haematologica Journal of the European Hematology Association Published by the Ferrata Storti Foundation
Editor-in-Chief Jan Cools (Leuven)
Deputy Editor Luca Malcovati (Pavia)
Managing Director Antonio Majocchi (Pavia)
Associate Editors Hélène Cavé (Paris), Ross Levine (New York), Claire Harrison (London), Pavan Reddy (Ann Arbor), Andreas Rosenwald (Wuerzburg), Juerg Schwaller (Basel), Monika Engelhardt (Freiburg), Wyndham Wilson (Bethesda), Paul Kyrle (Vienna), Paolo Ghia (Milan), Swee Lay Thein (Bethesda), Pieter Sonneveld (Rotterdam)
Assistant Editors Anne Freckleton (English Editor), Cristiana Pascutto (Statistical Consultant), Rachel Stenner (English Editor), Kate O’Donohoe (English Editor)
Editorial Board Omar I. Abdel-Wahab (New York); Jeremy Abramson (Boston); Paolo Arosio (Brescia); Raphael Bejar (San Diego); Erik Berntorp (Malmö); Dominique Bonnet (London); Jean-Pierre Bourquin (Zurich); Suzanne Cannegieter (Leiden); Francisco Cervantes (Barcelona); Nicholas Chiorazzi (Manhasset); Oliver Cornely (Köln); Michel Delforge (Leuven); Ruud Delwel (Rotterdam); Meletios A. Dimopoulos (Athens); Inderjeet Dokal (London); Hervé Dombret (Paris); Peter Dreger (Hamburg); Martin Dreyling (München); Kieron Dunleavy (Bethesda); Dimitar Efremov (Rome); Sabine Eichinger (Vienna); Jean Feuillard (Limoges); Carlo Gambacorti-Passerini (Monza); Guillermo Garcia Manero (Houston); Christian Geisler (Copenhagen); Piero Giordano (Leiden); Christian Gisselbrecht (Paris); Andreas Greinacher (Greifswals); Hildegard Greinix (Vienna); Paolo Gresele (Perugia); Thomas M. Habermann (Rochester); Claudia Haferlach (München); Oliver Hantschel (Lausanne); Christine Harrison (Southampton); Brian Huntly (Cambridge); Ulrich Jaeger (Vienna); Elaine Jaffe (Bethesda); Arnon Kater (Amsterdam); Gregory Kato (Pittsburg); Christoph Klein (Munich); Steven Knapper (Cardiff); Seiji Kojima (Nagoya); John Koreth (Boston); Robert Kralovics (Vienna); Ralf Küppers (Essen); Ola Landgren (New York); Peter Lenting (Le Kremlin-Bicetre); Per Ljungman (Stockholm); Francesco Lo Coco (Rome); Henk M. Lokhorst (Utrecht); John Mascarenhas (New York); Maria-Victoria Mateos (Salamanca); Simon Mendez-Ferrer (Madrid); Giampaolo Merlini (Pavia); Anna Rita Migliaccio (New York); Mohamad Mohty (Nantes); Martina Muckenthaler (Heidelberg); Ann Mullally (Boston); Stephen Mulligan (Sydney); German Ott (Stuttgart); Jakob Passweg (Basel); Melanie Percy (Ireland); Rob Pieters (Rotterdam); Stefano Pileri (Milan); Miguel Piris (Madrid); Andreas Reiter (Mannheim); Jose-Maria Ribera (Barcelona); Stefano Rivella (New York); Francesco Rodeghiero (Vicenza); Richard Rosenquist (Uppsala); Simon Rule (Plymouth); Claudia Scholl (Heidelberg); Martin Schrappe (Kiel); Radek C. Skoda (Basel); Gérard Socié (Paris); Kostas Stamatopoulos (Thessaloniki); David P. Steensma (Rochester); Martin H. Steinberg (Boston); Ali Taher (Beirut); Evangelos Terpos (Athens); Takanori Teshima (Sapporo); Pieter Van Vlierberghe (Gent); Alessandro M. Vannucchi (Firenze); George Vassiliou (Cambridge); Edo Vellenga (Groningen); Umberto Vitolo (Torino); Guenter Weiss (Innsbruck).
Editorial Office Simona Giri (Production & Marketing Manager), Lorella Ripari (Peer Review Manager), Paola Cariati (Senior Graphic Designer), Igor Ebuli Poletti (Senior Graphic Designer), Marta Fossati (Peer Review), Diana Serena Ravera (Peer Review)
Affiliated Scientific Societies SIE (Italian Society of Hematology, www.siematologia.it) SIES (Italian Society of Experimental Hematology, www.siesonline.it)
haematologica Journal of the European Hematology Association Published by the Ferrata Storti Foundation
Information for readers, authors and subscribers Haematologica (print edition, pISSN 0390-6078, eISSN 1592-8721) publishes peer-reviewed papers on all areas of experimental and clinical hematology. The journal is owned by a non-profit organization, the Ferrata Storti Foundation, and serves the scientific community following the recommendations of the World Association of Medical Editors (www.wame.org) and the International Committee of Medical Journal Editors (www.icmje.org). Haematologica publishes editorials, research articles, review articles, guideline articles and letters. Manuscripts should be prepared according to our guidelines (www.haematologica.org/information-for-authors), and the Uniform Requirements for Manuscripts Submitted to Biomedical Journals, prepared by the International Committee of Medical Journal Editors (www.icmje.org). Manuscripts should be submitted online at http://www.haematologica.org/. Conflict of interests. According to the International Committee of Medical Journal Editors (http://www.icmje.org/#conflicts), “Public trust in the peer review process and the credibility of published articles depend in part on how well conflict of interest is handled during writing, peer review, and editorial decision making”. The ad hoc journal’s policy is reported in detail online (www.haematologica.org/content/policies). Transfer of Copyright and Permission to Reproduce Parts of Published Papers. Authors will grant copyright of their articles to the Ferrata Storti Foundation. No formal permission will be required to reproduce parts (tables or illustrations) of published papers, provided the source is quoted appropriately and reproduction has no commercial intent. Reproductions with commercial intent will require written permission and payment of royalties. Detailed information about subscriptions is available online at www.haematologica.org. Haematologica is an open access journal. Access to the online journal is free. Use of the Haematologica App (available on the App Store and on Google Play) is free. For subscriptions to the printed issue of the journal, please contact: Haematologica Office, via Giuseppe Belli 4, 27100 Pavia, Italy (phone +39.0382.27129, fax +39.0382.394705, E-mail: info@haematologica.org). Rates of the International edition for the year 2016 are as following: Print edition
Institutional Euro 500
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Advertisements. Contact the Advertising Manager, Haematologica Office, via Giuseppe Belli 4, 27100 Pavia, Italy (phone +39.0382.27129, fax +39.0382.394705, e-mail: marketing@haematologica.org). Disclaimer. Whilst every effort is made by the publishers and the editorial board to see that no inaccurate or misleading data, opinion or statement appears in this journal, they wish to make it clear that the data and opinions appearing in the articles or advertisements herein are the responsibility of the contributor or advisor concerned. Accordingly, the publisher, the editorial board and their respective employees, officers and agents accept no liability whatsoever for the consequences of any inaccurate or misleading data, opinion or statement. Whilst all due care is taken to ensure that drug doses and other quantities are presented accurately, readers are advised that new methods and techniques involving drug usage, and described within this journal, should only be followed in conjunction with the drug manufacturer’s own published literature. Direttore responsabile: Prof. Edoardo Ascari; Autorizzazione del Tribunale di Pavia n. 63 del 5 marzo 1955. Printing: Tipografia PI-ME, via Vigentina 136, Pavia, Italy. Printed in January 2016.
haematologica calendar of events
Journal of the European Hematology Association Published by the Ferrata Storti Foundation
ESH International Conference on Aging and Hematological Malignancies: Biology and Therapy European School of Haematology (ESH) Chairs: B Löwenberg, L Balducci, P Fenaux, M Hallek March 11-13, 2016 Athens, Greece
Hematology Tutorial on managing complications in patients with hematologic malignancies in the era of new drugs EHA-ROHS-RSH Chairs: E Parovichnikova, I Poddubnaya, R Foà July 1-3, 2016 Moscow, Russian Federation
42nd EBMT Annual Meeting European Society for Bone and Marrow Transplantation Chairs: MS Sanz, A Urbano, JL Díez April 3-6, 2016 Valencia, Spain
EHA Scientific Conference on Bleeding Disorders Scientific Program Committee: C Balduini (Chair), A Falanga (Chair), F Rodeghiero, I Pabinger, M Makris September 14-17, 2016 Barcelona, Spain
8th International Conference on Thrombosis and Hemostasis Issues in Cancer - ICTHIC April8-10, 2016 Chairs: A Falanga, B Brenner, FR Rickles Bergamo, Italy
21st Congress of the European Hematology Association European Hematology Association June 9-12, 2016 Copenhagen, Denmark
Calendar of Events updated on February 10, 2016
haematologica Journal of the European Hematology Association Published by the Ferrata Storti Foundation
Table of Contents Volume 101, Issue 3: March 2016 Cover Figure Targeting epigenetic regulators - image accompanying the review article on page 269. (Image created by www.somersault1824.com)
Editorials 263
Innovation in the prognostication of chronic lymphocytic leukemia: how far beyond TP53 gene analysis can we go? Sarka Pospisilova, et al.
265
Securing reimbursement for patient centered haemophilia care: major collaborative efforts are needed Karin C. Berger, et al.
Review Article 269
Epigenetic regulators and their impact on therapy in acute myeloid leukemia Friederike Pastore and Ross L. Levine
Guideline Article 279
Consensus expert recommendations for identification and management of asparaginase hypersensitivity and silent inactivation Inge M. van der Sluis, et al.
Articles Hematopoiesis
286
Mathematical modeling reveals differential effects of erythropoietin on proliferation and lineage commitment of human hematopoietic progenitors in early erythroid culture Daniel Ward, et al.
Iron Metabolism & Its Disorders
297
Increased hepcidin in transferrin-treated thalassemic mice correlates with increased liver BMP2 expression and decreased hepatocyte ERK activation Huiyong Chen,et al.
Coagulation & Its Disorders
309
von Willebrand factor binds to the surface of dendritic cells and modulates peptide presentation of factor VIII Nicoletta Sorvillo, at al.
Bone Marrow Failure
319
Twenty years of the Italian Fanconi Anemia Registry: where we stand and what remains to be learned Antonio M. Risitano, et al.
Haematologica 2016; vol. 101 no. 3 - March 2016 http://www.haematologica.org/
haematologica Journal of the European Hematology Association Published by the Ferrata Storti Foundation
Acute Myeloid Leukemia
328
Prospective long-term minimal residual disease monitoring using RQ-PCR in RUNX1-RUNX1T1-positive acute myeloid leukemia: results of the French CBF-2006 trial Christophe Willekens, et al.
Acute Lymphoblastic Leukemia
336
Minimal residual disease monitoring by 8-color flow cytometry in mantle cell lymphoma: an EU-MCL and LYSA study Morgane Cheminant, et al.
Plasma Cell Disorders
346
Treatment with the HIV protease inhibitor nelfinavir triggers the unfolded protein response and may overcome proteasome inhibitor resistance of multiple myeloma in combination with bortezomib: a phase I trial (SAKK 65/08) Christoph Driessen, et al.
356
Comparison of serum free light chain and urine electrophoresis for the detection of the light chain component of monoclonal immunoglobulins in light chain and intact immunoglobulin multiple myeloma Thomas Dejoie, et al.
363
Impact of renal impairment on outcomes with lenalidomide and dexamethasone treatment in the FIRST trial, a randomized, open-label phase 3 trial in transplant-ineligible patients with multiple myeloma Meletios A. Dimopoulos, et al.
Stem Cell Transplantation
371
Analysis of memory-like natural killer cells in human cytomegalovirus-infected children undergoing αβ+T and B cell-depleted hematopoietic stem cell transplantation for hematological malignancies Letizia Muccio, et al.
382
Polymorphism in TGFB1 is associated with worse non-relapse mortality and overall survival after stem cell transplantation with unrelated donors Esteban Arrieta-Bolaños, et al.
Letters to the Editor Letters are available online only at www.haematologica.org/content/101/3.toc
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The anti-inflammatory effects of platelet-derived microparticles in human plasmacytoid dendritic cells involve liver X receptor activation Adam Ceroi, et al. http://www.haematologica.org/content/101/3/e72
e77
Jak2V617F driven myeloproliferative neoplasm occurs independently of interleukin-3 receptor beta common signalling Therese Vu, et al. http://www.haematologica.org/content/101/3/e77
e81
Expression and function of ABC-transporter protein ABCB1 correlates with inhibitory capacity of Ruxolitinib in vitro and in vivo Caroline Ebert, et al. http://www.haematologica.org/content/101/3/e81
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Hyperhomocysteinemia and high doses of nilotinib favor cardiovascular events in chronic phase Chronic Myelogenous Leukemia patients Gaëlle Fossard, et al. http://www.haematologica.org/content/101/3/e86
Haematologica 2016; vol. 101 no. 3 - March 2016 http://www.haematologica.org/
haematologica Journal of the European Hematology Association Published by the Ferrata Storti Foundation
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FGFR1OP2-FGFR1 induced myeloid leukemia and T-cell lymphoma in a mouse model Haiyan Qin, et al. http://www.haematologica.org/content/101/3/e91
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Clinical and molecular genetic characterization of wild-type MLL infant acute lymphoblastic leukemia identifies few recurrent abnormalities Marieke H. van der Linden, et al. http://www.haematologica.org/content/101/3/e95
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Combined CXCR3/CXCR4 measurements are of high prognostic value in chronic lymphocytic leukemia due to negative co-operativity of the receptors Sylvia Ganghammer, et al. http://www.haematologica.org/content/101/3/e99
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Brentuximab vedotin in refractory or relapsed peripheral T-cell lymphomas: the French named patient program experience in 56 patients Mathilde Lamarque, et al. http://www.haematologica.org/content/101/3/e103
e107
The anti-tumoral effect of lenalidomide is increased in vivo by hypoxia-inducible factor (HIF)-1Îą inhibition in myeloma cells Paola Storti, et al. http://www.haematologica.org/content/101/3/e107
e111
Ibrutinib and idelalisib target B cell receptor- but not CXCL12/CXCR4-controlled integrin-mediated adhesion in WaldenstrĂśm macroglobulinemia Martin F.M. de Rooij, et al. http://www.haematologica.org/content/101/3/e111
e116
Concomitant gain of 1q21 and MYC translocation define a poor prognostic subgroup of hyperdiploid multiple myeloma Niels Weinhold,et al. http://www.haematologica.org/content/101/3/e116
Comments Comments are available online only at www.haematologica.org/content/101/3.toc
e120
Dietary and pharmacological factors affecting iron absorption in mice and man Christina N Kontoghiorghe, et al. http://www.haematologica.org/content/101/3/e120
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Response to: Dietary and pharmacological factors affecting iron absorption in mice and man Carine Fillebeen, et al. http://www.haematologica.org/content/101/3/e121
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Comment on Lipsky et al.: Incidence and Risk Factors of Bleeding-Related Adverse Events in Patients with Chronic Lymphocytic Leukemia Treated with Ibrutinib Constantine S. Tam, et al. http://www.haematologica.org/content/101/3/e123
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Response to Comment on Incidence and Risk Factors of Bleeding-Related Adverse Events in Patients with Chronic Lymphocytic Leukemia Treated with Ibrutinib Andrew H. Lipsky, et al. http://www.haematologica.org/content/101/3/e124
Haematologica 2016; vol. 101 no. 3 - March 2016 http://www.haematologica.org/
haematologica Journal of the European Hematology Association Published by the Ferrata Storti Foundation
The origin of a name that reflects Europe’s cultural roots.
Ancient Greek
aÂma [haima] = blood a·matow [haimatos] = of blood lÒgow [logos]= reasoning
Scientific Latin
haematologicus (adjective) = related to blood
Scientific Latin
haematologica (adjective, plural and neuter, used as a noun) = hematological subjects
Modern English
The oldest hematology journal, publishing the newest research results. 2014 JCR impact factor = 5.814
Haematologica, as the journal of the European Hematology Association (EHA), aims not only to serve the scientific community, but also to promote European cultural identify.
EDITORIALS Innovation in the prognostication of chronic lymphocytic leukemia: how far beyond TP53 gene analysis can we go? Sarka Pospisilova,1* Lesley-Ann Sutton,2* Jitka Malcikova,1* Eugen Tausch,3 Davide Rossi,4 Emili Montserrat,5 Carol Moreno,6 Kostas Stamatopoulos,2,7 Gianluca Gaidano,4 Richard Rosenquist,2** and Paolo Ghia,8**# on behalf of the European Research Initiative on CLL (ERIC) 1
Center of Molecular Medicine, Central European Institute of Technology, Masaryk University, Brno, Czech Republic; 2Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Sweden; 3Department of Internal Medicine III, Ulm University, Germany; 4Division of Hematology, Department of Translational Medicine, Amedeo Avogadro University of Eastern Piedmont, Novara, Italy; 5Institute of Hematology and Oncology, Department of Hematology, Hospital Clinic, University of Barcelona, Spain; 6 Hematology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; 7Institute of Applied Biosciences, CERTH, Thessaloniki, Greece; and 8Division of Experimental Oncology and Department of Onco-Hematology, Università Vita-Salute San Raffaele and IRCCS Instituto Scientifico San Raffaele, Milan, Italy E-mail: ghia.paolo@hsr.it
doi:10.3324/haematol.2015.139246
* Contributed equally as first author ** Contributed equally as senior author
T
he prognostication of patients with chronic lymphocytic leukemia (CLL) currently relies on both clinical and biological parameters (Figure 1). The prime example concerns the TP53 gene, whereby inactivation of TP53, resulting from either a mutation or chromosome 17p deletion, is associated with a short time to progression, an early need for treatment, and an overall dismal outcome.1,2 The presence of TP53 aberrations is also a strong predictor of treatment response, as patients carrying such lesions respond poorly to standard chemoimmunotherapy (CIT) (i.e. fludarabine, cyclophosphamide, and rituximab).3 Although TP53 abnormalities are infrequent at diagnosis (5%-10%), they are found in 40%-50% of advanced or therapy-refractory cases, hence underscoring the need to re-assess TP53 gene status as the disease progresses and clones evolve.2,4,5 TP53 inactivation in CLL was identified more than 20 years ago in the context of advanced disease, chemo-refractoriness and poor clinical outcome. However, almost a decade passed before the analysis of TP53 defects by fluorescence in situ hybridization (FISH) was introduced into routine clinical practice, spurred on by the establishment of the Döhner hierarchical classification of cytogenetic abnormalities.1 In this model, patient stratification was based on the presence of recurrent chromosomal aberrations (17p deletion, including the TP53 locus, 11q deletion, including the ATM locus, trisomy 12 and 13q deletion), with patients carrying del(17p) having the shortest survival.1 More recently, studies have shown that approximately 80% of patients harboring del(17p) also carry a mutation on the second allele of the TP53 gene.4,6,7 Conversely, only approximately 60% of patients with TP53 mutations carry del(17p); nevertheless, studies have revealed that both biallelic and monoallelic defects (sole mutation or sole deletion) convey an equally poor prognosis. The poor prognosis conveyed by TP53 aberrations is mainly due to refractoriness to CIT treatment.4-8 The advent of agents acting independently of the p53 pathway looks promising for the treatment of CLL patients carrying TP53 aberrations. Recently, two inhibitors of B-cell receptor (BCR) signaling, namely ibrutinib [inhibitor of Bruton’s tyrosine kinase (BTK)], and idelalisib [inhibitor of phosphatidylinositol 3-kinase delta (PI3Kδ)] have been approved by both the Food and Drug administration (FDA) and the European Medicines Agency (EMA) for the first-line treatment of cases harboring del(17p)/TP53 mutations or the treatment of CLL cases refractory to CIT or unfit for CIT.9,10 haematologica | 2016; 101(3)
Additional small molecules targeting other pathophysiological processes are set to be approved, e.g. the BH3 mimetic ABT199 (Venetoclax), with preliminary results indicating efficacy in del(17p)/TP53 mutated CLL cases.11 These new therapeutic options have also accelerated the need to analyze del(17p)/TP53 mutations prior to therapy, and the European Research Initiative on CLL (ERIC) (www.ericll.org) has been at the forefront of this field by: i) formulating recommendations on the mutational analysis of TP53 in CLL, including guidance about when and in whom del(17p)/TP53 mutations should be investigated;5 ii) establishing the European CLL TP53 Network, aimed at promoting and advancing the analysis of TP53 gene aberrations across the medical community; iii) launching a certification system with external quality control for laboratories assessing TP53 mutations; and finally, iv) arranging dedicated educational workshops (the 1st ERIC workshop on TP53 analysis in CLL was held in October 2015 in Brno, the Czech Republic). While many laboratories currently utilize Sanger sequenc-
Figure 1. Gene and chromosomal defects in the management of chronic lymphocytic leukemia. Chronology of the introduction of clinically relevant chromosomal aberrations and gene mutations into clinical routine practice. FISH: fluorescence in situ hybridization; NGS: next generation sequencing. 263
Editorials
Figure 2. Ongoing and planned ERIC Gene Panel Projects. NGS: next generation sequencing; VAF: variant allelic frequency.
ing for TP53 analysis, other laboratories use pre-screening techniques such as denaturing high performance liquid chromatography (DHPLC) or functional analysis of separated allele in yeast (FASAY), in combination with Sanger sequencing.5 Technological progress over the last few years has led to the introduction of next generation sequencing (NGS), which will increasingly be applied to clinical diagnostics in the near future.12 In addition to monitoring the clonal population, targeted NGS, in which selected genes, e.g. TP53, are analyzed, now allows minor clones to be detected, even those below 1% variant allelic frequency (VAF), thus facilitating the study of clonal evolution. This is extremely important since resistance to CIT has been shown to be associated with the expansion of minor CLL clones carrying TP53 defects, which are already present but remain undetected at initiation of therapy, whereas the acquisition of novel TP53 defects in untreated patients is only rarely observed.13,14 In additon to cytotoxic therapy, other factors must be involved in clonal selection since TP53-mutated subclones persist at low allelic burden in some cases despite having received several lines of therapy.13-15 Next generation sequencing, specifically whole-genome sequencing (WGS) and whole-exome sequencing (WES), has been invaluable for deciphering the molecular heterogeneity of CLL. It is now known that CLL is characterized by a relatively well-defined set of recurrent mutations in addition to TP53, e.g. SF3B1, NOTCH1, ATM, BIRC3, NFKBIE and MYD88, as well as the known cytogenetic lesions.16 Although the molecular mechanisms by which some of the newly identified genetic lesions correlate with clinical aggressiveness are not fully understood, the fact that mutations often cluster in evolutionarily conserved hotspots or functional domains strongly suggests they have a role in the pathogenesis of CLL. In addition, the enrichment of some of these alterations in patients requiring treatment or refractory to therapy suggests a contributing role.16 In fact, it has recently been shown that CLL cases co-expressing mutations of TP53, NOTCH1 and BIRC3 have a poorer outcome than cases carrying only TP53 mutations.17 In line with this, studies have shown that mutations within the aformentioned genes may refine the prognostic value of the Dรถhner hierarchical clas264
sification.1,18,19 However, it is important to note that none of the recently identified mutations have yet been incorporated into clinical practice. Furthermore, while subclonal TP53 mutations may infer a poor prognosis, data regarding subclonal mutations within other recurrently mutated genes remain limited. Nevertheless, a few key studies have provided some insight into the intratumoral composition of mutations and their (sub)clonal behavior, and have demonstrated significant dynamic changes as the disease progresses, as well as the selection of subclones conveying poor prognosis.13,14,20,21 Thus, as we prepare for an era of tailored therapy, we need not only to appreciate the effect of mutations on response to treatment, but also the clinical impact of subclones harboring specific mutations. While WGS and WES remain primarily a discovery tool in biomedical cancer research, targeted NGS, in which a selected number of genes are sequenced, is emerging as a much more efficient means to identify genetic variants and contains many attractive features, including the possibility to: i) detect mutations present at low VAFs due to the high sensitivity achievable; ii) analyze all coding exons within a gene, irrespective of size; and iii) custom design a gene panel and screen several genes with known or predicted clinical implications in multiple patient samples simultaneously.12-14 In addition, technology is now evolving such that multiple types of lesions, i.e. single nucleotide variants, insertions, deletions and chromosomal rearrangements, can be analyzed in a single sequencing run; a task that cannot be achieved by any other stand-alone test. Although it is likely that NGS will change molecular diagnostics in CLL, the routine implementation of NGS is in its infancy, and there is still a long way to go before we consider NGS not just as a discovery tool for research, but also as a clinically useful prognostic tool. To begin with, standard analytical methods must be established and checked for accuracy and reproducibility. To help in this transition, ERIC has recently initiated a multi-center project to harmonize the analysis of clinically relevant genes in CLL, including TP53, using NGS (Figure 2). This initiative aims to reach a consensus on which genomic variants are clinically relevant in CLL and thereafter rigorously assess various gene panel technologies and bioinformatic approaches across the collaborative sites. These studies may help to set the stage for the wider adoption of targeted NGS gene panels in clinical practice by not only identifying the strengths and weaknesses of various technologies, but also by defining quality metrics that will improve accuracy and ensure consistency and reproducibility. Prior to incorporating NGS within a clinical setting, criteria should be harmonized at an international level, and these should include: i) the determination of performance specifications for NGS, such as ascertaining cut-off values for low frequency (subclonal) variants; ii) dealing with regions harboring a high GC content; iii) distinguishing between mean depth of coverage and uniformity of coverage; and iv) the standardization and evaluation of NGS at a clinical level. In conclusion, incredible progress has been made in CLL genomics during the last few years, largely due to the rise of NGS technology. The ERIC initiatives set out here will hopefully help in the clinical deployment of NGS as both haematologica | 2016; 101(3)
Editorials
a research and a clinical tool for CLL. An important caveat is that validity and utility differ between research and clinical settings, with crucial issues relating to: the cost per test, the time to actionable result, the possibility of establishing automated interpretation of the tests, and the actual clinical significance of each mutation. With regards to the latter, identifying mutations is only the first step, while determining the clinical significance of novel mutations can be a lengthy process, and elucidating which variants are clinically actionable or have clinical validity in CLL requires substantial research and prospective studies. Another important issue is the difference in capability of larger academic institutes/hospitals as compared to community or regional healthcare facilities. Therefore, a model has to be devised to cater for both; one such option is the provision of NGS at specialized, referral centers. Despite the challenges, NGS is (and will) change many aspects of modern medicine, and while its widespread implementation may take several years, the journey should be viewed with both hope and as an iterative process that will continually incorporate new findings while discarding the irrelevant. Acknowledgments This work was supported in part by research projects CEITEC CZ.1.05/1.1.00/02.68, H2020 Twinning (MEDGENET/20162018/no.692298) and MZ CR projects AZV 15-31834A and AZV 15-30015A; the Swedish Cancer Society, the Swedish Research Council, Selander’s Foundation and Lion’s Cancer Research Foundation, Uppsala, Sweden; H2020 “AEGLE, An analytics framework for integrated and personalized healthcare services in Europe”, by the EU; the Special Program in Molecular Clinical Oncology, 5 x 1000 No. #9965 and 10007, AIRC, Milan Italy, Ricerca Finalizzata RF-2010-2318823 and RF2011-02349712, Ministero della Salute, Rome, Italy; Else Kröner-Forschungskolleg Ulm. The authors would like to thank all the participants of the 1st TP53 ERIC workshop held in Brno, in October 2015, for the lively and constructive discussion on the present and future analysis of TP53 in CLL. Financial and other disclosures provided by the author using the ICMJE (www.icmje.org) Uniform Format for Disclosure of Competing Interests are available with the full text of this paper at www.haematologica.org.
References 1. Dohner H, Stilgenbauer S, Benner A, et al. Genomic aberrations and survival in chronic lymphocytic leukemia. N Engl J Med. 2000;343 (26):1910-1916. 2. Zenz T, Mertens D, Kuppers R, Dohner H, Stilgenbauer S. From patho-
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13. 14. 15. 16. 17. 18. 19. 20. 21.
genesis to treatment of chronic lymphocytic leukaemia. Nat Rev Cancer. 2010;10(1):37-50. Stilgenbauer S, Schnaiter A, Paschka P, et al. Gene mutations and treatment outcome in chronic lymphocytic leukemia: results from the CLL8 trial. Blood. 2014;123(21):3247-3254. Zenz T, Eichhorst B, Busch R, et al. TP53 mutation and survival in chronic lymphocytic leukemia. J Clin Oncol. 2010;28(29):4473-4479. Pospisilova S, Gonzalez D, Malcikova J, et al. ERIC recommendations on TP53 mutation analysis in chronic lymphocytic leukemia. Leukemia. 2012;26(7):1458-1461. Malcikova J, Smardova J, Rocnova L, et al. Monoallelic and biallelic inactivation of TP53 gene in chronic lymphocytic leukemia: selection, impact on survival, and response to DNA damage. Blood. 2009;114(26):5307-5314. Rossi D, Cerri M, Deambrogi C, et al. The prognostic value of TP53 mutations in chronic lymphocytic leukemia is independent of Del17p13: implications for overall survival and chemorefractoriness. Clin Cancer Res. 2009;15(3):995-1004. Gonzalez D, Martinez P, Wade R, et al. Mutational status of the TP53 gene as a predictor of response and survival in patients with chronic lymphocytic leukemia: results from the LRF CLL4 trial. J Clin Oncol. 2011;29(16):2223-2229. Brown JR, Byrd JC, Coutre SE, et al. Idelalisib, an inhibitor of phosphatidylinositol 3-kinase p110delta, for relapsed/refractory chronic lymphocytic leukemia. Blood. 2014;123(22):3390-3397. Byrd JC, Brown JR, O'Brien S, et al. Ibrutinib versus ofatumumab in previously treated chronic lymphoid leukemia. N Engl J Med. 2014;371(3):213-223. Stilgenbauer S, Eichhorst B, Schetelig J, et al. Venetoclax (ABT199/GDC-0199) Monotherapy Induces Deep Remissions, Including Complete Remission and Undetectable MRD, in Ultra-High Risk Relapsed/Refractory Chronic Lymphocytic Leukemia with 17p Deletion: Results of the Pivotal International Phase 2 Study. American Society of Hematology; 2015; Orlando, Florida. Sutton LA, Ljungstrom V, Mansouri L, et al. Targeted next-generation sequencing in chronic lymphocytic leukemia: a high-throughput yet tailored approach will facilitate implementation in a clinical setting. Haematologica. 2015;100(3):370-376. Rossi D, Khiabanian H, Spina V, et al. Clinical impact of small TP53 mutated subclones in chronic lymphocytic leukemia. Blood. 2014;123(14):2139-2147. Malcikova J, Stano-Kozubik K, Tichy B, et al. Detailed analysis of therapy-driven clonal evolution of TP53 mutations in chronic lymphocytic leukemia. Leukemia. 2015;29(4):877-885. Landau DA, Tausch E, Taylor-Weiner AN, et al. Mutations driving CLL and their evolution in progression and relapse. Nature. 2015;526 (7574):525-530. Sutton LA, Rosenquist R. Deciphering the molecular landscape in chronic lymphocytic leukemia: time frame of disease evolution. Haematologica. 2015;100(1):7-16. Guieze R, Robbe P, Clifford R, et al. Presence of multiple recurrent mutations confers poor trial outcome of relapsed/refractory CLL. Blood. 2015;126(18):2110-2117. Rossi D, Rasi S, Spina V, et al. Integrated mutational and cytogenetic analysis identifies new prognostic subgroups in chronic lymphocytic leukemia. Blood. 2013;121(8):1403-1412. Baliakas P, Hadzidimitriou A, Sutton LA, et al. Recurrent mutations refine prognosis in chronic lymphocytic leukemia. Leukemia. 2015;29(2):329-336. Landau DA, Carter SL, Stojanov P, et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell. 2013;152(4): 714-726. Schuh A, Becq J, Humphray S, et al. Monitoring chronic lymphocytic leukemia progression by whole genome sequencing reveals heterogeneous clonal evolution patterns. Blood. 2012;120(20):4191-4196.
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Securing reimbursement for patient centered haemophilia care: major collaborative efforts are needed Karin C. Berger,1 Brian M. Feldman,2 Joan Wasserman,3 Wolfgang Schramm,4 Victor Blanchette,5 and Kathelijn Fischer on behalf of the Outcome Measures Expert Working Group of the International Prophylaxis Study Group (IPSG) 1
University Hospital of Munich, Department of Haematology/Oncology, Germany; 2Division of Rheumatology, and The Research Institute, The Hospital for Sick Children, Department of Paediatrics, Medicine, and IHPME, University of Toronto, Canada; 3 National Institute on Minority Health and Health Disparities/NIH, Bethesda, MD, USA; 4Rudolf-Marx-Foundation, LudwigMaximilian University, Munich, Germany; 5Department of Paediatrics , University of Toronto, Medical Director, Paediatric Thrombosis and Haemostasis Program, Division of Haematology/Oncology, The Hospital for Sick Children, Canada; and 6K. Fischer, Van Creveldkliniek and Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands E-mail: karin.berger@med.uni-muenchen.de doi:10.3324/haematol.2015.139931
Challenges for patient-centered health care provision Policy makers and payers are increasingly mandating what physicians can prescribe and thus the need for proven evidence of the benefits and cost-effectiveness of new drugs has become imperative. A range of access hurdles are already in use. At their heart is the goal of improving effectiveness and value in healthcare as a way of balancing limited resources. Treatments for rare diseases, including hemophilia, must compete with treatments for other more common conditions in terms of their benefits and risks. Clotting factor concentrates are amongst the most expensive specialty drugs. With years of research finally bearing fruit, a selection of new clotting factor concentrates are now entering the therapeutic market.1 As more treatment options become available and their costs continue to rise, there is increasing scrutiny of the value they bring and demand for proof of their benefits. In this context, the present communication focuses on options that should be considered in a climate of increasing demands for high levels of evidence. Decision making for rare diseases including hemophilia has to follow the same paradigms as for common diseases, including evidence-based medicine (EBM), health technology assessment (HTA) and comparative effectiveness research (CER).2-4 EBM works with, integrates and evaluates the existing evidence considering both benefits and harms. It has as its goal the facilitation of making health care decisions by providing information about the improvement of outcomes and quality of care.1 As defined by EBM, recent regulations in several countries stipulate that randomized controlled trials (RCTs) generate the highest level of evidence possible.3 Outcome endpoints are required to focus on: morbidity, decrease in disease mortality, adverse events and the quality of life. HTAs evaluate published literature according to evidence-based criteria to present a high quality scientific synthesis of available evidence regarding clinical benefits, harms, economic consequences, and ethical or social issues. HTAs are increasingly leveraged as a basic requirement for healthcare decision making. Ideally a HTA is expected to give an evaluation of the long-term benefits and risks of a given medical intervention in relation to its costs.4 266
CER compares treatments, generates information about patients, caregivers, diagnostic and therapeutic methods during daily routine, and helps consumers, caregivers and policy makers to come to reasonable decisions concerning the best medical care for the individual patient.5,6 CER has to consider not only the benefits and risks, but also the costs.1.4 RCTs, which provide care in an experimental setting, are no longer considered to be the only source of information. Observational studies reflecting day-to-day practice are increasingly recognized to be important.5,7 New methods for rating clinical evidence allow for the appraisal of high quality nonrandomized comparative effectiveness trials. The current tool, ‘The Grading of Recommendations Assessment, Development and Evaluation’ (GRADE), used for rating the evidence levels of studies, ranks observational studies as low (2+) and randomized trials as high (4+), similar to previous rating nomenclatures. However, additional factors, i.e. strong association based on consistent evidence from 2 or more observational studies (+1) and very strong evidence of association based on direct evidence with no major threats to validity (+2), can increase the evidence grade from low (2+) to moderate (3+) and high (4+). Both Investigators and Regulators (or Regulatory Authorities) had taken into account that it is not always feasible to conduct randomized trials and that other factors should also be taken into consideration.8.9 In this context, the ‘Good Research for Comparative Effectiveness’ (GRACE) initiative was established to provide criteria to judge the quality of nonrandomized comparative effectiveness trials.10 The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) task force has further elaborated a detailed report on how to design and conduct prospective observational studies for the assessment of comparative effectiveness.11
Unique challenges of hemophilia to meet payer’s expectations Lack of requested evidence In hemophilia, variations in medical practice reflect the manifold therapeutic uncertainties and the lack of evidence for optimal standardized treatment strategies. A Swedish HTA on hemophilia states that prophylactic factor substitution is considered effective in patients with haematologica | 2016; 101(3)
Editorials
hemophilia, but data concerning many important relevant and related issues are still insufficient.12 Current questions concern dosing issues, the duration of prophylaxis, and prophylaxis in adult patients with hemophilia with and without co-morbidities.
Barriers to randomized trials There are several barriers that threaten the conduct of randomized trials in hemophilia. First, the number of patients required: many of the questions to be solved would require large patient numbers in order to get a statistically meaningful answer. In addition, research questions concerning dosing and prophylactic treatment would require decades of follow-up to assess hemophilic arthropathy and its consequences. Studies to address these questions would require observation periods of many years thus making randomized trials not feasible because of the problems of patient drop out, lack of adherence, and of course costs.13
Endpoints in hemophilia Information on ‘hard’ patient-relevant clinical endpoints has to be provided to prove the evidence of therapeutic benefit. These endpoints are morbidity, mortality and quality of life. Mortality was high before the introduction of factor concentrate replacement treatment, and still is in resource constrained countries.14,15 Repeated joint bleeds cause morphological changes and eventually lead to crippling arthropathy. Multifactorial influences lead to individual variations in a small patient population regarding bleeding type, the development of arthropathy, and the development of inhibitory antibodies. In this situation, the measurement of health-related quality of life (HRQoL) is a reasonable indicator of the long-term outcome and effectiveness of the therapeutic intervention chosen. HRQoL measurement may be most important from the payer’s perspective. It can also be converted into utility values to enable the assessment of quality-adjusted life years (QALYs) gained, and to calculate cost-effectiveness. Both hemophilia disease-specific instruments and generic instruments are available and should be implemented as a standard procedure. Consensus should be reached on the choice of instruments to allow for a more effective combination of data from different sources.
A key role for real-world evidence to prove requested effectiveness of hemophilia care Within the context of CER, the informative value and significance of observational studies and real-world data in comparison to randomized trials is the subject of ongoing debate.5,16 The evaluation of routine data in health service databases has thus far been challenging due to the limitations of available datasets, particularly in terms of reported outcomes measures.
Real-world evidence in hemophilia Expectations for future hemophilia treatment success are high due to a variety of treatment options in development. Yet, authorities fear rising costs and request evidence for rational treatment decisions. haematologica | 2016; 101(3)
In the United States, The Patient Protection and Affordable Care ACT (ACA) signed into law on March 23, 2010, enables comprehensive health insurance reforms that will increase access to health insurance and make health care coverage more affordable. For people with hemophilia, the benefits of the new law include the elimination of lifetime insurance caps, the ability to obtain health insurance regardless of pre-existing health conditions, and the expansion of Medicaid coverage. The ACA also includes provisions to improve the quality and comprehensiveness of health care coverage, thus creating a demand for evidenced-based decision making. Real-world evidence (RWE) holds the key to gathering the necessary information about all aspects of care, including clinical and patient-relevant outcomes, therapy profiles in real-life practice, as well as patient preferences. To satisfy the demand for higher levels of performance for reimbursement and market access in hemophilia, the ability to generate and access RWE will be essential. Several initiatives have already emerged in the past. The European Paediatric Network for Haemophilia Management (PEDNET) is a collaboration of pediatricians from European countries, established to promote clinical research and management of children with hemophilia by providing the necessary infrastructure.17 This initiative has generated important information, and demonstrates that high quality prospective observational studies can be conducted by the collaboration of many treatment centers through joint efforts. Important results associated with this kind of real-world data collection have the potential for lowering the rate of adverse events, thereby improving outcomes and achieving considerable savings in the future. Further initiatives worthy of mention are the European Haemophilia Safety Surveillance system (EUHASS),18 The International Factor IX Treatment Network Survey,19 and the ISTH-SSC international FIX inhibitor registry.20
Future needs To achieve the generation of RWE with a high quality standard requires amongst other factors: • To formulate research questions, to define the relevant patient subpopulations, and to determine the patient numbers necessary for a clear statement in the forefront of the intended study. • To combine data from different sources in the future. This will require connecting currently fragmented information through the use of technologies/methods which allow the linkage and integration of multiple patient-level datasets across the entire treatment journey. • To intensify national and international collaboration on current topics of interest. Eventually data interoperability at a national and international level may be achieved by leveraging alliances and technical platforms for datasharing. Randomized trials will remain the cornerstone for demonstrating the efficacy of medical technologies. The key challenge for reimbursement and market access in hemophilia as a rare disease will be to develop RWE approaches through national and international collaboration, thereby reducing the uncertainty for patients, physicians, payers, and other decision makers. 267
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Acknowledgements This work was supported by unrestricted grants to the International Prophylaxis Study Group (IPSG) from Bayer Healthcare; Baxalta (formerly Baxter BioScience); Biogen Idec, CSL Behring; Pfizer; and Novo Nordisk Healthcare AG; and was administered through The Hospital for Sick Children Foundation, Toronto, Canada.
References 1. Mahdi AJ, Obaji SG, Collins PW. Role of enhanced half-life factor VIII and IX in the treatment of haemophilia. Br J Haematol. 2015;169(6):768-776. 2. Walley T. Translating comparative effectiveness research into clinical practice: the UK experience. Drugs. 2012;72(2):163-170. 3. Gesetz zur Neuordnung des Arzneimittelmarktes in der gesetzlichen Krankenversicherung (Arzneimittelmarktneuordnungsgesetz AMNOG) vom 22. Dezember 2010. Bundesgesetzblatt. 2010;Teil 1 Nr. 67:2262 - 77. 4. Drummond MF, Schwartz JS, Jonsson B, et al. Key principles for the improved conduct of health technology assessments for resource allocation decisions. Int J Technol Assess Health Care. 2008 Summer;24(3):244-258; discussion 362-8. 5. Sox HC, Goodman SN. The methods of comparative effectiveness research. Annu Rev Public Health. 2012;33:425-445. 6. Tunis SR, Benner J, McClellan M. Comparative effectiveness research: Policy context, methods development and research infrastructure. Stat Med. 2010;29(19):1963-1976. 7. Hershman DL, Wright JD. Comparative effectiveness research in oncology methodology: observational data. J Clin Oncol. 2012;30(34):4215-4222. 8. Atkins D, Best D, Briss PA, et al. Grading quality of evidence and
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strength of recommendations. BMJ. 2004;328(7454):1490. 9. Balshem H, Helfand M, Schunemann HJ, et al. GRADE guidelines: 3. Rating the quality of evidence. J Clin Epidemiol. 2011;64(4):401-406. 10. Dreyer NA, Schneeweiss S, McNeil BJ, et al. GRACE principles: recognizing high-quality observational studies of comparative effectiveness. Am J Manag Care. 2010;16(6):467-471. 11. Berger ML, Dreyer N, Anderson F, Towse A, Sedrakyan A, Normand SL. Prospective observational studies to assess comparative effectiveness: the ISPOR good research practices task force report. Value Health. 2012;15(2):217-230. 12. Berntorp E, Astermark J, Baghaei F, et al. Report no. 208E of the Swedish Council on Health Technology Assessment (SBU): Treatment of Hemophilia A and B and von Willebrand Disease; ISBN: 978-9185413-44-7; ISSN: 1400-1403; May 2011. 13. Fischer K, Grobbee DE, van den Berg HM. RCTs and observational studies to determine the effect of prophylaxis in severe haemophilia. Haemophilia. 2007;13(4):345-350. 14. Evatt BL. Demographics of hemophilia in developing countries. Semin Thromb Hemost. 2005;31(5):489-494. 15. O'Mahony B, Black C. Expanding hemophilia care in developing countries. Semin Thromb Hemost. 2005;31(5):561-568. 16. Greenfield S, Platt R. Can observational studies approximate RCTs? Value Health. 2012;15(2):215-216. 17. Donadel-Claeyssens S. Current co-ordinated activities of the PEDNET (European Paediatric Network for Haemophilia Management). Haemophilia. 2006;12(2):124-127. 18. Makris M, Calizzani G, Fischer K, et al. EUHASS: The European Haemophilia Safety Surveillance system. Thromb Res. 2011;127 Suppl 2:S22-25. 19. Berntorp E, Waters J, Astermark J, Donfield SM, Shapiro AD. The international factor IX treatment network survey. Haemophilia. 2011;17(2)(2):367. 20. Chitlur M, Warrier I, Rajpurkar M, Lusher JM. Inhibitors in factor IX deficiency a report of the ISTH-SSC international FIX inhibitor registry (1997-2006). Haemophilia. 2009;15(5):1027-1031.
haematologica | 2016; 101(3)
REVIEW ARTICLE
Epigenetic regulators and their impact on therapy in acute myeloid leukemia Friederike Pastore1 and Ross L. Levine2
Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center; Leukemia Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
1 2
EUROPEAN HEMATOLOGY ASSOCIATION
Ferrata Storti Foundation
Haematologica 2016 Volume 101(3):269-278
ABSTRACT
G
enomic studies of hematologic malignancies have identified a spectrum of recurrent somatic alterations that contribute to acute myeloid leukemia initiation and maintenance, and which confer sensitivities to molecularly targeted therapies. The majority of these genetic events are small, site-specific alterations in DNA sequence. In more than two thirds of patients with de novo acute myeloid leukemia mutations epigenetic modifiers are detected. Epigenetic modifiers encompass a large group of proteins that modify DNA at cytosine residues or cause post-translational histone modifications such as methylations or acetylations. Altered functions of these epigenetic modifiers disturb the physiological balance between gene activation and gene repression and contribute to aberrant gene expression regulation found in acute myeloid leukemia. This review provides an overview of the epigenetic modifiers mutated in acute myeloid leukemia, their clinical relevance and how a deeper understanding of their biological function has led to the discovery of new specific targets, some of which are currently tested in mechanism-based clinical trials.
Correspondence: leviner@mskcc.org
Introduction Next-generation whole genome and whole exome sequencing of large AML patient cohorts has broadened our understanding and led to the discovery of new classes of mutations, including in genes involved in epigenetic regulation. At least 70% of patients with de novo AML display at least one mutation in an epigenetic modifier.1 Epigenetic modifiers include proteins that chemically modify DNA or catalyze post-translational modifications on histones. Abnormal epigenetic patterns caused by these mutations can lead to aberrant gene expression in AML. Several novel specific epigenetic therapies are in pre-clinical testing or have recently entered clinical trials.
Mutations in epigenetic regulators Cytosine modifications DNMT3A DNA methyltransferase 3A (DNMT3A) is a highly conserved 130 kDa protein that catalyzes de novo methylation of cytosine residues in DNA. Mutations in DNMT3A occur in 20-25% of de novo AML patients2-4 and were first identified in 2010. DNMT3A mutations often co-occur with NPM1 mutations and FLT3-ITD and confer adverse risk.4,5 Although mutations can occur in different functional domains, almost 60% of patients display a heterozygous substitution of arginine 882 in the catalytic domain that abrogates methyltransferase activity and DNA binding in vitro.6,7 The R882 mutation in AML patients correlates with global hypomethylation, especially at CpG islands, shores and promoters,8 although promoter hypermethylation has also been described.7,9,10 Mutant Dnmt3A - predominantly mutant R882 - has been shown to interact with wild-type Dnmt3A and Dnmt3B in a dominant negative manner inhibiting the wild-type methyltransferase activity of the tetrameric complex.8,11 haematologica | 2016; 101(3)
Received: December 18, 2015. Accepted: January 11, 2016. Pre-published: no prepublication. doi:10.3324/haematol.2015.140822
Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/100/3/269
Š2016 Ferrata Storti Foundation Material published in Haematologica is covered by copyright. All rights reserved to Ferrata Storti Foundation. Copies of articles are allowed for personal or internal use. A permission in writing by the publisher is required for any other use.
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Dnmt3A is required for the normal self-renewal capacity of HSCs in adult mice and for maintaining the differentiation potential of serially transplanted HSCs in wild-type recipients.12 Conditional deletion of DNMT3A in murine HSC causes a higher self-renewal capacity and reduced differentiation resulting in an accumulation of HSC in the bone marrow.10,13 In two studies, patients with DNMT3A mutations had higher survival rates when treated with high-dose daunorubicin (90 mg/m2) compared to standard-dose daunorubicin (45 mg/m2)14,15 although this has not been studied in other, well-annotated clinical trial cohorts. TET2 TET2 is a member of the Ten-Eleven translocation protein family of enzymes that regulate DNA methylation through the α-ketoglutarate and Fe(II)-dependent conversion of 5-methylcytosine (5mC) to 5-hydroxymethyl cytosine (5hmC).16 TET2 mutations are most often heterozygous with retained expression of the wild-type allele. They occur in 7-23% of AML patients depending on the cohort, and confer a poor prognosis in CN-AML.17,18 5hmC is thought to be critical in DNA demethylation19 but likely has other roles in regulating epigenetic state and transcriptional output. Biochemical analyses have revealed that TET2 mutations are associated with decreased levels of 5hmC20-22 and DNA hypermethylation, including at promoters and intragenic regions. Tet2 loss in murine models and in human cells leads to HSC self-renewal, stem cell and progenitor expansion and a skewing to the myelomonocytic and granulocytic lineages.23-26 WT1 The zinc finger DNA-binding protein Wilms' tumor 1 (WT1) is a sequence-specific transcription factor. 10-15% of patients with AML harbor mutations of WT1.1,27-29 WT1 mutations predominantly occur as truncating mutations or as nonsense mutations targeted by nonsense-mediated decay and resultant loss of WT1 protein expression.30 WT1 mutations are negatively correlated with TET2 and IDH1/2 mutations14,31 and to co-occur with FLT3-ITD or biallelic CEBPA mutations.32,33 In patients with cytogenetically normal AML, WT1 mutations are associated with chemo-resistant disease and a lower event-free, 5-year relapse-free and 5-year overall survival rate.6,29,32,33 DNA methylation analysis revealed similar hypermethylation signatures in IDH1/2, TET2 and WT1 mutated patient samples, with a significant overlap between the TET2 and WT1 mutant signatures31 consistent with a converging synergistic effect on DNA methylation. Liquid chromatography-mass spectrometry demonstrated WT1 mutant AML samples have reduced 5hmC levels, consistent with reduced TET2 enzymatic function. As is observed with shRNA-mediated Tet2 knockdown in murine HSC, shRNA-mediated knockdown of Wt1 reduces 5hmC levels.31,34 Co-immunoprecipitation studies revealed that Wt1 physically interacts with Tet2 via its zinc-finger domain31 and can also directly interact with Tet3. The significant overlap of differentially expressed genes in murine HSC with knockdown of Wt1 compared to knockdown of Tet2, and similar phenotype in functional studies, indicate similar effects on hematopoietic differentiation and a similar role in leukemic transformation. IDH1 and IDH2 IDH1 and IDH2 encode NADP-dependent isocitrate dehydrogenases, homodimeric enzymes which normally 270
catalyze the oxidative decarboxylation of isocitrate to alpha-ketoglutarate (α-KG) (synonymous: 2-oxoglutarate (2OG)) within the citric acid cycle. The IDH1 gene is located on 2q34, the IDH2 gene is located on 15q26.1. IDH1/2 mutations are hemizygous missense mutations in single arginine residues within the active site of the enzyme. IDH1 mutations almost always occur at arginine R132 (alteration predominantly R132H), and IDH2 mutations occur at the homologous arginine R172 (predominantly R172K) and at arginine R140 (predominantly R140Q).35,36 Mutations in the IDH1 gene were initially described in brain tumors.37 IDH1 mutations in AML were reported by Mardis et al.38 followed by the discovery of the IDH2 mutant in AML by Ward et al.35, Gross et al.39 and Marcucci et al.40 IDH1 and IDH2 mutations occur with a frequency of 6-7% and 9-11%, respectively, and are mutually exclusive with mutations of TET2.21,36,41,42 IDH1 mutations show a significant association with mutations in NPM1 and MLL43. IDH1 and IDH2 mutations are most common in cytogenetically normal AML (CN-AML) with frequencies of 10.4%36,38,43 and 12.1%-19%, respectively.40,44 IDH1/2 mutations have been identified in other myeloid diseases including MDS45 and MPN.46 IDH1 mutations are mainly associated with an inferior outcome, whereas the prognostic relevance of IDH2 mutations depends on the specific allele and on the choice of anti-leukemic therapy.47 All IDH1 and IDH2 mutations are novel gain-of-function mutations, leading to a neomorphic enzyme activity catalyzing the reaction from α-KG to 2-hydroxyglutarate (2-HG) and converting NADPH to NADP.35,39,48 This enzymatic reaction is performed by heterodimers formed by the mutated IDH1 or IDH2 protein together with the wild-type IDH protein. 2-HG enantiomers - R-2-HG and S-2-HG - are physiologically present within cells and tissues at low concentrations. IDH1/2 mutations in AML patients lead to the production and increased serum concentration of R-2-HG, but not of S-2-HG. Thus, R-2-HG has been proposed as a potential biomarker to assess treatment response and to follow minimal residual disease.39 Experiments in the human erythroleukemic cytokinedependent (GM-CSF) cell line TF-1, which is able to differentiate in the presence of erythropoietin (EPO),49 showed that the stable expression of IDH1R132H caused cytokine-independent growth and a block of differentiation.50 Moreover, R-2-HG itself, in the absence of an IDH1 mutation, was sufficient to induce transformation (cytokine independency and block of differentiation) in this cellular model. This effect was specific to R-2-HG, but not S-2-HG, and could be reversed by withdrawing R-2HG from the cell medium. Although the mechanisms of action of this oncometabolite R-2-HG in leukemic transformation have not been fully elucidated, many putative functions for R-2-HG have been suggested (for an extensive review please refer to Cairns et al.51). IDH1/2 mutations have been shown to competitively inhibit the 2ketoglutarate-dependent function of TET2, resulting in elevated 5-methylcytosine levels. Jumonji C (JmjC) domain-containing histone demethylases (JHDM) are 2ketoglutarate-dependent dioxygenases leading to a demethylation of mono-/di- and trimethylated lysines on histones in the presence of iron and 2-ketoglutarate.52 JHDM are competitively inhibited by 2-HG,53,54 followed by impaired histone demethylation and a block of differentiation.55 Experiments in mice with either a complete knock-out of Idh1 or knock-in with a heterozygous haematologica | 2016; 101(3)
Epigenetic regulators in AML Idh1R132H suggest that the tumor promoting effects of the Idh1 mutations are not caused by the lack of Idh1 protein, but rather by production of the oncometabolite R-2HG.51,56 In a conditional knock-in model expressing Idh1R132H from the endogenous locus, Sasaki et al. showed57 that Idh1R132H expression in all hematopoietic cells (Vav promoter) or in myeloid lineage cells (LysM promoter) resulted in an MDS-like phenotype (appearing within the first year) with expanded stem/progenitor numbers, anemia and extramedullary hematopoiesis. Lowe and colleagues have demonstrated that HSC derived from mice carrying NRas or Flt3-ITD mutations transduced with a vector containing mutant Idh2 results in AML. Moreover, Idh2 mutant-driven AML cells differentiated after inhibition of Brd4 in vitro.58
Polycomb group proteins and group interacting proteins Lineage specific regulation of gene expression in normal hematopoiesis is regulated by histone-modifying enzyme complexes, including polycomb group proteins (PcG) or trithorax group proteins (Trx). PRC1 complex The PRC1 complex consists of 4 members, CBX, BMI1 and the histone ubiquitin ligases RING1A and RING1B. PRC1 is involved in the maintenance of gene repression by recognizing H3K27me3 via CBX proteins, Histone H2A ubiquitination via RING1A and RING1B and the recruitment of DNA methyltransferases.59 BMI1 has been associated with increased HSC self-renewal and leukemic reprogramming of myeloid progenitors.60 Overexpression of BMI-1 and RING1A/RING1B have been detected in myeloid malignancies,61 whereas knock-out of BmiI in MLL-AF9 transformed GMP increased differentiation. PRC2 complex The canonical PRC2 complex is formed by EZH2, a methyltransferase, catalyzing the di- and trimethylation of the repressive chromatin mark H3K27,62 SUZ12, EED and RBAP48. Genetic defects in PRC2 components other than EZH2 are not common in myeloid malignancies, and occur more frequently in ALL.63-65 EZH2 mutations occur in 1-2% of AML patients, mostly as loss-of-function mutations in contrast to diffuse large cell lymphoma, where they appear as gain-of-function mutations reinforcing H3K27 methylation.66-68 Mutations in EZH2 are more common in MDS and are associated with cytopenias, but not with disease progression to AML.69 Conditional Ezh2 deficiency in murine hematopoietic cells leads to diminished generation of pre-B cells and immature B cells in the bone marrow70 and causes an MPN/MPD phenotype71 which is accentuated in the setting of concomitant Tet2 loss. Asxl1 The ASXL1 gene is 1 of 3 mammalian homologs of the additional sex combs (Asx) gene initially identified in Drosophila. It is located on chromosome 20q11.21, contains 13 exons and spans 81 kb.72 The gene belongs to the group of enhancers of trithorax and polycomb (ETP) genes that are involved in the regulation of HOX genes. ASXL1 has dual functions in the silencing and activation of gene expression. The ASXL1 protein consists of an N-terminal ASX homology (ASXH; amino acids 249-348) region containing 2 putative nuclear receptor coregulator binding haematologica | 2016; 101(3)
(NR box) motifs, the heterochromatin protein-1 (HP1) binding site and the lysine-specific demethylase 1 (LSD1) binding site, 3 other NR box motifs and a C-terminal zinc finger plant homeodomain (PHD; amino acids 1506-1537). ASXL1 is involved in the post-translational modification (PTM) of histones and is a member of a repressive complex containing histone H1.2.73 Abdel-Wahab et al. showed that ASXL1 regulates histone H3K27 methylation through its interaction with the polycomb-repressive complex 2 (PRC2) members EZH2, SUZ12 and EED. ASXL1 loss results in a global loss of H3K27 trimethylation (H3K27me3), although expression levels of PRC2 members remain the same.74 Furthermore, ASXL1 regulates histone H3K4 and H3K9 methylation by direct interaction of the N-terminal region of ASXL1 with HP1 and LSD1.75 This leads to an accumulation of methylated H3K9 (repressive histone mark) and unmethylated H3K4 (active histone mark). Heterozygous somatic mutations in the ASXL1 gene were described in MDS/MPN/AML by Gelsi-Boyer in 2009.76 Mutations are present throughout the entire coding region,77 but are clustered in the 5’ end of exon 12. The mutations are most commonly heterozygous frameshift and nonsense mutations, leading to the deletion of the PHD domain.76 Studies of ASXL1 protein expression in cell lines have indicated a reduced stability of the mutant forms of ASXL1 relative to wild-type and a more rapid degradation of the cDNA of ASXL1 mutant forms.78 Interestingly, a truncated ASXL1 protein could not be detected in most of these cases. Hence, leukemogenic ASXL1 mutations are loss-offunction mutations. ASXL1 loss results in a similar gene expression pattern as the mixed lineage leukemia-AF9 (MLL-AF9) gene expression signature including an upregulation of the expression of posterior HOXA genes.78 These findings suggest that ASXL1 may function as a tumor suppressor in malignancies of the myeloid lineage by affecting stem or progenitor cell self-renewal and/or differentiation. ASXL1 mutations have been found in patients with a spectrum of myeloid malignancies including myelodysplastic syndromes (MDS) (13-18.5%), chronic myelomonocytic leukemia (CMML) (43%), myeloproliferative neoplasms (MPN) (2-23%) and AML (9%-18%).79-81 ASXL1 mutations have been shown to co-occur with RUNX1 mutations and are inversely correlated with mutations of NPM1, DNMT3A, FLT3-ITD and FLT3-TKD.79,81 ASXL1 mutations are associated with an unfavorable overall survival rate (median overall survival 15.9 months vs. 22.3 months; P=0.019), and a significantly lower complete response rate to induction chemotherapy (61% vs. 79.6%; P=0.004) in AML.82 Asxl1 constitutive knock-out mice have partially penetrant perinatal lethality. Surviving knock-out mice exhibit defects in the frequency of differentiation of lymphoid and myeloid progenitors, but not in multipotent progenitors.83 Myeloerythroid lineage defects in mice with a homozygous deletion of Asxl1 (Asxl1tm1BC/Asxl1tm1BC) are mild.1,83 A conditional knock-out of Asxl1 in the hematopoietic compartment resulted in a myelodysplastic phenotype77,78 which was accentuated by concomitant Tet2 loss. JARID2 The Jumonji at rich interactive domain 2 (JARID2) gene is a PRC2 complex interacting protein, that recruits PRC2 complex to DNA target loci84 and inhibits the methyltransferase activity of PRC2. JARID2 mutations (deletions) are 271
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found in progression from chronic myeloid malignancies to acute leukemia.85
Trithorax genes MLL1 MLL genes belong to the family of SET domain containing protein lysine methyltransferases that methylate H3K4, a transcriptional activation mark. MLL translocations and MLL partial tandem duplications occur in AML with frequencies of 5-10% and 5-7%, respectively, and are associated with an inferior prognosis.86,87-91 Common target genes of MLL fusion proteins are HOXA cluster genes92 and MEIS1. Combinations of HOXA9 and MEIS1 as well as of the MLL fusion MLL-AF9 result in leukemic transformation of murine HSC and progenitor cells.93 Several studies have shown that the H3K79 methyltransferase DOT1L is crucial for the initiation and maintenance of MLL-AF9 rearranged AML.94-96 Menin, a component of the MLLSET1 like histone methyltransferase complex, is another indispensable protein that interacts with MLL in leukemogenesis. The inhibition of Menin has been shown to induce apoptosis and differentiation in leukemic blasts harboring MLL translocations.97,98 Other lysine methyltransferases and demethylases Fusions of the histone lysine methyltransferase NSD1 and Nup98 occur in a minority of AML patients, but are associated with a dismal prognosis.99-101 The methylation induced by NSD1 is physiologically antagonized by
demethylases LSD1 and LSD2. Mutations or translocations involving Jumanji C lysine demethylases e.g. JARID1 alter H3K4 methylation. 10% of AML patients with AML M7 harbor JARID1-NUP98 fusions, and expression of this fusion gene is associated with an aggressive course of disease and an adverse outcome.102,103 Histone acetyltransferases (HAT) Histone acetyltransferases alter chromatin compaction in favor of a less compact chromatin by acetylation of lysine residues. Alterations of enzymes belonging to this group e.g. CBP (mutations) or MOZ (translocations) have been detected in ALL and AML.104-106
Strategies for targeting epigenetic regulators Multiple studies substantiate that mutations of epigenetic modifiers result in increased self-renewal of murine HSC and HSPC, myeloproliferation and extramedullary hematopoiesis, but do not give rise to AML. Thus, epigenetic modifiers contribute to leukemogenesis, but are alone not capable of causing leukemia. The fact that these mutations can occur in early “pre-leukemic� stages e.g. in patients with preceding MDS, underscores that they are underlying mutations that will be detected in different malignant clones within the same patients at later time points, which makes them a relevant therapeutic target. Epigenetic modifications occur physiologically in the process of differentiation. Thus, these modifications are generally reversible and make them an attractive target (Figure 1 and Figure 2).
Figure 1. Methylation and hydroxymethylation of DNA and mechanisms for a targeted therapy. 272
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Epigenetic regulators in AML study, in which a short course of panobinostat was given in addition to the classical “7+3� induction chemotherapy in 22 patients >60 years with de novo AML or high-risk MDS, showed a CR/CRi rate of 40% and a median survival rate of 16 months in responders in this high-risk cohort.124
DNMT methyltransferase inhibitors The nucleoside analogs azacytidine (AZA) and 5-aza-2deoxycytidine [decitabine (DAC)] integrate into normal DNA during S phase, inhibit DNA methyltransferases by forming bonds with DNMTs and cause their degradation.107,108 Although both agents can induce robust global methylation changes (hypomethylation) in vitro and in vivo, overall response rates are low at 30-50%.109
IDH inhibitors
Histone deacetylase inhibitors The balance between histone acetylation and deacetylation plays a critical role in the regulation of gene expression. Histone acetylation induced by histone acetyl transferases (HATs) is associated with gene transcription, whereas histone hypoacetylation induced by histone deacetylases (HDACs) confers gene silencing.110 18 human HDACs can be assigned to four different classes of HDACs. HDACs have also been shown to deacetylate proteins involved in cell cycle control, differentiation and aopotosis.110,111 The potential of HDAC inhibitors as monotherapy in the treatment of MDS and AML has been assessed in a number of clinical trials. Although HDAC inhibition showed anti-leukemic activity in vivo, therapeutic outcome with single agent HDAC therapy remained poor with response rates of up to 17% for vorinostat,112-114 13% for the oral inhibitor MGCD0103115 and less for other inhibitors.116,117 Combinations of HDAC inhibitors with hypomethylating agents have been assessed in clinical studies and showed higher response rates and synergistic anti-leukemic effects.118-120 Combinations of HDAC inhibitor panobinostat with JQ1 inhibitors, EZH2 inhibitors or LSD1 inhibitors showed synergistically lethal effects on AML cells.121-123 A recently presented phase 1
Several small molecule targeting mutant IDH1/2 enzymes have been developed and tested in pre-clinical models and are currently being evaluated in clinical trials. The IDH1-R132H inhibitor AGI-5198 was first described in the context of glioma cells harboring IDH1 mutations and shown to reduce 2-ketoglutarate levels, demethylation of H3K9me3 and induce differentiation.125 Treatment of IDH1-mutated AML progenitor cells with HMS-101, another IDH1 inhibitor, resulted in a decrease of 2 HG levels and block of colony formation.126 First results of the clinical phase I trial of the orally administered IDH1 inhibitor, AG-120, in 66 patients with relapsed/primary refractory AML harboring an IDH1 mutation (NCT02074839) demonstrate an overall response rate of 36% with a CR rate of 18% and a median response duration of 5.6 months.127 The IDH1 inhibitor IDH305, and the IDH1 and IDH2 mutant inhibitor AG-881 are currently being tested in patients with Idh mutant malignancies in trials which are currently recruiting (NCT02381886, NCT02492737). The IDH2-R140Q inhibitor AGI-6780 binds allosterically to the IDH2R140Q dimeric interface and causes differentiation of the erythroleukemic cell line TF-1 and primary human AML blasts.128 The IDH2 inhibitor AG-221 decreased 2-HG levels by >90%, induced differentiation and prolonged survival in a dose-
Figure 2. Posttranslational methylation and acetylation of histones and mechanisms for a targeted therapy. Arg: Arginine; HAT: histone acetyltransferase; HDAC: histone deacetylase; KDM: Histone lysine demethylase; Lys: lysine; PKMT: protein lysine methyltransferase; PRMT: protein arginine methyltransferase. haematologica | 2016; 101(3)
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dependent manner in an AML IDH2R140Q xenograft model.129 Preliminary results of its first-in-human phase I/II dose escalation study (NCT01915498) in patients with advanced myeloid malignancies showed CR and PR rates of 18% and 15%, respectively, in 128 relapsed/refractory AML patients, irrespective of the number of prior treatment regimens. The median duration of response was 6 months. This clinical benefit is achieved by a differentiation of the malignant clone despite the persistence of the mutant IDH2 VAF.130
EZH2 inhibitors EZH2 inhibitors have proven efficacy in lymphoma and are currently being explored in these diseases in clinical trials. Of note, GSK126, a potent, highly selective EZH2 inhibitor decreased global H3K27me3 levels, and reduced proliferation of EZH2 mutant DLBCL cell lines and of EZH2 mutant DLBCL mouse xenograft models.131 Although EZH2 mutations occurring in AML are loss-offunction mutations, in contrast to lymphoma where the majority confers a gain-of-function, an intact EZH2 and PRC2 complex is required for aberrant self-renewal in MLL-rearranged AML.132,133 Deletion of EZH2 in MLL-rearranged mouse models impaired growth and progression of AML.134 Likewise, knockdown of EZH2 in HL60 cells promoted AML differentiation and reduced clonogeneic potential.135 Recent data have shown that EZH2 inhibitors e.g. 3-deazaneplanocin (DZNep) induced apoptosis in leukemic MLL-rearranged cells and reduced the frequency of leukemia initiating cells (LICs).136,137 UNC1999, a dual EZH1 and EZH2 inhibitor is a promising new oral target in MLL-rearranged leukemia.138
Bromodomain inhibitors BRDT, BRD2, BRD3, and BRD4 belong to the family of bromodomain and extraterminal (BET) human bromodomain proteins. They facilitate transcriptional activation by binding acetylated chromatin.
BRD4 inhibitors JQ1 is a cell-permeable small molecule that binds competitively to the acetyl-lysine recognition motif of BRD4.139 JQ1 has been demonstrated to have anti-leukemic activity in vitro and in vivo in diverse AML models.140,141 In FLT3-mutated AML, CD34+ human blast progenitor cells apoptosis was enforced by the combination of JQ1 with the FLT3 tyrosine kinase inhibitor ponatinib or AC220.121 Also, combinations of JQ1 with the HDAC inhibitor panobinostat increased apoptosis in human AML blast progenitor cells.122 A more stable and soluble derivate of JQ1 for clinical application, JQ2 (TEN-010), is currently being tested in a phase I study in patients with AML and MDS (NCT02308761). The BRD4 inhibitor, GSK525762, has entered early clinical trials in patients with relapsed refractory hematological malignancies (NCT01943851). The new BRD4 inhibitors in late pre-clinical trials - EP11313 and EP11336 - have demonstrated a favorable pharmacologic profile compared to GSK52762, and led to a growth inhibition of c-myc deregulated AML cell lines, which was more pronounced when combined with ATRA.142
BRD2/3/4 inhibitors OTX015 is another Bromodomain inhibitor, that specifically prevents BRD2, 3 and 4 from binding to acetylated histones which leads to the suppression of super-enhancer 274
driven oncogenes. OTX015 has proven antiproliferative efficacy in pre-clinical studies in lymphoid cell lines and mouse models.143 A dose-finding phase I trial of oral OXT015 (NCT01713582) in 41 patients with relapsed/refractory acute leukemia (37 AML, 1 high-risk MDS, 3 ALL), showed 2 complete remissions and 1 CR with incomplete count recovery. The main dose-dependent side effects were diarrhea and thrombocytopenia.144 The BRD inhibitor CPI-0610 in combination with MDM2 inhibition has shown efficacy in in vitro and murine in vivo experiments in eradicating p53 wild-type AML and sparing normal hematopoiesis.145 A phase 1 study of CPI-0610 in patients with pretreated AML, MDS and MPS is currently recruiting patients (NCT02158858).
LSD1 inhibitors The histone H3K4/K9 demethylase LSD1 can regulate gene activation and repression in epigenetic regulation and is a key effector of the differentiation block in MLL-rearranged leukemia. High LSD1 expression blocks differentiation and is associated with a poor prognosis in AML. LSD1 can be targeted by tranylcypromine analogs or downregulated by RNA interference which induces differentiation of MLLrearranged leukemic cells.146 The combination of ATRA and LSD1 inhibition by tranylcypromine in cell lines and primary AML samples showed a more potent antileukemic effect than either drug alone.147 The selective tranylcypromine derivative LSD1 inhibitors ORY-1001 developed by Oryzon Genomics (EudraCT number 2013002447-29) and GSK2879552 developed by GlaxoSmithKline (NCT02177812) have entered early phase clinical trials in patients with relapsed and refractory acute leukemia. A phase I clinical study of ATRA and tranylcypromine for adult patients with AML and MDS (NCT02273102), and a phase I/II trial of ATRA and tranylcypromine in patients with relapsed or refractory AML and no intensive treatment possibility (NCT02261779) are currently recruiting. Co-treatment of the LSD1 inhibitor 2509 and panobinostat showed synergistic lethality of primary AML blasts and prolonged survival in xenograft AML mouse models compared to either agent alone.123 IMG-98 is a novel LSD1 inhibitor that irreversibly binds to LSD1’s essential cofactor FAD and thereby leads to its inactivated enzyme form. Exposure of AML cell lines to IMG-98 has been shown to promote differentiation and growth inhibition of AML blasts, especially in combination with ATRA. The first clinical trials with an optimized drug closely related to IMG-98 are expected to start in early 2016.148
DOT1L inhibitors DOT1L inhibitors selectively impair DOT1L-mediated H3K79 methylation and inhibit the expression of leukemogenic genes. Several DOT1L inhibitors have been successfully tested in MLL-rearranged AML (MLL-AF6 and MLL-AF9) cells in xenograft models, where they have impeded proliferation and caused cell cycle arrest in cells expressing the MLL fusion.149-153 The DOT1L inhibitor Pinometostat (EPZ-5676) was able to cause sustained complete remission in a xenograft model. The first results from its phase I clinical trial for, thus far, 49 patients with advanced hematological malignancies, including relapsed/refractory MLL-rearranged AML (NCT01684150), revealed an overall response in 6 patients (2 of whom haematologica | 2016; 101(3)
Epigenetic regulators in AML achieved a CR) with an acceptable safety profile. Pinometostat plasma concentrations in these patients correlated with the inhibition of global H3K79 methylation in PBMC and reductions in methylation of MLL target genes.154 Data from Chen et al. demonstrated that DOT1L inhibition favors an open chromatin state by the inhibition of chromatin localization of the repressive SIRT1/H3K9 methyltransferase SUV39H1 complex.155 Of note, a combination therapy of a DOT1L inhibitor and SIRT activators have demonstrated enhanced antiproliferative activity against MLL-rearranged cell lines.155
JmjC-containing demethylases H3K27me3 negatively regulates gene transcription by promoting a compact chromatin structure.156,157 Mutations in members or associated proteins of the PRC2 complex such as EZH2, SUZ12, EED and ASXL1 result in a loss of H3K27me3, providing a rationale for a therapy that inhibits demethylation on H3K27.64,67,78 Demethylation by JmjC containing demethylases is α-KG-dependent and can be inhibited by small molecules. GSK-1 - or its corresponding ethyl ester prodrug GSK-4 - is a selective H3K27 demethylase inhibitor that inhibits JMJD3 and UTX and impairs TNF-α production by human primary macrophages in an H3K27-dependent manner.158,159
Summary In recent years our knowledge of the mutational landscape of AML has deeply improved and the relevance of mutations involving epigenetic modifiers has been highlighted. Epigenetic modifiers represent a new class of mutations that affect global chromatin state and DNA methylation, and control large numbers of genes and pathways by inducing alterations in DNA methylation or DNA hydroxymethylation and histone post-translational modi-
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fications. In vitro and in vivo models have delineated that mutations in epigenetic regulators are not by themselves sufficient to initiate AML, but can induce the expansion of a stem/progenitor clone that is susceptible to other mutations. In fact, mutations in epigenetic modifiers often cooccur with classical mutations in signaling effectors and transcription factors, but also with other epigenetic mutations. Furthermore mutations of epigenetic modifiers have not only been found in AML, but occur also in MPN and MDS, and even in subjects with clonal hematopoiesis in the absence of a myeloid neoplasia. The number of different epigenetic mutations that are potentially targetable for a specific personalized therapy currently outnumber the available inhibitors. However, some newly developed compounds demonstrate target inhibition and are currently been investigated in clinical trials with promising clinical efficacy. There is growing evidence that the inhibition of a specific epigenetic modifier may not kill the malignant clone, but in many cases rather leads to the differentiation of leukemia cells. Consequently, these agents will require a longer time to display their full therapeutic effects as compared to chemotherapy. A plausible clinical setting for these kinds of inhibitors could therefore be as an “add on” to conventional post-remission therapy. Pre-clinical studies have suggested that combination therapies of two or more epigenetic drugs, or a combination of an epigenetic drug combined with a kinase inhibitor, may have additional synergistic effects. Critical ongoing efforts include further accurate pre-clinical models to elucidate how mutations in epigenetic modifiers interact with other AML disease alleles, and clinical studies to assess the efficacy of epigenetic therapies alone or in combination with other anti-leukemic agents. Funding FP reports receiving grant PA 2541/1 from the German Research Foundation.
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GUIDELINE ARTICLE
Consensus expert recommendations for identification and management of asparaginase hypersensitivity and silent inactivation
EUROPEAN HEMATOLOGY ASSOCIATION
Ferrata Storti Foundation
Inge M. van der Sluis,1* Lynda M. Vrooman,2* Rob Pieters,3 Andre Baruchel,4 Gabriele Escherich,5 Nicholas Goulden,6 Veerle Mondelaers,7 Jose Sanchez de Toledo,8 Carmelo Rizzari,9 Lewis B. Silverman,2 and James A. Whitlock10
Department of Pediatric Hematology/Oncology, Erasmus Medical Center, Sophia Children’s Hospital, Rotterdam, The Netherlands; 2Department of Pediatric Oncology, Dana-Farber Cancer Institute, Division of Hematology/Oncology, Boston Children's Hospital, MA, USA; 3Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands; 4Department of Pediatric Hematology, Hôpital Robert Debré, Paris and University Paris Diderot, France; 5University Medical Centre Hamburg-Eppendorf, Clinic of Paediatric Hematology and Oncology, Hamburg, Germany; 6Great Ormond Street Hospital, London, UK; 7Pediatric Hematology/Oncology and Stem cell transplantation, Ghent University Hospital, Belgium; 8Department of Pediatric Hematology/Oncology, University Hospital Vall d'Hebron, Barcelona, Spain; 9Pediatric Hematology-Oncology Unit, Department of Pediatrics, University of Milano-Bicocca, Hospital S. Gerardo, Monza; 10Division of Haematology/Oncology, The Hospital for Sick Children and Department of Paediatrics, University of Toronto, Ontario, Canada 1
Haematologica 2016 Volume 101(3):279-285
*IMvdS and LMV contributed equally to this work.
ABSTRACT
L
-asparaginase is an integral component of therapy for acute lymphoblastic leukemia. However, asparaginase-related complications, including the development of hypersensitivity reactions, can limit its use in individual patients. Of considerable concern in the setting of clinical allergy is the development of neutralizing antibodies and associated asparaginase inactivity. Also problematic in the use of asparaginase is the potential for the development of silent inactivation, with the formation of neutralizing antibodies and reduced asparaginase activity in the absence of a clinically evident allergic reaction. Here we present guidelines for the identification and management of clinical hypersensitivity and silent inactivation with Escherichia coli- and Erwinia chrysanthemi- derived asparaginase preparations. These guidelines were developed by a consensus panel of experts following a review of the available published data. We provide a consensus of expert opinions on the role of serum asparaginase level assessment, indications for switching asparaginase preparation, and monitoring after change in asparaginase preparation.
Introduction L-asparaginase is an integral component of therapy for acute lymphoblastic leukemia (ALL). However, asparaginase-related complications, including the development of hypersensitivity reactions, pose clinical challenges. Children and adolescents with ALL who receive an inadequate course of planned asparaginase therapy due to either intolerable side effects or silent inactivation have been shown to have inferior outcomes compared with those who receive the majority of intended doses of asparaginase, highlighting the importance of maximizing the delivery of planned asparaginase therapy.1-3 L-asparaginase preparations are bacterial enzymes derived from either Escherichia coli (E. coli) or Erwinia chrysanthemi (Erwinia). E. coli-derived preparations include native E. coli-asparaginase, no longer available in the United States, and pegylated formulations (pegaspargase), in which the E. coli-derived enzyme is modified by the covalent attachment of polyethylene glycol. Erwinia asparaginase is an alternative haematologica | 2016; 101(3)
Correspondence: i.vandersluis@erasmusmc.nl or lynda_vrooman@dfci.harvard.edu
Received: October 14, 2015. Accepted: November 19, 2015. doi:10.3324/haematol.2015.137380
Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/100/3/279
©2016 Ferrata Storti Foundation Material published in Haematologica is covered by copyright. All rights reserved to Ferrata Storti Foundation. Copies of articles are allowed for personal or internal use. A permission in writing by the publisher is required for any other use.
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asparaginase preparation, antigenically distinct from E. coli-derived asparaginase forms. The clinical effectiveness of asparaginase is thought to be based upon the adequate depletion of asparagine. Lymphoblasts, dependent on extracellular sources of asparagine, are considered to be selectively vulnerable to asparagine depletion.4 Exposure to asparaginase, a foreign protein, has the capacity to trigger the development of anti-asparaginase antibodies. Prior studies have demonstrated the association of anti-asparaginase antibodies, which can neutralize enzymatic activity, with clinical hypersensitivity.3,5,6 Indeed, a major limitation in the capacity to deliver the intended up-front asparaginase therapy is the high rate of occurrence of hypersensitivity reactions, frequently reported in as many as 30% of patients receiving E. coliderived asparaginase, but with reported rates as high as 70%.1,3,7-9 Allergic reaction symptoms range from local reactions at the site of intramuscular injection to severe systemic reactions including anaphylaxis, which can occur with intramuscular or intravenous administration. Also demonstrated has been the phenomenon of “silent inactivation,� with the formation of neutralizing antibodies and reduced asparaginase activity in the absence of a clinically evident allergic reaction.2,3,10-12 The risk of development of clinical allergy and silent inactivation may be influenced by several factors including the formulation preparation of asparaginase, the route of administration, the schedule of administration (such as in schedules with intermittent dosing with gaps in asparaginase exposure followed by reintroduction of asparaginase), the line of treatment (i.e. relapse protocols) and the concurrent use of other chemotherapeutic agents including corticosteroids.13 An important clinical concern in the setting of both overt allergy to asparaginase and silent inactivation is that continued asparaginase therapy with the same formulation will be clinically harmful and therapeutically ineffective, ultimately contributing to poorer outcomes. Here we propose recommendations for the identification and management of clinical hypersensitivity and silent inactivation with E. coli- and Erwinia chrysanthemi-derived asparaginase preparations, with the goal of maximizing the delivery of the planned asparaginase therapy. We highlight the potential clinical role of therapeutic drug monitoring (TDM) through serum asparaginase level assessment, indications for switching asparaginase preparations, and recommendations for monitoring after changes in asparaginase preparation.
Serum Asparaginase Activity Assessment The ability to identify patients with inadequate asparaginase activity is of great value in clinical decision making and has the potential to improve clinical outcomes. There are, however, multiple considerations to take into account in the implementation of pharmacokinetic monitoring of asparaginase for clinical use.
What should be measured? Given that the aim of asparaginase therapy is asparagine depletion, the measurement of asparagine from the blood would appear to be the most direct assessment of asparaginase effectiveness. However, there are several limitations to directly measuring asparagine levels that make such a strategy impractical and unreliable for clinical use. The measurement of asparagine is technically difficult due to 280
the rapid ex vivo metabolism of asparagine in the presence of asparaginase. Reliable sample acquisition entails collection on ice water and centrifugation, extraction of serum, and de-proteinization/acidification to inhibit the reaction, in a very limited time-frame (less than 5-15 minutes). Because of these logistical challenges, the assessment of serum asparagine levels is not realistically achievable for broad clinical application.14-16 Furthermore, data from studies measuring asparagine levels are often difficult to interpret because different cut-off values have been used for the definition of complete asparagine depletion. The measurement of anti-asparaginase antibodies could also be considered, and are frequently measured in the context of clinical research investigations. However, there are no commercially clinically validated tests available at the present time. Moreover, the specificity of anti-asparaginase antibodies to predict inactivation has been found to be low compared with measuring asparaginase activity itself; many patients appear to develop anti-asparaginase antibodies without any signs of clinical allergy or inactivation of asparaginase, and antibody levels in patients with and without hypersensitivity overlap.12 Antibody assessment itself is therefore not well suited for current clinical use. The measurement of asparaginase activity levels is technically feasible, reproducible, and reliable, and is considered to best correlate with clinical effectiveness. Previously, asparaginase activity levels were only measured in the research setting, but a growing number of providers now have access to real-time, validated asparaginase activity measurements. Several European treatment protocols already recommend the monitoring of asparaginase activity in the context of routine clinical care. Currently, the assessment of asparaginase activity is often performed by the use of a reaction with indooxine.17 In North America, an FDA-approved asparaginase activity assay is currently commercially available (AIBio Tech, Richmond, VA, USA.)
What defines optimal asparaginase activity? The pharmacodynamic goal of asparaginase therapy is complete asparagine depletion. Nonetheless, the level of asparaginase activity necessary for complete asparagine depletion is unclear. A threshold of 0.1 IU/mL has been used in many research and treatment protocols to define therapeutic asparaginase activity, as levels above this threshold have been found to result in complete asparagine depletion.18-21 In 1981, Riccardi et al. administered E. coli and Erwinia asparaginase to rhesus monkeys and patients and found that plasma asparaginase activity levels above 0.1 IU/mL resulted in complete asparagine depletion in CSF and plasma.18 This cut-off of â&#x2030;Ľ 0.1 IU/mL has been confirmed and used in many clinical trials.9,19,22-24 The question arises whether a lower threshold, for example of 0.05 IU/mL, also leads to complete asparagine depletion. Rizzari and colleagues showed that trough asparaginase activity levels of < 0.05 IU/mL, obtained either with native E. Coli or Erwinia asparaginase, resulted in serum and CSF asparagine depletion in children with ALL.25 In some studies activity levels as low as 0.02 IU/mL26,27 or 0.03 IU/mL21,28 resulted in sufficient depletion. In contrast, the only study indicating that higher activity levels are needed is a recent COG study of two pegylated E. coli asparaginase preparations, calaspargase pegol and pegaspargase, in which the plasma asparagine level began haematologica | 2016; 101(3)
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to rebound once plasma asparaginase activity declined below 0.4 IU/mL.29 However, based on the literature to date, we consider that a nadir serum asparaginase activity level of ≥ 0.1 IU/mL appears to be an appropriate and safe target level, because complete depletion is observed less consistently with asparaginase activity levels below this cut-off. In addition, in the absence of further data, we consider a desirable level of activity for patients receiving pegaspargase to be defined as ≥ 0.1 IU/mL at 14 days postadministration. For patients receiving multiple doses per week of native E. coli or Erwinia asparaginase, we consider a desirable level of activity to be ≥ 0.1 IU/mL prior to each administered dose.
When should asparaginase activity be assessed? The timing of serum asparaginase assessment is another important aspect in the implementation of TDM for asparaginase therapy. The majority of childhood ALL trials now utilize pegaspargase, which has a plasma half-life notably longer than native E. coli asparaginase (5.73 ± 3.24 days, compared with 1.28 ± 0.3 days, respectively).30 Most reports use the trough level at day 14 to define the efficacy of the pegaspargase treatment. Information on the desirable target levels of asparaginase activity at time points prior to day 14 (that would ensure a level ≥ 0.1 IU/mL at day 14) is lacking. Still, assessments at earlier time points can be informative, as levels < 0.1 IU/mL prior to day 14 would indicate that the day 14 trough level will be too low. In summary: • Serum asparaginase activity levels are the best and most reliable indicators of asparaginase efficacy. • Trough asparaginase activity levels ≥ 0.1 IU/mL appears to be a safe target level to ensure therapeutic benefit. • Anti-asparaginase antibodies and asparagine measurements are not indicated for clinical decision making outside the context of a clinical trial.
Recommendations in the setting of clinical allergy The development of clinical hypersensitivity is considered a strong indicator that an individual patient has developed anti-asparaginase antibodies and will have reduced asparaginase activity. The ultimate concern is that continued use of asparaginase of the same formulation will be ineffective in the treatment of leukemia and may lead to poorer outcomes. Continuing the drug should be discouraged, even when it is clinically possible to administer the same preparation using premedication such as steroids and antihistamines or decreasing the infusion rate, as these measures reduce the symptoms of the allergy but do not prevent the inactivation of asparaginase by the antibodies. Clinical hypersensitivity reactions are characterized by a range of symptoms, from mild localized reactions at the site of intramuscular injection to severe systemic reactions with features such as urticaria, bronchospasm, angioedema, and anaphylaxis following either intramuscular or intravenous administration. When grading, the severity of reactions is based on the Common Terminology Criteria for Adverse Events v4.03 (CTCAE) classification (Table 1), haematologica | 2016; 101(3)
features of Grade 1 allergic reactions include transient flushing or rash without need for intervention, Grade 2 reactions include indication for intervention or interruption of infusion, Grade 3 are prolonged/recurrent reactions with the need for hospitalization for clinical sequelae, and Grade 4 reactions are life threatening. CTCAE v4.03 has a separate grading for anaphylaxis.31 However, in reviewing criteria such as these and in clinical practice, identifying a clinical allergy is not always straightforward. For example, it can be difficult to determine whether a Grade 1 reaction, with transient flushing, truly represents an allergic reaction. A concern in the setting of a questionable reaction would be the failure to positively identify and act upon true hypersensitivity. Indeed, some studies suggest that even a Grade 1 reaction can be associated with inactivation.12 With intravenously administered asparaginase, infusion reaction may occur, often late during an infusion, which can be confused with an allergic reaction.32 While there are signs and symptoms that mimic clinical allergy, these are not truly allergic reactions and are not associated with inactivation. In these cases asparaginase activity levels may be informative as to whether or not to continue the drug. Therefore, we propose the following recommendations based upon the severity of clinically evident allergic reaction and route of administration. In the setting of CTCAE Grade 2 or higher reactions, we recommend that switching asparaginase preparation is indicated, with no definite need for testing of asparaginase levels. In the setting of Grade 1 reactions, or when a reaction has occurred of questionable significance, we recommend real-time monitoring of serum asparaginase activity levels. Presuming the use of intravenous pegaspargase, we recommend checking a level within one week of dose administration. When the previous dose is truncated because of an allergic reaction it is hard to interpret an activity level following this dose. The main goal of this level recheck is to determine whether activity is present. If the level is nondetectable, then no further E. coli-derived asparaginase should be utilized and the patient should be switched to an Erwinia-derived preparation. If the level is detectable, we recommend rechecking a 14-day trough level, and a subsequent dose of pegaspargase may be carefully administered. Premedication with agents such as antihistamines or corticosteroids should not be used in the absence of checking asparaginase activity levels. Activity levels should be checked after 7 and 14 days and should be above 0.1 IU/mL. When the activity level is less than the desired threshold of 0.1 IU/mL, the asparaginase preparation should be switched. With intramuscular asparaginase, with any questionable reaction, we recommend checking a serum asparaginase level because the complete dose will have been administered and the level would therefore be informative (as there can be clinical confusion with regard to local irritation vs. clinical allergic reaction.) In summary: • Grade 1 reactions and questionable reaction following intravenous administration: Monitor serum asparaginase level in real-time within 7 days to identify inactivation. • Grade 2-4 reactions following intravenous or intramuscular administration: Switch asparaginase prepara281
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tion, without definite need to check asparaginase levels. â&#x20AC;˘ Any questionable reaction with intramuscular administration requires checking asparaginase activity level.
Recommendations in the setting of silent inactivation â&#x20AC;&#x153;Silent inactivationâ&#x20AC;? is the development of asparaginase antibodies and asparaginase inactivity without the development of overt or recognized allergy symptoms. From a clinical perspective, the concern is that with continued asparaginase administration in the setting of silent inactivation, the patient will not receive the benefit of effective asparaginase therapy which might otherwise be mitigated by switching to an alternative asparaginase preparation. Recent data suggest that the development of silent inactivation is clinically important in the context of leukemiadirected therapy, and that acting upon silent inactivation may improve outcome.2,3 The results of the DFCI ALL Consortium Protocol 00-01 suggested that the evaluation of nadir serum asparaginase levels in patients receiving native E. coli asparaginase, and changing asparaginase preparation in the setting of silent inactivation (as defined by persistently low nadir serum asparaginase activity, with or without antibody positivity), was associated with improved outcome in children with ALL.2 These results highlight the importance of switching asparaginase preparation in the setting of silent inactivation.
What is the definition of silent inactivation? Silent Inactivation is caused by neutralizing anti-drug antibodies (to asparaginase or PEG) resulting in asparaginase inactivity without the development of overt allergy symptoms. It can be identified with (trough) asparaginase activity levels below the lower limit of quantification (LLQ) occurring in patients without clinical allergy, preferably measured in 2 independent samples to minimize the number of patients tested false positive. Specifically, with the use of pegaspargase (given every two weeks), a day 7 asparaginase activity level below 0.1 IU/mL and/or a day 14 level below the LLQ would be consistent with silent inactivation. Although less frequently used in contemporary pediatric ALL trials, silent inactivation is demonstrat-
ed for native E. coli asparaginase if the 72 hours post-dose level is below the LLQ (in a two times a week schedule), or if the level 7 days post-dosing is below the LLQ (in a weekly administration schedule). With the use of Erwinia asparaginase (three times a week schedule), a 48 hours post-dose level below the LLQ would raise concern for silent inactivation (please see more detailed discussion of monitoring with the use of Erwinia asparaginase below). In addition, low activity levels should always be interpreted in the context of the dosing and frequency of the administration of asparaginase. For example, with the use of Erwinia asparaginase, based on large inter-individual differences in clearance, low trough levels at 72 hours and beyond could reflect a need for more frequent dosing, rather than silent inactivation itself.
Who should be screened for silent inactivation? As noted above, the concern that patients could continue to be exposed to an agent that has been rendered ineffective when alternative agents exist is compelling. Our conclusion is that screening for silent inactivation should be considered in all patients undergoing therapy for ALL with asparaginase. This may be particularly important following gaps in asparaginase therapy or in the setting of the treatment of relapsed leukemia.
When should patients be screened for silent inactivation? Silent inactivation of E. coli-derived asparaginase has been reported in induction and intensification phases.2,12 Some patients may have developed antibodies to PEG before the start of pegaspargase treatment, presumably due to previous exposure to PEG (for example in creams).33 We recommend the testing of serum asparaginase activity level after the first dose of E. coli-derived asparaginase. With the use of pegaspargase, this should be done within 7 days of the dose. If the level is detectable but less than 0.1 IU/mL, activity should be rechecked at day 14. The recommended frequency of screening after the first dose of asparaginase depends upon the dosing schedule. For patients due to receive multiple asparaginase doses without any prolonged gap between doses (e.g., pegylated asparaginase given every 14 days), it would be reasonable to confirm a low or undetectable level after a subsequent
Table 1. Common Terminology Criteria for Adverse Events v4.03 (CTCAE) classification for allergic reactions and anaphylaxis. Definition allergic reaction: A disorder characterized by an adverse local or general response from exposure to an allergen.
Grade 1 Transient flushing or rash, drug fever <38 degrees C (<100.4 degrees F); intervention not indicated 2 Intervention or infusion interruption indicated; responds promptly to symptomatic treatment (e.g., antihistamines, NSAIDS, narcotics); prophylactic medications indicated for <=24 hrs 3 Prolonged (e.g., not rapidly responsive to symptomatic medication and/or brief interruption of infusion); recurrence of symptoms following initial improvement; hospitalization indicated for clinical sequelae (e.g., renal impairment, pulmonary infiltrates) 4 life-threatening consequences; urgent intervention indicated Definition anaphylaxis: A disorder characterized by an acute inflammatory reaction resulting from the release of histamine and histamine-like substances from mast cells, causing a hypersensitivity immune response. Clinically, it presents with breathing difficulty, dizziness, hypotension, cyanosis and loss of consciousness and may lead to death.
Grade 3 symptomatic bronchospasm, with or without urticaria; parenteral intervention indicated; allergy-related edema/angioedema; hypotension. 4 life-threatening consequences; urgent intervention indicated 282
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dose, and to change to a different preparation (eg, Erwinia) if two consecutive levels are undetectable. When there is a gap between asparaginase doses, we recommend checking a level after the first dose of asparaginase administered after the gap, with a gap defined as a period in which asparaginase activity level will have decreased to < LLQ between doses. In practice, this is usually the case when there is an interval of at least 4 weeks between pegylated asparaginase doses. With native E. coli asparaginase, we recommend measuring a trough level after the first dose and after every reintroduction of asparaginase. Levels could be checked more frequently based on clinician discretion. If there are a limited number of asparaginase doses in the treatment plan and/or intermittent dosing (prolonged gaps between doses), we would recommend screening for silent inactivation after every asparaginase dose, and one could consider switching preparation based upon a single undetectable or low level rather than confirming it with two separate measurements. This monitoring plan would foster the maximization of asparaginase exposure. In summary: • All patients should undergo therapeutic drug monitoring for silent inactivation. • Silent Inactivation can be identified by the assessment of serum asparaginase activity, preferably measured in 2 independent samples. • Measure serum asparaginase activity level within 7 days of the first dose of pegaspargase in induction and following every reintroduction after a gap in asparaginase. With native E. coli asparaginase, consider measuring a trough level after the first dose and after every reintroduction. • Silent inactivation of pegaspargase (administered every other week) is defined as a day 7 level below 0.1 IU/mL and/or day 14 level below the LLQ. • Silent inactivation of native E. coli or Erwinia asparaginase is defined as a trough level below the LLQ. Trough levels should always be interpreted in the context of dosing and frequency e.g.: - Native E coli: asparaginase activity below the LLQ 72 hours post-dosing in a two times a week administration schedule, or below the LLQ 7 days post-dosing in a weekly administration schedule. - Erwinia: a 48 hour post-dose activity level below the LLQ in a three times a week schedule.
• Consider confirmation of a low or undetectable level based on the planned schedule of asparaginase administration.
Switching preparations When changing asparaginase preparation, those receiving native E. coli asparaginase who develop clinical allergy or silent inactivation should switch to either pegaspargase or to Erwinia asparaginase, with the choice of a second-line agent dependent upon protocol specifications and preparation availability. Patients initially receiving pegaspargase may only be switched to Erwinia asparaginase; switching to native E. coli asparaginase should not be considered an option. Erwinia asparaginase is antigenically distinct from E. coliderived asparaginase. The half-life of Erwinia asparaginase is shorter than other forms of asparaginase, and the shorter half-life of Erwinia asparaginase has demonstrated clinihaematologica | 2016; 101(3)
cal implications, with the need for more frequent dosing (every 2-3 days).10,34 When utilized in patients with a history of E. coli asparaginase hypersensitivity reactions, the majority of patients receiving Erwinia asparaginase have been shown to achieve goal nadir asparaginase activity levels. A subsequent allergy to Erwinia asparaginase (in patients who previously developed an allergy to E. coli derived-preparations) has been reported at rates of 333%.12,35,36 Tong and colleagues reported that 97% of patients who switched to Erwinia asparaginase after hypersensitivity to pegaspargase were able to complete their full planned course of asparaginase in a continuous dosing schedule.12 While Erwinia asparaginase has been utilized in the treatment of childhood ALL for several decades, there remains variability in practice with regard to optimal dosing, dosing interval, and route of administration. Intravenous administration has been routinely utilized in Europe in a dose of 20,000 IU/m2 three times a week. FDA-approved IM dosing of Erwinia asparaginase is 25,000 IU/m2 three times per week (Monday/Wednesday/Friday), for six total doses for each planned dose of pegaspargase. Twice weekly IM or IV dosing has also been utilized with apparent efficacy.12,35 Although IM administration has largely been utilized in the United States, Erwinia asparaginase is now approved for IV use as well.37 The optimal dose, schedule, and route of Erwinia asparaginase, however, remain unclear and are worthy of further investigation. Large inter-individual differences in clearance underscore the importance of individualized dosing schedules based on asparaginase activity.
Asparaginase activity monitoring after change in preparation to Erwinia asparaginase After switching to Erwinia asparaginase, the monitoring of serum asparaginase levels is of continued importance, however with a slightly different underlying rationale. After a switch to Erwinia asparaginase due to allergy to pegaspargase, there are no additional alternative preparations available. The emphasis on asparaginase activity monitoring with the use of Erwinia asparaginase is to inform the individualization of dosing rather than the switching of asparaginase preparation. Based on large inter-individual differences in clearance, low nadir levels at 72 hours or 96 hours after a dose of Erwinia may represent the need for more frequent dosing rather than silent inactivation, and levels should be interpreted in this context. In theory, persistently low 48 hour trough levels with appropriate dosing of Erwinia asparaginase could allow clinicians the possibility of considering alternative treatments to asparaginase, although the implementation of such alterations would be dependent on individual treatment protocols, and the impact of such alterations are unknown. We recommend checking trough serum asparaginase levels every 2 weeks in continuous schedules (and after each 2-week block of Erwinia asparaginase in intermittent dosing schedules). Specifically, levels after Erwinia asparaginase should be performed 48 hours after dosing, with an aim of asparaginase activity levels greater than 0.1 IU/mL. Because Erwinia asparaginase is frequently dosed on a Monday/Wednesday/Friday schedule, there may also be clinical value in checking a level 72 hours after dosing (after the Friday dose and before the following Monday dose). It may be reasonable to adjust dosing or interval to 283
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maintain a level greater than 0.1 IU/mL. In addition, there may be differences in the pharmacokinetics of IM compared with IV administered Erwinia asparaginase, making the assessment of levels of particular importance with IV administration.
Future directions and opportunities The ability to identify patients with inadequate asparaginase activity has the potential to inform clinical decision making. Previously, asparaginase activity levels were only measured in the context of research, but a growing number of providers now have access to real-time validated asparaginase activity measurements. Several European treatment protocols already recommend the monitoring of asparaginase activity. Since therapeutic drug monitoring has started to play a role in daily practice, guidelines are needed for identifying and managing hypersensitivity to asparaginase. The goal of asparaginase therapy is to achieve complete asparagine depletion. As noted above, due to the difficulties in directly measuring serum asparagine, it is much easier and more reliable to measure asparaginase activity levels to guarantee effective treatment. To date, an asparaginase activity level â&#x2030;Ľ 0.1 IU/mL appears to be a reasonable target level to ensure therapeutic benefit. Ultimately, in interpreting asparaginase activity levels and in considering the most appropriate target threshold, it is the association with clinical outcome that matters the most. More data are needed to more precisely determine the optimal target level and to more robustly characterize the relationship between asparaginase activity levels and long-term outcomes. Yet even without further data, monitoring drug levels to detect silent inactivation may potentially improve outcome and could prevent continued administration of a drug that is ineffective. In addition, more detailed pharmacokinetic analyses will be of value to improve individualized dosing strategies. Currently, most reports use asparaginase activity trough levels at day 14 to determine the efficacy of the pegaspargase treatment. Information on the optimal target levels of
References 5. 1. Silverman LB, Gelber RD, Dalton VK, et al. Improved outcome for children with acute lymphoblastic leukemia: results of DanaFarber Consortium Protocol 91-01. Blood. 2001;97(5):1211-1218. 2. Vrooman LM, Stevenson KE, Supko JG, et al. Postinduction dexamethasone and individualized dosing of Escherichia Coli Lasparaginase each improve outcome of children and adolescents with newly diagnosed acute lymphoblastic leukemia: results from a randomized study--Dana-Farber Cancer Institute ALL Consortium Protocol 00-01. J Clin Oncol. 2013;31(9):1202-1210. 3. Panosyan EH, Seibel NL, Martin-Aragon S, et al. Asparaginase antibody and asparaginase activity in children with higher-risk acute lymphoblastic leukemia: Children's Cancer Group Study CCG-1961. J Pediatr Hematol Oncol. 2004;26(4):217-226. 4. Muller HJ, Boos J. Use of L-asparaginase in
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asparaginase prior to day 14 (for example, at day 3 and day 7) to ensure an adequate level at day 14 is lacking. Due to hospital budget restrictions and the increasing costs of treatment of childhood ALL, more insight into the costs of treatment with the various asparaginase preparations would be very useful. Tong and colleagues demonstrated that administration of pegaspargase in first-line therapy was cost effective compared to native E.coli asparaginase, as it leads to a lower frequency of hypersensitivity and consequently less use of Erwinia asparaginase.38 Switching to Erwinia asparaginase in case of clinical allergy or silent inactivation has considerable impact on the total ALL treatment costs.38 Erwinia asparaginase is more expensive and patients require more visits due to the more frequent administration schedule. This change in frequency of hospital visits may negatively affect the quality of life of the child and parents. However, the change in preparation may also have a favorable impact on outcome. Health economic analyses, taking into account all the costs of switching to Erwinia asparaginase, gained life years or qualityadjusted life years (QALY) and downstream costs of treatment failure are complicated but necessary in order to inform discussion on whether the change in preparation in case of allergy or silent inactivation is cost-effective. This will depend on the intensity of asparaginase therapy, the costs of Erwinia asparaginase and other treatment costs, and detailed cost-effectiveness considerations will differ by country and by treatment plan. In addition to the detection of silent inactivation, therapeutic drug monitoring can be used to individualize dosing by adapting the dose based on activity levels. With the use of pegaspargase, activity levels are generally much higher than with native E. coli asparaginase. Therapeutic drug monitoring to inform dosing has the potential to reduce the pegaspargase dose and thereby to reduce medication costs. Future investigations could explore optimal maximum nadir serum asparaginase levels with regard to clinical efficacy, toxicity, and cost, considering also the additional costs of the therapeutic drug monitoring program itself. Further characterization of long-term outcomes and comprehensive cost analyses would further inform clinical practice.
childhood ALL. Crit Rev Oncol Hematol. 1998;28(2):97-113. Woo MH, Hak LJ, Storm MC, et al. Antiasparaginase antibodies following E. coli asparaginase therapy in pediatric acute lymphoblastic leukemia. Leukemia. 1998;12(10): 1527-1533. Zalewska-Szewczyk B, Andrzejewski W, Mlynarski W, Jedrychowska-Danska K, Witas H, Bodalski J. The anti-asparagines antibodies correlate with L-asparagines activity and may affect clinical outcome of childhood acute lymphoblastic leukemia. Leuk Lymphoma. 2007;48(5):931-936. Muller HJ, Beier R, Loning L, et al. Pharmacokinetics of native Escherichia coli asparaginase (Asparaginase medac) and hypersensitivity reactions in ALL-BFM 95 reinduction treatment. Br J Haematol. 2001;114(4):794-799. Woo MH, Hak LJ, Storm MC, et al. Hypersensitivity or development of antibodies to asparaginase does not impact treatment outcome of childhood acute lym-
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phoblastic leukemia. J Clin Oncol. 2000;18 (7):1525-1532. Appel IM, Kazemier KM, Boos J, et al. Pharmacokinetic, pharmacodynamic and intracellular effects of PEG-asparaginase in newly diagnosed childhood acute lymphoblastic leukemia: results from a single agent window study. Leukemia. 2008;22(9):1665-1679. Asselin BL. The three asparaginases. Comparative pharmacology and optimal use in childhood leukemia. Adv Exp Med Biol. 1999;457:621-629. Vieira Pinheiro JP, Ahlke E, Nowak-Gottl U, et al. Pharmacokinetic dose adjustment of Erwinia asparaginase in protocol II of the paediatric ALL/NHL-BFM treatment protocols. Br J Haematol. 1999;104(2):313-320. Tong WH, Pieters R, Kaspers GJ, et al. A prospective study on drug monitoring of PEGasparaginase and Erwinia asparaginase and asparaginase antibodies in pediatric acute lymphoblastic leukemia. Blood. 2014;123(13):2026-2033.
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13. Pieters R, Hunger SP, Boos J, et al. L-asparaginase treatment in acute lymphoblastic leukemia: a focus on Erwinia asparaginase. Cancer. 2011;117(2):238-249. 14. Gentili D, Zucchetti M, Conter V, Masera G, D'Incalci M. Determination of L-asparagine in biological samples in the presence of Lasparaginase. J Chromatogr B Biomed Appl. 1994;657(1):47-52. 15. Asselin BL, Lorenson MY, Whitin JC, et al. Measurement of serum L-asparagine in the presence of L-asparaginase requires the presence of an L-asparaginase inhibitor. Cancer Res. 1991;51(24):6568-6573. 16. Lanvers-Kaminsky C, Westhoff PS, D'Incalci M, Zucchetti M, Boos J. Immediate cooling does not prevent the ex vivo hydrolysis of Lasparagine by asparaginase. Ther Drug Monit. 2014;36(4):549-552. 17. Lanvers C, Vieira Pinheiro JP, Hempel G, Wuerthwein G, Boos J. Analytical validation of a microplate reader-based method for the therapeutic drug monitoring of L-asparaginase in human serum. Anal Biochem. 2002;309(1):117-126. 18. Riccardi R, Holcenberg JS, Glaubiger DL, Wood JH, Poplack DG. L-asparaginase pharmacokinetics and asparagine levels in cerebrospinal fluid of rhesus monkeys and humans. Cancer Res. 1981;41(11 Pt 1):45544558. 19. Avramis VI, Sencer S, Periclou AP, et al. A randomized comparison of native Escherichia coli asparaginase and polyethylene glycol conjugated asparaginase for treatment of children with newly diagnosed standard-risk acute lymphoblastic leukemia: a Children's Cancer Group study. Blood. 2002;99(6):1986-1994. 20. Boos J, Werber G, Ahlke E, et al. Monitoring of asparaginase activity and asparagine levels in children on different asparaginase preparations. Eur J Cancer. 1996;32A(9): 1544-1550. 21. Ahlke E, Nowak-Gottl U, Schulze-Westhoff P, et al. Dose reduction of asparaginase under pharmacokinetic and pharmacodynamic control during induction therapy in children with acute lymphoblastic leukaemia. Br J Haematol. 1997;96(4):675-681. 22. Strullu M, Corradini N, Audrain M, et al.
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Silent hypersensitivity to Escherichia coli asparaginase in children with acute lymphoblastic leukemia. Leuk Lymphoma. 2010;51(8):1464-1472. Avramis VI, Martin-Aragon S, Avramis EV, Asselin BL. Pharmacoanalytical assays of Erwinia asparaginase (erwinase) and pharmacokinetic results in high-risk acute lymphoblastic leukemia (HR ALL) patients: simulations of erwinase population PK-PD models. Anticancer Res. 2007;27(4C):2561-2572. Albertsen BK, Schroder H, Ingerslev J, et al. Comparison of intramuscular therapy with Erwinia asparaginase and asparaginase Medac: pharmacokinetics, pharmacodynamics, formation of antibodies and influence on the coagulation system. Br J Haematol. 2001;115(4):983-990. Rizzari C, Zucchetti M, Conter V, et al. Lasparagine depletion and L-asparaginase activity in children with acute lymphoblastic leukemia receiving i.m. or i.v. Erwinia C. or E. coli L-asparaginase as first exposure. Ann Oncol. 2000;11(2):189-193. Pieters R, Appel I, Kuehnel HJ, et al. Pharmacokinetics, pharmacodynamics, efficacy, and safety of a new recombinant asparaginase preparation in children with previously untreated acute lymphoblastic leukemia: a randomized phase 2 clinical trial. Blood. 2008;112(13):4832-4838. Tsurusawa M, Shimomura Y, Asami K, et al. Long-term results of the Japanese Childhood Cancer and Leukemia Study Group studies 811, 841, 874 and 911 on childhood acute lymphoblastic leukemia. Leukemia. 2010; 24(2):335-344. Rizzari C, Citterio M, Zucchetti M, et al. A pharmacological study on pegylated asparaginase used in front-line treatment of children with acute lymphoblastic leukemia. Haematologica. 2006;91(1):24-31. Angiolillo AL, Schore RJ, Devidas M, et al. Pharmacokinetic and pharmacodynamic properties of calaspargase pegol Escherichia coli L-asparaginase in the treatment of patients with acute lymphoblastic leukemia: results from Children's Oncology Group Study AALL07P4. J Clin Oncol. 2014;32(34): 3874-3882.
30. Asselin BL, Whitin JC, Coppola DJ, Rupp IP, Sallan SE, Cohen HJ. Comparative pharmacokinetic studies of three asparaginase preparations. J Clin Oncol. 1993;11(9):1780-1786. 31. Common Terminology Criteria for Adverse Events (CTCAE) Version 4.0 Published: 2009 (v4.03: June 14, 2010). U.S.DEPARTMENT OF HEALTH AND HUMAN SERVICES, National Institutes of Health National Cancer Institute. Available at http://evs.nci.nih.gov/ ftp1/CTCAE/CTCAE_4.03_2010-0614_QuickReference_5x7.pdf. 32. Lenz HJ. Management and preparedness for infusion and hypersensitivity reactions. Oncologist. 2007;12(5):601-609. 33. Garay RP, El-Gewely R, Armstrong JK, Garratty G, Richette P. Antibodies against polyethylene glycol in healthy subjects and in patients treated with PEG-conjugated agents. Expert Opin Drug Deliv. 2012;9(11): 1319-1323. 34. Moghrabi A, Levy DE, Asselin B, et al. Results of the Dana-Farber Cancer Institute ALL Consortium Protocol 95-01 for children with acute lymphoblastic leukemia. Blood. 2007;109(3):896-904. 35. Vrooman LM, Supko JG, Neuberg DS, et al. Erwinia asparaginase after allergy to E. coli asparaginase in children with acute lymphoblastic leukemia. Pediatr Blood Cancer. 2010;54(2):199-205. 36. Billett AL, Carls A, Gelber RD, Sallan SE. Allergic reactions to Erwinia asparaginase in children with acute lymphoblastic leukemia who had previous allergic reactions to Escherichia coli asparaginase. Cancer. 1992;70(1):201-206. 37. Vrooman LM, Kirov, II, Dreyer ZE, et al. Activity and Toxicity of Intravenous Erwinia Asparaginase Following Allergy to E. coliDerived Asparaginase in Children and Adolescents With Acute Lymphoblastic Leukemia. Pediatr Blood Cancer. 2015 Sep 16. [Epub ahead of print] 38. Tong WH, van der Sluis IM, Alleman CJ, et al. Cost-analysis of treatment of childhood acute lymphoblastic leukemia with asparaginase preparations: the impact of expensive chemotherapy. Haematologica. 2013;98(5): 753-759.
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ARTICLE EUROPEAN HEMATOLOGY ASSOCIATION
Hematopoiesis
Ferrata Storti Foundation
Mathematical modeling reveals differential effects of erythropoietin on proliferation and lineage commitment of human hematopoietic progenitors in early erythroid culture
Daniel Ward,1 Deborah Carter,2 Martin Homer,1 Lucia Marucci,1 and Alexandra Gampel2
Haematologica 2016 Volume 101(3):286-296
Department of Engineering Mathematics, Faculty of Engineering, University of Bristol; and 2School of Biochemistry, Faculty of Medical and Veterinary Science, University of Bristol, UK 1
ABSTRACT
E
Correspondence: a.gampel@bristol.ac.uk
Received: July 13, 2015. Accepted: November 18, 2015. Pre-published: November 20, 2015 doi:10.3324/haematol.2015.133637
Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/101/3/286
Š2016 Ferrata Storti Foundation Material published in Haematologica is covered by copyright. All rights reserved to Ferrata Storti Foundation. Copies of articles are allowed for personal or internal use. A permission in writing by the publisher is required for any other use.
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rythropoietin is essential for the production of mature erythroid cells, promoting both proliferation and survival. Whether erythropoietin and other cytokines can influence lineage commitment of hematopoietic stem and progenitor cells is of significant interest. To study lineage restriction of the common myeloid progenitor to the megakaryocyte/erythroid progenitor of peripheral blood CD34+ cells, we have shown that the cell surface protein CD36 identifies the earliest lineage restricted megakaryocyte/erythroid progenitor. Using this marker and carboxyfluorescein succinimidyl ester to track cell divisions in vitro, we have developed a mathematical model that accurately predicts population dynamics of erythroid culture. Parameters derived from the modeling of cultures without added erythropoietin indicate that the rate of lineage restriction is not affected by erythropoietin. By contrast, megakaryocyte/erythroid progenitor proliferation is sensitive to erythropoietin from the time that CD36 first appears at the cell surface. These results shed new light on the role of erythropoietin in erythropoiesis and provide a powerful tool for further study of hematopoietic progenitor lineage restriction and erythropoiesis.
Introduction The question of whether extrinsic signals, in particular cytokines, have a deterministic role in lineage commitment of hematopoietic stem and progenitor cells has been much debated. It is difficult to distinguish a definite role in commitment from the well-characterized roles of cytokines in promoting proliferation and survival. Recent work has provided strong evidence that lineage commitment of granulocyte/monocyte progenitors (GMP) to granulocytes or macrophages can be specified by the appropriate colony stimulating factor (CSF)1-3 but it is less clear whether cytokines can control the lineage restriction of less mature stem and progenitor cells.4,5 Common myeloid progenitors (CMPs) are immature hematopoietic progenitors that become restricted to either the erythroid lineage as megakaryocyte/erythroid progenitors (MEP), giving rise to red blood cells and megakaryocytes/ platelets, or the myeloid lineage as GMP, giving rise to neutrophils, eosinophils, mast cells and macrophages. Mutations in regulators of lineage commitment from CMP are often found in leukemia,6-9 highlighting the importance of understanding this key regulatory event. The erythroid master regulator erythropoietin (epo) is fundamental to the control of both homeostatic and stress erythropoiesis.10 Erythroid cell proliferation is dependent on epo during the early S phase of the second colony forming unit-erythroid (CFU-E) division, coincident with the appearance of the erythroid marker glycophorin A (GPA).11,12 Epo is also required for erythroid precursor cell survival haematologica | 2016; 101(3)
Modeling lineage commitment in erythroid culture
Table 1. Parameters of the mathematical model. Symbols and physiological meaning of the parameters used in the full (37 parameter) mathematical model, and (column 3) their values in the simplified (20 parameter) model.
Parameter
αi δi mI, mF mI mi Qδ, Rδ Qm, Rm tC,tM Di Gi
Description
Simplification
Proliferation rate: CMP generation i≥0 Proliferation rate: MEP generation i≥0 Maturation rates (initial, final): CMP to MEP generation 0 Maturation rate: CMP to MEP generation i>1 Logistic function parameters (bias, decay rate): CMP proliferation generation 0 Logistic function parameters (bias, decay rate): MEP proliferation generation 0 Logistic function parameters (bias, decay rate): CMP to MEP maturation generation 0 Proliferation initiation delays: CMP, MEP generation 0 ‘b’ compartment delay: CMP generation i≥1 ‘b’ compartment delay: MEP generation i≥1
through signal transducer and activator of transcription (Stat-5) mediated activation of the apoptotic inhibitor Bcell lymphoma-extra large (Bcl-XL).13 However, the role of epo in controlling proliferation, maturation and survival of the earliest erythroid progenitors is not completely understood. In vivo, epo is essential from the CFU-E stage onward as mutants defective for epo or epo receptor do not produce mature red cells.14 Such mutants are able to produce both early and late erythroid progenitors, burst forming unit-erythroid (BFU-E) and CFU-E, respectively, suggesting that epo signaling is not absolutely required for erythroid commitment. However, a recent study has suggested that high levels of epo can bias commitment toward the erythroid lineage over the myeloid lineage both in vivo and in vitro.15 To clarify these apparently contradictory findings on the role of epo in lineage commitment and erythropoiesis, we have determined the effect of epo on the kinetics of proliferation and lineage restriction during the maturation from CMP to MEP. Population dynamics of hematopoietic culture is a complex output generated by the interplay of proliferation, maturation and cell death. Proliferation and cell death can be followed directly using the carboxyfluorescein succinimidyl ester (CFSE) division-tracking dye and Annexin V/propidium iodide staining, respectively, but maturation associated with lineage restriction can only be analyzed indirectly as a measure of the number of mature cells that arise in culture which cannot be accounted for by proliferation or death. To disentangle the individual contributions of these different cell behaviors, we have developed a mathematical model of the early stages of erythroid culture during which cells become committed to the megakaryocyte/erythroid lineage. Mathematical models have been developed as tools to assist in the analysis of population dynamics during hematopoiesis,16-19 and to determine the transcription factor regulatory interactions that control hematopoietic differentiation pathways.20 To understand how proliferation integrates with lineage commitment and impacts on the overall population dynamics, a number of mathematical approaches for cell division have also been proposed, taking advantage of CFSE division-tracking. In particular, the Smith-Martin model that takes into account progression of cells through the cell cycle, has been successfully used to predict and model population dynamics of in vitro erythropoiesis.21-25 haematologica | 2016; 101(3)
mi ≥1= mF Fixed by dataset A Fixed by dataset A Fixed by dataset A Fixed by dataset A Di=D Gi≥4=G3
We have combined immunophenotyping, proliferation analysis and cytokine dependence to refine the analysis of early erythroid culture of CD34+ cells isolated from human peripheral blood (PB). Isolated PB CD34+ cells are CMP, and the appearance of detectable surface levels of the erythroid marker CD36 is the earliest identifier of progenitors restricted to the megakaryocyte/erythroid lineage.26 Interestingly, CMP give rise to MEP independently of cell division in erythroid culture and most of the starting CMP become lineage-restricted prior to the onset of proliferation. We have developed an adaptation of the SmithMartin mathematical model that describes the population dynamics of erythroid culture during lineage restriction from CMP to MEP. The model suggests that cells become responsive to epo as soon as they are committed to the megakaryocyte/erythroid lineage, but epo does not control lineage restriction.
Methods Antibodies and reagents Antibodies used were CD34-BV421, CD36-APC, CD90-PE, CD123-PE/Cy5, CD38-APC/Cy7, CD135-PE and CD45RABV421 (Biolegend) and CD36-PE, CD45-APC/Vio770, CD235 (GPA)-APC, CD61-APC/Vio770 (Miltenyi Biotec). AnnexinVBV421 (Biolegend) was used for analysis of dying cells. Monastrol (Merck Chemicals Ltd, UK) was used at 100 mM to block cells in M phase of the cell cycle.
Erythroid culture Peripheral blood mononuclear cells (PBMCs) were isolated from leukocyte cones by density purification over Histopaque (Sigma) from healthy donors with informed consent. Isolated cells were cultured in erythroid medium (EM); Stem Span (Stem Cell Technologies) + 10 ng/mL stem cell factor (SCF) (for Day 0 to Day 4) and 50 ng/mL (for Day 5 to Day 11), 40 ng/mL insulin-like growth factor1 (IGF-1), 1 ng/mL interleukin-3 (IL-3), 1 mM dexamethasone and with or without 2 U/mL epo (details in Online Supplementary Methods). Research was reviewed and approved by Southmead and Bristol Research Ethics Committee Centre (08/H0102/26 and 12/SW/0199, respectively).
Cell cycle analysis K562 cells were grown in Iscove modified Dulbecco medium + 20% fetal calf serum, fixed in ice cold 70% ethanol and rehydrated 287
D. Ward et al. Table 2. Parameter values of the mathematical model. Dataset A Dataset B Dataset C Dataset D
α0
α1
α2
α3
α4
α5
mI
mf
0.018 0.023 0.014 0.012
0.054 0.066 0.045 0.042
0.108 0.120 0.098 0.086
0.181 0.183 0.198 0.174
0.213 0.200 0.264 0.255
0.195 0.185 0.299 0.269
0.016 0.017 0.025 0.020
0.004 0.002 0.003 0.005
δ0
δ1
δ2
δ3
δ4
δ5
Dataset A Dataset B Dataset C Dataset D
0.128 0.143 0.118 0.088
0.123 0.150 0.121 0.114
0.119 0.124 0.158 0.119
0.127 0.125 0.181 0.146
0.129 0.121 0.279 0.206
0.110 0.125 0.269 0.199
Dataset A Dataset B Dataset C Dataset D
30.394 31.119 29.708 28.841
27.705 27.800 26.279 25.765
9.800 9.602 10.742 10.623
14.549 13.892 14.599 12.453
9.817 10.688 10.540 9.521
8.017 8.264 10.316 10.893
δ11
δ12
δ13
0.35 0.259
0.386 0.284
0.422 0.309
0.458 0.334
0.494 0.359
0.53 0.384
0.566 0.409
Dataset E Dataset F
tC
δ6
0.314 0.234
tM
δ7
D
δ8
G1
δ9
G2
δ10
G3
Rows 1, 5 and 9: parameters fitted using dataset A, used in simulations shown in Figure 5A-D, and Online Supplementary Figures S4 and S5 (red lines). Rows 2, 6 and 10: re-identified parameters used for validation (dataset B), simulation shown in Online Supplementary Figure S5 (black lines). Rows 3, 7 and 11: fitted parameters on the control data sets (dataset C), simulations shown in Figure 6C, E anf F, and Online Supplementary Figure S6A-C. Rows 4, 8 and 12: fitted parameters on the minus epo data sets (dataset D), simulations shown in Figure 6C, E and F, and Online Supplementary Figure S6 A-C. Rows 13 and 14: parameters 6-13 are the extrapolated proliferation rates used to predict the MEP cell counts from 90 h to 186 h (Figure 6E, datasets E and F, plus and minus epo, respectively), calculated by assuming a linear relationship between MEP proliferation rates above. Proliferation rates are divisions per hour and maturation rates are transitions per hour.
in phosphate buffered saline (PBS). Fixed cells were stained in PBS (Sigma) + 0.1% Triton X-100 + 10 mg/mL propidium iodide + 100 mg/mL RNase and analyzed on the MACSQuant VYB flow cytometer.
Flow cytometry and fluorescence assisted cell sorting Flow cytometry and fluorescence assisted cell sorting (FACS) was used to analyze cells stained with anti-human-specific antibodies on the MACSQuant and post-acquisition analysis was carried out with FlowJo v.7.6.5 for proliferation analysis and FlowJo v.X0.7 for all other analysis (details in Online Supplementary Methods).
CFSE tracking CD34+ cells were labeled on the day of isolation with CFSE (Biolegend) at 2 mM in PBS for 15 min at 37°C. Excess CFSE was quenched by incubating in Stem span + 10% fetal calf serum for 5 min at 37°C. Cells were washed in PBS and transferred into EM at a density of 2x104 cells/mL in multi-well dishes so that each time point was taken from a separate well. For extended cultures beyond Day 4, cells were fed by 1:2 partial medium change into EM with 100 ng/mL SCF.
Results CD36 expression marks megakaryocyte/erythroid restricted progenitors At the time of isolation, CD34+ cells from human PBMNC, are 85% CD34+CD38+IL3Rα−CD45RA−CD90−CD45lo (Figure 1A), previously defined as CMP27,28 and 15% of CD34+ cells are CD34+CD45lo IL−3Rlo CD38+CD90− CD45RA+, GMP as previously described,29,30 which do not persist in erythroid culture (Online Supplementary Figure S1). CD34+ cells cultured in serum-free medium supplemented with appro288
priate cytokines including SCF, IL-3 and epo mature into erythroid cells. The appearance of erythroid precursors and erythroblasts is easily detected by the appearance of the erythroid marker GPA (Ter119) at Day 7 to Day 10 of culture, depending on conditions. To identify the less mature erythroid progenitors, MEP, at the point of lineage restriction from CMP, prior to GPA expression, we examined the change during culture of the previously defined markers interleukin-3 receptor (IL-3R/CD123) and Fmslike tyrosine kinase 3 (Flt-3/CD135), which are present at low levels on CMP and GMP and absent on MEP isolated from human bone marrow and cord blood.27,31,32 Surface levels of both markers increase over the first two days in culture and then decrease to Day 4 on the bulk population, so they do not provide useful markers for early lineage restriction in erythroid culture (Figure 1B). The erythroid marker CD36 was originally identified as a cell surface marker of erythroblasts33 and later shown to be detectable prior to the appearance of the erythroid differentiation marker GPA.34 At isolation, PB CD34+ cells do not express CD36, but give rise to CD34+CD36+ cells within the first few days of culture (Figure 1C). To determine whether surface expression of CD36 marks the lineage restriction event, CD34+ cells were cultured for 2-4 days and then separated by FACS into CD34+CD36− and CD34+CD36+ populations. CD34+CD36− cells give rise to both erythroid (burst forming unit-erythroid, BFU-E) and myeloid [colony forming unit-granulocyte/monocyte (CFU-GM)] colonies in methylcellulose, indicating these cells are the CMP population. CD34+CD36+ cells give rise only to BFU-E (Figure 2A). BFU-E colonies contain small numbers of megakaryocytes (Figure 2B) consistent with the assignment of CD34+CD36+ cells as bipotent MEPs. This is further supported by the finding that CD34+CD36+ cells are CD61lo (Figure 2C, population I,) reflecting megakaryocyte potential.36 haematologica | 2016; 101(3)
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Maturation of CMP to MEP is independent of cell division Previous work had suggested that PB CD34+ cells divided asymmetrically dependent on unequal distribution of the determinants Notch and Numb.37 One possible explanation for the observed population dynamics of erythroid culture, in particular the retention of CMP concurrent with MEP production (Online Supplementary Figure S2), is that CMP might divide asymmetrically to produce 1 MEP and 1 CMP. To test this, we first examined whether maturation of CMP to MEP was coincident with cell division by following proliferation of PB CD34+ cells by CFSE tracking. Cells were CFSE-labeled at the time of isolation and the number of cells in each generation was determined separately for CMP and MEP by gating on CD36. After 24 h in culture, all
cells in both CMP and MEP populations remain in generation 0 [G(0)] and do not progress to G(1) until the second day of culture, indicating a lag phase for both CMP and MEP from the time of isolation to the initiation of proliferation between Days 1 and 2 (Figure 3A, top panels). Maturation of CMP to MEP begins immediately, as is clear from the emergence of CD36+ MEP on Day 1, before the appearance of G(1) cells (Figure 3B). This indicates that maturation does not require cell division, at least for G(0) CMP, and rules out the possibility that MEP arise from CMP by asymmetric division. This was confirmed by demonstrating that inhibiting cell cycle progression with Monastrol does not block maturation of CD34+ cells to MEP (Online Supplementary Figure S3). By Day 2, 75% MEP and 24% CMP have undergone a
A
B
C
Figure 1. Immunophenotyping of peripheral blood (PB) CD34+ cells in erythroid culture. (A) Surface expression of CD34, CD45, CD38, IL-3R (CD123), CD45RA and CD90 on PB CD34+ cells on the day of isolation. All populations were gated first on Propidium iodide incorporation and scatter properties. Cells were gated for CD34+ and CD45lo (left panel) prior to analysis of CD38 and CD90. IL-3R and CD45RA were analyzed on the total live population. Gates were set with isotype controls. (B) Surface expression of IL-3R and Flt3 on CD34+ cells cultured in EM for four days. Stained cells in blue and isotype controls in orange. Expression of both markers increases in the total population during the first three days of culture and then decreases. (C) Surface expression of CD36 over four days of culture demonstrating that CD36â&#x2C6;&#x2019; and CD36+ cells comprise distinct populations. Stained cells in blue and isotype controls in orange.
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first division and are in G(1) (Figure 3A). Overall, CMP proliferation is heterogeneous with an even distribution of cells over 5 generations from G(0-4). By contrast, all of the MEP cells divide within the first three days as indicated by the absence of cells in G(0) and, by Day 4, 84% MEP are found in 3 generations G(3-5) with a distinct peak (35%) in G(4), suggesting a largely synchronously proliferating population (Figure 3C). To determine whether cell death has a significant effect on population dynamics, dying and dead cells were quantified over a 4-day time course by Annexin V staining and Propidium Iodide incorporation, respectively. The total number of dead cells was insignificant compared to live cells (Figure 3D). Annexin V staining was undetectable.
Mathematical model of population dynamics of erythroid culture Understanding the role of extrinsic factors on CMP to MEP maturation and progenitor proliferation requires a means of measuring the relative parameters independently, which is not possible using population dynamics alone. Instead, we have developed a mathematical model that simulates the population dynamics, in terms of CMP and MEP numbers, dependent on a set of parameters (Table 1) derived from our experimental data, which include total cell number, CMP:MEP ratio and cell division by CFSE tracking with sampling every eight hours during the first four days of culture Our model is an adaptation of the Smith-Martin model. It separates the cell cycle into two distinct compartments
‘a’ and ‘b’, representing the G1 (‘a’) and the combined S, G2 and M phases (‘b’) of the cell cycle. We incorporated maturation of CMP to MEP, as shown schematically in Figure 4A. Cells originate in the ‘a’ compartment, with their transition governed by a stochastic process. This gives rise to rates αi and δi for generation i CMP and MEP cells, respectively, transitioning to the ‘b’ compartment, and rate mi for generation i CMP maturing to generation i MEP. Cells remain in the ‘b’ compartment for a fixed time (Di and Gi for CMP and MEP, respectively) before they divide and then re-enter the ‘a’ compartment as a next generation cell. In order to simplify the model whilst maintaining the key dynamics that relate to lineage restriction, we made the following assumptions based on data presented in Figure 3: 1) death rate plays an insignificant role over the time course; 2) cell maturation initiates prior to the onset of CMP and MEP proliferation and, thus, does not require transition through the cell cycle; 3) GMP are excluded from the analysis as this population does not contribute significantly to the population dynamics in the first four days of culture (Online Supplementary Figure S1). The CMP and MEP populations in each generation evolve according to a set of coupled delay differential equations (Figure 4B). To account for the observation that maturation initiates prior to the onset of proliferation (Figure 3B), we allowed the first generation proliferation and maturation rates to be time dependent. Constant rates for proliferation and maturation in G(0) do not provide a good qualitative or quantitative match to data, while piecewise constant rates (a single step function for each rate) substantially improve
A
B
290
C
Figure 2. Surface expression of CD36 marks the transition from CMP to MEP in erythroid culture. (A) CD34+ cells cultured in EM for two or four days were sorted by FACS into CD34+CD36− and CD34+CD36+ populations. Sorted cells were cultured in methylcellulose for two weeks and colonies enumerated. Results are from 3 independent experiments, 2 tests per experiment. Error bars are standard deviation. (B) Cytospin from an individual erythroid colony stained with May Grünwald/Giemsa showing a megakaryocyte (M) with multiple nuclei and surrounding erythroblasts (E). (C) The megakaryocyte marker CD61 is absent on cells at isolation (data not shown) and heterogeneous at Day 2 with low expression on CD36+ MEP (population I, 46% CD34+CD36+CD61lo/−) and largely absent on CD36− CMP (population II, 37% CD34+CD36− CD61−). There is a small population of CD61− cells (population III, 17% CD34+CD36+CD61−) which may be megakaryocyte progenitors directly descended from CMP as previously described for mouse multipotent progenitors.47
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the fit. To maintain physiological plausibility, we model G(0) rates as smoothed step functions, in the form of generalized logistic functions (Figure 4C). We then used optimization routines to find the set of parameters that minimizes the root mean square error (RMSE) to the experimental data (CFSE tracking) (see Online Supplementary Methods). The system was fitted to experimental data (Online Supplementary Table S1, dataset A) initially in the most general case, where all 37 parameters are generation dependent (Online Supplementary Table S2). This provided insight as to the system behavior and allowed us to make simplifications with no significant
A
increase in RMSE (see Online Supplementary Methods), and reduce the system to 20 parameters: 12 proliferation rates (α(0-5), δ(0-5)), 2 maturation rates (mI, mF), 4 compartmental delays (D, G(1-3)), and 2 proliferation initiation delays (tC, tM). We observed very good quantitative agreement between model and experiment for all generations (Figure 5A and B and Online Supplementary Figure S4A and B). To quantify the experimental error, standard deviation was calculated and normalized over total cell counts (shaded regions in Figure 5B). The standard deviation found within the experimental data was 222.44 (3.07%) for CMP, and 279.46 (2.75%) for MEP. The RMSE error between exper-
B
C
D
Figure 3. CFSE tracking highlights key features of early erythroid culture. CD34+ cells were labeled with CFSE on the day of isolation and cultured in EM for four days. (A) Cells were gated on CD34+CD36− (CMP) or CD34+CD36+ (MEP) and generation occupancy was determined by flow cytometry. Numbers above peaks indicate generation. After one day in culture, cells have not divided and all cells are in G(0), (top panels). Proliferation begins for CMP and MEP between Day 1 and Day 2 as demonstrated by the appearance of a peak at half the mean fluorescence intensity (MFI) of the original peak (G(0) MFI=63000 for G(0) and MFI=34000 for G(1)). This generation analysis was done manually in FlowJo vX0.7. (B) CD36 staining on Day 1 of the same cells as shown in the left panels demonstrates the appearance of CD36+ cells prior to the onset of proliferation. (C) Generational occupancy at Day 4 shows that MEP proliferation is more synchronized than CMP with 84% MEP within 3 generations, G(3-5), and 85% CMP evenly distributed in 5 generations, G(0-4). (D) Cell death is insignificant during the first four days of culture as measured by propidium iodide staining to identify dead cells (gray) as compared to live cells (green). Error bars are standard deviation.
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imental data and model predictions for optimal parameter values (Figure 5B) was 101.02 (1.38%) for CMP and 138.29 (1.85%) for MEP. Table 2 shows the rates (divisions per hour) and delays (hours) obtained from the optimization algorithm. The model was validated using a second, independent experimental data set (dataset B) in two different ways (Online Supplementary Methods and Online Supplementary Figure S5), and indicated high confidence in the model and parameter values. Further in silico experiments were conducted on an extensive set of simulated data, confirming robustness of the parameter identification methods used (Online Supplementary Methods and Online Supplementary Table S3). The identified parameters for dataset A (Table 2) show that the average MEP proliferation rate (δ) is 0.12 divisions per hour, and that this rate does not vary greatly from the onset of proliferation to Day 4 (Figure 5C). The rate of proliferation of CMP cells is initially 10% of the proliferation rate of MEP, and increases with each generation such that CMP transit from G(5) to G(6) is approximately twice the average MEP rate (Figure 5C). The maturation rate for G(0) (m0(t)) decreases over time, from 0.016 to 0.004 transitions per hour (Figure 5D). Importantly, the maturation rate, even at the beginning of the culture when it is at its highest, is an order of magnitude smaller than the MEP proliferation rate. These parameters show important features of the relative contributions of proliferation and maturation to overall population dynamics at different stages in the culture: while maturation plays a crucial role at the beginning, proliferation is the main effector of later culture stages.
The role of epo in maturation from CMP to MEP To investigate a possible role for epo in the earliest stages of erythropoiesis in vitro, CD34+ cells were isolated and cultured under standard conditions or in the absence of epo. After one day in culture, prior to the onset of proliferation, 20% CMP have matured to MEP under both conditions (Figure 6A), suggesting that CMP to MEP transition during the first 24 h is not responsive to epo in G(0). However, over nine days in culture, there is a dramatic difference in the number of MEP in control and minus epo cultures and by Day 9 there is over 20 times more MEP produced in cultures with epo (Figure 6B). The lower MEP production in the absence of epo could be due to defects in maturation, proliferation, or both. To resolve the individual effects of epo on maturation and proliferation, we used the model to fit parameters from generational CMP and MEP counts of 4-day cultures with and without epo (datasets C and D). The results are shown in Figure 6C and Online Supplementary Figure S6A and B. Parameter fitting shows that the proliferation rate of MEP (δ) is 21% lower in the absence of epo, whereas there is less than 10% difference in proliferation rates of CMP (ι) (Table 2). The traces from CFSE tracking also support this result in that a distinct delay in generation progression for MEP is detectable in cultures without epo (Figure 6D). Also, CFSE tracking shows no significant effect of epo on CMP generation occupancy (Figure 6D); this result, taken together with insignificant change in proliferation rate of CMP (Table 2), indicates that CMP proliferation is not epo-sensitive. Model validation, using fitted parameters for δ to reproduce MEP amplification in 2 additional experimental datasets (Online Supplementary Table
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Figure 4. Mathematical model of the system. (A) Schematic diagram of erythroid cell culture. The cell cycle is modelled as a 2-stage process, composed of compartments â&#x20AC;&#x2DC;aâ&#x20AC;&#x2122; approximating the G1 phase of the cell cycle, and â&#x20AC;&#x2DC;bâ&#x20AC;&#x2122; approximating the S/G2/M phases. CMP and MEP both originate in the â&#x20AC;&#x2DC;aâ&#x20AC;&#x2122; compartment. Their transition is governed by a stochastic process, with rates đ?&#x203A;źđ?&#x2018;&#x2013; and đ?&#x203A;żđ?&#x2018;&#x2013; for generation đ?&#x2018;&#x2013; CMP and MEP cells, respectively, to transition to the â&#x20AC;&#x2DC;bâ&#x20AC;&#x2122; compartment, and a rate đ?&#x153;&#x2021;đ?&#x2018;&#x2013; of maturing (transitioning) from CMP into samegeneration MEP cells. Cells remain in the â&#x20AC;&#x2DC;bâ&#x20AC;&#x2122; compartment for a fixed time (â&#x2C6;&#x2020;đ?&#x2018;&#x2013; and Î&#x201C;đ?&#x2018;&#x2013; for CMP and MEP, respectively) before they divide and then re-enter the â&#x20AC;&#x2DC;aâ&#x20AC;&#x2122; compartment as a next generation cell. (B) The size of the CMP and MEP populations in each generation at each time point are given by 4 delay differential equations (DDEs) where đ??śđ?&#x2018;&#x2013;(đ?&#x2018;Ą) and đ?&#x2018;&#x20AC;đ?&#x2018;&#x2013;(đ?&#x2018;Ą) are the generational populations of CMP and MEP, respectively. (C) G(0) proliferation and maturation rates are time-dependent, taking the form of generalized logistic functions, where đ?&#x2018;&#x192;đ?&#x203A;ź is the maximum proliferation rate for G(0) CMP, đ?&#x2018;&#x192;đ?&#x203A;ż is the maximum proliferation rate for G(0) MEP, and đ?&#x153;&#x2021;I and đ?&#x153;&#x2021;F are the initial and final maturation rates for G(0). The parameters đ?&#x2018;&#x201E;đ?&#x203A;ź,đ?&#x2018;&#x2026;đ?&#x203A;ź,đ?&#x2018;&#x201E;đ?&#x203A;ż,đ?&#x2018;&#x2026;đ?&#x203A;ż,đ?&#x2018;&#x201E;đ?&#x153;&#x2021;, đ?&#x2018;&#x2026;đ?&#x153;&#x2021;, đ?&#x153;?C and đ?&#x153;?M control the dynamics of the generalized logistic functions and were optimized for. Equations were solved using MATLABÂŽ solver dde23.48
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S1, datasets E and F), further supports this result (Online Supplementary Figure S6C). We have used the parameters calculated from 4-day cultures with and without epo to simulate MEP population expansion to Day 8 (Table 2 and Figure 6E, datasets E and F). For control cultures, there is good agreement between the predicted cell numbers and experimental data. For cultures without epo, there is good agreement for the first 56 days (up to time 130 h in Figure 6E), and then the experimental data show a significantly slower proliferation than predicted. This suggests that there is a change in epo sensitivity between Days 5 and 6, after which MEP proliferate very slowly without epo. The maturation rate for G(0) (m0(t) (Table 2) is not significantly different with or without epo (datasets C and D). Although the final maturation rate is slightly higher in the absence of epo, this cannot account for the decrease in MEP formation, suggesting that epo effects on population dynamics and MEP production are not mediated by
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altered maturation rates. To determine whether epo confers an erythroid bias, we used CFSE tracking to quantify CMP maturation and showed that epo does not affect the total number of CMP that mature to MEP (Figure 6F). These results provide strong evidence that neither the rate nor the bias of the CMP to MEP transition is influenced by epo.
Discussion Understanding the fundamental mechanisms controlling lineage commitment, proliferation and survival during erythropoiesis is becoming an achievable goal thanks to the advent of novel high throughput technologies to explore the transcriptome,39-42 proteome43 and epigenome.44,45 In order to maximize the specificity of the information afforded by these techniques, it is essential to start with well-defined populations. A recent report pro-
Figure 5. Fitted model simulations provide proliferation and maturation parameters and recapitulate population dynamics. (A) Fitted model simulations (solid lines) of CMP (left panel) and MEP (right panel) cell numbers in each generation. Experimental data (dataset A) are dashed lines, ± standard error (shaded regions). (B) Model simulation of total CMP (left panel) and MEP (right panel) cell numbers using model derived parameters (solid lines) together with experimental data (dashed lines) ± standard error (shaded regions). (C) Proliferation rates dataset A for CMP (α) in blue, and MEP (δ) in orange, as divisions per hour over the first 5 generations. (D) Changes for dataset A in maturation rate (m0) of G(0) CMP in the first 90 h of culture expressed as CMP to MEP transitions per hour.
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vided an excellent protocol to identify populations at distinct stages of erythroid differentiation,46 but early erythroid culture has proved more challenging. To achieve this, it is necessary to measure the transition event under experimental conditions where proliferation and survival can be controlled and/or measured simultaneously. We identified CD36 as a cell surface marker that distinguishes CMP and MEP as distinct populations. Identification of CD36 as an MEP marker provides a powerful tool to investigate the influence of extrinsic factors on lineage restriction of CMP to MEP. Epo is essential for steady-state and stress erythropoiesis and is used therapeutically to increase red cell production in clinical anemia. It is of considerable interest, therefore, to understand the precise role of epo in hematopoiesis. There is a significant body of work demonstrating that an essential role of epo is to promote proliferation of erythroid progenitors and survival of differentiating erythroblasts.47,48 This raises the question of whether epo acts on less mature progenitors. Early work from Lodish et al.14 showed that epo and the epo receptor are not required to generate early erythroid-specified progenitors in vivo, suggesting that epo does not have an instructive role in early hematopoiesis. On the other hand, a more recent study15 showed that early progenitors are responsive to epo, and that high lev294
Figure 6. MEP proliferation rate is epo-sensitive but CMP maturation is independent of epo. (A) Cell numbers for CMP and MEP after one day in control (blue) and minus epo (red) culture Âą standard deviation. Data are from 4 experiments, 3 samples per experiment. (B) Relative difference in MEP numbers in control and minus epo cultures (fold difference = N. control/N. minus epo MEP Âą standard deviation) from 3 experiments. (C) Model fitting simulations for control (blue) and minus epo (red) cultures compared to experimental data from datasets C and D (dots). Shaded regions represent standard error in the experimental data. (D) CFSE in CMP and MEP after four days in control culture (blue) or minus epo (red). Traces from 3 samples are overlaid. Generations 2, 3 and 5 are labeled. (E) Extrapolation of MEP cell counts from datasets E and F to Day 8 (186 h) using best-fit parameters in Table 2 for both control and minus epo populations. Additional proliferation rates (đ?&#x203A;ż6â&#x2C6;&#x2019;13) predicted by assuming a linear relationship between the optimized proliferation rates, found from the Day 0â&#x20AC;&#x201C;4 fitting for G(0) to G(5) MEP, allowing us to extrapolate forward for G(6) onwards. (F) Maturation over four days expressed as percentage of CMP matured from experimental data (circles) and model predictions (lines). Experimental values were calculated as the number of CMP taking into account cell division. CMP matured = N. CMP in G(0) + sum over 6 generations N. CMP G(i)/2i. Model predicted maturation was tracked from all generations of CMP throughout the simulations.
els of epo suppress GMP formation through transcriptional reprogramming, both in vivo and in vitro. Interestingly, this study showed that although there is a relative increase in erythroid progenitors compared to non-erythroid progenitors, the absolute number of erythroid progenitors is not affected by high levels of epo in vivo. Our results using mathematical modeling correspond well with these studies, demonstrating that the time-dependent maturation rate of CMP is not affected by epo. Importantly, equal numbers of CMP convert to MEP with and without epo, further supporting the idea that epo does not bias CMP commitment toward the erythroid lineage. Population dynamics of cord blood CD34+ cells in erythroid culture show similar dynamics to PB CD34+ cells (Online Supplementary Figure S7) suggesting that these results are likely to be applicable to CMP from other sources. This provides validation for the proposed model in demonstrating consistency with in vivo data. It is important to note that, while our results clearly indicate that the MEP population is homogeneous, the CMP population behaves heterogeneously in terms of proliferation. It is, therefore, possible that there is an earlier restricted progenitor within the CMP population that is indistinguishable from other CMP using known markers. Developing a transcriptomic fingerprint of CD34+CD36+ haematologica | 2016; 101(3)
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cells would allow us to investigate this possibility using single cell analysis applied to the CMP population. Although it is well established that epo is required for proliferation of late erythroid progenitors, a possible influence of epo on earlier progenitors has not been described. To test this, we determined the progenitor proliferation rates using the model. This showed that, from the initial stages of megakaryocyte/erythroid lineage commitment, cells are epo responsive and proliferation is slower in the absence of epo. By disentangling the independent contributions of proliferation and maturation to population dynamics, the model allowed a high-resolution quantification of proliferation rate that revealed the sensitivity of MEP to epo. This means of quantification will be highly beneficial to the efforts to amplify erythroid cells in culture for the production of red cells for clinical transfusion; a 4-5 day culture significantly decreases the cost and simplifies screening of changes in culture conditions as compared to end point analysis from 2-3 week cultures. Model simulations using a proliferation rate derived from the first four days of culture and extrapolating to Day 8 show that the derived proliferation rate faithfully reproduces the experimental data. This confirms the
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power of the model to describe erythroid culture. Interestingly, simulating MEP amplification to Day 8 in the absence of epo using the proliferation rate derived from the first four days of culture shows a marked change after Day 5. This suggests that there are two MEP stages with different epo sensitivity: an early stage (Days 1-4) with a small increase in proliferation rate in response to epo and a later stage (Days 4-8), which is increasingly dependent on epo for proliferation. The increased resolution of cell behavior parameters given by the mathematical model provides a valuable tool to further investigate both the mechanism of lineage restriction and proliferation behavior of hematopoietic progenitors in culture. Funding The work was supported by funding from the National Institute for Health Research program grant RP-PG-0310-1004AMT and from the EPSRC Doctoral Training Programme (DW). Acknowledgments The authors would like to thank Dr Ashley Toye and members of his group for critical reading of the manuscript, and anonymous reviewers for constructive comments that improved the manuscript.
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haematologica | 2016; 101(3)
ARTICLE
Iron Metabolism & its Disorders
Increased hepcidin in transferrin-treated thalassemic mice correlates with increased liver BMP2 expression and decreased hepatocyte ERK activation
EUROPEAN HEMATOLOGY ASSOCIATION
Ferrata Storti Foundation
Huiyong Chen,1 Tenzin Choesang, 1 Huihui Li,1,2 Shuming Sun, 1 Petra Pham,3 Weili Bao, 1 Maria Feola,1,4 Mark Westerman,5 Guiyuan Li,2 Antonia Follenzi,4 Lionel Blanc,6 Stefano Rivella,7 Robert E. Fleming,8 and Yelena Z. Ginzburg1*
Erythropoiesis Laboratory, LFKRI, New York Blood Center, NY, USA; 2Central South University, Changsha, PR China; 3Flow Cytometry Core Laboratory, LFKRI, New York Blood Center, NY, USA; 4University of Piemonte Orientale, Amedeo Avogadro, Novara, Italy; 5Intrinsic Lifesciences, LLC, La Jolla, CA, USA; 6The Feinstein Institute for Medical Research, Manhasset, NY, USA; 7Weill Cornell Medical College, NY, USA; and 8Saint Louis University, MO, USA 1
Haematologica 2016 Volume 101(3):297-308
ABSTRACT
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ron overload results in significant morbidity and mortality in β-thalassemic patients. Insufficient hepcidin is implicated in parenchymal iron overload in β-thalassemia and approaches to increase hepcidin have therapeutic potential. We have previously shown that exogenous apo-transferrin markedly ameliorates ineffective erythropoiesis and increases hepcidin expression in Hbbth1/th1 (thalassemic) mice. We utilize in vivo and in vitro systems to investigate effects of exogenous apo-transferrin on Smad and ERK1/2 signaling, pathways that participate in hepcidin regulation. Our results demonstrate that apo-transferrin increases hepcidin expression in vivo despite decreased circulating and parenchymal iron concentrations and unchanged liver Bmp6 mRNA expression in thalassemic mice. Hepatocytes from apo-transferrin-treated mice demonstrate decreased ERK1/2 pathway and increased serum BMP2 concentration and hepatocyte BMP2 expression. Furthermore, hepatocyte ERK1/2 phosphorylation is enhanced by neutralizing anti-BMP2/4 antibodies and suppressed in vitro in a dose-dependent manner by BMP2, resulting in converse effects on hepcidin expression, and hepatocytes treated with MEK/ERK1/2 inhibitor U0126 in combination with BMP2 exhibit an additive increase in hepcidin expression. Lastly, bone marrow erythroferrone expression is normalized in apo-transferrin treated thalassemic mice but increased in apo-transferrin injected wild-type mice. These findings suggest that increased hepcidin expression after exogenous apo-transferrin is in part independent of erythroferrone and support a model in which apo-transferrin treatment in thalassemic mice increases BMP2 expression in the liver and other organs, decreases hepatocellular ERK1/2 activation, and increases nuclear Smad to increase hepcidin expression in hepatocytes.
Introduction β-thalassemia is characterized by anemia, expanded erythropoiesis, and iron overload with iron overload principally causing morbidity and mortality in these patients.1 Although iron overload primarily results from transfused erythrocytes, transfusion-independent patients also develop iron overload from increased dietary iron absorption. Iron absorption and iron recycling are regulated by hepcidin, a peptide hormone produced predominantly in the liver. Hepcidin binds ferhaematologica | 2016; 101(3)
Correspondence: yginzburg@nybloodcenter.org
Received: April 2, 2015. Accepted: December 1, 2015. Pre-published:December 3, 2015. doi:10.3324/haematol.2015.127902
Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/101/3/297
©2016 Ferrata Storti Foundation Material published in Haematologica is covered by copyright. All rights reserved to Ferrata Storti Foundation. Copies of articles are allowed for personal or internal use. A permission in writing by the publisher is required for any other use.
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roportin (FPN1), the iron exporter on enterocytes, hepatocytes, and reticuloendothelial macrophages,2 and results in FPN1 degradation and decreased release of cellular iron, down-regulating dietary iron absorption, iron release from stores, and tissue iron recycling. Despite iron overload, hepcidin is inappropriately low and is thus implicated as the cause of iron overload in patients with and mouse models of β-thalassemia.3-7 This lack of appropriate hepcidin response, despite increased parenchymal iron stores, in β-thalassemia suggests a competing hepcidin-suppressing signal.6-8 In diseases of concurrent iron overload and ineffective erythropoiesis, hepcidin suppression results from secretion of bone marrow factors [(e.g. growth differentiation factor 15 (GDF15), twisted gastrulation 1 (TWSG1), GDF11, and erythroferrone (ERFE)].9-12 These erythroid regulators of hepcidin and their signaling pathways are active areas of investigation targeted for development of novel therapeutics in iron disorders. We previously demonstrated that exogenous apo-transferrin (apoTf) in Hbbth1/th1 (thalassemic) β-thalassemia intermedia mice markedly ameliorates ineffective erythropoiesis and increases hepcidin expression.13 Mechanisms of hepcidin regulation involve bone morphogenetic proteins (BMPs). Several BMP signaling molecules up-regulate hepcidin expression in vitro3,14-16 by binding BMP receptors. BMP6 is a principal endogenous BMP regulating hepcidin expression,16,17 and Bmp6 knockout mice exhibit hepcidin suppression with iron overload.17,18 Bmp6 mRNA is up-regulated in mouse liver following dietary iron overload, suggesting that transcriptional regulation of hepcidin by iron involves an autocrine or paracrine BMP6 effect.3 However, increased hepcidin in chronically iron-loaded Bmp6 knockout mice suggests that other pathways stimulate hepcidin expression in response to iron overload.19 Furthermore, when normalized to liver iron content, Bmp6 expression is not increased in β-thalassemic mice,5 suggesting that hepcidin regulation in conditions of chronic iron overload, such as β-thalassemia, may involve additional molecules. Other BMPs, including BMP2 and 4, also induce hepcidin regulation in vitro20 and neutralizing BMP2/4 antibodies suppress hepcidin-responsiveness to serum and/or holoTf similar to noggin (BMP receptor blocker) response.21 Lastly, BMP2 injection results in increased hepcidin expression in vivo,14 but its physiological role in hepcidin regulation has not been fully determined. Regulation of hepcidin expression in hepatocytes is dependent on the decapentaplegic (Smad) signaling pathway. BMP receptor binding induces phosphorylation of intracellular Smad1/5/8, the association of pSmad1/5/8 with Smad4, and the complex translocation to the nucleus where binding to regulatory elements induces hepcidin expression. Recent evidence suggests that BMP receptor signaling is complex and Smad signaling may integrate with other signaling pathways.22 Specifically, MAP kinase modulates Smad signaling,23 and, although the details have not been worked out, may regulate nuclear translocation or transcriptional activity of pSmad1/5/8.24 Most studies examining such crosstalk used transformed epithelial cell lines, with MEK/ERK1/2 pathway reported to enhance25 or inhibit26 Smad activity depending on cell type- or target gene-specificity. Several studies provide indirect evidence that hepatic MEK/ERK1/2 is involved in hepcidin regulation.21,27,28 In particular, MEK/ERK1/2 inhibition did not suppress hepcidin expression in HepG2 cells28 despite par298
allel increases of MEK/ERK1/2 and Smad signaling in response to BMP2 and holoTf. Thus, the physiological relevance of the interactions between these signaling pathways in iron homeostasis is still not completely understood. We postulate that apoTf systemically affects hepatocyte hepcidin expression via the purported “erythroid regulator”. In addition, we evaluate the role of addition BMPs in systemic and cellular iron regulation of hepcidin in apoTftreated mice. Lastly, we hypothesize that MEK/ERK1/2 suppression in hepatocytes is involved in stimulating hepcidin expression in apoTf-treated mice. To understand the mechanisms of hepcidin regulation from these perspectives in apoTf-treated thalassemic mice, we explore ironrelated parameters in circulation, in the liver, and in hepatocytes. Our findings demonstrate that reversal of ineffective erythropoiesis and increased hepcidin in apoTf-treated thalassemic mice correlate with decreased hepatocyte MEK/ERK1/2 signaling, increased circulating BMP2, and decreased ERFE expression in erythroid precursors, supporting the hypothesis that exogenous apoTf influences hepcidin expression both via erythropoiesis- and ironrelated pathways.
Methods Mice Hbbth1/th1 (thalassemic) mice were backcrossed onto a C57BL6 background, as previously described.13 Age- and gender-matched 8-10-week old thalassemic and C57BL6 (WT) mice were bred and housed in the animal facility under AAALAC guidelines. The experimental protocols were approved by the Institutional Animal Care and Use Committee. Standard Mouse Chow was used for all experiments (Lab Diet #5001, 270 ppm iron). All mice had access to food and water ad libitum.
Transferrin regimen Mice were treated with 10 mg (400 mg/kg/day) of human apoTf (Kamada, Israel) or same volume of PBS via intraperitoneal injections daily for 20 days. This course yielded results consistent with previously published 60 days of injections13 (Online Supplementary Figure S1A and B). Mice were sacrificed on day 3 after the last injection and samples processed for analyses.
Serum parameter analyses Mouse serum was separated and analyzed using ELISA kits for hepcidin (Hepcidin-Murine CompeteTM competitive ELISA, Intrinsic LifeSciences, LLC, La Jolla, CA, USA), BMP2 (Abnova, Taiwan), and the Integra 800 Automated Clinical Analyzer (Roche Diagnostics, IN, USA) for other circulating iron-related parameters.
Non-heme iron spectrophotometry Iron quantification was performed using the Torrance and Bothwell method.29 Briefly, desiccated tissue samples were digested in acid-digestion mixture, diluted, and mixed with chromogen reagent. Absorption was measured at 540 nm on a spectrophotometer (Multiskan MCC Microplate Reader, Fisher Scientific).
Primary culture of hepatocytes Wild-type and thalassemic mouse livers were perfused with PBS followed by Liberase TM (Roche Diagnostics, IN, USA) or filtered collagenase type 1 (Worthington, NJ, USA) using two-step liver perfusion. Live cells were purified by Percoll (Sigma) and plathaematologica | 2016; 101(3)
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ed, as previously described.15 Cells were allowed to attach, starved for 18 h, and treated for 24 h with 5% mouse serum with or without 20 mg/mL monoclonal anti-human BMP2/4 antibody (R&D systems). Alternatively, WT serum-treated primary mouse hepatocytes were incubated with increasing doses of MEK/ERK1/2 inhibitor, U0126 [(freshly diluted in DMSO (Promega)] or DMSO (Sigma) 2-2.5 h prior to cell harvest. Lastly, primary mouse hepatocytes were incubated with 5% FBS and increasing doses of BMP2 (Sigma) for 24 h with and without U0126.
Western blot Liver was homogenized with Protease and Phosphatase Inhibitor Cocktail (Sigma) and total protein extracted. Freshly isolated or cultured hepatocytes were directly lysed by Cell Lysis Buffer (Cell Signaling). Nuclear protein was prepared using NEPERTM Nuclear and Cytoplasmic Extraction Reagents (Thermo Scientific) as per the manufacturerâ&#x20AC;&#x2122;s instructions. Briefly, samples were homogenized, stained with 0.4% trypan blue, and analyzed by western blot using sub-fraction specific controls. Specifically,
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Figure 1. Effect of apo-transferrin injection on ironrelated parameters. Serum iron (A) and transferrin saturation (B) (measured in the serum as a ratio of serum iron and total transferrin binding capacity) in PBS-injected WT (n=8), PBS-injected thalassemic (n=6), apoTf-treated WT (n=13), and apoTf-treated thalassemic (n=6) mice. (C) Serum hepcidin concentration measured by ELISA in PBS-injected WT (n=24), PBS-injected thalassemic (n=13), apo-transferrintreated WT (n=22), and apoTf-treated thalassemic (n=14) mice. (D) Liver non-heme iron concentration measured using spectrophotometry (n=12-14 mice per group). (E) ERFE mRNA expression in sorted bone marrow orthochromatophilic erythroblasts (n=4 sorted samples per group, each sorted sample from 2-3 mice). Tf: transferrin; apo: apo-transferrin; WT: wild type; thal: thalassemic (Hbbth1/th1); ERFE: erythroferrone. 299
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membranes were incubated with primary antibodies [Smad1, pSmad1/5/8, Smad4, Ferritin H, ERK1/2, pERK1/2, and TBP (TATA box binding protein) (Cell Signaling)]; Smad7 (Sigma); TGIF, and Lamin B (Santa Cruz); GAPDH and β-actin (Thermo Scientific); and BMP2 (Novus Biologicals)] as well as HRP-conjugated secondary antibodies (Thermo Scientific) and detected using the SuperSignal West Dura Extended Duration Substrate (Thermo Scientific). The results were quantified using Image J (v1.45q, NIH, USA).
Immunofluorescence Samples were fixed in 4% paraformaldehyde, washed, permeabilized, blocked, and incubated overnight with primary rabbit anti-pSmad1/5/8 antibody. Control slides were incubated with rabbit IgG. Slides were washed and stained with goat anti-rabbit
secondary antibody [(Alexa Fluor® 488 conjugate (Molecular Probes)]. Coverslips were mounted and slides viewed using Zeiss LSM 510 Meta Laser Scanning Confocal microscope.
Fluorescence-activated cell-sorting analysis Bone marrow cells were processed and analyzed as described previously.30 Briefly, cells were incubated with anti-CD45 magnetic beads and CD45 negative cells collected, counted, and incubated with anti-mouse TER119-phycoerythrin-Cy7 (PE-Cy7) and CD44allophycocyanin (APC). Erythroid precursors were identified and sorted using TER119, CD44, and forward scatter on MoFlo® XDP High-Speed Cell Sorter (Beckman Coulter, Miami, FL, USA) using Summit Software (Beckman Coulter, Miami, FL, USA). Post-sort target population purity was confirmed by microscopic morphology evaluation of cytospins.
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Figure 2. Effect of apo-transferrin treatment on Smad and ERK1/2 signaling to hepcidin in hepatocytes. (A) Phosphorylated Smad1/5/8, Smad1, Smad4, and Smad7, phosphorylated ERK1/2, total ERK1/2, and Ferritin H in fresh hepatocytes detected by western blot (representative gel; n = 6-7 mice per group). (B) Statistical analysis of phosphorylated Smad1/5/8:total Smad1:GAPDH in fresh hepatocytes performed using ImageJ, presented as mean ± SEM (n = 6-7 mice per group). (C) Statistical analysis of phosphorylated ERK1/2:total ERK1/2 relative to GAPDH in fresh hepatocytes performed using ImageJ, presented as mean ± SEM (n=6-7 mice per group). apo: apo-transferrin; WT: wild type; thal: thalassemic (Hbbth1/th1). haematologica | 2016; 101(3)
Hepcidin regulation in transferrin-treated mice
RNA extraction and quantitative real-time RT-PCR RNA from hepatocytes or livers was purified using PureLink RNA Mini Kit (Ambion, Life Technology) and analyzed with SuperScript III Platinum SYBR Green One-Step qRT-PCR Kit (Invitrogen, Life Technology). Hepcidin and ERFE mRNA was detected, as previously reported.12 Primers for mouse BMP6 were designed and confirmed (Online Supplementary Table S1). We normalized mRNA concentrations to GAPDH.8
Statistical analysis All data are reported as mean±standard error (SEM). Analysis for statistically significant differences was performed using Student’s unpaired t-test.
Results Increased hepcidin and improved iron-related parameters in apoTf-treated thalassemic mice To evaluate apoTf's effect on iron metabolism, we meas-
ured circulating and cellular iron-related parameters in WT, thalassemic, and apoTf-treated WT and thalassemic mice. Serum iron concentration is higher in thalassemic compared to WT mice, and significantly decreases in apoTf-treated mice (Figure 1A). As previously demonstrated,13 WT and thalassemic mice exhibit similar transferrin saturations which significantly decrease in apoTf-treated mice (Figure 1B and Online Supplementary Figure S1B). Serum hepcidin concentration (Figure 1C) and liver Hamp1 mRNA expression (Online Supplementary Figure S2A) exhibit similar patterns; although no difference in hepcidin is observed between WT and thalassemic mice, apoTf increases hepcidin expression (Figure 1C and Online Supplementary Figure S2A). Furthermore, Id1 mRNA expression is significantly increased in apoTf-treated thalassemic mice (Online Supplementary Figure S2A). No differences are observed in other genes relevant to hepcidin regulation (e.g. Tfr2, HFE, HJV, and Tfr1) either between WT and thalassemic mice or between PBS-injected and apoTf-treated thalassemic mice (Online Supplementary Figure S2B and C).
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Figure 3. Effect of apo-transferrin injection on nuclear Smad signaling in hepatocytes. (A) Phosphorylated Smad1/5/8, Smad4, Smad7, and TGIF in hepatocyte nuclei detected by western blot (representative gel; n=4-6 mice per group in each experiment). Statistical analysis of phosphorylated Smad1/5/8 (B) and Smad 4 (C) relative to Lamin B (lower band) in hepatocyte nuclei performed using ImageJ, presented as mean ± SEM (n=4-6 mice per group). apo: apotransferrin; WT: wild type; thal: thalassemic (Hbbth1/th1). 301
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Hepcidin concentration is similar in WT and thalassemic mice despite increased liver iron concentration in thalassemic mice (Figure 1D). Importantly, increased hepcidin in apoTf-treated thalassemic mice occurs despite decreased liver iron concentration (Figure 1D), consistent with previously published work on thalassemic mice.13 ApoTf injections increase hepcidin expression relative to liver iron in both WT and thalassemic mice (Online Supplementary Figure S2D). These findings led us to evaluate known iron-responsive regulators of hepcidin expression, including the BMP/SMAD pathway.
Nuclear pSmad1/5/8 and Smad4 increased in apoTf-treated thalassemic hepatocytes No changes in pSmad1/5/8:Smad1:GAPDH are observed between freshly isolated primary hepatocytes from WT, thalassemic, and apoTf-treated mice (Figure 2A and B). Although thalassemic mice exhibit a greater iron concentration, both in the liver (Figure 1D) and in isolated hepatocytes (Figure 2A), pSmad1/5/8:Smad1:Ferritin H is reduced compared to WT mice and increased by apoTf treatment (Online Supplementary Figure S2E). This finding suggests that exogenous apoTf partially restores Smad pathway responsiveness to hepatocellular iron stores and that activation of Smad1/5/8 is inappropriately low in thalassemic mice, consistent with previous findings.5 No dif-
ference in total cellular Smad4 is observed (Figure 2A). To further assess the Smad pathway, we analyzed positive (Smad 1/5/8 and Smad 4) and negative (TGIF and Smad7) regulatory Smads in hepatocyte nuclear fractions.31,32 Nuclear pSmad1/5/8 and Smad4 are suppressed in thalassemic mice and increased in apoTf-treated thalassemic mice (Figure 3A-C). Increased hepcidin mRNA expression in apoTf-treated thalassemic mice occurs despite increased Smad7 mRNA expression (Online Supplementary Figure S2A) and independent of Smad7 protein concentration as no changes are observed in either total cellular or nuclear fractions (Figures 2A and 3A). TGIF remains unchanged in hepatocyte nuclear fractions from WT, thalassemic, and apoTf-treated thalassemic mice (Figure 3A). Taken together, these findings demonstrate that hepatocellular nuclear pSmad1/5/8 and Smad4 increase in apoTf-treated thalassemic mice despite decreased circulating and tissue iron concentrations.
MEK/ERK1/2 pathway inhibition in vivo and in vitro correlates with increased hepcidin expression Because MEK/ERK1/2 signaling has been proposed in hepcidin regulation, we investigate pERK1/2 in primary mouse hepatocytes. Both pERK1/2 and pERK1/2:ERK1/2:GAPDH are increased in freshly isolated hepatocytes from thalassemic mice (Figure 2A and C).
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Figure 4. Effects of MEK/ERK1/2 inhibitor U0126 on hepcidin expression in vitro. Hepcidin mRNA expression (A) as well as cellular ERK1/2 and Smad1/5/8 signaling (B and C) in primary hepatocytes from WT and thalassemic mice. Cells were cultured with WT mouse serum in the presence of 0, 2, 10, or 50 mM U0126 added 2.5 h prior to cell harvest, normalized to DMSO treated control cells. (D) Nuclear pSmad1/5/8 and Smad4 in WT primary hepatocytes treated with escalating doses of U0126 as above. These in vitro results represent 3-6 independent experiments. WT: wild type; TBP: TATA box binding protein. haematologica | 2016; 101(3)
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Both pERK1/2 and pERK1/2:ERK1/2:GAPDH are decreased in apoTf-treated mice (Figure 2A and C), suggesting that increased hepcidin in apoTf-treated mice is a consequence of ERK1/2 pathway suppression. No changes in MEK/ERK1/2 signaling were observed using liver tissue (data not shown), consistent with prior results in iron-overloaded mice.33 To confirm that ERK1/2 signaling inhibits hepcidin expression, we evaluated serum-treated primary WT and thalassemic hepatocytes and demonstrate a dose-depen-
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Figure 5. Effect of apo-transferrin treatment on hepcidin regulators BMP2 and BMP6. (A) Liver BMP6 mRNA expression measured by q-RT PCR relative to GAPDH mRNA and normalized to PBS-injected WT mice (n=8-12 mice per group). (B) Statistical analysis of BMP6 mRNA relative to non-heme liver iron, presented as mean Âą SEM (n=8-12 mice per group). (C) Serum BMP2 concentration measured by ELISA (n=6-9 mice per group). Western blot (D, representative gel) and statistical analysis (E) of liver BMP2 protein concentration (n=6-9 mice per group). apo: apo-transferrin; WT: wild type; thal: thalassemic (Hbbth1/th1). haematologica | 2016; 101(3)
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Figure 6. Effects of mouse serum, MEK/ERK1/2 inhibitor U0126, BMP2, and neutralizing BMP2/4 antibody on hepcidin expression in vitro. Hepcidin mRNA expression (A) and Smad and ERK1/2 pathway activation (B) in primary WT hepatocytes cultured with different mice sera, with and without the addition of neutralizing anti-BMP2/4 antibodies, and compared with primary cultured hepatocytes directly treated with apotransferrin. Results are normalized to untreated hepatocytes in culture. Concurrent treatment with serum and neutralizing anti-BMP2/4 antibodies compared with serum or anti-BMP2/4 antibody alone (*P<0.05 and **P<0.004 for each paired condition with and without added anti-BMP2/4 antibody). Gray: no serum. (C) Quantification of phosphorylated relative to total Smad in primary WT hepatocytes treated with WT or thalassemic mouse serum. Hepcidin mRNA (D) as well as ERK1/2 and Smad1/5/8 signaling (E) in WT primary hepatocytes treated with 20 ng/mL BMP2, 25 mM U0126, or the combination. These in vitro results represent 6 independent experiments. (F) Nuclear pSmad1/5/8 and Smad4 in WT primary hepatocytes treated with 20 ng/mL BMP2, 25 mM U0126, or the combination as above. These in vitro results represent 2 independent experiments. (G) Immunofluorescence using anti-pSmad1/5/8 antibodies in WT primary hepatocytes treated with 20 ng/mL BMP2, 25 mM U0126, or the combination as above. (H) Results were quantified using mean nuclear fluorescence intensity in ImageJ. These in vitro results represent 4 independent experiments. UT: untreated; apo: apo-transferrin; WT: wild-type; thal: thalassemic (Hbbth1/th1); RPL4: ribosomal protein L4; TBP: TATA box binding protein. haematologica | 2016; 101(3)
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BMP2 is associated with increased hepcidin expression in apoTf-treated thalassemic mice Because BMPs regulate hepcidin via Smad signaling, we investigated BMPs in PBS-injected and apoTf-treated mice. We utilized whole liver samples for Bmp6 mRNA analysis, in the light of evidence that BMP6 induction by dietary iron occurs primarily in liver non-parenchymal cells, rather than hepatocytes.34 In agreement with this, hepatocytes exposed in vitro to mouse serum exhibit unchanged Bmp6 expression (data not shown). Although liver Bmp6 expression is significantly increased in thalassemic mice (Figure 5A), consistent with higher nonheme liver iron in these mice (Figure 1D), serum hepcidin and liver hepcidin mRNA expression are unchanged from WT mice (Figure 1B and Online Supplementary Figure S2A). Liver Bmp6 expression relative to iron concentration is also suppressed in thalassemic mice (Figure 5B). However, no significant change in Bmp6 expression [either absolute (Figure 5A) or relative to liver iron (Figure 5B)] is observed in apoTf-treated thalassemic mice despite a decrease in liver iron (Figure 1D) and increased Bmp6 expression in apoTf-treated WT mice (Figure 5A and B). We, therefore, evaluated the potential role of other BMPs in hepcidin regulation in apoTf-treated thalassemic mice. Serum and liver BMP2 concentration are lower in thalassemic mice and increased in apoTf-treated thalassemic mice (Figure 5C-E); BMP2 is also increased in apoTf-treated WT mice (Figure 5D and E). BMP2 mRNA and protein expression are undetectable in sorted bone marrow erythroid precursors (data not shown), and BMP2 expression in hepatocytes is lower relative to liver with no difference between PBS-injected and apoTf-treated WT and thahaematologica | 2016; 101(3)
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Figure 7. Proposed working model of hepcidin regulation in apo-transferrin-treated thalassemic mice. Apo-transferrin treatment induces circulating BMP2, decreases circulating ERFE, and decreases activation of ERK1/2 in hepatocytes, resulting in increased nuclear Smad signaling and hepcidin expression. ERFE: erythroferrone; thal: thalassemic (Hbbth1/th1); apo: apo-transferrin.
lassemic mice (Online Supplementary Figure S3). No differences in serum BMP4 concentration (Online Supplementary Figure S4A) or mRNA expression in the liver or hepatocytes (Online Supplementary Figure S2B and C) are observed, and liver as well as bone marrow BMP4 are undetectable by western blot (data not shown). These findings suggest that like BMP6, non-parenchymal cells are the main source of BMP2 in the liver, correlating with the increased hepcidin expression in apoTf-treated mice. To further evaluate the role of BMP2 in apoTf-mediated hepcidin regulation, we analyzed the effect of neutralizing anti-BMP2/4 antibody on serum-treated cultured primary WT hepatocytes. To test the validity of this in vitro method, we demonstrate suppressed hepcidin expression and pSmad1/5/8 (Figure 6A-C) with unchanged Smad4 and Smad7 concentrations (data not shown) in hepatocytes treated with serum from thalassemic relative to WT mice, providing evidence of a robust culture system.35 Hepcidin expression is also increased in serum-treated relative to untreated cells (Figure 6A). No differences are observed in apoTf-treated relative to untreated hepatocytes or those treated with serum from apoTf- relative to PBS-treated mice (Figure 6A and B), providing evidence of nonparenchymal cell involvement via BMP2 and BMP6 on hepcidin expression. Primary hepatocytes concurrently treated with serum and neutralizing anti-BMP2/4 antibody exhibit suppressed hepcidin expression in each condition relative to serum alone conditions (Figure 6A), again suggesting the importance of extra-cellular BMP2 in hepcidin regulation. A similar pattern of pSmad1/5/8 mRNA and protein suppression is seen in cells exposed to serum with and without anti-BMP2/4 antibody (Figure 6A-C). As 305
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expected, Id1 mRNA expression mimics changes in hepcidin expression (Online Supplementary Figure S5). Surprisingly, MEK/ERK1/2 pathway activation is increased in hepatocytes treated by the neutralizing antibody (Figure 6B), suggesting that BMP2 is involved in hepcidin regulation via the MEK/ERK1/2 pathway. To further explore the relationships between BMP-Smad and MEK/ERK1/2 pathways, mouse primary hepatocytes were treated with different doses of BMP2 and U0126. In response to BMP2, we observe dose-dependent increases in hepcidin expression and pSmad1/5/8; and decrease in MEK/ERK1/2 (Online Supplementary Figure S6A and B). When primary hepatocytes are treated with BMP2 (20 ng/mL), U0126 (25 mM) or the combination, BMP2 and U0126 each induce hepcidin expression with an additive combined effect (Figure 6D). During these treatments, the ERK1/2 pathway is effectively suppressed by U0126 and by BMP2. Total cellular pSmad1/5/8 is minimally affected by the addition of BMP2 or U0126 individually but induced with the combination of BMP2 and U0126 (Figure 6E). However, BMP2, U0126, and their combination induce nuclear pSmad1/5/8 and especially Smad4 by western blot (Figure 6F) and immunofluorescence (Figure 6G and H). These findings demonstrate that BMP2 signaling and MEK/ERK1/2 suppression each up-regulate hepcidin by increasing nuclear pSMAD1/5/8 and especially Smad4 concentrations in an additive way.
ApoTf-induced hepcidin increase correlates with ERFE suppression only in thalassemic mice We evaluated candidate “erythroid regulators” in apoTftreated thalassemic mice. No differences were observed in GDF15 (data not shown), TWSG1 (Online Supplementary Figure S7), or GDF11 (Online Supplementary Figure S7) between bone marrow erythroblasts from WT, thalassemic, and apoTf-treated mice. However, ERFE expression is increased in bone marrow erythroblasts in thalassemic mice and normalized in apoTf-treated thalassemic mice but is surprisingly increased in apoTf-treated WT mice (Figure 1E). These findings support the importance of ERFE as an erythroid regulator in thalassemic mice, suggest that the effect in th1/th1 mice and th3/+ mice12 is comparable, and provide data consistent with our previously published evidence that apoTf treatment reverses ineffective erythropoiesis in thalassemic mice.13 Similar results have recently been reported in apoTf-treated th3/+ mice.36 Lastly, increased hepcidin expression in apoTf-treated WT mice occurs despite an increase in ERFE, suggesting that increased liver BMP2 in apoTf-treated WT and thalassemic mice functions, at least in part, through suppression of MEK/ERK1/2 pathway.
Discussion Thalassemia provides a model system for investigating the dual and competitive regulation of hepcidin by iron and erythropoiesis. Based on prior observations, we proposed that exogenous apoTf provides a window into the mechanisms responsible for this dual regulation. We found that in apoTf-treated thalassemic mice, hepcidin expression is increased despite a decrease in circulating and parenchymal iron; ERFE expression is decreased in the bone marrow, likely responsible for hepcidin de-repression; increased BMP2 but not BMP6 expression are also 306
involved; and MEK/ERK1/2 pathway activation inversely correlates with hepcidin expression. As previously reported,13 apoTf-treated mice did not exhibit any toxicity in our experiments, and exogenous apoTf’s effect on iron and erythropoiesis is similar whether administered to older (9-10 months)13 or younger (8-10 weeks) mice. Furthermore, 20 days of daily apoTf injections result in effects similar (Online Supplementary Table S2) to 60 days of injections (Online Supplementary Figure S1A and B). ApoTf-treated thalassemic mice exhibit a decrease in systemic iron overload. In addition, hepcidin mRNA expression is again unchanged in thalassemic relative to WT mice and increased in apoTf-treated thalassemic mice.13 A mouse serum hepcidin ELISA has recently been developed37 and demonstrates a strong correlation with hepcidin mRNA. Increased hepcidin expression in apoTf-treated thalassemic mice occurs despite decreased serum or liver iron, cytosolic Ferritin H, and circulating transferrin saturation, as previously reported,13 and thus does not reflect changes in iron status. Although BMP6 is important in hepcidin regulation, we previously demonstrated that Bmp6 expression is insufficiently increased relative to liver iron in th3/+ mice.5 Our current data further demonstrate that liver Bmp6 mRNA expression is unchanged in apoTf-treated thalassemic mice. Furthermore, Bmp6 is suppressed relative to liver iron, and although total pSmad1/5/8 is unchanged, nuclear pSmad1/5/8 and Smad4 are suppressed in thalassemic mice, together suggesting that BMP receptor stimulation is dampened and unable to increase nuclear pSmad despite increased Bmp6 expression. Similar findings have been demonstrated in th3/+ mice.38 Lastly, because liver Bmp6 mRNA expression is increased in apoTf--treated WT but not thalassemic mice, we hypothesize that BMP6 is involved in hepcidin regulation in response to more acute changes in iron status and not to chronic iron overload in thalassemic mice. Because other BMPs are involved in Smad1/5/8 activation and hepcidin expression in vitro,15 we explored the role of BMP2 in hepcidin regulation of apoTf-treated thalassemic mice. Our results demonstrate that BMP2 is decreased in the sera and livers of thalassemic mice and increased in sera and livers from apoTf-treated thalassemic mice. These findings are consistent with previously published work on BMP2 expression in human liver tissue and hepatoma-derived cell lines,20 and nonparenchymal cells in the rat liver.39 We hypothesize that increased hepcidin in apoTf-treated mice is at least in part due to liver-secreted BMP2 because: 1) BMP2 protein is not detectable in hepatocytes, suggesting that nonparenchymal cells in the liver are involved; 2) nonparenchymal cells in the liver are of mesenchymal origin, the same cells previously implicated in BMP6 secretion;34 3) BMP2 secretion has been demonstrated from cells of mesenchymal origin (e.g. vascular and skeletal cells).40,41 Taken together, these data strongly suggest that nonparenchymal cells of the liver and other organs secrete BMP2 to enable paracrine and endocrine effects on hepcidin regulation in the liver. A previous publication showed that exogenous BMP2 increases hepcidin expression and lowers serum-iron levels in mice14 and that BMP inhibitor, noggin, as well as neutralizing anti-BMP2/4 antibodies block hepcidin response to serum and transferrin.21 The increased BMP2 in human sera activates hepcidin expression in vitro, is haematologica | 2016; 101(3)
Hepcidin regulation in transferrin-treated mice
blocked by BMP2 immunodepletion,42 and hepcidin suppression correlates with BMP2 suppression in mice.43 This previous publication supports the role of BMP2 in apoTfinduced hepcidin upregulation. To further evaluate the effect of BMP2 on hepcidin expression, we treated primary hepatocytes with mouse serum and neutralizing BMP2/4 antibody. Although neutralizing anti-BMP2/4 antibodies suppress hepcidin expression and pSmad1/5/8 as expected, pERK1/2 is increased in neutralizing antiBMP2/4 antibody-treated hepatocytes relative to serum alone. These findings further support our finding that MEK/ERK1/2 signaling is increased in thalassemic mice and suppressed in apoTf-treated mice. Transferrin in primary hepatocyte cultures demonstrates complex changes in MEK/ERK1/2 signaling and relationship to Smad signaling. In vitro, transient induction of MEK/ERK1/2, Smad1/5/8, and hepcidin expression are observed in serum and/or transferrin-treated primary WT mouse hepatocytes, all blocked by U0126.21 In vivo, neither acute nor chronic iron induced increases in liver pERK1/2 in WT mice,4 despite associated changes in hepcidin expression. Taken together, these reported observations leave considerable uncertainty regarding the relationship between transferrin, pERK1/2, and hepcidin expression. We demonstrate for the first time increased pERK1/2 in hepatocytes from thalassemic relative to WT mice and decreased pERK1/2 in apoTf-treated thalassemic mice. Hepatocellular pERK1/2 is inversely correlated with hepcidin expression and nuclear Smad in thalassemic mice. In primary hepatocytes, U0126 results in the expected doseresponse inhibition of MEK/ERK1/2 and increased hepcidin expression. These in vitro experiments reveal, despite unchanged cellular or cytosolic pSmad1/5/8, increased nuclear pSmad1/5/8 and Smad4 when MEK/ERK1/2 is suppressed. In addition, BMP2- and U0126-treated primary mouse hepatocytes reveal an additive increase in hepcidin expression relative to treatment with either agent alone, suggesting a cross-acting function between BMP2Smad1/5/8 and MEK/ERK1/2 pathways. Supporting these observations, a screen using small-molecule kinase inhibitors found that MEK/ERK1/2 pathway inhibitors increase hepcidin in primary hepatocytes44 and MEK/ERK1/2 activators decrease BMP-dependent nuclear Smad.28,44,45 Taken together, our data suggest that activation of hepatocyte MEK/ERK1/2 pathway inhibits hepcidin expression by decreasing nuclear Smad, and that these effects are attenuated by treatment with apoTf. In thalassemic mice, exogenous apoTf reverses ineffective erythropoiesis and increases hepcidin expression13 likely by reducing circulating “erythroid regulator” suppression of hepcidin. Prior reports demonstrated that GDF15 does not play a role in erythroid regulation of hepcidin in mice.38,46 We also analyzed TWSG1 and GDF11 mRNA and did not observe any differences in erythroid precursors between PBS- and apoTf-treated thalassemic mice. However, ERFE mRNA expression is increased in thalassemic mice and normalized in apoTf-treated tha-
References 1. Nemeth E. Hepcidin in β-thalassemia. Ann Ny Acad Sci. 2010;1202(1):31-35. 2. Nemeth E, Tuttle MS, Powelson J, et al.
haematologica | 2016; 101(3)
lassemic mice. Because ERFE expression is dependent on STAT5 signaling via erythropoietin receptor,12 decreased ERFE expression in apoTf-treated thalassemic mice is likely a consequence of improved erythroid maturation and RBC survival, leading to a decrease in serum erythropoietin and reversal of splenomegaly.13 This finding confirms the importance of ERFE and its role in the reversal of ineffective erythropoiesis in apoTf-treated thalassemic mice. Furthermore, ERFE expression is increased in apoTf-treated WT mice, consistent with increased erythropoietin13 and reticulocyte counts (Online Supplementary Table S2). Thus, hepcidin expression in apoTf-treated WT mice is increased despite increased ERFE, suggesting that BMP2 provides a dominant effect to increase hepcidin expression and that increased hepcidin expression in apoTf-treated thalassemic mice is a combined effect of increased BMP2 and decreased ERFE. Reagents are currently under development to elucidate ERFE regulation of hepcidin in apoTftreated thalassemic mice. We hypothesize that liver and/or serum BMP2 is a previously unexplored upstream suppressor of the MEK/ERK1/2 pathway, inducing hepcidin expression (Figure 7). The mechanism by which this occurs is not yet clear. One possibility is that changes in BMP binding endothelial cell precursor-derived regulator (BMPER) affects BMP signaling. Indeed, the concept of BMPER regulation of hepcidin via BMP2 has recently been published.47 Smad-independent signaling for TGFβ family of ligands, including BMPs, has been proposed.22 BMP2 exerts its function through both MEK/ERK1/2 and Smad pathways in primary cultured osteoblasts48,49 and it has been proposed that cells of mesenchymal origin exhibit enhancement while cells of epithelial origin exhibit inhibition of Smad signaling by the MEK/ERK1/2 pathway.25 The details of a potentially significant cell autonomous cross-talk between the MEK/ERK1/2 and BMP/Smad pathways remain to be elucidated. Further studies are necessary to explore the potential use of exogenous apoTf to reverse ineffective erythropoiesis in β-thalassemia and other diseases of concurrent anemia and iron overload. Our data present mechanisms for hepcidin de-repression in apoTf-treated thalassemic mice, provide additional therapeutic targets in this pathway, and support our hypothesis that reversal of ineffective erythropoiesis and iron overload require concurrent management in β-thalassemia. Funding This work was supported by NHLBI (to YZG; HL105682), NIDDK (to YZG, SR, REF; DK095112), and New York Blood Center funding to the Erythropoiesis Laboratory. Acknowledgments We extend special thanks to the Jacobi Medical Center for sample analysis, E. Nemeth, M. Socolovsky, and P. Ney for stimulating discussions, and T. Ganz and M. Narla for unparalleled guidance and support.
Hepcidin regulates cellular iron efflux by binding to ferroportin and inducing its internalization. Science. 2004;306(5704):2090-2093. 3. Kautz L, Meynard D, Monnier A, et al. Iron regulates phosphorylation of Smad1/5/8
and gene expression of Bmp6, Smad7, Id1, and Atoh8 in the mouse liver. Blood. 2008;112(4):1503-1509. 4. Corradini E, Rozier M, Meynard D, et al. Iron regulation of hepcidin despite attenuated Smad1,5,8 signaling in mice without
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H. Chen et al. transferrin receptor 2 or Hfe. Gastroenterology. 2011;141(5):1907-1914. 5. Parrow NL, Gardenghi S, Ramos P, et al. Decreased hepcidin expression in murine beta-thalassemia is associated with suppression of Bmp/Smad signaling. Blood. 2012;119(13):3187-3189. 6. De Franceschi L, Daraio F, Filippini A, et al. Liver expression of hepcidin and other iron genes in two mouse models of beta-thalassemia. Haematologica. 2006;91(10): 1336-1342. 7. Gardenghi S, Marongiu MF, Ramos P, et al. Ineffective erythropoiesis in beta-thalassemia is characterized by increased iron absorption mediated by down-regulation of hepcidin and up-regulation of ferroportin. Blood. 2007;109(11):5027-5035. 8. Vokurka M, Krijt J, Sulc K, Necas E. Hepcidin mRNA levels in mouse liver respond to inhibition of erythropoiesis. Physiol Res. 2006;55(6):667-674. 9. Tanno T, Bhanu NV, Oneal PA, et al. High levels of GDF15 in thalassemia suppress expression of the iron regulatory protein hepcidin. Nat Med. 2007;13(9):1096-1101. 10. Tanno T, Porayette P, Sripichai O, et al. Identification of TWSG1 as a second novel erythroid regulator of hepcidin expression in murine and human cells. Blood. 2009;114(1):181-186. 11. Dussiot M, Maciel TT, Fricot A, et al. An activin receptor IIA ligand trap corrects ineffective erythropoiesis in beta-thalassemia. Nat Med. 2014;20(4):398-407. 12. Kautz L, Jung G, Valore EV, Rivella S, Nemeth E, Ganz T. Identification of erythroferrone as an erythroid regulator of iron metabolism. Nat Genet. 2014;46(7): 678-684. 13. Li H, Rybicki AC, Suzuka SM, et al. Transferrin therapy ameliorates disease in beta-thalassemic mice. Nat Med. 2010;16(2):177-182. 14. Babitt JL, Huang FW, Xia Y, Sidis Y, Andrews NC, Lin HY. Modulation of bone morphogenetic protein signaling in vivo regulates systemic iron balance. J Clin Invest. 2007;117(7):1933-1939. 15. Lin L, Valore EV, Nemeth E, Goodnough JB, Gabayan V, Ganz T. Iron transferrin regulates hepcidin synthesis in primary hepatocyte culture through hemojuvelin and BMP2/4. Blood. 2007;110(6):2182-2189. 16. Hentze MW, Muckenthaler MU, Galy B, Camaschella C. Two to tango: regulation of Mammalian iron metabolism. Cell. 2010;142(1):24-38. 17. Andriopoulos BJ, Corradini E, Xia Y, et al. BMP6 is a key endogenous regulator of hepcidin expression and iron metabolism. Nat Genet. 2009;41(4):482-487. 18. Meynard D, Kautz L, Darnaud V, CanonneHergaux F, Coppin H, Roth MP. Lack of the bone morphogenetic protein BMP6 induces massive iron overload. Nat Genet. 2009;41(4):478-481. 19. Ramos E, Kautz L, Rodriguez R, et al. Evidence for distinct pathways of hepcidin regulation by acute and chronic iron loading in mice. Hepatology. 2011;53(4):13331341. 20. Xia Y, Babitt JL, Sidis Y, Chung RT, Lin HY. Hemojuvelin regulates hepcidin expression via a selective subset of BMP ligands and receptors independently of neogenin.
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Blood. 2008;111(10):5195-5204. 21. Ramey G, Deschemin JC, Vaulont S. Crosstalk between the mitogen activated protein kinase and bone morphogenetic protein/hemojuvelin pathways is required for the induction of hepcidin by holotransferrin in primary mouse hepatocytes. Haematologica. 2009;94(6):765-772. 22. Derynck R, Zhang YE. Smad-dependent and Smad-independent pathways in TGFbeta family signalling. Nature. 2003;425 (6958):577-584. 23. Javelaud D, Mauviel A. Crosstalk mechanisms between the mitogen-activated protein kinase pathways and Smad signaling downstream of TGF-beta: implications for carcinogenesis. Oncogene. 2005; 24(37): 5742-5750. 24. Lutz M, Knaus P. Integration of the TGFbeta pathway into the cellular signalling network. Cell Signal. 2002;14(12):977-988. 25. Hayashida T, Decaestecker M, Schnaper HW. Cross-talk between ERK MAP kinase and Smad signaling pathways enhances TGFbeta-dependent responses in human mesangial cells. FASEB J. 2003;17(11):1576-1578. 26. Kretzschmar M, Doody J, Timokhina I, Massague J. A mechanism of repression of TGFbeta/ Smad signaling by oncogenic Ras. Genes Dev. 1999;13(7):804-816. 27. Calzolari A, Raggi C, Deaglio S, et al. TfR2 localizes in lipid raft domains and is released in exosomes to activate signal transduction along the MAPK pathway. J Cell Sci. 2006;119(Pt 21):4486-4498. 28. Poli M, Luscieti S, Gandini V, et al. Transferrin receptor 2 and HFE regulate furin expression via mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/Erk) signaling. Implications for transferrin-dependent hepcidin regulation. Haematologica. 2010;95(11):18321840. 29. Torrance JD, Bothwell TH. Tissue iron stores. In: Cook JD eds. Iron-Methods in hematology. New York, NY, USA: Churchill Lingstone; 1980; Vol.1:90-115. 30. Liu J, Zhang J, Ginzburg Y, et al. Quantitative analysis of murine terminal erythroid differentiation in vivo: novel method to study normal and disordered erythropoiesis. Blood. 2013;121(8):e43-9. 31. Wotton D, Knoepfler PS, Laherty CD, Eisenman RN, Massague J. The Smad transcriptional corepressor TGIF recruits mSin3. Cell Growth Differ. 2001;12(9):457463. 32. Mleczko-Sanecka K, Casanovas G, Ragab A, et al. SMAD7 controls iron metabolism as a potent inhibitor of hepcidin expression. Blood. 2010;115(13):2657-2665. 33. Corradini E, Meynard D, Wu Q, et al. Serum and liver iron differently regulate the bone morphogenetic protein 6 (BMP6)SMAD signaling pathway in mice. Hepatology. 2011;54(1):273-284. 34. Enns CA, Ahmed R, Wang J, et al. Increased iron loading induces Bmp6 expression in the non-parenchymal cells of the liver independent of the BMP-signaling pathway. Plos One. 2013;8(4):e60534. 35. Weizer-Stern O, Adamsky K, Amariglio N, et al. Downregulation of hepcidin and haemojuvelin expression in the hepatocyte cell-line HepG2 induced by thalassaemic sera. Br J Haematol. 2006;135(1):129-138.
36. Gelderman MP, Baek JH, Yalamanoglu A, et al. Reversal of hemochromatosis by apotransferrin in non-transfused and transfused Hbbth3/+ (heterozygous B1/B2 globin gene deletion) mice. Haematologica. 2015;100(5):611-622. 37. Gutschow P, Schmidt PJ, Han H, et al. A competitive enzyme-linked immunosorbent assay specific for murine hepcidin-1: correlation with hepatic mRNA expression in established and novel models of dysregulated iron homeostasis. Haematologica. 2015;100(2):167-177. 38. Frazer DM, Wilkins SJ, Darshan D, Badrick AC, McLaren GD, Anderson GJ. Stimulated erythropoiesis with secondary iron loading leads to a decrease in hepcidin despite an increase in bone morphogenetic protein 6 expression. Br J Haematol. 2012;157(5):615626. 39. Zhang AS, Anderson SA, Wang J, et al. Suppression of hepatic hepcidin expression in response to acute iron deprivation is associated with an increase of matriptase-2 protein. Blood. 2011;117(5):1687-1699. 40. Matsubara H, Hogan DE, Morgan EF, Mortlock DP, Einhorn TA, Gerstenfeld LC. Vascular tissues are a primary source of BMP2 expression during bone formation induced by distraction osteogenesis. Bone. 2012;51(1):168-180. 41. Martinovic S, Mazic S, Kisic V, et al. Expression of bone morphogenetic proteins in stromal cells from human bone marrow long-term culture. J Histochem Cytochem. 2004;52(9):1159-1167. 42. Maes K, Nemeth E, Roodman GD, et al. In anemia of multiple myeloma, hepcidin is induced by increased bone morphogenetic protein 2. Blood. 2010;116(18):3635-3644. 43. Liu S, Suragani RN, Han A, Zhao W, Andrews NC, Chen JJ. Deficiency of hemeregulated eIF2alpha kinase decreases hepcidin expression and splenic iron in HFE-/mice. Haematologica. 2008;93(5):753-756. 44. Goodnough JB, Ramos E, Nemeth E, Ganz T. Inhibition of hepcidin transcription by growth factors. Hepatology. 2012; 56(1):291-299. 45. Kretzschmar M, Doody J, Massague J. Opposing BMP and EGF signalling pathways converge on the TGF-beta family mediator Smad1. Nature. 1997;389(6651): 618-622. 46. Casanovas G, Vujic SM, Casu C, et al. The murine growth differentiation factor 15 is not essential for systemic iron homeostasis in phlebotomized mice. Haematologica. 2013;98(3):444-447. 47. Patel N, Masaratana P, Diaz-Castro J, et al. BMPER protein is a negative regulator of hepcidin and is up-regulated in hypotransferrinemic mice. J Biol Chem. 2012; 287(6):4099-4106. 48. Tang CH, Yang RS, Chien MY, Chen CC, Fu WM. Enhancement of bone morphogenetic protein-2 expression and bone formation by coumarin derivatives via p38 and ERK-dependent pathway in osteoblasts. Eur J Pharmacol. 2008;579(13):40-49. 49. Su JL, Chiou J, Tang CH, et al. CYR61 regulates BMP-2-dependent osteoblast differentiation through the {alpha}v{beta}3 integrin/integrin-linked kinase/ERK pathway. J Biol Chem. 2010;285(41):31325-31336.
haematologica | 2016; 101(3)
ARTICLE
Coagulation & Its Disorders
von Willebrand factor binds to the surface of dendritic cells and modulates peptide presentation of factor VIII
EUROPEAN HEMATOLOGY ASSOCIATION
Ferrata Storti Foundation
Nicoletta Sorvillo,1,2* Robin B. Hartholt,1,* Esther Bloem,1 Magdalena Sedek,1 Anja ten Brinke,3 Carmen van der Zwaan,1 Floris P. van Alphen,1 Alexander B. Meijer,4 and Jan Voorberg1
Department of Plasma Proteins, Sanquin-AMC Landsteiner Laboratory, Amsterdam, the Netherlands; 2Current address: Harvard Medical School Program in Cellular and Molecular Medicine, Boston Childrenâ&#x20AC;&#x2122;s Hospital, USA; 3Department of Immune Pathology, Sanquin-AMC Landsteiner Laboratory, Amsterdam, the Netherlands; and 4 Department of Plasma Proteins, Sanquin Blood Supply Foundation, Amsterdam and the Department of Pharmaceutics, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, the Netherlands 1
*
NS and RBH contributed equally
Haematologica 2016 Volume 101(3):309-318
ABSTRACT
I
t has been proposed that von Willebrand factor might affect factor VIII immunogenicity by reducing factor VIII uptake by antigen presenting cells. Here we investigate the interaction of recombinant von Willebrand factor with immature monocyte-derived dendritic cells using flow cytometry and confocal microscopy. Surprisingly, von Willebrand factor was not internalized by immature dendritic cells, but remained bound to the cell surface. As von Willebrand factor reduces the uptake of factor VIII, we investigated the repertoire of factor VIII presented peptides when in complex with von Willebrand factor. Interestingly, factor VIII-derived peptides were still abundantly presented on major histocompatibility complex class II molecules, even though a reduction of factor VIII uptake by immature dendritic cells was observed. Inspection of peptide profiles from 5 different donors showed that different core factor VIII peptide sequences were presented upon incubation with factor VIII/von Willebrand factor complex when compared to factor VIII alone. No von Willebrand factor peptides were detected when immature dendritic cells were pulsed with different concentrations of von Willebrand factor, confirming lack of von Willebrand factor endocytosis. Several von Willebrand factor derived peptides were recovered when cells were pulsed with von Willebrand factor/factor VIII complex, suggesting that factor VIII promotes endocytosis of small amounts of von Willebrand factor by immature dendritic cells. Taken together, our results establish that von Willebrand factor is poorly internalized by immature dendritic cells. We also show that von Willebrand factor modulates the internalization and presentation of factor VIII-derived peptides on major histocompatibility complex class II.
Introduction Hemophilia A is an X-linked bleeding disorder caused by reduced levels of functional human coagulation factor VIII (FVIII). Patients are treated with regular intravenous injections of FVIII concentrates.1 Approximately 25% of the severe hemophilia A patients [defined as <1 IU/dL (<1% FVIII activity)] develop inhibitory antibodies against FVIII. Both genetic and non-genetic risk factors for inhibitor formation have been identified.2-4 Genetic risk factors include F8 gene mutation5 and polyhaematologica | 2016; 101(3)
Correspondence: j.voorberg@sanquin.nl
Received: October 7, 2015. Accepted: November 27, 2015. Pre-published: December 3, 2015. doi:10.3324/haematol.2015.137067
Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/101/3/309
Š2016 Ferrata Storti Foundation Material published in Haematologica is covered by copyright. All rights reserved to Ferrata Storti Foundation. Copies of articles are allowed for personal or internal use. A permission in writing by the publisher is required for any other use.
309
N. Sorvillo et al. morphisms in IL10, TNFA, FCGR2A and CTLA4.6,7 Moreover, large epidemiological studies have shown that treatment intensity of hemophilia A patients is also linked to inhibitor development.8 The initial step in FVIII inhibitor formation is the endocytosis of FVIII by professional antigen presenting cells such as dendritic cells (DCs). Once endocytosed, FVIII is cleaved in endo-lysosomal compartments into discrete peptides that are loaded on MHC class II.9,10 The FVIII peptide-MHC class II complexes are then transported to the cell surface for recognition by antigen-specific CD4+ T-helper cells. Until now, most studies have focused on unravelling the mechanism of endocytosis and presentation of FVIII itself. However, the majority of FVIII circulates in complex with its carrier protein von Willebrand factor (VWF), a multimeric glycoprotein with two critical functions in hemostasis.11 Besides its role in platelet binding in primary hemostasis, VWF prevents premature activation of FVIII and increases FVIII half-life by preventing its degradation and clearance.12 Recently, VWF has also been shown to play an important role in FVIII inhibitor formation. It has been shown that VWF reduces the uptake of FVIII by DCs.13 The exact mechanism of interaction of VWF with DCs is still unknown. Here, the interaction and processing of VWF by DCs, alone or in complex with FVIII, was explored. Surprisingly, no endocytosis of VWF was observed when iDCs were treated with VWF alone or in complex with FVIII. Prolonged incubation times did not lead to internalization of VWF by iDCs; instead, VWF remained tightly bound to the cell surface. To determine the effect of VWF on FVIII peptide presentation, the repertoire of naturally presented FVIII-derived peptides by DCs on MHC class II molecules was analyzed by pulsing DCs with FVIII or FVIII/VWF complex. Interestingly, our findings show that although FVIII endocytosis is reduced in the presence of VWF, FVIII-derived peptides are still efficiently presented on MHC class II. In agreement with its lack of internalization, no VWF derived peptides could be detected when cells were treated with VWF alone, whereas a small number of VWF-derived peptides were presented on MHC class II when cells were pulsed with FVIII/VWF complex. Taken together these data suggest that VWF alone or in complex with FVIII binds to the cell surface, thereby modulating the internalization and peptide presentation of FVIII by DCs.
Methods Materials Spray dried ethylenediaminetetraacetic acid (EDTA) vacutainers (Greiner Bio-One, Kremsmuenster, Austria) were used for blood collection from healthy HLA class II-typed volunteers after giving informed consent in accordance with Dutch regulations and after approval from the Sanquin Ethical Advisory Board in accordance with the Declaration of Helsinki. Monocytes were isolated using Ficoll-Paque Plus (GE Healthcare, Uppsala, Sweden), CD14 microbeads (Miltenyi Biotech, Auburn, CA, USA) and cultured in Cellgro medium supplemented with human recombinant GM-CSF and IL-4 (CellGenix, Freiburg, Germany) in 6-well plates (Falcon, n. 353046, Corning, Amsterdam, the Netherlands). Human serum albumin was supplied by Sanquin Blood Supply, Amsterdam, the Netherlands. B-domain-deleted FVIII14 and recombinant 310
VWF15 were cultured in 6320 cm2 factories (Nunc, Roskilde, Denmark) in DMEM/F12 (Lonza, Walkerville, MD, USA) supplemented with 10% heat inactivated fetal calf serum (Bodinco, Alkmaar, the Netherlands), which is stepwise reduced to 8% FCS. Medium was concentrated using the hemoflow F5 HPS system (Fresenius Medical Care, Bad Homburg vor der Höhe, Germany) before protein purification. CNBr activated sepharose 4B and Q sepharose fast flow were purchased from GE Healthcare (Wauwatosa, WI, USA). Halt EDTA free protease inhibitor cocktail (100x) was used from Thermo Scientific (Bremen, Germany). Human collagen (VitroCol) used for quantification of VWF activity was obtained from Advanced BioMatrix (Carlsbad, CA, USA). Monoclonal antibodies against different FVIII domains CLB-CAg12, CLB-CAg117 and CLBEL14 have been described previously.16-19 Monoclonal antibody CLB-RAg20 against VWF has been described previously.14,20 DCSIGN and EEA1 antibodies were from AbD Serotec (Bio-Rad, Veenendaal, the Netherlands) and BD Biosciences (San Jose, CA, USA), respectively. Polyclonal antibody against human VWF from DAKO (Heverlee, Belgium) was used. Antibodies were labeled employing Alexa Fluor micro scale protein labeling kits (Molecular probes, Invitrogen, Breda, the Netherlands). Paraformaldehyde 20% EM grade was supplied by Electron Microscopy Sciences (Hatfield, PA, USA). FVIII activity was determined using the Chromogenix FVIII activity assay (Chromogenix Technologies, Llanelli, UK). For confocal analysis, cells were mounted using MOWIOL (Calbiochem, EMD Millipore, Billerica, MA, USA) supplemented with 2.5% triethylenediamine (Sigma-Aldrich, St. Louis, MO, USA) and 1 µg/mL Hoechst 33342 Fluorescent Stain (Life Technologies, Carlsbad, CA, USA).
Endocytosis/binding of VWF and FVIII Immature dendritic cells were harvested at day 5 and 2x105 cells were incubated with different concentrations of recombinant VWF ranging from 10 nM to 200 nM for 30 min at 37°C in 100 mL serum free medium (Cellgro). Next, cells were washed with Tris buffered saline (TBS) and fixed with 1% paraformaldehyde in TBS at room temperature for 15 min. Samples were incubated with quench buffer (50 mM NH4Cl with or without 0.2% saponine in TBS) for 15 min at room temperature and stained with a specific monoclonal antibody against VWF, CLBRAg20, in staining buffer (TBS supplemented with 0.5% human serum albumin), with or without 0.05% saponine, and subsequently with rabbit anti-mouse Alexa-568 conjugated secondary antibody. For FVIII uptake experiments, 25 nM of recombinant FVIII was used and samples were stained with Alexa Fluor 488labeled human monoclonal antibody CLB-EL14. FVIII/VWF complex was obtained by incubation of 25 nM FVIII or 50 nM of FVIII with 250 nM of VWF (ratio 1:10 or 1:5) in serum free medium for 30 min at 37°C. The ability of FVIII to bind to VWF under these conditions was confirmed by ELISA (Online Supplementary Figure S1). Uptake and binding of FVIII/VWF complex was performed as described above. Uptake and binding was analyzed by flow cytometry (LSR Fortessa, BD Biosciences, San Jose, CA, USA). Histograms were processed using FlowJo V10 software (Tree Star Inc., Ashland, OR, USA). Data are expressed as percentage of mean fluorescent intensity (MFI) where 100% of uptake/binding corresponds to the maximal fluorescent signal obtained. For each experiment, a sample which was stained for VWF or FVIII without adding VWF or FVIII was used as a negative control to determine background levels. To monitor uptake/binding by confocal microscopy, 5x105 cells were incubated with 50 nM of VWF for 30 min and/or 5 h at 37°C. Next, cells were washed with TBS and fixed with 4% haematologica | 2016; 101(3)
Surface bound VWF modulates FVIII presentation
PFA for 15 min at room temperature. Subsequently, samples were quenched and stained for VWF, as described above. Cells were also stained with antibodies against DC-SIGN (CD209) and early endosomal marker (EEA1), and subsequently with Alexa Fluor 488 labeled secondary antibody in TBS supplemented with 0.5 % HSA and 0.05% saponine. Stained cover slips were mounted with MOWIOL containing 2.5% triethylenediamine, imaged using 63x objective on a Leica TCS SP8 confocal microscope, and analyzed using Leica Application Suite X (Leica Microsystems, Wetzlar, Germany).
Purification and mass spectrometry analysis of HLA-DRbound peptides HLA-DR/peptide complexes were purified and analyzed by mass spectrometry, as described previously; further details are available in the Online Supplementary Methods.9,21,22
Characterization of peptides Peptides were identified using Proteome Discoverer 1.4 (Thermo Scientific, Bremen, Germany) and core peptides were predicted using NetMHCpan 2.8.23 The core peptide with the highest predicted binding affinity was used to indicate the location of that specific group of identified peptides. Further details are available in the Online Supplementary Methods.
Results VWF interacts with human monocyte-derived dendritic cells It is well established that FVIII and VWF circulate in plasma in a non-covalent complex.24 In vitro experiments suggest that the presence of VWF reduces interaction of FVIII with immature monocyte-derived dendritic cells (iDCs) and subsequent uptake by iDCs, leading to reduced immunogenicity.13 However, until now, it has not been established how VWF interacts with iDCs. IDCs were incubated with 25 nM of FVIII alone or in complex with VWF (ratio 1:10) for 30 min at 37°C and subsequently analyzed by flow cytometry. FVIII/VWF complex formation was confirmed by ELISA (Online Supplementary Figure S1). As expected, an increase in mean fluorescent intensity was observed when cells were incubated with FVIII alone, indicative for FVIII binding and/or uptake, while the presence of VWF reduced the FVIII signal (Figure 1A). These findings are in agreement with previous observations.13,18 Interestingly, an increase in mean fluorescent intensity was also observed when samples were stained with monoclonal antibody CLB-RAg20 directed against VWF (Figure 1B), indicating that VWF also interacts with iDCs. To
B
A
CLB-RAg20-568
CLB-EL14-488
C
CLB-RAg20-568
Figure 1. Interaction of von Willebrand factor (VWF) with monocyte-derived dendritic cells. (A) 25 nM of FVIII alone or in complex with VWF (1:10) was incubated with iDCs for 30 min at 37°C. Cells were analyzed by flow cytometry. Gray histograms represent control cells not pulsed with FVIII or VWF. (B) iDCs pulsed with FVIII/VWF complex (1:10) were stained with an antibody against VWF. (C) Uptake/binding was performed with increased concentrations of VWF (10-200 nM). Graphs represent data of 3 independent experiments ± SD. Uptake/binding is represented as percentage of mean fluorescent intensity (MFI) where 100% corresponds to the highest fluorescent signal for each individual experiment such as FVIII alone (A) and 200 nM of VWF (B). haematologica | 2016; 101(3)
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Figure 2. Binding of von Willebrand factor (VWF) to iDCs. (A) iDCs were pulsed with 50 nM VWF in the presence of saponin (continuous line) or without saponin (dotted line) for 30 min. Binding/uptake was analyzed by flow cytometry. (A) Representative histograms where the gray histrogram represents control cells not pulsed with VWF. The solid line and dotted line represent cells treated with or without saponine, respectively. (B) Uptake/binding is represented as percentage of mean fluorescent intensity (MFI) where condition with saponin is set at 100%. Graph represents data of 3 independent experiments ± SD. (C) Binding of VWF to iDCs assessed by confocal microscopy. Cells were incubated with 50 nM VWF for 30 min, subsequently cells were fixed, permeabilized and stained with VWF polyclonal antibody (DAKO) followed by a secondary antibody Alexa Fluor-568 (VWF staining in red) and EEA1 antibody or anti-DC-SIGN antibody followed by secondary antibody Alexa Fluor-488 (EEA1 and DC-SIGN staining in green; upper and lower panel, respectively) with nuclear staining in blue.
determine if the interaction of VWF with iDCs is dependent on FVIII, increasing concentrations of recombinant human VWF were incubated with iDCs. A dose-dependent increase in signal was observed (Figure 1C), further suggesting that VWF interacts with iDCs even when not in complex with FVIII.
VWF is not internalized by dendritic cells To establish whether VWF is endocytosed or bound to the cell surface, iDCs were incubated with 50 nM VWF for 30 min at 37°C and subsequently stained with anti-VWF antibody CLB-RAg20 in presence or absence of 0.05% 312
saponine. Surprisingly, a similar increase in mean fluorescent intensity was observed in both permeabilized and non-permeabilized cells. This suggests that VWF is predominantly bound and not internalized by iDCs (Figure 2A and B). To further assess the mechanism of VWF interaction, iDCs were incubated with VWF for 30 min at 37°C, and analyzed by confocal microscopy. Confocal microscopy revealed that VWF only binds to the cell surface and is not internalized (Figure 2C). In fact, no co-localization with early endosome marker EEA1 was observed (Figure 2C, upper panel), while VWF was detected in close proximity to the surface marker DC-SIGN. We next anahaematologica | 2016; 101(3)
Surface bound VWF modulates FVIII presentation
B
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Figure 3. Increased incubation times does not lead to von Willebrand factor (VWF) endocytosis. (A) iDCs were pulsed with 50 nM of VWF for 30, 150 and 300 min, respectively. Binding/uptake was analyzed by flow cytometry. (A) Representative histograms where the gray histograms represent control cells not pulsed with VWF. Black line VWF signal. (B) Quantification of time-dependent binding of VWF to iDCS. Signal is indicated as percentage of MFI and represents data from 3 independent experiments +/- SD. (C) Confocal analysis of VWF binding to iDCs. Cells were incubated with VWF for 6 h. Cells were fixed, permeabilized and stained with VWF polyclonal antibody (DAKO) followed by secondary antibody Alexa Fluor-568 (VWF staining in red) and EEA1 antibody or anti-DC-SIGN antibody followed by secondary antibody Alexa Fluor-488 (EEA1 and DC-SIGN staining in green; upper and lower panel, respectively) with nuclear staining in blue.
lyzed whether prolonged incubation results in the internalization of VWF by iDCs. Fifty nanomolar of VWF was incubated with iDCs for several time points ranging from 30 min to 5 h at 37°C and analyzed by flow cytometry. No increase in mean fluorescent intensity was observed after longer incubation times (Figure 3A and B). Confocal analysis revealed no co-localization with the early endosome marker EEA1 (Figure 3C, upper panel) whereas even after prolonged incubation times VWF was only detected in close proximity to DC-SIGN (Figure 3C, lower panel). These results suggest that VWF is not internalized by iDCs.
VWF modulates the presentation of FVIII derived peptides on MHC class II It is well established that after internalization by iDCs FVIII is efficiently presented on MHC class II.9 Several studies have shown that VWF reduces the uptake of FVIII by iDCs.13,18 However, whether VWF affects the processing and presentation of FVIII-derived peptides on MHC haematologica | 2016; 101(3)
class II has not been addressed. Immature DCs from 3 healthy HLA-DRB1-typed donors (donors A, B and C) were pulsed with 50 nM of FVIII alone or in complex with VWF (ratio 1:5) for 5 h at 37°C. Subsequently, the repertoire of MHC class II-derived peptides was identified by mass spectrometry. In agreement with previous observations, the majority of peptides presented on MHC class II molecules originate from endogenous proteins expressed by iDCs (data not shown). Figure 4 represents the repertoire of naturally presented FVIII peptides obtained from 3 different HLA typed donors, indicated as donor A, B and C. FVIII-derived peptides with the same predicted core MHC class II binding amino acid sequence were grouped. An overview of the complete set of identified peptides is provided in Online Supplementary Figure S3. Figure 4 shows that peptides originating from all FVIII domains were detected and differences in domain-specificity of the presented peptides were noted. Donor A predominantly presented peptides from the A2 and A3 domains. Cells from donor B additionally presented peptides from the A1 313
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Figure 4. FVIII-derived MHC class II core peptides identified from donors A, B and C. Cells from donors A, B and C were pulsed with 50 nM FVIII or a complex of 50 nM FVIII and 250 nM VWF. After maturation MHC class II ligands were extracted and analyzed using mass spectrometry. FVIII specific peptides were identified using Proteome Discoverer and subsequently HLA-DRB1 genotype specific core peptides for both HLA-DRB1 alleles were predicted using NetMHCpan 2.8. The first column shows predicted core peptides with corresponding residue numbers and corresponding FVIII domain (using HGVS numbering). When multiple core peptides are predicted, the core peptide with the highest predicted affinity for one of the two HLA alleles was used to represent the cluster of peptides identified (see Online Supplementary Figure S3 for all peptides identified for each donor). Subsequent columns depict which peptides are found for each donor upon incubation with FVIII or FVIII/VWF. Green: FVIII peptide detected following incubation with FVIII; yellow: FVIII peptide detected following incubation with FVIII and FVIII/VWF complex; red: FVIII peptide detection following incubation with FVIII/VWF; arrow: FVIII peptide not detected following incubation with FVIII/VWF.
domain, whereas cells from donor C showed the largest variation in presented peptides spanning most FVIII domains. Interestingly, FVIII-derived peptides were also detected when samples were pulsed with FVIII/VWF complex. We have previously shown that only small variations in peptides were found between duplicate samples using mass spectrometry.9 This allowed for the comparison of peptide repertories found in FVIII/VWF pulsed DCs with those found in DCs pulsed with FVIII only. Several FVIII-derived peptides were no longer detected in samples pulsed with FVIII/VWF complex (as indicated by the arrows) when compared to samples 314
incubated with FVIII alone. Interestingly, several FVIIIderived peptides were only observed in iDCs pulsed with FVIII/VWF complex (Figure 4, red boxes). The observed changes in peptide repertoire most likely arise from alterations in the levels of presentation of specific peptides. Indeed, analysis of the raw MS spectra confirmed the presence of low levels of â&#x20AC;&#x153;unidentifiedâ&#x20AC;? peptides based on their position in the ion-chromatogram. We then explored whether increasing the ratio of VWF over FVIII affected peptide presentation of FVIII. Immature DCs were incubated with 25 nM of FVIII alone or in complex with VWF in a ratio of 1:10 instead haematologica | 2016; 101(3)
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Figure 5. FVIII-derived MHC class II core peptides identified from donors D and E. Cells from donors D and E were pulsed with 25 nM FVIII or a complex of 25 nM FVIII with 250 nM VWF. After maturation of DCs MHC class II ligands were extracted and analyzed using mass spectrometry. FVIII specific peptides were identified using Proteome Discoverer and subsequently HLA-DRB1 genotype specific core peptides were predicted using NetMHCpan 2.8. The first column shows predicted core peptides with corresponding residue numbers and corresponding FVIII domain. The core peptides represent a cluster of identified FVIII derived peptides. The core peptide with the highest predicted affinity was selected when multiple core peptides for the same cluster of peptides were predicted. Online Supplementary Figure S3 provides an overview of all peptides identified. Subsequent columns depict which peptides are found for each donor for cells pulsed with FVIII or FVIII/VWF complex. Green: FVIII peptide detected following incubation with FVIII; yellow: FVIII peptide detected following incubation with FVIII and FVIII/VWF complex; red: FVIII peptide detection following incubation with FVIII/VWF; arrow: FVIII peptide not detected following incubation with FVIII/VWF.
of 1:5. After uptake, maturation and lysis of the cells, the MHC class II complexes were purified and the eluted peptides were identified, as described above. Figure 5 shows that a wide range of FVIII peptides was detected in samples pulsed with FVIII alone or with FVIII/VWF complex. In addition, under these conditions, VWF modulated the repertoire of FVIII peptides presented on MHC class II. Taken together these data indicate that, although a reduction in FVIII endocytosis by iDCs is observed, when iDCs are pulsed with FVIII/VWF complex there is still an appropriate presentation of FVIII-derived peptides in the presence of VWF. haematologica | 2016; 101(3)
Presentation of VWF-derived peptides on MHC class II molecules To investigate whether iDCs are able to present VWFderived peptides, iDCs were pulsed with either 250 nM of VWF alone or in complex with FVIII at a FVIII:VWF ratio of 1:5 (donors A, B and C) or 1:10 (donors D and E). After uptake and maturation of iDCs, cells were lysed and the MHC class II complexes were purified. The eluted peptides were analyzed by mass spectrometry. No VWFderived peptides were detected upon incubation with VWF alone (Figure 6), although a similar amount of endogenous peptides was eluted from the MHC class II 315
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Figure 6. VWF-derived MHC class II core peptides identified from donors A-E. Cells from donors A to E were pulsed with 250 nM VWF or complex of 25 nM FVIII with 250 nM VWF. After maturation MHC class II ligands were extracted and analyzed using mass spectrometry. VWF specific peptides were identified using Proteome Discoverer and subsequently HLA-DRB1 genotype specific core peptides were predicted using NetMHCpan 2.8. The first column shows predicted core peptides with corresponding residue numbers and corresponding VWF domain. Predicted core peptides are used to indicate the location of VWF-derived peptides identified. Subsequent columns depict which peptides are found for each donor for cells pulsed with VWF only or cells pulsed with FVIII/VWF complex. Green: VWF peptide detected following incubation with VWF; yellow: VWF peptide detected following incubation with VWF and FVIII/VWF complex; red: VWF peptide detection following incubation with FVIII/VWF.
molecules (Online Supplementary Figure S2). These results are consistent with the observation that VWF is not internalized by iDCs. Strikingly, when iDCs were pulsed with FVIII/VWF complex at a ratio of 1:5 (donors A, B and C) or 1:10 (donors D and E), VWF peptides were identified in 4 out of 5 donors (Figure 6). Donors A and B present peptides derived from the VWFA1 domain of VWF while donors C and D presented a single peptide derived from the VWFD3 domain. Donor D also presents two other peptides from the VWFA2 and CTCK domain, respectively, while for donor E, no VWF-derived peptides were identified. Taken together these data suggest that VWF, when in complex with FVIII, is internalized and can subsequently be presented on MHC class II molecules. The number of VWF peptides identified suggests that only a limited amount of VWF is being internalized under these conditions.
Discussion Formation of inhibitory antibodies in hemophilia A patients can lead to serious and even life-threatening complications.1 Endocytosis of FVIII by antigen-presenting cells is the initial step in this process, which can eventually lead to the development of long-living plasma cells which produce antibodies against FVIII.25 While VWF has been shown to modulate FVIII endocytosis by DCs, to our knowledge, no data regarding the interaction of VWF with iDCs have been described. Dendritic cells have robust antigen-presenting capacity due to the wide variety of endocytic mechanisms that are operational in these cells.26,27 Both receptor-mediated endocytosis, driven by ligand-binding to specific receptors, as well as non-specific internalization pathways such as macropinocytosis, have been shown to contribute to the efficient sampling of antigens from their environment.28 Unexpectedly, despite its binding to the cell surface, VWF is not internalized by iDCs. The lack of internalization of VWF is consistent 316
with the observation that VWF-derived peptides are not presented on MHC class II (Figure 5). To our knowledge, this provides a unique property of VWF since the majority of antigens are rapidly and efficiently endocytosed by iDCs. Based on the clusters of VWF that were observed by confocal microscopy, we speculate that VWF binds to discrete domains on the plasma membrane which apparently prohibit its endocytosis. Several cell surface receptors have previously been implicated in clearance of VWF (see review by Lenting et al.29). Recently, the C-type lectin receptor CLEC4M, the carbohydrate receptor Siglec-5 and scavenger receptor class A member 5 (SCARA5) have been shown to interact with VWF.30-32 Interestingly, transfected HEK293 cells over-expressing CLEC4M or Siglec-5 were shown to efficiently internalize VWF.30-32 The lack of VWF internalization observed in iDCs suggests that CLEC4M, Siglec-5 and SCARA5 are not involved in binding of VWF to dendritic cells. It has been established that VWF is predominantly cleared from the circulation by macrophages in spleen and liver.33,34 These observations have been confirmed using in vitro assays, which have demonstrated that shear stress is critical for endocytosis of VWF by human monocytederived macrophages.35 It is still unclear how shear stress affects VWF uptake. Shear stress may induce changes in cell-surface receptors present on macrophages; alternatively, it may induce conformational changes in VWF, thereby allowing for the exposure of structural determinants necessary for its uptake. As mentioned previously, VWF-derived peptides are not presented upon incubation of iDCs with increasing concentrations of VWF. Unexpectedly, upon incubation with FVIII/VWF complex a limited set of VWF-derived peptides was presented on MHC class II in 4 out of 5 donors analyzed (Figure 6). These data indicate that, in the presence of FVIII, small amounts of VWF are being internalized and processed for presentation on MHC class II. This suggests that FVIII facilitates VWF endocytosis by DCs. We speculate that VWF is co-internalized through its ability to interhaematologica | 2016; 101(3)
Surface bound VWF modulates FVIII presentation
act with FVIII. Development of allo-antibodies to VWF in response to replacement therapy occurs in approximately 5%-10% of the patients with severe von Willebrand disease.30 Our knowledge on the etiology and characteristics of this unwanted immune response remains limited.36 Genetic studies have shown that allo-antibody development to VWF occurs in patients with deletions, frameshift and nonsense mutations.37 It is likely that internalization of VWF by antigen-presenting cells is needed to allow CD4+ T cells to recognize VWF-derived peptides presented on MHC class II. Our study is the first to define which VWF-derived peptides are being presented on MHC class II. Peptides derived from the D’D3 domain assembly, the A1 and A3 domain and the carboxy-terminal CTCK domain were identified in this study. In view of its large size, the number of VWFderived core peptides presented on MHC class II appears to be limited. This may be related to the lack of internalization of VWF by iDCs. We speculate that this may contribute to the low frequency of allo-antibody development in patients with type 3 von Willebrand disease. In agreement with results from a previous study, we show that internalization of FVIII by dendritic cells is reduced in the presence of VWF.13,18 However, our data also show that VWF modulates the repertoire of FVIII-derived peptides presented on MHC class II. Overall, the number of FVIII peptides presented on MHC class II is reduced in the presence of VWF; depending on the donor analyzed, 1-10 peptides are not identified in the presence of VWF (Figures 4 and 5). This subset includes peptides containing the core sequence FIIMYSLDG, which has been identified as a promiscuous CD4+ T-cell epitope in the C1 domain of
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FVIII.38 In addition, peptides with core sequence FRNQASPRY (A3 domain; residues 1766-1786) are presented less efficiently in the presence of VWF. Interestingly, this peptide is presented by multiple MHC class II alleles9,10 and has also been described as a functional T-cell epitope in humanized E17 HLA-DRB1*1501 mice.39 These observations raise the possibility that VWF modulates CD4+ Tcell responses by affecting FVIII peptide presentation by antigen-presenting cells. The potential modulating effect of VWF on FVIII immunogenicity has been the subject of intense discussion.40 While hemophilia A patients have normal levels of VWF and are treated with VWF containing FVIII products, they can still develop inhibitors. This is in agreement with our data, as we show that even though FVIII endocytosis by iDCs is significantly reduced in the presence of VWF, it is still sufficient to allow for appropriate presentation of FVIII-derived peptides on MHC class II. We obtained evidence that the repertoire of FVIII peptides presented on MHC class II is modulated by VWF. Whether this effect translates into a potential benefit for FVIII/VWF concentrates when compared to highly purified plasma-derived or recombinant FVIII concentrates remains to be established. Acknowledgments The authors would like to thank members of the Department of Plasma Proteins for helpful discussions. Funding This work was supported by Sanquin and Landsteiner Foundation for Blood Transfusion Research (LSBR).
CANAL cohort study. Blood. 2007; 109(11):4648-4654. van Haren SD, Herczenik E, ten Brinke A, Mertens K, Voorberg J, Meijer AB. HLADR-presented peptide repertoires derived from human monocyte-derived dendritic cells pulsed with blood coagulation factor VIII. Mol Cell Proteomics. 2011; 10(6):M110.002246. van Haren SD, Wroblewska A, Herczenik E, et al. Limited promiscuity of HLA-DRB1 presented peptides derived of blood coagulation factor VIII. PLoS One. 2013;8(11):e80239. Zhou Y-F, Eng ET, Zhu J, et al. Sequence and structure relationships within von Willebrand factor. Blood. 2012;120(2):449458. Yee A, Gildersleeve RD, Gu S, et al. A von Willebrand factor fragment containing the D’D3 domains is sufficient to stabilize coagulation factor VIII in mice. Blood. 2014;124(3):445-52. Dasgupta S, Repessé Y, Bayry J, et al. VWF protects FVIII from endocytosis by dendritic cells and subsequent presentation to immune effectors. Blood. 2007;109(2):610612. van den Biggelaar M, Bierings R, Storm G, Voorberg J, Mertens K. Requirements for cellular co-trafficking of factor VIII and von Willebrand factor to Weibel-Palade bodies. J Thromb Haemost. 2007;5(11):2235-2242. Castro-Núñez L, Dienava-Verdoold I, Herczenik E, Mertens K, Meijer AB. Shear stress is required for the endocytic uptake of the factor VIII-von Willebrand factor complex by macrophages. J Thromb
Haemost. 2012;10(9):1929-1937. 16. Leyte A, Mertens K, Distel B, et al. Inhibition of human coagulation factor VIII by monoclonal antibodies. Mapping of functional epitopes with the use of recombinant factor VIII fragments. Biochem J. 1989;263(1):187-194. 17. van Den Brink EN, Turenhout EA, Davies J, et al. Human antibodies with specificity for the C2 domain of factor VIII are derived from VH1 germline genes. Blood. 2000; 95(2):558-563. 18. Herczenik E, van Haren SD, Wroblewska A, et al. Uptake of blood coagulation factor VIII by dendritic cells is mediated via its C1 domain. J Allergy Clin Immunol. 2012; 129(2):501-509. 19. van Helden PMW, van den Berg HM, Gouw SC, et al. IgG subclasses of anti-FVIII antibodies during immune tolerance induction in patients with hemophilia A. Br J Haematol. 2008;142(4):644-652. 20. Stel H V, Sakariassen KS, Scholte BJ, et al. Characterization of 25 monoclonal antibodies to factor VIII-von Willebrand factor: relationship between ristocetin-induced platelet aggregation and platelet adherence to subendothelium. Blood. 1984; 63(6):1408-1415. 21. Wroblewska A, van Haren SD, Herczenik E, et al. Modification of an exposed loop in the C1 domain reduces immune responses to factor VIII in hemophilia A mice. Blood. 2012;119(22):5294-5300. 22. Sorvillo N, van Haren SD, Kaijen PH, et al. Preferential HLA-DRB1*11-dependent presentation of CUB2-derived peptides by ADAMTS13-pulsed dendritic cells. Blood.
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haematologica | 2016; 101(3)
ARTICLE
Bone Marrow Failure
Twenty years of the Italian Fanconi Anemia Registry: where we stand and what remains to be learned
EUROPEAN HEMATOLOGY ASSOCIATION
Ferrata Storti Foundation
Antonio M. Risitano,1 Serena Marotta,1 Rita Calzone,2 Francesco Grimaldi,1 and Adriana Zatterale,2* on behalf of all RIAF Contributors 1 2
Hematology, Department of Clinical Medicine and Surgery, â&#x20AC;&#x153;Federico IIâ&#x20AC;? University; Genetics Unit, ASL Napoli 1, Naples, Italy
Haematologica 2016 Volume 101(3):319-327
ABSTRACT
T
he natural history of Fanconi anemia remains hard to establish because of its rarity and its heterogeneous clinical presentation; since 1994, the Italian Fanconi Anemia Registry has collected clinical, epidemiological and genetic data of Italian Fanconi Anemia patients. This registry includes 180 patients with a confirmed diagnosis of Fanconi anemia who have either been enrolled prospectively, at diagnosis, or later on. After enrollment, follow-up data were periodically collected to assess the clinical course, possible complications and long-term survival; the median follow up was 15.6 years. The main goal of the study was to describe the natural history of Fanconi anemia, focusing on the following variables: family history, disease presentation, development of hematological manifestations, development of malignancies, occurrence of hematopoietic stem cell transplantation and survival. Typical morphological and/or hematological abnormalities and/or growth retardation were the most common manifestations at diagnosis; the majority of patients (77%) exhibited hematological abnormalities at the initial presentation, and almost all (96%) eventually developed hematological manifestations. More than half of the patients (57%) underwent a bone-marrow transplant. The occurrence of cancer was quite rare at diagnosis, whereas the cumulative incidence of malignancies at 10, 20 and 30 years was 5%, 8% and 22%, respectively, for hematological cancers and 1%, 15% and 32%, respectively, for solid tumors. Overall survival at 10, 20 and 30 years were 88%, 56% and 37%, respectively; the main causes of death were cancer, complications of the hematological presentation and complications of transplantation. These data clearly confirm the detrimental outcome of Fanconi anemia, with no major improvement in the past decades.
Introduction Fanconi anemia (FA)1 is a rare inherited hematological disorder biologically characterized by hypersensitivity to DNA cross-linking agents. FA is mainly an autosomal recessive disease (except the rare X-linked FANC-B form), which was first reported in 1927 by the Swiss pediatrician Guido Fanconi as familial, infantile anemia.2 FA is now defined as a chromosomal instability (CI) syndrome, and it shows a wide clinical and genetic heterogeneity. Indeed, genetically FA can be caused by mutations in at least 18 different genes, mostly cooperating in a pathway which has not yet been fully elucidated. These gene products somehow interact with proteins encoded by genes which, when mutated, cause other CI syndromes, such as Ataxia Teleangiectasia, Bloom syndrome, and Nijmegen Breakage Syndrome.3 All these proteins cooperate in a pathway which appears to be involved in DNA and oxidative stress damage repair.4 The FA cellular phenotype is characterized by a G2 cell cycle phase delay5 and by CI, typically appearing as characteristic rearrangement haematologica | 2016; 101(3)
Correspondence: azatt@tin.it
Received: July 7, 2015. Accepted: November 27, 2015. Pre-published: December 3, 2015. doi:10.3324/haematol.2015.133520
Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/101/3/319
Š2016 Ferrata Storti Foundation Material published in Haematologica is covered by copyright. All rights reserved to Ferrata Storti Foundation. Copies of articles are allowed for personal or internal use. A permission in writing by the publisher is required for any other use.
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figures (triradial and quadriradial figures made by nonhomologous chromosomes). The CI is both spontaneous6 and/or induced by alkylating DNA cross-linking agents, such as mytomicin C (MMC)7 or the more specific diepoxybutane (DEB);8 the cytogenetic DEB-test makes the FA diagnosis possible in those patients not showing typical malformations or still asymptomatic, and also in patients not showing spontaneous CI at standard cytogenetic evaluation. The phenotype of FA patients is largely heterogeneous, since the natural history of the disease entails different clinical manifestations which may either be present at birth, or develop later during the course of the disease. Clinically, FA patients present bone marrow failure at various times in life, typically beginning in childhood as platelet deficiency, and then progressing to pancytopenia,1 eventually leading to life-threatening complications. FA patients can show variable congenital malformations9 and are prone to hematologic and solid neoplasias, which are ultimately the leading cause of death.1,4,10-12 At present, hematopoietic stem cell transplantation (HSCT) represents the only effective treatment for FA,13 although unfortunately it cannot improve the patient’s growth rate or reduce the propensity to develop non-hematological cancers. FA is a rare disease, with an incidence rate of 0.1-0.5 new cases for every 100000 newborn children;1,14 thus, large multicenter studies are needed to better describe the natural history of the disease. Ideally, to prevent any bias, such studies should include all patients diagnosed with FA in a broad but well-defined geographic area, possibly with a prospective collection of data and an adequate long-term follow-up. Until now, the most reliable data on FA has come from the International Fanconi Anemia Registry (IFAR)15,16 and some national Registries, mostly the North American Survey of Fanconi Anemia (NAS)12,17 and the German Fanconi Anemia Registry (GEFA).18 In a rare and highly heterogeneous disease such as FA, it is very difficult to establish the natural history of the disease, and even more difficult to organize research projects which require the collection of samples from patients showing common features. Moreover, some significant differences are possible among different populations, due to the existence of geographic isolates or genetic derives. A specific national registry, collecting patients’ clinical, epidemiological, genetic and familial data, becomes a powerful tool, which creates for physicians and the scientific community the possibility of better knowing the disease, hence preventing misdiagnosis and delayed diagnosis. A national registry also creates a network that facilitates the participation of patients in research projects and clinical trials. To fulfill the need for a National Database including most (if not all) FA patients diagnosed in Italy, in 1994 some of us started “Il Registro Italiano Anemia di Fanconi” (RIAF), which translates as 'The Italian Fanconi Anemia Registry'.19 This project was established within the Italian Public National Health System (NHS), at the Genetic Unit of the local health unit “ASL Napoli 1”, taking advantage of their established expertise in diagnostics and genetic counseling for CI syndromes. The aims of the project were: i. to create a National database, recording all Italian FA cases; ii. to collect information about the epidemiology of FA in Italy, as well as about its natural history and therapeutic interventions; iii. to promote a robust scientific network among health workers (physicians, but also scientists) dealing 320
with FA in Italy, eventually increasing awareness about the disease and promoting the strongest possible collaboration among Italian physicians and scientists; iv. to provide patients and their families with an established national network for the diagnosis, treatment and follow-up of FA patients; v. to share with the Italian authorities all the information above, aiming to assess the real impact of FA on the Italian NHS, eventually promoting further health policy strategies. Herein we report on the 20-year experience of this Registry, focusing on the natural history of the disease, frequent therapeutic modalities and long-term outcomes of FA patients. The major aim of this work was to identify possible factors affecting the survival of FA patients, as well as to identify possible changes in outcome emerging over the two decades of this study.
Methods Study design and data handling The RIAF was officially established in 1994 as a prospective, non-interventional study, approved by the local Government (serving as the institutional review board, IRB) and operated according to National laws within the NHS. The program was approved and funded by the Regional Government (Regione Campania), and supported by the Italian Association for Research on Fanconi Anemia (AIRFA). All patient data were collected through dedicated case report forms (CRFs), designed by the geneticists working at the ASL Napoli 1, and further developed thanks to the contributions of collaborating physicians (listed in the Appendix). In accordance with the Declaration of Helsinki, before enrollment all patients or their parents/guardians gave written, informed consent, after discussing the RIAF aims and policy and their own rights with an authorized delegate (geneticist or physician), as listed in a written information sheet. Follow-up CRFs were periodically filed by the geneticists or the physicians, through a continuous sharing of critical information with both treating physicians and patients or their families. The CRFs and informed consent were approved by the local Government/IRB. The data were stored both on paper and in digital records, strictly protected, accessible only to the authorized staff and always made anonymous for publications or sharing with other researchers.
Inclusion criteria, enrollment and data collection Patients were enrolled only after a positive DEB or MMC test, for the most part carried out or confirmed in our laboratory. The chromosome breakage assay was always performed on peripheral blood cells. Patients were enrolled either at the time of initial diagnosis, or later, during the course of the disease, according to patients’ and/or parents’ decisions. Following informed consent, family history and medical information were recorded by the treating physicians, according to the specific CRFs. Given the epidemiologic purpose of the RIAF, the data of patients belonging to non-Italian ethnic populations were collected separately and are not considered here. Indeed, the patients geographic designation was established on the basis of their parents’ and grandparents’ birthplaces (Caucasian ethnicity and proven Italian descent covering at least two generations); the same criteria were used to assign a patient to a specific Italian Region.
Data analysis and statistics Statistical analysis was performed on the population of 180 patients (fetal losses and miscarriages were excluded), focusing on the following categories: family history, disease presentation, hematologic manifestations, HSCT, treatment impact on survival, haematologica | 2016; 101(3)
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malignancies, overall and cancer-free survival, and causes of death. Standard descriptive statistical tests were applied as appropriate, using SPSS software (PSP, Bologna, Italy). Student’s t-test, MannWhitney test and Fisher’s exact test were used for most descriptive analysis. The time to developing specific disease presentation (i.e., hematological presentation, hematological malignancies, solid tumors) was presented as cumulative incidence, using a competing risk approach, with birth treated as the FA onset date; death and HSCT (the latter only for hematological presentation and hematological malignancies) were considered as competing risks. The Kaplan-Meier curve was used to estimate overall survival; again birth was considered as the FA onset date. The following variables were tested for a possible impact on survival: gender, date of birth, age at diagnosis, all congenital abnormalities (presence, total number, type; with and without skin abnormalities), hematological presentation (at diagnosis, or at any time), hematological malignancies, solid tumors (all together and head/neck) and HSCT. Univariate and multivariate analyses were performed using a Cox regression model on all patients, as well as separately on transplanted and non-transplanted patients.
Results Diagnosis and genetics of FA A preliminary diagnosis of FA was made by treating physicians, based on clinical presentation at birth, or later on; in some patients their family history was the main reason to hypothesize the presence of FA. The diagnosis of FA was based on a standard chromosome breakage test by exposure to DEB or MMC, performed on peripheral blood samples. Given the possible challenges in the diagnosis of CI, all tests were confirmed at the Genetic Unit of the ASL Napoli 1, or other laboratories with specific expertise for the diagnosis of FA, eventually limiting subjective interpretations and inter-laboratory technical variability. Lymphoblastoid cell lines were established for research aims and as diagnostic positive controls. A single DEB test was sufficient for the diagnosis in the majority of cases; however, the protocol adopted at the Genetic Unit of the ASL Napoli 1 was used to confirm the diagnosis on two different samples, allowing a robust consistency of data. Between 1989 and 2014, out of a total of 1340 DEB tests performed on 1185 subjects, the number of positive tests was 206 (for 135 patients). Notably, a prior misdiagnosis was proven in 11 patients: in 7 cases a previous diagnosis of FA was not confirmed, whereas in 4 patients the diagnosis of FA was missing. Mosaicism was suspected in 9% of patients.20 They showed chromosome breaks in <40% of their cells, but typical DEB-induced rearrangements were demonstrated; CI testing performed on different tissues, together with clinical, family and/or molecular data, confirmed the FA diagnosis. Patients lacking a confirmatory positive chromosome breakage test were not enrolled in the RIAF, irrespective of their clinical presentation; thus, possible revertant phenotypes may be underrepresented in this cohort. Complementation groups were available for 55 patients; the most common complementation group was A (91%), followed by G (5%) and D2 (4%) (Online Supplementary Table S1).
Subject characteristics Between 1994 and 2014, a total of 180 patients were included in the RIAF, belonging to 151 distinct families (median number of affected subjects per family was 1, haematologica | 2016; 101(3)
range 1-4; Table 1); a few (n=3) cases of miscarriage diagnosed as FA (by DEB and/or molecular tests on amniocytes or chorionic villi) were also recorded, but were not included in this study. The geographical distribution was spread throughout the country, even if a significant number of patients were from the North-East or the South of Italy;21 however, we were unable to identify any founder effect. The characteristics of enrolled patients are described in Table 2. There were 94 (52%) male and 86 (48%) female patients, with no statistical difference in gender. The median age at diagnosis was 7.48 years; when patients were divided according to the date of birth, by quartiles (≤1980, 1981-1987, 1988-1995, ≥1996), the age at diagnosis was significantly lower in patients born in more recent periods (Mann-Whitney test, P<0.001; Figure 1A).
Family history Family history was carefully collected for all patients included in the registry; parents consanguinity was recorded in 20 of the 151 families (14%; Table 1). For each family (considered as two parents), the median number of affected children was 1 (range 1-4), the median of unaffected siblings (all confirmed by DEB test: all alive siblings were tested even as potential HSCT donors) was 1 (range 0-10); the median of miscarriages was 0 (range 0-3). Globally, in the 151 families enrolled we recorded 310 babies, of whom 183 (60%) were FA cases (180 enrolled in the RIAF and 3 miscarriages), and 127 were unaffected siblings. Thirty-seven of the patients included in the registry reported a family history of FA. We also looked for the occurrence of hematological disorders and malignancies in the relatives of enrolled FA patients (up to the second degree of kinship); family history for cancer (taking into account up to the second degree) was 56% (85 out of 151). Family history for hematological disorders was demonstrated in 19% of patients. Morphological abnormalities in some relatives were recorded in 19% of patients.
Disease presentation In the majority of patients the diagnosis was suspected based on typical morphological and/or hematological abnormalities and/or growth retardation. As detailed in
Table 1. Characteristics of the 151 families.*
Variable Consanguinity between parents
N. of affected children°
N. of not affected children
N. of miscarriages
151 Families (%) None 1st degree 2nd degree >2nd degree 1 2 3 4 0 1 2 3 or >3 0 1 2 3 or >3
131 (86) 13 (9) 3 (2) 4 (3) 124 (82) 22 (14) 4 (3) 1 (1) 35 (23) 65 (43) 36 (24) 15 (10) 122 (81) 21 (14) 7 (5) 1 (<1)
*For this analysis, a family is intended as two parents with their children; data are expressed by family; °including abortions with confirmed FA.
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Table 2, congenital abnormalities were demonstrated in 90% of patients at the time of diagnosis; the most common were the typical abnormalities of skin pigmentation, which affected 96% of RIAF patients (Table 2); skeletal abnormalities were also very frequent (57%). Other common congenital abnormalities involved growth retardation (39%), the central nervous system (35%), the urinary system (34%), the genital tract (18%), the gastrointestinal tract (13%), the eyes (12%), the endocrine system (9%) or the cardiovascular system (7%). Hematological manifestations were defined according to the definition of aplastic anemia22 and in accordance with the WHO 2008 classification of myeloid malignancies.23 The majority of patients (77%) exhibited some hematological abnormalities at diagnosis, which in most cases was a mild-to-moderate cytopenia eventually associated with some degree of bone marrow failure (BMF), whereas hematological malignancies (e.g., myelodysplastic syndromes, MDS) and solid tumors were very rarely observed at diagnosis (Table 2). Thanks to the long-term follow-up of the enrolled patients, we were able to assess the further course of the disease with the development of the most common complications of FA, as well as the impact of different factors on survival.
Time to hematological manifestations: bone marrow failure and hematological malignancies Even if hematological abnormalities were present at diagnosis in only 77% of cases, a total of 172 (96%) of FA patients enrolled in the RIAF had some hematological manifestations during their disease course; in almost all cases (172, 96%) this included cytopenia due to BMF, whereas a hematological malignancy (mostly MDS or acute leukemia, n=9 and n=4, respectively) was recorded in 8% of patients (see Table 3). As expected, in many cases an initial BMF progressed to either MDS or more aggressive hematological cancers; 1 MDS, 1 leukemia and 1 lymphoma patient did not evolve from a previous BMF. Considering death and HSCT as competing events, the cumulative incidence of any hematological disorder was 62%, 88% and 94% at 10, 20 and 30 years respectively, whereas the incidence of hematological malignancies was 5%, 8% and 22% at 10, 20 and 30 years, respectively. 'The cumulative incidence of the first hematological presentation and of the first hematological malignancy is depicted in Figure 1B.
Time to hematopoietic stem cell transplantation The development of a hematological presentation is the main indication for HSCT in FA patients; indeed, more than half of the patients enrolled in the RIAF (102 out of 180, 57%) had received a HSCT from either a non-affected sibling or matched unrelated donor (Table 3). The first HSCT was performed from a non-affected sibling in 38% of cases, from a matched unrelated donor in 48%, and quite rarely from cord blood (4%) or a mismatched related donor (0.9%). The cumulative incidence of HSCT in our patient cohort was 33%, 64% and 72% at 10, 20 and 30 years, respectively, as depicted in Figure 1B. The age at transplant was significantly different according to the date of birth cohorts, since patients born in more recent years were transplanted earlier (Mann-Whitney test, P<0.001; Figure 1C). Since National and European Registries collecting transplant-specific information exist,15,16 in the RIAF we decided not to duplicate this information. A formal analy322
sis of HSCT outcome in these patients is beyond the scope of this study. However, follow-up data on survival and the possible development of malignancies were also collected for those RIAF patients who received a HSCT.
Cumulative incidence of solid tumors A total of 27 solid cancers were diagnosed in 20 of the 180 RIAF patients (11%); a few patients experienced multiple cancers. The most common sites of cancer were the head and neck (n=12, 44% of all solid tumors), liver (n=3, 11%), breast, thyroid and genital tract (n=2 for each, 7%) (see Table 3 for details). The cumulative incidence of solid tumors was 1%, 15% and 32% at 10, 20 and 30 years respectively, as depicted in Figure 1B. The incidence of all solid cancers and of head and neck tumors was not statistically different between patients who had received a HSCT and those who had not (P=0.43 and P=0.50, respec-
Table 2. Patient characteristics at diagnosis.
Variable
180 Patients (%)
Gender Female Male Age (median years at diagnosis) Family history no yes Family history for malformations no yes Family history for hematological diseases no yes Family history for cancer no yes Congenital abnormalities none/unknown skin only structural abnormalities* Type of malformation skin hyper- hypopigmentation skeletal abnormalities growth retardation central nervous system renal and urinary tract genital tract gastrointestinal tract eyes endocrine system cardiovascular system Hematological disease BMF* no yes Hematological malignancies^ no yes Solid tumors no yes
86 (48) 94 (52) 7,48 (0-37,7) 143 (79) 37 (21) 145 (81) 35 (19) 145 (81) 35 (19) 79 (44) 101 (56) 17 (10) 16 (9) 147 (81) 163 (96) 103 (57) 70 (39) 63 (35) 61 (34) 33 (18) 23 (13) 21 (12) 16 (9) 12 (7)
42 (23) 138 (77) 179 (99) 1 (1) 177 (98) 3 (2)
*As defined according to the International Agranulocytosis and Aplastic Anemia Study Group; ^As defined according to the WHO 2008 criteria.
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tively; Figure 1D), even if the analysis is limited by the small number of events. In transplanted patients, all but one tumor occurred after HSCT.
diagnosis (Figure 2C; P<0.001) and on higher numbers of structural abnormalities (Figure 2D; P<0.005). Notably, BMF at initial presentation, date of birth cohorts and HSCT seemed not to affect survival (Table 4). Indeed, looking at overall survival by quartiles of date of birth there was no improvement in survival over time (Figure 2E; P=n.s.). Similarly, grouping patients by HSCT, the 10, 20 and 30 year survival rate of non-transplanted patients (n=78, median follow-up 15.8 years) were 84%, 49% and 34%, respectively, while those of transplanted patients (n=102, median follow-up 16 years) were 90%, 62% and 40%, respectively (Figure 2F; P=0.17). Multivariate analysis was also performed separately on non-HSCT and HSCT patients; in this context, age at diagnosis remained associated with a better survival rate in both groups (Online Supplementary Table S3).
Overall survival and prognostic factors Ninety-four of the 180 patients were still alive at the time of the last follow-up. For all patients enrolled in the RIAF, overall survival was calculated starting from the day of birth. With a median follow up of 15.6 years, median survival was 22.5 years (Figure 2A); probabilities of survival at 10, 20 and 30 years were 88%, 56% and 37%, respectively (without censoring HSCT patients). Looking for the natural history of the disease, when patients who had received an allogeneic HSCT were censored at the time of transplant, the probabilities of survival at 10, 20 and 30 years were 85%, 39% and 24%, respectively (Figure 2B). In univariate analysis, no patients feature affected overall survival, except age at diagnosis and the number of structural abnormalities (excluding skin anomalies; Online Supplementary Table S2). In multivariate analysis, an older age at diagnosis (HR=0.873, P<0.001) and the presence of more than 10 structural abnormalities (excluding skin; HR=6.504, P=0.017) were associated with a better or worse survival rate, respectively. This distinction resulted in statistically significant differences in overall survival based on age at
Cause of death Eighty-six of the 180 FA patients enrolled in the RIAF died during their follow-up; the main causes of death are listed in Table 5. As expected, the causes of death were different in patients who had not received an HSCT as compared with those of transplanted patients (P<0.001; Chi-square test). Indeed, in non-HSCT patients the main causes of death were related to the underlying disease (i.e., for the most part the hematological abnormalities), such as
A
B
C
D
1. Time to most common FA complications. (A) Age at diagnosis, according to quartiles of date of birth (DOB); (B) Cumulative incidence of bone marrow failure (BMF), hematological malignancies (MDS and AML; HEM TUM), solid tumors (SOL TUM) and HSCT; (C) Age at HSCT, according to DOB quartiles; (D) Cumulative incidence of solid tumor and of head/neck tumors: HSCT vs. no HSCT.
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infections (n=14, 33% of total deaths in non-HSCT patients), bleeding (n=8, 18.5%) and solid tumors (n=9, 21%). In contrast, in HSCT patients the majority of deaths were somehow related to treatment complications, such as infections (n=11, 25.5% of total deaths in HSCT patients), graft versus host disease (GvHD) (n=11, 25.5%) and other transplant related mortality (TRM) (n=13, 30%). Solid tumors accounted for 9% of deaths (n=4).
Discussion The RIAF is the first population-based Italian database, run within the Italian NHS, focusing on FA, which is rare in the frequency of the disease, but the most common among inherited bone marrow failure syndromes. Herein we report a comprehensive analysis of all patients included in the registry over the past 20 years with their longterm follow-up, eventually providing a reliable description of the natural history of FA. In our series of 180 prospectively collected patient data, we have shown a median survival of 22 years, which unfortunately has not improved in the past two decades. Our efforts of creating a robust scientific network have increased the awareness of this disease in Italy, eventually leading to objective achievements. Indeed, the diagnosis has come to be made earlier over the past decades, and the time to the only curative treatment – namely HSCT – has decreased. Nevertheless, these improvements in the management of FA patients have not yet resulted in a better survival rate, and even the outcome of patients who have received a HSCT does not appear to be better than that of those who did not. Indeed, in our multivariate analysis, the only factors associated with a better outcome were an older age at diagnosis and a lower number of structural abnormalities, indicating that different clinical phenotypes may have a different life expectancy. The natural history of FA has been described in previous retrospective studies,15-18 which have highlighted the heterogeneity of clinical presentation. The RIAF includes only patients with a DEB test confirmed diagnosis of FA, who are unselected for specific disease presentations and have a long-term follow-up. Thus, selection biases (e.g., toward a severe phenotype) should be limited (with the only exception being a possible underestimation of patients beginning with cancers, who might not receive the correct FA diagnosis), eventually leading to a more accurate representation of the natural history of FA. However, like all registry studies, the RIAF suffers from potential limitations, since its completeness and accuracy largely depends on the commitment and dedication of collaborating physicians. Our database confirms that FA severely impairs the survival of affected patients; the median survival observed in our series (about 22 years) was slightly lower than those reported by the International Fanconi Anemia Registry16 and by the USA National Cancer Institute.24 In our Registry, we have not systematically investigated any genotype-phenotype correlation;25 however, genetic data from a subset of patients (as well as independent data on the genetics of FA in the same geographic area)26 seem predominantly influenced by the large prevalence of patients harboring FANCA mutations.21,27 Malignancies play an important role in the natural history of FA, the risk increasing with age for a wide array of cancer types;10-12,18 moreover, some patients can develop multiple cancers, possibly 324
also due to the increased risk associated with anti-cancer treatments (i.e., chemotherapy and radiotherapy). This report confirms the cancer propensity of FA patients and further stresses the need for frequent and careful tumor evaluations, aiming at early therapeutic interventions,24,28 the only effective strategy for improving long-term survival in FA patients. Our Registry was not designed to formally investigate the impact of specific therapeutic interventions on the natural history of the disease. However, even if a head-tohead comparison is impossible, we have separately looked for overall survival in FA patients who have received an HSCT, without showing any difference with non-transplanted patients. HSCT may cure the hematological disease associated with FA, but it does not reverse the phenotype that results from the involvement of extra-hematological tissues and organs.29-31 This is especially true for the intrinsic risk of cancer due to the genetic instability typical of FA, which might actually be increased by the pre-transplant conditioning regimen and possible detrimental
Table 3. Disease manifestations during the whole disease course.
Variable Hematological disease BMF no yes Hematological malignancies no yes MDS AML Lymphoma Solid tumors no yes Sites of solid tumors° head/neck liver breast thyroid genital tract lung central nervous system kidney soft tissues skin gastrointestinal tract HSCT no yes Sibling MUD CBU Haploidentical Unknown Alive yes no
180 Patients (%)
8 (4) 172 (96) 166 (92) 14 (8) 9 (5) 4 (2.5) 1 (0.5) 160 (89) 20 (11) 12 (7) 3 (2) 2 (1) 2 (1) 2 (1) 1 (0.5) 1 (0.5) 1 (0.5) 1 (0.5) 1 (0.5) 1 (0.5) 78 (43.5) 102 (56.5) 39* (22) 49§ (27) 4 (2) 1 (0.5) 9 (5) 94 (52) 86 (48)
The total number of tumors exceeds the number of patients with cancer, since some patients experienced multiple tumors: one patient had 4 tumors (genital tract, breast, skin and head/neck), 4 patients had 2 tumors (head/neck and gastrointestinal tract, head/neck and liver, head/neck and thyroid, central nervous system and Wilms). *2 from sibling cord blood units. §One was a second HSCT after a graft failure of a first HSCT from a sibling donor
°
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effects of GvHD.28,32 Notably, in our study the risk of solid tumors remains high even after HSCT, but apparently it is not increased over that of non-transplanted patients. However, these data should be confirmed with a longer follow-up, possibly within International studies designed to specifically investigate this endpoint. Our observation that the survival of transplanted and non-transplanted patients was not different is not surprising, because HSCT in the context of FA carries specific challenges. Beyond the fact that HSCT does not affect the extra-hematological phenotype of FA, other reasons may play a role: i. HSCT patients may be biased toward a more severe phenotype; ii. initial patients may have received a HSCT with a nonoptimized conditioning regimen;33 iii. initial patients have
A
B
C
D
E
F
received HSCT quite late in their disease course; and iv. longer follow-up is needed to let the positive impact of HSCT emerge. Unfortunately, even if in recent decades improvements in transplant procedures (e.g., the use of reduced intensity conditioning regimens) have significantly prolonged the overall survival rate of patients,31 HSCT for FA remains associated with a poor prognosis, with a high number of patients exposed to lethal complications. Since the RIAF was not designed to study HSCT in the context of FA, the actual impact of HSCT on the natural history of FA needs to be investigated in more specific studies that also deal with all the transplant-specific factors affecting the outcome of HSCT. Indeed, the question is whether more recent HSCT, performed according to
Figure 2. Overall survival. (A) Overall survival, HSCT not censored (filled area represents 95% confidence interval); (B) Overall survival, HSCT censored (filled area represents 95% confidence interval); (C) Overall survival, according to age at diagnosis (patients were grouped based on the median age at diagnosis of 7,48 years); (D) Overall survival, according to number of structural abnormalities; (E) Overall survival, according to DOB quartiles (HSCT not censored); (F) Overall survival: HSCT vs. no HSCT.
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A.M. Risitano et al. Table 4. Multivariate analysis.
Variable Gender Date of birth (quartiles) Age at diagnosis Congenital abnormalities (n>10, except skin) Skin hyper- hypopigmentation Skeletal abnormalities CNS and/or eyes abnormalities Renal and urinary tract abnormalities Genital tract abnormalities Gastrointestinal tract abnormalities Endocrine abnormalities Cardiovascular abnormalities BMF at diagnosis BMF anytime Hematological malignancies Solid tumours HSCT
HR
95% CI (lower)
95% CI (upper)
P
1,067 0,873 6,504 0,665 1,093 0,84 1,163 0,778 1,523 0,619 0,399 1,846 0,823 1,7 0,595 0,724
0,643 0,821 1,406 0,394 0,548 0,377 0,589 0,365 0,677 0,246 0,129 0,757 0,171 0,813 0,294 0,403
1,772 0,928 30,086 1,125 2,18 1,873 2,298 1,659 3,426 1,563 1,234 4,504 3,964 3,558 1,204 1,301
0,802 0,305 <0,001 0,017 0,128 0,8 0,67 0,663 0,516 0,31 0,311 0,111 0,178 0,808 0,159 0,149 0,28
transplant protocols which have been optimized over the past years,34 have improved the outcome of FA, as compared with natural history. One may anticipate that combining earlier therapeutic intervention with improved HSCT protocols may lead in the near future to improved long-term outcomes for FA patients,35,36 especially if a lack of an increased risk of malignancies is confirmed. In conclusion, our registry confirms the adverse natural history of FA, eventually leading to disappointing outcomes that have not improved over time; thus, there is an urgent need for effective treatment strategies. Our findings highlight that large collaborative studies are essential to investigate the impact of available therapeutic interventions (such as transplantation), to optimize their use and to define their role in the treatment algorithm of FA. It seems obvious that it will only be through stronger collaboration among physicians and scientists, National and International Registries, and healthcare networks, that we may hope to offer better long-term outcomes to patients affected by FA and to their families. Acknowledgements The authors are deeply grateful to the patients and their families for providing data and tissue samples, not identified here for privacy reasons. They are grateful to list in the Appendix all the physicians providing data and moral support. The authors are also grateful to Dr. G. Pagano, founder and past president of the Italian Association for Research in Fanconi Anemia (AIRFA), who strongly sustained the RIAF project and partially funded it, to Dr. M.R. Piemontese, A. Savoia and L. Zelante, who provided mutation data, to Dr. M. Amato, O. Catapano, F. D’Amico, M. Galgani, E. Montone, M. Rossi, D. Scafato, who followed each other in maintaining the Registry, and to Dr. V. Altieri, A. Lioniello and G. Peperna for daily cytogenetic and informational work. The authors thank Prof. Sharon Schuman for editing the manuscript. The authors wish to dedicate this paper to the beloved memory of Lisa Orsini, whose relatives funded this work, and to Prof. 326
Table 5. Causes of death.
Cause Infections Bleeding Tumor Liver failure Kidney failure Heart failure GvHD Other TRM Other Unknown
No HSCT (%)
HSCT (%)
Total (%)
14 (33) 8 (18.5) 9 (21) 1 (2) 2 (5) 1 (2) 0 (0) 0 (0) 0 (0) 8 (18.5) 43
11 (25.5) 0 (0) 4 (9) 0 (0) 0 (0) 0 (0) 11 (25.5) 13 (30) 1 (2) 3 (7) 43
25 (29) 8 (9.5) 13 (15) 1 (1) 2 (2.5) 1 (1) 11(13) 13 (15) 1 (1) 11(13) 86
Bruno Rotoli and Dr. Angelo Rosolen, who both first collaborated to create the Registry, and to all the patients who passed away. Appendix RIAF Contributors (Institutions, Physicians, number of Patients reported): Napoli - Oncoematologia AORN "Santobono-Pausilipon" Poggi V., Loffredo G., Misuraca A., Menna G., Ripaldi M., Parasole R., Marchese L., Schiavulli M., Boccalatte M.F. (18); Napoli – Servizio di Genetica ASL Napoli 1 – Zatterale A., Calzone R. (16); Genova - Ematologia e Oncologia Pediatrica Ist. “G. Gaslini” - Dufour C., Svahn J. (15); Padova Oncoematologia Pediatrica AOU - Bisogno G., Cesaro S., Rosolen A., Rossetti F., Sainati L.,Varotto S. (14); Torino - Dip. Scienze Pediatriche e dell’Adolescenza OIRM - Ramenghi U. (14); Napoli - Clinica Pediatrica AOU SUN - Nobili B., Matarrese S., Ferrara M., Perrotta S. (14); Roma - Ematologia Univ. "La Sapienza" - Mandelli F, Giona F., Arcese W., Rana I., Amendola A., Barberi W., Testi A.M. (12); Cagliari - Centro haematologica | 2016; 101(3)
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Trapianti Osp. Microcitemico - Arru L., Cossu F. (8); Monza Oncoematologia Pediatrica Policlinico "San Gerardo" - Longoni D. (8); Napoli - Ematologia AOU "Federico II" - Rotoli B., Catalano L., Fiorillo A., Risitano A. (7); Pescara - UOC Trapianto Emopoietico PO - Di Bartolomeo P. (5); Palermo Ematologia Osp. "Cervello" - Fabbiano F., Mirto S. (5); Palermo - Oncoematologia Pediatrica Osp.“Di Cristina” - Farruggia P. (4); Roma - Pediatria Osp. “Sant'Eugenio” - Del Principe D. (4); Bologna - Oncoematologia Pediatrica "Sant'Orsola" - Rosito P., Paolucci G. (3); Catania - Ematologia ed Oncologia Policlinico di Catania - Schilirò G. (3); Parma - Pediatria e Oncoematologia Pediatrica A.O. - Izzi G., Barone A. (3); Napoli - Dip. Pediatria AOU “Federico II” - Pignata C., Sebastio G., Scarcella A. (2); Napoli - Pediatria AO “A. Cardarelli” - Saviano A. (2); Nocera Inferiore - Pediatria AO “Umberto I” - Amendola G., Di Concilio R. (2); Pesaro - Pediatria Osp. "San Salvatore" - Felici
References 1. Alter BP, Kupfer G. Fanconi Anemia. In: Pagon RA, Adam MP, Ardinger HH, et al, eds. GeneReviews. Seattle (WA): University of Washington, 2013; pp. 1993-2015. 2. Fanconi G. Familiäre infantile perniziosaartige Anämie (pernizioses Blutbild und Konstitution). Jahrb Kinderh. 1927;117: 257280. 3. Schroeder TM. Genetically determined chromosome instability syndromes. Cytogenet Cell Genet. 1982;33(1-2):119-132. 4. Zatterale A. Fanconi Anemia clinical and genetic heterogeneity. In: Pagano G, ed. Fanconi Anemia and Oxidative Stress: Mechanistic Background and Clinical Prospects. NY: Nova Science Publishers Inc.; 2015:1-15. 5. Dutrillaux B, Aurias A, Dutrillaux AM. The cell cycle of lymphocytes in Fanconi anemia. Hum Genet. 1982;62(4):327-332. 6. Schroeder TM, Anschultz F, Knoff A. Spontane chromosome aberrationen bei familiärer Panmyelopathie. Humangenetik. 1964;1(2):194-196. 7. Sasaki MS, Tonomura A. A high susceptibility of Fanconi's anemia to chromosome breakage by DNA cross-linking agents. Cancer Res. 1973;33(8):1829-1836. 8. Auerbach AD. Fanconi anemia diagnosis and the diepoxybutane (DEB) test. Exp Hematol. 1993;21(6):731-733. 9. Glanz A, Fraser FC. Spectrum of anomalies in Fanconi anaemia. J Med Genet. 1982;19(6): 412-416. 10. Alter BP. Inherited bone marrow failure syndromes. In: Nathan DG, Oskin SH, Ginsburg D, Look AT, Oski FA, eds. Hematology of Infancy and Childhood. Philadelphia: PA Saunders; 2003. pp. 280–365. 11. Alter BP. Cancer in Fanconi's anemia, 1927– 2001. Cancer. 2003;97(2):425–440. 12. Rosenberg PS, Greene MH, Alter BP. Cancer incidence in persons with Fanconi anemia. Blood. 2003;101(3):822-826. 13. Rosenberg PS, Alter BP, Socié G, Gluckman E. Secular trends in outcomes for Fanconi anemia patients who receive transplants: implications for future studies. Biol Blood Marrow Transplant. 2005;11(9):672-679. 14. Orphanet. http://www.orpha.net/consor/cgibin/Disease_Search.php?lng=EN&data_id=6 34&MISSING%20CONTENT=Fanconi-anemia&search=Disease_Search_Simple&title= Fanconi-anemia
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L. (2); Roma - Oncoematologia pediatrica Policlinico "A. Gemelli" - Lasorella A., Mastrangelo S. (2); Trieste - Ematooncologia Pediatrica Univ. - Rabusin M. (2); Ancona Oncoematologia Pediatrica - Pierani P., Fabrizzi B. (2); Avellino - Ematologia AORN "San Giuseppe Moscati" - Volpe E., Cantore N. (2); Bari - Dip. Biomedicina Età Evolutiva UO Pediatrica I Policlinico - Martire B. (2); Catanzaro - Ematologia PO "Pugliese-Ciaccio" - Levato L. (1); Ferrara - Clinica Pediatrica Univ. - Borgna Pignatti C. (1); Genova - Div. Pediatria "Ospedali Galliera" - Melevendi C. (1); Perugia Pediatria AO - Mazzarino I. (1); Pisa - Oncoematologia Pediatrica AO - Favre C., De Marco E. (1); Reggio Calabria Genetica Medica OORR “Bianchi-Melacrino” - Laganà C. (1); Roma - UOC TCS Policlinico “Tor Vergata” - Cudillo L. (1); Siena - Italy -Clinica Pediatrica Univ. - Acquaviva A. (1); Zurich (Switzerland) - Kinderspital – Albisetti M. (1).
15. Auerbach AD, Schroeder TM. First announcement of the Fanconi anemia International Registry. Blood. 1982;60(4): 1054. 16. Kutler DI, Singh B, Satagopan J, et al. A 20year perspective on the International Fanconi Anemia Registry (IFAR). Blood. 2003;15(4): 1249-1256. 17. Rosenberg PS, Huang Y, Alter BP. Individualized risks of first adverse events in patients with Fanconi anemia. Blood. 2004;104(2):350-355. 18. Rosenberg PS, Alter BP, Ebell W. Cancer risks in Fanconi anemia: findings from the German Fanconi Anemia Registry. Haematologica. 2008;93(4):511-517. 19. Zatterale A, Calzone R, Montone E, Pagano G. Il Registro Italiano Anemia di Fanconi. Ann Ist Super Sanità. 1999;35(2):233-235. 20. Fargo JH, Rochowski A, Giri N, Savage SA, Olson SB, Alter BP. Comparison of chromosome breakage in non-mosaic and mosaic patients with Fanconi anemia, relatives, and patients with other inherited bone marrow failure syndromes. Cytogenet Genome Res. 2014;144(1):15-27. 21. Savoia A, Zatterale A, Del Principe D, Joenje H. Fanconi’s Anaemia in Italy: high prevalence of complementation group A in two geographic clusters. Hum Genet. 1996;97(5): 599-603. 22. International agranulocytosis and aplastic anemia study. Incidence of aplastic anemia: The relevance of diagnostic criteria. Blood. 1987;70(6):1718-1723. 23. Vardiman JW, Thiele J, Arber DA, et al. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood. 2009;114(5):937-951. 24. Alter BP, Giri N, Savage SA, et al. Malignancies and survival patterns in the National Cancer Institute inherited bone marrow failure syndromes cohort study. Br J Haematol. 2010;150(2):179-188. 25. Faivre L, Guardiola P, Lewis C, et al. Association of complementation group and mutation type with clinical outcome in Fanconi anemia. Blood. 2000;96(13):40644070. 26. De Rocco D, Bottega R, Cappelli E, et al. Molecular analysis of Fanconi anemia: the experience of the Bone Marrow Failure Study Group of the Italian Association of Pediatric Onco-Hematology. Haematologica. 2014;99 (6):1022-1031.
27. Savoia A, Piemontese MR, Savino M, et al. Linkage analysis of Fanconi anaemia in Italy and mapping of the complementation group A gene. Hum Genet. 1997;99(1):9397. 28. Masserot C, Peffault de Latour R, Rocha V, et al. Head and neck squamous cell carcinoma in 13 patients with Fanconi anemia after hematopoietic stem cell transplantation. Cancer. 2008;113(12):3315-3322. 29. Locatelli F, Zecca M, Pession A, et al. The outcome of children with Fanconi anemia given hematopoietic stem cell transplantation and the influence of fludarabine in the conditioning regimen: a report from the Italian pediatric group. Haematologica. 2007;92(10): 1381-1388. 30. Dufour C, Rondelli R, Locatelli F, et al. Stem cell transplantation from HLA-matched related donor for Fanconi's anaemia: a retrospective review of the multicentric Italian experience on behalf of AIEOP-GITMO. Br J Haematol. 2001;112(3):796-805. 31. Peffault de Latour R, Porcher R, Dalle JH, et al. Allogeneic hematopoietic stem cell transplantation in Fanconi anemia: the European Group for Blood and Marrow Transplantation experience. Blood. 2013; 122(26):4279-4286. 32. Rosenberg PS, Socié G, Alter BP, Gluckman E. Risk of head and neck squamous cell cancer and death in patients with Fanconi anemia who did and did not receive transplants. Blood. 2005;105(1):67-73. 33. Pasquini R, Carreras J, Pasquini MC, et al. HLA-matched sibling hematopoietic stem cell transplantation for fanconi anemia: comparison of irradiation and nonirradiation containing conditioning regimens. Biol Blood Marrow Transplant. 2008;14(10): 1141-1147. 34. Benajiba L, Salvado C, Dalle JH, et al. HLAmatched related-donor HSCT in Fanconi anemia patients conditioned with cyclophosphamide and fludarabine. Blood. 2015;125 (2):417-418. 35. Hutson SP, Han PK, Hamilton JG, et al. The use of haematopoietic stem cell transplantation in Fanconi anaemia patients: a survey of decision making among families in the US and Canada. Health Expect. 2015;18(5):929941. 36. Khan NE, Rosenberg PS, Lehmann HP, Alter BP. Preemptive Bone Marrow Transplantation for FANCD1/BRCA2. Biol Blood Marrow Transplant. 2015;21(10): 1796-1801.
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ARTICLE EUROPEAN HEMATOLOGY ASSOCIATION
Acute Myeloid Leukemia
Ferrata Storti Foundation
Prospective long-term minimal residual disease monitoring using RQ-PCR in RUNX1-RUNX1T1-positive acute myeloid leukemia: results of the French CBF-2006 trial
Christophe Willekens,1 Odile Blanchet,2 Aline Renneville,3 Pascale Cornillet-Lefebvre,4 Cécile Pautas,5 Romain Guieze,6 Norbert Ifrah,7 Hervé Dombret,8 Eric Jourdan,9 Claude Preudhomme3 and Nicolas Boissel,8 on behalf of the French AML Intergroup
Maladie du Sang, Hôpital Claude Huriez, Lille; 2Département Hématologie-Immunologie CHU Angers, Tumor Bank CHU-ICO, CRB-CHU Angers, BB-0033-00038, UMR Inserm 892 CNRS 6299 CRCNA, Université d'Anger; 3Laboratoire d’hématologie, Centre de BiologiePathologie, CHRU de Lille; Equipe 3 INSERM U837, JPARC Lille; 4Laboratoire d'Hématologie, Centre Hospitalier Universitaire de Reims; 5Hématologie Clinique, Centre Hospitalier Henri Mondor, Créteil; 6Hématologie Clinique, Centre Hospitalier Universitaire, Clermont-Ferrand; 7Hématologie Clinique, Centre Hospitalier Universitaire, Angers; 8 Département d’Hématologie, Hôpital Saint-Louis, EA3518, Institut Universitaire d’Hématologie, Université Paris 7; and 9Service d'Hématologie, Centre Hospitalier Universitaire de Nîmes, France 1
Haematologica 2016 Volume 101(3):328-335
ABSTRACT
Correspondence: nicolas.boissel@sls.aphp.fr
Received: June 18, 2015. Accepted: November 26, 2015. Pre-published: December 3, 2015. doi:10.3324/haematol.2015.131946
Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/101/3/328
©2016 Ferrata Storti Foundation Material published in Haematologica is covered by copyright. All rights reserved to Ferrata Storti Foundation. Copies of articles are allowed for personal or internal use. A permission in writing by the publisher is required for any other use.
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n t(8;21)(q22;q22) acute myeloid leukemia, the prognostic value of early minimal residual disease assessed with real-time quantitative polymerase chain reaction is the most important prognostic factor, but how long-term minimal residual disease monitoring may contribute to drive individual patient decisions remains poorly investigated. In the multicenter CBF-2006 study, a prospective monitoring of peripheral blood and bone marrow samples was performed every 3 months and every year, respectively, for 2 years following intensive chemotherapy in 94 patients in first complete remission. A complete molecular remission was defined as a (RUNX1-RUNX1T1/ABL1)x100 ≤ 0.001%. After the completion of consolidation therapy, a bone marrow complete molecular remission was observed in 30% of the patients, but was not predictive of subsequent relapse. Indeed, 8 patients (9%) presented a positive bone marrow minimal residual disease for up to 2 years of follow-up while still remaining in complete remission. Conversely, a peripheral blood complete molecular remission was statistically associated with a lower risk of relapse whatever the time-point considered after the completion of consolidation therapy. During the 2-year follow-up, the persistence of peripheral blood complete molecular remission was associated with a lower risk of relapse (4-year cumulative incidence, 8.2%), while molecular relapse confirmed on a subsequent peripheral blood sample predicted hematological relapse (4-year cumulative incidence, 86.9%) within a median time interval of 3.9 months. In t(8;21)(q22;q22) acute myeloid leukemia, minimal residual disease monitoring on peripheral blood every 3 months allows for the prediction of hematological relapse, and to identify patients who could potentially benefit from intervention therapy (ClinicalTrials.gov ID #NCT00428558). haematologica | 2016; 101(3)
RUNX1-RUNX1T1 MRD should be evaluated on blood
Introduction In young adult patients with core binding factor acute myeloid leukemia (CBF-AML, i.e. with t(8;21)(q22;q22) [CBFA-AML] or inv(16)(p13q22)/t(16;16)(p13;q22) [CBFBAML]), complete remission (CR) is reached in more than 90% of cases. However, around 20-30% of patients will experience hematological relapse.1,2 In CBFA-AML, many disease-related factors have been correlated to the risk of relapse, including extramedullary disease,3 hyperleucocytosis,4 CD56 expression,5 additional cytogenetic aberrations6 and gene mutations such as KIT and FLT3-ITD.7,8 Quantification of the leukemia associated fusion gene RUNX1-RUNX1T1 (formerly AML1-ETO) by RQ-PCR provides a perfect target for minimal residual disease (MRD) assessment. In the French CBF-2006 study, we previously showed that early reduction in MRD level (>3 logs) during consolidation treatment in AML with t(8;21) was the most powerful marker to predict relapse in multivariable analysis.2 During longer term post-consolidation follow-up, retrospective studies reported that MRD detection was associated with an increased risk of relapse in these patients.9,10 However, several studies also observed that a bone marrow positive MRD could be detectable in patients with long-
term persistent CR.1,10,11 Recently, the Medical Research Council (MRC) reported that MRD positivity at a rate of >500 RUNX1-RUNX1T1 copies in bone marrow (BM) and >100 copies in peripheral blood (PB) during follow-up was predictive of hematological relapse.1 We herein report the results of a prospective assessment of BM and PB MRD levels during the follow-up of 94 CBFAAML patients enrolled in the French CBF-2006 study.
Methods Patients and Treatment Protocol The diagnosis of CBFA-AML was defined by the presence of either the t(8;21) translocation by karyotype and/or fluorescence in situ hybridization analysis and/or evidence of RUNX1-RUNX1T1 fusion transcript. Ninety-seven patients aged 18-60 years and with newly diagnosed CBA-AML were enrolled at 35 French centers between July 2007 and November 2010 in the CBF-2006 trial. The CBF-2006 trial (EudraCT #2006-005163-26; ClinicalTrials.gov ID #NCT00428558) compared two intensive induction regimens in CBF leukemias.2 After induction, complete remission (CR) was obtained in 96 CBF-AML patients (1 early death). Patients received 3 monthly consolidation cycles with cytarabine at 3,000 mg/m2/12 h by 2-hour IV infusion on days 1, 3,
Figure 1. Patient flow chart.
Table 1. Correlation between paired BM and PB MRD transcript ratio at specific time-points. Only MRD>0.001% on both PB and BM were considered.
Pearson (r) At diagnosis (n = 68) After induction (n = 70) Before 2nd consolidation course (n = 40) Before 3rd consolidation course (n = 29) At the end of treatment (n = 18) Total - Samples with MRD > 1% (N = 89) - Samples with MRD < 1% (N = 179)
0.65 0.62 0.32 0.30 0.41 0.93 0.79 0.51
Spearman P
P
0.68 0.55 0.30 0.36 0.27 0.84 0.79 0.49
<0.0001 <0.0001 0.056 0.052 0.273 <0.0001 <0.0001 <0.0001
MRD: minimal residual disease; BM: bone marrow; PB: peripheral blood.
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and 5, followed by lenograstim granulocyte colony-stimulating factor starting at day 8 until neutrophil recovery. The study was approved by the ethics committee of Nimes University Hospital and by the Institutional Review Board of the French Regulatory Agency and was conducted in accordance with the Declaration of Helsinki. All samples were collected as part of the treatment protocol. Clinicians were prospectively informed of the MRD results. According to the protocol, patients with molecular recurrence defined by an MRD ratio increase of more than 1log on two successive samples were eligible to participate in the phase II clinical trial DasaCBF.12 Thus, 5 patients were preemptively treated with dasatinib 140mg once daily. All 5 patients rapidly presented a hematological relapse within a median of 1.8 months. Another patient was preemptively treated with high-dose chemotherapy (MIDAM; intermediate-dose cytarabine, mitoxantrone and gemtuzumab ozogamycin) after a molecular relapse confirmed on a subsequent sample without hematological relapse. This patient was censored at the time of an allogeneic transplant.
Samples and MRD evaluation Bone marrow and PB samples were requested at diagnosis and then during therapy, after induction chemotherapy and before the second and third consolidation chemotherapy. Results of early MRD evaluation have already been published.2 At the end of treatment, MRD was again assessed on PB and BM. During post-consolidation follow-up, PB samples were monitored every 3 months for 2 years and BM samples annually for 2 years. Among the 96 CR patients, 2 patients had no MRD monitoring during follow-up. Long-term MRD level monitoring was thus analyzed in 94 patients (Figure 1). MRD levels were monitored for RUNX1-RUNX1T1 transcript by RQ-PCR in 2 central laboratories (Angers, Lille), as previously described.13 Calibration curves were performed using Ipsogen plasmids (Ipsogen SA, Marseille, France) and ABL1 was amplified concomitantly as an internal reference. Results were expressed as a [RUNX1-RUNX1T1/ABL1] x 100 transcript ratio. The sensitivity of this quantification was 0.001%. A complete molecular response (CMR) was thus defined as a transcript ratio â&#x2030;¤0.001%, providing
that at least 20 000 copies of the ABL1 control gene had been amplified. Molecular relapse was arbitrarily defined as a positive MRD occurring after having reached CMR.
Statistical analysis The Spearman rank correlation coefficient and the Pearson correlation tests were used to calculate correlation between transcript ratio in BM and PB. The rate of PB-MRD increase was calculated in patients with an available PB-MRD at relapse as log10(PBMRDR/PB-MRDbefR)/DT, where PB-MRDR is the MRD at relapse, PB-MRDbefR is the MRD on the prior PB sample before relapse, and DT is the time between both assessments. The outcomes were updated as of August 2013, with a median follow-up of 44.7 months. Overall survival (OS) was estimated by the Kaplan-Meier method and compared by the log-rank test. Cumulative incidence of relapse (CIR) was estimated taking into account death in first CR for competing risk and compared by cause-specific hazards Cox models. Patients were censored at allogeneic stem cell transplant in first complete remission. Specific hazards of relapse (SHRs) and HRs are given with 95% confidence interval (CI). To evaluate the impact of time to CMR or time to molecular relapse, outcome data were estimated by the Mantel-Byar method, considering CMR or molecular relapse as a time-dependent covariate. This method, described by Simon and Makuch, was applied for an appropriate graphical representation of CMR and molecular relapse impact on OS and CIR.14,15 All statistical tests were performed with the Stata/IC 12.1 software (StataCorp, College Station, TX, USA).
Results A total of 479 BM samples and 800 PB samples were collected, corresponding respectively to 71.3% and 64.1% of the samples planned by the protocol. Seventy-four BM samples and 74 PB samples were assessed at the end of treatment time-point (after the third consolidation cycle),
Table 2. Characteristics of the 94 patients with CBFA-AML included in the CBF-2006 trial.
Patients, n
94
Median age, y (range) Gender (Male/Female), n. Median WBC, 109/L (range) Median BM blasts, % (range) Secondary AML, % (n.) Additional cytogenetic abnormalities, % (n.) loss of Y trisomy 8 del(9q) del (7q) / monosomy 7 Gene mutations, % (n.) KIT mutation KIT mutation exon 8 KIT mutation exon 17 FLT3 mutation FLT3-ITD FLT3-TKD N- or K-RAS mutation Figure 2. Peripheral blood (PB) and (BM) bone marrow MRD in 525 paired samples.
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40.8(18-60) 54/40 11.3 (0.72-94.5) 53 (17-98) 11% (10/94) 24% (33/94) 2% (2/94) 15% (14/94) 4% (4/94) 23% (21/93) 5% (5/93) 17% (16/93) 11% (10/93) 6% (6/93) 4% (4/93) 15% (14/93)
WBC: white blood cell count; BM: bone marrow; AML: acute myeloid leukemia; ITD: internal tandem duplication; TKD: tyrosine kinase domain.
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and 78 BM samples and 399 PB samples during longer follow-up. During the 2 years of follow-up, a median number of 5 PB samples per patient (range 0-9) were collected. Seventy-nine BM and 79 PB samples not planned by the protocol, corresponding to control of previous MRD results or relapse time-points, were also included in the analysis.
than more elevated values collected at diagnosis or at hematological relapse. The median value of BM-MRD in patients with persistent CHR was 0.03% (range: 0.001-125) and 70.8% (range: 0.8-383) in the case of hematological relapse. The corresponding PB-MRD levels were 0.001% (range: 0.001-27) and 64.6% (range: 0.1-368), respectively.
Correlation between MRD results obtained on BM and PB
Peripheral blood versus bone marrow MRD prognostic value
To evaluate the correlation between PB- and BM-MRD levels, 525 paired PB and BM samples were compared at diagnosis, after induction, before each consolidation course, at the end of treatment and during the follow-up. Analyses were restricted to paired samples with comparable ABL Ct values. With a threshold of 0.001%, a positive MRD was detected on BM but not on PB in 134 paired samples (25.5%). Conversely, only 8 (1.5%) of the paired samples presented a detectable MRD on PB but not on BM (Figure 2). A comparison of the 268 (51.0%) paired samples with both PB- and BM-MRD levels >0.001% was performed. This analysis was split into two groups of MRD levels lower or higher than 1%, corresponding to samples collected in CR or at diagnosis/relapse, respectively. A significant correlation was observed between BM and PB MRD levels, irrespective of the MRD subgroup (Table 1). However, low PB- and BM-MRD values (<1%) were less closely correlated
The clinical characteristics of the 94 patients included in this MRD study are shown in Table 2. All patients were in first CR after induction chemotherapy. Median follow-up was 44.7 months. For this entire cohort, the 4-year estimated CIR was 33.3% (95%CI: 24.4-44.4) and the 4-year estimated OS was 83.4% (95%CI: 74.0-89.7). Of note, 4 hematological relapses were observed after the 2 years of MRD follow-up planned by protocol. At the end of consolidation therapy, MRD (called MRD4) was assessed on PB and BM within a median time of 39 days after the third consolidation (range: 15-100). Of the 74 patients evaluated at the end of treatment, 52 (70%) obtained a PB complete molecular response (CMR) compared to 22 (30%) on BM. The persistence of a detectable BM-MRD was not associated with an increased risk of relapse (4-year CIR, 33.8% versus 28.2%; SHR 1.20, P= 0.71; Figure 3A) or death (4-year OS, 87.7% versus 86.4%; HR 0.95, P=0.94; Figure 3B). Conversely, detectable PB-MRD at
A
B
C
D
Figure 3. Outcome according to PB- or BM-MRD at the end of consolidation therapy (MRD4). Cumulative incidence of relapse and overall survival are shown according to BM-MRD4 (A and B, respectively) or to PB-MRD4 (C and D, respectively). A MRD >0.001% was considered as positive.
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the end of consolidation was significantly associated with both a higher risk of relapse (4-year CIR, 50.9% versus 23.6%; SHR= 2.97, P=.01; Figure 3C) and a shorter survival rate (4-year OS, 63.6% versus 96.0%; HR= 6.8, P=0.005; Figure 3D). Among the 94 patients studied, 8 patients (9%)
had a detectable BM-MRD for up to 2 years of follow-up (BM-MRD<0.1%; range: 0.002-0.076%), while still remaining in first CR and in PB-CMR (Figure 4). Notably, the 4 patients that were found to have both positive PB- and BMMRD at 2 years relapsed. After consolidation completion,
Figure 4. PB- and BM-MRD in 8 patients with persistent BM-MRD up to two years after end of treatment.
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RUNX1-RUNX1T1 MRD should be evaluated on blood
whatever the time-point, BM-MRD failed to predict subsequent outcome (data not shown). For these reasons, further analyses were performed using PB-MRD.
Prognostic impact of complete molecular response and time to complete molecular response During the follow-up, 77 patients (81.9%) reached a PBCMR. The median time from CR to PB-CMR was 2.5 months. Notably, PB-CMR was achieved up to 16 months after CR (Online Supplementary Figure S1A). Among these 77 patients, 18 experienced a hematological relapse. Among the 17 patients that never reached PB-CMR, 11 were in relapse within 13 months after CR. In time-dependent Mantel-Byar analysis, patients who achieved PB-CMR had a significantly reduced incidence of relapse (4-year CIR, 26.6% versus 51.2%; SHR .25, P=.001; Figure 5A) and a significantly better overall survival rate (4year OS, 89.8% versus 56.1%; HR 0.23, P=0.008; Figure 5B). In this favorable subgroup of patients, the presence of FLT3ITD was associated with a trend towards a longer time to achieve PB-CMR (7.8 months versus 2.4 months in FLT3ITDpos and FLT3-ITDneg, respectively; P=0.11, Online Supplementary Figure S1D) while RAS gene mutation was associated with a significantly shorter time to PB-CMR (1.8 months versus 2.59 months in RAS mutated and RAS wildtype, respectively; P=0.05, Online Supplementary Figure S1C). None of the other patient- or disease-related characteristics (i.e. age, WBC, bone marrow blast %, del(9q), loss of sexual chromosome, KIT and FLT3-TKD mutations) significantly impacted time to CMR (see Online Supplementary Figure S1B for KIT mutations). The prognostic impact of time to achieve CMR was investigated considering CMR as a time-dependent variable. Time to CMR was not predictive of outcome, neither when considered as a continuous (data not shown) nor as a categorical variable (Figure 5C).
P=0.604; Figure 6D). The median time from confirmed detectable PCR positivity to hematological relapse was 3.9 months (IQ, 3.3-6.9). Of note, in the case of loss of CMR with a PB-MRD â&#x2030;Ľ0.5%, hematological relapse was systematically observed within a median time of 28 days (range: 10-99 days).
Peripheral blood kinetic of molecular relapse In 22 out of the 29 patients who experienced a relapse, a PB-MRD was assessed at the time of relapse. Among these 22 patients, 15 of them had a previous positive PB-MRD assessment within the preceding 3 months. In these 15 patients, the median rate in PB-MRD increase was 1.25 log10/month (range: -0.14,3.58), which is in line with the
A
B
Prognostic impact of loss of complete molecular response Among the 77 patients who achieved PB-CMR, 23 patients presented a molecular relapse (MR), arbitrarily defined as MRD>0.001% on one PB sample. Among these 23 patients, 1 patient immediately received salvage therapy, 2 patients (9%) were simultaneously in hematological relapse, 13 patients (57%) presented a confirmed positive MRD on the subsequent sample and 7 patients (30%) had a negative MRD on the subsequent sample. Median time between PB-CMR and MR was 6.9 months (95%CI: 3.325.7). The median MRD level in patients with a MR not confirmed on a subsequent sample was 0.007% (range 0.003%-0.06%) compared to 0.04% (range: 0.02%-1.55%) in patients with a confirmed MR (P=0.07). In time-dependent Mantel-Byar analysis, a molecular relapse was associated with a higher cumulative incidence of hematological relapse when compared to persistent PBCMR (4-year CIR, 74.5% versus 8.2%; SHR= 16.5, P< 0.001; Figure 6A). This excess of relapse translated into a shorter OS (4-year OS, 78.6% versus 94.2%; HR=5.9, P=0.019; Figure 6B). The outcome of patients that presented a confirmed positive MRD was compared to those with a negative MRD on the subsequent sample. Patients with a confirmed molecular relapse had a higher cumulative incidence of molecular relapse (4-year CIR, 86.9% versus 23.4%; SHR= 5.7, P=0.026; Figure 6C) but a similar survival rate (4-year OS, 77.8% versus 77.8%; HR=0.6, haematologica | 2016; 101(3)
C
Figure 5. Impact of CMR achievement on patient outcome. Peripheral Blood CMR was evaluated as a time-dependent variable and Simon-Makuch representations are shown for cumulative incidence of relapse (A) and overall survival (B). Time to PB CMR was split into 4 quartiles (Q1-4) to evaluate its impact on cumulative incidence of relapse (C).
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C. Willekens et al. A
B
C
D
Figure 6. Impact of molecular relapse in patients with complete molecular remission. Simon-Makuch representations are shown for cumulative incidence of relapse (A) and overall survival (B) in patients who remain in CMR (CMR) or who experienced a molecular relapse (MRD+). Simon-Makuch representations are shown for cumulative incidence of relapse (C) and overall survival (D) in patients who experienced a molecular relapse confirmed (2 consecutive time-points, 2TP) or not confirmed (1 time-point, 1TP) on a subsequent sample.
3.9 months of median time from confirmed detectable PCR positivity to hematological relapse observed above. Individual PB-MRD evolutions for the 22 patients are shown in the Online Supplementary Figure S2.
Discussion In CBF-AML, recent reports have indicated that RUNX1RUNX1T1 MRD reduction in the BM after 1 or 2 consolidation courses was the most powerful prognostic factor of relapse.1,2,16 However, early MRD response to treatment does not allow for the prediction of all subsequent events, and patients with satisfying early response will still experience relapse in around 20% of cases.2 In this supplementary analysis, we showed that PB monitoring on a 3 monthly basis for up to 24 months after the end of treatment allowed for the detection of molecular relapse and the anticipation of hematological relapse in RUNX1-RUNX1T1 positive AML patients. This study is the first report of systematic prospective MRD monitoring in CBFA-AML patients homogeneously treated in a phase III study. In CBFA-AML patients, persistent detection of RUNX1-RUNX1T1 transcripts have been reported in BM and/or PB in patients with prolonged CR.1,10,11,17 Thus, no real consensus has emerged concerning the source of the sample to monitor MRD after the end of chemotherapy. We herein confirm the good correlation between BM and PB MRD levels in a large number of sam334
ples.18 Despite the use of a quantitative RT-PCR assay with a sensitivity of 0.001%, we observed a significant amount of positive BM-MRD with negative PB-MRD samples (25.5%). A difference in MRD kinetics upon therapy partially accounts for this disparity between PB and BM samples. However, as previously reported, the persistence of a positive BM MRD at 2 years was detected in 9% of patients who remained in long-term CR. The persistence of RUNX1-RUNX1T1 positive non-leukemic cells (hematopoietic stem cells, B-cells, monocytes and mast cells) in the BM has been suggested to explain this persistence of a positive BM signal.19,20 This probably contributes to the lack of prognostic impact of post-consolidation BM MRD, and strongly supports the use of PB MRD to monitor RUNX1-RUNX1T1 positive AML patients. Out of the 29 hematological relapses observed in our cohort, 11 (38%) occurred early, before 13 months of follow-up, in the context of persistent PB-MRD positivity, 2 (7%) were diagnosed at the same time as molecular relapse, 2 (7%) after molecular relapse with a negative MRD on a subsequent sample and 10 (34%) after molecular relapse confirmed on a subsequent sample. Among the 4 patients (14%) who relapsed without previous molecular relapse, one patient had a late relapse occurring after the last negative PB-MRD assessed at 2 years, and the other 3 patients were not monitored as scheduled. In 21 patients, the relapse was thus predicted either by the persistence of PB-MRD positivity or by a confirmed molecular relapse. Complete molecular response on PB occurred in 81.9% of patients and haematologica | 2016; 101(3)
RUNX1-RUNX1T1 MRD should be evaluated on blood
was associated with a reduced risk of relapse and a longer survival rate. However, our data do not suggest a preferable time-point to consider persistent PB MRD positivity for intervention, a positive PB-MRD at 3, 6 or 9 months after the end of treatment being associated with a 4-year CIR of around 60% (data not shown). Consistent with previous reports, a molecular relapse was highly correlated to a subsequent risk of hematological relapse. Indeed, Ommen et al., reporting on 42 patients, found that a MRD > 10-4 on PB or BM was highly predictive of hematological relapse within 3 months.10 In a more recent study, Yin JA et al. reported that MRD positivity at a rate of >500 RUNX1-RUNX1T1 copies on BM and >100 RUNX1-RUNX1T1 copies on PB were highly correlated to the risk of hematological relapse and to survival.1 In this study, median times between detectable PCR positivity (10-5 sensitivity, normalized to ABL1) to hematological relapse were 4.9 months and 4.5 months in BM and PB, respectively. All patients with loss of CMR and PB MRD â&#x2030;Ľ 0.5% relapsed, but we were not able to define an optimal threshold to predict hematological relapse. With a median time to hematological relapse of 3.9 months, it seems reasonable to recommend confirming a molecular relapse on a second sample before considering any therapeutic intervention. Nowadays, there are no recommendations concerning patients with CBF-AML in molecular relapse. Gemtuzumab ozogamicin has been suggested as a drug of interest in CBF-
References 1. Yin JA, O'Brien MA, Hills RK, Daly SB, Wheatley K, Burnett AK. Minimal residual disease monitoring by quantitative RT-PCR in core binding factor AML allows risk stratification and predicts relapse: results of the United Kingdom MRC AML-15 trial. Blood. 2012;120(14):2826-2835. 2. Jourdan E, Boissel N, Chevret S, et al. Prospective evaluation of gene mutations and minimal residual disease in patients with core binding factor acute myeloid leukemia. Blood. 2013;121(12):2213-2223. 3. Byrd JC, Weiss RB, Arthur DC, et al. Extramedullary leukemia adversely affects hematologic complete remission rate and overall survival in patients with t(8;21)(q22;q22): results from Cancer and Leukemia Group B 8461. J Clin Oncol. 1997;15(2):466-475. 4. Nguyen S, Leblanc T, Fenaux P, et al. A white blood cell index as the main prognostic factor in t(8;21) acute myeloid leukemia (AML): a survey of 161 cases from the French AML Intergroup. Blood. 2002;99(10):3517-3523. 5. Baer MR, Stewart CC, Lawrence D, et al. Expression of the neural cell adhesion molecule CD56 is associated with short remission duration and survival in acute myeloid leukemia with t(8;21)(q22;q22). Blood. 1997;90(4):1643-1648. 6. Schoch C, Haase D, Haferlach T, et al. Fiftyone patients with acute myeloid leukemia and translocation t(8;21)(q22;q22): an additional deletion in 9q is an adverse prognostic factor. Leukemia. 1996;10(8):1288-1295. 7. Paschka P, Marcucci G, Ruppert AS, et al. Adverse prognostic significance of KIT mutations in adult acute myeloid leukemia with inv(16) and t(8;21): a Cancer and Leukemia Group B Study. J Clin Oncol. 2006;24(24):3904-3911.
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AML therapy, in frontline and as part of salvage therapy, but it has been shown to be associated with increased liver toxicity.21,22 Allogeneic stem cell transplant in second CR is a standard recommendation in young adults with AML, but it remains a subject of discussion in CBF-AML.22,23 In conclusion, we suggest that MRD level monitoring by quantitative RT-PCR during follow-up after high-dose cytarabine-based therapy in CBFA-AML should be evaluated in PB every 3 months for up to two years after consolidation completion. In the case of persistent positive PB MRD after treatment, patients should be closely monitored. These data also suggest that the reappearance of a detectable MRD in PB should be confirmed in a second PB sample to more accurately predict hematological relapse. Further studies will be mandatory to define the best strategy in terms of therapeutic attitude according to molecular relapse. Trial registration This study has been part of the CBF 2006 trial referred in EudraCT #2006-005163-26 and ClinicalTrials.gov ID #NCT00428558. Acknowledgments The authors thank all hematologists who participated in the CBF 2006 trial for providing follow up samples and their helpful to extend clinical follow up and AnnaĂŤlle Beucher for technical support with MRD analysis.
8. Boissel N, Leroy H, Brethon B, et al. Incidence and prognostic impact of c-Kit, FLT3, and Ras gene mutations in core binding factor acute myeloid leukemia (CBFAML). Leukemia. 2006;20(6):965-970. 9. Schnittger S, Weisser M, Schoch C, Hiddemann W, Haferlach T, Kern W. New score predicting for prognosis in PMLRARA+, AML1-ETO+, or CBFBMYH11+ acute myeloid leukemia based on quantification of fusion transcripts. Blood. 2003;102(8):2746-2755. 10. Ommen HB, Schnittger S, Jovanovic JV, et al. Strikingly different molecular relapse kinetics in NPM1c, PML-RARA, RUNX1RUNX1T1, and CBFB-MYH11 acute myeloid leukemias. Blood. 2010;115(2): 198-205. 11. Krauter J, Gorlich K, Ottmann O, et al. Prognostic value of minimal residual disease quantification by real-time reverse transcriptase polymerase chain reaction in patients with core binding factor leukemias. J Clin Oncol. 2003;21(23):4413-'22. 12. Boissel N, Renneville A, Leguay T, et al. Dasatinib in high-risk core binding factor acute myeloid leukemia in first complete remission: a French Acute Myeloid Leukemia Intergroup trial. Haematologica. 2015;100(6):780-785. 13. Gabert J, Beillard E, van der Velden VH, et al. Standardization and quality control studies of 'real-time' quantitative reverse transcriptase polymerase chain reaction of fusion gene transcripts for residual disease detection in leukemia - a Europe Against Cancer program. Leukemia. 2003;17(12):2318-2357. 14. Simon R, Makuch RW. A non-parametric graphical representation of the relationship between survival and the occurrence of an event: application to responder versus nonresponder bias. Stat Med. 1984;3(1):35-44. 15. Mantel N. Evaluation of Response-Time Data Involving Transient States: An
16.
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Illustration Using Heart-Transplant Data. J Am Stat Assoc. 1974;69:81-86. Zhu HH, Zhang XH, Qin YZ, et al. MRDdirected risk stratification treatment may improve outcomes of t(8;21) AML in the first complete remission: results from the AML05 multicenter trial. Blood. 2013;121 (20):4056-4062. Tobal K, Yin JA. Monitoring of minimal residual disease by quantitative reverse transcriptase-polymerase chain reaction for AML1-MTG8 transcripts in AML-M2 with t(8; 21). Blood. 1996;88(10):3704-3709. Leroy H, de Botton S, Grardel-Duflos N, et al. Prognostic value of real-time quantitative PCR (RQ-PCR) in AML with t(8;21). Leukemia. 2005;19(3):367-372. Miyamoto T, Weissman IL, Akashi K. AML1/ETO-expressing nonleukemic stem cells in acute myelogenous leukemia with 8;21 chromosomal translocation. Proc Natl Acad Sci USA. 2000;97(13):7521-7526. Cornet E, Dumezy F, Roumier C, et al. Involvement of a common progenitor cell in core binding factor acute myeloid leukaemia associated with mastocytosis. Leuk Res. 2012;36(11):1330-1333. Hills RK, Castaigne S, Appelbaum FR, et al. Addition of gemtuzumab ozogamicin to induction chemotherapy in adult patients with acute myeloid leukaemia: a metaanalysis of individual patient data from randomised controlled trials. Lancet Oncol. 2014;15(9):986-996. Hospital MA, Prebet T, Bertoli S, et al. Corebinding factor acute myeloid leukemia in first relapse: a retrospective study from the French AML Intergroup. Blood. 2014;124 (8):1312-1319. Burnett AK, Goldstone A, Hills RK, et al. Curability of patients with acute myeloid leukemia who did not undergo transplantation in first remission. J Clin Oncol. 2013;31(10):1293-1301.
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ARTICLE EUROPEAN HEMATOLOGY ASSOCIATION
Acute Lymphoblastic Leukemia
Ferrata Storti Foundation
Haematologica 2016 Volume 101(3):336-345
Minimal residual disease monitoring by 8-color flow cytometry in mantle cell lymphoma: an EU-MCL and LYSA study
Morgane Cheminant,1,2 Coralie Derrieux,1 Aurore Touzart,1 Stéphanie Schmit,1 Adrien Grenier,1 Amélie Trinquand,1 Marie-Hélène Delfau-Larue,3 Ludovic Lhermitte,1 Catherine Thieblemont,4 Vincent Ribrag,5 Stéphane Cheze,6 Laurence Sanhes,7 Fabrice Jardin,8 François Lefrère,2 Richard Delarue,2 Eva Hoster,9,10 Martin Dreyling,10 Vahid Asnafi,1 Olivier Hermine,2 and Elizabeth Macintyre1
Biological Hematology, Paris Descartes – Sorbonne Paris Cité University, Institut Necker-Enfants Malades, AP-HP, France; 2Clinical Hematology, Paris Descartes – Sorbonne Paris Cité University, IMAGINE Institut, Necker Hospital, AP-HP, France; 3Biological Hematology and Immunology, AP-HP, Groupe Hospitalier Mondor, Créteil, France; 4Hemato-Oncology, Saint-Louis Hospital, APHP – Paris Diderot – Sorbonne Paris Cité University - INSERM U728 – Institut Universitaire d'Hematologie, France; 5Département de Médecine, Institut Gustave Roussy, Villejuif, France; 6Clinical Hematology, University Hospital of Caen, France; 7Clinical Hematology, Perpignan Hospital, France; 8 Clinical Hematology, INSERM U918, IRIB, Centre Henri Becquerel, Rouen, France; 9Institute of Medical Informatics, Biometry, and Epidemiology, University of Munich, Germany; and 10Department of Internal Medicine III, University Hospital Munich, Germany 1
ABSTRACT
Correspondence: elizabeth.macintyre@aphp.fr
Received: September 2, 2015. Accepted: December 18, 2015. Pre-published: December 24, 2015. doi:10.3324/haematol.2015.134957
Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/101/3/336
©2016 Ferrata Storti Foundation Material published in Haematologica is covered by copyright. All rights reserved to Ferrata Storti Foundation. Copies of articles are allowed for personal or internal use. A permission in writing by the publisher is required for any other use.
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uantification of minimal residual disease may guide therapeutic strategies in mantle cell lymphoma. While multiparameter flow cytometry is used for diagnosis, the gold standard method for minimal residual disease analysis is real-time quantitative polymerase chain reaction (RQ-PCR). In this European Mantle Cell Lymphoma network (EUMCL) pilot study, we compared flow cytometry with RQ-PCR for minimal residual disease detection. Of 113 patients with at least one minimal residual disease sample, RQ-PCR was applicable in 97 (86%). A total of 284 minimal residual disease samples from 61 patients were analyzed in parallel by flow cytometry and RQ-PCR. A single, 8-color, 10-antibody flow cytometry tube allowed specific minimal residual disease assessment in all patients, with a robust sensitivity of 0.01%. Using this cut-off level, the true-positive-rate of flow cytometry with respect to RQ-PCR was 80%, whereas the true-negative-rate was 92%. As expected, RQ-PCR frequently detected positivity below this 0.01% threshold, which is insufficiently sensitive for prognostic evaluation and would ideally be replaced with robust quantification down to a 0.001% (10-5) threshold. In 10 relapsing patients, the transition from negative to positive by RQ-PCR (median 22.5 months before relapse) nearly always preceded transition by flow cytometry (4.5 months), but transition to RQ-PCR positivity above 0.01% (5 months) was simultaneous. Pre-emptive rituximab treatment of 2 patients at minimal residual disease relapse allowed re-establishment of molecular and phenotypic complete remission. Flow cytometry minimal residual disease is a complementary approach to RQ-PCR and a promising tool in individual mantle cell lymphoma therapeutic management (clinicaltrials identifiers: 00209209 and 00209222).
Introduction Mantle cell lymphoma (MCL) is characterized by an advanced stage of disease at diagnosis, with most patients demonstrating peripheral blood or bone marrow infiltration. Historically, overall survival was short, but use of monoclonal anti-CD20 haematologica | 2016; 101(3)
MRD by flow cytometry in mantle cell lymphoma
antibody and intensive treatment, including high-dose cytarabine and autologous stem cell transplantation (ASCT) have improved prognosis.1-7 Novel strategies, such as maintenance or pre-emptive treatment, may improve progression-free survival and prevent clinical relapse8-10 but are best used in combination with precise, reproducible, quantification of minimal residual disease (MRD).11 In the European Mantle Cell Lymphoma network (EU-MCL) study, multivariate analysis showed that MRD status at the end of induction is one of the strongest independent prognostic factors.2,11 Moreover, MRD-based pre-emptive rituximab therapy restored PCR-negativity in 81% of MCL patients.8 The gold standard for monitoring MRD in MCL is realtime quantitative polymerase chain reaction (RQ-PCR) amplification of clonal immunoglobulin heavy chain (IgH) VDJ or IgH-BCL1 rearrangements, which are informative in 90% and 40% of patients, respectively.8,12 Classical IgH or BCL1-IgH allele specific oligonucleotide (ASO)-based strategies use diagnostic DNA with a known level of infiltration,13 as defined by multiparameter flow cytometry (MFC) for construction of a standard curve. This approach can be difficult for samples in which the infiltration is very
low (<1%), not known, (e.g. lymph node DNA), or in samples with unreliable MFC. It is necessary to distinguish quantifiable positivity from low-level MRD positivity. For this reason, it is common practice to specify for each patient undergoing RQ-PCR both the level of sensitivity of detection and the quantifiable range (QR), with values below the quantifiable range (BQR) but above sensitivity being positive but unquantifiable. These techniques are relatively long procedures, are costly and require significant expertise, justifying alternative MRD quantification techniques. Multi-color flow cytometry assays with at least 6 colors have been applied to MRD monitoring in acute lymphoblastic leukemia,14,15 multiple myeloma,16-18 chronic lymphocytic leukemia,19 and hairy cell leukemia.20 To date, there are no established criteria for MRD quantification by MFC in MCL. An MCL 4-color panel using surface light chain restriction in the CD19+CD5+ subpopulation lacked sensitivity for MRD quantification, being inferior to consensus, qualitative PCR.21 We, therefore, developed a single, 8-color MFC tube for use in MCL and performed a pilot study on samples collected prospectively for molecular RQ-PCR MRD moni-
Figure 1. Flow chart for EU-MCL patients. *Including 51 samples from the experimental cohort.
Table 1. Multiparameter flow cytometry positivity as a function of the level of RQ-PCR MRD positivity in 284 samples from 61 patients.
RQ-PCR samples (n)
â&#x2030;Ľ0.1% 26
Neg MFC (n) Pos MFC (n) Pos MFC/RQ-PCR (%)
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1 25 96%
Pos > QR (n=62) Pos BQR (n=68) <0.1% <0.01% BQR 3/3 BQR 2/3 BQR 1/3 â&#x2030;Ľ0.01% 23 13 23 26 19 9 14 61%
9 4 31%
16 7 30%
20 6 23%
18 1 5%
Neg
TOTAL
154
284
153 1 1%
226 58 20%
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toring in EU-MCL patients. We analyzed the suitability of 8-color MFC for regular MRD evaluation, with a view to pre-emptive treatment on MRD relapse.
Methods Patientsâ&#x20AC;&#x2122; characteristics and samples Patients with previously untreated, histologically confirmed MCL of Ann Arbor stages II-IV were registered to one of two randomized EU-MCL clinical trials according to age and eligibility to receive an ASCT: patients up to 65 years of age to the MCL Younger trial (clinicaltrials.gov identifier: 00209222)2 and patients older than 60 years to the MCL Elderly trial (clinicaltrials.gov identifier: 00209209).9 Both protocols were approved by the institutional review boards of all participating institutions and were conducted according to the Declaration of Helsinki. Peripheral blood (PB) and/or bone marrow (BM) samples were collected at diagnosis, mid-term staging, end-of-induction and post induction at 2-3-monthly intervals until clinical relapse in both trials.2,9,11 Full details of treatment are given in the Online Supplementary Appendix. Response duration (RD) was defined only for patients who achieved at least a partial response (PR) after induction treatment
and was calculated as the period from the completion of induction to documented progression or death from any cause, which were both considered as an event. Statistical analyses are detailed in the Online Supplementary Appendix. Minimal residual disease analysis in France was centralized in 2 reference centers, one of which (Necker Hospital) performed the comparison presented here. The comparison of MFC and RQ-PCR was performed for patients with diagnostic data and at least one follow up. The number of samples analyzed by these techniques is indicated in Online Supplementary Table S1.
Multiparameter flow cytometry Eight-color MFC was performed prospectively at diagnosis on fresh cells after Ficoll-separation, but on thawed cryopreserved cell for MRD samples. The cells were stained with a conjugated monoclonal antibody combination using CD3/CD14/CD56-(FITC), LAIR-1/CD305-(PE), CD19-(PeCy7), CD5-(PerCPCy5.5), CD11A(APC), Lambda (Alexa700), Kappa (Pacific Blue) and CD45 (V500). All antibodies were from Becton-Dickinson (San Jose, CA, USA) except Kappa and Lambda (Exbio, Prague, the Czech Republic). MFC was performed on a FACS canto-II flow cytometer with DIVA software (Becton-Dickinson, USA) and standardized Euroflow instrument settings.22
A
B
Figure 2. Gating strategy to assess mantle cell lymphoma involvement and evaluate LAIP in a diagnostic peripheral blood (PB) sample (A) and to quantify MFC MRD in a follow-up PB sample (B) from a patient included in the EU-MCL trial. 338
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MRD by flow cytometry in mantle cell lymphoma
For MRD assessment, 106 viable cells were stained with the diagnostic antibody panel and at least 200,000 non-gated events were acquired. In accordance with studies on MRD in acute leukemia,23-25 positive MFC MRD was defined by a homogenous cluster (>20 events) with the LAIP and scatter properties defined at diagnosis. MRD was quantified by dividing the number of MCL cells by the total number of events acquired (Online Supplementary Figure S1).
PCR-based MRD analysis DNA from PB or BM was extracted by standard techniques and identification of clonal IgH rearrangement assessed using qualitative IgH-VDJ FR1 and FR2 consensus PCR on DNA extracted from the mononuclear fraction used for MFC. Gene scanning and sequence analyses (ABI prism automated sequencer Applied Biosystems, San Francisco, CA, USA) and ASO clone specific PCR were performed as described13 adapted for lymphoma,11 with identification of minimal sensitivity and QR for each patient. The minimum sensitivity considered acceptable was 10-4 (0.01%). Positive RQ-PCR MRD results were quantified if within the QR, and considered as BQR if below this range. All analyses were performed using a 1Ct RQ-PCR cut-off from background (see Online Supplementary Table S3 for details), which maximizes the number of BQR samples. BQR samples were separated on the number of positive triplicate wells.
Results Sensitivity and specificity of MCL cell detection by MFC We initially evaluated an antibody panel allowing assessment of tumor infiltration and identification of leukemia-associated immunophenotype (LAIP) on 51 cryopreserved diagnostic EU-MCL samples (experimental cohort). We combined the LAIR-1 and CD11a antibodies described in Euroflow standardization protocols22 with a standard backbone in a 10-antibody 8-color single tube. LAIR-1 and CD11a are expressed on normal blood B lymphocytes and other chronic B lymphoproliferations but not MCL.21,26,27 The backbone included well-defined antibodies previously used in 4-color MCL MRD strategies: CD45, CD19, CD5, Kappa and Lambda light chains, CD3, CD14 and CD56. Peripheral blood cells from 10 healthy donors were used as normal controls to evaluate the specificity and sensitivity of the antigenic combination. Lymphocytes were identified by FSC/SSC properties and high CD45 expression. After exclusion of doublets, CD14+ monocytes, CD56+ NK cells and CD3+ T lymphocytes, MCL B cells were selected on a CD19/CD5 plot. The expression of CD11a and LAIR-1 and Kappa/Lambda isotypic restriction were then evaluated (Figure 2). Polyclonal B CD19+/CD5+ lymphocytes from 10 healthy donors expressed higher levels of both CD11a [MFI: median (range): 1729 (1426-2154)] and LAIR-1 [1859 (11502788)] compared to MCL samples, allowing the definition of a “physiological B-cell area”. In the 51 MCL patients, the intensity of expression [MFI: median (range)] of CD11a and LAIR-1 on pathological B cells was lower [681 (118-4986) and 416 (15-4013)], respectively. In 41 of 51 (80%) cases, MCL cells expressed low levels of CD11a and LAIR-1 and fell in an area defined as the “mantle box” (Figure 3, lower left quadrant). In the remaining cases, 5 of 51 (10%) were CD11alow/LAIR-1high or CD11ahigh/LAIR-1low. No CD11ahigh/LAIR-1high cases were detected (Figure 3). As such, the association of CD11a and LAIR-1 rescued cases haematologica | 2016; 101(3)
with high expression of one of these antigens, increasing LAIP discrimination power in comparison to a CD19/CD5/Kappa/Lambda combination. Among the 51 MCL cases, 49 (96%) were detected outside the “physiological B-cell area”. Only 2 (4%) expressed CD11a and LAIR-1 levels close to those of normal B cells but light chain isotypic restriction allowed accurate MRD evaluation for these cases. Thus, the single tube antibody panel allowed the identification of at least one specific LAIP in almost all MCL cases tested [49 of 51 (96%)]. We then evaluated the specificity and sensitivity of this single tube on 10 normal blood samples. Analysis of 1.106 cells gave less than 7.10-5 non-specific events in the “mantle box” [range 32 (3.10-5) to 63 (6.10-5) events], giving a theoretical sensitivity of at least 0.01%/10-4 (Online Supplementary Table S2 and Figure S1). Serial dilutions of diagnostic samples in normal PB showed that the sensitivity of discrimination of mantle cells from polyclonal background was 10-4 (data not shown). These data demonstrate that the use of a single, 8-color MFC tube allows specific flow cytometric MRD assessment in all patients tested, with a robust sensitivity of 10-4 .
Assessment of MCL by MFC A total of 195 diagnostic samples (115 PB and 80 BM) from 116 EU-MCL patients (including the 51 aforementioned samples) were analyzed with the MFC panel. No lymphoma cells were detected, with a 10-2 sensitivity, in 20 diagnostic samples (10 PB, 10 BM) from 11 MCL patients. MFC MRD was not performed for these patients. Infiltration at diagnosis was at least 1% in 164 of 175 (94%) samples (97 of 105 PB, 67 of 70 BM), with a median of 16%. Lower level infiltration between 0.01% and 0.9% was seen in 11 samples (Online Supplementary Figure S2). At least one LAIP, based on low CD11a and/or LAIR expression and Kappa/Lambda restriction, was identified in 175 samples, allowing MRD evaluation with a robust sensitivity of 0.01%/10-4 in 105 of 116 (91%) patients. A total of 294 MRD samples (211 PB and 83 BM) were quantified by MFC at specified time points (see Online Supplementary Table S1). Overall, MRD was undetectable, below 0.01%, in 236 (80%) samples (172 PB, 64 BM). MFC positivity was detected in 58 (20%) samples (39 PB,
Figure 3. CD11a and LAIR mean fluorescent intensity (MFI) on diagnostic mantle cell lymphoma samples in the experimental cohort (n=51 patients). 339
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19 BM), with 34 samples between 0.01%-0.09%, 11 between 0.1%-0.9% and 13 up to 1%.
MCL assessment by RQ-PCR At least one diagnostic PB, BM or lymph node sample was submitted for prospective RQ-PCR from 131 MCL patients. Of these patients, at least one MRD sample was available for 113. Nine (8%) patients had no available tissue DNA, less than 1% infiltration by MCL cells by MFC and no clearly detectable clonal population by IgH-VDJ and BCL1-IgH PCR. It was not possible to sequence or to obtain an ASO IgH or BCL1-IgH RQ-PCR system with acceptable sensitivity in 7 patients, despite at least 1% infiltration by MFC. In the remaining 97 of 113 (86%) patients, the RQ-PCR strategy was sufficiently sensitive (minimum 0.01%) and specific for MRD quantification (Figure 1). These 97 patients underwent RQ-PCR MRD quantification (Figure 1) of IgH-VDJ targets in 92, BCL1IgH in 5 and both in 11. Although the RQ-PCR sensitivity was below 0.01% in all patients, the QR was above 0.01% in 26 patients (27%); it was between 0.02% and 0.05% in 19 and between 0.06% and 0.2% in 7. Qualitative PCR allowed identification of a distinct clonal peak which could be sequenced in 95% (99 of 104) of diagnostic samples with a MFC population of at least 1%, but also in 9 samples for whom MFC was lower than 1% and 14 patients for whom MFC was not possible, including 7 lymph node DNA samples. In these patients, the diagnostic sample infiltration used for the patient-specific standard curves construction was deduced from its RQ-PCR CT value, using a MFC/PCR regression curve obtained from 100 diagnostic PB/BM MCL samples with at least 1% infiltration by MFC (Online Supplementary Figure S3). This increased the number of patients accessible to RQ-PCR MRD from 74 (65%) to 97 (86%) of the 113 EU-MCL patients with at least one MRD sample. Suboptimal QR were no more frequent in these patients (5 of 23, 22%) than those with MFC values above 1% (21 of 74, 28%). A total of 894 MRD samples (639 PB and 255 BM) from 97 EU-MCL patients were quantified by RQ-PCR (see Figure 4 and Online Supplementary Table 1S for time points), of which 173 (19%) were positive quantifiable, 180 (20%) positive below the quantifiable range (BQR), and 541 (61%) negative. As such, half the positive MRD samples were within the low-level non-quantifiable range. We, therefore, compared IgH-VDJ RQ-PCR with MFC assessment and with RQ-PCR quantification of BCL1-IgH, distinguishing BQR samples on the number of positive wells within the triplicates analyzed.
Comparison of RQ-PCR and MFC MRD values MFC was compared with RQ-PCR in 284 samples (207 PB and 77 BM) from 61 patients (32 younger and 29 elderly). These included 153 of 541 (28%) RQ-PCR negative and 131 of 355 (37%) RQ-PCR positive samples (Figure 1). Using cut-off levels of at least 0.01% positivity for RQPCR, 80% (39 of 49) of samples were also positive by MFC (Cohen kappa of 0.6666, P<0.0001), giving a truepositive rate of MFC MRD evaluation of 80% and a truenegative rate of 92% (Table 1). Below this level, agreement dropped due to the significant number of samples which were positive by RQ-PCR but negative by MFC. MFC/RQ-PCR+ samples included 19 positive quantifiable and 54 positive BQR results by RQ-PCR. Conversely, 340
Figure 4. RQ-PCR results from 97 (49 elderly and 48 younger) EUMCL patients.
MFC+/RQ-PCR-less-than-0.01% samples included 4 positive quantifiable samples, 14 BQR, and only one negative sample by RQ-PCR. A significantly higher proportion of BQR samples with at least 2 positive triplicates were MFC positive (17 of 62, 27%; Cohen kappa 0.5391, P<0.0001) compared to virtually none of those with only 1 or no triplicate above sensitivity (2 of 173, 1%) (Cohen kappa 0.4515, P<0.0001) (Online Supplementary Figure S4). If all levels of RQ-PCR positivity are considered, amongst 154 RQ-PCR negative samples, only one was positive by MFC, at 0.07% (RQ-PCR sensitivity and QR of 0.01%). Of the 62 RQ-PCR positive-quantifiable MRD samples, 43 (69%) were also positive by MFC, whereas 19 (31%) samples from 11 patients were considered negative by MFC. Overall, 14 of 68 (21%) BQR samples were also positive by MFC (Table 1 and Figure 5). The incidence of MFC positivity in samples with two or three positive triplicate PCR analyses was not significantly lower than quantifiable samples, below 0.01% by RQ-PCR (13 of 49 vs. 4 of 13 respectively; P=0.6667), whereas this incidence fell in samples with only 1 of 3 positive triplicates, when it was not significantly different from samples considered negative by PCR (1 of 19 vs. 1 of 154; P=0.2207). Restriction of criteria for MRD low-level positivity to samples with at least 2 of 3 positive triplicates reduced the overall incidence of positivity from 46% to 39% of the 284 samples analyzed (Table 1). The RQ-PCR BQR samples that were positive by MFC were all below 0.1%. Similarly, all but one of the RQ-PCR quantifiably positive samples, which were negative by MFC, were below 0.1% (18 of 19) (Table 1 and Figure 5). In 62 of 284 samples with quantifiable RQ-PCR MRD and positive or negative MFC MRD, values correlated well (Pearson correlation coefficient r=0.95, P<0.0001) (Figure 5). A Bland-Altman analysis28 showed higher MFC-based MRD values with a mean difference of 0.0862 log and a relatively wide range between the 95% limits of agreehaematologica | 2016; 101(3)
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ment (95%CI: -1.1525; 1.3249) (Online Supplementary Figure S5). The Euro-MRD network defined two criteria for definition of BQR positivity, with a 3Ct or 1Ct difference from first background positivity.13 Re-analysis of data using these more restrictive criteria for positivity (Online Supplementary Table S3) led to a diminution of BQR samples from 68 of 284 (24%) to 53 of 284 (19%). We also compared IgH-VDJ and BCL1-IgH ASO RQPCR results for 48 MRD samples from 11 patients. Twelve of 13 samples positive above 0.01% by IgH-VDJ or BCL1IgH gave identical results (Online Supplementary Figure S6). All 11 IgH-VDJ BQR results with 2 or 3 positive triplicates were also BCL1-IgH positive, in contrast to only 3 of 9 IgH BQR with one positive triplicate. These data are in keeping with the MFC-IgH RQ-PCR comparison. Taken together, this comparison shows comparable results for MFC and quantifiable positive RQ-PCR results (Pearson correlation coefficient r=0.95, P<0.0001) but a greater sensitivity for RQ-PCR, when the identification of at least 2 of 3 positive triplicate results detects veritable low level positivity.
Prognostic relevance of MFC MRD values A total of 33 patients with MRD data and a documented clinical remission after induction were evaluable for assessment of the prognostic impact of MFC MRD status at the end of induction. Patients achieving a negative MFC MRD after induction (n=26) demonstrated a non-significant trend for prolonged remission duration (RD) compared with patients with residual disease (n=7; P=0.1496) (Figure 6A), Comparable results were obtained for RD based on end-of-induction RQ-PCR MRD status with a 0.01% positivity cut off (28 of 33 MRD negative patients). In contrast, when using an RQ-PCR positive cut off, including BQR positivity, the 13 MRD negative patients required re-treatment significantly later than the 20 MRD
positive patients (P=0.0014) (Figure 6B). There was no difference in RD when patients with MRD results showing a single positive triplicate were considered negative (Figure 6B) in this small cohort (16 of 33 MRD negative patients). These data confirm that MFC and RQ-PCR are comparable above 0.01% sensitivity levels, but that the greater sensitivity of the latter improves predictive value for remission duration.
MRD relapse precedes clinical relapse and allows pre-emptive treatment Amongst 61 patients with at least one paired MRD analysis, 29 relapsed and 19 died. Analysis of relapse kinetics was restricted to PB MRD values, since regular monitoring makes this a more appropriate source of material. Ten relapsing patients had sufficient MRD points to assess the capacity of RQ-PCR or MFC to predict future clinical relapse. For relapse kinetics, samples with only one positive triplicate were considered negative but are represented graphically at 10-7, (Figure 7) whereas two or three positive triplicates were considered to be MRD positive (represented graphically at 10-6). Clinical relapse was preceded by MRD relapse in all patients by RQ-PCR, compared to 6 of 8 by MFC (Figure 6 and Online Supplementary Table S4). If only positive results above 0.01% were taken into account, MRD relapse preceded clinical relapse in 7 of 10 patients. The median latency for prediction by RQ-PCR when any increase to at least 2 positive triplicates was considered as MRD relapse was 22.5 months (range 1-48 months) and 5 months (range 2-11 months) when only results above 0.01% were considered positive. Latency by MFC was similar to the latter, at 4.5 months (range 2-18 months). No relapse tissue was available for the patients whose relapses had not been predicted (Online Supplementary Table S4). Although the number of patients is limited, these data suggest that regular MFC and RQ-PCR monitoring may
Figure 5. Visual representation of paired minimal residual disease (MRD) evaluated by multiparameter flow cytometry (MFC) and RQ-PCR. Underlined numbers refer to the total number of samples in the corresponding quadrant. Pearson correlation coefficient of the 62 MRD in which RQ-PCR provided quantifiable MRD results and MFC provided either positive or negative results was calculated with Pearson correlation test.
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facilitate pre-emptive treatment, with sufficient latency to allow appropriate therapeutic modification. As proof of principle, 2 patients treated locally off protocol underwent an MRD relapse (one relapsed twice) with no evidence of clinical or hematologic relapse, and were treated with rituximab with rapid molecular and phenotypic response (Figure 7B), followed by 3-monthly maintenance. This led to a 4-year remission in one patient, with the second currently in CR under maintenance.
Discussion Monitoring MRD in MCL clearly has a place in prognostic evaluation, therapeutic stratification,11 and assessment of pre-emptive treatment,8 underlining the need for optimal MRD surveillance during remission, with therapeutic modification at MRD conversion or relapse. Gold standard IgH RQ-PCR MRD quantification is sensitive but relatively complex and time consuming, and has a high proportion of low-level positive results below the quantifiable
range. As such, it may be less easily adapted for immediate, individual patient management than MFC. MFC MRD should be faster and cheaper than molecular analysis but FC was, until recently, insufficiently sensitive.21 We and others have shown that MFC can give comparable results to Ig/TCR MRD for levels above 0.01% in acute lymphoblastic leukemia,14 chronic lymphocytic leukemia,29 and hairy cell leukemia,20 with complementary informativity. We now demonstrate that this is also the case for MCL. At diagnosis, it was possible to identify at least one MCL LAIP in all patients followed by RQ-PCR. The latter was possible for 86% of patients overall, including 16 with no or insufficient MFC infiltration for identification of a diagnostic LAIP. Conversely, 7 patients had an identifiable MCL population by MFC but no satisfactory clone-specific RQ-PCR probes. Only 9 of 113 (8%) of patients could not access either approach, demonstrating that MFC and RQ-PCR are complementary. Phenotypic evolution during the course of the disease is a potential risk of MFC, but the 8-color strategy described here reduced the risk of false
A
B
Figure 6. Response duration (RD) according to minimal residual disease (MRD) status in peripheral blood (PB) and/or bone marrow (BM) after the end of induction in 33 mantle cell lymphoma (MCL) younger and elderly patients. RD according to multiparamter flow cytometry (MFC) (7 of 33 MRD positive patients) and RQ-PCR (5 of 33 MRD positive patients) with a 0.01% cut off (6A) and RQPCR with a negative/positive cut off, solid lines (20 of 33 MRD positive patients with at least one positive triplicate; 6B) or a 1 vs. 2 of 3 triplicate cut off, dashed line (17 of 33 MRD positive patients with at least 2 positive triplicates). Gray lines indicate negative MRD and black lines positive MRD. 342
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negative results by increasing the number of LAIP per patient. MFC MRD quantification is based on the detection of MCL cells within a large number of normal B lymphocytes. We demonstrate a robust sensitivity of at least 0.01%, on analysis of at least 200,000 cells. This could obviously be increased by increasing the number of cells analyzed, as long as the number of normal B lymphocytes in the â&#x20AC;&#x153;MCL-boxâ&#x20AC;? remains low/absent. Using a cut-off level of 0.01%, RQ-PCR and MFC MRD values correlated significantly, although MFC was negative in 10 of 49 samples (20%). It should be emphasized that this is a retrospective study of thawed cryopreserved Ficoll MRD samples, which may underestimate MFC sensitivity, relative
to prospective whole blood samples. MFC MRD was specific, as demonstrated by the virtual absence of RQ-PCR negative/MFC positive cases. Multiparameter flow cytometry, with a sensitivity limit of 0.01%, would be expected to be negative in the vast majority of RQ-PCR BQR samples. This was indeed the case for samples with only one positive triplicate (1 of 19, 5%) but not in samples with 2 or 3 positive triplicates (13 of 49, 27%), when the incidence of positivity was similar to quantifiable samples below 0.01% (4 of 13, 31%). This probably reflects the significant variability in QR values between patients, since 8 of 15 MFC+/BQR samples had QR above 0.01%, compared to 27% of patients overall.
A
B
Figure 7. Kinetics of minmal residual disease (MRD) evolution. RQ-PCR (gray) and multiparameter flow cytometry (MFC) (black) quantification of peripheral blood samples at indicated dates. Negative RQ-PCR values are shown as 1E-08, single triplicate BQR results at 1E-07 and 2 or 3 positive triplicates at 1E-06. The horizontal dotted lines represent individual QR and MFC sensitivity, as labeled. Negative MFC results are shown just below 10E-4 (0.01%). RQ-PCR and MFC MRD relapses, respectively, 26 and five months before clinical relapse (A). Pre-emptive treatment based on rising RQ-PCR and/or MFC MRD results (B). haematologica | 2016; 101(3)
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Inter-patient variability and acceptable QR limits should be optimized, ideally with a QR of at least 0.01% in all patients. The use of more stringent criteria for low-level MRD positivity will reduce the proportion of BQR samples by 5%-10% overall, or 10%-25% of positive samples. Whether this will improve the positive and negative predictive value of MRD needs to be evaluated in prospective studies. It is possible that even very low levels of MRD positivity should be used for therapeutic stratification, as demonstrated for remission duration in this study, in which case RQ-PCR is preferable to MFC, unless at least one log more cells can be quantified with the latter. It is probable that combined, integrated use of MFC and RQ-PCR will provide optimal MCL follow up. The recent demonstration that next generation sequencing strategies will improve informativity and sensitivity30 also underline the need for a combined approach, as will the use of droplet digital PCR.31 In practice, therapeutic modification is rarely based on a single MRD result. The clinical relevance of MFC and RQPCR MRD assessment could be analyzed for 10 patients undergoing first relapse. The transition from negative to positive, including BQR RQ-PCR (median 22.5 months) nearly always preceded negative to positive transition by MFC (4.5 months), but RQ-PCR positivity above 0.01% did not differ (5 months). These delays are compatible with pre-emptive treatment in the majority of relapsing patients. If therapeutic intervention is to be considered, the interval for PB surveillance should probably be 3monthly. It should, however, be noted that MRD gave less than a 3-month warning in 3 of 10 patients. Failure to detect relapse may reflect clonal evolution or relapse as purely nodal disease, or even the appearance of a novel Blymphoproliferative disorder. Distinguishing between these possibilities will require histological and molecular analysis of relapse tissue. As proof of concept, pre-emptive
References 1. Dreyling M, Lenz G, Hoster E, et al. Early consolidation by myeloablative radiochemotherapy followed by autologous stem cell transplantation in first remission significantly prolongs progression-free survival in mantle-cell lymphoma: results of a prospective randomized trial of the European MCL Network. Blood. 2005;105(7):2677–2684. 2. Hermine O, Hoster E, Walewski J, et al. Alternating courses of 3xCHOP and 3xDHAP plus rituximab followed by a high dose ARA-C containing myeloablative regimen and autologous stem cell transplantation increases overall survival when compared to 6 courses of CHOP plus rituximab followed by myeloablative radiochemotherapy and ASCT in mantle cell lymphoma: final analysis of the MCL Younger Trial of the European mantle cell lymphoma Network. Hematol Oncol. 2012: abstract 151. Presented at: ASH Annual Meeting, Atlanta, GA, USA, 8-11 December 2012. 3. Geisler CH, Kolstad A, Laurell A, et al. Long-term progression-free survival of mantle cell lymphoma after intensive frontline immunochemotherapy with in vivopurged stem cell rescue: a nonrandomized phase 2 multicenter study by the Nordic
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treatment of 2 patients at MRD conversion prior to clinical relapse allowed re-establishment of molecular and phenotypic complete remission and a durable second remission. It remains unknown what the MRD threshold for starting pre-emptive treatment should be, and analysis of individual MRD kinetics for a larger number of patients is clearly required. Similarly, MRD kinetics following novel agents such as idelalisib need to be studied. MFC (or limiting RQPCR positivity to samples with at least 2 of 3 positive triplicates) might also be useful in the interpretation of lowlevel positive results, potentially avoiding excessively early therapeutic stratification.8 Molecular remission is an independent prognostic factor of clinical outcome.11 With its greater sensitivity, RQ-PCR MRD is better suited to prognostic analysis but MFC MRD may be more clinically relevant and feasible in individual therapeutic management. We need to clarify the optimal integration of these complementary techniques, their timing and the definition of MRD relapse, in order to optimize comparison between studies. MFC clearly has a place in MCL management, which needs further evaluation in a prospective setting. Acknowledgments We thank Franck Morschhauser, Loic Ysebaert, François Boue, Celia Salanoubat, Michel Fabbro, Eric Jourdan, Sylvie Castaigne, Maud Janvier, Pierre Fenaux, Lina Al Jassem, Hugo Gonzalez and Marie Beaumont for contributing diagnostic and MRD samples for EU-MCL patients and Carine Deschodt and Fabienne Morand for help with sample management. Funding This work was supported by the Institut National du Cancer (INCa) PAIR Lymphoma program on Mantle Cell Lymphoma and by the Lymphoma Study Association (LYSA).
Lymphoma Group. Blood. 2008;112(7): 2687–2693. Romaguera JE, Fayad L, Rodriguez MA, et al. High rate of durable remissions after treatment of newly diagnosed aggressive mantle-cell lymphoma with rituximab plus hyper-CVAD alternating with rituximab plus high-dose methotrexate and cytarabine. J Clin Oncol. 2005;23(28):7013–7023. Lenz G, Dreyling M, Hoster E, et al. Immunochemotherapy with rituximab and cyclophosphamide, doxorubicin, vincristine, and prednisone significantly improves response and time to treatment failure, but not long-term outcome in patients with previously untreated mantle cell lymphoma: results of a prospective randomized trial of the German Low Grade Lymphoma Study Group (GLSG). J Clin Oncol. 2005;23(9):1984–1992. Delarue R, Haioun C, Ribrag V, et al. CHOP and DHAP plus rituximab followed by autologous stem cell transplantation in mantle cell lymphoma: a phase 2 study from the Groupe d’Etude des Lymphomes de l’Adulte. Blood. 2013;121(1):48–53. Lefrère F, Delmer A, Levy V, et al. Sequential chemotherapy regimens followed by high-dose therapy with stem cell transplantation in mantle cell lymphoma: an update of a prospective study. Haematologica. 2004;89(10):1275–1276.
8. Andersen NS, Pedersen LB, Laurell A, et al. Pre-emptive treatment with rituximab of molecular relapse after autologous stem cell transplantation in mantle cell lymphoma. J Clin Oncol. 2009; 27(26):4365–4370. 9. Kluin-Nelemans HC, Hoster E, Hermine O, et al. Treatment of older patients with mantle-cell lymphoma. N Engl J Med. 2012;367(6):520–531. 10. Le Gouill S, Thieblemont C, Oberic L, et al. Rituximab maintenance versus wait and watch after four courses of R-DHAP followed by autologous stem cell transplantation in previously untreated young patients with mantle cell lymphoma: first interim analysis of the phase III prospective LyMa trial, a Lysa study. Hematol Oncol. 2014: abstract 146. Presented at: ASH Annual Meeting, Atlanta, GA, USA, 6-9 December 2014. 11. Pott C, Hoster E, Delfau-Larue M-H, et al. Molecular remission is an independent predictor of clinical outcome in patients with mantle cell lymphoma after combined immunochemotherapy: a European MCL intergroup study. Blood. 2010;115(16): 3215–3223. 12. Pott C, Schrader C, Gesk S, et al. Quantitative assessment of molecular remission after high-dose therapy with autologous stem cell transplantation predicts long-term remission in mantle cell
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ARTICLE EUROPEAN HEMATOLOGY ASSOCIATION
Plasma Cell Disorders
Ferrata Storti Foundation
Treatment with the HIV protease inhibitor nelfinavir triggers the unfolded protein response and may overcome proteasome inhibitor resistance of multiple myeloma in combination with bortezomib: a phase I trial (SAKK 65/08)
Christoph Driessen,1 Marianne Kraus,1 Markus Joerger,1 Hilde Rosing,2 JĂźrgen Bader,1 Felicitas Hitz,1 Catherine Berset,3 Alexandros Xyrafas,3 Hanne Hawle,3 Gregoire Berthod,4 Hermann S. Overkleeft,5 Christiana Sessa,6 Alwin Huitema,2 Thomas Pabst,7 Roger von Moos,8 Dagmar Hess,1 and Ulrich J.M. Mey8
Department of Oncology/Hematology, Kantonsspital St.Gallen, Switzerland; Slotervaart Hospital/The Netherlands Cancer Institute, Amsterdam, the Netherlands; 3 SAKK Coordinating Center, Bern, Switzerland; 4University Hospital CHUV, Lausanne, Switzerland; 5University of Leiden, the Netherlands; 6San Giovanni hospital, Bellinzona, Switzerland; 7Department of Medical Oncology, Inselspital, University Hospital and University of Bern, Switzerland; 8Hematology & Medical Oncology, Kantonsspital Graubuenden, Chur, Switzerland 1 2
Haematologica 2016 Volume 101(3):346-355
ABSTRACT
D Correspondence: christoph.driessen@kssg.ch
Received: September 9, 2015. Accepted: December 4, 2015. Pre-published: December 11, 2015. doi:10.3324/haematol.2015.135780
Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/101/3/346
Š2016 Ferrata Storti Foundation Material published in Haematologica is covered by copyright. All rights reserved to Ferrata Storti Foundation. Copies of articles are allowed for personal or internal use. A permission in writing by the publisher is required for any other use.
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ownregulation of the unfolded protein response mediates proteasome inhibitor resistance in multiple myeloma.The Human Immunodeficieny Virus protease inhibitor nelfinavir activates the unfolded protein response in vitro. We determined dose-limiting toxicity and recommended dose for phase II of nelfinavir in combination with the proteasome inhibitor bortezomib. Twelve patients with advanced hematologic malignancies were treated with nelfinavir (2500-5000 mg/day p.o., days 114, 3+3 dose escalation) and bortezomib (1.3 mg/m2, days 1, 4, 8, 11; 21-day cycles). A run in phase with nelfinavir monotherapy allowed pharmakokinetic/pharmakodynamic assessment of nelfinavir in the presence or absence of concomittant bortezomib. End points included dose-limiting toxicity, activation of the unfolded protein response, proteasome activity, toxicity and response to trial treatment. Nelfinavir 2x2500 mg was the recommended phase II dose identified. Nelfinavir alone significantly up-regulated expression of proteins related to the unfolded protein response in peripheral blood mononuclear cells and inhibited proteasome activity. Of 10 evaluable patients in the dose escalation cohort, 3 achieved a partial response, 4 stable disease for 2 cycles or more, while 3 had progressive disease as best response. In an exploratory extension cohort with 6 relapsed, bortezomib-refractory, lenalidomide-resistant myeloma patients treated at the recommended phase II dose, 3 reached a partial response, 2 a minor response, and one progressive disease. The combination of nelfinavir with bortezomib is safe and shows promising activity in advanced, bortezomibrefractory multiple myeloma. Induction of the unfolded protein response by nelfinavir may overcome the biological features of proteasome inhibitor resistance (clinicaltrials.gov identifier: 01164709).
Introduction Proteasome inhibitors are the backbone of multiple myeloma (MM) therapy in Europe.1 However, the majority of MM patients ultimately develop proteasome inhibitor resistance, and proteasome inhibitor therapy yielded disappointing results haematologica | 2016; 101(3)
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in other hematologic malignancies. Response of bortezomib-refractory MM to next generation drugs (carfilzomib, pomalidomide) is in the 20%-30% range,2;3 leaving the majority of bortezomib-resistant patients currently without active therapy. Proteasome inhibitor sensitivity of MM cells is modulated by the unfolded protein response (UPR),4-6 a conserved pathway7 that prevents accumulation of misfolded and dysfunctional protein in the endoplasmic reticulum (ER) by acting on mRNA translation, protein
folding and destruction. The latter is orchestrated by the ER-associated degradation machinery (ERAD), with the proteasome as its rate-limiting terminal protease.8 Excessive activation of the UPR (terminal UPR) results in apoptosis and is a major mechanism of cytotoxicity of proteasome inhibitors in MM.6 The level of UPR pre-activation modulates both maturation stage and proteasome inhibitor-sensitivity of MM, so
Table 1. Characteristics of the patients, diseases, treatment durations and responses in the phase I dose escalation cohort of the trial.
Characteristic Age, years
N. of patients (n=12) Median Range
Sex Male Female Diagnosis Multiple myeloma Acute leukemia Malignant lymphoma Prior lines of therapy Median Range Performance status PS0 PS1 PS2 Refractory to last therapy Yes No Progression under last therapy Yes No % of dose delivered in cycles completed Bortezomib Nelfinavir Treatment discontinuation Before completion of 3 cycles After completion of 3 cycles
Dose levels Reasons for discontinuation before completing 3 cycles PD Toxicity Death Other Response after completing 3 cycles PR SD* PD NA Best response on trial therapy PR SD* PD NA
%
58 45-67 8 4
67 33
8 2 (1 ALL, 1 AML) 2 (1 MCL, 1 DLBCL)
67 17 17
4 3-4 2 8 2
17 67 17
7 5
56 42
8 4
67 33
96.2 97.8 9 3
75 25
DL0
DL1
DL2
1
2
2 1
1 2+
1 2
1 1 2
5
2 (MM, DLBCL) 1 (MM)
2 (MM) 2 (ALL, MM) 1 (MM) 1 (MM)
1 (MCL) 2 (AML,MM)
MM: multiple myeloma; AML: acute myeloid leukemia; MCL: mantle cell lymphoma; ALL: acute lymphoblastic leukemia; DLBCL: diffuse large B-cell lymphoma; PD: progressive disease; PR: partial response; SD: stable disease; NA: not applicable.*SD must have been maintained for at least 2 cycles. +Other reasons: Grade 4 ALT elevation leading to treatment termination per protocol; investigatorâ&#x20AC;&#x2122;s decision due to increasing hip pain in the absence of radiographic signs for MM progression.
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347
C. Driessen et al. Table 2. Overview of the number of adverse events graded according to Common Toxicity Criteria 4.0 per dose level in the phase I dose escalation cohort of the trial.
Toxicity Neutropenia
Thrombocytopenia
Anemia
Cardiovascular
Nausea/vomiting
Dyspnea
GI-toxicity, excluding diarrhea Diarrhea
Fatigue Transaminases (ALT)
Bilirubin
Hyperuricemia
Infection
Nervous system disorders
Dose level 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1
Grade 1-2 − − − − − − − 1 2 2 1 1 2 2 2 − 2 1 1 2 2 2 3 6 2 1 3 1 1 − − − 2 − − − 1 2 3 1 1
that pharmacological activation of the UPR may overcome proteasome inhibitor resistance.9 Activation of the UPR is initiated via three ER-resident transmembrane proteins, including inositol-requiring kinase 1 (IRE1). IRE1 drives activation of Xbox-binding protein (XBP1), a major regulator of chaperones and ERAD, while a pro-apoptotic pathway is triggered via CCAAT/-enhancer-binding protein homologous protein (CHOP) upon excessive UPR activation. Silencing of IRE1 or XBP1 in MM results in proteasome inhibitor resistance,4 and the response of MM to bortezomib correlates with high XBP1 expression.10 The status of UPR activation links proteasome inhibitor sensitivity of MM to the differentiation pathway from pre-plasmablasts to mature plasma cells. Full plasma cell maturation requires UPR activation via the IRE1/XBP1 axis11 and 348
N. of patients Grade 3 − − 1 − − 3 1 0 3 − 1 2 − − − − − 1 − 1 1 − − − − − 1 − − 1 − − − 1 − − 1 − 1 − −
Grade 4 3 − − 2 1 1 0 0 1 − − − − − − − − − − − − − − − − − − − − 1 − − − − 1 − − − 1 − −
Grade 5 − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − 1 −
results in a mature, proteasome inhibitor-sensitive MM cell type.4 In contrast, IRE1-/XBP1- MM cells are immature, proteasome inhibitor-resistant, lack a fully developed ER,12 and accumulate in proteasome inhibitor-resistant MM patients.4 While IRE1-targeting drugs are in early development,13 the HIV protease inhibitor nelfinavir has UPR- and IRE1/XBP1-inducing pre-clinical activity,14-16 allowing proof-of-concept clinical trials to test the effect of UPR induction on proteasome inhibitor-sensitivity of MM. The UPR-inducing activity of nelfinavir on mammalian cells may involve interference with UPR-activating proteases,17 the pAKT pathway18-20 and/or the proteasome.21-23 Nelfinavir has single agent pre-clinical activity against MM, leukemia and solid tumors in vitro and in vivo,15;24-28 haematologica | 2016; 101(3)
Phase I trial of bortezomib with nelfinavir
*Cycle 1 only: Run-in phase nelfinavir days -7 to -1 (=week -1)
Figure 1. Graphical overview of dosing of nelfinavir (dose levels DL0, DL1, DL2) and bortezomib together with the time points at which samples for pharmakokinetic/pharmakodynamic (PK/PD) analysis were collected (PK1-5). Nelfinavir dosing in week -1 was only performed in cycle 1.
and re-sensitizes proteasome inhibitor-resistant tumor cells, including MM, at low micromolar concentrations.21;22;29 Nelfinavir is registered at 2x1250 mg/day. A dose of 2x3125 mg is safe in patients with advanced solid tumors,30 and nelfinavir is under investigation as a sensitizer for chemotherapy or radiation.31 The primary aim of this trial was to evaluate the safety and establish the recommended dose for a phase II trial (RP2D) of nelfinavir in combination with standard-dose bortezomib in patients with hematologic malignancies, including MM. Molecular studies assessed the effect of nelfinavir on UPR and proteasome activity. Early signs of activity were explored in patients with bortezomib-refractory MM.
Methods Eligibility Patients with advanced MM, acute leukemia or malignant lymphoma lacking active standard treatment options were eligible for the trial. Eligibility criteria included less than 5 prior chemotherapy lines, ECOG performance score of 2 or under, adequate hematologic, hepatic and renal function. Major exclusion criteria included uncontrolled, clinically relevant medical conditions, CTC grade over 1 peripheral polyneuropathy, and use of strong CYP3A4 modulators. In the extension cohort of the trial, only bortezomibresistant MM patients were eligible after at least 2 lines of systemic treatment. As according to the protocol developed before 2011, “bortezomib-resistant” disease was defined as “nonresponsiveness or progression to bortezomib-containing therapy, or progression < 6 months after completing such therapy”. In agreement with IMWG criteria, we here refer to “bortezomib-refractory myeloma” as myeloma “nonresponsive while on bortezomibcontaining therapy, or progressive within 60 days of last bortezomib-containing therapy”.32 haematologica | 2016; 101(3)
Trial design The primary end point was dose-limiting toxicity (DLT). Secondary end points included adverse events (AE), pharmacodynamic/pharmacokinetic parameters and response to trial treatment. Dose escalation was performed in a classical 3+3 design. AE were graded according to CTCAE 4.0. DLT was assessed during cycle 1 in all patients who had received at least one dose of bortezomib in combination with nelfinavir, and defined as hematologic toxicity grade 3-4 persisting for more than two weeks, or any grade 3 or over non-hematologic AE judged to be possibly related to nelfinavir or bortezomib, excluding self-limiting grade 3 ALT, bilirubin or metabolic changes (cholesterol, blood glucose, triglycerides), grade 3 nausea/vomiting without adequate symptomatic therapy, or grade 3 neurotoxicity without dose reduction of bortezomib. After establishing the recommended dose, the protocol was amended to treat an exploratory extension cohort of 6 additional patients with bortezomib-resistant myeloma at the recommended dose to assess toxicity and detect early signs of activity. All patients gave written informed consent prior to trial inclusion, the trial was approved by the independent canton research ethics committees, performed in accordance with national Swiss law, ICH-GCP, and the Declaration of Helsinki, and registered at clinicaltrials.gov identifier:01164709.
Drug administration Bortezomib was given at 1.3 mg/m2 days 1, 4, 8, 11 i.v. for three cycles of 21 days. No dexamethasone was added during the dose escalation part. Nelfinavir [dose levels 1250 mg (DL0), 1875 mg (DL1) and 2500 mg (DL2)] was taken on days 1-14 as 625 mg capsules q 12 h p.o. together with a full meal. In cycle 1 only, combination treatment with bortezomib+nelfinavir was preceded by nelfinavir monotherapy on days -7 to -1 (run in phase for PK/PD assessment). Patients without disease progression after cycle 3 could continue trial therapy until completion of 7 cycles. 349
C. Driessen et al.
Figure 2. Overview of the best treatment response of all evaluable patients with multiple myeloma (MM) that have received trial treatment. The changes in paraprotein levels compared to baseline while on trial are displayed relative to baseline. Individual patients are represented by UPN numbers, patients of the phase I dose escalation cohort (UPN 1-12) in light gray, patients of the extension cohort (UPN 13-19) in dark gray. The streaked lines pattern denotes the only indivudual MM patient in the trial (UPN 08, dose escalation cohort) that was not bortezomib refractory by IMWG criteria before receiving bortezomib+nelfinavir therapy.
Dose-limiting toxicity and response assessment Adverse events were recorded throughout the trial for all patients. Response assessment was peformed centrally by the SAKK.
Pharmacokinetic and pharmacodynamic assessment For details regarding PK/PD samples collection and measurements, please refer to the Online Supplementary Methods. Further details of the methods used are available in the Online Supplementary Methods.
Results Patients and dose escalation The dose escalation cohort of the trial was treated between July 2010 and April 2012 at 4 academic hospitals in Switzerland. Twelve patients were included in the dose escalation cohort (Table 1). Treatment was performed as outlined (Figure 1). In cycle 1, more than 93% of the planned nelfinavir and bortezomib dose was administered per protocol. The only DLT was a G4 ALT elevation at DL2 that resolved after trial drug discontinuation. Nine patients discontinued treatment before completion of cycle 3: 5 progressive disease (PD), one due to toxicity, one death related to the underlying disease, 2 premature discontinuations for putative disease progression (see below). An average of 2.6 treatment cycles was administered in the dose escalation cohort.
Toxicity and anti-tumor activity Seven serious adverse events were observed in 5 patients (Table 2): one fatal thrombotic cerebral ischemia (DL0) occurring after withdrawl of the trial drug in a patient with leukemic mantle cell lymphoma who was hospitalized with pneumonia, 2 cases of pneumonia, one neutropenic fever, one gastrointestinal hemorrhage and one hyperviscosity syndrome (all grade 3), and one pulmonary embolism that resolved without sequelae. Except for one infection and the hyperviscosity syndrome, all 350
SAE were judged to be possibly related to trial medication. Because the thrombotic ischemia occurred after trial drug withdrawl, it was not considered a DLT. The other SAEs occurred after therapy cycle 1 and did not meet the criteria for DLT. Ten patients received at least one full cycle of trial therapy and were evaluable for best treatment response during trial therapy (Table 1). Of these, 3 achieved a partial response (PR), 4 remained in stable disease (SD) for at least 2 cycles, while 3 progressed within 2 therapy cycles. Therapy was prematurely stopped in 2 MM patients, based on putative progression of MM lesions on radiological images assessed by the local investigators; however, independent analysis of imaging results later concluded that there had been no objective evidence for MM progression. These patients were, therfore, considered nonevaluable for anti-tumor activity. Of the patients with malignancies other than MM, one patient with MCL achieved a PR, 2 patients with relapsed, refractory acute leukemia [1 acute myeloid leukemia (AML), 1 acute lymphoblastic leukemia (ALL)] remained stable for two cycles, while one patient with relapsed diffuse large B-cell lymphoma (DLBCL) experienced progressive disease (PD). Of the 3 patients from the dose escalation cohort that did complete three therapy cycles, 2 patients with MM experienced SD and one patient with DLBCL had PD.
Anti-tumor activity in relapsed, bortezomib-refractory myeloma Six additional patients with bortezomib-refractory myeloma were treated with the tentative recommended dose of nelfinavir 2x2500 mg p.o. in combination with bortezomib. The disease characteristics and prior therapies and responses of the included patients are summarized in Tables 3 and 4. Importantly, all patients were bortezomib-refractory according to IMWG criteria, and all in addition had lenalidomide-resistant disease. Five out of 6 of these patients received bortezomib+nelfinavir treatment within less than two months after having experienced PD under other bortezomib-based combination reghaematologica | 2016; 101(3)
Phase I trial of bortezomib with nelfinavir
A
B
Figure 3. Expression of unfolded protein response (UPR)-related proteins and proteasome activity in peripheral blood mononuclear cells (PBMC) from treated patients PBMC were collected at the indicated time points PK1 (base-line predose), PK2 (nelfinavir 4 h postdose), PK3 (nelfinavir trough + bortezomib pre dose), PK4 (nelfinavir+bortezomib 4 h post dose), PK5 (nelfinavir trough, 24 h post bortezomib). Equal amounts of cellular protein from PBMC was resolved by 12.5% SDS PAGE. (A) exemplary changes in expression of UPR-related protein and proteasome activity in PBMC from one patient (UPN04). Upper left: protein representing the UPR and its related apoptotic machinery (BIP, CHOP, PDI, PARP), as well as pAKT, was assessed from PBMC collected at the time points PK1-PK5, and visualized by western blot, GAPDH served as a control. Upper right: Quantitative comparison of proteasome activity (seperately for the β2-type and β1/β5-type subunits) at the different time points PK1-PK5 was achieved with activity based proteasome probes in the same PBMC samples (see Methods). (B) Mean quantitative changes in expression of UPR-related proteins and proteasome activity in PBMC for all patients Botom left: effect of nelfinavir monotherapy on expression of UPR related protein in PBMC. The relative change in expression of the UPR-associated proteins was assessed for all patients by western blot as (as shown for UPN04 above), and quantified by flourescence scanning comparing the time points PK1 (baseline) versus PK2 (post-nelfinavir); n= 11 patients, one technical failure). The mean relative difference from a base-line signal and standard deviation of expression of the proteins indicated is displayed for PK1 versus PK2. Bottom right: changes in proteasome activity in PBMC for the entire dose escalation cohort are shown after nelfinavir monotherapy (PK1 vs. PK3) and after treatment with nelfinavir in combination with bortezomib (PK1 vs. PK4). The relative changes in proteasome activity in PBMC were quantified using affinity-based probes and flourescence scanning. Mean changes from 11 patients and standard deviation are presented.
imens, inlcuding double and triple combinations with dexamethasone, bendamustine and cyclophosphamide, with no additional interposed lines of treatment. Only one patient received interposed treatment lines between the last regimen that defined bortezomib-refractory disease and the experimental bortezomib+nelfinavir therapy, and this patient had failed under both interposed therapies (thalidomide and bendamustine, respectively). Three of these patients with bortezomib-refractory MM achieved a PR with bortezomib+nelfinavir tretament. They all had been treated with bortezomib+nelfinavir immediately after (<2 months) progressing under therapies with bortezomib/bendamustine/dexamethasone (UPN 14) and bortezomib/bendamustine (UPN18) or bortezomib monotherapy (UPN 13), respectively. Another 2 patients achieved a minimal response (MR) with bortezomib+nelfinavir after having progressed under prior bortezomib/dexamethasone therapy. One of these patients, a 77-year old woman, refused trial continuation after suffering a myocardial infarction secondary to a bacteriemia from a portacath infection in cycle 2, whereas the other patient received 5 cycles per protocol. One patient did not respond to bortezomib+nelfinavir treatment. His MM had been refractory to lenalidomide/dexamethasone haematologica | 2016; 101(3)
first-line and refractory to bortezomib/cyclophosphamide/dexamethasone second-line therapy. An average number of 4.5 therapy cycles was administered in this cohort within the trial. There was no change in the toxicity profile. One patient with rapidly progressive MM relapse after allogeneic transplant (UPN 15) developed graft-versus-host disease (GvHD) and generalized EpsteinBarr virus (EBV) infection during the first week of trial treatment. She was excluded from the trial during the first week of treatment due to a rapidly worsening clinical condition, although her paraprotein decreased. The patient died of GvHD within two weeks, was replaced in the trial, and was not evaluated for efficacy. A patient chart-based additional analysis revealed that 3 patients treated in the extension cohort continued their bortezomib+nelfinavir therapy post trial on an off label basis for a total of 7, 10 and 17 cycles. They experienced 10-, 12-, and 12-month intervals to next antimyeloma treatment, respectively. Over all dose levels, of the 7 patients with bortezomibrefractory MM (according to IMWG criteria) treated in the trial, 4 achieved a PR, 2 an MR, and one PD. Of 11 patients with relapsed, bortezomib-resistant MM (according to trial criteria) treated, 4 achieved a PR, 2 an MR and 2 SD for 2 cycles or more with nelfinavir+bortezomib treat351
C. Driessen et al. Table 3. Patients with bortezomib-refractory multiple myeloma treated in the extension cohort of the trial. Overview of the disease characteristics, response to prior therapies, number of treatment cycles and response achieved on an individual patient basis.
Patient Age
PS Cytogenetics Prior Refractory Bortezomib- LENCycles Best Cycle# $ lines of to last resistant resistant administered response where therapy therapy within best last 12 response months was reached
13 14 16 17 18 19
2 1 1 2 1 1
66 52 77 70 63 72
UN t (4;14) UN del 1p UN UN
9 4 7 2 8 4
Yes Yes Yes Yes Yes Yes
No * Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes
#7 4 2 2 #7 #5
PR PR MR PD PR MR
3 2 1 1 7 3
% Reason paraprotein for change termination after 2 cycles, relative to a baseline -59 -61 -28 +35 -58 -32
Completed PD§ Pat. decision PD Completed Completed
Per protocol. *Bortezomib-refractory >12 months before inclusion. Patient therefore per protocol received bortezomib monotherapy in cycle 1, showed progressive disease, and received bortezomib + nelfinavir from cycle 2 onwards. #Continued the same therapy off trial on an off label basis. §Interrupted trial medication due to a perirectal abscess and surgical intervention, and experienced disease progression in the absence of trial medication. UN: unknown.
$
ment (Figure 2), corresponding to a clinical benefit (PR+MR) rate of 55% in this small sample.
Pharmacodynamic analysis Peripheral blood mononuclear cells (PBMC) (n=11, one technical failure) from the dose escalation phase of the trial were analyzed for protein expression indicative of UPR activation (PDI, BIP, CHOP, PARP), p-AKT, and proteasome activity (Figure 3). Comparison of protein expression between base-line (PK1) and peak nelfinavir plasma levels (PK2) revealed upregulation of CHOP and PARP (CHOP +56%, 95%CI: +17%-+95%, P=0.008; PARP + 57%, 95%CI: +2% - +112%, P=0.04; n=10), demonstrating induction of the UPR-mediated apoptotic machinery by nelfinavir monotherapy. PDI and BIP expression showed mean increases of 71% and 55% (P=0.19; P=0.17), also consistent with UPR activation by nelfinavir, while p-AKT decreased by 12% (P=0.37). Because activation of IRE1/XBP1 controls bortezomib-sensitivity of MM,4 we undertook a post hoc analysis of remaining PBMC lysate of one patient (UPN 04) for pIRE-1 protein by western blot, which suggestied pIRE1 induction by nelfinavir (Online Supplementary Figure S1). During nelfinavir treatment, and prior to bortezomib injection (PK3), we observed a mean decrease in proteasome β2 and β1/β5 subunit activity of 16% and 17% (P=0.01 and P=0.002), respectively, compared to baseline. Four hours after bortezomib injection (PK4), mean reduction in β1/β5 proteasome activity was 46%, compared to baseline (P<0.0001), while β2 activity levels normalized (mean difference 2% from baseline). Nelfinavir treatment, therefore, has proteasome-inhibiting and UPR-inducing activity in vivo.
Pharmacokinetics Nelfinavir plasma concentrations decreased during nelfinavir monotherapy for DL1 versus DL2 (PK2, mean plasma concentration DL1 13.3 mM vs. DL2 8.9 mM; P=0.08) (Figure 4). Peak plasma concentrations were higher during nelfinavir monotherapy compared to combination therapy with bortezomib (mean PK2 vs. PK4; 9.2 vs. 6.8 mM; 352
P=0.002), suggesting induction of nelfinavir clearance either by autoinduction, concomitant bortezomib application, or both. A population PK model is consistent with autoinduction of the clearance of both nelfinavir (69% increase of nelfinavir clearence for time points >5 days after initiation of treatment), and the active metabolite M8 (120% increase of M8 clearence) (Online Supplementary Table S1). Covariate testing suggested no significant impact of patient age, gender, BSA, renal or liver function on the clearance of nelfinavir. Goodness-of-fit plots of model-predicted and observed nelfinavir and M8 concentration-time data support the adequacy of the model (Online Supplementary Figure S2).
Discussion This trial demonstrates that nelfinavir at a dose of 2500 mg b.i.d. can safely be added to the approved bortezomib therapy in patients with advanced hematologic cancers. Oral nelfinavir therapy induced activation of the UPR, including induction of pIRE1, consistent with pre-clinical data.22;24 The high fraction of bortezomib-refractory MM patients experiencing MM control after treatment with bortezomib+nelfinavir in our trial is consistent with the model that low activation levels of the UPR, and in particular of IRE1-XBP1, are a major determinator of proteasome inhibitor resistance in MM.4 We provide clinical proof of concept that the pharmacological induction of UPR activation can re-sensitize proteasome inhibitor refractory MM for proteasome inhibitor treatment.22 With response rates of bortezomib-refractory MM to next generation drugs (carfilzomib, pomalidomide) in the 20%30% range,2,3 the signals of activity of bortezomib+nelfinavir observed in this patient group in our trial are encouraging: PR, including durable responses, was observed in 3 out of 5 patients with relapsed, bortezomib-refractory MM. Strikingly, all responding patients had experienced PD during bortezomib-containing combination therapy directly prior (< 60 days) to trial therapy, clearly indicating that the combination with nelfinavir is capable of overcoming bortezomib resistance in MM. haematologica | 2016; 101(3)
Phase I trial of bortezomib with nelfinavir
Figure 4. Mean nelfinavir plasma concentrations (mM) for the 4 pharmacokinetic sampling points and the 3 dosing cohorts (1250, 1850 and 2500 mg bid). Significance levels are given for the difference between mean NFV peak concentration (PK2) and either mean NFV through concnetration (PK3) or NFV peak concentration in combination with BTZ (PK4).
The low numbers of non-MM patients included in the trial limits an evaluation of potential activity in non-MM hematologic malignancies. One patient with relapsed, bortezomib-refractory mantle cell lymphoma with a clinically aggressive course achieved PR while another patient with relapsed ALL showed SD for 3 cycles. The addition of nelfinavir may, therfefore, also sensitize diseases other than MM for bortezomib treatment, consistent with preclinical data. Trial design originated in 2010 allowed inclusion of MM patients who had relapsed within six months after bortezomib-containing therapy, while the IMWG criteria for bortezomib-refractory MM32 published in 2011 require disease progression within less than 60 days of such therapy. Although not required per protocol, all patients treated in the extension cohort of our trial fully met the current IMWG criteria for bortezomib-refractory MM, and in addition had lenalidomide-resistant MM. It is likely that a sizable fraction of the “bortezomib-resistant” patients from the dose escalation cohort of the study also matched the IMWG cirteria for bortezomib-refractory disease (i.e. had progressed <60 days after bortezomib-therapy, and not only after < 6 months); however, this was not captured in sufficient detail in the prospective data analysis plan designed before 2011. Panobinostat combined with bortezomib/dexamethasone had an overall response rate of 34% and a combined MR+PR rate of 52% in relapsed, bortezomib refractory MM.33 In our trial, 9 patients with relpased, bortezomib-resistant MM were treated with bortezomib+nelfinavir at the RP2D, rsulting in 4 PR (44%) and 6 patients with clinical benefit (MR+PR, 67%). There was no apparent additional hematologic toxicity from combining bortezomib with nelfinavir, in contrast to panobinostat, which may be important for treating a heaviliy pre-treated population of MM patients. Dexamethasone is usually co-administered with bortezomib as anti-myeloma agent at doses between 40 and 160 mg weekly because of its synergistic activity. To apply the most stringent criteria for the identification of a clinical haematologica | 2016; 101(3)
synergy between nelfinavir and bortezomib, our protocol did not allow dexamethasone co-administration in the dose escalation part of the trial, and also in the extension cohort, no dexamethasone co-administration was allowed until completion of cycle 3. After cycle 3 only, patients who did not achieve a MR were allowed to co-administer 8 mg dexamethasone with bortezomib, while higher doses were excluded due to potential interactions with nelfinavir through the Cyp3A4 system. All patients in the extension cohort, except UPN18, achieved their best response already during cycles 1-3, i.e. without the addition of dexamethasone, and UPN 13 achieved a PR with bortezomib-nelfinavir therapy after showing PD under bortezomib monotherapy. One DLT (reversible CTC grade 4 AST elevation) was observed during the trial. No clinically significant hepatic toxicity was observed in subsequent therapy cycles or in the MM patients in the extension cohort where dexamethasone was added in some. Mild diarrhea was the most frequent adverse event; this was managed by supportive measures, including the early use of symptomatic loperamide suggested in the protocol in case of diarrhea, and did not result in treatment discontinuation. The patient experiencing DLT was transferred to follow up according to protocol, but was treated with the trial regimen at DL2 on an off label basis because she had experienced a PR and lacked alternative options. She received a total of 8 therapy cycles without hepatic toxicity before undergoing allogeneic stem cell transplant. We used PBMC as surrogate tissue for pharmacodynamic assessment because proteasome inhibition in PBMC largely parallels that in bone marrow.34 Nelfinavir moderately reduced β2 proteasome activity that is unaffected by bortezomib.22,35 Co-inhibition of β2 sensitizes myeloma cells for treatment with the β5-targeted drugs bortezomib and carfilzomib.36 Bortezomib-resistant myeloma cells upregulate β2 activity,37 presumably as an adaptive mechanism, and β2 proteasome activity increases during bortezomib treatment.35 The co-inhibition of β2 proteasome 353
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Table 4. Details of last bortezomib-containing therapy [(regimen, duration, best treatment response, relapsed and bortezomib-refractory per IMWG criteria, primary bortezomib-refractory per IMWG criteria, time to next treatment (TNT), time between last and current bortezomib-containing regimen (months), treatment regimens and respective response between last and current bortezomib-containing regimen)], in conjunction with details of the current bortezomib+nelfinavir therapy [(best response, total number of bortezomib+nelfinavir cycles administered during trial and during subsequent off label therapy, time to next treatment (TNT)] for the 6 MM patients treated in the extension cohort of the trial.
Last bortezomib-containing therapy before bortezomib+nelfinavir PatientRegimen Duration Best Relapsed and (months) response bortezomibrefractory$
Primary TNT Treatment Treatment Best Total TNT bortezomib- (months) between between response number (months) refractory$ last bortezomiblast bortezomibof cycles containing and containing and bortezomib+ bortezomib+nelfinavir bortezomib+nelfinavir nelfinavir therapy (months)
13 Vel 1 14 Vel/Benda/Dex 1 16 Vel/Dex 7
PD PD SD
yes yes yes
no no yes
1 2 8
17 18 19
PR PR SD
yes yes yes
no no no
7 21 4
VCD Vel/Benda Vel/Dex
6 20 2
Bortezomib+ nelfinavir
no no Thal (PD) Benda (PD) no no no
0 <2 11
PR PR MR
17 4 2
12 NA NA
<2 <2 <2
PD PR MR
2 10 7
NA 12 10
Per IMWG criteria.Vel: bortezomib; Dex: dexamethasone; Benda: bendamustine; Thal: thalidomide; VCD: bortezomib/cyclophosphamide/dexamethasone; PR: partial response; MR: minimal response; NA: not applicable because patients received no further myeloma-specific therapy. $
activity may, therefore, contribute to the combined activity of nelfinavir+bortezomib against bortezomib-resistant myeloma. However, similar to nelfinavir, saquinavir and lopinavir have shown UPR-inducing and bortezomib-sensitizing features in vitro in the absence of significant intrinsic proteasome inhibition.22 The proteasome inhibition caused by nelfinavir is, therefore, unlikely to be the only mechanism driving UPR induction, and other targets in the UPR regulatory machinery may be involved.17 Peak nelfinavir plasma concentrations were consistent with synergistic activity with proteasome inhibitors in vitro,22;24;29 and with autoinduction of clearance by high drug concentrations,30 suggesting that co-medication with bortezomib may decrease nelfinavir plasma levels. Other proteasome inhibitors, such as carfilzomib, may improve synergistic activity.22 This trial demonstrates feasibility and safety of combining full-dose proteasome inhibitor therapy with pharmacological activation of the UPR, using nelfinavir, an approved drug with a known safety profile. Furthermore, it provides proof of concept that nelfinavir can be used as an innovative, biology-driven approach to re-sensitize patients with proteasome inhibitor-refractory MM for proteasome inhibitor treatment.4 Future research should focus on the clinical activity of this combination in the bortezomibrefractory setting, but also assess whether bortezomib may be replaced by next generation drugs, e.g. carfilzomib, to avoid drug interactions and improve activity, and whether nelfinavir may likewise be used in combination with novel oral proteasome inhibitors to boost their low single agent
References 1. Moreau P, Richardson PG, Cavo M, et al. Proteasome inhibitors in multiple myeloma: 10 years later. Blood. 2012;120(5):947-959. 2. Siegel DS, Martin T, Wang M, et al. A phase
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activity.38 Alternative HIV protease inhibitors with similar UPR-inducing activity like lopinavir may provide more favorable pharmacology. Likewise, UPR activation with nelfinavir combined with lenalidomide or pomalidomide, which modulate the ubiquitination process upstream of the proteasome through interaction with cereblon, an ubiquitin ligase in the ubiquitin-proteasome system, may allow similar re-sensitizing mechanisms to be exploited. This ultimately suggests exploring the addition of HIV protease inhibitors to established combinations of proteasome inhibitors with immunomodulatory drugs, for example, in the carfilzomib/lenalidomide/dexamethasone regimen, one of the most powerful and tolerable regimens available to date for advanced multiple myeloma. The activity of bortezomib+nelfinavir in combination with standard dose dexamethasone is presently being studied in an SAKK phase II trial of the Swiss Group for Clinical Cancer Research (SAKK) in patients with bortezomib-refractory myeloma while nelfinavir in combination with lenalidomide in lenalidomide-refractory patients is being tested in an SAKK phase I/II trial. Funding The trial could be conducted through financial funding and support by the Swiss Group for Clinical Cancer Research (SAKK), the Swiss State Secretariat for Education, Research and Innovation (SERI), the Swiss Foundation for Clinical Cancer Research (SSKK), Janssen Cilag AG, and a translational research grant (31003A_143924) to CD from the Swiss National Research Foundation (SNF).
2 study of single-agent carfilzomib (PX-171003-A1) in patients with relapsed and refractory multiple myeloma. Blood. 2012;120(14):2817-2825. 3. San MJ, Weisel K, Moreau P, et al. Pomalidomide plus low-dose dexamethasone versus high-dose dexamethasone alone
for patients with relapsed and refractory multiple myeloma (MM-003): a randomised, open-label, phase 3 trial. Lancet Oncol. 2013;14(11):1055-1066. 4. Leung-Hagesteijn C, Erdmann N, Cheung G, et al. Xbp1s-negative tumor B cells and preplasmablasts mediate therapeutic protea-
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some inhibitor resistance in multiple myeloma. Cancer Cell. 2013;24(3):289-304. Neznanov N, Komarov AP, Neznanova L, Stanhope-Baker P, Gudkov AV. Proteotoxic stress targeted therapy (PSTT): induction of protein misfolding enhances the antitumor effect of the proteasome inhibitor bortezomib. Oncotarget. 2011;2(3):209-221. Obeng EA, Carlson LM, Gutman DM, et al. Proteasome inhibitors induce a terminal unfolded protein response in multiple myeloma cells. Blood. 2006;107(12):49074916. Walter P, Ron D. The unfolded protein response: from stress pathway to homeostatic regulation. Science. 2011; 334(6059):1081-1086. Mayor T. Navigating the ERAD interaction network. Nat Cell Biol. 2011;14(1):46-47. Orlowski RZ. Why proteasome inhibitors cannot ERADicate multiple myeloma. Cancer Cell. 2013;24(3):275-277. Ling SC, Lau EK, Al-Shabeeb A, et al. Response of myeloma to the proteasome inhibitor bortezomib is correlated with the unfolded protein response regulator XBP-1. Haematologica. 2012;97(1):64-72. Reimold AM, Iwakoshi NN, Manis J, et al. Plasma cell differentiation requires the transcription factor XBP-1. Nature. 2001;412(6844):300-307. Taubenheim N, Tarlinton DM, Crawford S, et al. High rate of antibody secretion is not integral to plasma cell differentiation as revealed by XBP-1 deficiency. J Immunol. 2012;189(7):3328-3338. Papandreou I, Denko NC, Olson M, et al. Identification of an Ire1alpha endonuclease specific inhibitor with cytotoxic activity against human multiple myeloma. Blood. 2011;117(4):1311-1314. Bernstein WB, Dennis PA. Repositioning HIV protease inhibitors as cancer therapeutics. Curr Opin HIV AIDS. 2008;3(6):666-675. Gills JJ, Lopiccolo J, Tsurutani J, et al. Nelfinavir, A lead HIV protease inhibitor, is a broad-spectrum, anticancer agent that induces endoplasmic reticulum stress, autophagy, and apoptosis in vitro and in vivo. Clin Cancer Res. 2007;13(17):51835194. Chow WA, Jiang C, Guan M. Anti-HIV drugs for cancer therapeutics: back to the future? Lancet Oncol. 2009;10(1):61-71. Guan M, Fousek K, Jiang C, et al. Nelfinavir induces liposarcoma apoptosis through inhibition of regulated intramembrane proteolysis of SREBP-1 and ATF6. Clin Cancer Res. 2011;17(7):1796-1806. Gupta AK, Cerniglia GJ, Mick R, McKenna WG, Muschel RJ. HIV protease inhibitors block Akt signaling and radiosensitize tumor cells both in vitro and in vivo. Cancer Res.
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2005;65(18):8256-8265. 19. Plastaras JP, Vapiwala N, Ahmed MS, et al. Validation and toxicity of PI3K/Akt pathway inhibition by HIV protease inhibitors in humans. Cancer Biol Ther. 2008;7(5): 628-635. 20. Yang Y, Ikezoe T, Nishioka C, et al. NFV, an HIV-1 protease inhibitor, induces growth arrest, reduced Akt signalling, apoptosis and docetaxel sensitisation in NSCLC cell lines. Br.J Cancer. 2006;95(12):1653-1662. 21. Bruning A, Vogel M, Mylonas I, Friese K, Burges A. Bortezomib targets the caspaselike proteasome activity in cervical cancer cells, triggering apoptosis that can be enhanced by nelfinavir. Curr Cancer Drug Targets. 2011;11(7):799-809. 22. Kraus M, Bader J, Overkleeft H, Driessen C. Nelfinavir augments proteasome inhibition by bortezomib in myeloma cells and overcomes bortezomib and carfilzomib resistance. Blood Cancer J. 2013;3e103. 23. Gupta AK, Li B, Cerniglia GJ, et al. The HIV protease inhibitor nelfinavir downregulates Akt phosphorylation by inhibiting proteasomal activity and inducing the unfolded protein response. Neoplasia. 2007;9(4):271-278. 24. Bono C, Karlin L, Harel S, et al. The HIV-1 protease inhibitor nelfinavir impairs proteasome activity and inhibits the multiple myeloma cells proliferation in vitro and in vivo. Haematologica. 2012;97(7):1101-1109. 25. Bruning A, Rahmeh M, Gingelmaier A, Friese K. The mitochondria-independent cytotoxic effect of nelfinavir on leukemia cells can be enhanced by sorafenib-mediated mcl-1 downregulation and mitochondrial membrane destabilization. Mol Cancer. 2010;9:19. 26. Ikezoe T, Saito T, Bandobashi K, et al. HIV-1 protease inhibitor induces growth arrest and apoptosis of human multiple myeloma cells via inactivation of signal transducer and activator of transcription 3 and extracellular signal-regulated kinase 1/2. Mol Cancer Ther. 2004;3(4):473-479. 27. Shim JS, Rao R, Beebe K, et al. Selective inhibition of HER2-positive breast cancer cells by the HIV protease inhibitor nelfinavir. J Natl Cancer Inst. 2012; 104(20):1576-1590. 28. Yang Y, Ikezoe T, Takeuchi T, et al. HIV-1 protease inhibitor induces growth arrest and apoptosis of human prostate cancer LNCaP cells in vitro and in vivo in conjunction with blockade of androgen receptor STAT3 and AKT signaling. Cancer Sci. 2005;96(7):425-433. 29. Kraus M, Muller-Ide H, Ruckrich T, et al. Ritonavir, nelfinavir, saquinavir and lopinavir induce proteotoxic stress in acute myeloid leukemia cells and sensitize them for proteasome inhibitor treatment at low micromolar drug concentrations. Leuk Res.
2014;38(3):383-392. 30. Pan J, Mott M, Xi B, et al. Phase I study of nelfinavir in liposarcoma. Cancer Chemother Pharmacol. 2012;70(6):791-799. 31. Brunner TB, Geiger M, Grabenbauer GG, et al. Phase I trial of the human immunodeficiency virus protease inhibitor nelfinavir and chemoradiation for locally advanced pancreatic cancer. J Clin Oncol. 2008;26(16):26992706. 32. Rajkumar SV, Harousseau JL, Durie B, et al. Consensus recommendations for the uniform reporting of clinical trials: report of the International Myeloma Workshop Consensus Panel 1. Blood. 2011;117(18): 4691-4695. 33. Durie BG, Harousseau JL, Miguel JS, et al. International uniform response criteria for multiple myeloma. Leukemia. 2006; 20(9):1467-1473. 34. Cheson BD, Pfistner B, Juweid ME, et al. Revised response criteria for malignant lymphoma. J Clin Oncol. 2007;25(5):579-586. 35. Blade J, Samson D, Reece D, et al. Criteria for evaluating disease response and progression in patients with multiple myeloma treated by high-dose therapy and haemopoietic stem cell transplantation. Myeloma Subcommittee of the EBMT. European Group for Blood and Marrow Transplant. Br J Haematol. 1998;102(5): 1115-1123. 36. Li N, Kuo CL, Paniagua G, et al. Relative quantification of proteasome activity by activity-based protein profiling and LCMS/MS. Nat Protoc. 2013;8(6):1155-1168. 37. Demo SD, Kirk CJ, Aujay MA, et al. Antitumor activity of PR-171, a novel irreversible inhibitor of the proteasome. Cancer Res. 2007;67(13):6383-6391. 38. Richardson PG, Schlossman RL, Alsina M, et al. PANORAMA 2: panobinostat in combination with bortezomib and dexamethasone in patients with relapsed and bortezomib-refractory myeloma. Blood. 2013;122(14):2331-2337. 39. Berkers CR, Verdoes M, Lichtman E, et al. Activity probe for in vivo profiling of the specificity of proteasome inhibitor bortezomib. Nat Methods. 2005;2(5):357-362. 40. Mirabella AC, Pletnev AA, Downey SL, et al. Specific cell-permeable inhibitor of proteasome trypsin-like sites selectively sensitizes myeloma cells to bortezomib and carfilzomib. Chem Biol. 2011;18(5):608-618. 41. Ruckrich T, Kraus M, Gogel J, et al. Characterization of the ubiquitin-proteasome system in bortezomib-adapted cells. Leukemia. 2009;23(6):1098-1105. 42. Richardson PG, Baz R, Wang M, et al. Phase 1 study of twice-weekly ixazomib, an oral proteasome inhibitor, in relapsed/refractory multiple myeloma patients. Blood. 2014; 124(7):1038-1046.
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ARTICLE EUROPEAN HEMATOLOGY ASSOCIATION
Plasma Cell Disorders
Ferrata Storti Foundation
Comparison of serum free light chain and urine electrophoresis for the detection of the light chain component of monoclonal immunoglobulins in light chain and intact immunoglobulin multiple myeloma Thomas Dejoie,1 Michel Attal,2 Philippe Moreau, 1 Jean-Luc Harousseau,3 and Herve Avet-Loiseau 4
Haematologica 2016 Volume 101(3):356-362
University Hospital Nantes; 2Institut Universitaire du Cancer, Toulouse; 3Institut de CancĂŠrologie de lâ&#x20AC;&#x2122;Ouest, Saint-Herblain; and 4Institut Universitaire du Cancer, University Hospital, CRCT, INSERM U1037, Toulouse, France
1
ABSTRACT
R Correspondence: avetloiseau.herve@iuct-oncopole.fr
Received: March 3, 2015. Accepted: November 26, 2015. Pre-published: December 3, 2015. doi:10.3324/haematol.2015.126797
Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/101/3/356
Š2016 Ferrata Storti Foundation Material published in Haematologica is covered by copyright. All rights reserved to Ferrata Storti Foundation. Copies of articles are allowed for personal or internal use. A permission in writing by the publisher is required for any other use.
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esponse criteria for multiple myeloma are based upon changes in monoclonal protein levels quantified using serum and/or urine protein electrophoresis. The latter lacks sensitivity at low monoclonal protein levels and since 2001, the serum free light chain test has been available and its clinical utility proven, yet guidelines have not recommended it as a replacement for urine assessment. Herein we evaluated responses using serum free light chain measurements and serum and urine electrophoresis after 2 and 4 cycles of therapy and after stem cell transplantation in 25 light chain and 157 intact immunoglobulin myeloma patients enrolled in the IFM 2007-02 MM trial. All 25 light chain patients had measurable disease by serum free light chain and urine methods at presentation. By contrast 98 out of 157 intact immunoglobulin patients had measurable disease by serum free light chain compared to 55 out of 157 by urine electrophoresis. In all patients there was substantial agreement between predicate (serum/urine protein electrophoresis) and test (serum protein electrophoresis and serum free light chain) methods for response assessment (Weighted Kappa=0.83). Urine immunofixation became negative in 47% light chain and 43% intact immunoglobulin patients after 2 cycles of therapy. At this time the serum free light chain ratio normalised in only 11% and 27% patients, respectively. In summary we found good agreement between methods for response assessment, but the serum free light chain test provided greater sensitivity than urine electrophoresis for monitoring. To our knowledge this is the first report comparing both methods for response assignment based on the International Myeloma Working Group guidelines. (Clinical Trials Register.eu identifier: 2007-005204-40).
Introduction Plasma cell dyscrasias are a disparate group of premalignant and malignant disorders. These conditions are commonly characterized by the production of monoclonal proteins (M-protein) which may be intact immunoglobulins (M-Ig), free light chains (FLC) or, less frequently, free heavy chains. Rarely do the disorders present without the production of any M-protein. The monoclonal components are usually identified and quantified by electrophoresis and immunofixation of serum (SPE + sIFE) and urine (UPE + uIFE) proteins; such approaches are required for the diagnosis and monitoring of patients with multiple myeloma (MM).1 Whilst these techniques are adequate for the majority of MM patients, those with light chain only MM (LCMM) and oligosecretory MM can be challenging to monitor.2 In these patients, 24h UPE is recommended for monitoring Bence Jones pro-
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tein (BJP) changes during follow-up; however, (i) BJP levels in urine are influenced by renal function, particularly when produced at low concentrations; (ii) there can be significant fluctuations in BJP levels measured by UPE during monitoring of individual patients; and (iii) up to 19% of urine samples contain monoclonal intact immunoglobulin that may interfere with BJP measurements.3-5 In addition, the provision of urine at the time of diagnosis and during monitoring can be an issue due to incomplete urine collection and variable compliance of between 5%-52%.6-9 The introduction of the polyclonal antibody based Freelite® assays in 2001 was an important addition to the laboratory and physicians’ armamentarium for the diagnosis,2,10,11 monitoring12-15 and prognosis16-18 of patients with monoclonal gammopathies (MG). The largest screening study to date comparing the utility of SPE, sIFE, UPE, uIFE and serum free light chain (sFLC) for screening for MG disorders included 1877 patients and concluded that SPE and sFLC provide a simple first-line methodology for screening for high tumour burden MG; and urine tests and sIFE can be ordered more selectively.2 These results were independently confirmed in another study of 923 patients.19 Subsequently, international guidelines recommended the use of sFLC in combination with SPE and sIFE for the diagnosis of MG, negating the need for urine analysis other than when AL amyloidosis is suspected.20 Monitoring sFLC concentrations for response assignment is currently only recommended for patients with nonmeasurable disease by electrophoretic methods and for determining stringent complete response (sCR); since FLC concentrations in the serum and urine of individual patients do not correlate and response assessment may differ between methods, guidelines do not recommend the use of the sFLC assay as a replacement for 24h urine collections for monitoring MM patients.20 However, Bradwell et al. studied 82 LCMM patients and indicated that urine analysis may overestimate the response to therapy by becoming negative in 32% patients, compared to only 11% patients whose sFLC ratio normalized.4 The discrepancy is clinically relevant since normalisation of serum FLC levels and ratio has been associated with improved outcomes in both LCMM21 and IIMM22 patients. The aim of this study was to compare the performance of sFLC as a replacement for urine tests for quantifying monoclonal protein expression at presentation and for response assignment during the monitoring of LCMM and IIMM patients.
those obtained by 24h urine measurements at the time of collection.
Laboratory methods Serum FLCκ and FLCλ concentrations were measured by Freelite® (The Binding Site Group Ltd, UK) on a Dade Behring BNTMII nephelometer (Siemens GmbH, Germany). Reference ranges for sFLC have been previously published (normal range: sFLCκ 3.3-19.4 mg/L, sFLCλ 5.7-26.3 mg/L, sFLCκ/λ ratio 0.261.65).24 The sFLC results were compared with serum and urine electrophoresis and immunofixation data collected at the time of the original clinical trial.25
Response assessment We applied the International Myeloma Working Group (IMWG) criteria for measurable disease and response assessment.20,26,27 Briefly, measurable disease by SPE refers to M-Ig >10 g/L, by UPE to BJP concentrations ≥200 mg/24h, and by sFLC as an abnormal sFLC ratio with involved (tumour) FLC (iFLC) concentrations ≥100 mg/L. sFLC responses were scored based on changes in dFLC (difference between the involved and uninvolved FLC concentrations). In accordance with IMWG guidelines, partial response (PR) was defined by urine tests as a decrease in BJP by ≥90% or to <200 mg/24h and by sFLC as >50% decrease in dFLC; very good partial response (VGPR) was defined as detectable BJP by uIFE but not by UPE, or BJP levels <100 mg/24h by UPE, and by sFLC as >90% decrease in dFLC. Bone marrow information was lacking, thus preventing the assignment of complete response (CR); instead negative serum IFE, negative uIFE and normalisation of sFLC ratio were used as best possible response. Progressive disease (PD) by urine tests was assigned by a 25% increase in BJP with ≥200 mg/24h increase in absolute values, and by sFLC by 25% increase in dFLC with >100 mg/L increase in absolute values. Stable disease (SD) was assigned to patients not fitting any of the above response criteria. For IIMM patients each level of response was assigned based on serum and urine changes as specified above; where methods disagreed the lowest response was assigned.
Statistical analyses Correlations between sFLC and UPE measurements were carried out using Pearson’s correlation analyses and calculation of the correlation coefficient (r) using Analyse-it (v. 2.25) software. Concordance in response assignment by either method was studied using quadratic Weighted Kappa analyses. Weighted Kappa values >0.81 correspond to near perfect agreement; values >0.61, >0.41, and >0.21 represent substantial, moderate and fair agreement, respectively.28 Percentage agreement corresponds to the number of samples with concordant responses.
Ethical considerations Methods Patients and serum samples We selected 182 patients (25 LCMM, 157 IIMM) from the InterGroupe Francophone du Myélome (IFM) 2007-02 MM trial (Clinical Trials Register.eu identifier: 2007-005204-40) who had serum and 24h urine samples collected at presentation and at least one follow-up sample at the end of any second or fourth cycle of induction therapy or post ASCT (median 4 samples, range 2-5). In accordance with the trial protocol patients had been centrally randomised equally into two treatment arms to receive 4 cycles of bortezomib and dexamethasone (VD) or bortezomib and thalidomide plus dexamethasone (VtD), followed by 1 cycle of high-dose melphalan plus ASCT.23 Serum samples were retrospectively analysed for FLC concentrations and the results compared with haematologica | 2016; 101(3)
The study was approved by the local ethical committee and conducted in accordance with the Declaration of Helsinki and Good Clinical Practice Guidelines. Written informed consent from participating patients was required.
Results Diagnostic sensitivity and measurable disease All 25 LCMM patients had abnormal sFLC ratios and positive UPE at presentation and monoclonal protein levels consistent with measurable disease (Figure 1 and Table 1), although correlation between sFLC and urine assays for monoclonal serum free light chain concentrations was poor (r=0.27, data not shown). A number of patients 357
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appeared to have very high levels of monoclonal protein according to one test but only modest levels according to the other: 4 out of 25 patients had iFLC levels >10000 mg/L (median 12500 (10400-22000)) but BJP levels by UPE of 3800 (1200-7200) mg/24h; by contrast 3 out of 25 patients had BJP >10000 mg/24h (median 12500 (1000042000)) but median iFLC levels were 6200 (6500-8600) mg/L. In IIMM patients, by contrast, sFLC ratios were abnormal in 154 out of 157 (98%) IIMM patients whereas 85 out of 157 (54%) were positive by uIFE and 67 out of 157 (43%) by UPE (Table 1 and Figure 1B). 98 out of 157 (62%) patients had measurable disease by sFLC and 55 out of 157 (35%) by UPE. Correlation between sFLC and UPE measurements was poor (r=0.36), as was the correlation between intact immunoglobulin by SPE and sFLC (r=0.06) or UPE (r=-0.26) (data not shown).
Response assessment In all patients (25 LCMM and 157 IIMM; in 10 patients two responses were scored: at maximum response and at progression as determined by at least one test), comparison in response evaluation between predicate methods (serum and/or urine electrophoresis) using IMWG criteria and test assessment (serum electrophoresis and serum FLC) demonstrated concordance in 155 out of 192 (81%) cases (6 PD, 6 SD, 47 PR, 43 VGPR and 53 patients in whom all serum and urine tests normalised). Weighted Kappa analysis indicated near perfect agreement between methods for response assignment (WK (95%CI): 0.85 (0.68-0.98)). The most significant difference among the 37 out of 192 discrepant responses was for 1 LCMM patient who progressed at cycle 4 by serum FLC assessment (iFLC=200mg/L) whilst urine IFE was negative. Conversely, UPE identified progressive disease in 2 LCMM patients in whom sFLC assessment indicated stable disease. In addition, relapse involving an apparent “light chain escape” was seen in 3 IIMM patients: in 2 of these 3 patients light chain escape was identified by both sFLC and UPE, while in the third patient it was detected by sFLC analysis alone. A direct comparison between urine and serum FLC assessment during follow-up showed that the sFLC ratio had normalised in only 2 out of 19 (11%) LCMM patients with serum and urine measurements available at the end of cycle 2, compared to 9 out of 19 (47%) patients in whom uIFE had become negative. Similarly, the sFLC ratio had normalised in 3 out of 21 (14%) and 8 out of 21 (38%) patients at cycles 4 and post-ASCT, respectively, compared to 14 out of 21 (67%) patients with negative uIFE at either time point (Figure 2A). Likewise in IIMM patients with measurable disease and complete data by serum and urine IFE and serum FLC for each time point analysed, 19 out of 44 (43%) patients had a negative uIFE at the end of cycle 2 compared to 12 out of 44 (27%) patients in whom the FLC ratio had normalised and 4 out of 44 (9%) patients for whom serum IFE had become negative. Similar discrepant results were found at the end of cycle 4 and postASCT (Figure 2B). Three case studies are presented of LCMM patients in whom sFLC remained abnormal while urine tests normalised during monitoring. In all three cases urine assessment became negative for the presence of BJP at cycle 2, whereas sFLC remained abnormal and serum immunofixation was positive (Figure 3). Finally, 5 out of 157 IIMM 358
patients had M-Ig <10g/L by SPE but measurable levels of disease by both UPE and sFLC. During monitoring UPE became negative in all 5 patients by cycle 2, whereas an abnormal sFLC ratio and positive serum immunofixation indicated persistent disease (data not shown).
Discussion 24h urine collection can help distinguish between glomerular and tubular proteinuria.29,30 However, the collection of samples is cumbersome and inconvenient for the patient, leading to poor compliance,7-9 and the laboratory treatment of the samples varies and can be laborious.31,32 In addition, the excretion of BJP is affected by kidney function, and electrophoretic methods for quantifying the monoclonal protein in urine from patients with plasma cell dyscrasias can be insensitive.4 Therefore there is a need for alternative tests that overcome the limitations imposed by urine testing. In recent years the use of sFLC analysis for screening and monitoring patients with monoclonal gammopathies has gained importance and has been proposed as a potential substitute for 24h urine
A
B
Figure 1. Scatter charts of serum κFLC and λFLC. (A) All 25 LCMM patients had an abnormal sFLC ratio and were positive by uIFE. (B) 154/157 IIMM patients presented with an abnormal sFLC ratio. uIFE was positive in 85/157 patients (positive uIFE open red circles, negative uIFE solid red circles, black diamonds: normal blood donor sera (n=282), parallel lines: 100 percentile normal range for sFLC (ratio, 0.26-1.65). haematologica | 2016; 101(3)
sFLC for monitoring multiple myeloma patients
assessment, as it may resolve some of the difficulties associated with this approach. In keeping with previous publications33 we found poor correlation between sFLC and BJP urine excretion levels in both LCMM (r=0.27) and IIMM (r=0.36) patients. In 4 out of 25 LCMM patients, sFLC reported disproportionately high concentrations of the monoclonal protein relative to UPE; these values may be influenced by the aggregation state of the FLC as previously reported.34-36 Conversely, in 3 out of 25 patients it was urine measurements that
reported improbably high BJP levels by UPE densitometry; aggregation is unlikely to have caused these results, and an analytical error or the presence of intact immunoglobulin fragments cannot be discounted.5 Acknowledging the quantitative differences between the assays, sFLC tests were more sensitive than UPE and uIFE during follow-up in LCMM patients, with sIFE data confirming clonality and corroborating the sFLC results in some patients. For instance, UPE became negative in 2 LCMM patients but reappeared at the next assessment,
Table 1. Baseline characteristics.
ISS stage*: I II III Abnormal sFLC kappa/lambda ratio kappa lambda sFLC measurable disease: iFLC (kappa or lambda), mg/L iFLC (kappa), mg/L iFLC (lambda), mg/L uIFE positive UPE positive UPE measurable disease: Bence Jones protein, mg/24h Immunophenotype by sIFE: IgG kappa IgG lambda IgA kappa IgA lambda
LCMM (n=25) n (%) Median (range)
IIMM (n=157)
10 (40) 10 (40) 5 (20) 25 (100) 14 (56) 532 (42-2401) 11 (44) 0.0009 (0.00001-0.0171) 25 (100) 3620 (689-22000) 3740 (689-13100) 3000 (875-22000) 25 (100) 25 (100) 25 (100) 1940 (490-42000)
46 (30) 75 (48) 35 (22) 154 (98) −
98 (62) 483 (101-15600) 491 (101-15600) 441 (101-14100) 85 (54) 67 (43) 55 (35) 1000 (210-9200)
− − − −
79 (50) 34 (22) 26 (17) 18 (11)
−
*one IIMM patient missing data.
Table 2. Weighted Kappa analysis comparing IMWG responses as determined by predicate (serum and/or urine electrophoresis) and test (serum electrophoresis and/or serum FLC) methods in 25 LCMM and 157 IIMM patients.
Predicate method: SPE/IFE and / or UPE/uIFE assigned responses PD SD PR VGPR Negative Serum and urine IFE
Test method: SPE/IFE and / or FLC assigned responses
Total % agreement Weighted Kappa (95% CI)
Total
PD SD
6 2
2 6
2 2
2 1
1 1
13 12
PR VGPR Negative serum IFE and normal FLC ratio
0 0
1 0
47 3
1 43
0 15
49 61
0
0
0
4
53
57
8 75
9 67
54 87 0.83 (0.68-0.98)
51 84
70 76
192
Comparison includes maximum responses assigned in all 182 patients; and disease progression detected in 10/182 patients.Weighted Kappa analysis showed near perfect agreement (0.83) between methods for response assessment. Lack of bone marrow data prevented assignment of complete response (CR); instead, normalisation of serum FLC ratio and negative sIFE and uIFE were considered as the best possible response by either method.
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indicating relapse; whereas FLC indicated stable disease. In both of these patients positive sIFE confirmed the presence of clonal light chains, indicating that, in some instances, treatment responses may be overestimated by urine analysis. More clinically relevant was the identification of one LCMM patient with progressive disease by an increase in iFLC >200mg/L at the end of cycle 4, which would have qualified the patient for receiving treatment even in the absence of clinical symptoms, according to IMWG;37 urine tests at this time point were negative. We also found that sFLC had greater sensitivity than urine analysis for the detection of monoclonal FLC in patients expressing intact monoclonal immunoglobulins. This result may not be surprising as FLC production in IIMM is generally lower than that in LCMM,12 and it is probable that the lack of FLC in the urine was due to reabsorption in the kidneys. In support of this we observed the rapid disappearance of BJP in urine in a disproportionately high percentage of patients after only 2 cycles of therapy, compared to serum assessment. This suggests that sFLC quantification may reflect the tumour’s response to therapy better than BJP measurements. This might be particularly relevant for monitoring IIMM patients with nonmeasurable disease by SPE (M-Ig <10g/L). Our data supports a role for sFLC measurement for monitoring these patients, as UPE overestimated the response in all 5 oligosecretory patients in our cohort by becoming negative by cycle 2, whereas an abnormal sFLC ratio and elevated iFLC indicated the presence of disease at this stage, which was supported by a positive IFE. Importantly, these discrepancies may have clinical impact as normalisation of the sFLC ratio to achieve stringent CR,26 and indeed at any level of response,22,38,39 associates with improved survival outcomes. We further report on 3 patients undergoing clonal changes consistent with light chain escape, one of whom was identified by sFLC analysis only. Changes in the production of monoclonal protein by MM tumour cells (“light chain escape”) were described over 50 years ago via urine analysis40,41 and are readily identified by sFLC analysis.42-44 The MRCIX trial has provided the largest and most comprehensive study to date into this phenomenon, and reported that nearly 50% of patients with light chain escape as identified by sFLC monitoring were missed by urine assessment.44 Importantly the study also demonstrated an association of light chain escape with poorer prognosis. Recent research has shown that the use of novel, more intensive therapies has brought about an increase in the frequency of light chain escape at relapse;45 in this context it would appear that the superior sensitivity of sFLC over urine measurements provides additional clinical information, making a case for the use of serum over urine assessments for monitoring MM patients. The sensitivity of the tests is likely to be influenced by renal function.3,4 A previous study suggested that serum concentrations of 133 mg/L kappa and 278 mg/L lambda light chains are required to overwhelm the reabsorption capacity of the kidney and allow detection of FLCs in urine;3 the impact of charge46 and blood pressure47 may influence the presence of FLC in urine, adding to the subjective nature of the electrophoretic assessment and making these cut-offs somewhat subjective. sFLC assessment can also be affected by renal function, with a proportional increase of kappa over lambda FLC concentrations as renal function deteriorates that may result in a shift in the 360
A
B
Figure 2. Normalisation of serum and urine tests during follow-up. A) Bar chart shows percentage of LCMM patients whose sFLC ratio (blue bar) or uIFE (red bar) had normalised at the end of cycle 2 (n=19), cycle 4 (n=21) and post autologous stem cell transplant (ASCT; n=21). B) Bar chart shows percentage of IIMM patients whose sFLC ratio (blue bar), uIFE (red bar) or serum IFE (green bar) had normalised at the end of cycle 2 (n=44), cycle 4 (n=48) and post-ASCT (n=38). Analyses include patients with measurable levels of disease by each method at presentation as per IMWG guidelines, and complete data for all tests at each time point.
sFLCκ/λ ratio. Hence a renal reference range has been proposed that corrects for renal function in patients with kidney disease.48,49 It is improbable that renal function affected our findings since the study protocol excluded all patients with renal impairment (median creatininemia = 88 (71103) mM);23 furthermore, when tested, the use of the renal reference range during monitoring in our population had no impact on the results (data not shown). A limitation of our study is the lack of bone marrow data, which prevented us from assigning complete responses (CR) as defined by IMWG guidelines. Instead, we reported patients in which BJP was no longer detected in urine electrophoresis and those whose sFLC ratio and SPE had normalised, as a surrogate for CR. It must be pointed out though that about 86% of these patients would have achieved a conventional CR should bone marrow information have been available.50 Future studies must formally address the degree of response indicated by the various serum and urine assessments together with bone marrow infiltration and immunohistology/flow cytometry data, with the ultimate goal of determining haematologica | 2016; 101(3)
sFLC for monitoring multiple myeloma patients
A
B
C
Figure 3. Case studies of 3 LCMM patients monitored during the course of therapy. At presentation all three patients were UPE and uIFE positive and had abnormal sFLC ratios. In all three patients the sFLC ratio remained abnormal throughout monitoring. (A) dFLC indicated no response to therapy in this ÎşFLC patient. UPE and uIFE became negative at cycle 2; monoclonal bands reappeared post-transplantation, indicating disease progression. (B) and (C) depict two ÎťFLC patients; in both cases dFLC indicated VGPR at the end of cycle 2 whereas UPE and uIFE became negative. In all cases sIFE confirmed the presence of residual disease. dFLC: green lines; UPE: red lines; positive uIFE: +ve; negative uIFE: -ve; inserted gels show sIFE results at the end of cycle 2.
which method provides the most relevant clinical information in terms of progression and survival outcome. In summary, we have shown that replacing urine for sFLC measurements does not significantly affect response evaluation in a combined population of LCMM and IIMM patients. However sFLC assessment provides a more sensitive measure of tumour FLC production than
References 1. Kyle R, Child JA, Anderson K, et al. Criteria for the classification of monoclonal gammopathies, multiple myeloma and related disorders: a report of the International Myeloma Working Group. Br J Haematol. 2003;121(5)749-757. 2. Katzmann JA, Kyle RA, Benson J, et al. Screening panels for detection of monoclonal gammopathies. Clin Chem. 2009;55(8): 1517-1522. 3. Nowrousian MR, Brandhorst D, Sammet C, et al. Serum free light chain analysis and
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urine analysis, and has a greater concordance with serum monoclonal immunoglobulin markers for the presence of monoclonal protein during follow-up. On this basis, we feel our results add to previous reports suggesting that sFLC analysis could be considered as a suitable alternative to urine electrophoresis when monitoring myeloma patients.
urine immunofixation electrophoresis in patients with multiple myeloma. Clin Cancer Res. 2005;11(24 Pt 1):8706-8714. 4. Bradwell AR, Carr-Smith HD, Mead GP, Harvey TC, Drayson MT. Serum test for assessment of patients with Bence Jones myeloma. Lancet. 2003;361(9356):489-491. 5. Siegel DS, McBride L, Bilotti E, et al. Inaccuracies in 24-hour urine testing for monoclonal gammopathies. Lab Med. 2009;40(6):341-344. 6. Fidler CJ, Hussein AKA, Gandhi N, et al. Evaluating trends in diagnostic and prognostic testing for multiple myeloma. Blood. 2011;118(21):2067 Abstr.
7. Robson EJD, Taylor J, Beardsmore C, Basu S, Mead G, Lovatt T. Utility of serum free light chain analysis when screening for lymphoproliferative disorders. Lab Med. 2009;40(6): 325-329. 8. Beetham R, Wassell J, Wallage MJ, Whiteway AJ, James JA. Can serum free light chains replace urine electrophoresis in the detection of monoclonal gammopathies? Ann Clin Biochem. 2007;44(Pt 6):516-522. 9. Holding S, Spradbery D, Hoole R, et al. Use of serum free light chain analysis and urine protein electrophoresis for detection of monoclonal gammopathies. Clin Chem Lab Med. 2011;49(1):83-88.
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T. Dejoie et al. 10. Bradwell AR, Carr-Smith HD, Mead GP, et al. Highly sensitive, automated immunoassay for immunoglobulin free light chains in serum and urine. Clin Chem. 2001;47(4): 673-680. 11. Katzmann JA, Dispenzieri A, Kyle RA, et al. Elimination of the need for urine studies in the screening algorithm for monoclonal gammopathies by using serum immunofixation and free light chain assays. Mayo Clin Proc. 2006;81(12):1575-1578. 12. Mead GP, Carr-Smith HD, Drayson MT, Morgan GJ, Child JA, Bradwell AR. Serum free light chains for monitoring multiple myeloma. Br J Haematol. 2004;126(3): 348-354. 13. Abraham RS, Clark RJ, Bryant SC, et al. Correlation of serum immunoglobulin free light chain quantification with urinary Bence Jones protein in light chain myeloma. Clin Chem. 2002;48(4):655-657. 14. Drayson M, Tang LX, Drew R, Mead GP, Carr-Smith H, Bradwell AR. Serum free lightchain measurements for identifying and monitoring patients with nonsecretory multiple myeloma. Blood. 2001;97(9):2900-2902. 15. Lachmann HJ, Gallimore R, Gillmore JD, et al. Outcome in systemic AL amyloidosis in relation to changes in concentration of circulating free immunoglobulin light chains following chemotherapy. Br J Haematol. 2003;122(1):78-84. 16. Dispenzieri A, Lacy MQ, Katzmann JA, et al. Absolute values of immunoglobulin free light chains are prognostic in patients with primary systemic amyloidosis undergoing peripheral blood stem cell transplantation. Blood. 2006;107(8):3378-3383. 17. Dispenzieri A, Kyle RA, Katzmann JA, et al. Immunoglobulin free light chain ratio is an independent risk factor for progression of smoldering (asymptomatic) multiple myeloma. Blood. 2008;111(2):785-789. 18. Rajkumar SV, Kyle RA, Therneau TM, et al. Serum free light chain ratio is an independent risk factor for progression in monoclonal gammopathy of undetermined significance. Blood. 2005;106(3):812-817. 19. Hill PG, Forsyth JM, Rai B, Mayne S. Serum free light chains: an alternative to the urine Bence Jones proteins screening test for monoclonal gammopathies. Clin Chem. 2006; 52(9):1743-1748. 20. Dispenzieri A, Kyle R, Merlini G, et al. International Myeloma Working Group guidelines for serum-free light chain analysis in multiple myeloma and related disorders. Leukemia. 2009;23(2):215-224. 21. Boyle E, Brioli A, Leleu X, et al. The value of serum free light chain monitoring compared to urinary Bence-Jones measurement in light chain only myeloma. Blood. 2013;122(21): 1895a 22. Kapoor P, Kumar SK, Dispenzieri A, et al. Importance of achieving stringent complete response after autologous stem-cell transplantation in multiple myeloma. J Clin
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Oncol. 2013;31(36):4529-4535. 23. Moreau P, Avet-Loiseau H, Facon T, et al. Bortezomib plus dexamethasone versus reduced-dose bortezomib, thalidomide plus dexamethasone as induction treatment before autologous stem cell transplantation in newly diagnosed multiple myeloma. Blood. 2011;118(22):5752-5758. 24. Katzmann JA, Clark RJ, Abraham RS, et al. Serum reference intervals and diagnostic ranges for free kappa and free lambda immunoglobulin light chains: relative sensitivity for detection of monoclonal light chains. Clin Chem. 2002;48(9):1437-1444. 25. Moreau P, Avet-Loiseau H, Facon T, et al. Bortezomib plus dexamethasone versus reduced-dose bortezomib, thalidomide plus dexamethasone as induction treatment before autologous stem cell transplantation in newly diagnosed multiple myeloma. Blood. 2011;118(22):5752-5758. 26. Durie BG, Harousseau JL, Miguel JS, et al. International uniform response criteria for multiple myeloma. Leukemia. 2006;20(9): 1467-1473. 27. Rajkumar SV, Harousseau JL, Durie B, et al. Consensus recommendations for the uniform reporting of clinical trials: report of the International Myeloma Workshop Consensus Panel 1. Blood. 2011;117(18): 4691-4695. 28. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159-174. 29. Bottini PV, Ribeiro Alves MA, Garlipp CR. Electrophoretic pattern of concentrated urine: comparison between 24-hour collection and random samples. Am J Kidney Dis. 2002;39(1):E2 30. Levinson SS. Polyclonal free light chain of Ig may interfere with interpretation of monoclonal free light chain / ratio. Ann Clin Lab Sci. 2010;40(4):348-353. 31. Levinson SS, Keren DF. Free light chains of immunoglobulins: clinical laboratory analysis. Clin Chem. 1994;40(10):1869-1878. 32. Kaplan JS and Horowitz GL. Twenty-fourhour Bence-Jones protein determinations: can we ensure accuracy? Arch Pathol Lab Med. 2011;135(8):1048-1051. 33. Dispenzieri A, Zhang L, Katzmann JA, et al. Appraisal of immunoglobulin free light chain as a marker of response. Blood. 2008;111(10):4908-4915. 34. Abraham RS, Charlesworth MC, Owen BA, et al. Trimolecular complexes of lambda light chain dimers in serum of a patient with multiple myeloma. Clin Chem. 2002;48(10): 1805-1811. 35. Harding SJ, Sharp K, Steiner A, et al. Quantification of polymerising serum free light chains. Clin Lymphoma Myeloma. 2009;9(s1):S101-S102. 36. Harding S, Provot F, Beuscart JB, et al. Aggregated serum free light chains may prevent adequate removal by high cut-off haemodialysis. Nephrol Dial Transplant.
2011;26(4):1438-1440. 37. Rajkumar SV, Harousseau JL, Durie B, et al. Consensus recommendations for the uniform reporting of clinical trials: report of the International Myeloma Workshop Consensus Panel 1. Blood. 2011;117(18): 4691-4695. 38. Alhaj MM, Rajkumar SV, Dispenzieri A, et al. Utility of serum free light chain measurements in multiple myeloma patients not achieving complete response to therapy. Leukemia. 2015;29(10):2033-2038. 39. Drayson MT, Berlanga O, Plant T, Newnham NJ, Young P, Harding S. Immunoglobulin heavy/light chain measurements during monitoring provide prognostic information of relapse after therapy in myeloma patients. Blood. 2012;120(21): 3964 Abstr. 40. Hobbs JR. Growth rates and responses to treatment in human myelomatosis. Br J Haematol. 1969;16(6):607-617. 41. Hobbs JR. Monitoring myelomatosis. Arch Intern Med. 1975;135(1):125-130. 42. Dawson MA, Patil S, Spencer A. Extramedullary relapse of multiple myeloma associated with a shift in secretion from intact immunoglobulin to light chains. Haematologica. 2007;92(1):143-144. 43. Hobbs JA, Drayson MT, Sharp K, Harding S, Bradwell AR, Mead GP. Frequency of altered monoclonal protein production at relapse of multiple myeloma. Br J Haematol. 2009;148 (4):659-661. 44. Brioli A, Giles H, Pawlyn C, et al. Serum free immunoglobulin light chain evaluation as a marker of impact from intraclonal heterogeneity on myeloma outcome. Blood. 2014;123(22):3414-3419. 45. Kuhnemund A, Liebisch P, Bauchmuller K, et al. 'Light-chain escape-multiple myeloma'an escape phenomenon from plateau phase: report of the largest patient series using LCmonitoring. J Cancer Res Clin Oncol. 2009;135(3):477-484. 46. Klassen RB, Allen PL, Batuman V, Crenshaw K, Hammond TG. Light chains are a ligand for megalin. J Appl Physiol. 2005;98(1):257263. 47. Ritz E. Nephrology beyond JASN: Plasma exchange for acute renal failure of myeloma - logical, yet ineffective. J Am Soc Nephrol. 2006;17:914-916. 48. Hutchison CA, Harding S, Hewins P, et al. Quantitative assessment of serum and urinary polyclonal free light chains in patients with chronic kidney disease. Clin J Am Soc Nephrol. 2008;3(6):1684-1690. 49. Hutchison CA, Plant T, Drayson M, et al. Serum free light chain measurement aids the diagnosis of myeloma in patients with severe renal failure. BMC Nephrol. 2008;9:11 50. Chee CE, Kumar S, Larson DR, et al. The importance of bone marrow examination in determining complete response to therapy in patients with multiple myeloma. Blood. 2009;114(13):2617-2618.
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ARTICLE
Plasma Cell Disorders
Impact of renal impairment on outcomes with lenalidomide and dexamethasone treatment in the FIRST trial, a randomized, open-label phase 3 trial in transplant-ineligible patients with multiple myeloma
EUROPEAN HEMATOLOGY ASSOCIATION
Ferrata Storti Foundation
Meletios A. Dimopoulos,1 Matthew C. Cheung,2 Murielle Roussel,3 Ting Liu,4 Barbara Gamberi,5 Brigitte Kolb,6 H. Guenter Derigs,7 HyeonSeok Eom,8 Karim Belhadj,9 Pascal Lenain,10 Richard Van der Jagt,11 Sophie Rigaudeau,12 Mamoun Dib,13 Rachel Hall,14 Henry Jardel,15 Arnaud Jaccard,16 Axel Tosikyan,17 Lionel Karlin,18 William Bensinger,19 Rik Schots,20 Nicolas Leupin,21 Guang Chen,21 Jennifer Marek,21 Annette Ervin-Haynes,21 and Thierry Facon22
1 National and Kapodistrian University of Athens, Athens, Greece; 2Odette Cancer Centre, Toronto, ON, Canada; 3CHU Purpan/IUCT Oncopole, Toulouse, France; 4West China Hospital of Sichuan University, Chengdu, China; 5Arcispedale S. Maria Nuova, Reggio Emilia, Italy; 6Hôpital Robert Debré, Paris, France; 7Staedtische Kliniken Frankfurt am Main Höchst, Frankfurt, Germany; 8National Cancer Center, Goyang-si Gyeonggi-do, South Korea; 9Hôpital Henri Mondor, Creteil, France; 10Centre Henri Becquerel, Rouen, France; 11Ottawa Hospital, ON, Canada; 12Hematology, Hôpital de Versailles, Le Chesnay, France; 13CHU Angers, Angers, France; 14Royal Bournemouth Hospital, Dorset, England, UK; 15Hospital Center, Vannes, France; 16CHU Limoges, France; 17Hôpital du Sacré-Coeur de Montréal, QB, Canada; 18Centre Hospitalier Lyon Sud, Pierre-Bénite, France; 19Fred Hutchinson Cancer Center, Seattle, WA, USA; 20University Hospital VUB-Myeloma Center Brussels, Vrije Universiteit Brussels, Brussels, Belgium; 21Celgene Corporation, Summit, NJ, USA; and 22Service des Maladies du Sang, Hôpital Claude Huriez, CHRU Lille, France
Haematologica 2016 Volume 101(3):363-370
ABSTRACT
R
enal impairment is associated with poor prognosis in myeloma. This analysis of the pivotal phase 3 FIRST trial examined the impact of renally adapted dosing of lenalidomide and dexamethasone on outcomes of patients with different degrees of renal impairment. Transplant-ineligible patients not requiring dialysis were randomized 1:1:1 to receive continuous lenalidomide and dexamethasone until disease progression (n=535) or for 18 cycles (72 weeks; n=541), or melphalan, prednisone, and thalidomide for 12 cycles (72 weeks; n=547). Follow-up is ongoing. Patients were grouped by baseline creatinine clearance into no (≥ 80 mL/min [n=389]), mild (≥ 50 to < 80 mL/min [n=715]), moderate (≥ 30 to < 50 mL/min [n=372]), and severe impairment (< 30 mL/min [n=147]) subgroups. Continuous lenalidomide and dexamethasone therapy reduced the risk of progression or death in no, mild, and moderate renal impairment subgroups vs. melphalan, prednisone, and thalidomide therapy (HR = 0.67, 0.70, and 0.65, respectively). Overall survival benefits were observed with continuous lenalidomide and dexamethasone treatment vs. melphalan, prednisone, and thalidomide treatment in no or mild renal impairment subgroups. Renal function improved from baseline in 52.6% of lenalidomide and dexamethasone–treated patients. The safety profile of continuous lenalidomide and dexamethasone was consistent across renal subgroups, except for grade 3/4 anemia and rash, which increased with increasing severity of renal impairment. Continuous lenalidomide and dexamethasone treatment, with renally adapted lenalidomide dosing, was effective for most transplant-ineligible patients with myeloma and renal impairment. Trial registration: ClinicalTrials.gov (NCT00689936); EudraCT (2007004823-39). Funding: Intergroupe Francophone du Myélome and the Celgene Corporation.
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Correspondence: mdimop@med.uoa.gr
Received: August 6, 2015. Accepted: November 26, 2015. Pre-published: December 11, 2015. doi:10.3324/haematol.2015.133629
Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/101/3/363
©2016 Ferrata Storti Foundation Material published in Haematologica is covered by copyright. All rights reserved to Ferrata Storti Foundation. Copies of articles are allowed for personal or internal use. A permission in writing by the publisher is required for any other use.
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Introduction Renal impairment (RI) is a major disease complicating factor for many patients with multiple myeloma (MM).1 Twenty percent or more of patients with newly diagnosed MM (NDMM) present with some degree of RI, which is associated with poor outcomes, including poor survival and risk of early death.1-4 The reversal of RI is associated with prolonged survival.1,3 Newer agents, including immunomodulatory agents and proteasome inhibitors, are effective in patients with RI, leading to outcomes similar to those in non–renally impaired patients, and often improving renal function.1,5 Currently bortezomib-based regimens are often used for treating patients with MM and RI.4,6 However, the evaluation of lenalidomide in patients with RI has been limited by the exclusion of patients with severe RI from clinical trials.4 Lenalidomide, a non-nephrotoxic compound, is predominantly excreted renally.7 However, because it is minimally metabolized, it is recommended that the starting dose be adjusted according to the level of renal function.7,8 With appropriate dose adjustments, lenalidomide plus dexamethasone has been demonstrated to be effective and tolerable in renally impaired patients with relapsed/refractory MM (RRMM).8 Improvement in renal function has also been reported in up to 72% of patients with RRMM with lenalidomide plus dexamethasone treatment.8,9 However, little is known about the efficacy and tolerability of newer therapies in patients with NDMM with RI because many phase 3 clinical trials exclude patients with moderate to severe RI.10-12 The Frontline Investigation of Revlimid and Dexamethasone vs. Standard Thalidomide (FIRST) study is a phase 3, international, randomized, open-label trial of lenalidomide plus low-dose dexamethasone (Rd) in patients with NDMM who are ineligible for stem cell transplant (SCT).13 The FIRST trial is notable for enrolling patients with NDMM with any level of RI, excluding only those requiring dialysis. Patients were randomized to Rd until disease progression (Rd continuous); Rd for 72 weeks (18 cycles; Rd18); or melphalan, prednisone, and thalidomide (MPT) for 72 weeks. In the overall study population, Rd continuous resulted in a reduced risk of progression or death vs. MPT (hazard ratio [HR], 0.72; P<0.001; data cutoff, May 2013). Overall survival (OS) was also improved with Rd continuous vs. MPT (HR, 0.78). Both Rd continuous and Rd18 had lower rates of hematologic toxicity than MPT, but slightly higher rates of grade 3-4 infections. In this study, lenalidomide dosing was adaptable based on renal function and recovery: the starting dose was decreased for patients with moderate to severe RI and could be increased as renal function improved to maintain effective lenalidomide exposure. The goal of this analysis was to assess the effect of lenalidomide treatment, with appropriate dose adjustments for renal function, in combination with low-dose dexamethasone on outcomes in patients with varying degrees of RI in the FIRST study.
Patients were enrolled between August 2008 and March 2011. The trial was approved by the institutional review board of each site and registered with ClinicalTrials.gov (NCT00689936) and the European Clinical Trials Database (2007-004823-39).
Patients Eligible patients had previously untreated, symptomatic, and measurable MM.13 Patients were aged ≥ 65 years or otherwise unable to receive SCT. RI of any degree was allowed, except that requiring hemodialysis or peritoneal dialysis. Full eligibility criteria are in the Online Supplementary Methods.
Study design Patients were randomly assigned 1:1:1 to 3 treatment arms: lenalidomide (25 mg/day, days 1-21) and dexamethasone (40 mg/day, days 1, 8, 15, and 22) in 28-day cycles until disease progression (Rd continuous); lenalidomide and dexamethasone as above in 28-day cycles for 72 weeks (18 cycles; Rd18); or melphalan (0.25 mg/kg/day, days 1-4), prednisone (2 mg/kg/day, days 14), and thalidomide (200 mg/day) in twelve 42-day cycles for 72 weeks (MPT). Starting dose adjustments were based on renal function and age (Online Supplementary Methods). Randomization was performed using a validated interactive voice response system. Patients were stratified by age (≤ 75 vs. > 75 years), International Staging System (ISS) disease stage, and country. Renal function subgroups were defined as the following: no RI, creatinine clearance (CrCl) at baseline ≥ 80 mL/min; mild RI, ≥ 50 to < 80 mL/min; moderate RI, ≥ 30 to < 50 mL/min; and severe RI, < 30 mL/min.
Endpoints and assessments This analysis was based on an unplanned update at the request of regulatory authorities, with a data cut-off of March 3, 2014 (data cut-off for the primary analysis was May 24, 201313). The objective of this secondary analysis was to evaluate the efficacy and safety of Rd continuous treatment in patients with varying degrees of RI. The primary endpoint was progression-free survival (PFS) for Rd continuous vs. MPT. Secondary endpoints included OS, overall response rate (ORR; ≥ partial response [PR]), time to second-line anti-myeloma treatment, improvement in CRAB criteria (calcium, renal, anemia, bone; including improvement of renal function from baseline by observing improvement in CrCl), and safety. Response was investigator-assessed using the International Myeloma Working Group (IMWG) criteria14 after each treatment cycle and every 28 days during PFS follow-up. Baseline CrCl was estimated from serum creatinine by a central laboratory at screening using the Cockcroft-Gault formula,15,16 and reassessed on day 1 (± 3 days) of each treatment cycle. Per-protocol improvement in renal function was defined as an increase of ≥ 1 renal function subgroup (by CrCl as defined above) from baseline at any point during treatment. As an additional retrospective analysis, renal response was assessed according to IMWG criteria4 (Online Supplementary Methods). Safety was evaluated until 28 days after the last dose of the study drug; adverse events (AEs) were graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events v3.0.
Results Methods Patient characteristics Full study design details were reported previously and are in the Online Supplementary Methods.13 FIRST was a randomized, openlabel, phase 3 trial conducted at 246 treatment centers in 18 countries in Europe, North America, and the Asia-Pacific region. 364
Of the 1623 patients randomized, 389 (24.0%) had no RI, 715 (44.1%) had mild RI, 372 (22.9%) had moderate RI, and 147 (9.1%; the smallest subgroup) had severe RI (excluding patients requiring dialysis). Renal subgroups haematologica | 2016; 101(3)
Impact of renal impairment on Rd treatment in NDMM
were defined using CrCl-based thresholds that correspond to levels at which lenalidomide dose reductions are recommended in patients with RI.17,18 The median duration of follow-up was 45.5 months with a data cut-off of March 3, 2014 (an update of the previously published final PFS analysis, which had a data cut-off of May 24, 201313). Protocol violations involving incorrect starting dose of lenalidomide or melphalan for level of renal function at randomization, or incorrect dose adjustment due to change in renal function during treatment were rare (< 2%), and only 3 patients were lost to follow-up (Online Supplementary Figure S1). Baseline characteristics were well balanced among treatment groups (Table 1). An increasing degree of RI was associated with older age, higher ISS stage, and higher Eastern Cooperative Oncology Group (ECOG) performance status score.
Efficacy Efficacy results reported here are focused on the Rd continuous and MPT arms (primary comparators in the FIRST trial). A PFS benefit was seen for Rd continuous compared with MPT in all subgroups of patients except those with severe RI (Figure 1). The HR for risk of progression or death for Rd continuous vs. MPT was 0.67 in patients with no RI (P=0.015), 0.70 in patients with mild RI (P=0.002), and 0.65 in patients with moderate RI (P=0.005). In patients with severe RI, no clear PFS benefit with Rd con-
tinuous vs. MPT could be determined (HR, 0.80; P=0.394). Four-year PFS was increased with Rd continuous vs. MPT in all renal subgroups: 41.3% vs. 18.4% in patients with no RI, 34.3% vs. 12.7% in patients with mild RI, 26.9% vs. 11.8% in patients with moderate RI, and 22.2% vs. 0% in patients with severe RI. Similarly, PFS was extended with Rd continuous vs. Rd18 in patients with no RI to moderate RI. Across all treatment arms, a worse level of renal function was associated with a shorter PFS. OS improvements were observed in patients with no RI or mild RI who were treated with Rd continuous vs. MPT (HR, 0.59 and 0.73, respectively; Table 2). No OS benefits were observed in patients with moderate or severe RI with Rd continuous vs. MPT. Rates of 4-year survival were higher with Rd continuous compared with MPT for all renal subgroups: 69.7% vs. 58.4% in patients with no RI, 63.4% vs. 54.4% in those with mild RI, 50.6% vs. 45.0% in those with moderate RI, and 41.6% vs. 29.5% in those with severe RI. OS was similar with Rd continuous and Rd18, regardless of renal function. Compared with MPT, Rd continuous extended the time to second-line anti-myeloma treatment in patients with no RI (HR, 0.66), mild RI (HR, 0.66), and moderate RI (HR, 0.55; Table 2). An increase in time to second-line antimyeloma treatment was not observed in patients with severe RI receiving Rd continuous vs. those receiving MPT. Rd continuous treatment resulted in higher ORRs (â&#x2030;Ľ PR) vs. MPT treatment in patients with mild or moderate RI
A
B
C
D
Figure 1. Kaplan-Meier curves of progression-free survival (PFS) in patients with (A) no renal impairment (RI), (B) mild RI, (C) moderate RI, and (D) severe RI in all renal subgroups. CrCl: creatinine clearance; HR: hazard ratio; MPT: melphalan, prednisone, and thalidomide; Rd: lenalidomide and low-dose dexamethasone; Rd18: Rd for 18 cycles.
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(Table 3). ORR was 80.1% with Rd continuous vs. 70.7% with MPT (odds ratio [OR], 1.66) in patients with mild RI and 84.9% vs. 58.7% (OR, 3.96) in patients with moderate RI. No difference in ORR was shown with Rd continuous vs. MPT in the no RI or severe RI subgroups. Patients with moderate RI showed improved ORR with Rd continuous treatment compared with those receiving Rd18 (84.9% vs. 70.0%; OR, 2.41). No differences were observed between Rd continuous and Rd18 for ORRs in other renal function subgroups. Patients with severe RI had lower ORRs than those with better renal function across all treatment arms.
Improvement of renal function (per protocol) Per-protocol improvement of renal function was defined as an increase of ≥ 1 renal function level (defined by the same CrCl thresholds used for the subgroup analysis) from baseline at any point during treatment. For this analysis, results for Rd continuous and Rd18 arms were pooled. Improved renal function was generally observed across all combined Rd and the MPT treatment groups (Figure 2A). All patients with improved renal function in the pooled Rd group ameliorated within the first 18 treatment cycles. Of the patients with severe RI (CrCl < 30
Table 1. Patient baseline characteristics.
No RI CrCl ≥ 80 mL/min (n = 389) Rd Cont Rd18 MPT (n = 123) (n = 122) (n = 144)
Mild RI CrCl ≥ 50 to < 80 mL/min (n = 715) Rd Cont Rd18 MPT (n = 241) (n = 252) (n = 222)
Moderate RI CrCl ≥ 30 to < 50 mL/min (n = 372) Rd Cont Rd18 MPT (n = 126) (n = 120) (n = 126)
Severe RI CrCl < 30 mL/min (n = 147) Rd Cont Rd18 MPT (n = 45) (n = 47) (n = 55)
Median age 68 (44-84) 69 (40-82) 69 (53-88) (range), y ≥ 65 y, n. (%) 105 (85.4) 107 (87.7) 127 (88.2) > 75 y, n. (%) 14 (11.4) 15 (12.3) 21 (14.6) Male, n. (%) 77 (62.6) 80 (65.6) 89 (61.8) ISS stage, n. (%) I/II 106 (86.2) 105 (86.1) 120 (83.3) III 17 (13.8) 17 (13.9) 24 (16.7) ECOG PS, n. (%) 0 38 (30.9) 41 (33.6) 45 (31.3) 1 62 (50.4) 62 (50.8) 63 (43.8) 2 22 (17.9) 19 (15.6) 34 (23.6) 3 1 (0.8) 0 0 NA 0 0 2 (1.4) High-risk 8 (6.5) 6 (4.9) 4 (2.8) cytogenetics,a n. (%)
73 (53-86) 73 (54-84) 73 (56-86)
76 (61-91) 77 (59-89) 76 (65-92)
77 (61-87) 75 (56-89)76 (51-90)
231 (95.9) 242 (96.0) 215 (96.8) 75 (31.1) 86 (34.1) 73 (32.9) 122 (50.6) 134 (53.2) 111 (50.0)
124 (98.4) 114 (95.0) 126 (100.0) 72 (57.1) 71 (59.2) 66 (52.4) 74 (58.7) 43 (35.8) 65 (51.6)
44 (97.8) 44 (93.6) 52 (94.5) 25 (55.6) 21 (44.7) 28 (50.9) 21 (46.7) 16 (34.0) 22 (40.0)
165 (68.5) 167 (66.3) 155 (69.8) 76 (31.5) 85 (33.7) 67 (30.2)
43 (34.1) 83 (65.9)
43 (35.8) 77 (64.2)
42 (33.3) 84 (66.7)
5 (11.1) 7 (14.9) 6 (10.9) 40 (88.9) 40 (85.1) 49 (89.1)
73 (30.3) 85 (33.7) 110 (45.6) 117 (46.4) 58 (24.1) 48 (19.0) 0 2 (0.8) 0 0 17 (7.1) 16 (6.3)
38 (30.2) 62 (49.2) 24 (19.0) 1 (0.8) 1 (0.8) 11 (8.7)
28 (23.3) 63 (52.5) 29 (24.2) 0 0 8 (6.7)
30 (23.8) 65 (51.6) 29 (23.0) 2 (1.6) 0 12 (9.5)
6 (13.3) 9 (19.1) 9 (16.4) 23 (51.1) 21 (44.7) 31 (56.4) 15 (33.3) 17 (36.2) 15 (27.3) 0 0 0 1 (2.2) 0 0 4 (8.9) 4 (8.5) 4 (7.3)
72 (32.4) 116 (52.3) 33 (14.9) 0 1 (0.5) 21 (9.5)
a del(17p) and/or t(4;14). Cont: continuous; CrCl: creatinine clearance; ECOG PS: Eastern Cooperative Oncology Group performance status; ISS: International Staging System; MPT: melphalan, prednisone, and thalidomide; NA: not applicable; Rd: lenalidomide and low-dose dexamethasone; Rd18: Rd for 18 cycles; RI: renal impairment.
Table 2. OS and time to second anti-myeloma treatment in renal subgroups.
No RI CrCl ≥ 80 mL/min (n = 389) 4-year OS, % (SE) Rd continuous 69.7 (4.8) Rd18 70.4 (4.5) MPT 58.4 (4.5) HR (95% CI) Rd continuous vs. MPT 0.59 (0.38-0.91) Rd continuous vs. Rd18 0.78 (0.49-1.24) Rd18 vs. MPT 0.73 (0.49-1.10) Median time to second-line anti-myeloma treatment, mo Rd continuous 43.7 Rd18 31.3 MPT 31.3 HR (95% CI) Rd continuous vs. MPT 0.66 (0.47-0.92) Rd continuous vs. Rd18 0.80 (0.56-1.12) Rd18 vs. MPT 0.84 (0.61-1.14)
Mild RI CrCl ≥ 50 to < 80 mL/min (n = 715)
Moderate RI CrCl ≥ 30 to < 50 mL/min (n = 372)
Severe RI CrCl < 30 mL/min (n = 147)
63.4 (3.3) 57.9 (3.4) 54.4 (3.6)
50.6 (4.8) 44.6 (4.9) 45.0 (4.9)
41.6 (8.0) 46.0 (8.1) 29.5 (7.5)
0.73 (0.55-0.97) 0.91 (0.69-1.21) 0.80 (0.61-1.05)
0.83 (0.58-1.18) 0.83 (0.58-1.17) 1.01 (0.71-1.43)
0.92 (0.55-1.53) 1.27 (0.72-2.21) 0.76 (0.45-1.28)
37.0 29.9 27.8
35.8 24.1 21.8
17.5 23.7 18.1
0.66 (0.52-0.84) 0.77 (0.61-0.98) 0.85 (0.68-1.06)
0.55 (0.39-0.78) 0.56 (0.40-0.79) 0.94 (0.68-1.29)
0.74 (0.42-1.29) 1.02 (0.58-1.80) 0.86 (0.51-1.42)
CrCl: creatinine clearance; HR: hazard ratio; MPT: melphalan, prednisone, and thalidomide; OS: overall survival; Rd: lenalidomide and low-dose dexamethasone; Rd18: Rd for 18 cycles; RI: renal impairment; SE: standard error.
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Impact of renal impairment on Rd treatment in NDMM
mL/min) at baseline who were treated with Rd, 61.4% shifted to a better renal function category (CrCl ≥ 30 mL/min) during treatment, compared with 55.8% of those treated with MPT. For patients with mild (CrCl ≥ 50 to < 80 mL/min) or moderate RI (CrCl ≥ 30 to < 50 mL/min) at baseline, rates of renal function improvement were similar with Rd compared with MPT (mild: 45.5% vs. 47.8%,
respectively; moderate: 63.8% vs. 62.0%, respectively). Normal renal function was achieved by 4.8% of Rd-treated patients and 0% of MPT-treated patients with severe RI at baseline, and by 9.4% and 1.9%, respectively, of patients with moderate RI at baseline. Mild RI was achieved by 18.1% of patients with severe RI who received Rd and 11.6% who received MPT. However,
Table 3. Response to treatment in renal subgroups.
No RI CrCl ≥ 80 mL/min Rd Cont Rd18 MPT (n = 123) (n = 122) (n = 144) ORR (≥ PR), % CR VGPR PR SD, % PD, % NE, % OR (95% CI) Rd Cont vs. MPT Rd Cont vs. Rd18 Rd18 vs. MPT
83.7 20.3 31.7 31.7 14.6 1.6 0
86.1 22.1 32.0 32.0 10.7 1.6 1.6 1.78 (0.97-3.27) 0.83 (0.41-1.68) 2.14 (1.13-4.03)
74.3 20.1 15.3 38.9 20.1 1.4 4.2
Mild RI CrCl ≥ 50 to < 80 mL/min Rd Cont Rd18 MPT (n = 241) (n = 252) (n = 222) 80.1 24.5 25.7 29.9 12.4 2.5 5.0
81.3 22.6 24.6 34.1 13.1 0.8 4.8 1.66 (1.08-2.55) 0.92 (0.59-1.44) 1.81 (1.18-2.77)
70.7 8.1 20.3 42.3 19.8 3.2 6.3
Moderate RI CrCl ≥ 30 to < 50 mL/min Rd Cont Rd18 MPT (n = 126) (n = 120) (n = 126) 84.9 18.3 28.6 38.1 8.7 1.6 4.8
70.0 11.7 28.3 30.0 21.7 1.7 6.7 3.96 (2.16-7.23) 2.41 (1.29-4.51) 1.64 (0.97-2.78)
58.7 9.5 17.5 31.7 20.6 5.6 15.1
Severe RI CrCl < 30 mL/min Rd Cont Rd18 MPT (n = 45) (n = 47) (n = 55) 64.4 15.6 15.6 33.3 15.6 0 20.0
66.0 25.5 21.3 19.1 23.4 0 10.6
54.5 12.7 20.0 21.8 32.7 1.8 10.9
1.51 (0.67-3.39) 0.94 (0.40-2.21) 1.61 (0.72-3.61)
Cont: continuous; CR: complete response; CrCl: creatinine clearance; MPT: melphalan, prednisone, and thalidomide; NE: not evaluable; OR: odds ratio; ORR: overall response rate; PD: progressive disease; PR: partial response; Rd: lenalidomide and low-dose dexamethasone; Rd18: Rd for 18 cycles; RI: renal impairment; SD: stable disease; VGPR: very good partial response.
A
Figure 2. Renal improvement per-protocol (A) and based on IMWG criteria (B). aPercentages represent patients who improved from baseline to most extreme post-baseline CrCl value, divided by the total number of patients with baseline and post-baseline CrCl data. b Based on Modification of Diet in Renal Disease equation. CR: complete response; CrCl: creatinine clearance; eGFR: estimated glomerular filtration rate; IMWG: International Myeloma Working Group; MPT: melphalan, prednisone, and thalidomide; MR: minor response; PR: partial response; Rd: lenalidomide and low-dose dexamethasone; Rd18: Rd for 18 cycles; RI: renal impairment.
B
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patient numbers within each of these improvement subgroups were small. Overall, < 5% of patients in either treatment group experienced worsening renal function during treatment (Rd, 2.5%; MPT, 2.7%).
Improvement in renal function (IMWG criteria) IMWG-defined complete renal response (CRrenal), a sustained improvement in renal function to near normal levels (CrCl ≥ 60 mL/min), was achieved by 23.8% of patients receiving Rd and 14.3% of patients receiving MPT treatment (Figure 2B). Partial renal response was achieved by 11.1% of patients receiving Rd and 7.7% of patients receiving MPT. Similar rates of minimal renal response were observed across arms. Of the 68 Rd-treated patients who achieved CRrenal, 58 (85.3%) did so within the first 3 cycles of treatment. Eleven of the 68 patients (16.2%) who achieved CRrenal received an increased dose of lenalidomide after exhibiting renal improvement; 10 of these 11 patients tolerated the increased dose for the remainder of the study.
with severe RI had the shortest median duration of treatment of only 6.8 months (0.3-56.2 months). Actual lenalidomide dosing on the Rd continuous treatment arm was close to planned dosing for patients with no, mild, or moderate RI, with a median relative dose intensity of ≥ 0.9. Patients with severe RI had a median relative dose intensity of only 0.7 with Rd continuous. The discontinuation rate due to AEs was similar across renal subgroups (Table 4). The most frequent hematologic grade 3/4 AEs across renal subgroups were anemia (15%-27% with Rd continuous and 15%-35% with MPT) and neutropenia (22%-32% for Rd continuous and 29%-53% for MPT; Table 4). In all treatment arms, the incidence of anemia increased with the degree of RI. Rash also occurred at a higher rate in patients with severe RI treated with Rd continuous or Rd18. The most frequent nonhematologic AE was infection (28%-31% with Rd continuous and 14%24% with MPT). Rates of deep vein thrombosis and pulmonary embolism were not impacted by renal function.
Safety
Discussion
The median duration of treatment in the Rd continuous arm was 22.4 months (range, 0.9-66.2 months) for patients with no RI, 18.9 months (range, 0.3-61.7 months) for those with mild RI, and 18.9 months (0.2-60.0 months) for those with moderate RI (Online Supplementary Table S1). Patients
RI is present in a substantial number of patients with NDMM, is often related to comorbidities such as hypertension and diabetes in addition to the MM insult, and is associated with increased morbidity and mortality.1-3,19
Table 4. Discontinuations due to AEs and grade 3/4 AEs.
Variable, n (%)
Discontinuation of any study drug due to AEs Any grade 3/4 AE
No RI CrCl ≥ 80 mL/min (n = 389) Rd Cont Rd18 MPT (n = 123) (n = 122) (n = 144)
Mild RI CrCl ≥ 50 to < 80 mL/min (n = 715) Rd Cont Rd18 MPT (n = 240) (n = 252) (n = 222)
Moderate RI CrCl ≥ 30 to < 50 mL/min (n = 363) Rd Cont Rd18 MPT (n = 124) (n = 119) (n = 120)
Severe RI CrCl < 30 mL/min (n = 147) Rd Cont Rd 18 MPT (n = 45) (n = 47) (n = 55)
40 (33)
13 (11)
36 (25)
69 (29)
56 (22)
63 (28)
44 (36)
23 (19)
38 (32)
14 (31)
17 (36)
16 (29)
108 (88)
90 (74)
130 (90)
195 (81)
206 (82)
199 (90)
112 (90)
96 (81)
98 (82)
39 (87)
41 (87)
53 (96)
Hematologic AEs (≥ 10% in any renal subgroup of any treatment arm) Neutropenia 27 (22) 25 (21) 76 (53) 76 (32) Anemia 18 (15) 11 (9) 22 (15) 42 (18) Thrombocytopenia 7 (6) 6 (5) 20 (14) 24 (10) Leukopenia 4 (3) 6 (5) 17 (12) 12 (5)
72 (29) 33 (13) 22 (9) 12 (5)
112 (51) 34 (15) 26 (12) 22 (10)
37 (30) 27 (22) 10 (8) 8 (7)
35 (29) 29 (24) 10 (8) 8 (7)
39 (33) 27 (23) 11 (9) 10 (8)
11 (24) 12 (27) 4 (9) 0
11 (23) 12 (26) 5 (11) 4 (9)
16 (29) 19 (35) 3 (6) 4 (7)
53 (21) 23 (9) 13 (5) 23 (9) 13 (5) 3 (1) 6 (2) 1 (< 1) 11 (4)
36 (16) 12 (5) 12 (5) 10 (5) 8 (4) 3 (1) 2 (1) 22 (10) 5 (2)
39 (31) 5 (4) 8 (7) 11 (9) 8 (7) 3 (2) 10 (8) 1 (1) 4 (3)
30 (25) 10 (8) 6 (5) 8 (7) 8 (7) 4 (3) 6 (5) 1 (1) 4 (3)
24 (20) 9 (8) 7 (6) 5 (4) 11 (9) 5 (4) 7 (6) 9 (8) 2 (2)
14 (31) 4 (9) 4 (9) 2 (4) 9 (20) 1 (2) 5 (11) 1 (2) 5 (11)
12 (26) 3 (6) 1 (2) 7 (15) 5 (11) 8 (17) 3 (6) 0 1 (2)
13 (24) 3 (6) 2 (4) 8 (15) 3 (6) 7 (13) 2 (4) 4 (7) 2 (4)
4 (2) 1 (< 1)
2 (1) 0
12 (10) 4 (3)
8 (7) 1 (1)
3 (3) 2 (2)
3 (7) 5 (11)
5 (11) 3 (6)
2 (4) 3 (6)
Nonhematologic AEs (≥ 10% in any renal subgroup of any treatment arm) Infections 38 (31) 23 (19) 20 (14) 68 (28) Pneumonia 11 (9) 9 (7) 7 (5) 25 (10) Back pain 8 (7) 14 (12) 7 (5) 19 (8) Fatigue 7 (6) 8 (7) 8 (6) 20 (8) Rash 5 (4) 2 (2) 6 (4) 11 (5) Renal failure 2 (2) 3 (3) 1 (1) 7 (3) Renal failure, acute 1 (1) 1 (1) 2 (1) 2 (1) PSN 1 (1) 0 16 (11) 3 (1) General physical 1 (1) 0 3 (2) 6 (3) health deterioration Hypocalcemia 0 2 (2) 1 (1) 8 (3) Blood creatinine 0 0 1 (1) 1 (< 1) increased
AE: adverse event; Cont: continuous; CrCl: creatinine clearance; MPT: melphalan, prednisone, and thalidomide; PSN: peripheral sensory neuropathy; Rd: lenalidomide and low-dose dexamethasone; Rd18: Rd for 18 cycles; RI: renal impairment.
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When addressed in a timely manner, myeloma-associated RI tends to improve or normalize.1,3 This secondary cohort analysis of the FIRST trial assessed the activity and tolerability of continuous Rd therapy, with renally adapted dosing of lenalidomide, in patients with NDMM and RI who were ineligible for SCT. Rd continuous treatment reduced the risk of progression or death by 33%, 30%, and 35% in patients with no, mild, or moderate RI, respectively, compared with MPT. Rd continuous also improved ORR and extended time to second-line anti-myeloma treatment compared with MPT in patients with mild and moderate RI, and provided OS benefits for patients with mild RI. These data are consistent with efficacy results in patients with normal renal function and the overall study population from the FIRST trial.13 In patients with severe RI, the efficacy benefits of Rd continuous compared with MPT could not be clearly demonstrated due to wide confidence intervals, which limited a definitive interpretation of the results. Analysis was further limited by the low number of patients with severe RI (only 9.1% of the overall trial population) and their short duration of treatment (median, 6.8 months, compared with 18.9 months for other patients with RI). Severe RI has been shown to be an adverse prognostic factor for outcomes with regimens containing novel agents, including bortezomib-based combinations.20 This was shown even for patients whose renal function improved with therapy, suggesting that the poor prognostic impact of severe RI may be related to myeloma biology.20 Indeed, in the current study, severe RI was associated with worse baseline disease characteristics, including higher disease stage and ECOG performance status score. The shorter duration of lenalidomide treatment in patients with severe RI, compared with those with better renal function, was likely not due to decreased tolerability of lenalidomide given that the treatment discontinuation rate due to AEs was similar across renal subgroups. Instead, patients with severe RI likely progressed more rapidly despite treatment, as evidenced by lower rates of ORR and shorter PFS across all treatment arms. Previous analyses have demonstrated the efficacy of lenalidomide plus dexamethasone treatment in patients with RRMM and mild to moderate RI.9 However, in these studies, standard lenalidomide dosing was associated with increased toxicity and more frequent use of subsequent dose reductions and interruptions due to AEs in patients with moderate to severe RI vs. patients with mild or no RI.9 Recommendations have since been made for lenalidomide starting dose adjustments for moderate to severe RI based on pharmacokinetic data.7,17 Lenalidomide is not nephrotoxic,18 but is primarily renally excreted,7 such that RI greatly affects its pharmacokinetics. Therefore, doses are adjusted in patients with RI to match exposure levels achieved by standard dosing in patients with normal renal
References 1. Chanan-Khan AA, San Miguel JF, Jagannath S, Ludwig H, Dimopoulos MA. Novel therapeutic agents for the management of patients with multiple myeloma and renal impairment. Clin Cancer Res. 2012;18(8):2145-2163. 2. Augustson BM, Begum G, Dunn JA, et al.
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function.7 In a series of 50 patients with RRMM, lenalidomide plus dexamethasone, with starting dose adjustments of lenalidomide based on renal function, was shown to be active with similar rates of AEs in patients with and without RI.8 This analysis demonstrated that applying dose adjustments of lenalidomide, adapted to the level of renal function, results in a similar safety profile across all levels of RI in patients with NDMM. An exception was anemia, which increased with the degree of RI in all treatment arms even with dose adjustments, suggesting that anemia may be a consequence of RI, independent of treatment. Physicians should be vigilant about monitoring for and managing anemia in RI patients. Rd continuous was well tolerated across renal function groups, with similar rates of discontinuation due to AEs for patients with and without RI. Patients with mild to moderate RI also tolerated sustained treatment with lenalidomide for durations similar to those tolerated by patients without RI. Reversibility of RI is an important treatment goal and is associated with improved survival outcomes.3 Although severe RI was only a small subset of the FIRST trial and patients requiring dialysis were excluded, Rd continuous or Rd18 treatment improved renal function using the perprotocol assessment in most patients (52.6%), including 61.4% of those with severe RI. Additionally, the rate of IMWG-defined CRrenal was 23.8% in Rd-treated patients. Compared with MPT, Rd treatment resulted in a greater degree of renal improvement, although patient numbers were small. Results were consistent with previous observations of improvement in renal function in both NDMM and RRMM with lenalidomide-based treatment.6,8,9 Overall, lenalidomide represents an active treatment option for patients with NDMM and RI, especially those who may have contraindications for bortezomib such as preexisting peripheral neuropathy. Challenges remain in assessing renal function in patients with MM.1,4,21 There is a lack of consensus regarding the most appropriate assessment method, and there are additional difficulties in assessing renal function in the elderly, including lower production of serum creatinine.1,9,21 Despite these limitations, continuous therapy with Rd until disease progression improved renal function and provided PFS benefits in a large proportion of patients with mild to moderate RI with a manageable safety profile. With renally adapted dosing of lenalidomide, Rd continuous represents an effective and tolerable treatment option for many patients with NDMM with RI who are ineligible for SCT. Acknowledgments The authors would like to thank MediTech Media (Jennifer Leslie, PhD, and Nicola Hanson, PhD) for editorial assistance sponsored by Celgene Corporation.
Early mortality after diagnosis of multiple myeloma: analysis of patients entered onto the United Kingdom Medical Research Council trials between 1980 and 2002-Medical Research Council Adult Leukaemia Working Party. J Clin Oncol. 2005;23(36):9219-9226. 3. Knudsen LM, Hjorth M, Hippe E. Renal failure in multiple myeloma: reversibility and impact on the prognosis. Nordic Myeloma
Study Group. Eur J Haematol. 2000;65(3): 175-181. 4. Dimopoulos MA, Terpos E, Chanan-Khan A, et al. Renal impairment in patients with multiple myeloma: a consensus statement on behalf of the International Myeloma Working Group. J Clin Oncol. 2010;28(33): 4976-4984. 5. Dimopoulos MA, Delimpasi S, Katodritou E, et al. Significant improvement in the sur-
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vival of patients with multiple myeloma presenting with severe renal impairment after the introduction of novel agents. Ann Oncol. 2014;25(1):195-200. Dimopoulos MA, Rossou M, Gkotzmanidou M, et al. The role of novel agents on the reversibility of renal impairment in newly diagnosed symptomatic patients with multiple myeloma. Leukemia. 2013;27(2):423-429. Chen N, Lau H, Kong L, et al. Pharmacokinetics of lenalidomide in subjects with various degrees of renal impairment and in subjects on hemodialysis. J Clin Pharmacol. 2007;47(12):1466-1475. Dimopoulos MA, Christoulas D, Roussou M, et al. Lenalidomide and dexamethasone for the treatment of refractory/relapsed multiple myeloma: dosing of lenalidomide according to renal function and effect on renal impairment. Eur J Haematol. 2010;85 (1):1-5. Dimopoulos M, Alegre A, Stadtmauer EA, et al. The efficacy and safety of lenalidomide plus dexamethasone in relapsed and/or refractory multiple myeloma patients with impaired renal function. Cancer. 2010;116(16):3807-3814.
10. Palumbo A, Hajek R, Delforge M, et al. Continuous lenalidomide treatment for newly diagnosed multiple myeloma. N Engl J Med. 2012;366(19):1759-1769. 11. Facon T, Mary JY, Hulin C, et al. Melphalan and prednisone plus thalidomide versus melphalan and prednisone alone or reducedintensity autologous stem cell transplantation in elderly patients with multiple myeloma (IFM 99-06): a randomised trial. Lancet. 2007;370(9594):1209-1218. 12. Hulin C, Facon T, Rodon P, et al. Efficacy of melphalan and prednisone plus thalidomide in patients older than 75 years with newly diagnosed multiple myeloma: IFM 01/01 trial. J Clin Oncol. 2009;27(22):3664-3670. 13. Benboubker L, Dimopoulos MA, Dispenzieri A, et al. Lenalidomide and dexamethasone in transplant-ineligible patients with myeloma. N Engl J Med. 2014;371(10):906-917. 14. Durie BGM, Harousseau J, Miguel JS, et al. International uniform response criteria for multiple myeloma. Leukemia. 2006;20(9):1467-1473. 15. Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron. 1976;16(1):31-41.
16. Luke DR, Halstenson CE, Opsahl JA, Matzke GR. Validity of creatinine clearance estimates in the assessment of renal function. Clin Pharmacol Ther. 1990;48(5): 503-508. 17. RevlimidÂŽ (lenalidomide) [package insert]. Summit, NJ: Celgene Corporation; 2015. 18. Dimopoulos MA, Palumbo A, Attal M, et al. Optimizing the use of lenalidomide in relapsed or refractory multiple myeloma: consensus statement. Leukemia. 2011;25(5):749-760. 19. Ludwig H, San Miguel J, Dimopoulos MA, et al. International Myeloma Working Group recommendations for global myeloma care. Leukemia. 2014;28(5):981-982. 20. Khan R, Apewokin S, Grazzutti M, et al. Renal insufficiency retains adverse prognostic implications despite renal function improvement following Total Therapy for newly diagnosed multiple myeloma. Leukemia. 2015;29(5):1195-1201. 21. Hudson JQ, Bean JR, Burger CF, Stephens AK, McFarland MS. Estimated glomerular filtration rate leads to higher drug dose recommendations in the elderly compared with creatinine clearance. Int J Clin Pract. 2015;69(3):313-320.
haematologica | 2016; 101(3)
ARTICLE
Stem Cell Transplantation
Analysis of memory-like natural killer cells in human cytomegalovirus-infected children undergoing αβ+T and B cell-depleted hematopoietic stem cell transplantation for hematological malignancies
EUROPEAN HEMATOLOGY ASSOCIATION
Ferrata Storti Foundation
Letizia Muccio,1 Alice Bertaina,2 Michela Falco,3 Daniela Pende,4 Raffaella Meazza,4 Miguel Lopez-Botet,5 Lorenzo Moretta,6 Franco Locatelli,2,7 Alessandro Moretta,1* and Mariella Della Chiesa1*
Dipartimento di Medicina Sperimentale and Centro di Eccellenza per la Ricerca Biomedica, Università di Genova, Italy; 2Dipartimento di Oncoematologia, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ospedale Pediatrico Bambino Gesù, Roma, Italy; 3IRCCS, Istituto Giannina Gaslini, Genova, Italy; 4IRCCS, Azienda Ospedaliera Universitaria San Martino-Istituto Nazionale per la Ricerca sul Cancro, Genova, Italy; 5Universitat Pompeu Fabra and Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain; 6Dipartimento di Immunologia, IRCCS Ospedale Pediatrico Bambino Gesù, Roma, Italy; and 7Dipartimento di Scienze Pediatriche, Università di Pavia, Italy 1
*These authors contributed equally to this study
Haematologica 2016 Volume 101(3):371-381
ABSTRACT
W
e analyzed the impact of human cytomegalovirus infection on the development of natural killer cells in 27 pediatric patients affected by hematological malignancies, who had received a HLA-haploidentical hematopoietic stem cell transplantation, depleted of both α/β+ T cells and B cells. In line with previous studies in adult recipients of umbilical cord blood transplantation, we found that human cytomegalovirus reactivation accelerated the emergence of mature natural killer cells. Thus, most children displayed a progressive expansion of a memory-like natural killer cell subset expressing NKG2C, a putative receptor for human cytomegalovirus, and CD57, a marker of terminal natural killer cell differentiation. NKG2C+CD57+ natural killer cells were detectable by month 3 following hematopoietic stem cell transplantation and expanded until at least month 12. These cells were characterized by high killer Ig-like receptors (KIRs) and leukocyte inhibitory receptor 1 (LIR-1) and low Siglec-7, NKG2A and Interleukin-18Rα expression, killed tumor targets and responded to cells expressing HLA-E (a NKG2C ligand). In addition, they were poor Interferon-γ producers in response to Interleukin-12 and Interleukin-18. The impaired response to these cytokines, together with their highly differentiated profile, may reflect their skewing toward an adaptive condition specialized in controlling human cytomegalovirus. In conclusion, in pediatric patients receiving a type of allograft different from umbilical cord blood transplantation, human cytomegalovirus also induced memory-like natural killer cells, possibly contributing to controlling infections and reinforcing anti-leukemia effects.
Introduction Natural killer (NK) cells are innate lymphocytes that play an important role in anti-viral and anti-tumor responses.1 Their function is finely regulated by an array of both activating and inhibitory surface receptors2-4 and can be strongly influenced by several other factors, such as exposure to cytokines and/or PAMPs,5 developmental stage,6 and licensing.7,8 A fundamental role is played by HLA-class I specific haematologica | 2016; 101(3)
Correspondence: alemoret@unige.it
Received: July 22, 2015. Accepted: December 4, 2015. Pre-published: December 11, 2015. doi:10.3324/haematol.2015.134155
Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/101/3/371
©2016 Ferrata Storti Foundation Material published in Haematologica is covered by copyright. All rights reserved to Ferrata Storti Foundation. Copies of articles are allowed for personal or internal use. A permission in writing by the publisher is required for any other use.
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inhibitory receptors including: killer Ig-like receptors (KIRs) distinguishing among allotypic determinants of the HLA-A, -B and -C;9 the HLA-E-specific CD94/NKG2A heterodimer10 and the leukocyte inhibitory receptor 1 (LIR-1 or ILT2) broadly recognizing HLA class I alleles.11 Activating KIRs, as well as CD94/NKG2C, represent the activating counterpart of HLA-I specific inhibitory receptors, although the ligand specificity is known only for selected receptors (i.e. KIR2DS1, KIR2DS4 and CD94/NKG2C).10,12-14 Since NK cells are the first lymphocyte population to emerge after hematopoietic stem cell transplantation (HSCT), their role in early recovery of immunity after the allograft is considered crucial, contributing to protection from both tumor recurrence and viral infections before the full restoration of T cell immunity. In KIR/KIR-L mismatched haplo-HSCT recipients, alloreactive NK cells, generated 6-8 weeks after HSCT,15 are capable of killing residual tumor cells, thus critically improving patients outcome.16,17 The first wave of NK cells after HSCT is represented by immature CD56bright CD94/NKG2Abright NK cells, while more differentiated CD56dim KIR+ NKG2A- NK cells, containing alloreactive NK cells, only emerge later.15,18,19 To reduce the time window required for fully competent NK cell generation, a new method of graft manipulation has been developed and applied; this approach is based on the elimination of αβ+ T cells (to prevent graft-versus-host disease, GvHD) and of B cells (to avoid EBV-related posttransplant lymphoproliferative disorders). Notably, together with high numbers of CD34+ HSC, this graft contains donor-derived, mature NK cells and γδ T cells20 which may confer prompt protection against both leukemia recurrence and infections.21 As recently shown, NK cell reconstitution after HSCT can be highly accelerated by human cytomegalovirus (HCMV) infection/reactivation.22,23 Indeed, in patients receiving umbilical cord blood transplantation (UCBT), HCMV infection induced a rapid development of mature NK cells characterized by the KIR+NKG2A- phenotype. Importantly, these cells expressed NKG2C. Although the exact mechanism(s) involved in HCMV-induced NKG2C+ NK cell expansion has not been clarified, it is likely that NKG2C may play a role in HCMV recognition and in promoting the expansion and/or maturation of NKG2C+ cells,24 as well as in the control of HCMV infection, as suggested in the case of a T cell deficient patient.25 In addition, a correlation between early HCMV reactivation and a reduced incidence of leukemia relapse has been reported in adult patients with acute myeloid leukemia (AML) receiving allo-HSCT.26 In the present study, we analyzed the impact of HCMV reactivation on the development of NK cells in a cohort of pediatric patients affected by hematological malignancies who received α/β+ T cell- and B cell-depleted HSCT. We observed a great expansion of memory-like NK cells in HCMV-reactivating/infected patients that express NKG2C, a putative receptor for HCMV and CD57, a marker of terminal differentiation.27,28
Methods Patients and samples A cohort of 27 pediatric patients affected by hematological malignancies (mainly acute lymphoblastic leukemia, ALL) was 372
enrolled in a phase I/II trial (Clinical Trials.gov Identifier NCT01810120) and received HLA-haploidentical HSCT after removal of both αβ+T cells and CD19+ B cells.20 Patients were transplanted between November 2010 and May 2012. Among them, 13 experienced HCMV infection after transplantation. Of these 13 patients, 12 had a positive HCMV serology, and 1 had a negative HCMV serology. However, for the sake of brevity, we consider all 13 patients together as the HCMV-reactivating group throughout this article. The clinical characteristics of patients and details on graft composition are summarized in the Online Supplementary Table S1. The assessment of HCMV serology, episodes of HCMV infection/reactivation and therapy are detailed in the Online Supplementary Methods and summarized in the Online Supplementary Table S2. Peripheral blood samples were collected at 1, 3, 6 and 12 months after transplantation. Peripheral blood mononuclear cells (PBMC) were separated from blood samples by ficoll-hypaque gradients (Sigma-Aldrich, St. Louis, MO, USA) and used directly for flow cytometry analyses and functional assays, or frozen and subsequently thawed for further investigations, whenever indicated. NK cell reconstitution was analyzed at the above time points, from month 1 to month 12 for 21 patients, and from month 1 to month 6 for 6 patients. PBMC collected from adult healthy donors (HD) were used as controls. Frozen samples from HSC donors peripheral blood (PB) or leukapheresis were also analyzed. To compare NK cell subsets differentiation, PBMC from 10 pediatric UCBT recipients and 5 pediatric recipients of positively selected CD34+HSC from a HLA-haploidentical parent were also analyzed. Patients were transplanted at the Bambino Gesù Children’s Hospital, Rome, Italy. Patients’ parents gave their informed consent to participation in this study, which was approved by the Azienda Ospedaliera Universitaria San Martino (Genoa, Italy), by the University of Genoa (Genoa, Italy) and by the Bambino Gesù Children’s Hospital (Rome, Italy) ethics committees and was conducted in accordance with the tenants of the Declaration of Helsinki.
Monoclonal antibodies and flow cytometry, functional assays, KIR-ligands and KIR genes profile analyses and statistical analysis See Online Supplementary Methods for details.
Results HCMV reactivation/infection accelerates NK cell maturation in αβ+T/B cell-depleted HSCT pediatric patients We analyzed NK cell reconstitution in 27 pediatric patients undergoing αβ+T/B cell-depleted HSCT and compared, at different time intervals post-HSCT, data in children who experienced HCMV reactivation (or primary infection in 1 case) (n=13) with those of children who did not (n=14). In all cases, reactivation/infection occurred within month 2 after HSCT and the virus was cleared by month 6. The cells infused with this type of transplantation contain not only CD34+ HSC, but also donor-derived NK and γδ T cells (see Online Supplementary Table S1 for details). Thus, at early time points after transplantation, peripheral blood NK cells contain mature NK cells together with HSC-derived NK cells. Although, due to technical limitahaematologica | 2016; 101(3)
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tions of NKG2C+ NK cells and were also characterized by high percentages of NKG2A-KIR+ NK cells (see dots in individual plots in Figure 1A,C).
tion, the mature NK cells could not be distinguished from de novo generated NK cells, a remarkable difference could be detected between patients who either did or did not reactivate HCMV. HCMV reactivation/infection accelerated the differentiation of mature NK cells, as shown by the higher frequency of KIR+NKG2A- NK cells by month 3 after HSCT in HCMVreactivating patients (Figure 1A). Major differences emerged at 6 months after HSCT between HCMV-reactivating and non-reactivating patients (two representative patients are shown in Figure 1B). In line with previous studies,22,23,29 HCMV reactivation induced a strong imprinting in NK cell development not only by accelerating KIR+NKG2A- NK cell differentiation, but also by inducing a remarkable increase of CD56dim NKG2C+ NK cells (Figure 1C,D). Notably, two patients in the HCMV-reactivating group did not expand NKG2C+ NK cells, while two others in the HCMV non-reactivating group developed high propor-
In line with our previous study showing the emergence of high proportions of hypofunctional and phenotypically aberrant CD56-CD16+ NK cells in HCMV-reactivating adult recipients of UCBT,22 in our pediatric cohort we also found the presence of this peculiar NK cell subset in significantly higher frequencies in HCMV-reactivating patients than in non-reactivating ones (Figure 2A). However, the proportion of these cells at month 6 was significantly lower in the αβ+T/B cell-depleted cohort of HCMV-reactivating patients (Figure 2B and Online Supplementary Figure S1) than in the other two cohorts analyzed in parallel (namely, one undergoing UCBT and the other receiving only megadoses of positively selected CD34+ cells from haploidentical donors).
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Figure 1. HCMV induces rapid differentiation of NKG2A-KIR+ NKG2C+ NK cells in patients receiving αβ+T/B cell-depleted haplo-HSCT. Freshly collected PBNK cells from the various patients were analyzed by multicolor immunofluorescence and FACS analysis at different time intervals after HSCT. NK cells were gated from PBMC samples as CD3-CD19- lymphocytes. In (A) the expression of NKG2A in combination with KIRs was analyzed and the percentages of NKG2A-KIR+ NK cells in patients who did (empty circles, n=13) or did not (filled black squares, n=14) experience HCMV after transplantation are reported at 1, 3, 6 and 12 months after HSCT. 95% CI for the mean and statistical significance are indicated (*P<0.05; ** P<0.01; *** P<0.001). In (B) reciprocal expression of NKG2A and KIR are shown for two representative patients, one reactivating (left panel) and the other non-reactivating HCMV (right panel), at 6 months after transplantation. The percentage of NKG2AKIR+ NK cells is indicated in the lower right quadrant. In (C), after gating on CD56dim NK cells, the percentages of CD56dim NK cells expressing NKG2C are reported, at the different time points, in patients experiencing (empty circles, n=13) or not (filled black squares, n=14) HCMV reactivation after transplantation. 95% CI for the mean and statistical significance are indicated. In (D) NK cells from two representative patients are shown at 6 months after HSCT. The percentages of NKG2C+ NK cells are depicted in the upper right quadrants. Numbers in brackets represent the percentages of NKG2C+ NK cells by gating on the CD56dim NK cell subset.
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High proportions of “memory-like” NKG2C+ CD57+ CD56dim NK cells develop in patients experiencing HCMV reactivation In both solid organ transplanted patients and HSCT recipients, HCMV infection can induce the expansion of NKG2C+ CD57+ NK cells.23,30 This subset is present at variable proportions, also in HCMV-seropositive (HCMV+) healthy individuals,31 and may possibly be endowed with “memory” properties. Thus, we analyzed the development of such NKG2C± CD57± NK cell subsets in our cohort of patients. In Figure 3A, the distribution of different NKG2C/CD57 NK cell subsets (gated on CD56dim NK cells) is reported at different time points after HSCT for patients who did or did not reactivate HCMV (the gating strategy is shown in Online Supplementary Figure S2A). By month 3, a substantial increase of NKG2C+CD57+ NK cells was detectable in HCMV-reactivating patients. More marked differences were found at month 6, when much higher proportions of NKG2C+CD57+ cells, paralleled by a sharp decrease of NKG2C- CD57- NK cells, were detectable in HCMV-reactivating patients. Figure 3B shows data from two representative patients, one reactivating and the other not reactivating HCMV. A progressive expansion of the NKG2C+CD57+ NK cell subset (gating on the CD56dim subset) can be observed in the patient reactivating HCMV. Further analysis of PBNK cells from HCMV+ and HCMV- healthy donors (HDs) (Figure 3C and Online Supplementary Figure S2B) showed that the NKG2C+CD57+ NK cell subset was present in significantly lower frequencies in HCMV+ HDs than in HCMV-reactivating HSCT patients, both at month 6 and 12 (Online Supplementary Figure S2C). When PBNK cells from the various HSC donors of HCMV-reactivating patients were analyzed, we found that NKG2C+CD57+ NK cells were present at a low medi-
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an frequency (11%, Online Supplementary Figure S2D) although all the HSC donors analyzed were HCMV+ (Online Supplementary Table S1). Thus, 3-6 months after HSCT, in most HCMV-reactivating patients, the frequency of NKG2C+CD57+ NK cells exceeded that measured in their respective donors (Figure 3B, Online Supplementary Figure S2D and data not shown). In view of this observation, we can suggest that NKG2C+CD57+ NK cells emerging in HCMV-reactivating patients are mostly de novo generated.
Memory-like NK cells from recipients of αβ+T/B celldepleted HSCT are characterized by the KIR+ NKG2ASiglec7- LIR1+/- IL-18Ralow NCRlow surface phenotype
We analyzed in more detail the phenotype of the HCMV-induced NKG2C+CD57+ CD56dim NK cell subset, focusing on HCMV-reactivating patients at month 6 after HSCT, i.e. when these cells were maximally expanded. HCMV+ HDs were analyzed for comparison. Representative dot plots indicating the gating strategy are shown in Figure 4A. Both NKG2C+CD57- and NKG2C+CD57+ NK cells were mostly KIR+NKG2A- in both transplanted patients and HCMV+ HDs (Figure 4B). Notably, expanded NKG2C+ KIR+ NK cells preferentially expressed KIRs specific for the respective donor KIR ligands (Online Supplementary Figure S3 and data not shown). As previously reported in UCBT patients, Siglec-7 was significantly down-regulated in NKG2C+ NK cells isolated from patients, but only marginally in NK cells from HCMV+ HDs (Figure 4B).22 Importantly, Siglec-7 down-regulation was correlated with HCMV recurrence. Indeed, non-reactivating patients did not significantly down-regulate this marker at any time points after HSCT (Online Supplementary Figure S4), with the exception of the two outliers mentioned before. The analysis of LIR-1 in our cohort of patients revealed
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Figure 2. Modest accumulation of aberrant CD56-CD16+ NK cells in patients undergoing HCMV reactivation after αβ+ T/B cell-depleted transplant as compared to patients receiving UCBT or purified CD34+ cells. PBNK cells from the various patients were analyzed for the expression of CD56 and CD16 at 1, 3, 6 and 12 months after HSCT. In (A), the percentages of CD56-CD16+ NK cells in patients experiencing HCMV (empty circles, n=13) or not (filled black squares, n=14) after transplantation are reported at the different time points. 95% CI for the mean and statistical significance are indicated (*P<0.05; ** P<0.01; *** P<0.001). In (B), the percentages of CD56-CD16+ NK cells measured in αβ+ T/B cell-depleted haplo-HSCT (n=13) patients are compared to those measured in two different groups of pediatric patients reactivating HCMV after transplantation who received either cord blood transplantation (UCBT) (n=5) or positively selected CD34+ HSC (CD34+ haploHSCT) (n=5). The values reported correspond to 6 months after HSCT for all patients. 95% CI for the mean and statistical significance are indicated.
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a more frequent expression in NKG2C+CD57+ than in NKG2C+CD57- NK cells. This different expression was more evident in HCMV+ HDs, where most NKG2C+CD57+ NK cells were LIR-1+ (Figure 4B), in line with previous studies.31 The NKG2C+CD57- and NKG2C+CD57+ NK cells subsets of both HSCT recipients and HDs displayed remarkable differences in the expression levels of IL-18Rα (Figure 4C). This is in line with previous reports that terminally differentiated CD57+ NK cells express low levels of different cytokine receptors.27,28 Interestingly, both the proportion of IL-18Rα (data not shown) and the median surface intensity (Figure 4C) were lower in patients than in HDs in all CD56dim NK cell subsets. Figure 4C also shows that NKG2C+CD57+ (both in patients and HDs) display lower expression of NKp46 and NKp30 as compared to NKG2C+
CD57- NK cells. No significant differences were detected for other triggering receptors including CD16, DNAM1, NKG2D and 2B4 (Figure 4C and Online Supplementary Figure S5).
Memory-like NKG2C+CD57+ NK cells respond efficiently to tumor cells and HLA-E+ targets, but show impaired IFN-γ production in response to rhIL-12 plus rhIL-18 stimulation. We next evaluated the functional capabilities of the expanded NKG2C+CD57+ memory-like NK cell subset (in comparison with the other subsets identified by the expression of NKG2C/CD57) by assessing degranulation and IFN-γ production upon K562 stimulation and/or exposure to cytokines. Figure 5A (left panel) shows that the different NK subsets (analyzed by gating on CD56dim NK
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Figure 3. Different NK cell subsets identified by NKG2C/CD57 expression: progressive expansion of memory-like NKG2C+CD57+ NK cells in patients reactivating HCMV after HSCT. PBNK cells from the various patients were analyzed for the expression of NKG2C and CD57 at 1, 3, 6 and 12 months after HSCT. After gating on CD56+CD3-CD19- lymphocytes, CD56dim NK cells were evaluated. In (A) the size of the different subsets identified by NKG2C and CD57 on CD56dim NK cells (i.e. NKG2C+CD57-, NKG2C+CD57+, NKG2C-CD57- and NKG2C-CD57+, indicated for brevity as 2C and 57) in patients experiencing (empty circles, n=13) or not (filled black squares, n=14) HCMV reactivation after transplantation are shown from month 1 to month 12 after HSCT. 95% CI for the mean and statistical significance are indicated (*P<0.05; ** P<0.01; *** P<0.001). In (B), reciprocal expression of NKG2C and CD57 on CD56dim NK cells from two representative patients, one reactivating (upper panels), and the other non-reactivating HCMV (lower panels), is shown at different time intervals after transplantation in comparison to NK cells isolated from their respective HSC donors (left panels). The percentages of positive cells are indicated in each quadrant. In (C) reciprocal expression of NKG2C and CD57 on CD56dim NK cells from three HDs (one HCMV-, left panel; two HCMV+, middle and right panels) are shown. The percentages of positive cells are indicated in each quadrant.
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cells) displayed comparable levels of CD107a degranulation, further increased by overnight exposure to rhIL-15. Figure 5A (right panel) shows that, in the absence of exogenous rhIL-15, PBNK cells from HCMV+ HDs displayed slightly higher degranulation as compared to patients. Regarding the production of IFN-γ, upon stimulation with K562, both the NKG2C+CD57- and NKG2C+CD57+ NK cell subsets (both in patients and HDs) were slightly better producers than NKG2C- NK cell subsets. Also, IFN-
γ production was increased upon overnight exposure to rhIL-15 (Figure 5B). In parallel experiments, the ability of the various NKG2C/CD57 subsets to produce IFN-γ was assessed after overnight incubation in the presence of rhIL-12 plus rhIL-18. Remarkably, under these conditions, NKG2C+CD57+ memory-like NK cells from patients resulted as poor IFN-γ producers (Figure 5B), while the less differentiated NKG2C-CD57- NK cells were the best producers. Intermediate levels of IFN-γ were produced by the
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Figure 4. NKG2C+ CD57+ NK cells expanded in patients receiving αβ+ T/B cell-depleted HSCT and experiencing HCMV are characterized by a KIR+ NKG2A- Siglec7- LIR1+/IL18Ralow NCRlow surface phenotype. PBNK cells collected at 6 months after HSCT from HCMV-reactivating patients and from HCMV+ HDs were analyzed for the expression of the indicated surface markers, after gating on the different CD56dim NK cell subsets identified by NKG2C and CD57 (i.e. NKG2C+CD57-, NKG2C+CD57+, NKG2C-CD57- and NKG2CCD57+, indicated for brevity as 2C and 57). In (A) the gating strategy is shown for a representative patient and a donor, in (B) 95% CI for the mean percentage of positive CD56dim NK cells is shown for HCMV-reactivating patients (black bars, n=8) and for HCMV+ HDs (grey bars, n=7). In (C) 95% CI for the median fluorescence intensity (mfi) is similarly shown. Statistical significance was calculated for NKG2C+CD57- vs. NKG2C+CD57+ NK cell subsets for each surface marker (*P<0.05; ** P<0.01; *** P<0.001).
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NKG2C+CD57- and NKG2C-CD57+ NK subsets. These differences in response to rhIL-12 plus rhIL-18 might reflect, at least in part, the levels of expression of IL-18Rα (Figure 4C) in the different NKG2C/CD57 subsets. It is of note, in this context, that all the NK subsets analyzed expressed IL-12R at similar levels (data not shown). A lower production of IFN-γ, in response to rhIL-12 plus rhIL-18, was also detected in NKG2C+CD57+ NK cells from HDs. However, down-regulation of IL-18Rα expression was less marked and IFN-γ production was only partially compromised. At variance with IFN-γ production, exposure to rhIL-12 plus rhIL-18 induced comparable levels of degranulation in the different NKG2C/CD57 NK cell subsets after stimulation with K562 (Online Supplementary Figure S6). In another set of experiments, NKG2C+ NK cells from HCMV-reactivating patients were assessed for degranulation in reverse ADCC assays, either in the presence or in the absence of specific anti-NKG2C mAb. Antibodymediated triggering of NKG2C efficiently induced degranulation in both NKG2C+CD57- and NKG2C+CD57+ NK cell subsets at levels comparable to those induced by anti-
CD16 mAbs (Figure 6A). The simultaneous mAb-mediated triggering of NKG2C and LIR-1 did not affect degranulation induced by anti-NKG2C mAb alone (data not shown). On the contrary, the simultaneous mAb-mediated triggering of NKG2C and KIR almost completely abolished NKG2C-triggered degranulation (Figure 6A). Similar results were obtained in the analysis of HCMV+ HD NK cells (Online Supplementary Figure S7). Next, we examined the ability of NKG2C+ NK cell subsets (either CD57+ or CD57-) to recognize HLA-E, a specific ligand of NKG2C,10 by assessing their degranulation, in CD107a assays, in the presence of the HLA-E expressing (transfected) 721.221 cell line (221.AEH).32 As shown in Figure 6B, both NKG2C+CD57- and NKG2C+CD57+ NK cell subsets displayed enhanced degranulation as compared to 221wt (i.e. lacking HLA-E surface expression, Online Supplementary Figure S8A), indicating that NKG2C recognizes HLA-E on target cells and induces activation/degranulation of NKG2C+ cells. This finding was further substantiated by masking experiments performed using a specific anti-NKG2C mAb. In the presence
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Figure 5. Memory-like NKG2C+CD57+ NK cells from patients are capable of both degranulating and producing IFN-γ in response to tumor targets, but show impaired IFN-γ production in response to rhIL-12 plus rhIL-18 stimulation. Freshly drawn PBMC from HCMV-reactivating patients (n=8) at 6 months after HSCT, and from HCMV+ HDs (n=6) were cultured overnight in the presence or in the absence of rhIL-15 or rhIL-12 plus rhIL-18. Then PB cells were incubated with either medium alone (data not shown) or K562 for 3 hours. In (A), after incubation with K562, CD107a expression was evaluated in the different NKG2C/CD57 CD56dim NK cell subsets shown in Fig.4. Black bars represent cells cultured overnight in medium alone, while white bars represent cells cultured overnight in rhIL-15. In B), parallel cultures were assessed for intracellular IFN-γ production upon overnight culture with medium alone (black bars) or rhIL-15 (white bars) followed by stimulation for 3 hours in the presence of K562 or upon overnight exposure to rhIL-12 plus rhIL-18 (grey bars). 95% CI for the mean of CD107a/IFN-γ positive NK cells is shown for each subset. Left panels show data relative to patients and right panels show data relative to HDs.
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of this mAb, degranulation of NKG2C+ NK cells in response to 221.AEH was inhibited (data not shown). On the contrary, NKG2C- CD57- and NKG2C-CD57+ NK cells that express high levels of NKG2A (Figure 4B), displayed decreased degranulation against 221.AEH than 221wt, in line with the inhibitory effect mediated by NKG2A upon interaction with HLA-E. Similar results were obtained in parallel experiments with HCMV+ HDs. However, in this case, NKG2C+ NK cells displayed a lesser increase in degranulation when comparing 221wt to 221.AEH (Online Supplementary Figure S8B). This is likely due to the higher levels of NKG2A expressed on the surface of NKG2C+ NK cells from HDs (Figure 4B). In line with this possibility, when degranulation was evaluated after gating on NK cells lacking NKG2A (Online Supplementary Figure S8C), the increment became significant also in HDs (Online Supplementary Figure S8D).
Discussion NK cells play a crucial role in early immunity following HSCT in leukemic patients;33 therefore the possibility to improve and to control NK cell maturation and function in HSCT recipients may result in important clinical benefits. We have recently documented that in UCBT recipients HCMV infection/reactivation early after HSCT results in a remarkable acceleration of NK cell reconstitution and maturation.22 In agreement with this study, we also found that in pediatric patients receiving a different cell composition in the HLA-haploidentical HSCT setting, depleted of TCR-αβ+T and CD19+ B cells, HCMV reactivation could accelerate the development of mature NK cells character-
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ized by the KIR+NKG2A-NKG2C+Siglec7- phenotypic signature. This is noteworthy, as compared to pediatric patients receiving either UCBT or positively-selected CD34+ HSC from a HLA-haploidentical relative, this cohort of patients had reduced proportions of hypofunctional CD56-CD16+ NK cells. Since these cells usually develop in subjects infected by HCMV when T cell immunity is impaired,22,34 their reduced presence suggests that NK and γδ T cells contained in the graft may exert a protective role against severe infections. It is well documented that HCMV infection influences NK cell maturation and induces a long-term reconfiguration of the NK cell receptor repertoire.35-37 This imprinting, induced by HCMV infection, also suggested that NK cells might keep the memory of past infections, thus sharing features with the cells of the adaptive immune system. Indeed, in mice, the expansion and persistence of memory Ly49H+ NK cells, endowed with specific anti-MCMV properties, has been clearly documented.38 It is possible that NKG2C+ NK cells that expand in humans after HCMV infection and preferentially acquire CD57 may represent the human counterpart of murine memory Ly49H+ NK cells. In the present study, we show that most pediatric patients reactivating HCMV display a progressive expansion of this putative memory NK cell population expressing both NKG2C and CD57. The frequency of NKG2C+ NK cells (both CD57+ and CD57-) at 6 and 12 months after HSCT was higher than in both adult HCMV-seropositive HDs and in the respective HSC donors (Online Supplementary Figure S2, Figure 3B). This may reflect a recent or ongoing HCMV infection occurring in patients, but could also depend on the status of immunosuppression allowing a better imprinting of NK cells.5,37 Notably,
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Figure 6. Both anti-NKG2C mAbs and HLA-E+ target cells efficiently trigger degranulation of the memory-like NKG2C+CD57+ NK cell subset of HCMV-reactivating transplanted patients. Thawed PBMC from HCMV-reactivating patients, collected at 6 months after HSCT, were cultured in the presence of rhIL-15. In (A), after overnight culture, cells were incubated for 1h with the FcγR+ murine cell line p815 either in the presence or in the absence of anti-CD16, anti-NKG2C and anti-KIR specific mAbs alone or in combination (for each patient an anti-KIR mAb recognizing the KIR that was most expanded on NKG2C+CD57+ cells was used, i.e the KIR specific for the corresponding donor KIR ligand). CD107a expression is shown for each NK cell subset as 95% CI for the mean summarizing data for n=7 HCMV-reactivating HSCT patients. (dark bars: NKG2C+CD57-, light grey: NKG2C+CD57+, dark grey: NKG2C-CD57-, white bars: NKG2C-CD57+). CTR indicates NK cells cultured in the presence of p815 and in the absence of mAbs. In B), after 3 days of culture with rhIL-15, cells were incubated in medium alone (black bars) or with 221wt (light grey bars) or with 221 expressing HLA-E (221.AEH, dark grey bars). CD107a expression is shown for each NK cell subset as 95% CI for the mean summarizing data for n=5 HCMV-reactivating HSCT patients. Statistical significance is indicated (*P<0.05).
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we found a correlation between the duration of the antiviral treatment (Online Supplementary Table S2) and the percentage of Siglec-7 negative NK cells, which was only a tendency at month 3 (Spearman r=0,47 P=0,14) and became significant at month 6 (Spearman r=0,6 P=0,04), possibly indicating that the loss of Siglec-7 might be a marker of an efficient anti-HCMV response. The phenotypic characterization of NK cells developing in our patients reactivating HCMV revealed that NKG2C+CD57+ NK cells represent a highly differentiated subset, displaying lower levels of expression of Siglec-7, IL-18Rα, NKG2A, NKp46 and NKp30 and higher levels of KIRs and LIR-1 than NKG2C+CD57- NK cells. It is likely that NKG2C+CD57- NK cells first emerge in response to HCMV infection and rapidly shift to a more differentiated CD57+LIR-1+ phenotype. It cannot be ruled out that LIR-1 expression may be progressively acquired by NKG2C+ NK cells following HCMV infection, and represent a viral evasion strategy. Indeed, the HCMV-derived viral glycoprotein UL-18 is a high affinity ligand for LIR-1.39 Although in reverse ADCC experiments LIR-1 did not substantially inhibit NKG2C activation, it is possible that, in vivo, UL-18 expressed by infected cells may efficiently engage LIR-1 and weaken NKG2C-mediated signaling. Whether the NKG2C+CD57+ NK cell subset can persist for a long time after resolution of infection, or is continuously replenished by differentiating NKG2C+CD57- NK cells36,40 is still unknown. In support of the first hypothesis is the finding that HCMV infection can induce resistance to cell death in NK cells developing after UCBT.41 In some patients, we could observe an increase in NK cell numbers after HCMV infection (Online Supplementary Table S3) that was followed by the acquisition of a memory-like phenotype, suggesting that both proliferation and differentiation are likely contributing to the generation of this subset. In our cohort of patients, the expanded memory-like NKG2C+CD57+ NK cells were functionally competent in terms of cytokine production and cytotoxicity/degranulation in response to tumor targets. On the other hand, they displayed poor capabilities of producing IFN-γ in response to rhIL-12 plus rhIL-18. This may be consequent, at least in part, to the reduced expression of IL-18Rα. However, we cannot exclude the involvement of other mechanisms affecting the signaling pathway downstream of the receptors. The diminished responsiveness to cytokines of NKG2C+CD57+ NK cells may reflect their specialization in controlling HCMV infection and their memory-like signature, in agreement with recent findings in mice.42 Indeed, in mice, MCMV-induced memory Ly49H+ NK cells show an impaired response to cytokines alone (IL-12 and IL-18). Whether this poor response was determined by the reduced expression of cytokine receptors, or by an altered signaling downstream of the receptors, has not been established. On the other hand, these MCMV-induced memory Ly49H+ NK cells were characterized by a higher responsiveness to m157 antigen (the specific viral ligand for Ly49H) in the presence of cytokines, as compared to naive Ly49H+ NK cells.42 In this context, we also show that cytokine-treated NKG2C+ CD57+ NK cells can efficiently degranulate in response to HLA-E+ targets (HLA-E is a ligand of NKG2C) (Figure 6).10 It is of note that differently to mice, the putative viral ligands recognized by NKG2C, expressed by HCMVinfected cells, have not been identified. They may be HLAE molecules bound to viral peptides (e.g. UL40-derived peptides),37,43 as well as other undefined molecules. haematologica | 2016; 101(3)
Interestingly, two patients receiving grafts from HCMVseropositive donors containing donor-derived NKG2C+ NK cells did not experience HCMV reactivation after transplantation, but displayed a significant expansion of highly differentiated NKG2C+ NK cells. These data suggest that donor-derived transplanted NK cells may persist in the recipient and favor anti-viral responses. Since these patients received grafts from seropositive donors, it is likely that transplanted NKG2C+ NK cells, primed by a previous encounter with HCMV in the donor, had undergone expansion in response to viral antigens present in low levels in infected peripheral tissues of the recipient (subclinical HCMV reactivation). This would be in line with previous data in recipients receiving T cell-replete HSCT.44,45 Interestingly, one of these two patients (pt #26) expanding NKG2C+ NK cells, experienced infection with viruses other than HCMV, namely adenovirus and BK, early after HSCT (Online Supplementary Table S1). Thus, it cannot be ruled out that these viral infections could have favored the expansion of HCMV-primed, donor-derived NKG2C+ NK cells, in agreement with a previous study showing that hantavirus infection could induce the expansion of NKG2C+ NK cells in HCMV+ individuals.46 The response to HCMV may also be influenced by the number of donor-derived mature NK cells contained in the graft, which is highly variable among patients receiving this type of HSCT (Online Supplementary Table S1). It is possible that protection from HCMV reactivation is achieved only with suitable numbers of NKG2C+KIR+ NK cells in the graft. Notably, the two patients displaying high frequencies of NKG2C+ NK cells and the absence of HCMV reactivation received grafts containing high numbers of donor-derived NK cells (pts #26 and #27, Online Supplementary Table S1). Thus, in HCMV-reactivating patients, as well as in the few non-reactivating ones who were transplanted with seropositive donors, the proportions of mature NKG2C+CD57+ NK cells were significantly higher than those of non-reactivating patients. The capability of killing patient leukemia blasts and of protecting patients from acute HCMV infection, and also from other viral infections, should be investigated in assays against autologous leukemia blasts and autologous infected targets. However, it is conceivable that these cells, which respond efficiently against tumors and HLA-E+ targets, may play a beneficial role. Indeed AML blasts (and to a lesser extent ALL blasts) express HLA-E at significant levels47,48 and could be directly targeted by NKG2C+NK cells (especially when a KIR/KIRL mismatch occurs in the graft-versus-host direction). Along this line, a protective role for NKG2C+CD57+ CD56dim NK cells emerging after HSCT in HCMV-reactivating recipients has been suggested recently, although in a different transplantation setting.49 Interestingly, it has recently been shown that NKG2C+CD57+ NK cells, isolated from HCMV+ individuals, are characterized by an epigenetic remodeling at the IFN-γ locus, which is similar to the one found in memory CD8+ T cells or Th1 cells. This epigenetic imprinting could be responsible for the enhanced IFN-γ production observed in NKG2C+ NK cells, and may be involved in the regulation of NK cell adaptive immune system mechanisms.40 Moreover, very recently, an altered pattern of expression of signalling proteins has been described in HCMV-induced memory-like NK cells.50 In particular, HCMV infection could promote the generation of adap379
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tive NK cells that lack the expression of FcεRγ, EAT-2 and SyK. These molecular characteristics depend on given DNA methylation patterns that memory NK cells share in part with CTLs. Such epigenetic alterations could be responsible for the functional skewing shown by HCMVinduced NK cells that appear to be specialized in target cell recognition, especially via ADCC mechanisms, and impaired in cytokine-induced responses (with IL-12 and IL-18 at least). Indeed, previous studies showed that memory-like NKG2C+CD57+NK cells, isolated from HCMV+ HDs, can efficiently kill HCMV-infected targets in the presence of anti-HCMV antibodies, i.e. through CD16 cross-linking.31,51 Notably, in HSCT recipients, memorylike NKG2C+CD57+ NK cells displayed efficient mAbmediated CD16 triggering (Figure 6A) and could kill infected cells via ADCC. The precise definition of the signals capable of inducing this selective imprinting confined to NKG2C+ NK cells would be important for providing a molecular basis for the regulation and, possibly, the manipulation of adaptive features in innate cells.
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ARTICLE EUROPEAN HEMATOLOGY ASSOCIATION
Stem Cell Transplantation
Ferrata Storti Foundation
Polymorphism in TGFB1 is associated with worse non-relapse mortality and overall survival after stem cell transplantation with unrelated donors
Esteban Arrieta-Bolaños,1,2,3 Neema P. Mayor,1,2 Steven G.E. Marsh,1,2 J. Alejandro Madrigal,1,2 Jane F. Apperley,4 Keiren Kirkland,5 Stephen Mackinnon,6 David I. Marks,7 Grant McQuaker,8 Julia Perry,5 Michael N. Potter,9 Nigel H. Russell,10 Kirsty Thomson,11 and Bronwen E. Shaw1,2
Anthony Nolan Research Institute, London, UK; 2Cancer Institute, University College London, UK; 3Centro de Investigaciones en Hematología y Trastornos Afines (CIHATA), Universidad de Costa Rica, San José, Costa Rica; 4Haematology Department, Hammersmith Hospital, London, UK; 5BSBMT Data Registry, Guy’s Hospital, London, UK; 6 Department of Haematology, University College London, Royal Free Campus, UK; 7Adult BMT Unit, University Hospitals Bristol NHS Trust, Bristol, UK; 8Bone Marrow Transplant Unit, Beatson West of Scotland Cancer Centre, Glasgow, UK; 9Section of Haemato-oncology, Royal Marsden Hospital, London, UK; 10Centre for Clinical Haematology, Nottingham University Hospital, and Academic Haematology, Nottingham University Hospitals, UK; and 11Department of Haematology, University College Hospital, London, UK 1
Haematologica 2016 Volume 101(3):382-390
ABSTRACT
ransforming growth factor β-1, encoded by the TGFB1 gene, is a cytokine that plays a central role in many physiological and pathogenic processes. We have sequenced TGFB1 regulatory region and assigned allelic genotypes in a large cohort of hematopoietic stem cell transplantation patients and donors. In this study, we analyzed 522 unrelated donor-patient pairs and examined the combined effect of all the common polymorphisms in this genomic region. In univariate analysis, we found that patients carrying a specific allele, ‘p001’, showed significantly reduced overall survival (5-year overall survival 30.7% for p001/p001 patients vs. 41.6% others; P=0.032) and increased non-relapse mortality (1-year nonrelapse mortality: 39.0% vs. 25.4%; P=0.039) after transplantation. In multivariate analysis, the presence of a p001/p001 genotype in patients was confirmed as an independent factor for reduced overall survival [hazard ratio=1.53 (1.04-2.24); P=0.031], and increased non-relapse mortality [hazard ratio=1.73 (1.06-2.83); P=0.030]. In functional experiments we found a trend towards a higher percentage of surface transforming growth factor β1-positive regulatory T cells after activation when the cells had a p001 allele (P=0.07). Higher or lower production of transforming growth factor β-1 in the inflammatory context of hematopoietic stem cell transplantation may influence the development of complications in these patients. Findings indicate that TGFB1 genotype could potentially be of use as a prognostic factor in hematopoietic stem cell transplantation risk assessment algorithms.
T
Correspondence: a.madrigal@ucl.ac.uk
Received: August 10, 2015. Accepted: November 20, 2015. Pre-published: November 26, 2015. doi:10.3324/haematol.2015.134999
Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/101/3/382
©2016 Ferrata Storti Foundation Material published in Haematologica is covered by copyright. All rights reserved to Ferrata Storti Foundation. Copies of articles are allowed for personal or internal use. A permission in writing by the publisher is required for any other use.
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Introduction Hematopoietic stem cell transplantation (HSCT) is a medical procedure used to treat malignant and non-malignant diseases of the blood, as well as solid tumors. The outcome of HSCT is influenced both by clinical and genetic factors. Compatibility between the recipient and the donor in terms of HLA is a wellknown limiting factor for the success of allogeneic HSCT.1 In addition, genes other than those of the HLA system, in particular those that are highly polymorphic, have been proposed as potential factors affecting the success of this therapy.2 haematologica | 2016; 101(3)
TGFB1 polymorphism reduces survival after HSCT
One of the genes that are likely to play an important role in the outcome of allogeneic HSCT is TGFB1, which encodes transforming growth factor-β1 (TGF-β1). TGF-β1 is a cytokine that plays a central role in many physiological and pathogenic processes, having pleiotropic effects on cell proliferation, differentiation, migration and survival, as well as being a fundamental component of the immune system. TGF-β1 is likely to be relevant for both therapeutic and pathogenic immune processes associated with the different stages of HSCT.3 Genetic variation resulting in differences in its production and/or function could play a role in the way that this cytokine modifies these immune processes. Regulatory activity for this gene, located at chromosome 19q13.1-q13.3, has been mapped to approximately 3.0 kilobases (kb) from positions -2665 to +423 in its exon 1 (+1 being the translation start site). This region includes two promoter sites, two negative regulatory elements and two enhancers lying upstream of the first promoter.4 Several polymorphisms in TGFB1 regulatory region have been identified, and these are known to cause alterations in cytokine secretion in several settings.4 Previous work allowed for the definition of 17 TGFB1 regulatory region and exon 1 alleles, which are formed by the combination of 18 SNPs and other kinds of variation (Online Supplementary Table S1).4,5 We have recently expanded this inventory of TGFB1 alleles with the discovery of other less common variant combinations.6 The role of polymorphism in TGFB1 in the outcome of HSCT has been examined in some studies.7 However, the results have not been consistent. In this study, we aimed at comprehensively analyzing the role of genetic variation in TGFB1 regulatory region and exon 1 in a large cohort of UD-HSCT recipients and donors. In addition, since regulatory T cells (Treg) are major producers of TGF-β1 and have the unique ability of expressing its latent form on their surface upon stimulation,8 as well as being likely effectors or targets during the immunological events taking place prior, during and after HSCT, we have performed functional assays to further understand the effect of this variation on the way that TGF-β1 is expressed by human regulatory Treg.
Methods Patients, donors, and clinical data Hematopoietic stem cell transplantation patient and donor samples are part of the Anthony Nolan Research Institute’s stem cell transplantation sample repository (www.myresearchproject.org.uk, application number MREC 01/8/31). Healthy volunteer donors were used to obtain mononuclear cells for functional experiments. Patients’ clinical data were collected by the Anthony Nolan Research Institute in collaboration with the British Society for Blood and Marrow Transplantation. All samples were collected according to the Anthony Nolan Research Institute’s review board-approved guidelines and written informed consent was obtained from all participants.
Sequencing of the regulatory region of TGFB1 The 3.0 kb upstream regulatory region of TGFB1 was analyzed for polymorphism by Sanger sequencing, as explained elsewhere.6 Briefly, based on the studies by Shah et al.,4,5 the region extending from -2,664 to +423 according to this gene’s translation start site was sequenced and the sequenced fragments were then analyzed, haematologica | 2016; 101(3)
Table 1. Patients’, donor and transplant characteristics in the hematopoietic stem cell transplantation cohort (n=504*). Patient age (years) Median 27.9 (range 0.4-63.8) 0-20 20-40 40-60 >60 Donor age (years) Median 35.0 (range 19.2-60.4) 0-20 20-40 40-60 >60 Sex (male) Patients Donors Sex matching Patient-donor Male-male Male-female Female-female Female-male HLA-matching 10/10 matched 1 mismatch >1 mismatch Disease AML ALL CML MDS Other1 Disease status Complete remission/chronic phase Other Unknown CMV status Patient(+)-donor(+) Patient(+)-donor(-) Patient(-)-donor(+) Patient(-)-donor(-) Unknown TBI Yes No Unknown SC source BM PB Both Unknown T-cell depletion Yes No Unknown GvHD prophylaxis None Cyclosporin Cyclosporin+MTX Other Unknown Transplant year 1996-2001 2002-2009
N
%
176 201 126 1
34.9 39.9 25.0 0.2
2 369 132 1
0.4 73.2 26.2 0.2
322 382
63.9 75.8
256 66 56 126
50.8 13.1 11.1 25.0
358 100 46
71.0 19.8 9.1
136 155 111 45 57
27.0 30.8 22.0 8.9 11.3
417 77 10
82.7 15.3 2.0
64 88 51 282 19
12.7 17.4 10.1 56.0 3.8
434 55 15
86.1 10.9 3.0
340 159 2 3
67.5 31.5 0.4 0.6
427 22 55
84.7 4.4 10.9
4 131 329 28 12
0.8 26.0 65.3 5.6 2.4
253 251
50.2 49.8
1 Includes secondary acute leukemia, non-Hodgkin lymphoma, primary immune deficiency, bone marrow failure, multiple myeloma, metabolic disease, myeloproliferative neoplasia, biphenotypic acute leukemia, Hodgkin disease, undifferentiated acute leukemia. *18 pairs out of 522 eligible lacked clinical data. AML: acute myeloid leukemia; ALL: acute lymphoid leukemia; CML: chronic myeloid leukemia; BM: bone marrow; CMV: cytomegalovirus; GvHD: graft- versus-host disease; HLA: human leukocyte antigen; MDS: myelodysplastic syndrome; MTX: methotrexate; PB: peripheral blood; SC: stem cell; TBI: total body irradiation.
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E. Arrieta-Bolaños et al.
Table 2. TGFB1 regulatory region and exon 1 allele frequencies found in the UD-HSCT patient-donor cohort.
Allele Frequency p001 p003 p006 p009 p013 p014 Total
0.3004 0.5411 0.0753 0.0000 0.0020 0.0773 0.9961
Patients Copies Carriers 307 553 77 0 2 79 1018
264 415 73 0 2 75
Carrier Frequency frequency 0.5166 0.8121 0.1429 0.0000 0.0039 0.1468
and used to assign a TGFB1 regulatory region and exon 1 allelic genotype4,5 based on the genotypes for 18 known polymorphic positions. In cases where there were theoretical ambiguities, the phase of the relevant polymorphic positions was defined by allelespecific amplification strategies using different primer combinations.6
Cellular assays CD4+CD25– and CD4+CD25+ cells were isolated from peripheral blood mononuclear cells (PBMC) with a human CD4+CD25+ Regulatory T-Cell Isolation Kit (Miltenyi Biotec GmbH, Bergisch Gladbach, Germany). Isolated cell fractions were stained with antibodies against CD4 (PerCP, clone SK3, BD Biosciences, Oxford, UK; APC, clone RPAT4, eBioscience, San Diego, USA), CD127 (FITC, clone eBioRDR5, eBioscience; PerCP, clone eBioRDR5, eBioscience, San Diego, USA), and CD25 (APC, clone 2A3, BD Biosciences, Oxford, UK; PerCP-Cy5.5, clone BC96, BioLegend, San Diego, USA). Surface TGF-β1 expression on isolated Treg was assessed by staining of its latency-associated peptide (αLAP-PE, clone 27232, R&D Systems, Abingdon, UK) on resting and activated CD4+CD25+CD127lo cells. The cells were activated with antibodies against CD3 and CD28 (NA/LE mouse, clones HIT3a and CD28.2, respectively, BD Biosciences, Oxford, UK) at 10 mg/mL. Non-stimulated and platebound antibody-stimulated cells were used as controls.
Statistical analysis The Z-test was used to compare TGFB1 regulatory region and exon 1 allele variant and genotype frequencies between the HSCT patient and donor cohorts (Online Supplementary Table S2). Deviation from Hardy-Weinberg equilibrium was assessed with Fisher’s exact test or χ2 test (Online Supplementary Tables S3 and S4). Detailed information on SNP and allele frequencies and the assignment of TGFB1 regulatory region and exon 1 genotypes is available in the Online Supplementary Appendix. The main clinical end point was overall survival (OS). Secondary end points were event-free survival (EFS), non-relapse mortality (NRM), acute graft-versus-host disease (aGvHD) (grades I-IV, II-IV or III-IV), and relapse. EFS was defined as survival without relapse (an event was death of any cause or relapse). For univariate analysis of time-to-event data (OS, EFS), the Kaplan-Meier method was used. Log rank statistics were used to compare OS and EFS probabilities between groups of interest. The probabilities of NRM and relapse were estimated by the cumulative incidence method, and compared using Gray’s test, with relapse and death without relapse as competing events, respectively. aGvHD frequencies were compared by means of the χ2 test, or by Fisher’s exact test. 384
0.2865 0.5331 0.0867 0.0010 0.0000 0.0877 0.9951
Donors Copies Carriers 294 547 89 1 0 90 1021
256 405 88 1 0 87
Carrier frequency
Z
P
0.4990 0.7895 0.1715 0.0019 0.0000 0.1696
0.7 0.4 0.9 N/A N/A 0.9
0.49 0.72 0.34 N/A N/A 0.39
Multivariate analyses were performed using Cox’s regression (OS, EFS), the Fine-Gray method (NRM, relapse), or logistic regression (aGvHD) as appropriate. Clinical variables with P≤0.2 in univariate models for association with transplant outcome were selected for multivariate analyses. Kruskal-Wallis and Mann-Whitney tests were used to compare Treg LAP expression levels between TGFB1 regulatory region and exon 1 genotype groups.
Results Cohort The cohort was composed of 522 unrelated myeloablative transplants performed between 1996 and 2009. Typing was possible for only patient or donor DNA for 9 and 11 pairs, respectively. Although permission for genetic testing was granted, permission for use of clinical data was not granted in 18 cases [patient TGFB1 genotypes: p001/p003 (n=7), p003/p003 (n=6), p001/p001 (n=2), p006/p014 (n=2), and p014/p014 (n=1)] and these were thus excluded. Consequently, when clinical data were analyzed, the final number of pairs included for patient and donor genotypes were 493 and 495, respectively (‘whole cohort’). The characteristics of the patients, their donors and the transplants are presented in Table 1. T-cell depletion with alemtuzumab was used in 85% of the patients.
Descriptive results for the typing of TGFB1 regulatory region and exon 1 alleles in the patient-donor cohort Only six of the previously reported alleles were seen in the cohort: p001, p003, p006, p009, p013, and p014, four of which were predominant (Table 2). Online Supplementary Table S5 shows the genotype frequencies observed. Neither the allele nor the genotype frequencies differed significantly between patients and donors (Z test; P>0.050). Nine samples (5 donors and 4 patients) showed genotypes that did not correspond with any allelic combination based on the previously known 17 TGFB1 regulatory region and exon 1 alleles. These samples were shown to carry a combination of a known allele and a novel allele.6
Survival analysis Median follow up in the cohort was 20.5 months (range 0.2-178.9 months). Five-year OS and EFS in the whole cohort were 40.9% (95%CI: 36.6%-45.2%) and 30.4% (95%CI: 26.3%-34.5%), respectively. Median OS was haematologica | 2016; 101(3)
TGFB1 polymorphism reduces survival after HSCT
21.6 months (95%CI: 11.5-31.6 months). Median EFS was 9.9 months (95%CI: 7.6-12.2 months). One-year cumulative incidence for NRM was 26.8% in the whole cohort. Five-year relapse cumulative incidence was 39.0%. Median time to relapse was 51.6 months (95%CI: 9.5-93.8 months). The effect of TGFB1 polymorphism on survival was assessed in three models: recessive allelic, dominant allelic, and SNP (-1347C>T)-associated effects. Both the effect of donor and patient-borne polymorphism was independently assessed.
A
Recessive models Variation in patient TGFB1 regulatory region and exon 1 had a significant effect on the OS of the whole cohort. When homozygosity for alleles p001 and p003 and heterozygosity were compared (Figure 1A), significant differences were found (n=486 when excluding patients homozygous for p006 and p014 due to low numbers; P=0.041). Patients homozygous for p003 (n=132) had the highest median OS (43.8 months), while patients homozygous for the p001 allele (n=41) had the lowest (7.9 months). When pairwise comparisons were considered, there was a significant difference between patients homozygous for p001 and p003 (P=0.014), and a trend between p001 and the heterozygous group (n=313; P=0.071). Patients with a p001/p001 genotype (n=41) show significantly lower OS than the rest of the patients of any other genotype (n=452; 5-year OS 30.7% for p001/p001 patients vs. 41.6% others; P=0.032) (Figure 1B). No differences in OS according to donor allele were found (n=491; P=0.47). Other TGFB1 alleles could not be tested due to low numbers of homozygotes. Among all patient genotypes with n>20, only p001/p001 shows a significant effect on OS in the whole cohort when compared to the rest of the genotypes (data not shown).
Dominant models
B
C
No effect of patient alleles was seen using this model (Figure 1C for p001). However, patients whose donors carried at least one copy of p001 had worse OS than patients whose donor lacked this allele (median OS 13.7 vs. 39.5 months, respectively; P=0.043). Alleles p003, p006 and p014 did not have statistically significant dominant donor effects on OS in this cohort.
TGFB1 -1347C>T (rs1800469) The -1347T variant was close to a marker for allele p001 in this cohort (41/43 TGFB1 -1347TT patients were p001/p001). There was no statistical evidence for a recessive effect of either patient (n=493; P=0.11) or donor (n=495; P=0.11) genotype on OS (Figure 2A). When the dominant model for -1347C was tested (i.e. TT vs. CT+CC) (Figure 2B), patients that had the -1347TT genotype (n=43) had significantly lower OS than that of patients bearing at least one C variant (median OS 7.9 vs. 25.1 months; P=0.036). No evidence of a donor genotype effect on OS was found in this model (P=0.82) (Figure 2C).
Analysis of EFS, NRM, relapse and aGvHD There was a significant increase in the incidence of NRM among patients that bear the p001 allele (1-year NRM: 39.0%; P=0.039) or the -1347T (1-year NRM: 39.5%; P=0.029) in a homozygous manner when compared to other genotypes (1-year NRM: 25.4% and 25.3%, haematologica | 2016; 101(3)
Figure 1. Survival analysis according to the effects of patient TGFB1 regulatory region and exon 1 allele for the whole cohort. (A) Patients homozygous for allele p001 (n=41) show significantly worse overall survival (OS) when compared to patients homozygous for p003 (n=132; P=0.014). A trend for lower OS in patients homozygous for p001 was also found when compared to heterozygous patients (n=309; P=0.071). (B) Patients homozygous for TGFB1 allele p001 (n=41) show reduced OS when compared with all other genotypes (n=452; P=0.032). (C) Patients with at least one copy of TGFB1 allele p001 (n=255) do not show significantly different OS from patients with other genotypes (n=238; P=0.37). 385
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respectively) (Figure 3A-C). There was no effect of the dominant presence of p001 among donors (Figure 3D). None of the models tested impacted on EFS, disease relapse or aGvHD (grades I-IV, II-IV or III-IV).
Multivariate analyses Based on the univariate analyses for the clinical factors (Table 3), patient age, donor age, patient sex, HLA matching, disease status, cytomegalovirus (CMV) matching, and use of total body irradiation (TBI) were selected for inclusion in the multivariate model for OS. Likewise, patient age, HLA matching, CMV matching, use of TBI, and use
of T-cell depletion were selected for the NRM model. For OS, disease status at transplant and patient age together with the recessive allelic model were significant factors associated with this outcome (Table 4). When the -1347C dominant and the ‘p001/p001 versus other genotype’ models were examined, both were found to be significantly associated with OS together with patient age, HLA matching, and disease status. Overall, patients older than 40 years of age, not transplanted in complete remission/chronic phase nor from 10/10 HLA-matched donors, and being homozygous for TGFB1 p001 (or -1347T) were associated with decreased OS.
Table 3. Analysis of the univariate association between clinical factors and overall survival and non-relapse mortality in the whole cohort (n=504).
Risk factor Patient age > 40 years < 40 years Donor age > 30 years < 30 years Sex Female patients Male patients Female donors Male donors Sex matching Overall matched Overall mismatched Female to male Other HLA-matching 0 mismatches 1 mismatch >1 mismatch Disease status Low risk1 High risk CMV status Matched2 Not matched TBI presence Yes No SC source BM PB T-cell depletion Yes No GvHD prophylaxis None Cyclosporin Cyclosporin+MTX Other Unknown Transplant year 1996-2001 2002-2009
Median OS (months)
P
NRM (cumulative incidence)3
P
7.6 40.4
<0.001
36.2 23.6
0.002
17.0 45.0
0.081
28.8 21.3
0.38
40.5 17.0 34.0 18.7
0.15
24.7 28.0 27.9 26.4
0.37
22.4 21.3 12.5 22.4
0.84
26.9 26.6 30.3 26.2
0.72
28.6 11.3 8.6
0.051
23.7 31.0 41.3
0.048
26.4 7.6
0.004
26.4 32.5
0.44
27.2 12.2
0.035
24.0 35.5
0.012
18.7 N/A
0.089
27.9 18.1
0.080
19.5 27.8
0.66
29.4 21.4
0.24
22.1 14.4
0.95
27.4 13.6
0.18
7.8 19.1 25.7 8.9 7.9
0.23
N/A 26.7 26.4 28.6 33.3
0.74
14.4 32.1
0.23
29.6 23.9
0.36
0.32
0.59
0.98
0.69
1 Complete remission/chronic phase; 2cytomegalovirus (CMV) matching: CMV positive-CMV positive; CMV negative-CMV negative; 31-year NRM cumulative incidence. BM: bone marrow; GvHD: graft-versus-host disease; HLA: human leukocyte antigen; MTX: methotrexate; NRM: non-relapse mortality; OS: overall survival; PB: peripheral blood; SC: stem cell; TBI: total body irradiation.
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For NRM, patient homozygosity for TGFB1 p001 (or 1347T), patient age older than 40 years, and the presence of one or more allelic HLA mismatches (i.e. ≤9/10) were associated with increased probability of death (Table 4).
A
Functional consequences of TGFB1 regulatory region and exon 1 alleles in Treg When Treg from healthy donors were stimulated with antibodies against CD3 and CD28, an upregulation of surface LAP, which peaked at 24 h of culture, was detected. This upregulation was observed only on the CD4+, CD127lo cells and CD25+ cells, as previously described.8 As shown in Figure 4, TGFB1 genotype appears to influence the levels of LAP expressed by Treg upon TCR stimulation. A trend towards higher LAP+ levels was seen when the sample expressed a p001 allele (Mann-Whitney test; P=0.07). An analysis of p001/p001 individuals on their own was not possible because of the reduced frequency of this genotype among available healthy volunteer donors.
B
Discussion The present study revealed that homozygosity for a TGFB1 p001 allele in UD-HSCT patients was associated with significantly worse OS and NRM. Cellular experiments suggest a potential functional effect of TGFB1 p001, as there was a trend toward higher expression of surface TGF-β1 on in vitro stimulated Treg that bore this allele. This study is the largest performed so far on the role of TGFB1 polymorphisms in HSCT. In contrast to previous studies, the analysis encompassed the combined effect of various polymorphisms organized in defined alleles in a genomic region of approximately 3 kb. A few studies have analyzed TGFB1 polymorphism in HSCT, but their heterogeneity makes comparisons difficult.7 Most previous studies are small (54% included less than 100 pairs) or have investigated rare alleles. Moreover, most of the studies have focused their analysis on one or two SNPs and only on their impact on GvHD. Two early studies also used pre-existing classifications of the genotypes in “high producer” and “low producer” groups, potentially introducing a bias in their analyses.9-11 Studies in mostly related donor cohorts have found no association for TGFB1 +29T>C and +74G>C or their combined +29~+74 genotypes with OS, GvHD, engraftment or infections.9,12 In a larger study with mismatched UD-HSCT, there was no consistent association of TGFB1 -1347C>T with OS, engraftment or GvHD, despite initial findings in a discovery cohort.13 Finally, in two recent reports analyzing relatively large cohorts of mostly related donor transplants, TGFB1 -1347 TT and CT patients showed increased incidence of aGvHD, but no effect on OS, EFS, or NRM.14,15 TGFB1 -1347T was found to be a risk factor for skin aGvHD but protective against lung cGvHD.15 In our analyses, there was no statistical confirmation of a role of +29T>C16 (data not shown). An explanation for this difference could be the fact that +29CC genotypes could include both p001 and p014.4 The presence of p014 could not be analyzed for a recessive effect in our cohort. However, since OS in +29CC patients was not statistically different from +29TT and TC individuals, this could suggest the lack of effect from p014. Interestingly, a study performed in Chinese HSCT patients (n=240) found lower haematologica | 2016; 101(3)
C
Figure 2. Survival analysis according to the effects of patient TGFB1 -1347C>T for the whole cohort. (A) Among the whole cohort (n=493), patients bearing a -1347TT genotype (n=43) show a trend toward lower OS (P=0.11) when compared to other genotypes. When pairwise comparisons are made, patients with a -1347TT genotype (n=43) have significantly lower OS than patients with -1347CC (n=237; P=0.039). In a dominant model for the C variant, (B) the presence of a TGFB1 -1347TT in patients results in significantly lower overall survival (OS) [(median OS TT (n=43) vs. CC+CT (n=450) 7.9 vs. 25.1 months; P=0.036)]. (C) No effect of the donor genotype (TT, n=35; CC+CT, n=460) was suggested by this dominant model (P=0.82).
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incidence of aGvHD in patients whose donors were TGFB1 -1347TT individuals, and also in patients who bore at least one copy of the T variant, but with no effect on OS, NRM or relapse.17 However, it is uncertain if TGFB1 -1347T correlates with allele p001 in the Chinese population. In the present study, the effect seen for allele p001 on OS and NRM could not be explained by increases in the
incidence of aGvHD. This might be due to the fact that most of the transplants included in our cohort were T-cell depleted, and the incidence of aGvHD was low. Despite this, there remains the possibility that this cytokine could modify this complication, for example, by affecting the generation of Th17 cells.18 Alternatively, the genetic association with NRM could potentially be explained by
Table 4. Analysis of the multivariate association between clinical factors and TGFB1 regulatory region and exon 1 polymorphism and their effect on overall survival and non-relapse mortality in the whole cohort.
Outcome OS
Model
Factor1
HR [95%CI]
P
Recessive allelic (n=476)
Patient p001/p001 vs. heterozygous Patient p003/p003 vs. heterozygous Patient age < 40 vs. > 40 Disease status (low risk vs. high risk) Patient -1347 TT vs. CC+CT Patient age < 40 vs. > 40 Disease status (low risk vs. high risk) HLA matched 10/10 vs. ≤ 9/10 Patient p001/p001 vs. other Patient age < 40 vs. > 40 Disease status (low risk vs. high risk) HLA matched 10/10 vs. ≤ 9/10 Patient p001/p001 vs. heterozygous Patient p003/p003 vs. heterozygous HLA matched 10/10 vs. ≤ 9/10 Patient age < 40 vs. > 40 Patient -1347 TT vs. CC+CT HLA matched 10/10 vs. ≤ 9/10 Patient age < 40 vs. > 40 Patient p001/p001 vs. other HLA matched 10/10 vs. ≤ 9/10 Patient age < 40 vs. > 40
1.38 [0.94-2.04] 0.78 [0.59-1.02] 0.61 [0.47-0.78] 0.72 [0.53-0.97] 1.52 [1.04-2.21] 0.59 [0.46-0.76] 0.73 [0.54-0.98] 0.77 [0.60-0.98] 1.53 [1.04-2.24] 0.59 [0.46-0.76] 0.73 [0.54-0.98] 0.77 [0.61-0.98] 1.80 [1.08-3.00] 1.11 [0.76-1.60] 0.73 [0.52-1.01] 0.60 [0.43-0.84] 1.79 [1.09-2.92] 0.70 [0.50-0.97] 0.58 [0.42-0.81] 1.73 [1.06-2.83] 0.70 [0.50-0.97] 0.58 [0.42-0.81]
0.103 0.065 <0.001 0.032 0.031 <0.001 0.038 0.034 0.031 <0.001 0.039 0.035 0.024 0.580 0.065 0.003 0.020 0.031 0.001 0.030 0.032 0.001
Dominant -1347C (n=483)
p001/p001 vs. other genotypes (n=483)
NRM
Recessive allelic (n=486)
Dominant -1347C (n=493)
p001/p001 vs. other genotypes (n=493)
Factors are compared to the last one listed for their hazard ratio (HR). NRM: non-relapse mortality; OS: overall survival.
1
A
C
388
B
D
Figure 3. Survival analysis according to the effects of patient and donor TGFB1 regulatory region and exon 1 genotype on nonrelapse mortality (NRM) for the whole cohort. Allele p001 and -1347T homozygosity in patients showed a significant increase in NRM in the whole cohort. No effect on NRM was observed for the donor p001 dominant model. (A) Recessive patient allelic model [(p001/p001 (n=41) versus p003/p003 (n=132) versus heterozygous (n=309); overall P=0.099)]; (B) patient p001/p001 (n=41) versus other (n=452; P=0.039); (C) dominant patient 1347C (-1347 TT (n=43) versus CT+CC (n=450); P=0.029); (D) dominant donor p001 (p001/- (n=246) versus other (n=249); P=0.440).
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TGFB1 polymorphism reduces survival after HSCT
another cause of death in which TGF-β1 might play a role, such as impaired early immune responses to infectious agents,19 organ damage,20 or complications such as hepatic veno-occlusive disease.21 However, this remains unclear. Interestingly, a recent report22 has shown evidence of a role for TGF-β1 in limiting both the growth and function of the thymic medulla, another potential niche for its influence on the outcome of HSCT. Our typing results revealed that four TGFB1 regulatory region and exon 1 alleles predominated. Even though this was not a population study, our results provide insight into TGFB1 regulatory region allelic diversity and frequencies and are a potential reference for future studies. Overall, the frequencies for variant polymorphic positions and for TGFB1 regulatory region and exon 1 alleles agree widely with data previously reported by other studies.23 We speculated that the strong detrimental effect of patient p001 observed in this study was related to differences in functionality between TGFB1 regulatory region and exon 1 alleles. Our study showed that the level of surface TGF-β1 on Treg after TCR stimulation appears to be modified by the presence of the p001 allele in TGFB1.
A
B
Figure 4. Surface TGF-β1 (LAP) expression on Treg upon TCR stimulation differs according to TGFB1 regulatory region and exon 1 genotype in healthy donors. (A) Percentage of LAP+ cells between different TGFB1 regulatory region and exon 1 genotypes. (B) The percentage of LAP+ cells within the CD4+CD25+CD127lo gate shows a trend toward higher expression when a p001 allele at TGFB1 regulatory region is present. p001/other: p001/p001 (2), p001/p014 (2), p001/new (1). Shown are mean and standard error. haematologica | 2016; 101(3)
Even though it did not reach statistical significance, TGFB1 p001/x genotypes showed results that suggested higher generation of LAP+ cells when compared to TT individuals, following previous observations in other cell types and experimental systems. The -1347T variant has been previously associated with higher TGF-β1 plasma levels,24 as well as with a significant increase in in vitro TGF-β1 expression25 via alteration of promoter interactions with transcription factors Yin Yang 126 and AP1.27 Combining both the observations made for TGFB1 -1347C>T and those made for +29T>C,28 Shah et al. proposed that TGFB1 alleles that share a -1347T and +29C would represent a high production phenotype.4 Allele p001 would be the sole representative of this cluster seen with significant frequencies in our cohort. Interestingly, a couple of studies have found opposite results and associated -1347T and +29C with lower plasma concentrations of this cytokine and lower reporter gene activities,29 and a TGFB1 upstream haplotype congruent with allele p001 with weaker promoter activity than another haplotype fitting with allele p003.30 However, the genomic region examined in the latter study only partially spanned the one studied here and included different SNPs not characterized in this study. Finally, one study associated allelic variants carrying a proline either in codon 10 (+29C) or 25 (+74C) with reduced expression, but only included TGFB1 coding region in in vitro constructs.31 In addition to a -1347T variant, a feature that is unique to p001 is the absence of the -2389AGG duplication (rs11466313), and this has been associated with the gain of allele DNA-protein complexes, potentially leading to novel transcription factor binding site motifs.30 Low frequency of homozygotes for some of the TGFB1 alleles precluded thorough analysis of their effects. A much larger study would be needed for it to be possible to assess homozygous individuals. In addition, since our cohort was comprised mainly of alemtuzumab-T-cell- depleted transplants, changes in its dosage or dosage schedule could have taken place over the 13-year observation period, potentially having an impact on our results. Unfortunately, this information is not available for assessment. Finally, we do not have data on replication of these results in an independent cohort. Hence, these analyses should be confirmed in other settings, such as non-myeloablative transplants or transplants performed with alternative donors. In conclusion, the fact that patients having a p001/p001 genotype have significantly higher probabilities of dying early after the transplant could potentially allow for better pre-emptive measures to improve the prognosis for these patients. However, further research is needed to understand the mechanism of this effect and the cause(s) of death associated with it. Acknowledgments The authors would like to thank all of the UK transplant teams who have contributed patients’ data and samples to this study. We thank Dr. Richard Szydlo for statistical advice and Prof. Katharina Fleischhauer for critically reviewing this manuscript. Funding This work was supported by grants from University College London, the University of Costa Rica, and the Costa Rican National Council for Scientific and Technologic Research (CONICIT) to EAB. 389
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21. Reimer J, Bien S, Ameling S, et al. Antineoplastic agent busulfan regulates a network of genes related to coagulation and fibrinolysis. Eur J Clin Pharmacol. 2012;68 (6):923-935. 22. Hauri-Hohl M, Zuklys S, Hollander GA, Ziegler SF. A regulatory role for TGF-beta signaling in the establishment and function of the thymic medulla. Nat Immunol. 2014;15(6):554-561. 23. Hoffmann SC, Stanley EM, Cox ED, et al. Ethnicity greatly influences cytokine gene polymorphism distribution. Am J Transplant. 2002;2(6):560-567. 24. Grainger DJ, Heathcote K, Chiano M, et al. Genetic control of the circulating concentration of transforming growth factor type beta1. Hum Mol Genet. 1999;8(1):93-97. 25. Luedecking EK, DeKosky ST, Mehdi H, Ganguli M, Kamboh MI. Analysis of genetic polymorphisms in the transforming growth factor-beta1 gene and the risk of Alzheimer's disease. Hum Genet. 2000;106(5):565-569. 26. Silverman ES, Palmer LJ, Subramaniam V, et al. Transforming growth factor-beta1 promoter polymorphism C-509T is associated with asthma. Am J Respir Crit Care Med. 2004;169(2):214-219. 27. Shah R, Hurley CK, Posch PE. A molecular mechanism for the differential regulation of TGF-beta1 expression due to the common SNP -509C-T (c. -1347C > T). Hum Genet. 2006;120(4):461-469. 28. Dunning AM, Ellis PD, McBride S, et al. A transforming growth factorbeta1 signal peptide variant increases secretion in vitro and is associated with increased incidence of invasive breast cancer. Cancer Res. 2003;63(10): 2610-2615. 29. Wang H, Zhao YP, Gao CF, et al. Transforming growth factor beta 1 gene variants increase transcription and are associated with liver cirrhosis in Chinese. Cytokine. 2008;43(1):20-25. 30. Healy J, Dionne J, Belanger H, et al. Functional impact of sequence variation in the promoter region of TGFB1. Int J Cancer. 2009;125(6):1483-1489. 31. Mohren S, Weiskirchen R. Non-synonymous gene polymorphisms in the secretory signal peptide of human TGF-beta1 affect cellular synthesis but not secretion of TGF-beta1. Biochem Biophys Res Commun. 2009;379(4): 1015-1020.
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