Chromosomal instability in cell free dna as a highly specific biomarker for detection of ovarian can

1

Chromosomal instability in cell-free DNA as a highly specific

2

biomarker for detection of ovarian cancer in women with

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adnexal masses

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Adriaan Vanderstichele 1,2, Pieter Busschaert 1,2, Dominiek Smeets 3,4, Chiara Landolfo 1, Els

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Van Nieuwenhuysen 1,2, Karin Leunen 1,2, Patrick Neven 1,2, Frédéric Amant 1,2,9, Sven Mahner

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5,6

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Lambrechts 3,4,*, Ignace Vergote 1,2

, Ioana Braicu 7, Robert Zeilinger 8, An Coosemans 1,2, Dirk Timmerman 1, Diether

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1

Department of Gynaecology and Obstetrics, University Hospitals Leuven, Leuven, Belgium

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2

Division of Gynaecological Oncology, Leuven Cancer Institute, KU Leuven, Leuven, Belgium

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3

Vesalius Research Center, VIB, Leuven, Belgium

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4

Laboratory for Translational Genetics, Department of Oncology, KU Leuven, Leuven,

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Belgium

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5

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Cancer Center Hamburg-Eppendorf (UCCH), Germany

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Department of Gynecology and Obstetrics, University of Munich – LMU, Munich, Germany

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Department of Gynecology, Campus Virchow-Klinikum, Charité University Hospital,

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European Competence Center for Ovarian Cancer Berlin, Germany

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8

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Cancer Center, Gynecologic Cancer Unit, Medical University of Vienna, Austria

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9

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Amsterdam, the Netherlands

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*

Department of Gynecology, Hamburg-Eppendorf University Medical Center, University

Department of Obstetrics and Gynecology, Molecular Oncology Group, Comprehensive

Center for Gynecologic Oncology Amsterdam (CGOA), Netherlands Cancer Institute (NKI),

Corresponding author

25 26

Running title

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Cell-free DNA testing in women with adnexal masses

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Keywords

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Cell-free DNA; whole-genome sequencing; epithelial ovarian cancer; high-grade serous

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ovarian cancer; chromosomal instability

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1

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Additional information

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Financial support

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This work was supported by the Belgian Cancer Plan Action 29 of the Ministry of Health of

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Belgium (grant number NKP-29-039), by the Vriendtjes tegen Kanker fund (grant number

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EVO-FOVTK1-O2010) and by The Flemish League against Cancer (grant number EVO-

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ZKD0344). FA and DT are Senior Clinical researchers for the Research Fund-Flanders (FWO-

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V). AC is a postdoctoral researcher for the Research Fund-Flanders (FWO-V).

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Corresponding Author

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Prof Diether Lambrechts

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Laboratory for Translational Genetics

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Department of Oncology, VIB, KU Leuven

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Herestraat 49, Box 912

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3000 Leuven, Belgium

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Phone +32 16 37 32 09 / Fax + 32 16 37 25 85

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diether.lambrechts@vib-kuleuven.be

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Conflict of interest disclosure statement

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The authors declare no potential conflicts of interest.

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Manuscript metrics

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Abstract: 249 words

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Translational relevance: 138 words

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Article: 3618 words (without references), 4711 words (including references),

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5322 words (including references and image legends)

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1 table, 4 figures

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Translational relevance

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With a 5-10% lifetime risk for women to undergo surgery for a suspected ovarian

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malignancy, the accurate detection of malignancy in women presenting with adnexal masses

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is important to select them for adequate surgery, increased surveillance or expectant

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management. However, current detection methods lack sufficient specificity. Sequencing

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studies have shown that chromosomal instability is inherent to ovarian tumors. Here we

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develop a novel method that uses chromosomal instability detected by whole-genome

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sequencing on cell-free DNA, which can be non-invasively obtained from plasma. We show

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that our proposed method outperforms current detection methods based on serum CA-125

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testing and ultrasound imaging to predict ovarian malignancy. Our findings can easily

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translate into clinical practice, since its analytical protocols are very similar to those applied

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for non-invasive prenatal testing (NIPT) in pregnant women, which is broadly adopted in

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routine clinical practice.

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Abstract

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Purpose: Chromosomal instability is a hallmark of ovarian cancer. Here, we explore copy

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number alteration (CNA) profiling in cell-free DNA as a potential biomarker to detect

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malignancy in patients presenting with an adnexal mass.

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Experimental design: We prospectively enrolled 68 patients with an adnexal mass, of which

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57 were diagnosed with invasive or borderline carcinoma and 11 with benign disease. Cell-

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free DNA was extracted from plasma and analyzed by low-coverage whole-genome

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sequencing.

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Results: Patterns of chromosomal instability were detectable in cell-free DNA using 44

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healthy individuals as a reference. Profiles were representative of those observed in

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matching tumor tissue and contained CNAs enriched in 2 large datasets of high-grade serous

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ovarian cancer (HGSOC). Quantitative measures of chromosomal instability, referred to as

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genome-wide z-scores, were significantly higher in patients with ovarian carcinoma than in

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healthy individuals or patients with benign disease. Cell-free DNA testing improved

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malignancy detection (AUC 0.89) over serum CA-125 (AUC 0.78) or the risk of malignancy

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index (RMI, AUC 0.81). AUC values of cell-free DNA testing even further increased for HGSOC

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patients specifically (AUC 0.94). At a specificity of 99.6%, a theoretical threshold required for

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ovarian cancer screening, sensitivity of cell-free DNA testing was 2 to 5-fold higher

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compared to CA-125 and RMI testing.

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Conclusions: This is the first study evaluating the potential of cell-free DNA for the diagnosis

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of primary ovarian cancer using chromosomal instability as a read-out. We present a

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promising method to increase specificity of presurgical prediction of malignancy in patients

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with adnexal masses.

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Introduction

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Ovarian cancer is the fifth most common cause of female cancer mortality and remains the

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leading cause of gynecological cancer mortality in developed countries (1). Peritoneal spread

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often precedes the onset of symptoms, which explains the high percentage of patients

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presenting with advanced-stage disease at primary diagnosis. Despite the introduction of

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improved surgical techniques, chemotherapy regimens and targeted therapies, overall

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survival rates for these patients have not improved significantly since the introduction of

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platinum-based chemotherapy (5-year overall survival rate of ca. 30%) (2). Multiple efforts

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have been made to evaluate early screening methods based on serum CA-125

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concentrations and/or transvaginal ultrasound (3, 4). Until now, these methods did not meet

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the standards required to advocate population-based screening. Indeed, the recent

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UKCTOCS trial failed to demonstrate a mortality reduction with ultrasound and/or CA-125-

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based screening for ovarian cancer (4), illustrating the urgent need for additional cancer-

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specific diagnostic biomarkers (5).

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Due to rapid developments in the field of non-invasive prenatal testing (NIPT), the analysis

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of tumor-specific CNAs in cell-free DNA has gained momentum as a potential cancer

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screening tool. Indeed, we and others reported that occult maternal cancers can be

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discovered by NIPT testing (6, 7), leading to the diagnosis of a pre-symptomatic advanced-

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stage ovarian cancer in a 41-year old patient at 15 weeks of gestation. As chromosomal

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instability is an important hallmark of cancer (8), it is indeed expected that NIPT testing,

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which is designed to detect specific fetal chromosomal anomalies (aneuploidy), is also able

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to reveal tumor-specific patterns of chromosomal instability in the plasma from cancer

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patients (9).

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Elaborating on these findings, we here intend to study whether low-coverage whole-genome

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sequencing can be used in patients presenting with adnexal masses, as a tool to select which

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patients should undergo surgery. With 5-10% of women undergoing surgery for a suspected

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ovarian neoplasm in the course of their life, adnexal uterine masses represent a frequent

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medical problem (10). The accurate detection of malignancy in these patients is of

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paramount importance to select patients for adequate surgery (in case of suspected

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malignancy), continued surveillance (if the suspicion of malignancy is low) or expectant

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management (in case of a benign adnexal mass). Many efforts have been made to assist in

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the decision process and to improve the management of these patients, by implementing

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mathematical models consisting of clinical data, such as ultrasound imaging, and serum

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biomarkers, such as CA-125 (cancer antigen 125) and HE-4 (human epididymis protein 4)

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(11, 12). Nevertheless, current models still lack specificity (13) and in order to decrease the

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number of unnecessary surgical interventions and to improve patient selection for tertiary

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oncological referral, it is thus imperative to develop improved screening models.

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Here, we propose to implement cell-free DNA testing in the pre-operative evaluation of

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patients presenting with an adnexal mass. We developed and validated a whole-genome

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sequencing method and analysis pipeline to detect ovarian cancer-specific patterns of

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chromosomal instability in cell-free DNA and compared its specificity for predicting

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malignancy in patients with an adnexal mass to other modalities, such as ultrasound imaging

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and serum CA-125.

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Materials and methods

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Patients and clinical data

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We collected pre-treatment blood samples from patients with an adnexal mass suspicious

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for malignancy, referred to the department of gynecological oncology and the department

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of gynecological ultrasound of the University Hospitals Leuven (Leuven, Belgium). All

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samples were collected between September 2014 and October 2015. Blood samples were

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also obtained from a cohort of healthy female individuals. The ethical committee of the

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University Hospitals Leuven approved this study (S/55904) and all included patients gave

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written informed consent. We measured pre-treatment serum CA-125 levels and calculated

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the risk of malignancy index (14), using data from a pre-operative transvaginal ultrasound.

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Cell-free DNA extraction and whole-genome low-coverage sequencing

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Plasma was extracted through a 2-step centrifugation procedure and DNA was extracted

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using the QIAamp circulating nucleic acid kit (Qiagen, Hilden, Germany). DNA sequencing

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libraries were prepared using the KAPA DNA Library Preparation Kit (KAPA Biosystems,

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Wilmington, MA, USA). Whole-genome sequencing was performed on a HiSeq2500

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(Illumina, San Diego, CA, USA) using a V4 flowcell generating 1 Ă— 50 bp reads.

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Bio-informatics pipeline

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Raw sequencing reads were mapped to the human reference genome Hg19 using BWA

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v0.7.1 (15). Duplicate and low-quality reads were removed by Picard Tools v1.11 and

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Samtools v0.1.18 respectively (16). The QDNAseq package v.1.0.5 was used to create bins of

6

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1000 kb along the genome (17). Distributions of bin counts for plasma samples were shifted

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and rescaled based on distributions for healthy individuals to correct for the total number of

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reads. Furthermore, bin values were smoothed by taking moving window averages based on

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windows of 50 Mb (i.e., 50 adjacent bins), which move in steps of 1000 kb (i.e., 1 bin). We

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calculated z-scores for these windows, i.e. the number of standard deviations that a given

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window deviates from the population mean. A detailed description of the pipeline can be

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found in the Supplementary Methods.

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Tumor tissue correlation

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Matching fresh-frozen tumor specimens were collected at baseline (staging laparoscopy or

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debulking surgery). DNA was extracted after macrodissection and sequencing libraries were

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generated in a similar way as for plasma DNA. Bio-informatic processing comprised the use

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of the QDNAseq package (17) and the ASCAT algorithm (18).

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Genome-wide z-score

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For each sample i, we calculated the sum of squares Si of window z-values. To quantify

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chromosomal instability, we calculated the genome-wide z-score (19) as the Si normalized

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based on the healthy individuals (HI): =

− SD

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Statistical analysis

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Receiver operation characteristic (ROC) curves were constructed and the corresponding area

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under the curve (AUC) values were calculated using the ‘ROCR’ package in R (20).

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Sensitivities and specificities were calculated using the ‘caret’ package (21). All data was

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processed in R version 3.1.3.

225 226 227

Results

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Assessing chromosomal instability in cell-free DNA

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We obtained plasma DNA samples from 68 patients presenting with an adnexal mass prior

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to undergoing surgery or receiving any treatment, as well as from 44 healthy female

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individuals. Clinical characteristics for all 112 included samples are summarized in Table 1

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(detailed characteristics in Supplementary Table S4). Briefly, 54 patients were diagnosed

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with invasive carcinoma, 3 with borderline carcinoma and 11 with a benign mass. Whole-

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genome short-read sequencing was performed on all cell-free DNA samples with a median

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of 7.6â‹…106 sequencing reads per sample, corresponding to a median read depth of 0.12Ă—

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(IQR 0.08-0.16Ă—, Supplementary Table S1).

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For each healthy individual, we counted the number of reads per 1 Mb bin. To reduce noise

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across these bins, we used a sliding window approach and calculated for each window of 50

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Mb a z-score using the remaining healthy individuals as a reference. Relative to the observed

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distribution of z-scores in the healthy individuals, absolute z-scores higher than 3 were

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considered indicative of regions with a significant chromosomal gain or loss (19). Overall,

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0.3% of all windows in healthy individuals exhibited z-scores >3, whereas in cell-free DNA

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samples from patients with an invasive tumor (n=54) 14% of windows exhibited a z-score >3.

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The heatmap in Figure 1 shows the genome-wide distribution of over- and

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underrepresented 50 Mb windows (z-score >3) obtained for all samples: all genomic regions

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that are overrepresented and thus indicative of somatic amplifications are shown in red,

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while genomic regions that are underrepresented and thus indicative of somatic deletions

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are shown in blue. Visual inspection shows that a large degree of chromosomal instability is

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detectable in patients with an invasive carcinoma, especially in the 45 samples of the HGSOC

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subtype. On the other hand, patients with borderline carcinoma (n=3) or a benign ovarian

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mass (n=11) demonstrate a stable chromosomal pattern comparable to the profiles

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obtained from healthy individuals.

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Chromosomal instability in cell-free DNA and matching tumor biopsies

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To show that CNAs observed in cell-free DNA from cancer patients were similar to those in

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the corresponding tumor, we generated copy number estimates by whole-genome

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sequencing of DNA (17) obtained from 8 pre-treatment HGSOC tumor biopsies. To visualize

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the similarity between the log2 ratios from tumor tissue and z-scores in cell-free DNA

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samples, all values within a tumor sample were rank transformed, and ranks were plotted

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using a color scale from blue (bins with lowest ranks) to red (bins with highest ranks), as

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shown in Supplementary Figure 1. The median Spearman correlation coefficient between

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cell-free DNA and tumor DNA profiles was 0.51 (IQR: 0.37-0.62, p<0.01 for all samples), thus

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confirming the specificity of the plasma profiles. We also included 4 patients for which

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multiple tissue biopsies from different tumor sites were available (Supplementary Fig. S2

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and S3). Although the number of biopsies assessed was small, prominent CNAs shared

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between the tumor biopsies were also detectable in the cell-free DNA.

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In order to further demonstrate the specificity of the observed CNAs, we focused on the 45

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cell-free DNA samples obtained from patients diagnosed with high-grade serous ovarian

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cancer (HGSOC). We retrieved segmented copy number data from 160 HGSOC cases,

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previously characterized by SNP array profiling of fresh-frozen tumoral tissue-derived DNA in

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the context of the OVCAD project (22), as well as from 565 HGSOC cases included in The

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Cancer Genome Atlas (TCGA) project (23). To visualize the similarity between copy number

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profiles generated on cell-free DNA samples and those obtained from tissue-derived DNA,

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we plotted the average profiles of CNAs for the 3 cohorts (Fig. 2; Supplementary Methods).

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The 2 upper plots depict the average copy number profile for the 2 sets of HGSOC cases,

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while the lower plot depicts the average z-scores value per window across 45 HGSOC cell-

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free DNA samples. Spearman correlation coefficients between cell-free and tumor tissue

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samples was 0.67 for both cohorts, whereas the correlation between OVCAD and TCGA was

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0.92 (p<0.01 for all 3 comparisons).

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Genome-wide z-scores as a quantitative measure of chromosomal instability

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Next, using a sum of square approach to reflect the overall deviation in z-scores across all

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windows, we constructed for each cell-free sample a global measure of genomic instability

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(see Supplementary Methods), hereafter referred to as the genome-wide z-score. The

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median genome-wide z-score of healthy individuals was -0.1. There was no correlation (R2 =

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0.0034, p = 0.70) between the genome-wide z-score and the age (or menopausal status) of

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the healthy individuals (Supplementary Fig. S6). For patients presenting with a benign

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adnexal mass the median genome-wide z-score was -0.6, while for patients with borderline

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or invasive carcinoma it increased to a median value of 11 (see Supplementary Table S2; Fig.

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3). Genome-wide z-scores further increased to 12.9 when only considering patients with an

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invasive carcinoma. Indeed, borderline ovarian tumors are not typically characterized by

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large-scale chromosomal instability, except when associated with a high-grade invasive

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tumor component (24). Furthermore, we observed a positive correlation between FIGO

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stage and the genome-wide z-score for HGSOC tumors (Fig. 3). Indeed, although numbers

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were limited, cell-free DNA sequencing yielded genome-wide z-scores that were lower for

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lower-stage HGSOCs (median score of ~ 6.5 for 15 stage I/II/IIIA-B cases) compared to more

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advanced HGSOCs (median score of ~ 37 for 30 stage IIIC/IV cases). Median genome-wide z-

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scores for early-stage HGSOC tumors were still higher than the values observed in healthy

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individuals and patients with benign ovarian disease (Fig. 3, Supplementary Table S2).

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Additionally, elevated genome-wide z-scores were also observed for some of the non-

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HGSOC cases tested, i.e., a LGSOC tumor (OV1465, FIGO IIIB, score of 45.92) and a clear cell

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ovarian tumor (OV1529, FIGO IIIC, score of 1540.37). Indeed, it is known that ovarian clear

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cell carcinomas and other rare ovarian cancer subtypes constitute a heterogeneous disease

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at the genomic level, with only a subset of tumors displaying genomic instability.

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Consequently, we failed to observe elevated genome-wide z-scores in 7 out of 9 non-HGSOC

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cases (Supplementary Table S2 and Fig. S8).

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We sequenced approximately 8 million reads per sample, corresponding to a median

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coverage of 0.12Ă&#x2014;. For 6 samples, over 40 million sequencing reads were available and the

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influence of sequencing coverage on the genome-wide z-score was evaluated by selecting

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random subsets of sequencing reads, and plotting the genome-wide z-score as a function of

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sequencing coverage. We failed to observe an increase in the genome-wide z-score from 5

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to 10 million sequencing reads onwards (Supplementary Fig. S7), thereby supporting our

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approach of applying a low-coverage sequencing approach (~ 0.10Ă&#x2014;).

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Overall, these data suggests that genome-wide z-scores can be used to distinguish patients

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with an invasive carcinoma of high-grade serous histology from healthy individuals and

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patients with a benign adnexal mass, and this independently from FIGO stage.

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Performance of genome-wide z-score for prediction of malignancy

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Next, by applying different thresholds of the genome-wide z-score to classify cell-free DNA

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samples either as cancer patients or healthy individuals, and assessing the number of true-

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positives and false-positives at that specific threshold, we generated ROC curves. First, we

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assessed the area under the curve (AUC) to discriminate patients with borderline and

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invasive carcinoma (n=57) from patients with a benign adnexal mass (n=11). This is relevant

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as the first set of patients is generally selected for surgery and the latter undergoes

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expectant management. We also repeated this analysis for HGSOC alone (n=45). The

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obtained AUC values were 0.89 and 0.94, for both scenarios respectively (Fig. 4A).

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We also explored whether assessing specific genomic regions, known to be recurrently

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amplified or deleted in HGSOC tumors, could improve assay performance for detection of

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HGSOC cases. We applied both a shrinkage algorithm or selected genomic regions enriched

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in the TCGA cohort, but could not observe an improvement in the detection of malignancy

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(Supplementary Fig. S5A and S5B).

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Genome-wide z-score compared to CA-125 and RMI as diagnostic biomarkers

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Patients presenting with an adnexal mass underwent pre-operative transvaginal ultrasound

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scanning (n=60, 8 patients with missing data), as well as serum CA-125 testing (n=67, 1

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patient with missing data). Using these ultrasound data and pre-operative CA-125 values, we

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333

calculated the risk of malignancy index (RMI) values for each patient (see Supplementary

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Methods). Both CA-125 and RMI values were then used to construct ROC curves, as

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described for the genome-wide z-score. When considering patients with borderline and

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invasive carcinoma relative to patients with a benign adnexal mass, the pre-operative CA-

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125 value exhibited an AUC of 0.78 (Fig. 4B). The RMI score, based on combined CA-125 and

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ultrasound data, had a slightly better AUC of 0.81 (Fig. 4C). When only considering patients

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with HGSOC (n=45) and discriminating those from patients with a benign adnexal mass

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(n=11), the AUC increased to 0.82 for CA-125 (Fig. 4B) and to 0.85 for RMI testing (Fig. 4C).

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Thus, AUC values obtained using genome-wide z-scores in cell-free DNA were higher than

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those obtained by ultrasound or CA-125 testing.

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Interestingly, integrating the genome-wide z-score with RMI values in a logistic regression

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model also improved the performance of RMI testing: AUC 0.87 for the combined model

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versus AUC 0.81 for RMI alone (benign versus borderline and invasive carcinoma), and AUC

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0.92 for the combined model versus AUC 0.85 for RMI alone (benign versus HGSOC, see

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Supplementary Fig. S9).

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Next, we compared the sensitivity and specificity of each biomarker (Supplementary Table

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S3). Using the established cut-off of 35 U/mL, serum CA-125 yielded 86% sensitivity and 64%

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specificity to detect malignancy (borderline or invasive carcinoma), whereas RMI showed

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80% sensitivity and 64% specificity using the established cut-off of 200 (14). The optimal cut-

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off for the genome-wide z-score in our dataset, according to ROC analysis, was 0.71. This

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corresponds to 74% sensitivity and 91% specificity to detect malignancy (borderline and

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invasive ovarian carcinoma).

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Furthermore, we calculated sensitivities for the 3 modalities at a fixed â&#x20AC;&#x2DC;idealâ&#x20AC;&#x2122; specificity of

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99.6% (25). At this specificity, the corresponding sensitivity of the genome-wide z-score to

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distinguish benign tumors from malignant tumors was 67%. When only considering HGSOC

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cases, the sensitivity increased towards 78%. For serum CA-125 and RMI, sensitivities at

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99.6% specificity were only 13% and 37% for malignant tumors, and 11% and 42% for

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HGSOC cases, respectively. Notably, a specificity of 99.6% corresponds to a genome-wide z-

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score cut-off of 1.48 for detection of malignancy and HGSOC in our patient cohort. The

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theoretical probability of observing a genome-wide z-score higher than this cut-off was 7.3%

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among healthy individuals (see Supplementary Fig. S4).

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Discussion

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This study is the first to evaluate chromosomal instability in cell-free DNA as a potential

368

marker to discriminate patients with an adnexal mass caused by an invasive carcinoma from

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patients presenting with an adnexal mass that is benign. Indeed, by applying whole-genome

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sequencing at low coverage, we demonstrate that it is feasible to detect copy number

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changes in cell-free DNA. Particularly, the somatic copy number alterations (SCNAs) in cell-

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free DNA correlate well with those observed in tumor biopsies. We also develop a genome-

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wide z-score to quantify chromosomal instability in cell-free DNA and show how this score at

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a given threshold can be used to predict presence of chromosomal instable tumors in cell-

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free DNA. When comparing genome-wide z-scores to serum CA-125 measurements and

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ultrasound (RMI), chromosomal instability in cell-free DNA clearly outperformed CA-125 and

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RMI. Moreover, our method increases specificity to detect malignancy, while maintaining a

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sensitivity of ~70%. Although RMI is well established, its shortcomings have been well

379

documented (11, 26). Indeed, serum CA-125 levels can be normal in borderline and early

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stage invasive ovarian cancer, while often increased in benign tumors or conditions such as

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endometriosis, fibroids, pregnancy and infection, ultimately generating a low specificity. Our

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data now suggest that assessing chromosomal instability in cell-free DNA may serve as an

383

important addition to the currently used mathematical models and scoring systems for the

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detection of ovarian cancer in patients with adnexal masses (11), especially because it may

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increase specificity in combination with established methods (27). Preliminary data indeed

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show that the performance of RMI testing improves when combined with cell-free DNA

387

analysis (Supplementary Fig. S9).

388

It should be noted, however, that the majority of study patients were recruited through our

389

tertiary center for gynecological oncology. Our sample set is therefore enriched for patients

390

with malignancy, whereas in routine clinical practice the majority of patients with adnexal

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masses are characterized by benign disease. It is therefore important to independently

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confirm our observations in prospective studies. In this context, the International Ovarian

393

Tumor Analysis Group, IOTA (28), has initiated a prospective study to formally explore the

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integration of cell-free DNA testing in their IOTA prediction models (amendment to IOTA-5

395

study, NCT01698632).

396

A particular strength of assessing chromosomal instability at the whole-genome level is that

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this unbiased approach can successfully be applied to the majority of ovarian tumors

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without prior knowledge about the tumorâ&#x20AC;&#x2122;s genetic architecture. A previous report (29)

12

399

already successfully assessed tumor-specific mutations in circulating tumor DNA collected

400

from HGSOC patients, but the approach required prior genetic characterization of the tumor

401

using whole-exome sequencing. Except for TP53 mutations, HGSOC tumors do not

402

frequently carry oncogenic driver mutations. In contrast, HGSOC tumors are almost always

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characterized by a high degree of chromosomal instability (23, 30). Furthermore, since

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performing low-coverage whole-genome sequencing to detect chromosomal instability is

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less complicated compared to first characterizing and then detecting tumor-specific

406

mutations in cell-free DNA, our proposed method might be much more straight-forward to

407

apply in a routine diagnostic setting. Likewise, assessing chromosomal instability in cell-free

408

DNA could also detect tubal carcinoma, which is a type of ovarian cancer that is challenging

409

to detect by ultrasound, and usually is characterized by TP53 mutations and chromosomal

410

instability arising due to BRCA1/2 deficiency (31). On the other hand, a limited number of

411

cases in our study did not have an increased genome-wide z-score, which probably is due to

412

a tumor fraction in plasma that is below the limit of detection of our low coverage approach

413

(32). Additionally, some tumors lacking chromosomal instability will not be detected. This is

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the case for a number of non-HGSOC tumors, which are chromosomal stable types of

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ovarian cancer (i.e. â&#x20AC;&#x2DC;type 1â&#x20AC;&#x2122; tumors, such as low-grade serous or mucinous ovarian cancer).

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These could be detectable by complementing our method of assessing chromosomal

417

instability in cell-free DNA with a targeted sequencing panel for recurrent somatic mutations

418

such as KRAS and PIK3CA, which are characteristic for these tumor types (33).

419

Another intriguing point is that, until now, detection of chromosomal instability in cell-free

420

DNA has mainly been evaluated in metastatic cancer patients, in which plasma typically

421

contains a relatively high fraction of tumor-derived cell-free DNA (19, 34, 35). Few other

422

studies have assessed the feasibility of using chromosomal instability for the early detection

423

of cancer. Xia et al. (36) already examined plasma-derived cell-free DNA collected from 8

424

early-stage lung cancer patients versus 8 healthy individuals, and demonstrated subtle but

425

detectable differences in chromosomal instability. Additionally, SchĂźtz et al. (37) analyzed a

426

large dataset consisting of 204 patients with prostate cancer versus 207 healthy individuals,

427

and assessed a limited number of chromosomal regions for instability in cell-free DNA. They

428

reported a similar AUC of 0.92 to discriminate patients from healthy individuals. Our data

429

elaborate on these findings and our AUC value of 0.94 for HGSOC detection shows that our

430

approach of assessing chromosomal instability in cell-free DNA is equally effective for

431

patients with primary diagnosed (high-grade serous) ovarian cancer. Judging from these first

432

positive results, we believe that, at least from a technical point-of-view, cell-free DNA testing

13

433

for chromosomal instability could quickly be implemented into clinical practice. Indeed, our

434

analytical protocols are very similar to those applied for non-invasive prenatal testing (NIPT),

435

which already is widely adopted in routine clinical practice in most developed countries.

436

In conclusion, our proposed methodology provides distinct advantages to discriminate

437

malignancy from a benign lesion in patients presenting with an adnexal mass. This can form

438

the basis of a larger validation study in which results from cell-free DNA testing can be

439

integrated into improved ultrasound-driven prediction models.

440 441 442

Acknowledgements

443

We thank Annick Van den Broeck, Gilian Peuteman, Thomas Van Brussel, Evy Vanderheyden

444

and AnaĂŻs Van Hoylandt for their technical and logistic support.

445 446 447

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566

FIGURE LEGENDS

567 568

Figure 1. Heatmap of the chromosomal instability profiles of all 112 included samples.

569

Genomic regions, estimated to be amplified and deleted based on window z-score

570

calculation, are shown in red and blue respectively where red indicates z-scores larger than

571

3, light red z-scores between 2 and 3, blue indicates z-scores lower then -3 and light blue z-

572

scores between -2 and -3. Profiles of the 44 healthy individuals are labeled in green and

573

show a limited number of over- or underrepresented window z-scores, as is the case for the

574

11 patients with benign ovarian disease and the 3 patients with borderline carcinoma shown

575

underneath and respectively labeled in blue and orange. On the lower half of the plot, all 54

576

patients with invasive ovarian cancer are displayed. FIGO stages are annotated for all cases

577

of borderline and invasive ovarian cancer. Non-HGSOC cases are labeled in red and HGSOC

578

cases in dark red. The abnormal genomic representation of a majority of invasive ovarian

579

cancers can be easily perceived from this heatmap, especially in the HGSOC group where a

580

characteristic pattern of genomic gains and losses is displayed.

581 582

FIgure 2. Correlation of plasma profiles with tissue-derived profiles in HGSOC cases.

583

The 2 upper plots show the average copy number for each bin, calculated from segmented

584

copy number data of 565 HGSOC cases from the TCGA project (23) and 160 HGSOC cases

585

from the OVCAD project (22). All values were centered by the median value. The lower plot

586

depicts the average of z-scores per bin across 45 HGSOC plasma samples. The Spearman

587

correlation coefficients of the plasma samples in comparison with the TCGA and OVCAD

588

cohort were 0.78 and 0.77 respectively (p-values < 0.01). The correlation between the TCGA

589

and OVCAD cohort was 0.92 (p-value < 0.01).

590 591

Figure 3. Distribution of genome-wide z-scores according to FIGO stage and histology.

592

Genome-wide z-score distributions are shown for 44 healthy individuals (‘Healthy’), 11

593

patients with benign ovarian disease (‘Benign’), 3 patients with borderline ovarian

594

carcinoma (‘BOT’) and 54 patients with invasive ovarian carcinoma (‘Invasive’). The group of

595

patients with an invasive cancer was divided according to FIGO stage: 8 patients with FIGO

596

stage I-II disease, 12 patients with FIGO stage IIIA-B disease, 14 patients with FIGO stage IIIC

597

disease and 20 patients with FIGO stage IV disease. Filled dots represent HGSOC cases,

598

whereas unfilled dots represent non-HGSOC cases. A positive correlation between FIGO

599

stage and the genome-wide z-score for HGSOC tumors was observed. Elevated genome-

18

600

wide z-scores, compared to the benign cases, were also observed for some of the non-

601

HGSOC cases. Further descriptive statistics are detailed in Supplementary Table S2 and

602

information on individual samples is shown in Supplementary Table S4.

603 604

Figure 4. ROC analyses for genome-wide z-score calculation, CA-125 and RMI testing.

605

AUC values were calculated, validated cut-offs for RMI and CA-125 were annotated on the

606

respective plots (35 U/mL for CA-125 and 200 for RMI). A. Performance of genome-wide z-

607

score calculation to discriminate patients with benign ovarian disease (n=11) from patients

608

with borderline and invasive carcinoma (n=57, left plot) and patients with high-grade serous

609

ovarian cancer specifically (n=45, right plot). AUC values were 0.89 and 0.94 respectively. B.

610

Performance of CA-125 testing to discriminate patients with benign ovarian disease (n=11)

611

from patients with borderline and invasive carcinoma (n=56, left plot) and patients with

612

high-grade serous ovarian cancer specifically (n=44, right plot). AUC values were 0.78 and

613

0.82 respectively. C. Performance of risk of malignancy index (RMI) testing to discriminate

614

patients with benign ovarian disease (n=11) from patients with borderline and invasive

615

carcinoma (n=49, left plot) and patients with high-grade serous ovarian cancer specifically

616

(n=38, right plot). AUC values were 0.81 and 0.85 respectively.

617 618 619 620 621 622 623 624 625 626 627 628 629 630 19

631

SUPPLEMENTARY INFORMATION

632

Supplementary Methods and References

633

Supplementary Tables S1-S4

634

Supplementary Figures S1-S9

635 636

20

1

TABLES

2 3

Table 1. Clinical characteristics of the study population.

4

The following abbreviations were used: IQR = interquartile range, RMI = risk of malignancy

5

index, LGSOC = low-grade serous carcinoma, HGSOC = high-grade serous carcinoma, BOT =

6

borderline ovarian tumor.

7 Healthy individuals (n = 44) Age (in years) Median IQR Adnexal histology Cystadenoma Cystadenofibroma Fibroma Teratoma Serous borderline Low-grade serous Mucinous Endometrioid Clear-cell High-grade serous FIGO stage IA IB IC1 IC2 IC3 IIB IIIA1 IIIA2 IIIB IIIC IVB Menopausal Premenopausal Postmenopausal CA-125 (in mU/L) Median IQR RMI Median IQR

8

Patients with an adnexal mass (n = 68) Borderline Invasive Benign mass carcinoma carcinoma (n = 11) (n=3) (n=54)

48 25 – 56

58 36 – 75

44 40 – 47

63 59 – 73

-

2 3 4 2 -

3 -

5 2 1 1 45

-

-

1 2 -

3 1 1 2 1 3 1 8 14 20

26 18

4 7

3 -

6 48

-

29 21 – 68

17 15 – 2494

310 78 – 883

-

105 74 – 348

51 45 – 7482

2076 592 – 7052