u Bioinformatic analysis of fibroblast-mediated therapy resistance in HER2+ breast cancer

Bioinformatic analysis of fibroblast-mediated therapy resistance in HER2+ breast cancer

Jacob C. McDonalda and Ioannis K. Zervantonakisa, b

aTumor Microenvironment Engineering Laboratory, Department of Bioengineering, bUPMC Hillman Cancer Center

Jacob C. McDonald Jacob McDonald is a junior bioengineering student at the University of Pittsburgh with a focus in cellular engineering. After graduating, he plans to further his education and bioengineering research by attending graduate school.

Ioannis Zervantonakis is an Assistant Professor at the Department of Bioengineering, University of Pittsburgh and at the Hillman Cancer Center UPMC. He serves as an Early-Career Associate Scientific Advisor to Science Translational Medicine, has received the 2020 Hillman Early-Career Fellow Ioannis K. Zervantonakis for Innovative Cancer Research Award and his laboratory is funded by an Elsa Pardee Foundation Award.

Significance Statement

Interactions between tumor cells and stromal fibroblasts in the tumor microenvironment have been shown to mediate drug resistance in some HER2 overexpressing breast cancers. This study identifies signaling pathways within tumor cells that are critical to fibroblast-mediated drug resistance and proposes targeting specific proteins to restore treatment sensitivity.

Category: Computational Research

Keywords: breast cancer, tumor microenvironment,

fibroblasts, drug resistance

Abstract

Multiple targeted therapies have been developed for the treatment of HER2 overexpressing (HER2+) breast cancers, such as the dual HER2/EGFR kinase inhibitor lapatinib, but many patients eventually develop resistance to these treatments. One proposed cause of HER2 therapy resistance is the interaction between tumor cells and stromal fibroblasts in the tumor microenvironment, which has been reported to activate signaling pathways in HER2+ tumor cells that lead to a decrease in drug sensitivity. Here, we identify and compare both protein-expression and gene-expression signatures of breast cancer cells that exhibit fibroblast-mediated lapatinib resistance. We then integrate proteomic data with cell treatment response data to identify optimal protein targets for combination therapy that may restore lapatinib sensitivity in fibroblast-protected cancer cells. Our signatures for fibroblast protection suggest that fibroblasts reduce lapatinib sensitivity in HER2+ breast cancer cells through reactivation of the PI3K/Akt and mTOR signaling pathways, which results in increased cell cycle signaling and changes in lipid metabolism. Specifically, we found that lapatinib resulted in greater inhibition of proteins in the Akt/mTOR, cell cycle, and lipid synthesis pathways in cell lines that exhibited high sensitivity to lapatinib (i.e. low cell viability). Together, our findings suggest that inhibition of these pathways may be sufficient to restore lapatinib sensitivity in fibroblast-protected breast cancer cells.

1. Introduction

HER2 overexpressing (HER2+) breast cancer accounts for ~20% of all breast cancer cases [1]. The HER2 receptor is a receptor tyrosine kinase in the epidermal growth factor receptor family (ErbB), and heterodimerization of HER2 with other members of the ErbB family leads to signal transduction through the PI3K/Akt and MAPK survival pathways [2]. As a result, overexpression of HER2 can lead to increased cancer cell survival, proliferation, and invasion.

Multiple targeted therapies have been developed to treat HER2+ breast cancer, such as the dual HER2/ EGFR kinase inhibitor lapatinib. Lapatinib acts by binding to the kinase domain of the HER2 receptor, preventing its autophosphorylation and therefore its ability to transduce pro-survival signaling [3]. However, even though lapatinib treatment may initially be successful in treating HER2+ breast cancers, many patients will eventually develop resistance to these treatments. Several mechanisms of resistance to lapatinib treatment have been proposed, such as the activation of compensatory signaling pathways through other kinases, mutation of the HER2 kinase domain, or gene amplification [3]. Another proposed cause of HER2 therapy resistance is the interaction between tumor cells and stromal fibroblasts in the tumor microenvironment, which can occur through direct cell-cell contact or through the secretion of soluble factors. These tumor-fibroblast interactions have been reported to activate signaling pathways in some HER2+ tumor cell lines, such as the mTOR

and JAK2/STAT3 pathways, which leads to a decrease in lapatinib sensitivity [4, 5].

By performing an unbiased bioinformatic analysis of the tumor cell proteome and transcriptome, this study aimed to identify the signaling pathways within tumor cells that are critical to fibroblast-mediated lapatinib resistance. After identifying the signaling pathways most affected by fibroblast coculture, we then assessed the relationship between tumor cell viability and the level of protein expression in these pathways. We hypothesized that protein expression in the signaling pathways upregulated by fibroblast coculture would directly correlate with tumor cell viability. Specifically, we proposed to identify proteomic predictors of drug sensitivity (proteins whose lapatinib-induced decrease in protein expression correlates with a reduction in cell growth) and drug resistance (proteins whose lapatinib-induced decrease in protein expression correlates with an increase in cell growth).

2. Methods

2.1 Cell Lines and Culture

A panel of six HER2+ breast cancer cell lines was used in this study. The cell lines EFM192, BT474, and HCC202 have been shown to have decreased sensitivity to lapatinib treatment when cocultured with AR22 fibroblasts, and have therefore been classified as fibroblast-protected. The cell lines HCC1954, HCC1419, and SUM225 do not show an increase in viability in fibroblast coculture and are classified as fibroblast-insensitive [4]. Cells were grown in 96 well plates and treated with either 0.1µM lapatinib or with a dimethyl sulfoxide (DMSO) control for 96 hours. For the tumor-fibroblast coculture, fibroblasts were physically separated from the tumor cells using transwell filters, allowing for the exchange of secreted factors between the fibroblasts and tumor cells without cell-cell interactions due to physical contact. Data for these experiments was previously collected and further details of the experiments performed are provided in the supplementary methods in [4]. The focus of this project was a bioinformatic analysis of this data. 2.2 Protein and Gene Signatures

After 48 hours of treatment, protein expression in the tumor cells was quantified using reverse phase protein arrays (RPPA) and gene expression was quantified using RNA sequencing. RPPA data for 409 proteins was obtained as previously described [6], and RNA was extracted using the Qiagen RNA extraction kit and submitted for sequencing to the Harvard Bauer Core.

The fibroblast-protected cell line EFM192 was chosen to derive the fibroblast-protection signatures due to its large increase in viability in AR22 fibroblast coculture [4]. Proteins in lapatinib-treated EFM192 that had a greater than 1.4-fold change in expression when cocultured with AR22 fibroblasts compared to monoculture were selected for the protein expression signature using R. This threshold was chosen to be higher than variation in protein expression between biological replicates, yet low enough to identify multiple proteins for follow-up functional analysis. A total of 52/409 proteins were found to be differentially expressed using these criteria. The gene expression signature was identified using the R package ballgown and consisted of genes with a greater than 2-fold change in expression when cocultured with fibroblasts (q < .05). To identify the cellular functions most significantly affected by fibroblast coculture, Gene Ontology enrichment analysis was performed using R packages clusterProfiler and enrichplot. 2.3 Identification of Protein Targets

Tumor cell response to lapatinib treatment was tracked every 4 hours for 96 hours using IncuCyte live cell analysis. The relationship between lapatinib-induced protein inhibition and tumor cell viability was then assessed by performing a linear regression of the log2 fold change in cell number vs the log2 fold change in protein expression after 48 hours of lapatinib treatment. Proteins from the fibroblast-protection signature whose change in expression had a non-zero correlation with tumor cell viability (p < .05) were identified as potential targets to restore lapatinib sensitivity in fibroblast-protected tumor cells.

Figure 1. Protein Signature - Lapatinib induced protein expression changes (log2 transformed ratios of lapatinib-treated samples normalized by untreated samples) show restoration of pro-survival signaling only in EFM192 and HCC202 fibroblast-protected cell lines that are cocultured with fibroblasts. Blue corresponds to negative log2 ratios indicating protein downregulation while red corresponds to positive log2 ratios indicating protein upregulation. White values indicate no change in expression due to lapatinib treatment.

3. Results

The fibroblast-protection signature consisted of 52 proteins that were identified as being differentially expressed between lapatinib-treated EFM192 monoculture and fibroblast coculture. Compared to monoculture, coculture of lapatinib-treated EFM192 with fibroblasts resulted in ineffective inhibition of proteins in the Akt, mTOR, cell cycle, and lipid metabolism pathways, while the expression of pro-apoptotic proteins was reduced (Fig. 1). This fibroblast-induced effect on protein expression was also observed in the fibroblast-protected cell line HCC202, but it was not as evident in fibroblast-protected BT474 or the fibroblast-insensitive cell line HCC1954.

Like the protein signature, Gene Ontology enrichment analysis also revealed the pro-survival effects of fibroblast coculture, with cell cycle regulation (specifically mitosis) and lipid metabolism regulation being among the most significantly enriched groups of genes (Fig. 2).

Figure 2. Gene Ontology Enrichment Analysis - Top 15 most significantly enriched Gene Ontology terms based on p-value (p < 10-9). Plotted is the distribution of log2(coculture/monoculture) fold changes of the core genes in each Gene Ontology term. A distribution to the right of the vertical line indicates that the cellular function is upregulated as a result of fibroblast coculture.

The extent of protein inhibition due to lapatinib treatment correlated with the viability of tumor cells for 44 of 52 proteins in the fibroblast-protection signature, again including many proteins involved in Akt, mTOR, cell cycle, and lipid metabolism pathways (p < .05, Fig. 3).

Figure 3: Identifying Protein Targets – Lapatinib-induced inhibition of proteins in the protein signature such as phospho-S6 (r2 = .620, p < .001) correlates with the viability of HER2+ cell lines. As the log2 fold change in phospho-S6 expression becomes more negative (greater inhibition), the post-treatment live cell number is reduced. Each dot represents a different biological replicate.

4. Discussion

The protein and gene expression signatures derived for fibroblast-protected cell lines suggest that tumor-fibroblast interactions reduce lapatinib sensitivity through reactivation of the PI3K/Akt and mTOR signaling pathways, which results in increased cell cycle signaling and changes in lipid metabolism. These findings are consistent with previous studies on drug resistance in HER2+ breast cancer,

which have shown that the PI3K/Akt and mTOR pathways can mediate resistance to HER2 targeted drugs such as lapatinib or trastuzumab [7, 8]. Other studies also reveal changes in cellular metabolism as a possible mechanism for drug resistance, such as Ruprecht et al. who found that some lapatinib resistant HER2+ cancers mediate resistance through a reprogramming of glycolytic activity [9].

There does appear to be some heterogeneity in protein response to fibroblast coculture between the fibroblast-protected cell lines. For example, while lapatinib-treated EFM192 and HCC202 show a recovery of proteins in the Akt /mTOR and cell cycle pathways when cocultured with fibroblasts, these proteins still seem to be inhibited in the fibroblast-protected cell line BT474 (Fig. 1). It is possible that fibroblast-protection in BT474 is mediated by mechanisms other than the Akt/mTOR pathways. Also, protein recovery in the lipid synthesis pathway is seen in the cell line EFM192, but much less so in HCC202 or BT474. This suggests that EFM192 may have a higher dependency on lipid synthesis for survival and proliferation than the other fibroblast-protected cell lines. The variation in proteome response to fibroblast coculture between fibroblast-protected cell lines highlights the complexity of fibroblast-mediated lapatinib resistance.

The extent of lapatinib-induced inhibition for proteins in the Akt/mTOR, cell cycle, and lipid synthesis pathways correlated with tumor cell number, supporting our hypothesis that fibroblasts upregulate proteins in tumor cells that directly correlate with tumor cell viability. Our results suggest that inhibition of these pathways may reduce tumor cell viability and restore lapatinib sensitivity in fibroblast-protected cells. It has previously been shown that inhibition of mTOR in combination with lapatinib can overcome fibroblast-mediated lapatinib resistance [4], and based on the results of the linear regression, targeting proteins in the cell cycle or lipid synthesis pathways could also potentially have anti-tumor effects and be used in lapatinib combination therapy. In the future, we plan to perform mechanistic studies to evaluate the causal role of these proteins on drug sensitivity using pharmacological and genetic approaches.

5. Conclusion

It is evident that interactions between cancer cells and the tumor microenvironment play a significant role in sensitivity to drug treatments. Here, we identified several signaling pathways that are activated by fibroblast coculture and could mediate drug resistance in HER2+ breast cancers. Development of lapatinib combination therapies targeting these pathways may help restore the effects of lapatinib treatment in patients who develop resistance. We also highlighted the variation present in proteome response to the presence of fibroblasts, and how different HER2+ cell lines might utilize different mechanisms of resistance. It is important that we continue to gain a better understanding of tumor-fibroblast interactions to develop more effective, patient-specific HER2+ breast cancer treatments.

6. Acknowledgments

Funding was provided by the Department of Bioengineering at the University of Pittsburgh. Fellow lab members provided helpful discussion and advice during the completion of this project. We thank Dr. Gordon Mills and Dr. Yiling Lu for the RPPA studies.

7. References

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