Tumor cell sensitivity to vemurafenib can be predicted from protein expression in a BRAF-V600E basket trial setting

David Page,Molly J Carroll,Carl R Parent,Pamela K Kreeger

doi:10.1186/s12885-019-6175-2

David Page, Molly J Carroll + Show 2 more

Open Access

https://doi.org/10.1186/s12885-019-6175-2

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Abstract

BackgroundGenetics-based basket trials have emerged to test targeted therapeutics across multiple cancer types. However, while vemurafenib is FDA-approved for BRAF-V600E melanomas, the non-melanoma basket trial was unsuccessful, suggesting mutation status is insufficient to predict response. We hypothesized that proteomic data would complement mutation status to identify vemurafenib-sensitive tumors and effective co-treatments for BRAF-V600E tumors with inherent resistance.MethodsReverse Phase Proteomic Array (RPPA, MD Anderson Cell Lines Project), RNAseq (Cancer Cell Line Encyclopedia) and vemurafenib sensitivity (Cancer Therapeutic Response Portal) data for BRAF-V600E cancer cell lines were curated. Linear and nonlinear regression models using RPPA protein or RNAseq were evaluated and compared based on their ability to predict BRAF-V600E cell line sensitivity (area under the dose response curve). Accuracies of all models were evaluated using hold-out testing. CausalPath software was used to identify protein-protein interaction networks that could explain differential protein expression in resistant cells. Human examination of features employed by the model, the identified protein interaction networks, and model simulation suggested anti-ErbB co-therapy would counter intrinsic resistance to vemurafenib. To validate this potential co-therapy, cell lines were treated with vemurafenib and dacomitinib (a pan-ErbB inhibitor) and the number of viable cells was measured.ResultsOrthogonal partial least squares (O-PLS) predicted vemurafenib sensitivity with greater accuracy in both melanoma and non-melanoma BRAF-V600E cell lines than other leading machine learning methods, specifically Random Forests, Support Vector Regression (linear and quadratic kernels) and LASSO-penalized regression. Additionally, use of transcriptomic in place of proteomic data weakened model performance. Model analysis revealed that resistant lines had elevated expression and activation of ErbB receptors, suggesting ErbB inhibition could improve vemurafenib response. As predicted, experimental evaluation of vemurafenib plus dacomitinb demonstrated improved efficacy relative to monotherapies.Conclusions: Combined, our results support that inclusion of proteomics can predict drug response and identify co-therapies in a basket setting.

Highlights

Genetics-based basket trials have emerged to test targeted therapeutics across multiple cancer types
Tumors exhibit heterogeneous protein expression and sensitivity to vemurafenib To examine the ability of protein expression and activity to predict response in BRAF-V600E tumor cells to the BRAF inhibitor vemurafenib, appropriate cell line models were explored
Of the cell lines characterized by the Cancer Cell Line Encyclopedia (CCLE) that possess a BRAF-V600E mutation (n = 94), and the Reverse Phase Protein Array (RPPA) data available from the MD Anderson Cell Line Project (MCLP, n = 650), 26 overlapped and had data pertaining to vemurafenib sensitivity in the Cancer Therapeutic Response Portal (CTRP) (Fig. 1 a, Additional file 1: Table S1)

Summary

Introduction

Genetics-based basket trials have emerged to test targeted therapeutics across multiple cancer types. There has been a shift to add targeted therapeutics (e.g., Herceptin) to standard cancer treatment approaches such as surgery, chemotherapy, and radiation. This is due, in part, to the emergence of largescale DNA sequence analysis that has identified actionable genetic mutations across multiple tumor types [1, 2]. Conducting a clinical trial for a targeted therapeutic can be challenging due to slow patient accrual, for tumor types that harbor the mutation at a low frequency [2]. Results of the basket clinical trial of vemurafenib for non-melanoma tumors with the BRAF-V600E mutation indicated that other cancers, including colorectal, lung, and ovarian responded poorly to vemurafenib monotherapy [7]. A subset of colorectal patients achieved a partial response when combined with cetuximab, suggesting that the effects of vemurafenib are subject to the larger cellular network context

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