Abstract 279A: Cell panel profiling of 162 small molecule therapeutics identifies response biomarkers for PARP, BET-family and proteasome inhibitors

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Abstract The collective effort of cancer research has resulted in a large diversity of small molecule therapeutics that are tested clinically as precision medicine in specific patient populations. The applications of these therapeutics could be further extended and refined by the identification of new genomic biomarkers that are predictive for response. One of the best ways to do this, is by in vitro profiling in cancer cell line panels. Oncolines is a panel of 102 genetically characterized cell lines from diverse tumour origins, on which proliferation assays are run in parallel. Earlier analysis has shown that the Oncolines workflow generates highly reproducible data, as required for biomarker discovery [1]. In this cell panel, we profiled 162 different cancer therapeutics, including many standard of care chemotherapeutic agents, approved and pre-clinical kinase inhibitors, epigenetic modulators and compounds acting by other mechanisms. Assays were based on ATP-lite read-out, with a nine-point duplicate dilution series of the compounds. Drug response was quantified by manual curve fitting. Response was associated with the genomic status of the cell lines as retrieved from the COSMIC and Cancer Cell Line Encyclopedia (CCLE) databases. Mutations in patient hotspot locations and copy number changes in well-characterized cancer genes were used as input for ANOVA tests. Basal gene expression levels of 383 clinically actionable genes and sets of perturbation-response genes were applied to pathway analysis. Because various alternative metrics were proposed recently to quantify cell line response, we first investigated which metric resulted in the most sensitive identification of biomarkers. Responses were quantified as IC50, GI50, growth rate inhibition (GR50), DSS score, AUC and maximum effect. Known responder populations were analysed such as BRAF(V600E) mutant cell lines for BRAF inhibitors and EGFR amplified lines for EGFR inhibitors, to identify the optimal response metric for biomarker identification. The full spectrum of cell line responses was analyzed with a variety of clustering and principal component methods to define groups of therapeutic agents based on common biological mechanisms. We further analyzed novel clusters of the Poly (ADP-ribose) polymerase (PARP) inhibitors olaparib, niraparib, rucaparib, and talazoparib, the proteasome inhibitors MG-132, bortezomib and carfilzomib, the ubiquitin activating enzyme inhibitor MLN-7243 (TAK-243) and the BET-family inhibitors JQ1 and I-BET-762 and identified several response biomarkers based on mutation, gene expression and pathway analysis.