Abstract B20: Discovery of combination therapies in a pan-cancer context through functional complementarity and convergence analysis of oncogenic drivers

Abstract

Abstract Inherent genetic alterations and tissue-specific variations in cancer present a range of unique vulnerabilities which can be targeted by precision cancer therapies. An understanding of these alterations is a crucial first step in developing novel therapeutic hypotheses in a personalized context. An unbiased method to correlate responses to treatment with small-molecules is cancer cell line (CCL) sensitivity profiling. This allows the understanding of single-agent therapies and associated mechanisms of resistance by employing unbiased combination screening. However, performing such studies in a principled manner to understand multiple potential combinations is limited by the large scale of the required experiments. The area under the concentration-response curve can be used as a measure of sensitivity and the relationship between the sensitivity profiles for a large number of drugs helps build a network based on similarity of activity. Hence, it would be possible to identify ‘modules' or ‘clusters' that correspond to small molecules with highly correlated response across a number of cell lines. However, these clusters are often non-informative in predicting or determining potential combinatorial treatments. By leveraging recent whole-exome and RNA sequencing efforts across a diverse panel of human cancer cell lines coupled with small-molecule sensitivity information, this study aims at applying a pan-cancer exome-wide approach to identify potentially synergistic drug combinations. We define a ‘feature' as an alteration resulting from a single nucleotide variant, genomic amplification or deletion. We first perform feature selection to extract a functionally coupled set of genomic alterations using drug sensitivity as the phenotypic readout. This was achieved through an existing information-theoretic framework which iteratively maximizes the conditional information coefficient of the each potential feature with the target phenotype conditioned on prior selected features. We then integrate gene expression profiles into the model through a regression-based approach. Incorporating sensitivity measurements across a set of 545 small molecules allow us to derive functionally complementary genomic alterations unique to each drug. We find that our model is capable of identifying distinct features even for sets of small molecules that are known to have the same oncogenic target thereby revealing the mechanisitic intricacies that underlie drug activity. This knowledge transforms the drug similarity network. We notice that different small molecules functionally correspond to partially overlapping sets of genomic alterations which belong to the same signalling pathway. Therefore, this enables targeted identification of small molecules or their combinations which are specifically effective against a spectrum of genomic or transcriptomic alterations. We further expand the model to discover features that could confer resistance to therapy. For this case, we systematically identify a number of genomic deletions in tumor suppressors, epigenetic modifiers and genes linked to cell death. Interestingly, these events do not converge onto a single oncogenic pathway, thereby indicating potentially distinct and drug-dependent modes of therapeutic resistance. We believe that the proposed framework presents an unbiased method towards revealing crucial relationships and prospective synergies between different classes of targeted therapeutics. We anticipate that this approach will serve as a template for future efforts focusing on discovery of predictive biomarkers of small molecule sensitivity. Citation Format: Karthik Murugadoss, Manolis Kellis. Discovery of combination therapies in a pan-cancer context through functional complementarity and convergence analysis of oncogenic drivers [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr B20.