Abstract
The identification of genetic alteration combinations as drivers of a given phenotypic outcome, such as drug sensitivity, gene or protein expression, and pathway activity, is a challenging task that is essential to gaining new biological insights and to discovering therapeutic targets. Existing methods designed to predict complementary drivers of such outcomes lack analytical flexibility, including the support for joint analyses of multiple genomic alteration types, such as somatic mutations and copy number alterations, multiple scoring functions, and rigorous significance and reproducibility testing procedures. To address these limitations, we developed Candidate Driver Analysis or CaDrA, an integrative framework that implements a step-wise heuristic search approach to identify functionally relevant subsets of genomic features that, together, are maximally associated with a specific outcome of interest. We show CaDrA's overall high sensitivity and specificity for typically sized multi-omic datasets using simulated data, and demonstrate CaDrA's ability to identify known mutations linked with sensitivity of cancer cells to drug treatment using data from the Cancer Cell Line Encyclopedia (CCLE). We further apply CaDrA to identify novel regulators of oncogenic activity mediated by Hippo signaling pathway effectors YAP and TAZ in primary breast cancer tumors using data from The Cancer Genome Atlas (TCGA), which we functionally validate in vitro. Finally, we use pan-cancer TCGA protein expression data to show the high reproducibility of CaDrA's search procedure. Collectively, this work demonstrates the utility of our framework for supporting the fast querying of large, publicly available multi-omics datasets, including but not limited to TCGA and CCLE, for potential drivers of a given target profile of interest.