Mar 7, 2019

CaDrA: A Computational Framework for Performing Candidate Driver Analyses Using Genomic Features

Frontiers in Genetics
Vinay K KarthaStefano Monti


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...Continue Reading

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Mentioned in this Paper

The Cancer Genome Atlas
Somatic Mutation
Pharmacologic Substance
Protein S
Tumor Cells, Malignant
Genomic DNA

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