An Integrated Approach to Uncover Drivers of Cancer

SummarySystematic characterization of cancer genomes has revealed a staggering number of diverse aberrations that differ among individuals, such that the functional importance and physiological impact of most tumor genetic alterations remain poorly defined. We developed a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. We demonstrate the utility of this framework using a melanoma data set. Our analysis correctly identified known drivers of melanoma and predicted multiple tumor dependencies. Two dependencies, TBC1D16 and RAB27A, confirmed empirically, suggest that abnormal regulation of protein trafficking contributes to proliferation in melanoma. Together, these results demonstrate the ability of integrative Bayesian approaches to identify candidate drivers with biological, and possibly therapeutic, importance in cancer.

Graphical AbstractFigure optionsDownload high-quality image (432 K)Download as PowerPoint slideHighlights
► Genetic aberrations and phenotypic signature together identify drivers and their roles
► Expression of a driver, not its copy number, drives phenotype
► TBC1D16 and RAB27A, vesicular trafficking genes, are dependencies in melanoma