Cancer driver gene discovery through an integrative genomics approach in a non-parametric Bayesian framework

Abstract

Motivation: Comprehensive catalogue of genes that drive tumor initiation and progression in cancer is key to advancing diagnostics, therapeutics and treatment. Given the complexity of cancer, the catalogue is far from complete yet. Increasing evidence shows that driver genes exhibit consistent aberration patterns across multiple-omics in tumors. In this study, we aim to leverage complementary information encoded in each of the omics data to identify novel driver genes through an integrative framework. Specifically, we integrated mutations, gene expression, DNA copy numbers, DNA methylation and protein abundance, all available in The Cancer Genome Atlas (TCGA) and developed iDriver, a non-parametric Bayesian framework based on multivariate statistical modeling to identify driver genes in an unsupervised fashion. iDriver captures the inherent clusters of gene aberrations and constructs the background distribution that is used to assess and calibrate the confidence of driver genes identified through multi-dimensional genomic data. Results: We applied the method to 4 cancer types in TCGA and identified candidate driver genes that are highly enriched with known drivers. (e.g.: P < 3.40 × 10-36 for breast cancer). We are particularly interested in novel genes and observed multiple lines of supporting evidence. Using systematic evaluation from multiple independent aspects, we identified 45 candidate driver genes that were not previously known across these 4 cancer types. The finding has important implications that integrating additional genomic data with multivariate statistics can help identify cancer drivers and guide the next stage of cancer genomics research.