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Identifying genes underlying cancer development is critical to cancer biologyand has important implications across prevention, diagnosis and treatment.Cancer sequencing studies aim at discovering genes with high frequencies ofsomatic mutations in specific types of cancer, as these genes are potentialdriving factors (drivers) for cancer development. We introduce a hierarchicalBayesian methodology to estimate gene-specific mutation rates and driverprobabilities from somatic mutation data and to shed light on the overallproportion of drivers among sequenced genes. Our methodology applies todifferent experimental designs used in practice, including one-stage, two-stageand candidate gene designs. Also, sample sizes are typically small relative tothe rarity of individual mutations. Via a shrinkage method borrowing strengthfrom the whole genome in assessing individual genes, we reinforce inference andaddress the selection effects induced by multistage designs. Our simulationstudies show that the posterior driver probabilities provide a nearly unbiasedfalse discovery rate estimate. We apply our methods to pancreatic and breastcancer data, contrast our results to previous estimates and provide estimatedproportions of drivers for these two types of cancer.

Hierarchical Bayesian analysis of somatic mutation data in cancer