A generative Bayesian model to identify cancer driver genes

Cancer is a disease characterized largely by the accumulation of somatic mutations during the lifetime of a patient. Distinguishing driver mutations from passenger mutations had posed a challenge in modern cancer research. With the state of art of microarray technologies and clinical studies, a large numbers of candidate genes are extracted. Extracting informative genes out of them is essential. In our project we aim to find the cancer driver genes using somatic mutation data and protein protein interaction data. We developed a generative mixture model coupled with Bayesian parameter estimation to estimate background mutation rates and driver probabilities of each gene as well as the proportion of drivers among all sequenced genes. We choose suitable prior distributions for modelling both driver probabilities and background mutations of each gene. We apply our method to ovarian cancer data and numerically estimated the solution. Upon convergence, we are able to discover and identify some new candidate cancer driver genes.