Finding Driver Pathways in Cancer: Models and Algorithms

Abstract

AbstractCancer sequencing projects are now measuring somatic mutations in large numbers of cancer genomes. A key challenge in interpreting these data is to distinguish driver mutations, mutations important for cancer development, from passenger mutations that have accumulated in somatic cells but without functional consequences. A common approach to identify genes harboring driver mutations is a single gene test that identifies individual genes that are mutated in a significant number of cancer genomes. However, the power of this test is reduced by the mutational heterogeneity in most cancer genomes and by the necessity of estimating the background mutation rate (BMR). We investigate the problem of discovering driver pathways, groups of genes containing driver mutations, directly from cancer mutation data and without prior knowledge of pathways or other interactions between genes. We introduce two generative models of somatic mutations in cancer and study the algorithmic complexity of discovering driver pathways in both models. We show that a single gene test for driver genes is highly sensitive to the estimate of the BMR. In contrast, we show that an algorithmic approach that maximizes a straightforward measure of the mutational properties of a driver pathway successfully discovers these groups of genes without an estimate of the BMR. Moreover, this approach is also successful in the case when the observed frequencies of passenger and driver mutations are indistinguishable, a situation where single gene tests fail.KeywordsMarkov Chain Monte CarloSomatic MutationGreedy AlgorithmDriver MutationDriver GeneThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.