PathMEx: Pathway-Based Mutual Exclusivity for Discovering Rare Cancer Driver Mutations

摘要

The genetic material we carry today is different from that we were born with: our DNA is prone to mutations. Some of these mutations can make a cell divide without control, resulting in a growing tumor. Typically, in a cancer sample from a patient, a large number of mutations can be detected, and only a few of those are drivers - mutations that positively contribute to tumor growth. The majority are passenger mutations that either accumulated before the onset of the disease but did not cause it, or are byproducts of the genetic instability of cancer cells. One of the key questions in understanding the process of cancer development is which mutations are drivers, and should be analyzed as potential diagnostic markers or targets for therapeutics, and which are passengers. We propose PathMEx, a novel method based on simultaneous optimization of patient coverage, mutation mutual exclusivity, and pathway overlap among putative cancer driver genes. Compared to state-of-the-art method Dendrix, the proposed algorithm finds sets of putative driver genes of higher quality in three sets of cancer samples: brain, lung, and breast tumors. The genes in the solutions belong to pathways with known associations with cancer. The results show that PathMEx is a tool that should be part of a state-of-the-art toolbox in the driver gene discovery pipeline. It can help detect low-frequency driver genes that can be missed by existing methods.