CoMEt: A Statistical Approach to Identify Combinations of Mutually Exclusive Alterations in Cancer

TL;DR

CoMEt is a novel statistical algorithm that identifies mutually exclusive genetic alterations in cancer without prior pathway knowledge, improving detection of cancer driver mutations and revealing new insights into cancer genetics.

Contribution

It introduces an exact statistical test and a comprehensive framework for de novo detection of mutually exclusive alterations, surpassing existing methods in sensitivity and scope.

Findings

Outperforms existing methods on simulated and real data.

Identifies known and novel mutually exclusive gene sets in cancer.

Reveals potential new cancer driver genes.

Abstract

Cancer is a heterogeneous disease with different combinations of genetic and epigenetic alterations driving the development of cancer in different individuals. While these alterations are believed to converge on genes in key cellular signaling and regulatory pathways, our knowledge of these pathways remains incomplete, making it difficult to identify driver alterations by their recurrence across genes or known pathways. We introduce Combinations of Mutually Exclusive Alterations (CoMEt), an algorithm to identify combinations of alterations de novo, without any prior biological knowledge (e.g. pathways or protein interactions). CoMEt searches for combinations of mutations that exhibit mutual exclusivity, a pattern expected for mutations in pathways. CoMEt has several important feature that distinguish it from existing approaches to analyze mutual exclusivity among alterations. These include: an exact statistical test for mutual exclusivity that is more sensitive in detecting combinations containing rare alterations; simultaneous identification of collections of one or more combinations of mutually exclusive alterations; simultaneous analysis of subtype-specific mutations; and summarization over an ensemble of collections of mutually exclusive alterations. These features enable CoMEt to robustly identify alterations affecting multiple pathways, or hallmarks of cancer. We show that CoMEt outperforms existing approaches on simulated and real data. Application of CoMEt to hundreds of samples from four different cancer types from TCGA reveals multiple mutually exclusive sets within each cancer type. Many of these overlap known pathways, but others reveal novel putative cancer genes. *Equal contribution.