Identifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive Sets

An important goal of cancer genomic research is to identify the driving pathways underlying disease mechanisms and the heterogeneity of cancers. It is well known that somatic genome alterations (SGAs) affecting the genes that encode the proteins within a common signaling pathway exhibit mutual exclusivity, in which these SGAs usually do not co-occur in a tumor. With some success, this characteristic has been utilized as an objective function to guide the search for driver mutations within a pathway. However, mutual exclusivity alone is not sufficient to indicate that genes affected by such SGAs are in common pathways. Here, we propose a novel, signal-oriented framework for identifying driver SGAs. First, we identify the perturbed cellular signals by mining the gene expression data. Next, we search for a set of SGA events that carries strong information with respect to such perturbed signals while exhibiting mutual exclusivity. Finally, we design and implement an efficient exact algorithm to solve an NP-hard problem encountered in our approach. We apply this framework to the ovarian and glioblastoma tumor data available at the TCGA database, and perform systematic evaluations. Our results indicate that the signal-oriented approach enhances the ability to find informative sets of driver SGAs that likely constitute signaling pathways.

An important goal of studying cancer genomics is to identify critical pathways that, when perturbed by somatic genomic alterations (SGAs) such as somatic mutations, copy number alterations and epigenomic alterations, cause cancers and underlie different clinical phenotypes. In this study, we present a framework for discovering perturbed signaling pathways in cancers by integrating genome alteration data and transcriptomic data from the Cancer Genome Atlas (TCGA) project. Since gene expression in a cell is regulated by cellular signaling systems, we used transcriptomic changes to reveal perturbed cellular signals in each tumor. We then combined the genomic alteration data to search for SGA events across multiple tumors that affected a common signal, thus identifying the candidate members of cancer pathways. Our results demonstrate the advantage of the signal-oriented pathway approach over previous methods.