Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors

• Published in: BMC medical genomics Vol. 4; no. 1; p. 34
• Main Authors: Miller, Christopher A, Settle, Stephen H, Sulman, Erik P, Aldape, Kenneth D, Milosavljevic, Aleksandar
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 14.04.2011; BioMed Central; BMC
• Subjects: Cancer; Computer Simulation; Development and progression; E1A-Associated p300 Protein - genetics; Gene mutations; Gene Regulatory Networks - genetics; Genetic aspects; Genome, Human - genetics; Glioblastoma - genetics; Gliomas; Health aspects; Humans; Mutation - genetics; Neoplasms - genetics; Relapse; Risk factors; Signal Transduction - genetics; Survival Analysis; United States
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/1755-8794-4-34

Assays of multiple tumor samples frequently reveal recurrent genomic aberrations, including point mutations and copy-number alterations, that affect individual genes. Analyses that extend beyond single genes are often restricted to examining pathways, interactions and functional modules that are already known. We present a method that identifies functional modules without any information other than patterns of recurrent and mutually exclusive aberrations (RME patterns) that arise due to positive selection for key cancer phenotypes. Our algorithm efficiently constructs and searches networks of potential interactions and identifies significant modules (RME modules) by using the algorithmic significance test. We apply the method to the TCGA collection of 145 glioblastoma samples, resulting in extension of known pathways and discovery of new functional modules. The method predicts a role for EP300 that was previously unknown in glioblastoma. We demonstrate the clinical relevance of these results by validating that expression of EP300 is prognostic, predicting survival independent of age at diagnosis and tumor grade. We have developed a sensitive, simple, and fast method for automatically detecting functional modules in tumors based solely on patterns of recurrent genomic aberration. Due to its ability to analyze very large amounts of diverse data, we expect it to be increasingly useful when applied to the many tumor panels scheduled to be assayed in the near future.