title: Identifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive Sets.
abstract: 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.
authors: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors: Songjian
authors: S
authors: Lu
authors: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors: Kevin N
authors: KN
authors: Lu
authors: Department of Neurology, Northwestern Brain Tumor Institute, Center for Genetic Medicine, The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.
authors: Shi-Yuan
authors: SY
authors: Cheng
authors: Department of Neurology, Northwestern Brain Tumor Institute, Center for Genetic Medicine, The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.
authors: Bo
authors: B
authors: Hu
authors: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors: Xiaojun
authors: X
authors: Ma
authors: Pittsburgh Supercomputing Center, Pittsburgh, Pennsylvania, United States of America.
authors: Nicholas
authors: N
authors: Nystrom
authors: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors: Xinghua
authors: X
authors: Lu
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D053263:Gene Regulatory Networks / Q000235:genetics; D016678:Genome / Q000235:genetics*; D006801:Humans; D009154:Mutation / Q000235:genetics; D009369:Neoplasms / Q000235:genetics*