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

Lu, S and Lu, KN and Cheng, SY and Hu, B and Ma, X and Nystrom, N and Lu, X (2015) Identifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive Sets. PLoS Computational Biology, 11 (8). ISSN 1553-734X

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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.

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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Lu, Ssongjian@pitt.eduSONGJIAN
Lu, KN
Cheng, SY
Hu, B
Ma, X
Nystrom, N
Lu, Xxinghua@pitt.eduXINGHUA
Contributors:
ContributionContributors NameEmailPitt UsernameORCID
EditorBeerenwinkel, NikoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date: 1 August 2015
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: PLoS Computational Biology
Volume: 11
Number: 8
DOI or Unique Handle: 10.1371/journal.pcbi.1004257
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Biomedical Informatics
Refereed: Yes
ISSN: 1553-734X
Date Deposited: 23 Aug 2016 13:43
Last Modified: 30 Mar 2021 13:56
URI: http://d-scholarship.pitt.edu/id/eprint/28511

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