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

PLoS Computational Biology
Songjian LuXinghua Lu

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 approa...Continue Reading

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Citations

Jan 24, 2017·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Junhua Zhang, Shihua Zhang
Mar 11, 2016·PLoS Computational Biology·Yoo-Ah KimTeresa M Przytycka
Mar 19, 2016·Human Mutation·Abhishek Niroula, Mauno Vihinen
May 2, 2018·Scientific Reports·Francesco IorioJulio Saez-Rodriguez
Sep 22, 2020·Briefings in Bioinformatics·Gunnar VölkelHans A Kestler
May 18, 2021·The American Journal of Pathology·Zhongguang ChenJunyan Tao

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Methods Mentioned

BETA
SGA
xenografts

Software Mentioned

Dendrix
IPA
uw
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De n ovo Dri ver Exclusivity ( Dendrix [
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top200
Dendix

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