MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations

Sisheng Liu; Jinpeng Liu; Yanqi Xie; Tingting Zhai; Eugene W Hinderer; Arnold J Stromberg; Nathan L Vanderford; Jill M Kolesar; Hunter N B Moseley; Li Chen; Chunming Liu; Chi Wang

doi:10.1093/bioinformatics/btaa957

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MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations

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MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations

Sisheng Liu, Jinpeng Liu, Yanqi Xie, Tingting Zhai, Eugene W Hinderer, Arnold J Stromberg, Nathan L Vanderford, Jill M Kolesar, Hunter N B Moseley, Li Chen, …

Bioinformatics (Oxford, England), Vol.37(9), pp.1189-1197

06/09/2021

DOI: 10.1093/bioinformatics/btaa957

PMCID: PMC8189684

PMID: 33165532

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Abstract

Motivation Cancer somatic driver mutations associated with genes within a pathway often show a mutually exclusive pattern across a cohort of patients. This mutually exclusive mutational signal has been frequently used to distinguish driver from passenger mutations and to investigate relationships among driver mutations. Current methods for de novo discovery of mutually exclusive mutational patterns are limited because the heterogeneity in background mutation rate can confound mutational patterns, and the presence of highly mutated genes can lead to spurious patterns. In addition, most methods only focus on a limited number of pre-selected genes and are unable to perform genome-wide analysis due to computational inefficiency. Results We introduce a statistical framework, MEScan, for accurate and efficient mutual exclusivity analysis at the genomic scale. Our framework contains a fast and powerful statistical test for mutual exclusivity with adjustment of the background mutation rate and impact of highly mutated genes, and a multi-step procedure for genome-wide screening with the control of false discovery rate. We demonstrate that MEScan more accurately identifies mutually exclusive gene sets than existing methods and is at least two orders of magnitude faster than most methods. By applying MEScan to data from four different cancer types and pan-cancer, we have identified several biologically meaningful mutually exclusive gene sets.

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Title: Subtitle: MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations
Creators: Sisheng Liu - Bellevue Hospital Center
Jinpeng Liu - University of Kentucky
Yanqi Xie - University of Kentucky
Tingting Zhai - University of Kentucky
Eugene W Hinderer - University of Kentucky
Arnold J Stromberg - University of Kentucky
Nathan L Vanderford - Markey Cancer Center
Jill M Kolesar - University of Kentucky
Hunter N B Moseley - Bellevue Hospital Center
Li Chen - Markey Cancer Center
Chunming Liu - Markey Cancer Center
Chi Wang - Markey Cancer Center
Resource Type: Journal article
Publication Details: Bioinformatics (Oxford, England), Vol.37(9), pp.1189-1197
DOI: 10.1093/bioinformatics/btaa957
PMID: 33165532
PMCID: PMC8189684
NLM abbreviation: Bioinformatics
ISSN: 1367-4803
eISSN: 1367-4811
Publisher: Oxford University Press
Grant note: PO2 415 1400004000; PO2 415 1600001032 / ; ; P30CA177558 / ; ; ; ; R21CA205778; UL1TR001998; P20GM103436-15 / ; ;
Language: English
Date published: 06/09/2021
Academic Unit: Pharmacy; Pharmaceutical Sciences and Experimental Therapeutics
Record Identifier: 9984696547902771

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