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.
|Number of pages||9|
|State||Published - May 1 2021|
Bibliographical noteFunding Information:
This work was supported by the National Institutes of Health [R21CA205778, UL1TR001998, P20GM103436-15 and the Cloud Credits Model Pilot, a component of the National Institutes of Health Big Data to Knowledge (BD2K) program]; the Kentucky Lung Cancer Research Program [PO2 415 1400004000, PO2 415 1600001032]; and the Biostatistics and Bioinformatics Shared Resource Facility of the University of Kentucky Markey Cancer Center [P30CA177558].
© The Author(s) 2020. Published by Oxford University Press. All rights reserved.
ASJC Scopus subject areas
- Statistics and Probability
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics