Sisheng Liu1, Jinpeng Liu2, Yanqi Xie3, Tingting Zhai4, Eugene W Hinderer3, Arnold J Stromberg4, Nathan L Vanderford2,5, Jill M Kolesar2,6, Hunter N B Moseley2,3, Li Chen2,7, Chunming Liu2,3, Chi Wang2,7. 1. Adcolony Inc., Bellevue, WA 98004, USA. 2. Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA. 3. Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536, USA. 4. Department of Statistics, University of Kentucky, Lexington, KY 40536, USA. 5. Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40536, USA. 6. Department of Pharmacy Practice and Science, University of Kentucky, Lexington, KY 40536, USA. 7. Department of Internal Medicine, University of Kentucky, Lexington, KY 40536, USA.
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. AVAILABILITY AND IMPLEMENTATION: MEScan is available as an R package at https://github.com/MarkeyBBSRF/MEScan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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. AVAILABILITY AND IMPLEMENTATION: MEScan is available as an R package at https://github.com/MarkeyBBSRF/MEScan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Xing Hua; Paula L Hyland; Jing Huang; Lei Song; Bin Zhu; Neil E Caporaso; Maria Teresa Landi; Nilanjan Chatterjee; Jianxin Shi Journal: Am J Hum Genet Date: 2016-02-18 Impact factor: 11.043
Authors: Bernard Pereira; Suet-Feung Chin; Oscar M Rueda; Hans-Kristian Moen Vollan; Elena Provenzano; Helen A Bardwell; Michelle Pugh; Linda Jones; Roslin Russell; Stephen-John Sammut; Dana W Y Tsui; Bin Liu; Sarah-Jane Dawson; Jean Abraham; Helen Northen; John F Peden; Abhik Mukherjee; Gulisa Turashvili; Andrew R Green; Steve McKinney; Arusha Oloumi; Sohrab Shah; Nitzan Rosenfeld; Leigh Murphy; David R Bentley; Ian O Ellis; Arnie Purushotham; Sarah E Pinder; Anne-Lise Børresen-Dale; Helena M Earl; Paul D Pharoah; Mark T Ross; Samuel Aparicio; Carlos Caldas Journal: Nat Commun Date: 2016-06-06 Impact factor: 14.919