Mutation-profile-based methods for understanding selection forces in cancer somatic mutations: a comparative analysis
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Zhan Zhou1,4,*, Yangyun Zou1,*, Gangbiao Liu1, Jingqi Zhou1, Jingcheng Wu4, Shimin Zhao2, Zhixi Su1 and Xun Gu3,1
1Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
2State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
3Department of Genetics, Development and Cell Biology, Program of Bioinformatics and Computational Biology, Iowa State University, Ames, Iowa, USA
4College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
*These authors have contributed equally to this work
Zhixi Su, email: firstname.lastname@example.org
Xun Gu, email: email@example.com
Keywords: cancer somatic mutations, mutation profile, natural selection, cancer-associated genes, evolution
Received: October 26, 2016 Accepted: July 12, 2017 Published: July 19, 2017
Human genes exhibit different effects on fitness in cancer and normal cells. Here, we present an evolutionary approach to measure the selection pressure on human genes, using the well-known ratio of the nonsynonymous to synonymous substitution rate in both cancer genomes (CN/CS) and normal populations (pN/pS). A new mutation-profile-based method that adopts sample-specific mutation rate profiles instead of conventional substitution models was developed. We found that cancer-specific selection pressure is quite different from the selection pressure at the species and population levels. Both the relaxation of purifying selection on passenger mutations and the positive selection of driver mutations may contribute to the increased CN/CS values of human genes in cancer genomes compared with the pN/pS values in human populations. The CN/CS values also contribute to the improved classification of cancer genes and a better understanding of the onco-functionalization of cancer genes during oncogenesis. The use of our computational pipeline to identify cancer-specific positively and negatively selected genes may provide useful information for understanding the evolution of cancers and identifying possible targets for therapeutic intervention.
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