TY - JOUR T1 - A New Mutation-Profile-Based Method for Understanding the Evolution of Cancer Somatic Mutations JF - bioRxiv DO - 10.1101/021147 SP - 021147 AU - Zhan Zhou AU - Yangyun Zou AU - Gangbiao Liu AU - Jingqi Zhou AU - Shiming Zhao AU - Zhixi Su AU - Gu Xun Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/06/20/021147.abstract N2 - Human genes perform different functions and exhibit different effects on fitness in cancer and normal cell populations. Here, we present an evolutionary approach to measuring the selective pressure on human genes in cancer and normal cell genomes using the well-known dN/dS (nonsynonymous to synonymous substitution rate) ratio. We develop a new method called the mutation-profile-based Nei-Gojobori (mpNG) method, which applies sample-specific nucleotide substitution profiles instead of conventional substitution models to calculating dN/dS ratios in cancer and normal populations. Using 7,042 exome sequences from tumor-normal pairs, and germline variations from 6,500 exome sequences (ESP6500) as references, we found a significant relaxation of selective constraint for human genes in cancer cells. Compared with previous studies that focused on positively selected genes in cancer genomes, which potentially represent the driving force behind tumor initiation and development, we employed an alternative approach to identifying cancer constrained genes that strengthen negative selection pressure in tumor cells. As a conservative estimate of positively and negatively selected genes in cancer, we found 45 genes under intensified positive selection and 16 genes under strengthened purifying selection in cancer cells compared with germline cells. The cancer-specific positively selected genes are enriched for cancer genes and human essential genes, while several cancer-specific negatively selected genes have been reported as prognostic biomarkers for cancers. Therefore, our computation pipeline used to identify cancer positively and negatively genes may provide useful information for understanding the evolution of cancer somatic mutations. ER -