Dataset Information

Mutation-profile-based methods for understanding selection forces in cancer somatic mutations: a comparative analysis.

ABSTRACT: 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 (C_N /C_S ) and normal populations (p_N /p_S ). 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 C_N /C_S values of human genes in cancer genomes compared with the p_N /p_S values in human populations. The C_N /C_S 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.

SUBMITTER: Zhou Z

PROVIDER: S-EPMC5601697 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Mutation-profile-based methods for understanding selection forces in cancer somatic mutations: a comparative analysis.

Zhou Zhan Z Zou Yangyun Y Liu Gangbiao G Zhou Jingqi J Wu Jingcheng J Zhao Shimin S Su Zhixi Z Gu Xun X

Oncotarget 20170719 35

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 s ...[more]

PMID: 28938601

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Project description:Spatial heterogeneity in tumors is generally thought to result from branching clonal evolution driven by random mutations that accumulate during tumor development. However, this concept rests on the implicit assumption that cancer cells never evolve to a fitness maximum because they can always acquire mutations that increase proliferative capacity. In this study, we investigated the validity of this assumption. Using evolutionary game theory, we demonstrate that local cancer cell populations will rapidly converge to the fittest phenotype given a stable environment. In such settings, cellular spatial heterogeneity in a tumor will be largely governed by regional variations in environmental conditions, for example, alterations in blood flow. Model simulations specifically predict a common spatial pattern in which cancer cells at the tumor-host interface exhibit invasion-promoting, rapidly proliferating phenotypic properties, whereas cells in the tumor core maximize their population density by promoting supportive tissue infrastructures, for example, to promote angiogenesis. We tested model predictions through detailed quantitative image analysis of phenotypic spatial distribution in histologic sections of 10 patients with stage 2 invasive breast cancers. CAIX, GLUT1, and Ki67 were upregulated in the tumor edge, consistent with an acid-producing invasive, proliferative phenotype. Cells in the tumor core were 20% denser than the edge, exhibiting upregulation of CAXII, HIF-1Î±, and cleaved caspase-3, consistent with a more static and less proliferative phenotype. Similarly, vascularity was consistently lower in the tumor center compared with the tumor edges. Lymphocytic immune responses to tumor antigens also trended to higher level in the tumor edge, although this effect did not reach statistical significance. Like invasive species in nature, cancer cells at the leading edge of the tumor possess a different phenotype from cells in the tumor core. Our results suggest that at least some of the molecular heterogeneity in cancer cells in tumors is governed by predictable regional variations in environmental selection forces, arguing against the assumption that cancer cells can evolve toward a local fitness maximum by random accumulation of mutations. Cancer Res; 76(11); 3136-44. ©2016 AACR.

Project description:Exploring the molecular mechanisms of breast cancer is essential for the early prediction, diagnosis, and treatment of cancer patients. The large scale of data obtained from the high-throughput sequencing technology makes it difficult to identify the driver mutations and a minimal optimal set of genes that are critical to the classification of cancer. In this study, we propose a novel method without any prior information to identify mutated genes associated with breast cancer. For the somatic mutation data, it is processed to a mutated matrix, from which the mutation frequency of each gene can be obtained. By setting a reasonable threshold for the mutation frequency, a mutated gene set is filtered from the mutated matrix. For the gene expression data, it is used to generate the gene expression matrix, while the mutated gene set is mapped onto the matrix to construct a co-expression profile. In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. In the stage of evaluation, we propose a weighted metric to modify the traditional accuracy to solve the sample imbalance problem. We apply the proposed method to The Cancer Genome Atlas breast cancer data and identify a mutated gene set, among which the implicated genes are oncogenes or tumor suppressors previously reported to be associated with carcinogenesis. As a comparison with the integrative network, we also perform the optimal model on the individual gene expression and the gold standard PMA50. The results show that the integrative network outperforms the gene expression and PMA50 in the average of most metrics, which indicate the effectiveness of our proposed method by integrating multiple data sources, and can discover the associated mutated genes in breast cancer.

Dataset Information

Mutation-profile-based methods for understanding selection forces in cancer somatic mutations: a comparative analysis.

Publications

Mutation-profile-based methods for understanding selection forces in cancer somatic mutations: a comparative analysis.

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