title: Studying tumorigenesis through network evolution and somatic mutational perturbations in the cancer interactome.
abstract: Cells govern biological functions through complex biological networks. Perturbations to networks may drive cells to new phenotypic states, for example, tumorigenesis. Identifying how genetic lesions perturb molecular networks is a fundamental challenge. This study used large-scale human interactome data to systematically explore the relationship among network topology, somatic mutation, evolutionary rate, and evolutionary origin of cancer genes. We found the unique network centrality of cancer proteins, which is largely independent of gene essentiality. Cancer genes likely have experienced a lower evolutionary rate and stronger purifying selection than those of noncancer, Mendelian disease, and orphan disease genes. Cancer proteins tend to have ancient histories, likely originated in early metazoan, although they are younger than proteins encoded by Mendelian disease genes, orphan disease genes, and essential genes. We found that the protein evolutionary origin (age) positively correlates with protein connectivity in the human interactome. Furthermore, we investigated the network-attacking perturbations due to somatic mutations identified from 3,268 tumors across 12 cancer types in The Cancer Genome Atlas. We observed a positive correlation between protein connectivity and the number of nonsynonymous somatic mutations, whereas a weaker or insignificant correlation between protein connectivity and the number of synonymous somatic mutations. These observations suggest that somatic mutational network-attacking perturbations to hub genes play an important role in tumor emergence and evolution. Collectively, this work has broad biomedical implications for both basic cancer biology and the development of personalized cancer therapy.
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University School of Medicine.
authors.forename: Feixiong
authors.initials: F
authors.lastname: Cheng
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University School of Medicine.
authors.forename: Peilin
authors.initials: P
authors.lastname: Jia
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University School of Medicine.
authors.forename: Quan
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University School of Medicine.
authors.forename: Chen-Ching
authors.initials: CC
authors.lastname: Lin
authors.affiliation: Department of Ecology and Evolution, University of ChicagoBiodiversity Research Center and Genomics Research Center, Academia Sinica, Taipei, Taiwan.
authors.forename: Wen-Hsiung
authors.initials: WH
authors.lastname: Li
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University School of MedicineDepartment of Cancer Biology, Vanderbilt University School of MedicineDepartment of Psychiatry, Vanderbilt University School of MedicineCenter for Quantitative Sciences, Vanderbilt University Medical Center zhongming.zhao@vanderbilt.edu.
authors.forename: Zhongming
authors.initials: Z
authors.lastname: Zhao
mesh_terms: D063646:Carcinogenesis / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D053263:Gene Regulatory Networks; D020043:Genes, Essential; D006801:Humans; D008957:Models, Genetic; D009154:Mutation; D025941:Protein Interaction Mapping / Q000379:methods; D012641:Selection, Genetic
keywords: TCGA; network evolution; network-attacking perturbation; somatic mutation; tumorigenesis