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      Ancient genes establish stress-induced mutation as a hallmark of cancer

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          Abstract

          Cancer is sometimes depicted as a reversion to single cell behavior in cells adapted to live in a multicellular assembly. If this is the case, one would expect that mutation in cancer disrupts functional mechanisms that suppress cell-level traits detrimental to multicellularity. Such mechanisms should have evolved with or after the emergence of multicellularity. This leads to two related, but distinct hypotheses: 1) Somatic mutations in cancer will occur in genes that are younger than the emergence of multicellularity (1000 million years [MY]); and 2) genes that are frequently mutated in cancer and whose mutations are functionally important for the emergence of the cancer phenotype evolved within the past 1000 million years, and thus would exhibit an age distribution that is skewed to younger genes. In order to investigate these hypotheses we estimated the evolutionary ages of all human genes and then studied the probability of mutation and their biological function in relation to their age and genomic location for both normal germline and cancer contexts. We observed that under a model of uniform random mutation across the genome, controlled for gene size, genes less than 500 MY were more frequently mutated in both cases. Paradoxically, causal genes, defined in the COSMIC Cancer Gene Census, were depleted in this age group. When we used functional enrichment analysis to explain this unexpected result we discovered that COSMIC genes with recessive disease phenotypes were enriched for DNA repair and cell cycle control. The non-mutated genes in these pathways are orthologous to those underlying stress-induced mutation in bacteria, which results in the clustering of single nucleotide variations. COSMIC genes were less common in regions where the probability of observing mutational clusters is high, although they are approximately 2-fold more likely to harbor mutational clusters compared to other human genes. Our results suggest this ancient mutational response to stress that evolved among prokaryotes was co-opted to maintain diversity in the germline and immune system, while the original phenotype is restored in cancer. Reversion to a stress-induced mutational response is a hallmark of cancer that allows for effectively searching “protected” genome space where genes causally implicated in cancer are located and underlies the high adaptive potential and concomitant therapeutic resistance that is characteristic of cancer.

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          Most cited references49

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            TimeTree: a public knowledge-base of divergence times among organisms.

            Biologists and other scientists routinely need to know times of divergence between species and to construct phylogenies calibrated to time (timetrees). Published studies reporting time estimates from molecular data have been increasing rapidly, but the data have been largely inaccessible to the greater community of scientists because of their complexity. TimeTree brings these data together in a consistent format and uses a hierarchical structure, corresponding to the tree of life, to maximize their utility. Results are presented and summarized, allowing users to quickly determine the range and robustness of time estimates and the degree of consensus from the published literature. TimeTree is available at http://www.timetree.net
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              The significance of responses of the genome to challenge.

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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                25 April 2017
                2017
                : 12
                : 4
                : e0176258
                Affiliations
                [1 ]NantOmics, Tempe, Arizona, United States of America
                [2 ]BEYOND Center for Fundamental Concepts in Science, Arizona State University, Tempe, Arizona, United States of America
                [3 ]Department of Biomedical Informatics, Arizona State University, Tempe, Arizona, United States of America
                [4 ]School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
                [5 ]Department of Psychology, Arizona State University, Tempe, Arizona, United States of America
                [6 ]Planetary Science Institute, Research School of Astronomy and Astrophysics and Research School of Earth Sciences, Australian National University, Canberra, Australian Capital Territory, Australia
                CNR, ITALY
                Author notes

                Competing Interests: KJB and LC are employees of NantOmics, LLC. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                • Conceptualization: LC KJB AJO MM CHL PD.

                • Formal analysis: LC KJB MM.

                • Funding acquisition: PD.

                • Investigation: LC KJB AJO.

                • Methodology: LC KJB AJO MM CHL.

                • Software: LC KJB AJO.

                • Supervision: PD.

                • Visualization: LC KJB.

                • Writing – original draft: LC KJB AJO MM CHL PD.

                • Writing – review & editing: LC KJB AJO MM CHL PD.

                Author information
                http://orcid.org/0000-0003-1001-8207
                Article
                PONE-D-16-37303
                10.1371/journal.pone.0176258
                5404761
                28441401
                9b417886-c602-472d-8e4b-6fc7896bd2bb
                © 2017 Cisneros et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 16 September 2016
                : 8 April 2017
                Page count
                Figures: 6, Tables: 5, Pages: 22
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: U54CA143862
                Award Recipient :
                This work was supported by NIH grant U54CA143862 ( https://projectreporter.nih.gov) and NantOmics, LLC. The NIH had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. NantOmics provided support in the form of salaries for authors KJB and LC, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section. Data used in this study from the International Cancer Genomics Consortium (ICGC) was generated with the support of the following: Institut National de la Santé et de la Recherche Medicale (Inserm) within the framework of the ICGC, Federal Ministry of Education and Research (BMBF), National Health and Medical Research Council (NHMRC), Queensland State Government, University of Queensland, Institute for Molecular Bioscience, The Cancer Council NSW, Garvan Institute of Medical Research, Cancer Institute NSW, Italian Ministry of Education, University, and Research, University of Verona, German Cancer Aid (DKH), Ontario Institute for Cancer Research, Prostate Cancer Canada, Pio XII Foundation - Barretos Cancer Hospital, and René Rachou Research Center (FIOCRUZ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data from the 1000 Genomes Project is maintained and supported by the International Genome Sample Resource (IGSR), funded by the Wellcome Trust grant number WT104947/Z/14/Z. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Genetics
                Biology and Life Sciences
                Genetics
                Mutation
                Medicine and Health Sciences
                Oncology
                Basic Cancer Research
                Cancer Genomics
                Biology and Life Sciences
                Genetics
                Genomics
                Genomic Medicine
                Cancer Genomics
                Biology and Life Sciences
                Computational Biology
                Genome Evolution
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Evolution
                Biology and Life Sciences
                Evolutionary Biology
                Molecular Evolution
                Genome Evolution
                Biology and Life Sciences
                Genetics
                Mutation
                Somatic Mutation
                People and Places
                Demography
                Age Distribution
                Medicine and Health Sciences
                Oncology
                Cancer Risk Factors
                Genetic Causes of Cancer
                Biology and life sciences
                Genetics
                DNA
                DNA repair
                Biology and life sciences
                Biochemistry
                Nucleic acids
                DNA
                DNA repair
                Custom metadata
                All data in this study are publicly available for analysis without restriction. Cancer data from this study is available at https://dcc.icgc.org/releases/release_19. Publication restrictions are in place for data SKCA-BR until June 15, 2017. Permission was obtained to use the data prior to the corresponding project publication as per ICGC publication policy. Please see the ICGC website for the contact information to gain permission for publishing these data ( http://docs.icgc.org/portal/publication/). Normal tissue variant data are available from the Complete Genomics Indices database in the 1000 Genome Project (release 20130502, ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/cgi_variant_calls/).

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