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      Negative selection in tumor genome evolution acts on essential cellular functions and the immunopeptidome

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          Abstract

          Background

          Natural selection shapes cancer genomes. Previous studies used signatures of positive selection to identify genes driving malignant transformation. However, the contribution of negative selection against somatic mutations that affect essential tumor functions or specific domains remains a controversial topic.

          Results

          Here, we analyze 7546 individual exomes from 26 tumor types from TCGA data to explore the portion of the cancer exome under negative selection. Although we find most of the genes neutrally evolving in a pan-cancer framework, we identify essential cancer genes and immune-exposed protein regions under significant negative selection. Moreover, our simulations suggest that the amount of negative selection is underestimated. We therefore choose an empirical approach to identify genes, functions, and protein regions under negative selection. We find that expression and mutation status of negatively selected genes is indicative of patient survival. Processes that are most strongly conserved are those that play fundamental cellular roles such as protein synthesis, glucose metabolism, and molecular transport. Intriguingly, we observe strong signals of selection in the immunopeptidome and proteins controlling peptide exposition, highlighting the importance of immune surveillance evasion. Additionally, tumor type-specific immune activity correlates with the strength of negative selection on human epitopes.

          Conclusions

          In summary, our results show that negative selection is a hallmark of cell essentiality and immune response in cancer. The functional domains identified could be exploited therapeutically, ultimately allowing for the development of novel cancer treatments.

          Electronic supplementary material

          The online version of this article (10.1186/s13059-018-1434-0) contains supplementary material, which is available to authorized users.

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          Most cited references52

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          Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.

          The Cancer Genome Atlas revealed the genomic landscapes of human cancers. In parallel, immunotherapy is transforming the treatment of advanced cancers. Unfortunately, the majority of patients do not respond to immunotherapy, making the identification of predictive markers and the mechanisms of resistance an area of intense research. To increase our understanding of tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers and created The Cancer Immunome Atlas (https://tcia.at/). Cellular characterization of the immune infiltrates showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. Using machine learning, we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. The immunophenoscore was a superior predictor of response to anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) and anti-programmed cell death protein 1 (anti-PD-1) antibodies in two independent validation cohorts. Our findings and this resource may help inform cancer immunotherapy and facilitate the development of precision immuno-oncology.
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            The clonal evolution of tumor cell populations.

            P C Nowell (1976)
            It is proposed that most neoplasms arise from a single cell of origin, and tumor progression results from acquired genetic variability within the original clone allowing sequential selection of more aggressive sublines. Tumor cell populations are apparently more genetically unstable than normal cells, perhaps from activation of specific gene loci in the neoplasm, continued presence of carcinogen, or even nutritional deficiencies within the tumor. The acquired genetic insta0ility and associated selection process, most readily recognized cytogenetically, results in advanced human malignancies being highly individual karyotypically and biologically. Hence, each patient's cancer may require individual specific therapy, and even this may be thwarted by emergence of a genetically variant subline resistant to the treatment. More research should be directed toward understanding and controlling the evolutionary process in tumors before it reaches the late stage usually seen in clinical cancer.
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              Gapped sequence alignment using artificial neural networks: application to the MHC class I system.

              Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8-11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment.
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                Author and article information

                Contributors
                +34 61 6014 041 , stephan.ossowski@med.uni-tuebingen.de
                +34 93 3160 258 , martin.schaefer@crg.eu
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                31 May 2018
                31 May 2018
                2018
                : 19
                : 67
                Affiliations
                [1 ]GRID grid.473715.3, Genomic and Epigenomic Variation in Disease Group, Centre for Genomic Regulation (CRG), , The Barcelona Institute of Science and Technology, ; Dr. Aiguader 88, 08003 Barcelona, Spain
                [2 ]ISNI 0000 0001 1271 4623, GRID grid.18886.3f, Centre for Evolution and Cancer, The Institute of Cancer Research, ; London, UK
                [3 ]GRID grid.473715.3, Evolutionary Genomics Group, Centre for Genomic Regulation (CRG), , The Barcelona Institute of Science and Technology, ; Dr. Aiguader 88, 08003 Barcelona, Spain
                [4 ]GRID grid.473715.3, Institute for Research in Biomedicine (IRB Barcelona), , The Barcelona Institute of Science and Technology, ; Baldiri Reixac, 10, 08028 Barcelona, Spain
                [5 ]GRID grid.473715.3, Design of Biological Systems Group, Centre for Genomic Regulation (CRG), , The Barcelona Institute of Science and Technology, ; Dr. Aiguader 88, 08003 Barcelona, Spain
                [6 ]ISNI 0000 0001 2172 2676, GRID grid.5612.0, Universitat Pompeu Fabra (UPF), ; Barcelona, Spain
                [7 ]ISNI 0000 0000 9601 989X, GRID grid.425902.8, Institució Catalana de Recerca i Estudis Avançats (ICREA), ; Pg. Lluis Companys 23, 08010 Barcelona, Spain
                [8 ]ISNI 0000000404312247, GRID grid.33565.36, IST Austria (Institute of Science and Technology Austria), ; Am Campus 1, 3400 Klosterneuburg, Austria
                [9 ]ISNI 0000 0001 2190 1447, GRID grid.10392.39, Institute of Medical Genetics and Applied Genomics, University of Tübingen, ; Tübingen, Germany
                Author information
                http://orcid.org/0000-0001-7503-6364
                Article
                1434
                10.1186/s13059-018-1434-0
                5984361
                29855388
                8e1bb077-b8f7-48f0-9b64-a58562be1471
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 30 December 2017
                : 20 April 2018
                Funding
                Funded by: Ministerio de Economía y Competitividad (ES)
                Award ID: BIO2012-39754
                Award Recipient :
                Funded by: Ministerio de Economía y Competitividad (ES)
                Award ID: BFU2012-31329
                Funded by: FundRef http://dx.doi.org/10.13039/501100003329, Ministerio de Economía y Competitividad;
                Award ID: BFU2015-68723-P
                Funded by: FundRef http://dx.doi.org/10.13039/501100003330, Secretaría de Estado de Investigacion, Desarrollo e Innovacion;
                Award ID: SEV-2012–0208
                Award Recipient :
                Funded by: European Union Seventh Framework Programme
                Award ID: HEALTH-F4-2011–278568
                Award Recipient :
                Funded by: Horizon 2020 ()
                Award ID: 635290
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000011, Howard Hughes Medical Institute;
                Award ID: 55007424
                Funded by: Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat’s AGAUR program
                Award ID: 2014 SGR 0974
                Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: 335980
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: SCHA 1933/1-1
                Categories
                Research
                Custom metadata
                © The Author(s) 2018

                Genetics
                tumor evolution,negative selection,cancer-essential genes,neoepitopes,cancer immunology
                Genetics
                tumor evolution, negative selection, cancer-essential genes, neoepitopes, cancer immunology

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