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

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          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.


          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.


          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 references 96

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          Hallmarks of Cancer: The Next Generation

          The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              Comprehensive molecular portraits of human breast tumors

              Summary We analyzed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, mRNA arrays, microRNA sequencing and reverse phase protein arrays. Our ability to integrate information across platforms provided key insights into previously-defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at > 10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the Luminal A subtype. We identified two novel protein expression-defined subgroups, possibly contributed by stromal/microenvironmental elements, and integrated analyses identified specific signaling pathways dominant in each molecular subtype including a HER2/p-HER2/HER1/p-HER1 signature within the HER2-Enriched expression subtype. Comparison of Basal-like breast tumors with high-grade Serous Ovarian tumors showed many molecular commonalities, suggesting a related etiology and similar therapeutic opportunities. The biologic finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biologic subtypes of breast cancer.

                Author and article information

                [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
                +34 61 6014 041 ,
                ORCID:, +34 93 3160 258 ,
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                31 May 2018
                31 May 2018
                : 19
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, 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 ( applies to the data made available in this article, unless otherwise stated.

                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, Ministerio de Economía y Competitividad;
                Award ID: BFU2015-68723-P
                Funded by: FundRef, 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, 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, European Research Council;
                Award ID: 335980
                Funded by: FundRef, Deutsche Forschungsgemeinschaft;
                Award ID: SCHA 1933/1-1
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                © The Author(s) 2018


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


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