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      Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig

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          Abstract

          The type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we present TrackSig, a new method that reconstructs these trajectories using optimal, joint segmentation and deconvolution of mutation type and allele frequencies from a single tumour sample. In simulations, we find TrackSig has a 3–5% activity reconstruction error, and 12% false detection rate. It outperforms an aggressive baseline in situations with branching evolution, CNA gain, and neutral mutations. Applied to data from 2658 tumours and 38 cancer types, TrackSig permits pan-cancer insight into evolutionary changes in mutational processes.

          Abstract

          Cancers evolve as they progress under differing selective pressures. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, the authors present the method TrackSig the estimates evolutionary trajectories of somatic mutational processes from single bulk tumour data.

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          Most cited references19

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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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            The evolutionary history of 2,658 cancers

            Cancer develops through a process of somatic evolution 1,2 . Sequencing data from a single biopsy represent a snapshot of this process that can reveal the timing of specific genomic aberrations and the changing influence of mutational processes 3 . Here, by whole-genome sequencing analysis of 2,658 cancers as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) 4 , we reconstruct the life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer. Early oncogenesis is characterized by mutations in a constrained set of driver genes, and specific copy number gains, such as trisomy 7 in glioblastoma and isochromosome 17q in medulloblastoma. The mutational spectrum changes significantly throughout tumour evolution in 40% of samples. A nearly fourfold diversification of driver genes and increased genomic instability are features of later stages. Copy number alterations often occur in mitotic crises, and lead to simultaneous gains of chromosomal segments. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. Together, these results determine the evolutionary trajectories of cancer, and highlight opportunities for early cancer detection.
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              Tobacco smoke carcinogens, DNA damage and p53 mutations in smoking-associated cancers.

              It is estimated that cigarette smoking kills over 1 000 000 people each year by causing lung cancer as well as many other neoplasmas. p53 mutations are frequent in tobacco-related cancers and the mutation load is often higher in cancers from smokers than from nonsmokers. In lung cancers, the p53 mutational patterns are different between smokers and nonsmokers with an excess of G to T transversions in smoking-associated cancers. The prevalence of G to T transversions is 30% in smokers' lung cancer but only 12% in lung cancers of nonsmokers. A similar trend exists, albeit less marked, in laryngeal cancers and in head and neck cancers. This type of mutation is infrequent in most other tumors aside from hepatocellular carcinoma. At several p53 mutational hotspots common to all cancers, such as codons 248 and 273, a large fraction of the mutations are G to T events in lung cancers but are almost exclusively G to A transitions in non-tobacco-related cancers. Two important classes of tobacco smoke carcinogens are the polycyclic aromatic hydrocarbons (PAH) and the nicotine-derived nitrosamines. Recent studies have indicated that there is a strong coincidence of G to T transversion hotspots in lung cancers and sites of preferential formation of PAH adducts along the p53 gene. Endogenously methylated CpG dinucleotides are the preferred sites for G to T transversions, accounting for more than 50% of such mutations in lung tumors. The same dinucleotide, when present within CpG-methylated mutational reporter genes, is the target of G to T transversion hotspots in cells exposed to the model PAH compound benzo[a]pyrene-7,8-diol-9,10-epoxide. As summarized here, a number of other tobacco smoke carcinogens also can cause G to T transversion mutations. The available data suggest that p53 mutations in lung cancers can be attributed to direct DNA damage from cigarette smoke carcinogens rather than to selection of pre-existing endogenous mutations.
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                Author and article information

                Contributors
                morrisq@mskcc.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                5 February 2020
                5 February 2020
                2020
                : 11
                : 731
                Affiliations
                [1 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Department of Computer Science, , University of Toronto, ; Toronto, ON M5S 2E4 Canada
                [2 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Donnelly Centre for Cellular and Biomolecular Research, , University of Toronto, ; Toronto, ON M5S 3E1 Canada
                [3 ]GRID grid.494618.6, Vector Institute, ; Toronto, ON M5G 1M1 Canada
                [4 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Department of Molecular Genetics, , University of Toronto, ; Toronto, ON M5S 1A8 Canada
                [5 ]ISNI 0000 0004 0626 690X, GRID grid.419890.d, Ontario Institute for Cancer Research, ; Toronto, ON M5G 0A3 Canada
                [6 ]ISNI 0000 0001 2171 9952, GRID grid.51462.34, Computational and Systems Biology Program, Sloan Kettering Institute, , Memorial Sloan Kettering Cancer Center, ; New York, NY 10065 USA
                [7 ]ISNI 0000 0004 1795 1830, GRID grid.451388.3, The Francis Crick Institute, ; London, NW1 1AT UK
                [8 ]ISNI 0000 0004 0606 5382, GRID grid.10306.34, Wellcome Trust Sanger Institute, ; Cambridge, CB10 1SA UK
                [9 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Big Data Institute, , University of Oxford, ; Oxford, OX3 7LF UK
                [10 ]GRID grid.66859.34, Broad Institute of MIT and Harvard, ; Cambridge, MA 02142 USA
                [11 ]ISNI 0000 0000 9709 7726, GRID grid.225360.0, European Molecular Biology Laboratory, , European Bioinformatics Institute (EMBL-EBI), ; Cambridge, CB10 1SD UK
                [12 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, University of Toronto, ; Toronto, ON M5S 3E1 Canada
                [13 ]GRID grid.494618.6, Vector Institute, ; Toronto, ON M5G 1L7 Canada
                [14 ]ISNI 0000 0001 2291 4776, GRID grid.240145.6, The University of Texas MD Anderson Cancer Center, ; Houston, TX 77030 USA
                [15 ]ISNI 0000000121885934, GRID grid.5335.0, Cancer Research UK Cambridge Institute, , University of Cambridge, ; Cambridge, CB2 0RE UK
                [16 ]ISNI 0000 0000 9758 5690, GRID grid.5288.7, Molecular and Medical Genetics, , Oregon Health and Science University, ; Portland, OR 97231 USA
                [17 ]ISNI 0000 0001 2106 9910, GRID grid.65499.37, Dana-Farber Cancer Institute, ; Boston, MA 02215 USA
                [18 ]ISNI 0000 0004 0626 690X, GRID grid.419890.d, Ontario Institute for Cancer Research, ; Toronto, ON M5G 0A3 Canada
                [19 ]ISNI 0000 0000 9632 6718, GRID grid.19006.3e, University of California, ; Los Angeles, CA 90095 USA
                [20 ]ISNI 0000000403978434, GRID grid.1055.1, Peter MacCallum Cancer Centre, ; Melbourne, VIC 3000 Australia
                [21 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, University of Melbourne, ; Melbourne, VIC 3010 Australia
                [22 ]GRID grid.1042.7, Walter and Eliza Hall Institute, ; Melbourne, VIC 3000 Australia
                [23 ]ISNI 0000 0000 8580 3777, GRID grid.6190.e, University of Cologne, ; 50931 Cologne, Germany
                [24 ]ISNI 0000 0001 0668 7884, GRID grid.5596.f, University of Leuven, ; B-3000 Leuven, Belgium
                [25 ]ISNI 0000 0004 1936 7494, GRID grid.61971.38, Simon Fraser University, ; Burnaby, BC V5A 1S6 Canada
                [26 ]ISNI 0000 0001 0684 7796, GRID grid.412541.7, Vancouver Prostate Centre, ; Vancouver, BC V6H 3Z6 Canada
                [27 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, German Cancer Research Center (DKFZ), ; 69120 Heidelberg, Germany
                [28 ]ISNI 0000 0001 2190 4373, GRID grid.7700.0, Heidelberg University, ; 69120 Heidelberg, Germany
                [29 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Massachusetts General Hospital Center for Cancer Research, ; Charlestown, MA 02129 USA
                [30 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Department of Pathology, , Massachusetts General Hospital, ; Boston, MA 02114 USA
                [31 ]ISNI 000000041936754X, GRID grid.38142.3c, Harvard Medical School, ; Boston, MA 02215 USA
                [32 ]ISNI 000000041936877X, GRID grid.5386.8, Weill Cornell Medicine, ; New York, NY 10065 USA
                [33 ]GRID grid.429884.b, New York Genome Center, ; New York, NY 10013 USA
                [34 ]ISNI 0000 0001 0721 6013, GRID grid.8954.0, University of Ljubljana, ; 1000 Ljubljana, Slovenia
                [35 ]ISNI 0000 0004 0400 4439, GRID grid.240372.0, NorthShore University HealthSystem, ; Evanston, IL 60201 USA
                [36 ]ISNI 0000 0004 1936 7822, GRID grid.170205.1, The University of Chicago, ; Chicago, IL 60637 USA
                [37 ]ISNI 0000 0001 0740 6917, GRID grid.205975.c, University of California Santa Cruz, ; Santa Cruz, CA 95064 USA
                [38 ]ISNI 0000000121885934, GRID grid.5335.0, University of Cambridge, ; Cambridge, CB2 0QQ UK
                [39 ]ISNI 0000 0004 0410 2071, GRID grid.7737.4, University of Helsinki, ; 00014 Helsinki, Finland
                [40 ]ISNI 0000 0004 0445 5969, GRID grid.253692.9, Carleton College, ; Northfield, MN 55057 USA
                [41 ]ISNI 0000 0001 2097 5006, GRID grid.16750.35, Princeton University, ; Princeton, NJ 08540 USA
                [42 ]ISNI 0000 0001 0790 959X, GRID grid.411377.7, Indiana University, ; Bloomington, IN 47405 USA
                [43 ]ISNI 0000 0001 0840 2678, GRID grid.222754.4, Korea University, ; Seoul, 02481 Republic of Korea
                [44 ]ISNI 0000 0001 2160 926X, GRID grid.39382.33, Human Genome Sequencing Center, , Baylor College of Medicine, ; Houston, TX 77030 USA
                [45 ]ISNI 0000 0001 2193 314X, GRID grid.8756.c, University of Glasgow, ; Glasgow, G12 8RZ UK
                [46 ]GRID grid.454382.c, Oxford NIHR Biomedical Research Centre, ; Oxford, OX4 2PG UK
                Author information
                http://orcid.org/0000-0002-9243-9648
                http://orcid.org/0000-0002-2760-6999
                Article
                14352
                10.1038/s41467-020-14352-7
                7002414
                32024834
                e9e6375a-f33d-45cc-be2a-8d2ea8f7c37c
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 November 2018
                : 23 December 2019
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                © The Author(s) 2020

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                cancer genetics,computational models
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                cancer genetics, computational models

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