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      Genomic basis for RNA alterations in cancer

      research-article
      PCAWG Transcriptome Core Group, 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 2 , 10 , 3 , 4 , 6 , 7 , 3 , 4 , 6 , 7 , 10 , 11 , 12 , 2 , 2 , 13 , 14 , 13 , 14 , 15 , 16 , 15 , 10 , 15 , 17 , 18 , 19 , 13 , 14 , 20 , 21 , 18 , 12 , 20 , 9 , 13 , 22 , 23 , 28 , 29 , 24 , 3 , 4 , 6 , 7 , 13 , 14 , 9 , 25 , 18 , 15 , 13 , 14 , 15 , 26 , 27 , 23 , 28 , 29 , 30 , 13 , 14 , 13 , PCAWG Transcriptome Working Group, 2 , , 12 , 23 , 28 , , 9 , 31 , 3 , 4 , 5 , 6 , 7 , , 2 , 20 , 32 , 33 , 2 , 18 , 33 , 10 , PCAWG Consortium
      Nature
      Nature Publishing Group UK
      Cancer genomics, Data integration

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          Abstract

          Transcript alterations often result from somatic changes in cancer genomes 1 . Various forms of RNA alterations have been described in cancer, including overexpression 2 , altered splicing 3 and gene fusions 4 ; however, it is difficult to attribute these to underlying genomic changes owing to heterogeneity among patients and tumour types, and the relatively small cohorts of patients for whom samples have been analysed by both transcriptome and whole-genome sequencing. Here we present, to our knowledge, the most comprehensive catalogue of cancer-associated gene alterations to date, obtained by characterizing tumour transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) 5 . Using matched whole-genome sequencing data, we associated several categories of RNA alterations with germline and somatic DNA alterations, and identified probable genetic mechanisms. Somatic copy-number alterations were the major drivers of variations in total gene and allele-specific expression. We identified 649 associations of somatic single-nucleotide variants with gene expression in cis, of which 68.4% involved associations with flanking non-coding regions of the gene. We found 1,900 splicing alterations associated with somatic mutations, including the formation of exons within introns in proximity to Alu elements. In addition, 82% of gene fusions were associated with structural variants, including 75 of a new class, termed ‘bridged’ fusions, in which a third genomic location bridges two genes. We observed transcriptomic alteration signatures that differ between cancer types and have associations with variations in DNA mutational signatures. This compendium of RNA alterations in the genomic context provides a rich resource for identifying genes and mechanisms that are functionally implicated in cancer.

          Abstract

          Integrative analyses of transcriptome and whole-genome sequencing data for 1,188 tumours across 27 types of cancer are used to provide a comprehensive catalogue of RNA-level alterations in cancer.

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          Most cited references120

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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                Author and article information

                Contributors
                anbrooks@ucsc.edu
                raetsch@inf.ethz.ch
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                5 February 2020
                5 February 2020
                2020
                : 578
                : 7793
                : 129-136
                Affiliations
                [2 ]ISNI 0000 0000 9709 7726, GRID grid.225360.0, European Molecular Biology Laboratory, , European Bioinformatics Institute, ; Hinxton, UK
                [3 ]ISNI 0000 0001 2156 2780, GRID grid.5801.c, ETH Zurich, ; Zurich, Switzerland
                [4 ]ISNI 0000 0001 2171 9952, GRID grid.51462.34, Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [5 ]ISNI 000000041936877X, GRID grid.5386.8, Weill Cornell Medical College, ; New York, NY USA
                [6 ]ISNI 0000 0001 2223 3006, GRID grid.419765.8, SIB Swiss Institute of Bioinformatics, ; Lausanne, Switzerland
                [7 ]ISNI 0000 0004 0478 9977, GRID grid.412004.3, University Hospital Zurich, ; Zurich, Switzerland
                [8 ]ISNI 0000 0001 2180 6431, GRID grid.4280.e, National University of Singapore, ; Singapore, Singapore
                [9 ]ISNI 0000 0004 0620 715X, GRID grid.418377.e, Genome Institute of Singapore, ; Singapore, Singapore
                [10 ]ISNI 0000 0001 2256 9319, GRID grid.11135.37, Peking University, ; Beijing, China
                [11 ]ISNI 0000 0001 2151 536X, GRID grid.26999.3d, The University of Tokyo, ; Minato-ku, Japan
                [12 ]ISNI 0000 0001 0740 6917, GRID grid.205975.c, University of California, Santa Cruz, ; Santa Cruz, CA USA
                [13 ]ISNI 0000 0001 2034 1839, GRID grid.21155.32, BGI-Shenzhen, ; Shenzhen, China
                [14 ]China National GeneBank-Shenzhen, Shenzhen, China
                [15 ]ISNI 0000 0004 0626 690X, GRID grid.419890.d, Ontario Institute for Cancer Research, Toronto, ; Ontario, Canada
                [16 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, University of California, San Francisco, ; San Francisco, CA USA
                [17 ]ISNI 0000 0001 2193 314X, GRID grid.8756.c, University of Glasgow, ; Glasgow, UK
                [18 ]ISNI 0000 0004 0495 846X, GRID grid.4709.a, European Molecular Biology Laboratory, , Genome Biology Unit, ; Heidelberg, Germany
                [19 ]ISNI 0000000122483208, GRID grid.10698.36, The University of North Carolina at Chapel Hill, ; Chapel Hill, NC USA
                [20 ]ISNI 0000 0001 1014 0849, GRID grid.419491.0, Berlin Institute for Medical Systems Biology, , Max Delbruck Center for Molecular Medicine, ; Berlin, Germany
                [21 ]ISNI 0000000121901201, GRID grid.83440.3b, University College London, ; London, UK
                [22 ]ISNI 0000 0004 1937 0626, GRID grid.4714.6, Karolinska Institutet, ; Stockholm, Sweden
                [23 ]GRID grid.66859.34, Broad Institute, ; Cambridge, MA USA
                [24 ]GRID grid.410712.1, Ulm University and Ulm University Medical Center, ; Ulm, Germany
                [25 ]ISNI 0000 0004 0385 0924, GRID grid.428397.3, Duke-NUS Medical School, ; Singapore, Singapore
                [26 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, University of Toronto, ; Toronto, Ontario Canada
                [27 ]ISNI 0000 0001 2160 926X, GRID grid.39382.33, Baylor College of Medicine, ; Houston, TX USA
                [28 ]ISNI 0000 0001 2106 9910, GRID grid.65499.37, Dana-Farber Cancer Institute, ; Boston, MA USA
                [29 ]ISNI 000000041936754X, GRID grid.38142.3c, Harvard Medical School, ; Boston, MA USA
                [30 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, University of Toronto, Toronto, ; Ontario, Canada
                [31 ]ISNI 0000 0004 0620 9745, GRID grid.410724.4, National Cancer Centre Singapore, ; Singapore, Singapore
                [32 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, German Cancer Consortium (DKTK), ; partner site Berlin, Germany
                [33 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, German Cancer Research Center (DKFZ), ; Heidelberg, Germany
                [37 ]ISNI 0000 0001 2291 4776, GRID grid.240145.6, The UT MD Anderson Cancer Center, ; Houston, TX USA
                [38 ]Ludwig Center at Harvard, Boston, MA USA
                [39 ]ISNI 0000000121885934, GRID grid.5335.0, University of Cambridge, ; Cambridge, UK
                [40 ]ISNI 0000 0004 1937 0503, GRID grid.22098.31, The Azrieli Faculty of Medicine, , Bar-Ilan University, ; Safed, Israel
                [41 ]ISNI 0000 0001 1956 2722, GRID grid.7048.b, Aarhus University, ; Aarhus, Denmark
                [42 ]ISNI 0000 0004 0408 3720, GRID grid.417691.c, HudsonAlpha Institute for Biotechnology, ; Huntsville, AL USA
                [43 ]ISNI 0000000106344187, GRID grid.265892.2, University of Alabama at Birmingham, ; Birmingham, AL USA
                Article
                1970
                10.1038/s41586-020-1970-0
                7054216
                32025019
                12ee18a6-7648-4972-9635-648735b11ee3
                © 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
                : 29 March 2018
                : 11 December 2019
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                © The Author(s), under exclusive licence to Springer Nature Limited 2020

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                cancer genomics,data integration
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                cancer genomics, data integration

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