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      Whole-genome sequencing reveals novel tandem-duplication hotspots and a prognostic mutational signature in gastric cancer

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          Abstract

          Genome-wide analysis of genomic signatures might reveal novel mechanisms for gastric cancer (GC) tumorigenesis. Here, we analysis structural variations (SVs) and mutational signatures via whole-genome sequencing of 168 GCs. Our data demonstrates diverse models of complex SVs operative in GC, which lead to high-level amplification of oncogenes. We find varying proportion of tandem-duplications (TDs) among individuals and identify 24 TD hotspots involving well-established cancer genes such as CCND1, ERBB2 and MYC. Specifically, we nominate a novel hotspot involving the super-enhancer of ZFP36L2 presents in approximately 10% GCs from different cohorts, the oncogenic role of which is further confirmed by experimental data. In addition, our data reveal a mutational signature, specifically occurring in noncoding region, significantly enriched in tumors with cadherin 1 mutations, and associated with poor prognoses. Collectively, our data suggest that TDs might serve as an important mechanism for cancer gene activation and provide a novel signature for stratification.

          Abstract

          Structural variations in gastric cancer impact progression. Here, the authors perform whole-genome sequencing on 168 gastric cancer patients and identified tandem-duplications of super-enhancer ZFP36L2 in 10% of gastric cancer, and mutational signatures in tumors with cadherin 1 mutations that associated with poor prognoses.

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          Most cited references11

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          Oncotator: cancer variant annotation tool.

          Oncotator is a tool for annotating genomic point mutations and short nucleotide insertions/deletions (indels) with variant- and gene-centric information relevant to cancer researchers. This information is drawn from 14 different publicly available resources that have been pooled and indexed, and we provide an extensible framework to add additional data sources. Annotations linked to variants range from basic information, such as gene names and functional classification (e.g. missense), to cancer-specific data from resources such as the Catalogue of Somatic Mutations in Cancer (COSMIC), the Cancer Gene Census, and The Cancer Genome Atlas (TCGA). For local use, Oncotator is freely available as a python module hosted on Github (https://github.com/broadinstitute/oncotator). Furthermore, Oncotator is also available as a web service and web application at http://www.broadinstitute.org/oncotator/.
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            Diverse mechanisms of somatic structural variations in human cancer genomes.

            Identification of somatic rearrangements in cancer genomes has accelerated through analysis of high-throughput sequencing data. However, characterization of complex structural alterations and their underlying mechanisms remains inadequate. Here, applying an algorithm to predict structural variations from short reads, we report a comprehensive catalog of somatic structural variations and the mechanisms generating them, using high-coverage whole-genome sequencing data from 140 patients across ten tumor types. We characterize the relative contributions of different types of rearrangements and their mutational mechanisms, find that ~20% of the somatic deletions are complex deletions formed by replication errors, and describe the differences between the mutational mechanisms in somatic and germline alterations. Importantly, we provide detailed reconstructions of the events responsible for loss of CDKN2A/B and gain of EGFR in glioblastoma, revealing that these alterations can result from multiple mechanisms even in a single genome and that both DNA double-strand breaks and replication errors drive somatic rearrangements. Copyright © 2013 Elsevier Inc. All rights reserved.
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              SvABA: genome-wide detection of structural variants and indels by local assembly

              Structural variants (SVs), including small insertion and deletion variants (indels), are challenging to detect through standard alignment-based variant calling methods. Sequence assembly offers a powerful approach to identifying SVs, but is difficult to apply at scale genome-wide for SV detection due to its computational complexity and the difficulty of extracting SVs from assembly contigs. We describe SvABA, an efficient and accurate method for detecting SVs from short-read sequencing data using genome-wide local assembly with low memory and computing requirements. We evaluated SvABA's performance on the NA12878 human genome and in simulated and real cancer genomes. SvABA demonstrates superior sensitivity and specificity across a large spectrum of SVs and substantially improves detection performance for variants in the 20–300 bp range, compared with existing methods. SvABA also identifies complex somatic rearrangements with chains of short (<1000 bp) templated-sequence insertions copied from distant genomic regions. We applied SvABA to 344 cancer genomes from 11 cancer types and found that short templated-sequence insertions occur in ∼4% of all somatic rearrangements. Finally, we demonstrate that SvABA can identify sites of viral integration and cancer driver alterations containing medium-sized (50–300 bp) SVs.
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                Author and article information

                Contributors
                xingrui@bjmu.edu.cn
                jijiafu@hsc.pku.edu.cn
                fangxd@gzucm.edu.cn
                youyonglu@bjmu.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                2 May 2019
                2 May 2019
                2019
                : 10
                : 2037
                Affiliations
                [1 ]ISNI 0000 0001 0027 0586, GRID grid.412474.0, Laboratory of Molecular Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), , Peking University Cancer Hospital & Institute, ; 100142 Beijing, China
                [2 ]Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, CUHK Shenzhen Research Institute, Shenzhen, 518000 China
                [3 ]ISNI 0000 0004 0368 8293, GRID grid.16821.3c, Department of Surgery, Rui-jin Hospital, , Shanghai Jiaotong University School of Medicine, ; Shanghai, 200025 China
                [4 ]ISNI 0000 0004 1761 4404, GRID grid.233520.5, State Key Laboratory of Cancer Biology, Xijing Hospital of Digestive Diseases, , Fourth Military Medical University, ; Shanxi, 710032 China
                [5 ]ISNI 0000 0004 0368 8293, GRID grid.16821.3c, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Key Laboratory for Endocrine Tumours, Rui-Jin Hospital, , Shanghai Jiao-Tong University School of Medicine, ; 200025 Shanghai, China
                [6 ]ISNI 0000 0000 8848 7685, GRID grid.411866.c, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, ; 510120 Guangzhou, China
                [7 ]ISNI 0000 0004 1790 4137, GRID grid.35155.37, College of Life Science and Technology, , Huazhong Agricultural University, ; 430070 Wuhan, China
                [8 ]ISNI 0000 0004 0447 1045, GRID grid.414350.7, Department of Medical Oncology, , Beijing Hospital, ; 100730 Beijing, China
                Author information
                http://orcid.org/0000-0003-3435-6550
                http://orcid.org/0000-0002-2466-5002
                Article
                9644
                10.1038/s41467-019-09644-6
                6497673
                31048690
                99ca5e9d-d7ea-4826-8791-25af75b9c728
                © The Author(s) 2019

                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
                : 11 July 2018
                : 22 March 2019
                Categories
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                Custom metadata
                © The Author(s) 2019

                Uncategorized
                cancer,computational biology and bioinformatics,biomarkers,health care,oncology
                Uncategorized
                cancer, computational biology and bioinformatics, biomarkers, health care, oncology

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