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      Copy number signatures predict chromothripsis and clinical outcomes in newly diagnosed multiple myeloma

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          Abstract

          Chromothripsis is detectable in 20–30% of newly diagnosed multiple myeloma (NDMM) patients and is emerging as a new independent adverse prognostic factor. In this study we interrogate 752 NDMM patients using whole genome sequencing (WGS) to investigate the relationship of copy number (CN) signatures to chromothripsis and show they are highly associated. CN signatures are highly predictive of the presence of chromothripsis (AUC = 0.90) and can be used identify its adverse prognostic impact. The ability of CN signatures to predict the presence of chromothripsis is confirmed in a validation series of WGS comprised of 235 hematological cancers (AUC = 0.97) and an independent series of 34 NDMM (AUC = 0.87). We show that CN signatures can also be derived from whole exome data (WES) and using 677 cases from the same series of NDMM, we are able to predict both the presence of chromothripsis (AUC = 0.82) and its adverse prognostic impact. CN signatures constitute a flexible tool to identify the presence of chromothripsis and is applicable to WES and WGS data.

          Abstract

          Chromothripsis is associated with unfavourable outcomes in multiple myeloma (MM), but its detection usually requires whole genome sequencing. Here the authors develop an approach to detect chromothripsis in MM based on copy-number signatures that also works with whole exome sequencing data.

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          The repertoire of mutational signatures in human cancer

          Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature 1 . Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium 2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses 3–15 , enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated—but distinct—DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer.
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            Pan-cancer analysis of whole genomes

            Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale 1–3 . Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter 4 ; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation 5,6 ; analyses timings and patterns of tumour evolution 7 ; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity 8,9 ; and evaluates a range of more-specialized features of cancer genomes 8,10–18 .
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              Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications.

              : We describe Manta, a method to discover structural variants and indels from next generation sequencing data. Manta is optimized for rapid germline and somatic analysis, calling structural variants, medium-sized indels and large insertions on standard compute hardware in less than a tenth of the time that comparable methods require to identify only subsets of these variant types: for example NA12878 at 50× genomic coverage is analyzed in less than 20 min. Manta can discover and score variants based on supporting paired and split-read evidence, with scoring models optimized for germline analysis of diploid individuals and somatic analysis of tumor-normal sample pairs. Call quality is similar to or better than comparable methods, as determined by pedigree consistency of germline calls and comparison of somatic calls to COSMIC database variants. Manta consistently assembles a higher fraction of its calls to base-pair resolution, allowing for improved downstream annotation and analysis of clinical significance. We provide Manta as a community resource to facilitate practical and routine structural variant analysis in clinical and research sequencing scenarios.
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                Author and article information

                Contributors
                fxm557@med.miami.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                27 August 2021
                27 August 2021
                2021
                : 12
                : 5172
                Affiliations
                [1 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Myeloma Service, Department of Medicine, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [2 ]GRID grid.55325.34, ISNI 0000 0004 0389 8485, Institute for Cancer Research, , Oslo University Hospital Radiumhospitalet, ; Oslo, Norway
                [3 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Department of Epidemiology and Biostatistics, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [4 ]GRID grid.26790.3a, ISNI 0000 0004 1936 8606, Myeloma Service, Sylvester Comprehensive Cancer Center, , University of Miami, ; Miami, FL USA
                [5 ]GRID grid.7605.4, ISNI 0000 0001 2336 6580, Department of Molecular Biotechnologies and Health Sciences, , University of Turin, ; Turin, Italy
                [6 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, Myeloma Research Program, NYU Langone, Perlmutter Cancer Center, ; New York, NY USA
                [7 ]GRID grid.4708.b, ISNI 0000 0004 1757 2822, Department of Oncology and Hemato-Oncology, , University of Milan, ; Milan, Italy
                [8 ]GRID grid.414818.0, ISNI 0000 0004 1757 8749, Hematology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, ; Milan, Italy
                [9 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Cytogenetics Laboratory, Department of Pathology, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [10 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Hematopathology Service, Department of Pathology, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [11 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Medicine, , Weill Cornell Medical College, ; New York, NY USA
                Author information
                http://orcid.org/0000-0001-7873-4854
                http://orcid.org/0000-0003-2178-8493
                http://orcid.org/0000-0002-8638-9365
                http://orcid.org/0000-0002-9045-6495
                http://orcid.org/0000-0001-6576-5256
                http://orcid.org/0000-0002-4271-6360
                http://orcid.org/0000-0001-6485-4839
                http://orcid.org/0000-0002-5017-1620
                Article
                25469
                10.1038/s41467-021-25469-8
                8397708
                34453055
                115ac4d1-5bf9-4d3c-a9dd-96f13337a53b
                © The Author(s) 2021

                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
                : 6 December 2020
                : 2 August 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100011541, U.S. Department of Health & Human Services | NIH | NCI | Division of Cancer Epidemiology and Genetics, National Cancer Institute (National Cancer Institute Division of Cancer Epidemiology and Genetics);
                Award ID: P30 CA 240139
                Award Recipient :
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                © The Author(s) 2021

                Uncategorized
                myeloma,data processing,prognostic markers
                Uncategorized
                myeloma, data processing, prognostic markers

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