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      Genomic landscape and chronological reconstruction of driver events in multiple myeloma

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      1 , 2 , 3 , 3 , 4 , 2 , 5 , 2 , 2 , 2 , 2 , 2 , 6 , 7 , 2 , 8 , 7 , 2 , 9 , 2 , 7 , 7 , 10 , 10 , 10 , 3 , 4 , 7 , 2 , 8 , 11 , 6 , 12 , 7 , 13 , , 2 ,
      Nature Communications
      Nature Publishing Group UK
      Cancer genomics, Myeloma

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          Abstract

          The multiple myeloma (MM) genome is heterogeneous and evolves through preclinical and post-diagnosis phases. Here we report a catalog and hierarchy of driver lesions using sequences from 67 MM genomes serially collected from 30 patients together with public exome datasets. Bayesian clustering defines at least 7 genomic subgroups with distinct sets of co-operating events. Focusing on whole genome sequencing data, complex structural events emerge as major drivers, including chromothripsis and a novel replication-based mechanism of templated insertions, which typically occur early. Hyperdiploidy also occurs early, with individual trisomies often acquired in different chronological windows during evolution, and with a preferred order of acquisition. Conversely, positively selected point mutations, whole genome duplication and chromoplexy events occur in later disease phases. Thus, initiating driver events, drawn from a limited repertoire of structural and numerical chromosomal changes, shape preferred trajectories of evolution that are biologically relevant but heterogeneous across patients.

          Abstract

          Multiple myeloma evolves continuously. Here the authors chronologically reconstruct driver events in multiple myeloma, noting a limited repertoire of initiating driver events that shape the evolutionary trajectory of the disease.

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          Most cited references17

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          Genomic complexity of multiple myeloma and its clinical implications

          In the past 5 years, results from large-scale whole-exome sequencing studies have brought new insight into the clonal heterogeneity and evolution of multiple myeloma, a genetically complex disease. Herein, the authors describe the driver gene alterations and sequential acquisition of the main genomic aberrations involved in this disease, with a focus on the clonal heterogeneity of multiple myeloma and its clinical implications.
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            A high-risk, Double-Hit, group of newly diagnosed myeloma identified by genomic analysis

            Patients with newly diagnosed multiple myeloma (NDMM) with high-risk disease are in need of new treatment strategies to improve the outcomes. Multiple clinical, cytogenetic, or gene expression features have been used to identify high-risk patients, each of which has significant weaknesses. Inclusion of molecular features into risk stratification could resolve the current challenges. In a genome-wide analysis of the largest set of molecular and clinical data established to date from NDMM, as part of the Myeloma Genome Project, we have defined DNA drivers of aggressive clinical behavior. Whole-genome and exome data from 1273 NDMM patients identified genetic factors that contribute significantly to progression free survival (PFS) and overall survival (OS) (cumulative R 2 = 18.4% and 25.2%, respectively). Integrating DNA drivers and clinical data into a Cox model using 784 patients with ISS, age, PFS, OS, and genomic data, the model has a cumlative R2 of 34.3% for PFS and 46.5% for OS. A high-risk subgroup was defined by recursive partitioning using either a) bi-allelic TP53 inactivation or b) amplification (≥4 copies) of CKS1B (1q21) on the background of International Staging System III, comprising 6.1% of the population (median PFS = 15.4 months; OS = 20.7 months) that was validated in an independent dataset. Double-Hit patients have a dire prognosis despite modern therapies and should be considered for novel therapeutic approaches.
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              Constitutional and somatic rearrangement of chromosome 21 in acute lymphoblastic leukaemia

              Changes in gene dosage are a major driver of cancer, engineered from a finite, but increasingly well annotated, repertoire of mutational mechanisms 1 . This can potentially generate correlated copy number alterations across hundreds of linked genes, as exemplified by the 2% of childhood acute lymphoblastic leukemia (ALL) with recurrent amplification of megabase regions of chromosome 21 (iAMP21) 2,3 . We used genomic, cytogenetic and transcriptional analysis, coupled with novel bioinformatic approaches, to reconstruct the evolution of iAMP21 ALL. We find that individuals born with the rare constitutional Robertsonian translocation between chromosomes 15 and 21, rob(15;21)(q10;q10)c, have ~2700-fold increased risk of developing iAMP21 ALL compared to the general population. In such cases, amplification is initiated by a chromothripsis event involving both sister chromatids of the Robertsonian chromosome, a novel mechanism for cancer predisposition. In sporadic iAMP21, breakage-fusion-bridge cycles are typically the initiating event, often followed by chromothripsis. In both sporadic and rob(15;21)c-associated iAMP21, the final stages frequently involve duplications of the entire abnormal chromosome. The end-product is a derivative of chromosome 21 or the rob(15;21)c chromosome with gene dosage optimised for leukemic potential, showing constrained copy number levels over multiple linked genes. Thus, dicentric chromosomes may be an important precipitant of chromothripsis, as we show rob(15;21)c to be constitutionally dicentric and breakage-fusion-bridge cycles generate dicentric chromosomes somatically. Furthermore, our data illustrate that several cancer-specific mutational processes, applied sequentially, can co-ordinate to fashion copy number profiles over large genomic scales, incrementally refining the fitness benefits of aggregated gene dosage changes.
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                Author and article information

                Contributors
                nikhil_munshi@dfci.harvard.edu
                pc8@sanger.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                23 August 2019
                23 August 2019
                2019
                : 10
                : 3835
                Affiliations
                [1 ]ISNI 0000 0001 2171 9952, GRID grid.51462.34, Myeloma Service, Department of Medicine, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [2 ]ISNI 0000 0004 0606 5382, GRID grid.10306.34, The Cancer, Ageing and Somatic Mutation Programme, , Wellcome Sanger Institute, ; Hinxton, Cambridgeshire CB10 1SA UK
                [3 ]ISNI 0000 0004 1757 2822, GRID grid.4708.b, Department of Medical Oncology and Hemato-Oncology, , University of Milan, ; Milan, Italy
                [4 ]ISNI 0000 0001 0807 2568, GRID grid.417893.0, Department of Medical Oncology and Hematology, , Fondazione IRCCS Istituto Nazionale dei Tumori, ; Milan, Italy
                [5 ]ISNI 0000 0001 0942 6946, GRID grid.8356.8, School of Computer Science and Electronic Engineering, , University of Essex, ; Colchester, UK
                [6 ]ISNI 0000 0000 9709 7726, GRID grid.225360.0, European Bioinformatics Institute, , European Molecular Biology Laboratory (EMBL-EBI), ; Hinxton, UK
                [7 ]ISNI 000000041936754X, GRID grid.38142.3c, Jerome Lipper Multiple Myeloma Center, Dana–Farber Cancer Institute, , Harvard Medical School, ; Boston, MA USA
                [8 ]ISNI 0000000109410645, GRID grid.11794.3a, CIMUS - Molecular Medicine and Chronic Diseases Research Centre, , University of Santiago de Compostela, ; Santiago de Compostela, Spain
                [9 ]ISNI 0000 0001 2171 9952, GRID grid.51462.34, Epidemiology and Biostatistics, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [10 ]GRID grid.4817.a, CRCINA, INSERM, CNRS, , Université d’Angers, Université de Nantes, ; Nantes, France
                [11 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, University of Oxford, Big Data Institute, ; Oxford, UK
                [12 ]IUC-Oncopole, and CRCT INSERM U1037, 31100 Toulouse, France
                [13 ]ISNI 0000 0004 4657 1992, GRID grid.410370.1, Veterans Administration Boston Healthcare System, ; West Roxbury, MA USA
                Author information
                http://orcid.org/0000-0003-0761-9503
                http://orcid.org/0000-0001-5685-4580
                http://orcid.org/0000-0002-6201-1587
                http://orcid.org/0000-0001-8112-9073
                http://orcid.org/0000-0001-6709-963X
                Article
                11680
                10.1038/s41467-019-11680-1
                6707220
                31444325
                04da6ca6-8f6a-44c4-8f04-56e25ff8c150
                © 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
                : 13 December 2018
                : 23 July 2019
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                © The Author(s) 2019

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                cancer genomics,myeloma
                Uncategorized
                cancer genomics, myeloma

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