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      Comprehensive molecular profiling of 718 Multiple Myelomas reveals significant differences in mutation frequencies between African and European descent cases

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          Abstract

          Multiple Myeloma (MM) is a plasma cell malignancy with significantly greater incidence and mortality rates among African Americans (AA) compared to Caucasians (CA). The overall goal of this study is to elucidate differences in molecular alterations in MM as a function of self-reported race and genetic ancestry. Our study utilized somatic whole exome, RNA-sequencing, and correlated clinical data from 718 MM patients from the Multiple Myeloma Research Foundation CoMMpass study Interim Analysis 9. Somatic mutational analyses based upon self-reported race corrected for ancestry revealed significant differences in mutation frequency between groups. Of interest, BCL7A, BRWD3, and AUTS2 demonstrate significantly higher mutation frequencies among AA cases. These genes are all involved in translocations in B-cell malignancies. Moreover, we detected a significant difference in mutation frequency of TP53 and IRF4 with frequencies higher among CA cases. Our study provides rationale for interrogating diverse tumor cohorts to best understand tumor genomics across populations.

          Author summary

          This study represents the largest comprehensive molecular analysis of ethnically defined newly diagnosed treatment naïve Multiple Myeloma (MM). We revealed significant differences in mutation frequencies for important cancer genes between AA and CA MM. This study provides support for interrogating diverse tumor cohorts to best understand tumor genomics across populations.

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          Most cited references27

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          A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

          Heng Li (2011)
          Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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            A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data

            (2013)
            Motivation: Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. Results: We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. Availability: http://samtools.sourceforge.net. Contact: hengli@broadinstitute.org.
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              Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms

              We introduce Sailfish, a computational method for quantifying the abundance of previously annotated RNA isoforms from RNA-seq data. Because Sailfish entirely avoids mapping reads, a time-consuming step in all current methods, it provides quantification estimates much faster than do existing approaches (typically 20 times faster) without loss of accuracy. By facilitating frequent reanalysis of data and reducing the need to optimize parameters, Sailfish exemplifies the potential of lightweight algorithms for efficiently processing sequencing reads.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: MethodologyRole: Software
                Role: MethodologyRole: Project administrationRole: Resources
                Role: Data curationRole: Formal analysisRole: MethodologyRole: Software
                Role: Data curation
                Role: Data curation
                Role: MethodologyRole: Project administrationRole: Resources
                Role: Project administrationRole: Resources
                Role: MethodologyRole: VisualizationRole: Writing – review & editing
                Role: Project administrationRole: Resources
                Role: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: Visualization
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                22 November 2017
                November 2017
                : 13
                : 11
                : e1007087
                Affiliations
                [1 ] Department of Translational Genomics, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
                [2 ] Translational Genomics Research Institute, Phoenix, AZ, United States of America
                [3 ] Van Andel Research Institute, Grand Rapids, MI, United States of America
                [4 ] Department of Surgery, Division of Population Genetics, University of Arizona, Tuscon, AZ, United States of America
                [5 ] Multiple Myeloma Research Foundation, Norwalk, CT, United States of America
                UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-9705-4088
                http://orcid.org/0000-0002-5698-7735
                http://orcid.org/0000-0002-7359-0182
                http://orcid.org/0000-0002-8551-2835
                http://orcid.org/0000-0002-1784-9196
                http://orcid.org/0000-0001-5151-3058
                http://orcid.org/0000-0003-4375-7399
                Article
                PGENETICS-D-17-01063
                10.1371/journal.pgen.1007087
                5699827
                29166413
                e05e12be-a86d-4c4a-9a5e-e3ed62717f3e
                © 2017 Manojlovic et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 30 May 2017
                : 23 October 2017
                Page count
                Figures: 5, Tables: 2, Pages: 18
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100001253, Multiple Myeloma Research Foundation;
                Award ID: TGen/USC
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100008786, Keck School of Medicine of USC;
                Award ID: Startup
                Award Recipient :
                This work was supported from Multiple Myeloma Research Foundation (CoMMpass) MMRF-TGen Carpten and USC-Carpten and Start-up Fund, University of Southern California to JDC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Hematologic Cancers and Related Disorders
                Myelomas and Lymphoproliferative Diseases
                Myelomas
                Multiple Myeloma
                Medicine and Health Sciences
                Hematology
                Hematologic Cancers and Related Disorders
                Myelomas and Lymphoproliferative Diseases
                Myelomas
                Multiple Myeloma
                Medicine and Health Sciences
                Hematology
                Plasma Cell Disorders
                Multiple Myeloma
                Biology and Life Sciences
                Genetics
                Mutation
                Somatic Mutation
                People and places
                Population groupings
                Ethnicities
                African American people
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Research and analysis methods
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Biology and Life Sciences
                Genetics
                Mutation
                Point Mutation
                Biology and Life Sciences
                Genetics
                Gene Identification and Analysis
                Mutation Detection
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Sequencing Techniques
                Genome Sequencing
                Research and Analysis Methods
                Molecular Biology Techniques
                Sequencing Techniques
                Genome Sequencing
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Blood Cells
                White Blood Cells
                Plasma Cells
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Immune Cells
                White Blood Cells
                Plasma Cells
                Biology and Life Sciences
                Immunology
                Immune Cells
                White Blood Cells
                Plasma Cells
                Medicine and Health Sciences
                Immunology
                Immune Cells
                White Blood Cells
                Plasma Cells
                Custom metadata
                The Multiple Myeloma Research Foundation (MMRF) CoMMpass (Relating Clinical Outcomes in MM to Personal Assessment of Genetic Profile) trial (NCT01454297) is a longitudinal observation study of 1000 newly diagnosed myeloma patients receiving various standard approved treatments that aim at collecting tissue samples, genetic information, Quality of Life (QoL) and various disease and clinical outcomes over 10 years. Study Weblink: https://urldefense.proofpoint.com/v2/url?u=https-3A__www.themmrf.org_&d=DwICAw&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=rvfVNh4sABPo4MYhXxHnN6nyBTCIX6CKfeq-EEXJQfQ&m=aHWkANVSUoNo8KRzmN8nffZUUiSrG1bTUr5aciciswk&s=KgM_JmmIPfg5auOrKOd0v42b5eVTiZMSCyTD9KiCErc&e= Study Type: "CoMMpass Longitudinal. The study has been registered with dbGAP (dbGaP Study Accession: phs000748).

                Genetics
                Genetics

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