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      Functional Genomic Landscape of Acute Myeloid Leukemia

      research-article
      1 , 2 , 2 , 3 , 4 , 2 , 5 , 2 , 5 , 6 , 2 , 3 , 1 , 2 , 2 , 3 , 1 , 2 , 2 , 3 , 1 , 2 , 2 , 7 , 2 , 5 , 2 , 3 , 1 , 2 , 2 , 3 , 2 , 8 , 2 , 3 , 2 , 9 , 2 , 10 , 2 , 3 , 1 , 2 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 3 , 1 , 2 , 2 , 3 , 2 , 3 , 2 , 10 , 2 , 8 , 2 , 3 , 2 , 10 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 11 , 2 , 3 , 2 , 8 , 2 , 3 , 2 , 3 , 2 , 12 , 2 , 3 , 2 , 3 , 1 , 2 , 1 , 2 , 2 , 11 , 13 , 14 , 2 , 3 , 2 , 3 , 5 , 6 , 1 , 2 , 5 , 6 , 2 , 8 , 2 , 5 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 3 , 2 , 10 , 2 , 5 , 5 , 6 , 1 , 2 , 15 , 16 , 17 , 18 , 19 , 15 , 20 , 21 , 22 , 23 , 24 , 25 , 24 , 21 , 26 , 26 , 27 , 28 , 29 , 18 , 2 , 5 , 6 , 2 , 3 , 4
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          Abstract

          The implementation of targeted therapies for acute myeloid leukemia has been challenged by complex mutational patterns within and across patients as well as a dearth of pharmacologic agents for most mutational events. Here, we report initial findings from the Beat AML program on a cohort of 672 tumor specimens collected from 562 patients. We assessed these specimens using whole exome sequencing, RNA-sequencing, and ex vivo drug sensitivity analyses. Our data reveal novel mutational events not previously detected in AML. We show association of drug response with mutational status, including instances of drug sensitivity that are specific to combinatorial mutational events. Integration with RNA-sequencing also revealed gene expression signatures, which predict a role of specific gene networks in drug response. Collectively, this report offers a dataset, accessible by the Beat AML data viewer ( www.vizome.org), that can be leveraged to address clinical, genomic, transcriptomic, and functional inquiries into the biology of AML.

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          Most cited references31

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Is Open Access

            featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

            , , (2013)
            Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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              Prognostic relevance of integrated genetic profiling in acute myeloid leukemia.

              Acute myeloid leukemia (AML) is a heterogeneous disease with respect to presentation and clinical outcome. The prognostic value of recently identified somatic mutations has not been systematically evaluated in a phase 3 trial of treatment for AML. We performed a mutational analysis of 18 genes in 398 patients younger than 60 years of age who had AML and who were randomly assigned to receive induction therapy with high-dose or standard-dose daunorubicin. We validated our prognostic findings in an independent set of 104 patients. We identified at least one somatic alteration in 97.3% of the patients. We found that internal tandem duplication in FLT3 (FLT3-ITD), partial tandem duplication in MLL (MLL-PTD), and mutations in ASXL1 and PHF6 were associated with reduced overall survival (P=0.001 for FLT3-ITD, P=0.009 for MLL-PTD, P=0.05 for ASXL1, and P=0.006 for PHF6); CEBPA and IDH2 mutations were associated with improved overall survival (P=0.05 for CEBPA and P=0.01 for IDH2). The favorable effect of NPM1 mutations was restricted to patients with co-occurring NPM1 and IDH1 or IDH2 mutations. We identified genetic predictors of outcome that improved risk stratification among patients with AML, independently of age, white-cell count, induction dose, and post-remission therapy, and validated the significance of these predictors in an independent cohort. High-dose daunorubicin, as compared with standard-dose daunorubicin, improved the rate of survival among patients with DNMT3A or NPM1 mutations or MLL translocations (P=0.001) but not among patients with wild-type DNMT3A, NPM1, and MLL (P=0.67). We found that DNMT3A and NPM1 mutations and MLL translocations predicted an improved outcome with high-dose induction chemotherapy in patients with AML. These findings suggest that mutational profiling could potentially be used for risk stratification and to inform prognostic and therapeutic decisions regarding patients with AML. (Funded by the National Cancer Institute and others.).
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                13 September 2018
                17 October 2018
                October 2018
                17 April 2019
                : 562
                : 7728
                : 526-531
                Affiliations
                [1 ]Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR
                [2 ]Knight Cancer Institute, Oregon Health & Science University, Portland, OR
                [3 ]Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR
                [4 ]Howard Hughes Medical Institute
                [5 ]Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR
                [6 ]Oregon Clinical & Translational Research Institute, Oregon Health & Science University, Portland, OR
                [7 ]Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR
                [8 ]Integrated Genomics Laboratories, Oregon Health & Science University, Portland, OR
                [9 ]Technology Transfer & Business Development, Oregon Health & Science University, Portland, OR
                [10 ]Division of Hematology and Oncology, Department of Pediatrics, Oregon Health & Science University, Portland, OR
                [11 ]Biostatistics Shared Resource, Oregon Health & Science University, Portland, OR
                [12 ]Dapartment of Pathology, Oregon Health & Science University, Portland, OR
                [13 ]Oregon Health & Science University-Portland State University School of Public Health, Portland, OR
                [14 ]High-Throughput Screening Services Laboratory, Oregon State University, Corvalis, OR
                [15 ]Department of Medicine, Division of Hematology and Oncology, University of Florida, Gainesville, FL
                [16 ]Department of Internal Medicine/ Hematology Oncology, University of Texas Southwestern Medical Center, Dallas, TX
                [17 ]Molecular Therapeutics Program, Fox Chase Cancer Center, Philadelphia, PA
                [18 ]Fox Chase Cancer Center Biosample Repository Facility, Philadelphia, PA
                [19 ]Division of Hematology & Hematologic Malignancies, Department of Internal Medicine, Unviersity of Utah, Salt Lake City, UT
                [20 ]National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD
                [21 ]Division of Hematology, University of Colorado, Denver, CO
                [22 ]Bone Marrow Transplant Program, Fox Chase Cancer Center, Philadelphia, PA
                [23 ]Division of Hematologic Malignancies & Cellular Therapeutics, University of Kansas, Kansas City, KS
                [24 ]Clinical Research Services, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL
                [25 ]Department of Medicine – Hematology, Stanford University, Stanford, CA
                [26 ]Department of Hematology, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL
                [27 ]Department of Toxicology, Pharmacology and Therapeutics, University of Kansas Medical Center, Kansas City, KS
                [28 ]Department of Medicine, Division of Medical Oncology, University of Kansas Medical Center, Kansas City, KS
                [29 ]Blood Cell Development and Function Program, Fox Chase Cancer Center, Philadelphia, PA
                Author notes

                Contact Shannon McWeeney, mcweeney@ 123456ohsu.edu , Brian Druker, drukerb@ 123456ohsu.edu

                Author Contributions JWT, CET, DB, BW, SEK, SLS, NL, ARS, ET, SKM, and BJD contributed equally to this work. JWT, CET, SKM, AA, and BJD provided project oversight for experimental design, data management, data analysis and Interpretation, methods development, and development of Vizome platform; BW assisted in the development of Vizome platform, project oversight for experimental design, workflow development, data processing, management, analysis and dissemination, and methods development; ET provided patient sample acquisition, clinical data structure, collection, and analysis; SS, AR-S, MA, IE, AR assisted in patient sample processing, ex vivo drug screening, DNA/RNA extractions and sequencing sample submissions; NL, R, JD, CV provided curation and entry of patient clinical annotations; SK provided variant confirmation, and data analysis; DB assisted in workflow development for pre-processing and analysis of RNA-seq, Exome-Seq and Ex Vivo Drug Screening, RNA-seq, clinical data curation and integration, methods development, multivariate modeling and Integration, and development of Vizome platform; JR, RS, AY provided clinical data integration from OHSU Research Data Warehouse; TK assisted in recruiting patients and harvesting samples for analysis; CRC, RTS were co-Investigators for the repository protocol, edited and provided feedback on manuscript; CTJ, DAP collected samples for repository protocol, provided feedback on manuscript; MEM, RRP consented patients and collected samples for the repository protocol and aided with clinical annotation; DN assisted in the creation of drug screening replicate plates; CAE, KW-S, HZ assisted in data analysis; DKE provided data analysis, data curation, and development of analytical processes; AK, TM assisted in the development of the ex vivo drug screening processing workflow; SJW enabled, facilitated and mentored basic, translational and clinical research activities arising from data generated by Beat AML at the University of Kansas Cancer Center site, participated in the Beat AML Symposia to share research and create new research projects; ES, AC, RH, CL, RS provided exome and RNA-Seq library creation, sequencing, and data processing; DD, CN, JP assisted in genomic isolation, curation of samples and entry of patient clinical annotations; DLW served as a Liaison for sample acquisition; MWD served as a Local Principal Investigator for repository protocol, consented patients and collected samples for repository protocol, aided with clinical annotation, edited and provided feedback on manuscript; RLW served as a manager for their repository protocol, consented patients and collected samples for the repository, aided with clinical annotation, edited and provided feedback on manuscript; MML assisted in patient sample acquisition, IRB protocol development and maintenance, clinical data structure, collection, and analysis; UM, BHC, RC, AD, K-HD, JL, SEP assisted in patient sample acquisition; JB, RB, CC, MD, JG, HH, AJ, KJ, RJ, SL, SL, JMac, JMar, RS, TS, AT, JWa, JWo provided patient sample processing and ex vivo drug screening; CSH served as Principal Investigator for their local protocol; consented patients and collected samples for repository protocol, aided with clinical annotation, edited and provided feedback on manuscript; JMW served as Principal Investigator for their local repository protocol, edited and provided feedback on manuscript; BCM served as Principal Investigator for their local repository protocol, consented patients and collected samples for repository protocol, aided with clinical annotation, edited and provided feedback on manuscript; DCC, TLL, RHC served as Principal Investigators for their local repository protocol, consented patients and collected samples for repository protocol, aided with clinical annotation, edited and provided feedback on manuscript; TM provided regulatory oversight; BJ, KW provided regulatory oversight, curation and entry of patient clinical annotations; AB provided targetome overlay; JC, CO assisted in tech transfer, and project development.

                Correspondence and requests for materials should be addressed to SKM ( mcweeney@ 123456ohsu.edu ) or BJD ( drukerb@ 123456ohsu.edu ).
                Article
                NIHMS1504008
                10.1038/s41586-018-0623-z
                6280667
                30333627
                5639d5b2-38b0-4a46-b74e-b54a7de373c9

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