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Nature. 2018 Oct;562(7728):526-531. doi: 10.1038/s41586-018-0623-z. Epub 2018 Oct 17.

Functional genomic landscape of acute myeloid leukaemia.

Author information

1
Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR, USA.
2
Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.
3
Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA.
4
Howard Hughes Medical Institute, Portland, OR, USA.
5
Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.
6
Oregon Clinical & Translational Research Institute, Oregon Health & Science University, Portland, OR, USA.
7
Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA.
8
Integrated Genomics Laboratories, Oregon Health & Science University, Portland, OR, USA.
9
Technology Transfer & Business Development, Oregon Health & Science University, Portland, OR, USA.
10
Division of Hematology and Oncology, Department of Pediatrics, Oregon Health & Science University, Portland, OR, USA.
11
Biostatistics Shared Resource, Oregon Health & Science University, Portland, OR, USA.
12
Dapartment of Pathology, Oregon Health & Science University, Portland, OR, USA.
13
Oregon Health & Science University-Portland State University School of Public Health, Portland, OR, USA.
14
High-Throughput Screening Services Laboratory, Oregon State University, Corvalis, OR, USA.
15
Department of Medicine, Division of Hematology and Oncology, University of Florida, Gainesville, FL, USA.
16
Department of Internal Medicine/Hematology Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
17
Molecular Therapeutics Program, Fox Chase Cancer Center, Philadelphia, PA, USA.
18
Fox Chase Cancer Center Biosample Repository Facility, Philadelphia, PA, USA.
19
Division of Hematology & Hematologic Malignancies, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA.
20
National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
21
Division of Hematology, University of Colorado, Denver, CO, USA.
22
Bone Marrow Transplant Program, Fox Chase Cancer Center, Philadelphia, PA, USA.
23
Division of Hematologic Malignancies & Cellular Therapeutics, University of Kansas, Kansas City, KS, USA.
24
Clinical Research Services, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL, USA.
25
Department of Medicine-Hematology, Stanford University, Stanford, CA, USA.
26
Department of Hematology, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL, USA.
27
Department of Toxicology, Pharmacology and Therapeutics, University of Kansas Medical Center, Kansas City, KS, USA.
28
Department of Medicine, Division of Medical Oncology, University of Kansas Medical Center, Kansas City, KS, USA.
29
Blood Cell Development and Function Program, Fox Chase Cancer Center, Philadelphia, PA, USA.
30
Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA. mcweeney@ohsu.edu.
31
Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA. mcweeney@ohsu.edu.
32
Oregon Clinical & Translational Research Institute, Oregon Health & Science University, Portland, OR, USA. mcweeney@ohsu.edu.
33
Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA. drukerb@ohsu.edu.
34
Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA. drukerb@ohsu.edu.
35
Howard Hughes Medical Institute, Portland, OR, USA. drukerb@ohsu.edu.

Abstract

The implementation of targeted therapies for acute myeloid leukaemia (AML) has been challenging because of the 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 programme on a cohort of 672 tumour specimens collected from 562 patients. We assessed these specimens using whole-exome sequencing, RNA sequencing and analyses of ex vivo drug sensitivity. Our data reveal mutational events that have not previously been detected in AML. We show that the response to drugs is associated 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 for specific gene networks in the drug response. Collectively, we have generated a dataset-accessible through the Beat AML data viewer (Vizome)-that can be leveraged to address clinical, genomic, transcriptomic and functional analyses of the biology of AML.

PMID:
30333627
PMCID:
PMC6280667
[Available on 2019-04-17]
DOI:
10.1038/s41586-018-0623-z

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