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Cancer Cell. 2018 Mar 12;33(3):450-462.e10. doi: 10.1016/j.ccell.2018.01.021.

Systematic Functional Annotation of Somatic Mutations in Cancer.

Author information

1
Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
2
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
3
Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
4
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA.
5
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
6
Division of Oncology, Department of Medicine, Washington University, St. Louis, MO 63108, USA.
7
Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
8
Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
9
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA.
10
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Oncology, Johns Hopkins Medicine, Baltimore, MD 21287, USA.
11
Division of Oncology, Department of Medicine, Washington University, St. Louis, MO 63108, USA; Siteman Cancer Center, Washington University, St. Louis, MO 63108, USA.
12
HKU Shenzhen Institute of Research and Innovation, Shenzhen, China; School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR.
13
Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
14
Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. Electronic address: syi2@mdanderson.org.
15
Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA. Electronic address: nsahni@mdanderson.org.
16
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA. Electronic address: hliang1@mdanderson.org.

Abstract

The functional impact of the vast majority of cancer somatic mutations remains unknown, representing a critical knowledge gap for implementing precision oncology. Here, we report the development of a moderate-throughput functional genomic platform consisting of efficient mutant generation, sensitive viability assays using two growth factor-dependent cell models, and functional proteomic profiling of signaling effects for select aberrations. We apply the platform to annotate >1,000 genomic aberrations, including gene amplifications, point mutations, indels, and gene fusions, potentially doubling the number of driver mutations characterized in clinically actionable genes. Further, the platform is sufficiently sensitive to identify weak drivers. Our data are accessible through a user-friendly, public data portal. Our study will facilitate biomarker discovery, prediction algorithm improvement, and drug development.

KEYWORDS:

TCGA; cellular assay; clinical marker; driver mutation; drug sensitivity; functional genomics; functional proteomics; therapeutic target

PMID:
29533785
PMCID:
PMC5926201
DOI:
10.1016/j.ccell.2018.01.021
[Indexed for MEDLINE]
Free PMC Article

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