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Nature. 2019 May;569(7757):503-508. doi: 10.1038/s41586-019-1186-3. Epub 2019 May 8.

Next-generation characterization of the Cancer Cell Line Encyclopedia.

Author information

1
Broad Institute of Harvard and MIT, Cambridge, MA, USA.
2
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
3
University of California San Francisco, San Francisco, CA, USA.
4
Novartis Institutes for Biomedical Research, Cambridge, MA, USA.
5
Massachusetts General Hospital Cancer Center, Boston, MA, USA.
6
Department of Pathology, Massachusetts General Hospital, Boston, MA, USA.
7
Harvard Medical School, Boston, MA, USA.
8
The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
9
Belfer Center for Applied Cancer Science, Boston, MA, USA.
10
Novartis Institutes for Biomedical Research, Basel, Switzerland.
11
New York Genome Center, New York, NY, USA.
12
Department of Pathology and Laboratory Medicine, Englander Institute for Precision Medicine, Institute for Computational Biomedicine, and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
13
National Cancer Institute, Rockville, MD, USA.
14
Howard Hughes Medical Institute, Chevy Chase, MD, USA.
15
Novartis Institutes for Biomedical Research, Cambridge, MA, USA. wsellers@broadinstitute.org.
16
Broad Institute of Harvard and MIT, Cambridge, MA, USA. wsellers@broadinstitute.org.

Abstract

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

PMID:
31068700
DOI:
10.1038/s41586-019-1186-3

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