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      The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity

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          Abstract

          The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacologic annotation is available 1 . Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacologic profiles for 24 anticancer drugs across 479 of the lines, this collection allowed identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Altogether, our results suggest that large, annotated cell line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of “personalized” therapeutic regimens 2 .

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          Most cited references 30

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          Regularization and variable selection via the elastic net

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            A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes.

            Recent studies suggest that thousands of genes may contribute to breast cancer pathophysiologies when deregulated by genomic or epigenomic events. Here, we describe a model "system" to appraise the functional contributions of these genes to breast cancer subsets. In general, the recurrent genomic and transcriptional characteristics of 51 breast cancer cell lines mirror those of 145 primary breast tumors, although some significant differences are documented. The cell lines that comprise the system also exhibit the substantial genomic, transcriptional, and biological heterogeneity found in primary tumors. We show, using Trastuzumab (Herceptin) monotherapy as an example, that the system can be used to identify molecular features that predict or indicate response to targeted therapies or other physiological perturbations.
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              The landscape of somatic copy-number alteration across human cancers

              A powerful way to discover key genes playing causal roles in oncogenesis is to identify genomic regions that undergo frequent alteration in human cancers. Here, we report high-resolution analyses of somatic copy-number alterations (SCNAs) from 3131 cancer specimens, belonging largely to 26 histological types. We identify 158 regions of focal SCNA that are altered at significant frequency across multiple cancer types, of which 122 cannot be explained by the presence of a known cancer target gene located within these regions. Several gene families are enriched among these regions of focal SCNA, including the BCL2 family of apoptosis regulators and the NF-κB pathway. We show that cancer cells harboring amplifications surrounding the MCL1 and BCL2L1 anti-apoptotic genes depend upon expression of these genes for survival. Finally, we demonstrate that a large majority of SCNAs identified in individual cancer types are present in multiple cancer types.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                5 March 2012
                28 March 2012
                29 September 2012
                : 483
                : 7391
                : 603-607
                Affiliations
                [1 ]The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
                [2 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02115, USA
                [3 ]Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02115, USA
                [4 ]Novartis Institutes for Biomedical Research, Cambridge, Massachusetts 02139, USA
                [5 ]Genomics Institute of the Novartis Research Foundation, San Diego, California 92121, USA
                [6 ]Novartis Institutes for Biomedical Research, Emeryville, California 94608, USA
                [7 ]Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA
                [8 ]Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA
                Author notes
                []Correspondence and Requests for materials should be addressed to: Levi A. Garraway ( Levi_Garraway@ 123456dfci.harvard.edu ) or Robert Schlegel ( robert.schlegel@ 123456novartis.com )
                [*]

                These authors contributed equally to this work: see Author Contributions section for details

                [9]

                Present address: Novartis Institutes for Biomedical Research, Cambridge, Massachusetts 02139, USA

                [10]

                Present address: Sage Bionetworks, 1100 Fairview Ave. N., Seattle, WA 98109, USA

                [11]

                Present address: Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065

                nihpa361223
                10.1038/nature11003
                3320027
                22460905

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                Funding
                Funded by: Office of the Director : NIH
                Award ID: DP2 OD002750-01 || OD
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