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      Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells

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          Abstract

          Alterations in cancer genomes strongly influence clinical responses to treatment and in many instances are potent biomarkers for response to drugs. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) is the largest public resource for information on drug sensitivity in cancer cells and molecular markers of drug response. Data are freely available without restriction. GDSC currently contains drug sensitivity data for almost 75 000 experiments, describing response to 138 anticancer drugs across almost 700 cancer cell lines. To identify molecular markers of drug response, cell line drug sensitivity data are integrated with large genomic datasets obtained from the Catalogue of Somatic Mutations in Cancer database, including information on somatic mutations in cancer genes, gene amplification and deletion, tissue type and transcriptional data. Analysis of GDSC data is through a web portal focused on identifying molecular biomarkers of drug sensitivity based on queries of specific anticancer drugs or cancer genes. Graphical representations of the data are used throughout with links to related resources and all datasets are fully downloadable. GDSC provides a unique resource incorporating large drug sensitivity and genomic datasets to facilitate the discovery of new therapeutic biomarkers for cancer therapies.

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          Most cited references 5

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          Reorganizing the protein space at the Universal Protein Resource (UniProt)

          The mission of UniProt is to support biological research by providing a freely accessible, stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and querying interfaces. UniProt is comprised of four major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase, the UniProt Reference Clusters and the UniProt Metagenomic and Environmental Sequence Database. A key development at UniProt is the provision of complete, reference and representative proteomes. UniProt is updated and distributed every 4 weeks and can be accessed online for searches or download at http://www.uniprot.org.
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            Exploring the genomes of cancer cells: progress and promise.

            The description and interpretation of genomic abnormalities in cancer cells have been at the heart of cancer research for more than a century. With exhaustive sequencing of cancer genomes across a wide range of human tumors well under way, we are now entering the end game of this mission. In the forthcoming decade, essentially complete catalogs of somatic mutations will be generated for tens of thousands of human cancers. Here, I provide an overview of what these efforts have revealed to date about the origin and behavioral features of cancer cells and how this genomic information is being exploited to improve diagnosis and therapy of the disease.
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              Identification of genotype-correlated sensitivity to selective kinase inhibitors by using high-throughput tumor cell line profiling.

              Kinase inhibitors constitute an important new class of cancer drugs, whose selective efficacy is largely determined by underlying tumor cell genetics. We established a high-throughput platform to profile 500 cell lines derived from diverse epithelial cancers for sensitivity to 14 kinase inhibitors. Most inhibitors were ineffective against unselected cell lines but exhibited dramatic cell killing of small nonoverlapping subsets. Cells with exquisite sensitivity to EGFR, HER2, MET, or BRAF kinase inhibitors were marked by activating mutations or amplification of the drug target. Although most cell lines recapitulated known tumor-associated genotypes, the screen revealed low-frequency drug-sensitizing genotypes in tumor types not previously associated with drug susceptibility. Furthermore, comparing drugs thought to target the same kinase revealed striking differences, predictive of clinical efficacy. Genetically defined cancer subsets, irrespective of tissue type, predict response to kinase inhibitors, and provide an important preclinical model to guide early clinical applications of novel targeted inhibitors.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                January 2013
                January 2013
                22 November 2012
                22 November 2012
                : 41
                : D1 , Database issue
                : D955-D961
                Affiliations
                1Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK, 2Center for Molecular Therapeutics, Massachusetts General Hospital Cancer Center, Harvard Medical School, Charlestown, MA 02129, USA, 3Core Software Services, Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK and 4Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
                Author notes
                *To whom correspondence should be addressed. Tel: +44 1223 494878; Fax: +44 1226 494919; Email: mg12@ 123456sanger.ac.uk
                Correspondence may also be address to Ultan McDermott. Tel: +44 1223 494856; Fax: +44 1226 494919; Email: um1@ 123456sanger.ac.uk
                Article
                gks1111
                10.1093/nar/gks1111
                3531057
                23180760
                © The Author(s) 2012. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com.

                Page count
                Pages: 7
                Categories
                Articles

                Genetics

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