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Systematic identification of genomic markers of drug sensitivity in cancer cells

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      Abstract

      Clinical responses to anticancer therapies are often restricted to a subset of patients. In some cases, mutated cancer genes are potent biomarkers of response to targeted agents. To uncover new biomarkers of sensitivity and resistance to cancer therapeutics, we screened a panel of several hundred cancer cell lines, which represent much of the tissue-type and genetic diversity of human cancers, with 130 drugs under clinical and preclinical investigation. In aggregate, we found mutated cancer genes were associated with cellular response to most currently available cancer drugs. Classic oncogene addiction paradigms were modified by additional tissue-specific or expression biomarkers, and some frequently mutated genes were associated with sensitivity to a broad range of therapeutic agents. Unexpected relationships were revealed, including the marked sensitivity of Ewing’s sarcoma cells harboring the EWS-FLI1 gene translocation to PARP inhibitors. By linking drug activity to the functional complexity of cancer genomes, systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies.

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      Most cited references 39

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      Cytoscape: a software environment for integrated models of biomolecular interaction networks.

      Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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        A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

        When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations. We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably. Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org. Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html
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          Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib.

          Most patients with non-small-cell lung cancer have no response to the tyrosine kinase inhibitor gefitinib, which targets the epidermal growth factor receptor (EGFR). However, about 10 percent of patients have a rapid and often dramatic clinical response. The molecular mechanisms underlying sensitivity to gefitinib are unknown. We searched for mutations in the EGFR gene in primary tumors from patients with non-small-cell lung cancer who had a response to gefitinib, those who did not have a response, and those who had not been exposed to gefitinib. The functional consequences of identified mutations were evaluated after the mutant proteins were expressed in cultured cells. Somatic mutations were identified in the tyrosine kinase domain of the EGFR gene in eight of nine patients with gefitinib-responsive lung cancer, as compared with none of the seven patients with no response (P<0.001). Mutations were either small, in-frame deletions or amino acid substitutions clustered around the ATP-binding pocket of the tyrosine kinase domain. Similar mutations were detected in tumors from 2 of 25 patients with primary non-small-cell lung cancer who had not been exposed to gefitinib (8 percent). All mutations were heterozygous, and identical mutations were observed in multiple patients, suggesting an additive specific gain of function. In vitro, EGFR mutants demonstrated enhanced tyrosine kinase activity in response to epidermal growth factor and increased sensitivity to inhibition by gefitinib. A subgroup of patients with non-small-cell lung cancer have specific mutations in the EGFR gene, which correlate with clinical responsiveness to the tyrosine kinase inhibitor gefitinib. These mutations lead to increased growth factor signaling and confer susceptibility to the inhibitor. Screening for such mutations in lung cancers may identify patients who will have a response to gefitinib. Copyright 2004 Massachusetts Medical Society
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            Author and article information

            Affiliations
            [1 ]Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK
            [2 ]Massachusetts General Hospital Cancer Center, Harvard Medical School, Charlestown MA 02129, USA
            [3 ]Department of Cancer Biology, Dana Farber Cancer Institute, 44 Binney Street, Boston MA 02115, USA
            [4 ]Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Ave. Boston MA 02115, USA
            [5 ]EMBL-EBI, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK
            [6 ]Laboratoire de génétique et biologie des cancers, Institut Curie, 75248 Paris, Cedex 05, France
            [7 ]Division of Experimental Pathology, Institute of Pathology, Centre Hospitalier Universitaire Vaudois (CHUV), University of Lausanne, Lausanne, Switzerland
            [8 ]Howard Hughes Medical Institute, Chevy Chase MD 20815, USA
            Author notes
            Correspondence and requests for material should be addressed to U.M. ( um1@ 123456sanger.ac.uk ) or C.B. ( cbenes@ 123456partners.org )
            [†]

            Present Address: Department of Computing, University of East Anglia, Norwich NR4 7TJ, UK.

            [‡]

            Present Address: The Genome Analysis Centre, Norwich Research Park, Norwich NR4 7UH, UK

            [§]

            Present address: Oncology Drug Discovery, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139

            [*]

            These authors contributed equally to this work.

            Journal
            0410462
            6011
            Nature
            Nature
            Nature
            0028-0836
            1476-4687
            13 March 2012
            28 March 2012
            29 September 2012
            : 483
            : 7391
            : 570-575
            22460902
            3349233
            10.1038/nature11005
            UKMS47188

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            Funding
            Funded by: Wellcome Trust :
            Award ID: 086357 || WT
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