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      Systematic identification of genomic markers of drug sensitivity in cancer cells

      1 , 2 , 1 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 3 , 4 , 1 , 5 , 6 , 2 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 1 , 1 , 2 , 1 , 2 , 1 , 3 , 4 , 1 , 3 , 4 , 1 , 2 , 1 , 3 , 4 , 1 , 3 , 4 , 1 , 3 , 4 , 3 , 4 , 2 , 7 , 2 , 2 , 6 , 5 , 3 , 4 , 2 , 1 , 2 , 8 , 1 , 2 , 1 , 2

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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 24

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          A National Cancer Institute Workshop on Microsatellite Instability for cancer detection and familial predisposition: development of international criteria for the determination of microsatellite instability in colorectal cancer.

          In December 1997, the National Cancer Institute sponsored "The International Workshop on Microsatellite Instability and RER Phenotypes in Cancer Detection and Familial Predisposition," to review and unify the field. The following recommendations were endorsed at the workshop. (a) The form of genomic instability associated with defective DNA mismatch repair in tumors is to be called microsatellite instability (MSI). (b) A panel of five microsatellites has been validated and is recommended as a reference panel for future research in the field. Tumors may be characterized on the basis of: high-frequency MSI (MSI-H), if two or more of the five markers show instability (i.e., have insertion/deletion mutations), and low-frequency MSI (MSI-L), if only one of the five markers shows instability. The distinction between microsatellite stable (MSS) and low frequency MSI (MSI-L) can only be accomplished if a greater number of markers is utilized. (c) A unique clinical and pathological phenotype is identified for the MSI-H tumors, which comprise approximately 15% of colorectal cancers, whereas MSI-L and MSS tumors appear to be phenotypically similar. MSI-H colorectal tumors are found predominantly in the proximal colon, have unique histopathological features, and are associated with a less aggressive clinical course than are stage-matched MSI-L or MSS tumors. Preclinical models suggest the possibility that these tumors may be resistant to the cytotoxicity induced by certain chemotherapeutic agents. The implications for MSI-L are not yet clear. (d) MSI can be measured in fresh or fixed tumor specimens equally well; microdissection of pathological specimens is recommended to enrich for neoplastic tissue; and normal tissue is required to document the presence of MSI. (e) The "Bethesda guidelines," which were developed in 1996 to assist in the selection of tumors for microsatellite analysis, are endorsed. (f) The spectrum of microsatellite alterations in noncolonic tumors was reviewed, and it was concluded that the above recommendations apply only to colorectal neoplasms. (g) A research agenda was recommended.
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            Discovery of drug mode of action and drug repositioning from transcriptional responses.

            A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmacology. We developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA. We constructed a "drug network" of 1,302 nodes (drugs) and 41,047 edges (indicating similarities between pair of drugs). We applied network theory, partitioning drugs into groups of densely interconnected nodes (i.e., communities). These communities are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compound-targeted biological pathways. New compounds can be integrated into the network to predict their therapeutic and off-target effects. Using this network, we correctly predicted the MoA for nine anticancer compounds, and we were able to discover an unreported effect for a well-known drug. We verified an unexpected similarity between cyclin-dependent kinase 2 inhibitors and Topoisomerase inhibitors. We discovered that Fasudil (a Rho-kinase inhibitor) might be "repositioned" as an enhancer of cellular autophagy, potentially applicable to several neurodegenerative disorders. Our approach was implemented in a tool (Mode of Action by NeTwoRk Analysis, MANTRA, http://mantra.tigem.it).
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              Awakening guardian angels: drugging the p53 pathway.

              Currently, around 11 million people are living with a tumour that contains an inactivating mutation of TP53 (the human gene that encodes p53) and another 11 million have tumours in which the p53 pathway is partially abrogated through the inactivation of other signalling or effector components. The p53 pathway is therefore a prime target for new cancer drug development, and several original approaches to drug discovery that could have wide applications to drug development are being used. In one approach, molecules that activate p53 by blocking protein-protein interactions with MDM2 are in early clinical development. Remarkable progress has also been made in the development of p53-binding molecules that can rescue the function of certain p53 mutants. Finally, cell-based assays are being used to discover compounds that exploit the p53 pathway by either seeking targets and compounds that show synthetic lethality with TP53 mutations or by looking for non-genotoxic activators of the p53 response.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                13 March 2012
                28 March 2012
                29 September 2012
                : 483
                : 7391
                : 570-575
                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.

                Article
                UKMS47188
                10.1038/nature11005
                3349233
                22460902

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