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      A Landscape of Pharmacogenomic Interactions in Cancer

      research-article
      1 , 2 , 20 , 3 , 4 , 20 , 4 , 20 , 2 , 20 , 1 , 5 , 20 , 1 , 4 , 6 , 1 , 2 , 2 , 1 , 2 , 17 , 7 , 4 , 8 , 8 , 9 , 10 , 11 , 18 , 7 , 10 , 11 , 2 , 7 , 2 , 10 , 11 , 10 , 11 , 9 , 9 , 19 , 2 , 12 , 12 , 13 , 8 , 9 , 13 , 14 , 10 , 11 , 7 , 15 , 2 , 7 , 4 , 6 , 16 , 21 , 1 , 5 , 21 , 2 , 21 , , 2 , 21 , ∗∗
      Cell
      Cell Press

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          Summary

          Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.

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          Highlights

          • We integrate heterogeneous molecular data of 11,289 tumors and 1,001 cell lines

          • We measure the response of 1,001 cancer cell lines to 265 anti-cancer drugs

          • We uncover numerous oncogenic aberrations that sensitize to an anti-cancer drug

          • Our study forms a resource to identify therapeutic options for cancer sub-populations

          Abstract

          A look at the pharmacogenomic landscape of 1,001 human cancer cell lines points to new treatment applications for hundreds of known anti-cancer drugs.

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          Most cited references11

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          Systematic variation in gene expression patterns in human cancer cell lines.

          We used cDNA microarrays to explore the variation in expression of approximately 8,000 unique genes among the 60 cell lines used in the National Cancer Institute's screen for anti-cancer drugs. Classification of the cell lines based solely on the observed patterns of gene expression revealed a correspondence to the ostensible origins of the tumours from which the cell lines were derived. The consistent relationship between the gene expression patterns and the tissue of origin allowed us to recognize outliers whose previous classification appeared incorrect. Specific features of the gene expression patterns appeared to be related to physiological properties of the cell lines, such as their doubling time in culture, drug metabolism or the interferon response. Comparison of gene expression patterns in the cell lines to those observed in normal breast tissue or in breast tumour specimens revealed features of the expression patterns in the tumours that had recognizable counterparts in specific cell lines, reflecting the tumour, stromal and inflammatory components of the tumour tissue. These results provided a novel molecular characterization of this important group of human cell lines and their relationships to tumours in vivo.
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            A community effort to assess and improve drug sensitivity prediction algorithms.

            Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.
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              An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules.

              The high rate of clinical response to protein-kinase-targeting drugs matched to cancer patients with specific genomic alterations has prompted efforts to use cancer cell line (CCL) profiling to identify additional biomarkers of small-molecule sensitivities. We have quantitatively measured the sensitivity of 242 genomically characterized CCLs to an Informer Set of 354 small molecules that target many nodes in cell circuitry, uncovering protein dependencies that: (1) associate with specific cancer-genomic alterations and (2) can be targeted by small molecules. We have created the Cancer Therapeutics Response Portal (http://www.broadinstitute.org/ctrp) to enable users to correlate genetic features to sensitivity in individual lineages and control for confounding factors of CCL profiling. We report a candidate dependency, associating activating mutations in the oncogene β-catenin with sensitivity to the Bcl-2 family antagonist, navitoclax. The resource can be used to develop novel therapeutic hypotheses and to accelerate discovery of drugs matched to patients by their cancer genotype and lineage. Copyright © 2013 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Cell
                Cell
                Cell
                Cell Press
                0092-8674
                1097-4172
                28 July 2016
                28 July 2016
                : 166
                : 3
                : 740-754
                Affiliations
                [1 ]European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
                [2 ]Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
                [3 ]Institute for Systems Biology, Seattle, WA 98109, USA
                [4 ]Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam 1066 CX, The Netherlands
                [5 ]Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen 52057, Germany
                [6 ]Department of EEMCS, Delft University of Technology, Delft 2628 CD, the Netherlands
                [7 ]Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
                [8 ]Genetically Defined Diseases and Genomics, Bristol-Myers Squibb Research and Development, Hopewell, NJ 08534, USA
                [9 ]Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet 08908, Barcelona, Catalonia, Spain
                [10 ]Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
                [11 ]Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
                [12 ]Research Program on Biomedical Informatics, IMIM Hospital del Mar Medical Research Institute and Universitat Pompeu Fabra, Barcelona 08003, Spain
                [13 ]Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Catalonia, Spain
                [14 ]Department of Physiological Sciences II of the School of Medicine, University of Barcelona, L’Hospitalet 08908, Barcelona, Catalonia, Spain
                [15 ]Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
                [16 ]Cancer Genomics Netherlands, Uppsalalaan 8, Utrecht 3584CT, the Netherlands
                Author notes
                []Corresponding author um1@ 123456sanger.ac.uk
                [∗∗ ]Corresponding author mg12@ 123456sanger.ac.uk
                [17]

                Present address: Bioinformatics and Biostatistics Hub, C3BI, USR 3756 IP CNRS, Institut Pasteur, 75015 Paris, France

                [18]

                Present address: Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, 361102 Xiamen, China

                [19]

                Present address: Institute of Molecular Biology, Mainz 55128, Germany

                [20]

                Co-first author

                [21]

                Co-senior author

                Article
                S0092-8674(16)30746-2
                10.1016/j.cell.2016.06.017
                4967469
                27397505
                f8e04090-a8b0-48f3-860d-1b1aeb805ede
                © 2016 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 7 August 2015
                : 23 December 2015
                : 3 June 2016
                Categories
                Resource

                Cell biology
                Cell biology

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