New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Benjamin Sadacca, Anne-Sophie Hamy-Petit, Cécile Laurent, Pierre Gestraud, Hélène Bonsang-Kitzis, Alice Pinheiro, Judith Abecassis, Pierre Neuvial, Fabien Reyal

ABSTRACT

One of the most challenging problems in the development of new anticancer drugs is the very high attrition rate. The so-called "drug repositioning process" propose to find new therapeutic indications to already approved drugs. For this, new analytic methods are required to optimize the information present in large-scale pharmacogenomics datasets. We analyzed data from the Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia studies. We focused on common cell lines (n = 471), considering the molecular information, and the drug sensitivity for common drugs screened (n = 15). We propose a novel classification based on transcriptomic profiles of cell lines, according to a biological network-driven gene selection process. Our robust molecular classification displays greater homogeneity of drug sensitivity than cancer cell line grouped based on tissue of origin. We then identified significant associations between cell line cluster and drug response robustly found between both datasets. We further demonstrate the relevance of our method using two additional external datasets and distinct sensitivity metrics. Some associations were still found robust, despite cell lines and drug responses' variations. This study defines a robust molecular classification of cancer cell lines that could be used to find new therapeutic indications to known compounds. More... »

PAGES

15126

References to SciGraph publications

  • 2004-08. Drug repositioning: identifying and developing new uses for existing drugs in NATURE REVIEWS DRUG DISCOVERY
  • 2009-12. A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability in BMC BIOINFORMATICS
  • 2012-12. Prognostic gene signatures for patient stratification in breast cancer - accuracy, stability and interpretability of gene selection approaches using prior knowledge on protein-protein interactions in BMC BIOINFORMATICS
  • 2003-07. Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data in MACHINE LEARNING
  • 2016-02. Correlating chemical sensitivity and basal gene expression reveals mechanism of action in NATURE CHEMICAL BIOLOGY
  • 2012-12. Improving biomarker list stability by integration of biological knowledge in the learning process in BMC BIOINFORMATICS
  • 2012-03. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity in NATURE
  • 2016-05. Reproducible pharmacogenomic profiling of cancer cell line panels in NATURE
  • 2015-03. A comprehensive transcriptional portrait of human cancer cell lines in NATURE BIOTECHNOLOGY
  • 2012-03. Systematic identification of genomic markers of drug sensitivity in cancer cells in NATURE
  • 2013-12. Inconsistency in large pharmacogenomic studies in NATURE
  • 2013-11. Metrics other than potency reveal systematic variation in responses to cancer drugs in NATURE CHEMICAL BIOLOGY
  • 2011-12. Jetset: selecting the optimal microarray probe set to represent a gene in BMC BIOINFORMATICS
  • 2011-09. Phase II trial of modified FOLFOX6 and erlotinib in patients with metastatic or advanced adenocarcinoma of the oesophagus and gastro-oesophageal junction in BRITISH JOURNAL OF CANCER
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-017-14770-6

    DOI

    http://dx.doi.org/10.1038/s41598-017-14770-6

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1092522968

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29123141


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1112", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Oncology and Carcinogenesis", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Medical and Health Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "University of \u00c9vry Val d'Essonne", 
              "id": "https://www.grid.ac/institutes/grid.8390.2", 
              "name": [
                "Residual Tumor & Response to Treatment Laboratory (RT2Lab), PSL Research University, Translational Research Department, F-75248, Paris, France", 
                "U932 Immunity and Cancer; INSERM; Institut Curie, Paris, France", 
                "Laboratoire de Math\u00e9matiques et Mod\u00e9lisation d\u2019Evry, Universit\u00e9 d\u2019\u00c9vry Val d\u2019Essonne, UMR CNRS 8071, ENSIIE, USC INRA, Evry Val d\u2019Essonne, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sadacca", 
            "givenName": "Benjamin", 
            "id": "sg:person.01115571167.79", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01115571167.79"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Paris Sciences Lettres Research University", 
              "id": "https://www.grid.ac/institutes/grid.440907.e", 
              "name": [
                "Residual Tumor & Response to Treatment Laboratory (RT2Lab), PSL Research University, Translational Research Department, F-75248, Paris, France", 
                "U932 Immunity and Cancer; INSERM; Institut Curie, Paris, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hamy-Petit", 
            "givenName": "Anne-Sophie", 
            "id": "sg:person.01354204255.50", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01354204255.50"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Paris Sciences Lettres Research University", 
              "id": "https://www.grid.ac/institutes/grid.440907.e", 
              "name": [
                "Residual Tumor & Response to Treatment Laboratory (RT2Lab), PSL Research University, Translational Research Department, F-75248, Paris, France", 
                "U932 Immunity and Cancer; INSERM; Institut Curie, Paris, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Laurent", 
            "givenName": "C\u00e9cile", 
            "id": "sg:person.01160440255.76", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01160440255.76"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Institute Curie", 
              "id": "https://www.grid.ac/institutes/grid.418596.7", 
              "name": [
                "Institut Curie, PSL Research University, Mines Paris Tech, Bioinformatics and Computational Systems Biology of Cancer, INSERM U900, F-75005, Paris, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Gestraud", 
            "givenName": "Pierre", 
            "id": "sg:person.01326105644.76", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326105644.76"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Institute Curie", 
              "id": "https://www.grid.ac/institutes/grid.418596.7", 
              "name": [
                "Residual Tumor & Response to Treatment Laboratory (RT2Lab), PSL Research University, Translational Research Department, F-75248, Paris, France", 
                "U932 Immunity and Cancer; INSERM; Institut Curie, Paris, France", 
                "Department of Surgery, Institut Curie, F-75248, Paris, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Bonsang-Kitzis", 
            "givenName": "H\u00e9l\u00e8ne", 
            "id": "sg:person.01007271611.01", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01007271611.01"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Paris Sciences Lettres Research University", 
              "id": "https://www.grid.ac/institutes/grid.440907.e", 
              "name": [
                "Residual Tumor & Response to Treatment Laboratory (RT2Lab), PSL Research University, Translational Research Department, F-75248, Paris, France", 
                "U932 Immunity and Cancer; INSERM; Institut Curie, Paris, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Pinheiro", 
            "givenName": "Alice", 
            "id": "sg:person.01307106174.48", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01307106174.48"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Institute Curie", 
              "id": "https://www.grid.ac/institutes/grid.418596.7", 
              "name": [
                "Residual Tumor & Response to Treatment Laboratory (RT2Lab), PSL Research University, Translational Research Department, F-75248, Paris, France", 
                "U932 Immunity and Cancer; INSERM; Institut Curie, Paris, France", 
                "Mines Paristech, PSL-Research University, CBIO-Centre for Computational Biology, Mines ParisTech, F-77300, Fontainebleau, France", 
                "Institut Curie, PSL Research University, Mines Paris Tech, Bioinformatics and Computational Systems Biology of Cancer, INSERM U900, F-75005, Paris, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Abecassis", 
            "givenName": "Judith", 
            "id": "sg:person.011366632005.43", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011366632005.43"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Toulouse Mathematics Institute", 
              "id": "https://www.grid.ac/institutes/grid.462146.3", 
              "name": [
                "Laboratoire de Math\u00e9matiques et Mod\u00e9lisation d\u2019Evry, Universit\u00e9 d\u2019\u00c9vry Val d\u2019Essonne, UMR CNRS 8071, ENSIIE, USC INRA, Evry Val d\u2019Essonne, France", 
                "Institut de Math\u00e9matiques de Toulouse; UMR5219 Universit\u00e9 de Toulouse; CNRS UPS IMT, F-31062, Toulouse Cedex 9, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Neuvial", 
            "givenName": "Pierre", 
            "id": "sg:person.01023524737.59", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023524737.59"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Institute Curie", 
              "id": "https://www.grid.ac/institutes/grid.418596.7", 
              "name": [
                "Residual Tumor & Response to Treatment Laboratory (RT2Lab), PSL Research University, Translational Research Department, F-75248, Paris, France", 
                "U932 Immunity and Cancer; INSERM; Institut Curie, Paris, France", 
                "Department of Surgery, Institut Curie, F-75248, Paris, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Reyal", 
            "givenName": "Fabien", 
            "id": "sg:person.01104374270.94", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01104374270.94"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.ijrobp.2009.10.012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000811714"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1089/omi.2011.0118", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003008024"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.1004900107", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003168562"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.1018854108", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004692269"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nchembio.1986", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004967104", 
              "https://doi.org/10.1038/nchembio.1986"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-10-389", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005885894", 
              "https://doi.org/10.1186/1471-2105-10-389"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s1470-2045(09)70002-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006353914"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature11005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008312342", 
              "https://doi.org/10.1038/nature11005"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/mcb.01661-07", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008854103"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt.3080", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009734077", 
              "https://doi.org/10.1038/nbt.3080"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrd1468", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010423625", 
              "https://doi.org/10.1038/nrd1468"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrd1468", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010423625", 
              "https://doi.org/10.1038/nrd1468"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nchembio.1337", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011931121", 
              "https://doi.org/10.1038/nchembio.1337"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ccr.2012.09.016", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013646138"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1200/jco.2007.14.4147", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015966665"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature17987", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016017505", 
              "https://doi.org/10.1038/nature17987"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkv007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016098431"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1056/nejmoa1502309", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017367308"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.1464-410x.2009.09101.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018103616"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.1464-410x.2009.09101.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018103616"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/biostatistics/kxp059", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020140411"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/biostatistics/kxp059", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020140411"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/0008-5472.can-09-3788", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021328466"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1517/17460441.2013.768984", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022146823"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/2162402x.2015.1061176", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023716679"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkq973", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025608399"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/1535-7163.mct-11-0884", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026110324"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1200/jco.2011.38.8595", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026251512"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.bbamcr.2006.11.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029827623"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/0008-5472.can-05-1182", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030336226"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1001/jamaoncol.2015.34", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031106554"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1001/jamaoncol.2015.34", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031106554"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/1535-7163.mct-08-0972", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034599532"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature12831", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034683846", 
              "https://doi.org/10.1038/nature12831"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature11003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036060000", 
              "https://doi.org/10.1038/nature11003"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1023949509487", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036378730", 
              "https://doi.org/10.1023/a:1023949509487"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/bjc.2011.280", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037249387", 
              "https://doi.org/10.1038/bjc.2011.280"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/biostatistics/4.2.249", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037543114"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0506580102", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037705714"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0506580102", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037705714"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1172/jci45014", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037799511"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-12-474", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037968770", 
              "https://doi.org/10.1186/1471-2105-12-474"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0140-6736(13)62422-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038142448"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0140-6736(13)62422-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038142448"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0140-6736(13)62422-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038142448"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0140-6736(13)62422-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038142448"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-13-s4-s22", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039838678", 
              "https://doi.org/10.1186/1471-2105-13-s4-s22"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/1078-0432.ccr-13-0915", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041334148"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1056/nejmoa1103782", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041864150"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-13-69", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048982272", 
              "https://doi.org/10.1186/1471-2105-13-69"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-13-69", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048982272", 
              "https://doi.org/10.1186/1471-2105-13-69"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/1078-0432.ccr-12-1627", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051028259"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/03610927408827101", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1058331720"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.275.5298.343", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062555516"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1200/jco.2005.23.16_suppl.5504", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1079368973"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2017-12", 
        "datePublishedReg": "2017-12-01", 
        "description": "One of the most challenging problems in the development of new anticancer drugs is the very high attrition rate. The so-called \"drug repositioning process\" propose to find new therapeutic indications to already approved drugs. For this, new analytic methods are required to optimize the information present in large-scale pharmacogenomics datasets. We analyzed data from the Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia studies. We focused on common cell lines (n\u2009=\u2009471), considering the molecular information, and the drug sensitivity for common drugs screened (n\u2009=\u200915). We propose a novel classification based on transcriptomic profiles of cell lines, according to a biological network-driven gene selection process. Our robust molecular classification displays greater homogeneity of drug sensitivity than cancer cell line grouped based on tissue of origin. We then identified significant associations between cell line cluster and drug response robustly found between both datasets. We further demonstrate the relevance of our method using two additional external datasets and distinct sensitivity metrics. Some associations were still found robust, despite cell lines and drug responses' variations. This study defines a robust molecular classification of cancer cell lines that could be used to find new therapeutic indications to known compounds.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1038/s41598-017-14770-6", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1045337", 
            "issn": [
              "2045-2322"
            ], 
            "name": "Scientific Reports", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "7"
          }
        ], 
        "name": "New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels", 
        "pagination": "15126", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "3db2f2281a06c310f3dd4b641fcec0363cf2bc4ec0f1d29d375af85a89145e34"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "29123141"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "101563288"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1038/s41598-017-14770-6"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1092522968"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1038/s41598-017-14770-6", 
          "https://app.dimensions.ai/details/publication/pub.1092522968"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T13:32", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8659_00000609.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://www.nature.com/articles/s41598-017-14770-6"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41598-017-14770-6'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-017-14770-6'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-017-14770-6'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-017-14770-6'


     

    This table displays all metadata directly associated to this object as RDF triples.

    293 TRIPLES      21 PREDICATES      75 URIs      21 LITERALS      9 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1038/s41598-017-14770-6 schema:about anzsrc-for:11
    2 anzsrc-for:1112
    3 schema:author Nb06fb7ff0f374c46b682f0bc62bdf95f
    4 schema:citation sg:pub.10.1023/a:1023949509487
    5 sg:pub.10.1038/bjc.2011.280
    6 sg:pub.10.1038/nature11003
    7 sg:pub.10.1038/nature11005
    8 sg:pub.10.1038/nature12831
    9 sg:pub.10.1038/nature17987
    10 sg:pub.10.1038/nbt.3080
    11 sg:pub.10.1038/nchembio.1337
    12 sg:pub.10.1038/nchembio.1986
    13 sg:pub.10.1038/nrd1468
    14 sg:pub.10.1186/1471-2105-10-389
    15 sg:pub.10.1186/1471-2105-12-474
    16 sg:pub.10.1186/1471-2105-13-69
    17 sg:pub.10.1186/1471-2105-13-s4-s22
    18 https://doi.org/10.1001/jamaoncol.2015.34
    19 https://doi.org/10.1016/j.bbamcr.2006.11.009
    20 https://doi.org/10.1016/j.ccr.2012.09.016
    21 https://doi.org/10.1016/j.ijrobp.2009.10.012
    22 https://doi.org/10.1016/s0140-6736(13)62422-8
    23 https://doi.org/10.1016/s1470-2045(09)70002-6
    24 https://doi.org/10.1056/nejmoa1103782
    25 https://doi.org/10.1056/nejmoa1502309
    26 https://doi.org/10.1073/pnas.0506580102
    27 https://doi.org/10.1073/pnas.1004900107
    28 https://doi.org/10.1073/pnas.1018854108
    29 https://doi.org/10.1080/03610927408827101
    30 https://doi.org/10.1080/2162402x.2015.1061176
    31 https://doi.org/10.1089/omi.2011.0118
    32 https://doi.org/10.1093/biostatistics/4.2.249
    33 https://doi.org/10.1093/biostatistics/kxp059
    34 https://doi.org/10.1093/nar/gkq973
    35 https://doi.org/10.1093/nar/gkv007
    36 https://doi.org/10.1111/j.1464-410x.2009.09101.x
    37 https://doi.org/10.1126/science.275.5298.343
    38 https://doi.org/10.1128/mcb.01661-07
    39 https://doi.org/10.1158/0008-5472.can-05-1182
    40 https://doi.org/10.1158/0008-5472.can-09-3788
    41 https://doi.org/10.1158/1078-0432.ccr-12-1627
    42 https://doi.org/10.1158/1078-0432.ccr-13-0915
    43 https://doi.org/10.1158/1535-7163.mct-08-0972
    44 https://doi.org/10.1158/1535-7163.mct-11-0884
    45 https://doi.org/10.1172/jci45014
    46 https://doi.org/10.1200/jco.2005.23.16_suppl.5504
    47 https://doi.org/10.1200/jco.2007.14.4147
    48 https://doi.org/10.1200/jco.2011.38.8595
    49 https://doi.org/10.1517/17460441.2013.768984
    50 schema:datePublished 2017-12
    51 schema:datePublishedReg 2017-12-01
    52 schema:description One of the most challenging problems in the development of new anticancer drugs is the very high attrition rate. The so-called "drug repositioning process" propose to find new therapeutic indications to already approved drugs. For this, new analytic methods are required to optimize the information present in large-scale pharmacogenomics datasets. We analyzed data from the Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia studies. We focused on common cell lines (n = 471), considering the molecular information, and the drug sensitivity for common drugs screened (n = 15). We propose a novel classification based on transcriptomic profiles of cell lines, according to a biological network-driven gene selection process. Our robust molecular classification displays greater homogeneity of drug sensitivity than cancer cell line grouped based on tissue of origin. We then identified significant associations between cell line cluster and drug response robustly found between both datasets. We further demonstrate the relevance of our method using two additional external datasets and distinct sensitivity metrics. Some associations were still found robust, despite cell lines and drug responses' variations. This study defines a robust molecular classification of cancer cell lines that could be used to find new therapeutic indications to known compounds.
    53 schema:genre research_article
    54 schema:inLanguage en
    55 schema:isAccessibleForFree true
    56 schema:isPartOf N5900e19d8a21434cbeaa1054fb6b3d54
    57 N98feceac956e47d78c5f61dcb2a8c08f
    58 sg:journal.1045337
    59 schema:name New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels
    60 schema:pagination 15126
    61 schema:productId N13d141576fee4a3a9736198bb2f506db
    62 N46a07689cf63475d99429259d2935d56
    63 N47b59492916c422797b0786aa59925a4
    64 N5ab5f5c6350c4a709b3b36da85fe6cad
    65 Nf5cadd1686fd4e4f8cb5ab597a549009
    66 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092522968
    67 https://doi.org/10.1038/s41598-017-14770-6
    68 schema:sdDatePublished 2019-04-10T13:32
    69 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    70 schema:sdPublisher Nd0c72b6ee8ea441a86b6b9162bba9bb8
    71 schema:url https://www.nature.com/articles/s41598-017-14770-6
    72 sgo:license sg:explorer/license/
    73 sgo:sdDataset articles
    74 rdf:type schema:ScholarlyArticle
    75 N13d141576fee4a3a9736198bb2f506db schema:name dimensions_id
    76 schema:value pub.1092522968
    77 rdf:type schema:PropertyValue
    78 N3a481a691255445fbfc0411c6e4fbcfd rdf:first sg:person.01307106174.48
    79 rdf:rest N734cda7eb71f47c99c78f0bace4549d2
    80 N3ce34f11b65348d1bdf349642ded2eb0 rdf:first sg:person.01160440255.76
    81 rdf:rest N67422d7ec6a1485daae236d6d1ceb7a6
    82 N43004ac0c029471d940ed26d104d3c62 rdf:first sg:person.01354204255.50
    83 rdf:rest N3ce34f11b65348d1bdf349642ded2eb0
    84 N46a07689cf63475d99429259d2935d56 schema:name nlm_unique_id
    85 schema:value 101563288
    86 rdf:type schema:PropertyValue
    87 N47b59492916c422797b0786aa59925a4 schema:name readcube_id
    88 schema:value 3db2f2281a06c310f3dd4b641fcec0363cf2bc4ec0f1d29d375af85a89145e34
    89 rdf:type schema:PropertyValue
    90 N5900e19d8a21434cbeaa1054fb6b3d54 schema:volumeNumber 7
    91 rdf:type schema:PublicationVolume
    92 N5ab5f5c6350c4a709b3b36da85fe6cad schema:name doi
    93 schema:value 10.1038/s41598-017-14770-6
    94 rdf:type schema:PropertyValue
    95 N5e6f1037f4e94d0e97204c45f31a6e3a rdf:first sg:person.01104374270.94
    96 rdf:rest rdf:nil
    97 N67422d7ec6a1485daae236d6d1ceb7a6 rdf:first sg:person.01326105644.76
    98 rdf:rest N79bdfcbbdf0d400888c011d72ced1e52
    99 N734cda7eb71f47c99c78f0bace4549d2 rdf:first sg:person.011366632005.43
    100 rdf:rest Nddf7e5647ef14d8d933a1917669271e1
    101 N79bdfcbbdf0d400888c011d72ced1e52 rdf:first sg:person.01007271611.01
    102 rdf:rest N3a481a691255445fbfc0411c6e4fbcfd
    103 N98feceac956e47d78c5f61dcb2a8c08f schema:issueNumber 1
    104 rdf:type schema:PublicationIssue
    105 Nb06fb7ff0f374c46b682f0bc62bdf95f rdf:first sg:person.01115571167.79
    106 rdf:rest N43004ac0c029471d940ed26d104d3c62
    107 Nd0c72b6ee8ea441a86b6b9162bba9bb8 schema:name Springer Nature - SN SciGraph project
    108 rdf:type schema:Organization
    109 Nddf7e5647ef14d8d933a1917669271e1 rdf:first sg:person.01023524737.59
    110 rdf:rest N5e6f1037f4e94d0e97204c45f31a6e3a
    111 Nf5cadd1686fd4e4f8cb5ab597a549009 schema:name pubmed_id
    112 schema:value 29123141
    113 rdf:type schema:PropertyValue
    114 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    115 schema:name Medical and Health Sciences
    116 rdf:type schema:DefinedTerm
    117 anzsrc-for:1112 schema:inDefinedTermSet anzsrc-for:
    118 schema:name Oncology and Carcinogenesis
    119 rdf:type schema:DefinedTerm
    120 sg:journal.1045337 schema:issn 2045-2322
    121 schema:name Scientific Reports
    122 rdf:type schema:Periodical
    123 sg:person.01007271611.01 schema:affiliation https://www.grid.ac/institutes/grid.418596.7
    124 schema:familyName Bonsang-Kitzis
    125 schema:givenName Hélène
    126 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01007271611.01
    127 rdf:type schema:Person
    128 sg:person.01023524737.59 schema:affiliation https://www.grid.ac/institutes/grid.462146.3
    129 schema:familyName Neuvial
    130 schema:givenName Pierre
    131 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023524737.59
    132 rdf:type schema:Person
    133 sg:person.01104374270.94 schema:affiliation https://www.grid.ac/institutes/grid.418596.7
    134 schema:familyName Reyal
    135 schema:givenName Fabien
    136 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01104374270.94
    137 rdf:type schema:Person
    138 sg:person.01115571167.79 schema:affiliation https://www.grid.ac/institutes/grid.8390.2
    139 schema:familyName Sadacca
    140 schema:givenName Benjamin
    141 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01115571167.79
    142 rdf:type schema:Person
    143 sg:person.011366632005.43 schema:affiliation https://www.grid.ac/institutes/grid.418596.7
    144 schema:familyName Abecassis
    145 schema:givenName Judith
    146 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011366632005.43
    147 rdf:type schema:Person
    148 sg:person.01160440255.76 schema:affiliation https://www.grid.ac/institutes/grid.440907.e
    149 schema:familyName Laurent
    150 schema:givenName Cécile
    151 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01160440255.76
    152 rdf:type schema:Person
    153 sg:person.01307106174.48 schema:affiliation https://www.grid.ac/institutes/grid.440907.e
    154 schema:familyName Pinheiro
    155 schema:givenName Alice
    156 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01307106174.48
    157 rdf:type schema:Person
    158 sg:person.01326105644.76 schema:affiliation https://www.grid.ac/institutes/grid.418596.7
    159 schema:familyName Gestraud
    160 schema:givenName Pierre
    161 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326105644.76
    162 rdf:type schema:Person
    163 sg:person.01354204255.50 schema:affiliation https://www.grid.ac/institutes/grid.440907.e
    164 schema:familyName Hamy-Petit
    165 schema:givenName Anne-Sophie
    166 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01354204255.50
    167 rdf:type schema:Person
    168 sg:pub.10.1023/a:1023949509487 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036378730
    169 https://doi.org/10.1023/a:1023949509487
    170 rdf:type schema:CreativeWork
    171 sg:pub.10.1038/bjc.2011.280 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037249387
    172 https://doi.org/10.1038/bjc.2011.280
    173 rdf:type schema:CreativeWork
    174 sg:pub.10.1038/nature11003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036060000
    175 https://doi.org/10.1038/nature11003
    176 rdf:type schema:CreativeWork
    177 sg:pub.10.1038/nature11005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008312342
    178 https://doi.org/10.1038/nature11005
    179 rdf:type schema:CreativeWork
    180 sg:pub.10.1038/nature12831 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034683846
    181 https://doi.org/10.1038/nature12831
    182 rdf:type schema:CreativeWork
    183 sg:pub.10.1038/nature17987 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016017505
    184 https://doi.org/10.1038/nature17987
    185 rdf:type schema:CreativeWork
    186 sg:pub.10.1038/nbt.3080 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009734077
    187 https://doi.org/10.1038/nbt.3080
    188 rdf:type schema:CreativeWork
    189 sg:pub.10.1038/nchembio.1337 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011931121
    190 https://doi.org/10.1038/nchembio.1337
    191 rdf:type schema:CreativeWork
    192 sg:pub.10.1038/nchembio.1986 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004967104
    193 https://doi.org/10.1038/nchembio.1986
    194 rdf:type schema:CreativeWork
    195 sg:pub.10.1038/nrd1468 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010423625
    196 https://doi.org/10.1038/nrd1468
    197 rdf:type schema:CreativeWork
    198 sg:pub.10.1186/1471-2105-10-389 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005885894
    199 https://doi.org/10.1186/1471-2105-10-389
    200 rdf:type schema:CreativeWork
    201 sg:pub.10.1186/1471-2105-12-474 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037968770
    202 https://doi.org/10.1186/1471-2105-12-474
    203 rdf:type schema:CreativeWork
    204 sg:pub.10.1186/1471-2105-13-69 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048982272
    205 https://doi.org/10.1186/1471-2105-13-69
    206 rdf:type schema:CreativeWork
    207 sg:pub.10.1186/1471-2105-13-s4-s22 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039838678
    208 https://doi.org/10.1186/1471-2105-13-s4-s22
    209 rdf:type schema:CreativeWork
    210 https://doi.org/10.1001/jamaoncol.2015.34 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031106554
    211 rdf:type schema:CreativeWork
    212 https://doi.org/10.1016/j.bbamcr.2006.11.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029827623
    213 rdf:type schema:CreativeWork
    214 https://doi.org/10.1016/j.ccr.2012.09.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013646138
    215 rdf:type schema:CreativeWork
    216 https://doi.org/10.1016/j.ijrobp.2009.10.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000811714
    217 rdf:type schema:CreativeWork
    218 https://doi.org/10.1016/s0140-6736(13)62422-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038142448
    219 rdf:type schema:CreativeWork
    220 https://doi.org/10.1016/s1470-2045(09)70002-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006353914
    221 rdf:type schema:CreativeWork
    222 https://doi.org/10.1056/nejmoa1103782 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041864150
    223 rdf:type schema:CreativeWork
    224 https://doi.org/10.1056/nejmoa1502309 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017367308
    225 rdf:type schema:CreativeWork
    226 https://doi.org/10.1073/pnas.0506580102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037705714
    227 rdf:type schema:CreativeWork
    228 https://doi.org/10.1073/pnas.1004900107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003168562
    229 rdf:type schema:CreativeWork
    230 https://doi.org/10.1073/pnas.1018854108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004692269
    231 rdf:type schema:CreativeWork
    232 https://doi.org/10.1080/03610927408827101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058331720
    233 rdf:type schema:CreativeWork
    234 https://doi.org/10.1080/2162402x.2015.1061176 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023716679
    235 rdf:type schema:CreativeWork
    236 https://doi.org/10.1089/omi.2011.0118 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003008024
    237 rdf:type schema:CreativeWork
    238 https://doi.org/10.1093/biostatistics/4.2.249 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037543114
    239 rdf:type schema:CreativeWork
    240 https://doi.org/10.1093/biostatistics/kxp059 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020140411
    241 rdf:type schema:CreativeWork
    242 https://doi.org/10.1093/nar/gkq973 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025608399
    243 rdf:type schema:CreativeWork
    244 https://doi.org/10.1093/nar/gkv007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016098431
    245 rdf:type schema:CreativeWork
    246 https://doi.org/10.1111/j.1464-410x.2009.09101.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1018103616
    247 rdf:type schema:CreativeWork
    248 https://doi.org/10.1126/science.275.5298.343 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062555516
    249 rdf:type schema:CreativeWork
    250 https://doi.org/10.1128/mcb.01661-07 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008854103
    251 rdf:type schema:CreativeWork
    252 https://doi.org/10.1158/0008-5472.can-05-1182 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030336226
    253 rdf:type schema:CreativeWork
    254 https://doi.org/10.1158/0008-5472.can-09-3788 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021328466
    255 rdf:type schema:CreativeWork
    256 https://doi.org/10.1158/1078-0432.ccr-12-1627 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051028259
    257 rdf:type schema:CreativeWork
    258 https://doi.org/10.1158/1078-0432.ccr-13-0915 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041334148
    259 rdf:type schema:CreativeWork
    260 https://doi.org/10.1158/1535-7163.mct-08-0972 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034599532
    261 rdf:type schema:CreativeWork
    262 https://doi.org/10.1158/1535-7163.mct-11-0884 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026110324
    263 rdf:type schema:CreativeWork
    264 https://doi.org/10.1172/jci45014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037799511
    265 rdf:type schema:CreativeWork
    266 https://doi.org/10.1200/jco.2005.23.16_suppl.5504 schema:sameAs https://app.dimensions.ai/details/publication/pub.1079368973
    267 rdf:type schema:CreativeWork
    268 https://doi.org/10.1200/jco.2007.14.4147 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015966665
    269 rdf:type schema:CreativeWork
    270 https://doi.org/10.1200/jco.2011.38.8595 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026251512
    271 rdf:type schema:CreativeWork
    272 https://doi.org/10.1517/17460441.2013.768984 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022146823
    273 rdf:type schema:CreativeWork
    274 https://www.grid.ac/institutes/grid.418596.7 schema:alternateName Institute Curie
    275 schema:name Department of Surgery, Institut Curie, F-75248, Paris, France
    276 Institut Curie, PSL Research University, Mines Paris Tech, Bioinformatics and Computational Systems Biology of Cancer, INSERM U900, F-75005, Paris, France
    277 Mines Paristech, PSL-Research University, CBIO-Centre for Computational Biology, Mines ParisTech, F-77300, Fontainebleau, France
    278 Residual Tumor & Response to Treatment Laboratory (RT2Lab), PSL Research University, Translational Research Department, F-75248, Paris, France
    279 U932 Immunity and Cancer; INSERM; Institut Curie, Paris, France
    280 rdf:type schema:Organization
    281 https://www.grid.ac/institutes/grid.440907.e schema:alternateName Paris Sciences Lettres Research University
    282 schema:name Residual Tumor & Response to Treatment Laboratory (RT2Lab), PSL Research University, Translational Research Department, F-75248, Paris, France
    283 U932 Immunity and Cancer; INSERM; Institut Curie, Paris, France
    284 rdf:type schema:Organization
    285 https://www.grid.ac/institutes/grid.462146.3 schema:alternateName Toulouse Mathematics Institute
    286 schema:name Institut de Mathématiques de Toulouse; UMR5219 Université de Toulouse; CNRS UPS IMT, F-31062, Toulouse Cedex 9, France
    287 Laboratoire de Mathématiques et Modélisation d’Evry, Université d’Évry Val d’Essonne, UMR CNRS 8071, ENSIIE, USC INRA, Evry Val d’Essonne, France
    288 rdf:type schema:Organization
    289 https://www.grid.ac/institutes/grid.8390.2 schema:alternateName University of Évry Val d'Essonne
    290 schema:name Laboratoire de Mathématiques et Modélisation d’Evry, Université d’Évry Val d’Essonne, UMR CNRS 8071, ENSIIE, USC INRA, Evry Val d’Essonne, France
    291 Residual Tumor & Response to Treatment Laboratory (RT2Lab), PSL Research University, Translational Research Department, F-75248, Paris, France
    292 U932 Immunity and Cancer; INSERM; Institut Curie, Paris, France
    293 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...