{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T05:21:09Z","timestamp":1784697669816,"version":"3.55.0"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2016,10,12]],"date-time":"2016-10-12T00:00:00Z","timestamp":1476230400000},"content-version":"vor","delay-in-days":400,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Recent large-scale omics initiatives have catalogued the somatic alterations of cancer cell line panels along with their pharmacological response to hundreds of compounds. In this study, we have explored these data to advance computational approaches that enable more effective and targeted use of current and future anticancer therapeutics.<\/jats:p>\n               <jats:p>Results: We modelled the 50% growth inhibition bioassay end-point (GI50) of 17\u2009142 compounds screened against 59 cancer cell lines from the NCI60 panel (941\u2009831 data-points, matrix 93.08% complete) by integrating the chemical and biological (cell line) information. We determine that the protein, gene transcript and miRNA abundance provide the highest predictive signal when modelling the GI50 endpoint, which significantly outperformed the DNA copy-number variation or exome sequencing data (Tukey\u2019s Honestly Significant Difference, P &amp;lt;0.05). We demonstrate that, within the limits of the data, our approach exhibits the ability to both interpolate and extrapolate compound bioactivities to new cell lines and tissues and, although to a lesser extent, to dissimilar compounds. Moreover, our approach outperforms previous models generated on the GDSC dataset. Finally, we determine that in the cases investigated in more detail, the predicted drug-pathway associations and growth inhibition patterns are mostly consistent with the experimental data, which also suggests the possibility of identifying genomic markers of drug sensitivity for novel compounds on novel cell lines.<\/jats:p>\n               <jats:p>Contact: \u00a0terez@pasteur.fr; ab454@ac.cam.uk<\/jats:p>\n               <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv529","type":"journal-article","created":{"date-parts":[[2015,9,9]],"date-time":"2015-09-09T00:19:01Z","timestamp":1441757941000},"page":"85-95","source":"Crossref","is-referenced-by-count":116,"title":["Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel"],"prefix":"10.1093","volume":"32","author":[{"given":"Isidro","family":"Cort\u00e9s-Ciriano","sequence":"first","affiliation":[{"name":"1 Unit\u00e9 de Bioinformatique Structurale, Institut Pasteur and CNRS UMR 3825, Structural Biology and Chemistry Department, 75\u2009724 Paris, France,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerard J. P.","family":"van Westen","sequence":"additional","affiliation":[{"name":"2 Medicinal Chemistry, Leiden Academic Centre for Drug Research, Einsteinweg 55, 2333CC, Leiden,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guillaume","family":"Bouvier","sequence":"additional","affiliation":[{"name":"1 Unit\u00e9 de Bioinformatique Structurale, Institut Pasteur and CNRS UMR 3825, Structural Biology and Chemistry Department, 75\u2009724 Paris, France,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Nilges","sequence":"additional","affiliation":[{"name":"1 Unit\u00e9 de Bioinformatique Structurale, Institut Pasteur and CNRS UMR 3825, Structural Biology and Chemistry Department, 75\u2009724 Paris, France,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John P.","family":"Overington","sequence":"additional","affiliation":[{"name":"3 European Molecular Biology Laboratory European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD, Hinxton, Cambridge, UK and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Bender","sequence":"additional","affiliation":[{"name":"4 Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, CB2 1EW Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Th\u00e9r\u00e8se E.","family":"Malliavin","sequence":"additional","affiliation":[{"name":"1 Unit\u00e9 de Bioinformatique Structurale, Institut Pasteur and CNRS UMR 3825, Structural Biology and Chemistry Department, 75\u2009724 Paris, France,"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2015,9,8]]},"reference":[{"key":"2023020110240772500_btv529-B1","doi-asserted-by":"crossref","first-page":"4372","DOI":"10.1158\/0008-5472.CAN-12-3342","article-title":"The exomes of the NCI-60 panel: a genomic resource for cancer biology and systems pharmacology","volume":"73","author":"Abaan","year":"2013","journal-title":"Cancer Res."},{"key":"2023020110240772500_btv529-B2","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1081\/CNV-120030218","article-title":"Development of the proteasome inhibitor Velcade (Bortezomib)","volume":"22","author":"Adams","year":"2004","journal-title":"Cancer Invest."},{"key":"2023020110240772500_btv529-B3","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1021\/ci500152b","article-title":"Integrative and personalized QSAR analysis in cancer by kernelized bayesian matrix factorization","volume":"54","author":"Ammad-ud-din","year":"2014","journal-title":"J. Chem. Inf. Model."},{"key":"2023020110240772500_btv529-B4","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2023020110240772500_btv529-B5","first-page":"170","article-title":"Molecular similarity searching using atom environments, information-based feature selection, and a Na\u00efve Bayesian classifier","volume":"44","author":"Bender","year":"2004","journal-title":"J. Chem. Inf. Model."},{"key":"2023020110240772500_btv529-B6","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1021\/ci800249s","article-title":"How similar are similarity searching methods? A principal component analysis of molecular descriptor space","volume":"49","author":"Bender","year":"2009","journal-title":"J. Chem. Inf. Model."},{"key":"2023020110240772500_btv529-B7","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1038\/463858a","article-title":"How accurate are cancer cell lines?","volume":"463","author":"Borrell","year":"2010","journal-title":"Nature"},{"key":"2023020110240772500_btv529-B8","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1021\/ci400354b","article-title":"Functional motions modulating VanA ligand binding unraveled by self-organizing maps","volume":"54","author":"Bouvier","year":"2014","journal-title":"J. Chem. Inf. Model."},{"key":"2023020110240772500_btv529-B9","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"2023020110240772500_btv529-B10","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s10822-014-9743-1","article-title":"Computational chemogenomics: is it more than inductive transfer? J","volume":"28","author":"Brown","year":"2014","journal-title":"Comput. Aided Mol. Des."},{"key":"2023020110240772500_btv529-B11","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s10822-004-4077-z","article-title":"Statistical variation in progressive scrambling","volume":"18","author":"Clark","year":"2004","journal-title":"J. Comput. Aided Mol. Des."},{"key":"2023020110240772500_btv529-B12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-014-0049-z","article-title":"Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling","volume":"7","author":"Cortes-Ciriano","year":"2014","journal-title":"J. Cheminf."},{"key":"2023020110240772500_btv529-B13","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1021\/acs.jcim.5b00101","article-title":"Comparing the influence of simulated experimental errors on 12 machine learning algorithms in bioactivity modelling using 12 diverse data sets","volume":"55","author":"Cortes-Ciriano","year":"2015","journal-title":"J. Chem. Inf. Model."},{"key":"2023020110240772500_btv529-B14","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1039\/C4MD00216D","article-title":"Polypharmacology modelling using proteochemometrics: recent developments and future prospects","volume":"6","author":"Cortes-Ciriano","year":"2015","journal-title":"Med. Chem. Comm."},{"key":"2023020110240772500_btv529-B15","doi-asserted-by":"crossref","first-page":"3446","DOI":"10.1021\/acs.jctc.5b00153","article-title":"Temperature accelerated molecular dynamics with soft-ratcheting criterion orients enhanced sampling by low-resolution information","volume":"11","author":"Cortes-Ciriano","year":"2015","journal-title":"J. Chem. Theory Comput."},{"key":"2023020110240772500_btv529-B16","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1038\/nbt.2877","article-title":"A community effort to assess and improve drug sensitivity prediction algorithms","volume":"32","author":"Costello","year":"2014","journal-title":"Nat. Biotechnol."},{"key":"2023020110240772500_btv529-B17","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1124\/mol.112.084152","article-title":"Structure-based identification of OATP1B1\/3 inhibitors","volume":"83","author":"De Bruyn","year":"2013","journal-title":"Mol. Pharmacol."},{"key":"2023020110240772500_btv529-B18","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1038\/nchembio.1337","article-title":"Metrics other than potency reveal systematic variation in responses to cancer drugs","volume":"9","author":"Fallahi-Sichani","year":"2013","journal-title":"Nat. Chem. Biol."},{"key":"2023020110240772500_btv529-B19","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1038\/nature11005","article-title":"Systematic identification of genomic markers of drug sensitivity in cancer cells","volume":"483","author":"Garnett","year":"2012","journal-title":"Nature"},{"key":"2023020110240772500_btv529-B20","doi-asserted-by":"crossref","first-page":"R47","DOI":"10.1186\/gb-2014-15-3-r47","article-title":"Clinical drug response can be predicted using baseline gene expression levels and in\u00a0vitro drug sensitivity in cell lines","volume":"15","author":"Geeleher","year":"2014","journal-title":"Genome Biol."},{"key":"2023020110240772500_btv529-B21","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1016\/j.celrep.2013.07.018","article-title":"Global proteome analysis of the NCI-60 cell line panel","volume":"4","author":"Gholami","year":"2014","journal-title":"Cell Reports"},{"key":"2023020110240772500_btv529-B22","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/S1093-3263(01)00123-1","article-title":"Beware of q2! J","volume":"20","author":"Golbraikh","year":"2002","journal-title":"Mol. Graph. Modell."},{"key":"2023020110240772500_btv529-B23","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/nature12831","article-title":"Inconsistency in large pharmacogenomic studies","volume":"504","author":"Haibe-Kains","year":"2013","journal-title":"Nature"},{"key":"2023020110240772500_btv529-B24","doi-asserted-by":"crossref","first-page":"2149","DOI":"10.1093\/bioinformatics\/btn409","article-title":"Protein\u2013ligand interaction prediction: an improved chemogenomics approach","volume":"24","author":"Jacob","year":"2008","journal-title":"Bioinformatics"},{"key":"2023020110240772500_btv529-B25","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1186\/1471-2105-9-363","article-title":"Virtual screening of GPCRs: an in silico chemogenomics approach","volume":"9","author":"Jacob","year":"2008","journal-title":"BMC Bioinformatics"},{"key":"2023020110240772500_btv529-B26","article-title":"Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data","volume":"63\u201374","author":"Jang","year":"2014","journal-title":"Pac. Symp. Biocomput."},{"key":"2023020110240772500_btv529-B27","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1021\/ci400469u","article-title":"How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space","volume":"54","author":"Koutsoukas","year":"2013","journal-title":"J. Chem. Inf. Model."},{"key":"2023020110240772500_btv529-B28","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1038\/nbt1397","article-title":"A modular approach for integrative analysis of large-scale gene-expression and drug-response data","volume":"26","author":"Kutalik","year":"2008","journal-title":"Nat. Biotechnol."},{"key":"2023020110240772500_btv529-B29","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1093\/bioinformatics\/btr260","article-title":"Molecular signatures database (MSigDB) 3.0","volume":"27","author":"Liberzon","year":"2011","journal-title":"Bioinformatics"},{"key":"2023020110240772500_btv529-B30","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1158\/1535-7163.MCT-08-0921","article-title":"DNA fingerprinting of the NCI-60 cell line panel","volume":"8","author":"Lorenzi","year":"2009","journal-title":"Mol. Cancer Ther."},{"key":"2023020110240772500_btv529-B31","doi-asserted-by":"crossref","first-page":"e61318","DOI":"10.1371\/journal.pone.0061318","article-title":"Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties","volume":"8","author":"Menden","year":"2013","journal-title":"PLoS One"},{"key":"2023020110240772500_btv529-B32","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s13321-015-0086-2","article-title":"Chemistry aware model builder (camb): an R package for predictive bioactivity modeling","volume":"7","author":"Murrell","year":"2015","journal-title":"J. Cheminform."},{"key":"2023020110240772500_btv529-B33","doi-asserted-by":"crossref","first-page":"14229","DOI":"10.1073\/pnas.2331323100","article-title":"Proteomic profiling of the NCI-60 cancer cell lines using new high-density reverse-phase lysate microarrays","volume":"100","author":"Nishizuka","year":"2003","journal-title":"Proc. Natl Acad. Sci. U.S.A."},{"key":"2023020110240772500_btv529-B34","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.1021\/ci5001168","article-title":"Introducing conformal prediction in predictive modeling. A transparent and flexible alternative to applicability domain determination","volume":"54","author":"Norinder","year":"2014","journal-title":"J. Chem. Inf. Model."},{"key":"2023020110240772500_btv529-B35","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1093\/jnci\/81.14.1088","article-title":"Display and analysis of patterns of differential activity of drugs against human tumor cell lines: development of mean graph and COMPARE algorithm","volume":"81","author":"Paull","year":"1989","journal-title":"J. Natl Cancer Inst."},{"key":"2023020110240772500_btv529-B36","first-page":"2825","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"2023020110240772500_btv529-B37","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1158\/0008-5472.CAN-09-3528","article-title":"Exon array analyses across the NCI-60 reveal potential regulation of TOP1 by transcription pausing at guanosine quartets in the first intron","volume":"70","author":"Reinhold","year":"2010","journal-title":"Cancer Res."},{"key":"2023020110240772500_btv529-B38","doi-asserted-by":"crossref","first-page":"3499","DOI":"10.1158\/0008-5472.CAN-12-1370","article-title":"CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set","volume":"72","author":"Reinhold","year":"2012","journal-title":"Cancer Res."},{"key":"2023020110240772500_btv529-B39","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1093\/bioinformatics\/btq628","article-title":"Predicting in\u00a0vitro drug sensitivity using Random Forests","volume":"27","author":"Riddick","year":"2011","journal-title":"Bioinformatics"},{"key":"2023020110240772500_btv529-B40","doi-asserted-by":"crossref","first-page":"2837","DOI":"10.1021\/ci400482e","article-title":"Using random forest to model the domain applicability of another random forest model","volume":"53","author":"Sheridan","year":"2013","journal-title":"J. Chem. Inf. Model."},{"key":"2023020110240772500_btv529-B41","doi-asserted-by":"crossref","first-page":"S17","DOI":"10.1186\/1471-2105-10-S9-S17","article-title":"Structural similarity assessment for drug sensitivity prediction in cancer","volume":"10","author":"Shivakumar","year":"2009","journal-title":"BMC Bioinformatics"},{"key":"2023020110240772500_btv529-B42","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1038\/nrc1951","article-title":"The NCI60 human tumour cell line anticancer drug screen","volume":"6","author":"Shoemaker","year":"2006","journal-title":"Nat. Rev. Cancer."},{"key":"2023020110240772500_btv529-B43","doi-asserted-by":"crossref","first-page":"10787","DOI":"10.1073\/pnas.191368598","article-title":"Chemosensitivity prediction by transcriptional profiling","volume":"98","author":"Staunton","year":"2001","journal-title":"Proc. Natl Acad. Sci. U.S.A."},{"key":"2023020110240772500_btv529-B44","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1007\/978-3-642-16001-1_4","article-title":"Structured output prediction of anti-cancer drug activity","volume-title":"Pattern Recognition in Bioinformatics","author":"Su","year":"2010"},{"key":"2023020110240772500_btv529-B45","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.ccr.2004.06.026","article-title":"Predicting drug sensitivity and resistance: profiling ABC transporter genes in cancer cells","volume":"6","author":"Szakacs","year":"2004","journal-title":"Cancer Cell"},{"key":"2023020110240772500_btv529-B46","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1039\/C0MD00165A","article-title":"Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets","volume":"2","author":"Van Westen","year":"2011","journal-title":"Med. Chem. Commun."},{"key":"2023020110240772500_btv529-B47","doi-asserted-by":"crossref","first-page":"e92047","DOI":"10.1371\/journal.pone.0092047","article-title":"High resolution copy number variation data in the NCI-60 cancer cell lines from whole genome microarrays accessible through CellMiner","volume":"9","author":"Varma","year":"2014","journal-title":"PLoS One"},{"key":"2023020110240772500_btv529-B48","doi-asserted-by":"crossref","first-page":"e101183","DOI":"10.1371\/journal.pone.0101183","article-title":"An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge","volume":"9","author":"Wan","year":"2014","journal-title":"PLoS One"},{"key":"2023020110240772500_btv529-B49","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1038\/483544a","article-title":"Drug discovery: cell lines battle cancer","volume":"483","author":"Weinstein","year":"2012","journal-title":"Nature"},{"key":"2023020110240772500_btv529-B50","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1126\/science.1411538","article-title":"Neural computing in cancer drug development: predicting mechanism of action","volume":"258","author":"Weinstein","year":"1992","journal-title":"Science"},{"key":"2023020110240772500_btv529-B51","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1126\/science.275.5298.343","article-title":"An information-intensive approach to the molecular pharmacology of cancer","volume":"275","author":"Weinstein","year":"1997","journal-title":"Science"},{"key":"2023020110240772500_btv529-B52","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1038\/nrg3352","article-title":"Cancer pharmacogenomics: strategies and challenges","volume":"14","author":"Wheeler","year":"2013","journal-title":"Nat. Rev. Genet."},{"key":"2023020110240772500_btv529-B53","doi-asserted-by":"crossref","first-page":"i246","DOI":"10.1093\/bioinformatics\/btq176","article-title":"Drug\u2013target interaction prediction from chemical, genomic and pharmacological data in an integrated framework","volume":"26","author":"Yamanishi","year":"2010","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/1\/85\/49016525\/bioinformatics_32_1_85.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/1\/85\/49016525\/bioinformatics_32_1_85.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T21:26:47Z","timestamp":1675286807000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/32\/1\/85\/1742995"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,9,8]]},"references-count":53,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2016,1,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btv529","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2016,1,1]]},"published":{"date-parts":[[2015,9,8]]}}}