{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T01:13:18Z","timestamp":1775697198085,"version":"3.50.1"},"reference-count":50,"publisher":"Oxford University Press (OUP)","issue":"22","license":[{"start":{"date-parts":[[2018,6,1]],"date-time":"2018-06-01T00:00:00Z","timestamp":1527811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001348","name":"Agency for Science, Technology and Research","doi-asserted-by":"publisher","award":["A*STAR"],"award-info":[{"award-number":["A*STAR"]}],"id":[{"id":"10.13039\/501100001348","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,11,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>As we move toward an era of precision medicine, the ability to predict patient-specific drug responses in cancer based on molecular information such as gene expression data represents both an opportunity and a challenge. In particular, methods are needed that can accommodate the high-dimensionality of data to learn interpretable models capturing drug response mechanisms, as well as providing robust predictions across datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose a method based on ideas from \u2018recommender systems\u2019 (CaDRReS) that predicts cancer drug responses for unseen cell-lines\/patients based on learning projections for drugs and cell-lines into a latent \u2018pharmacogenomic\u2019 space. Comparisons with other proposed approaches for this problem based on large public datasets (CCLE and GDSC) show that CaDRReS provides consistently good models and robust predictions even across unseen patient-derived cell-line datasets. Analysis of the pharmacogenomic spaces inferred by CaDRReS also suggests that they can be used to understand drug mechanisms, identify cellular subtypes and further characterize drug-pathway associations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Source code and datasets are available at https:\/\/github.com\/CSB5\/CaDRReS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty452","type":"journal-article","created":{"date-parts":[[2018,5,31]],"date-time":"2018-05-31T07:19:28Z","timestamp":1527751168000},"page":"3907-3914","source":"Crossref","is-referenced-by-count":134,"title":["Predicting Cancer Drug Response using a Recommender System"],"prefix":"10.1093","volume":"34","author":[{"given":"Chayaporn","family":"Suphavilai","sequence":"first","affiliation":[{"name":"Department of Computer Science, School of Computing, National University of Singapore, Singapore"},{"name":"Computational and Systems Biology, Genome Institute of Singapore, Singapore"}]},{"given":"Denis","family":"Bertrand","sequence":"additional","affiliation":[{"name":"Computational and Systems Biology, Genome Institute of Singapore, Singapore"}]},{"given":"Niranjan","family":"Nagarajan","sequence":"additional","affiliation":[{"name":"Computational and Systems Biology, Genome Institute of Singapore, Singapore"}]}],"member":"286","published-online":{"date-parts":[[2018,6,1]]},"reference":[{"key":"2023012712351249000_bty452-B25","first-page":"i455","article-title":"Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization","volume-title":"Bioinformatics","author":"Ammad-ud-din","year":"2016"},{"key":"2023012712351249000_bty452-B1","first-page":"820","article-title":"Computational models for predicting drug responses in cancer research","volume":"18","author":"Azuaje","year":"2016","journal-title":"Brief. Bioinform"},{"key":"2023012712351249000_bty452-B2","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2023012712351249000_bty452-B3","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1038\/nrc3007","article-title":"Strategies to improve radiotherapy with targeted drugs","volume":"11","author":"Begg","year":"2011","journal-title":"Nat. Rev. Cancer"},{"key":"2023012712351249000_bty452-B4","first-page":"35","volume-title":"Proceedings of KDD Cup and Workshop","author":"Bennett","year":"2007"},{"key":"2023012712351249000_bty452-B5","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1158\/0008-5472.CAN-17-1345","article-title":"ConsensusDriver improves upon individual algorithms for predicting driver alterations in different cancer types and individual patients\u2014a toolbox for precision","volume":"78","author":"Bertrand","year":"2018","journal-title":"Cancer Research"},{"key":"2023012712351249000_bty452-B6","first-page":"401","article-title":"The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data","author":"Cerami","year":"2012"},{"key":"2023012712351249000_bty452-B7","doi-asserted-by":"crossref","first-page":"1.","DOI":"10.1093\/biostatistics\/kxw022","article-title":"Prediction of cancer drug sensitivity using high-dimensional omic features","volume":"18","author":"Chen","year":"2017","journal-title":"Biostatistics"},{"key":"2023012712351249000_bty452-B8","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1093\/bib\/bbv066","article-title":"Drug\u2013target interaction prediction: databases, web servers and computational models","volume":"17","author":"Chen","year":"2016","journal-title":"Brief. Bioinform"},{"key":"2023012712351249000_bty452-B9","doi-asserted-by":"crossref","first-page":"e1004975.","DOI":"10.1371\/journal.pcbi.1004975","article-title":"NLLSS: predicting synergistic drug combinations based on semi-supervised learning","volume":"12","author":"Chen","year":"2016","journal-title":"PLoS Comput. Biol"},{"key":"2023012712351249000_bty452-B10","doi-asserted-by":"crossref","first-page":"435.","DOI":"10.1038\/s41467-017-00451-5","article-title":"Phenotype-driven precision oncology as a guide for clinical decisions one patient at a time","volume":"8","author":"Chia","year":"2017","journal-title":"Nat. Commun"},{"key":"2023012712351249000_bty452-B11","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1093\/bioinformatics\/btv529","article-title":"Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel","volume":"32","author":"Cort\u00e9s-Ciriano","year":"2016","journal-title":"Bioinformatics"},{"key":"2023012712351249000_bty452-B12","doi-asserted-by":"crossref","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":"2023012712351249000_bty452-B13","doi-asserted-by":"crossref","first-page":"2891","DOI":"10.1093\/bioinformatics\/btw344","article-title":"Evaluating the molecule-based prediction of clinical drug responses in cancer","volume":"32","author":"Ding","year":"2016","journal-title":"Bioinformatics"},{"key":"2023012712351249000_bty452-B14","first-page":"489","article-title":"Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection","volume":"15","author":"Dong","year":"2015","journal-title":"BMC"},{"key":"2023012712351249000_bty452-B15","doi-asserted-by":"crossref","first-page":"5710","DOI":"10.1128\/MCB.00197-09","article-title":"Mutation of the Rb1 pathway leads to overexpression of mTor, constitutive phosphorylation of Akt on serine 473, resistance to anoikis, and a block in c-Raf activation","volume":"29","author":"El-Naggar","year":"2009","journal-title":"Mol. Cell. Biol"},{"key":"2023012712351249000_bty452-B16","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1158\/1535-7163.1427.3.11","article-title":"Specific inhibition of cyclin-dependent kinase 4\/6 by PD 0332991 and associated antitumor activity in human tumor xenografts","volume":"3","author":"Fry","year":"2004","journal-title":"Mol. Cancer Ther"},{"key":"2023012712351249000_bty452-B17","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/S0092-8674(03)00234-4","article-title":"MTA3, a Mi-2\/NuRD complex subunit, regulates an invasive growth pathway in breast cancer","volume":"113","author":"Fujita","year":"2003","journal-title":"Cell"},{"key":"2023012712351249000_bty452-B18","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1200\/JCO.2010.31.6208","article-title":"Phase I study of Navitoclax (ABT-263), a novel Bcl-2 family inhibitor, in patients with small-cell lung cancer and other solid tumors","volume":"29","author":"Gandhi","year":"2011","journal-title":"J. Clin. Oncol"},{"key":"2023012712351249000_bty452-B19","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 vitro drug sensitivity in cell lines","volume":"15","author":"Geeleher","year":"2014","journal-title":"Genome Biol"},{"key":"2023012712351249000_bty452-B20","doi-asserted-by":"crossref","first-page":"23857.","DOI":"10.1038\/srep23857","article-title":"Prioritization of anticancer drugs against a cancer using genomic features of cancer cells: a step towards personalized medicine","volume":"6","author":"Gupta","year":"2016","journal-title":"Sci. Rep"},{"key":"2023012712351249000_bty452-B21","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":"2023012712351249000_bty452-B22","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1677\/erc.1.00600","article-title":"Epidermal growth factor receptor inhibition strategies in oncology","volume":"11","author":"Harari","year":"2004","journal-title":"Endocr. Relat. Cancer"},{"key":"2023012712351249000_bty452-B23","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1038\/nature17987","article-title":"Reproducible pharmacogenomic profiling of cancer cell line panels","volume":"533","author":"Haverty","year":"2016","journal-title":"Nature"},{"key":"2023012712351249000_bty452-B24","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1016\/j.cell.2016.06.017","article-title":"A landscape of pharmacogenomic interactions in cancer","volume":"166","author":"Iorio","year":"2016","journal-title":"Cell"},{"key":"2023012712351249000_bty452-B26","first-page":"30","article-title":"Matrix factorization techniques for recommender systems","volume":"42","author":"Koren","year":"2009","journal-title":"Computer (Long. Beach. Calif.)"},{"key":"2023012712351249000_bty452-B27","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":"2023012712351249000_bty452-B28","doi-asserted-by":"crossref","first-page":"11851","DOI":"10.1158\/0008-5472.CAN-06-1377","article-title":"Sorafenib blocks the RAF\/MEK\/ERK pathway, inhibits tumor angiogenesis, and induces tumor cell apoptosis in hepatocellular carcinoma model PLC\/PRF\/5","volume":"66","author":"Liu","year":"2006","journal-title":"Cancer Res"},{"key":"2023012712351249000_bty452-B29","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1038\/sj.bjc.6603884","article-title":"Downstream signalling and specific inhibition of c-MET\/HGF pathway in small cell lung cancer: implications for tumour invasion","volume":"97","author":"Ma","year":"2007","journal-title":"Br. J. Cancer"},{"key":"2023012712351249000_bty452-B30","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res"},{"key":"2023012712351249000_bty452-B31","doi-asserted-by":"crossref","first-page":"2890","DOI":"10.1158\/1078-0432.CCR-06-3043","article-title":"KRAS mutation is an important predictor of resistance to therapy with epidermal growth factor receptor tyrosine kinase inhibitors in non\u2013small-cell lung cancer","volume":"13","author":"Massarelli","year":"2007","journal-title":"Clin. Cancer Res"},{"key":"2023012712351249000_bty452-B32","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1016\/j.bbamcr.2006.10.001","article-title":"Roles of the Raf\/MEK\/ERK pathway in cell growth, malignant transformation and drug resistance","volume":"1773","author":"McCubrey","year":"2007","journal-title":"Biochim. Biophys. Acta Mol. Cell Res"},{"key":"2023012712351249000_bty452-B33","doi-asserted-by":"crossref","first-page":"1563.","DOI":"10.1126\/science.1234139","article-title":"Cancer pharmacogenomics: early promise, but concerted effort needed","volume":"339","author":"McLeod","year":"2013","journal-title":"Science"},{"key":"2023012712351249000_bty452-B34","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.1016\/j.clinthera.2008.08.008","article-title":"Lapatinib: a dual inhibitor of human epidermal growth factor receptor tyrosine kinases","volume":"30","author":"Medina","year":"2008","journal-title":"Clin. Ther"},{"key":"2023012712351249000_bty452-B35","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":"2023012712351249000_bty452-B36","author":"M\u00fcllner","year":"2011"},{"key":"2023012712351249000_bty452-B37","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.gene.2005.10.018","article-title":"Epidermal growth factor receptor (EGFR) signaling in cancer","volume":"366","author":"Normanno","year":"2006","journal-title":"Gene"},{"key":"2023012712351249000_bty452-B38","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res"},{"key":"2023012712351249000_bty452-B39","doi-asserted-by":"crossref","first-page":"19192","DOI":"10.1074\/jbc.M000238200","article-title":"Two different signal transduction pathways are implicated in the regulation of initiation factor 2B activity in insulin-like growth factor-1-stimulated neuronal cells","volume":"275","author":"Quevedo","year":"2000","journal-title":"J. Biol. Chem"},{"key":"2023012712351249000_bty452-B40","doi-asserted-by":"crossref","first-page":"12.","DOI":"10.1186\/1758-2946-1-12","article-title":"Small Molecule Subgraph Detector (SMSD) toolkit","volume":"1","author":"Rahman","year":"2009","journal-title":"J. Cheminform"},{"key":"2023012712351249000_bty452-B41","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1109\/JBHI.2015.2412522","article-title":"Optimal drug prediction from personal genomics profiles","volume":"19","author":"Sheng","year":"2015","journal-title":"IEEE J. Biomed. Health Inform"},{"key":"2023012712351249000_bty452-B42","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1158\/0008-5472.CAN-06-3958","article-title":"Notch signaling, gamma-secretase inhibitors, and cancer therapy","volume":"67","author":"Shih","year":"2007","journal-title":"Cancer Res"},{"key":"2023012712351249000_bty452-B43","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.lungcan.2014.09.005","article-title":"MEK inhibition in non-small cell lung cancer","volume":"86","author":"Stinchcombe","year":"2014","journal-title":"Lung Cancer"},{"key":"2023012712351249000_bty452-B44","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1038\/nrclinonc.2011.121","article-title":"Predictive biomarkers: a paradigm shift towards personalized cancer medicine","volume":"8","author":"La Thangue","year":"2011","journal-title":"Nat. Rev. Clin. Oncol"},{"key":"2023012712351249000_bty452-B45","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1038\/nature06915","article-title":"Enabling personalized cancer medicine through analysis of gene-expression patterns","volume":"452","author":"Veer","year":"2008","journal-title":"Nature"},{"key":"2023012712351249000_bty452-B46","doi-asserted-by":"crossref","first-page":"513.","DOI":"10.1186\/s12885-017-3500-5","article-title":"Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization","volume":"17","author":"Wang","year":"2017","journal-title":"BMC Cancer"},{"key":"2023012712351249000_bty452-B47","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1126\/science.aac7041","article-title":"Identification and characterization of essential genes in the human genome","volume":"350","author":"Wang","year":"2015","journal-title":"Science"},{"key":"2023012712351249000_bty452-B48","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1038\/ng.2764","article-title":"The Cancer Genome Atlas Pan-Cancer analysis project","volume":"45","author":"Weinstein","year":"2013","journal-title":"Nat. Genet"},{"key":"2023012712351249000_bty452-B49","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":"2023012712351249000_bty452-B50","doi-asserted-by":"crossref","first-page":"bar026.","DOI":"10.1093\/database\/bar026","article-title":"International cancer genome consortium data portal\u2013a one-stop shop for cancer genomics data","volume":"2011","author":"Zhang","year":"2011","journal-title":"Database (Oxford)"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/22\/3907\/48920790\/bioinformatics_34_22_3907.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/22\/3907\/48920790\/bioinformatics_34_22_3907.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,2]],"date-time":"2023-09-02T18:35:20Z","timestamp":1693679720000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/34\/22\/3907\/5026663"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2018,6,1]]},"references-count":50,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2018,11,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bty452","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/215327","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2018,11,15]]},"published":{"date-parts":[[2018,6,1]]}}}