{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:25:33Z","timestamp":1772821533325,"version":"3.50.1"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T00:00:00Z","timestamp":1715126400000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"TUBA\u2014GEBIP 2017"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Combination drug therapies are effective treatments for cancer. However, the genetic heterogeneity of the patients and exponentially large space of drug pairings pose significant challenges for finding the right combination for a specific patient. Current in silico prediction methods can be instrumental in reducing the vast number of candidate drug combinations. However, existing powerful methods are trained with cancer cell line gene expression data, which limits their applicability in clinical settings. While synergy measurements on cell line models are available at large scale, patient-derived samples are too few to train a complex model. On the other hand, patient-specific single-drug response data are relatively more available.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this work, we propose a deep learning framework, Personalized Deep Synergy Predictor (PDSP), that enables us to use the patient-specific single drug response data for customizing patient drug synergy predictions. PDSP is first trained to learn synergy scores of drug pairs and their single drug responses for a given cell line using drug structures and large scale cell line gene expression data. Then, the model is fine-tuned for patients with their patient gene expression data and associated single drug response measured on the patient ex vivo samples. In this study, we evaluate PDSP on data from three leukemia patients and observe that it improves the prediction accuracy by 27% compared to models trained on cancer cell line data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>PDSP is available at https:\/\/github.com\/hikuru\/PDSP.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae134","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T14:21:47Z","timestamp":1715178107000},"source":"Crossref","is-referenced-by-count":10,"title":["From cell lines to cancer patients: personalized drug synergy prediction"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4356-8846","authenticated-orcid":false,"given":"Halil Ibrahim","family":"Kuru","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Bilkent University , Ankara 06800, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8613-6619","authenticated-orcid":false,"given":"A Ercument","family":"Cicek","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Bilkent University , Ankara 06800, Turkey"},{"name":"Computational Biology Department, Carnegie Mellon University , Pittsburgh 15213, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7058-5372","authenticated-orcid":false,"given":"Oznur","family":"Tastan","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Natural Sciences, Sabanci University , Istanbul 34956, Turkey"}]}],"member":"286","published-online":{"date-parts":[[2024,5,8]]},"reference":[{"key":"2024070110213016900_btae134-B1","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/nbt.2284","article-title":"Combinatorial drug therapy for cancer in the post-genomic era","volume":"30","author":"Al-Lazikani","year":"2012","journal-title":"Nat Biotechnol"},{"key":"2024070110213016900_btae134-B2","doi-asserted-by":"crossref","first-page":"D711","DOI":"10.1093\/nar\/gky964","article-title":"Arrayexpress update\u2014from bulk to single-cell expression data","volume":"47","author":"Athar","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2024070110213016900_btae134-B3","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1038\/nbt.3052","article-title":"A community computational challenge to predict the activity of pairs of compounds","volume":"32","author":"Bansal","year":"2014","journal-title":"Nat Biotechnol"},{"key":"2024070110213016900_btae134-B4","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1111\/j.1744-7348.1939.tb06990.x","article-title":"The toxicity of poisons applied jointly 1","volume":"26","author":"Bliss","year":"1939","journal-title":"Ann Appl Biol"},{"key":"2024070110213016900_btae134-B5","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1093\/bioinformatics\/19.2.185","article-title":"A comparison of normalization methods for high density oligonucleotide array data based on variance and bias","volume":"19","author":"Bolstad","year":"2003","journal-title":"Bioinformatics"},{"key":"2024070110213016900_btae134-B6","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.drudis.2015.09.003","article-title":"Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives","volume":"21","author":"Bulusu","year":"2016","journal-title":"Drug Discov Today"},{"key":"2024070110213016900_btae134-B7","doi-asserted-by":"crossref","first-page":"8683","DOI":"10.1021\/acs.jmedchem.9b02147","article-title":"Transfer learning for drug discovery","volume":"63","author":"Cai","year":"2020","journal-title":"J Med Chem"},{"key":"2024070110213016900_btae134-B8","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1093\/bioinformatics\/btt105","article-title":"ChemoPy: freely available python package for computational biology and chemoinformatics","volume":"29","author":"Cao","year":"2013","journal-title":"Bioinformatics"},{"key":"2024070110213016900_btae134-B9","first-page":"1","article-title":"Cancer drug response profile scan (cdrscan): a deep learning model that predicts drug effectiveness from cancer genomic signature","volume":"8","author":"Chang","year":"2018","journal-title":"Sci Rep"},{"key":"2024070110213016900_btae134-B10","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1038\/s41467-019-09186-x","article-title":"Network-based prediction of drug combinations","volume":"10","author":"Cheng","year":"2019","journal-title":"Nat Commun"},{"key":"2024070110213016900_btae134-B11","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ Psychol Measur"},{"key":"2024070110213016900_btae134-B12","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.pharmthera.2013.01.016","article-title":"Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review","volume":"138","author":"Csermely","year":"2013","journal-title":"Pharmacol Ther"},{"key":"2024070110213016900_btae134-B13","doi-asserted-by":"crossref","first-page":"i103","DOI":"10.1093\/bioinformatics\/btad234","article-title":"Transfer learning for drug\u2013target interaction prediction","volume":"39","author":"Dalk\u0131ran","year":"2023","journal-title":"Bioinformatics"},{"key":"2024070110213016900_btae134-B14","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1186\/s13073-016-0369-x","article-title":"Approaches to modernize the combination drug development paradigm","volume":"8","author":"Day","year":"2016","journal-title":"Genome Med"},{"key":"2024070110213016900_btae134-B15","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1038\/nrd2424","article-title":"The design of drugs for HIV and HCV","volume":"6","author":"De Clercq","year":"2007","journal-title":"Nat Rev Drug Discov"},{"key":"2024070110213016900_btae134-B16","doi-asserted-by":"crossref","first-page":"3714","DOI":"10.3390\/cancers12123714","article-title":"Predicting and quantifying antagonistic effects of natural compounds given with chemotherapeutic agents: applications for high-throughput screening","volume":"12","author":"Hackman","year":"2020","journal-title":"Cancers (Basel)"},{"key":"2024070110213016900_btae134-B17","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/978-1-4939-7493-1_17","volume-title":"Cancer Systems Biology","author":"He","year":"2018"},{"key":"2024070110213016900_btae134-B18","doi-asserted-by":"crossref","first-page":"2407","DOI":"10.1158\/0008-5472.CAN-17-3644","article-title":"Patient-customized drug combination prediction and testing for t-cell prolymphocytic leukemia patients","volume":"78","author":"He","year":"2018","journal-title":"Cancer Res"},{"key":"2024070110213016900_btae134-B19","doi-asserted-by":"crossref","first-page":"e1003390","DOI":"10.1371\/journal.pgen.1003390","article-title":"Genetic and genomic architecture of the evolution of resistance to antifungal drug combinations","volume":"9","author":"Hill","year":"2013","journal-title":"PLoS Genet"},{"key":"2024070110213016900_btae134-B20","doi-asserted-by":"crossref","first-page":"2125","DOI":"10.1016\/S0140-6736(09)60953-3","article-title":"Rosiglitazone evaluated for cardiovascular outcomes in oral agent combination therapy for type 2 diabetes (record): a multicentre, randomised, open-label trial","volume":"373","author":"Home","year":"2009","journal-title":"Lancet"},{"key":"2024070110213016900_btae134-B21","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":"2024070110213016900_btae134-B22","author":"Joseph","year":"2018"},{"key":"2024070110213016900_btae134-B23","doi-asserted-by":"crossref","first-page":"1632","DOI":"10.1056\/NEJMoa1908075","article-title":"Encorafenib, binimetinib, and cetuximab in BRAF v600e\u2013mutated colorectal cancer","volume":"381","author":"Kopetz","year":"2019","journal-title":"N Engl J Med"},{"key":"2024070110213016900_btae134-B24","doi-asserted-by":"crossref","first-page":"2334","DOI":"10.1109\/TCBB.2021.3086702","article-title":"Matchmaker: a deep learning framework for drug synergy prediction","volume":"19","author":"Kuru","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2024070110213016900_btae134-B25","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1038\/s41586-019-1005-x","article-title":"Effective breast cancer combination therapy targeting bach1 and mitochondrial metabolism","volume":"568","author":"Lee","year":"2019","journal-title":"Nature"},{"key":"2024070110213016900_btae134-B26","doi-asserted-by":"crossref","first-page":"2007","DOI":"10.1093\/bioinformatics\/btv080","article-title":"Large-scale exploration and analysis of drug combinations","volume":"31","author":"Li","year":"2015","journal-title":"Bioinformatics"},{"key":"2024070110213016900_btae134-B27","author":"Li","year":"2017"},{"key":"2024070110213016900_btae134-B28","doi-asserted-by":"crossref","first-page":"e1008653","DOI":"10.1371\/journal.pcbi.1008653","article-title":"Transynergy: mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations","volume":"17","author":"Liu","year":"2021","journal-title":"PLoS Comput Biol"},{"key":"2024070110213016900_btae134-B29","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1158\/1535-7163.MCT-10-0925","article-title":"Combinatorial effects of lapatinib and rapamycin in triple-negative breast cancer cells combined treatment in triple-negative breast cells","volume":"10","author":"Liu","year":"2011","journal-title":"Mol Cancer Ther"},{"key":"2024070110213016900_btae134-B30","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1038\/nrd3368","article-title":"Impact of high-throughput screening in biomedical research","volume":"10","author":"Macarron","year":"2011","journal-title":"Nat Rev Drug Discov"},{"key":"2024070110213016900_btae134-B31","first-page":"1","article-title":"Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen","volume":"10","author":"Michael","year":"2019","journal-title":"Nat Commun"},{"key":"2024070110213016900_btae134-B32","doi-asserted-by":"crossref","first-page":"1678","DOI":"10.1016\/j.cell.2017.11.009","article-title":"Combination cancer therapy can confer benefit via patient-to-patient variability without drug additivity or synergy","volume":"171","author":"Palmer","year":"2017","journal-title":"Cell"},{"key":"2024070110213016900_btae134-B33","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1093\/bioinformatics\/btu046","article-title":"Combinatorial therapy discovery using mixed integer linear programming","volume":"30","author":"Pang","year":"2014","journal-title":"Bioinformatics"},{"key":"2024070110213016900_btae134-B34","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1093\/bioinformatics\/btx806","article-title":"Deepsynergy: predicting anti-cancer drug synergy with deep learning","volume":"34","author":"Preuer","year":"2017","journal-title":"Bioinformatics"},{"key":"2024070110213016900_btae134-B35","doi-asserted-by":"crossref","first-page":"8481","DOI":"10.1038\/ncomms9481","article-title":"Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer","volume":"6","author":"Sun","year":"2015","journal-title":"Nat Commun"},{"key":"2024070110213016900_btae134-B36","doi-asserted-by":"crossref","first-page":"3564","DOI":"10.1158\/0008-5472.CAN-17-0489","article-title":"The national cancer institute ALMANAC: a comprehensive screening resource for the detection of anticancer drug pairs with enhanced therapeutic activity","volume":"77","author":"Susan","year":"2017","journal-title":"Cancer Res"},{"key":"2024070110213016900_btae134-B37","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1089\/adt.2012.503","article-title":"A high-throughput yeast assay identifies synergistic drug combinations","volume":"11","author":"Torres","year":"2013","journal-title":"Assay Drug Dev Technol"},{"key":"2024070110213016900_btae134-B38","doi-asserted-by":"crossref","first-page":"bbab390","DOI":"10.1093\/bib\/bbab390","article-title":"Deepdds: deep graph neural network with attention mechanism to predict synergistic drug combinations","volume":"23","author":"Wang","year":"2022","journal-title":"Brief Bioinf"},{"key":"2024070110213016900_btae134-B39","doi-asserted-by":"crossref","first-page":"2238","DOI":"10.1039\/D1SC04515F","article-title":"Orally administered bismuth drug together with n-acetyl cysteine as a broad-spectrum anti-coronavirus cocktail therapy","volume":"13","author":"Wang","year":"2022","journal-title":"Chem Sci"},{"key":"2024070110213016900_btae134-B40","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.cels.2017.09.001","article-title":"Folding membrane proteins by deep transfer learning","volume":"5","author":"Wang","year":"2017","journal-title":"Cell Syst"},{"key":"2024070110213016900_btae134-B41","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.cels.2015.12.003","article-title":"Prediction of synergism from chemical-genetic interactions by machine learning","volume":"1","author":"Wildenhain","year":"2015","journal-title":"Cell Syst"},{"key":"2024070110213016900_btae134-B42","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1002\/psp4.1","article-title":"Digre: drug-induced genomic residual effect model for successful prediction of multidrug effects","volume":"4","author":"Yang","year":"2015","journal-title":"CPT Pharmacometrics Syst Pharmacol"},{"key":"2024070110213016900_btae134-B43","author":"Yu","year":"2015"},{"key":"2024070110213016900_btae134-B44","doi-asserted-by":"crossref","first-page":"W43","DOI":"10.1093\/nar\/gkz337","article-title":"Drugcomb: an integrative cancer drug combination data portal","volume":"47","author":"Zagidullin","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2024070110213016900_btae134-B45","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/978-1-0716-0849-4_12","article-title":"Synergistic drug combination prediction by integrating multiomics data in deep learning models","volume":"2194","author":"Zhang","year":"2021","journal-title":"Transl Bioinf Therapeutic Dev"},{"key":"2024070110213016900_btae134-B46","doi-asserted-by":"crossref","first-page":"206ra140","DOI":"10.1126\/scitranslmed.3006548","article-title":"Systems pharmacology of adverse event mitigation by drug combinations","volume":"5","author":"Zhao","year":"2013","journal-title":"Sci Transl Med"},{"key":"2024070110213016900_btae134-B47","doi-asserted-by":"crossref","first-page":"e1002323","DOI":"10.1371\/journal.pcbi.1002323","article-title":"Prediction of drug combinations by integrating molecular and pharmacological data","volume":"7","author":"Zhao","year":"2011","journal-title":"PLoS Comput Biol"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btae134\/57452228\/btae134.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/5\/btae134\/58376007\/btae134.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/5\/btae134\/58376007\/btae134.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T06:22:23Z","timestamp":1719814943000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btae134\/7667298"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2024,5,1]]},"references-count":47,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,5,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btae134","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.02.13.528276","asserted-by":"object"}]},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,5,1]]},"published":{"date-parts":[[2024,5,1]]},"article-number":"btae134"}}