{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T06:32:20Z","timestamp":1781332340843,"version":"3.54.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000},"content-version":"vor","delay-in-days":53,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Institute of Information & Communications Technology Planning & Evaluation"},{"DOI":"10.13039\/501100014188","name":"MSIT","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Development of Intelligent SW Systems for Uncovering Genetic Variation and Developing Personalized Medicine for Cancer Patients with Unknown Molecular Genetic Mechanisms","award":["2019-0-00567"],"award-info":[{"award-number":["2019-0-00567"]}]},{"name":"Artificial Intelligence Graduate School Program","award":["2019-0-01842"],"award-info":[{"award-number":["2019-0-01842"]}]},{"name":"Human Biobank of Seoul National University Hospital"},{"name":"Korea Biobank Network","award":["KBN4_A03"],"award-info":[{"award-number":["KBN4_A03"]}]},{"name":"Seoul National University Hospital Cancer Tissue Bank"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Pharmacogenomics aims to provide personalized therapy to patients based on their genetic variability. However, accurate prediction of cancer drug response (CDR) is challenging due to genetic heterogeneity. Since clinical data are limited, most studies predicting drug response use preclinical data to train models. However, such models might not be generalizable to external clinical data due to differences between the preclinical and clinical datasets. In this study, a Precision Medicine Prediction using an Adversarial Network for Cancer Drug Response (PANCDR) model is proposed. PANCDR consists of two sub-models, an adversarial model and a CDR prediction model. The adversarial model reduces the gap between the preclinical and clinical datasets, while the CDR prediction model extracts features and predicts responses. PANCDR was trained using both preclinical data and unlabeled clinical data. Subsequently, it was tested on external clinical data, including The Cancer Genome Atlas and brain tumor patients. PANCDR outperformed other machine learning models in predicting external test data. Our results demonstrate the robustness of PANCDR and its potential in precision medicine by recommending patient-specific drug candidates. The PANCDR codes and data are available at https:\/\/github.com\/DMCB-GIST\/PANCDR.<\/jats:p>","DOI":"10.1093\/bib\/bbae088","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T07:01:05Z","timestamp":1710486065000},"source":"Crossref","is-referenced-by-count":18,"title":["PANCDR: precise medicine prediction using an adversarial network for cancer drug response"],"prefix":"10.1093","volume":"25","author":[{"given":"Juyeon","family":"Kim","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology , 61005, Gwangju , South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung-Hye","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine , 03080, Seoul , South Korea"},{"name":"Neuroscience Research Institute, Seoul National University College of Medicine , 03080, Seoul , South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2389-7183","authenticated-orcid":false,"given":"Hyunju","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology , 61005, Gwangju , South Korea"},{"name":"Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology , 61005, Gwangju , South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,3,14]]},"reference":[{"issue":"5439","key":"2024031506423425100_ref1","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1126\/science.286.5439.487","article-title":"Pharmacogenomics: translating functional genomics into rational therapeutics","volume":"286","author":"Evans","year":"1999","journal-title":"Science"},{"issue":"10","key":"2024031506423425100_ref2","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1038\/s41588-018-0209-6","article-title":"Pharmacogenomic landscape of patient-derived tumor cells informs precision oncology therapy","volume":"50","author":"Lee","year":"2018","journal-title":"Nat Genet"},{"issue":"3","key":"2024031506423425100_ref3","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"},{"issue":"7391","key":"2024031506423425100_ref4","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"},{"issue":"11","key":"2024031506423425100_ref5","doi-asserted-by":"crossref","first-page":"1210","DOI":"10.1158\/2159-8290.CD-15-0235","article-title":"Harnessing connectivity in a large-scale small-molecule sensitivity dataset","volume":"5","author":"Seashore-Ludlow","year":"2015","journal-title":"Cancer Discov"},{"issue":"10","key":"2024031506423425100_ref6","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1101\/gr.221077.117","article-title":"Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies","volume":"27","author":"Geeleher","year":"2017","journal-title":"Genome Res"},{"issue":"1","key":"2024031506423425100_ref7","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"},{"issue":"2","key":"2024031506423425100_ref8","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1158\/1541-7786.MCR-17-0378","article-title":"Precision oncology beyond targeted therapy: combining omics data with machine learning matches the majority of cancer cells to effective therapeutics","volume":"16","author":"Ding","year":"2018","journal-title":"Mol Cancer Res"},{"issue":"10","key":"2024031506423425100_ref9","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"},{"issue":"14","key":"2024031506423425100_ref10","doi-asserted-by":"crossref","first-page":"i501","DOI":"10.1093\/bioinformatics\/btz318","article-title":"Moli: multi-omics late integration with deep neural networks for drug response prediction","volume":"35","author":"Sharifi-Noghabi","year":"2019","journal-title":"Bioinformatics"},{"issue":"1","key":"2024031506423425100_ref11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-021-04146-z","article-title":"Super. 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