{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T20:03:30Z","timestamp":1792181010834,"version":"4.4.0"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62225209"],"award-info":[{"award-number":["62225209"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2025JJ30025"],"award-info":[{"award-number":["2025JJ30025"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["B18059"],"award-info":[{"award-number":["B18059"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["357952"],"award-info":[{"award-number":["357952"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"name":"iCAN Digital Precision Cancer Medicine Finland"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Intratumoral cellular heterogeneity limits therapeutic efficacy in cancer patients. Although single-cell transcriptomics offers high-resolution profiling, translating these insights into clinical drug response prediction remains challenging. Recently, transfer learning approaches have attempted to predict patient drug response by leveraging pre-clinical data. However, these approaches operate at the bulk level, often masking the cellular heterogeneity essential for prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this study, we propose scTAPE, a disentangled transfer learning framework to predict patient drug response using tumor single-cell transcriptomics. scTAPE follows a pre-training and fine-tuning paradigm. During the pre-training stage, scTAPE uses a disentangled learning strategy to extract intrinsic pharmacological signals masked by confounding factors from the matched bulk and single-cell expression profiles. Subsequently, a supervised drug response model is trained on labeled cell-line data to fine-tune the aligned common embedding, thereby achieving cross-domain generalization to unseen datasets. Experimental results demonstrate that scTAPE successfully predicts drug response across cell-line datasets and two independent clinical cohorts, outperforming state-of-the-art single-cell-based predictors. Furthermore, by analyzing tumor cell subpopulations, scTAPE not only predicts patient drug response to both single and combination treatments but also identifies potential therapeutic agents targeting drug-resistant subpopulations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The implementation of scTAPE is available via https:\/\/github.com\/xinliangSun\/scTAPE.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag269","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T16:33:38Z","timestamp":1783442018000},"source":"Crossref","is-referenced-by-count":1,"title":["A disentangled transformer-based transfer learning framework to predict patient drug response from tumor single-cell transcriptomics"],"prefix":"10.1093","volume":"42","author":[{"given":"Xinliang","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha, Hunan 410083,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Shen","sequence":"additional","affiliation":[{"name":"Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki , Helsinki 00290,","place":["Finland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linconghua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Central South University , Changsha, Hunan 410083,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha, Hunan 410083,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhangli","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha, Hunan 410083,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7480-7710","authenticated-orcid":false,"given":"Jing","family":"Tang","sequence":"additional","affiliation":[{"name":"Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki , Helsinki 00290,","place":["Finland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0188-1394","authenticated-orcid":false,"given":"Min","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha, Hunan 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