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However, discrepancies exist between cancer cell line models (CCLs) and patient\u2010derived tumor models, particularly in translating findings to clinical settings. To bridge this gap, computational approaches such as artificial intelligence\u2013based domain adaptation can assist in aligning laboratory and patient\u2010derived molecular data, thereby improving the translation of preclinical findings into personalized treatment strategies. We developed a deep unsupervised domain adaptation (UDA) algorithm to align features between source and target domains. It was trained on labeled CCLs data from the source domain and unseen, unlabeled CCL data from the target domain. The trained model was applied to predict the dependency map of breast cancer (BC) patients in The Cancer Genome Atlas (TCGA). To validate its performance, we used the predicted BC dependency map to classify ER\u2009+\u2009\/HER2\u2009+\u2009BC subtype statuses and identify synthetic lethality (SL) gene pairs for drug discovery. Our model demonstrated high accuracy in predicting cancer dependency maps for patient\u2010derived tumors. The generated maps showed excellent performance in predicting ER\u2009+\u2009\/HER2+ subtype statuses, with an area under the curve of the receiver operating characteristic (AUC\u2010ROC) of 0.99. Notably, our analysis also identified two potential synthetic lethality gene pairs: PBRM1\u2010NF2 and PBRM1\u2010CTNND2, which can be potentially used for developing precision therapies for ER\u2009+\u2009\/HER2+ breast cancer. Domain adaptation is a promising approach for transferring biological knowledge between different cancer models and improving patient\u2010specific treatment strategies.<\/jats:p>","DOI":"10.1002\/gepi.70044","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T11:22:04Z","timestamp":1782300124000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics"],"prefix":"10.1002","volume":"50","author":[{"given":"Yu","family":"Shi","sequence":"first","affiliation":[{"name":"Biostatistics Division, Dalla Lana School of Public Health University of Toronto Toronto Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Xu","sequence":"additional","affiliation":[{"name":"Biostatistics Division, Dalla Lana School of Public Health University of Toronto Toronto Ontario Canada"},{"name":"Biostatistics Department Princess Margaret Cancer Center Toronto Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9546-2245","authenticated-orcid":false,"given":"Pingzhao","family":"Hu","sequence":"additional","affiliation":[{"name":"Biostatistics Division, Dalla Lana School of Public Health University of Toronto Toronto Ontario Canada"},{"name":"Department of Biochemistry Western University London Ontario Canada"},{"name":"Department of Computer Science Western University London Ontario Canada"},{"name":"Department of Oncology Western University London Ontario Canada"},{"name":"Department of Epidemiology and Biostatistics Western University London Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,24]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"Current DepMap Release data including CRISPR Screens PRISM Drug Screens Copy Number Mutation Expression and Fusions. 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