{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:04:01Z","timestamp":1755219841517,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>Pharmacogenomics (PGx) is pivotal in personalized medicine, particularly in cancer care, where drug efficacy and toxicity often vary with genetic variability. This study investigates the prevalence of CYP2D6-related medications and phenotypes in a cohort of 5,576 female breast cancer patients using genomic and electronic health record (EHR) data from the NIH\u2019s All of Us Research Program. A total of 77% of patients were prescribed at least one CYP2D6 metabolized drug. A customized pipeline was developed to determine CYP2D6 genotypes and phenotypes, identifying actionable phenotypes in 12.5% of patients. The prevalence of CYP2D6-associated drugs, including Tamoxifen, ondansetron, and tramadol, were widely prescribed and increased significantly following cancer diagnosis. Approximately 25% of phenotyped patients exhibited non-normal metabolizer types, emphasizing the importance of pharmacogenomic considerations in clinical decision-making. These findings emphasize the potential of incorporating PGx guidelines into routine clinical practice to optimize breast cancer treatment and improve patient outcomes.<\/jats:p>","DOI":"10.3233\/shti251005","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:37:51Z","timestamp":1754566671000},"source":"Crossref","is-referenced-by-count":0,"title":["Unveiling Pharmacogenomic Patterns in Breast Cancer Through Big Data"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3149-6501","authenticated-orcid":false,"given":"Huiyi","family":"Yang","sequence":"first","affiliation":[{"name":"University of Utah"}]},{"given":"Joseph","family":"Finkelstein","sequence":"additional","affiliation":[{"name":"University of Utah"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251005","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:37:51Z","timestamp":1754566671000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251005"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251005","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}