{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T23:36:37Z","timestamp":1783208197369,"version":"3.54.6"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:00:00Z","timestamp":1665446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Mol. Biosci."],"abstract":"<jats:p>Endocrine therapy remains the primary treatment choice for ER+ breast cancers. However, most advanced ER+ breast cancers ultimately develop resistance to endocrine. This acquired resistance to endocrine therapy is often driven by the activation of the PI3K\/AKT\/mTOR signaling pathway. Everolimus, a drug that targets and inhibits the mTOR complex has been shown to improve clinical outcomes in metastatic ER+ breast cancers. However, there are no biomarkers currently available to guide the use of everolimus in the clinic for progressive patients, where multiple therapeutic options are available. Here, we utilized gene expression signatures from 9 ER+ breast cancer cell lines and 23 patients treated with everolimus to develop and validate an integrative machine learning biomarker of mTOR inhibitor response. Our results show that the machine learning biomarker can successfully distinguish responders from non-responders and can be applied to identify patients that will most likely benefit from everolimus treatment.<\/jats:p>","DOI":"10.3389\/fmolb.2022.981962","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T05:50:58Z","timestamp":1665467458000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Predicting clinical response to everolimus in ER+ breast cancers using machine-learning"],"prefix":"10.3389","volume":"9","author":[{"given":"Aritro","family":"Nath","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick A.","family":"Cosgrove","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey T.","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea H.","family":"Bild","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,10,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1093\/bioinformatics\/btu638","article-title":"Htseq--A Python framework to work with high-throughput sequencing data","volume":"31","author":"Anders","year":"2015","journal-title":"Bioinformatics"},{"key":"B2","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1016\/S1470-2045(14)70138-X","article-title":"Everolimus for women with trastuzumab-resistant, her2-positive, advanced breast cancer (Bolero-3): A randomised, double-blind, placebo-controlled phase 3 trial","volume":"15","author":"Andre","year":"2014","journal-title":"Lancet. 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