Student and Faculty Publications
Publication Date
1-1-2022
Journal
Frontiers in Molecular Biosciences
Abstract
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.
Keywords
machine-learning, biomarker, everolimus, estrogen receptor positive breast cancer, prognostic model, random forest, feature selection
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Medical Molecular Biology Commons, Oncology Commons
Comments
Supplementary Materials
PMID: 36304922