ArticleFrontiers in molecular biosciences2022
Predicting clinical response to everolimus in ER+ breast cancers using machine-learning.
Article in Frontiers in molecular biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- System analysis links SMARCD3 regulons to growth signaling and MEK inhibitor response in everolimus-resistant ER+ breast cancer cells.Cell reports. Medicine · 2025Article
- Bridging technology and medicine: artificial intelligence in targeted anticancer drug delivery.RSC advances · 2025Review
- Cuproptosis-related genes and agents: implications in tumor drug resistance and future perspectives.Frontiers in pharmacology · 2025Review
- Single-Cell Transcriptomics Sheds Light on Tumor Evolution: Perspectives from City of Hope's Clinical Trial Teams.Journal of clinical medicine · 2024Article
- Small molecule inhibitors as adjuvants in cancer immunotherapy: enhancing efficacy and overcoming resistance.Frontiers in immunology · 2024Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.