Predicting clinical response to everolimus in ER+ breast cancers using machine-learning
Aritro Nath1, Patrick A Cosgrove1, Jeffrey T Chang2
1City of Hope Comprehensive Cancer Center, Department of Medical Oncology and Therapeutics, Monrovia, CA, United States.
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.
Insights
Researchers developed a machine learning biomarker to predict response to everolimus, an mTOR inhibitor, in advanced ER+ breast cancer patients. This biomarker helps identify individuals likely to benefit from this targeted therapy, addressing treatment resistance.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Endocrine therapy is standard for ER+ breast cancer, but resistance is common.
- PI3K/AKT/mTOR pathway activation drives acquired resistance to endocrine therapy.
- Everolimus, an mTOR inhibitor, improves outcomes in metastatic ER+ breast cancer, but lacks predictive biomarkers.
Purpose of the Study:
- To develop and validate an integrative machine learning biomarker for predicting response to everolimus.
- To identify patients with advanced ER+ breast cancer who will most likely benefit from everolimus treatment.
Main Methods:
- Utilized gene expression signatures from ER+ breast cancer cell lines and patients treated with everolimus.
- Developed and validated an integrative machine learning model to distinguish responders from non-responders.
Main Results:
- The developed machine learning biomarker successfully distinguished between responders and non-responders to everolimus.
- The biomarker demonstrates potential for clinical application in guiding everolimus treatment decisions.
Conclusions:
- An integrative machine learning biomarker can predict everolimus response in ER+ breast cancer.
- This biomarker may help personalize treatment strategies for patients with advanced ER+ breast cancer, overcoming resistance.
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