Predicting clinical response to everolimus in ER+ breast cancers using machine-learning
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 d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Molecular Biosciences |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2022.981962/full |
| _version_ | 1828726586163068928 |
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| author | Aritro Nath Patrick A. Cosgrove Jeffrey T. Chang Andrea H. Bild |
| author_facet | Aritro Nath Patrick A. Cosgrove Jeffrey T. Chang Andrea H. Bild |
| author_sort | Aritro Nath |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T13:45:28Z |
| format | Article |
| id | doaj.art-12b7c7e610184dfe834172148ac48ce3 |
| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-04-12T13:45:28Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-12b7c7e610184dfe834172148ac48ce32022-12-22T03:30:42ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2022-10-01910.3389/fmolb.2022.981962981962Predicting clinical response to everolimus in ER+ breast cancers using machine-learningAritro Nath0Patrick A. Cosgrove1Jeffrey T. Chang2Andrea H. Bild3City of Hope Comprehensive Cancer Center, Department of Medical Oncology and Therapeutics, Monrovia, CA, United StatesCity of Hope Comprehensive Cancer Center, Department of Medical Oncology and Therapeutics, Monrovia, CA, United StatesDepartment of Integrative Biology and Pharmacology, University of Texas Health Science Center at Houston, Houston, TX, United StatesCity of Hope Comprehensive Cancer Center, Department of Medical Oncology and Therapeutics, Monrovia, CA, United StatesEndocrine 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.https://www.frontiersin.org/articles/10.3389/fmolb.2022.981962/fullmachine-learningbiomarkereverolimusestrogen receptor positive breast cancerprognostic modelrandom forest |
| spellingShingle | Aritro Nath Patrick A. Cosgrove Jeffrey T. Chang Andrea H. Bild Predicting clinical response to everolimus in ER+ breast cancers using machine-learning Frontiers in Molecular Biosciences machine-learning biomarker everolimus estrogen receptor positive breast cancer prognostic model random forest |
| title | Predicting clinical response to everolimus in ER+ breast cancers using machine-learning |
| title_full | Predicting clinical response to everolimus in ER+ breast cancers using machine-learning |
| title_fullStr | Predicting clinical response to everolimus in ER+ breast cancers using machine-learning |
| title_full_unstemmed | Predicting clinical response to everolimus in ER+ breast cancers using machine-learning |
| title_short | Predicting clinical response to everolimus in ER+ breast cancers using machine-learning |
| title_sort | predicting clinical response to everolimus in er breast cancers using machine learning |
| topic | machine-learning biomarker everolimus estrogen receptor positive breast cancer prognostic model random forest |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2022.981962/full |
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