Nath Aritro, Cosgrove Patrick A, Chang Jeffrey T, Bild Andrea H
City of Hope Comprehensive Cancer Center, Department of Medical Oncology and Therapeutics, Monrovia, CA, United States.
Department of Integrative Biology and Pharmacology, University of Texas Health Science Center at Houston, Houston, TX, United States.
Front Mol Biosci. 2022 Oct 11;9:981962. doi: 10.3389/fmolb.2022.981962. eCollection 2022.
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.
内分泌治疗仍然是雌激素受体阳性（ER+）乳腺癌的主要治疗选择。然而，大多数晚期ER+乳腺癌最终会对内分泌治疗产生耐药性。这种对内分泌治疗的获得性耐药通常是由PI3K/AKT/mTOR信号通路的激活所驱动的。依维莫司是一种靶向并抑制mTOR复合物的药物，已被证明可改善转移性ER+乳腺癌的临床疗效。然而，目前尚无生物标志物可用于指导依维莫司在临床上对有多种治疗选择的进展期患者的使用。在此，我们利用来自9个ER+乳腺癌细胞系和23例接受依维莫司治疗患者的基因表达特征，开发并验证了一种mTOR抑制剂反应的综合机器学习生物标志物。我们的结果表明，该机器学习生物标志物能够成功区分反应者和无反应者，并可用于识别最有可能从依维莫司治疗中获益的患者。
