Department of Medical Oncology, National Cancer Institute, Cairo University, Giza, Egypt.
Virology and Immunology Unit, Cancer Biology Department, National Cancer Institute, Cairo University, Giza, Egypt.
Biochem Genet. 2022 Dec;60(6):1963-1985. doi: 10.1007/s10528-022-10199-3. Epub 2022 Feb 19.
Estrogen receptor-α (ESR1) single nucleotide polymorphisms (SNPs) have been related to breast cancer (BC) susceptibility. In this retrospective study we investigated ESR1 SNPs in association with survival and treatment response in BC patients. Seven ESR1 SNPs were genotyped using TaqMan probe assay in 100 formalin-fixed paraffin embedded blocks of Egyptian ERBC patients. Log-binomial regression was used to assess the association of 5 ESR1 SNPs with relative risk of non-response to adjuvant-hormonal treatment. We compared the performance of five machine learning classification models for prediction of treatment response. Predictive models were developed using rs1801132, rs2228480, and rs9322354 that were significantly associated with increased risk for non-response along with the relevant clinical features. Survival analysis was performed to detect prognostic significance of ESR1 SNPs in ESRBC patients. rs1801132 (C), rs2228480 (A), and rs9322354 (G) minor alleles significantly increased the risk of non-response to tamoxifen by more than 81, 84, and 117%, respectively, in ERBC patients on anthracycline/anthracycline-taxanes-based chemotherapy. Multivariate Cox regression survival analysis revealed that rs1801132 (C) and large tumor size were independent predictors for poor survival outcome in ERBC. The best response predictive model was a combination random forest, K-nearest neighbor, and decision tree having an area under the curve of 0.94 and an accuracy of 90.8%. Our proposed predictive model based on ESR1 rs1801132, rs2228480, and rs9322354 SNPs represents a promising genetic risk stratification for selection patients who could benefit from tamoxifen therapy in such a way that might facilitate personalized medicine required to improve ERBC patients' outcome.
雌激素受体-α(ESR1)单核苷酸多态性(SNP)与乳腺癌(BC)易感性有关。在这项回顾性研究中,我们研究了 ESR1 SNP 与 BC 患者的生存和治疗反应的关系。使用 TaqMan 探针法在 100 例埃及 ERBC 患者的福尔马林固定石蜡包埋块中对 7 个 ESR1 SNP 进行了基因分型。使用对数二项式回归评估 5 个 ESR1 SNP 与辅助激素治疗无反应的相对风险之间的关联。我们比较了 5 种机器学习分类模型在预测治疗反应方面的性能。使用与无反应风险增加相关的 rs1801132、rs2228480 和 rs9322354 以及相关临床特征,开发了预测模型。生存分析用于检测 ESR1 SNP 在 ESRBC 患者中的预后意义。rs1801132(C)、rs2228480(A)和 rs9322354(G)的次要等位基因使 ERBC 患者在接受蒽环类药物/蒽环类药物-紫杉烷类化疗的情况下,对他莫昔芬无反应的风险分别增加了 81%、84%和 117%。多变量 Cox 回归生存分析显示,rs1801132(C)和大肿瘤大小是 ERBC 患者生存不良的独立预测因子。最佳反应预测模型是随机森林、K-最近邻和决策树的组合,曲线下面积为 0.94,准确率为 90.8%。我们基于 ESR1 rs1801132、rs2228480 和 rs9322354 SNP 提出的预测模型代表了一种有前途的遗传风险分层方法,可选择可能从他莫昔芬治疗中获益的患者,从而有助于实现改善 ERBC 患者预后所需的个体化医疗。