PD-L1 Protein Expression Is Associated With Good Clinical Outcomes and Nomogram for Prediction of Disease Free Survival and Overall Survival in Breast Cancer Patients Received Neoadjuvant Chemotherapy.
PD-L1 Protein Expression Is Associated With Good Clinical Outcomes and Nomogram for Prediction of Disease Free Survival and Overall Survival in Breast Cancer Patients Received Neoadjuvant Chemotherapy.
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PD-L1 蛋白表达与良好的临床结果和诺模图相关，用于预测接受新辅助化疗的乳腺癌患者的无病生存期和总生存期
DOI：
10.3389/fimmu.2022.849468
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发表时间：
2022
影响因子：
7.3
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中科院分区：
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本研究旨在探讨程序性死亡配体-1（PD-L1）蛋白在接受新辅助化疗（NACT）的乳腺癌患者肿瘤细胞中表达的潜在预后意义。采用免疫组化半定量法检测PD-L1蛋白在乳腺癌组织中的表达。采用卡方检验或Fisher精确检验分析PD-L1蛋白表达与临床病理特征的相关性。生存曲线源自Kaplan-Meier分析，对数秩检验用于比较生存分布与个体指数水平。采用单变量和多变量考克斯比例风险回归模型分析PD-L1蛋白表达与生存结局之间的相关性。根据多变量考克斯模型的结果建立了预测诺模图模型。校准分析和决策曲线分析（DCA）进行诺模图模型的校准，并随后采用诺模图模型的准确性和效益进行评估。共有104例接受NACT的乳腺癌患者入组本研究。根据IHC半定量评分将患者分为低PD-L1组（61例）和高PD-L1组（43例）。与PD-L1蛋白表达较低的患者相比，PD-L1蛋白表达较高的患者的无病生存期（DFS）（平均：48.21个月vs 31.16个月; P=0.011）和总生存期（OS）（平均：83.18个月vs 63.31个月; P=0.019）更长。单因素和多因素分析显示PD-L1、新辅助治疗时间、E-Cadherin、靶向治疗是影响患者DFS和OS的独立预后因素，基于这些独立预后因素的Nomogram用于评估DFS和OS时间。校准图显示基于PD-L1的诺模图预测与1年、3年和5年DFS和OS时间评估的实际观察结果基本一致。DCA曲线表明，基于PD-L1的列线图分别在3年和5年DFS和OS的预后评估方面具有更好的预测临床应用。在乳腺癌患者中，PD-L1蛋白高表达与显著更好的预后以及更长的DFS和OS相关。此外，PD-L1蛋白表达被认为是接受NACT患者的重要预后因素。
This study aims to investigate the potential prognostic significance of programmed death ligand-1 (PD-L1) protein expression in tumor cells of breast cancer patients received neoadjuvant chemotherapy (NACT). Using semiquantitative immunohistochemistry, the PD-L1 protein expression in breast cancer tissues was analyzed. The correlations between PD-L1 protein expression and clinicopathologic characteristics were analyzed using Chi-square test or Fisher’s exact test. The survival curve was stemmed from Kaplan-Meier assay, and the log-rank test was used to compare survival distributions against individual index levels. Univariate and multivariate Cox proportional hazards regression models were accessed to analyze the associations between PD-L1 protein expression and survival outcomes. A predictive nomogram model was constructed in accordance with the results of multivariate Cox model. Calibration analyses and decision curve analyses (DCA) were performed for the calibration of the nomogram model, and subsequently adopted to assess the accuracy and benefits of the nomogram model. A total of 104 breast cancer patients received NACT were enrolled into this study. According to semiquantitative scoring for IHC, patients were divided into: low PD-L1 group (61 cases) and high PD-L1 group (43 cases). Patients with high PD-L1 protein expression were associated with longer disease free survival (DFS) (mean: 48.21 months vs. 31.16 months; P=0.011) and overall survival (OS) (mean: 83.18 months vs. 63.31 months; P=0.019) than those with low PD-L1 protein expression. Univariate and multivariate analyses indicated that PD-L1, duration of neoadjuvant therapy, E-Cadherin, targeted therapy were the independent prognostic factors for patients’ DFS and OS. Nomogram based on these independent prognostic factors was used to evaluate the DFS and OS time. The calibration plots shown PD-L1 based nomogram predictions were basically consistent with actual observations for assessments of 1-, 3-, and 5-year DFS and OS time. The DCA curves indicated the PD-L1 based nomogram had better predictive clinical applications regarding prognostic assessments of 3- and 5-year DFS and OS, respectively. High PD-L1 protein expression was associated with significantly better prognoses and longer DFS and OS in breast cancer patients. Furthermore, PD-L1 protein expression was found to be a significant prognostic factor for patients who received NACT.