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投稿时间：2024-11-15 网络发布日期：2025-11-26
投稿时间：2024-11-15 网络发布日期：2025-11-26
中文摘要: 目的 评估前列腺癌患者中程序性死亡配体 1（PD-L1）抑制剂的治疗效果，并构建基于PD-L1表达水平和肿瘤突变负荷（TMB）的个体化预测模型，以预测患者对免疫治疗的反应。方法 本研究为单中心、前瞻性、观察性队列研究，选择2022年6月至2024年6月内江市第一人民医院接受PD-L1抑制剂治疗的106例前列腺癌患者为研究对象。根据肿瘤比例评分（TPS）评分将患者分为3组：低表达组（TPS<1%，n=41）、中等表达组（TPS1%～49%，n=39）和高表达组（TPS≥50%，n=26）。所有纳入患者均接受PD-L1抑制剂阿替利珠单抗治疗。通过免疫组织化学检测患者的 PD-L1 表达水平，并使用外显子组测序测定 TMB。根据实体瘤疗效评价标准（RECIST）1.1评估疗效。采用Cox回归模型和受试者工作特征（ROC）曲线分别评估PD-L1、TMB及其他临床变量对生存预后的独立影响和预测作用。结果 不同 PD-L1 表达水平患者的免疫治疗效果差异有统计学意义（Z=10.980，P=0.004）。高 TMB 组和低 TMB 组的疗效差异有统计学意义（Z=2.660，P=0.008）。高 PD-L1 表达（TPS≥50%）和高 TMB（>10 mut/Mb）的患者在免疫治疗中的疗效最佳，完全缓解率为 38.9%，疾病进展率为11.1%，而低PD-L1/低TMB患者的完全缓解率为5.9%，疾病进展率为41.2%（P=0.002）。Cox回归分析表明，PD-L1（HR=0.72，P=0.002）和TMB（HR=0.80，P=0.027）为无进展生存期和总生存期的独立影响因素。PD-L1和TMB组合的个体化预测模型的AUC 为0.81。结论 PD-L1和TMB可作为前列腺癌患者免疫治疗效果的重要预测标志物，基于二者的个体化预测模型能够有效筛选对免疫治疗敏感的患者。
Abstract:Objective To evaluate the short-term efficacy of programmed death-ligand 1（PD-L1）inhibitors in patients with prostate cancer and to construct an individualized predictive model based on PD-L1 expression levels and tumor mutational burden（TMB）to predict patients'responses to immunotherapy. Methods A single- center，prospective，observational cohort study was conducted，and 106 prostate cancer patients who received PD-L1 inhibitor treatment atthe First People's Hospital of Neijiang from June 2022 to June 2024 were selected. Based on the tumor proportion score（TPS），patients were categorized into the three groups：the low-expression group（TPS<1%，n=41），the intermediate-expression group（TPS 1%-49%，n=39），and the high-expression group（TPS≥50%，n=26）. All patients were treated with atezolizumab. PD - L1 expression levels were detected using immunohistochemistry，and TMB was determined through exome sequencing. The treatment efficacy was evaluated according to RECIST 1.1 criteria. The independent effects and predictive roles of PD-L1，TMB，and other clinical variables on survival prognosis were evaluated using the Cox regression model and receiver operating characteristic（ROC）curves，respectively. Results The difference in immunotherapy efficacy among patients with different PD-L1 expression levels was statistically significant（Z=10.980，P=0.004）. A statistically significant difference in treatment response was also observed between the high TMB and low TMB groups（Z=2.660，P=0.008）. Patients with both high PD-L1 expression（TPS≥50%）and high TMB（>10 mut/Mb）achieved the best outcomes from immunotherapy，demonstrating a complete response rate of 38.9% and a disease progression rate of 11.1%. In contrast, patients with low PD-L1 expression and low TMB had a complete response rate of only 5.9% and a disease progression rate of 41.2%（P=0.002）. Cox regression analysis identified both PD-L1（HR=0.72，P=0.002）and TMB（HR=0.8，P=0.027）as independent factors influencing both progression-free survival and overall survival. The personalized prediction model combining PD-L1 and TMB achieved an AUC of 0.81. Conclusion PD-L1 and TMB can serve as important predictive biomarkers for the efficacy of immunotherapy in prostate cancer patients. An individualized predictive model based on these two factors can effectively identify patients sensitive to immunotherapy.
文章编号： 中图分类号：R737.25 文献标志码：A
基金项目:内江市科技计划项目（Z202061）
附件
| Author Name | Affiliation |
| HUANG Yiming，CHEN Zhibin，LYU Tianbing，LI Xiaorong | Department of Urology,The First People's Hospital of Neijiang,Neijiang,Sichuan 641000,China |
引用文本：
黄一鸣，陈智彬，吕天兵，李小荣.前列腺癌患者中精准免疫治疗的短期疗效评估及基于PD⁃L1/TMB的个体化预测模型构建[J].中国临床研究,2025,38(11):1751-1755，1760.
黄一鸣，陈智彬，吕天兵，李小荣.前列腺癌患者中精准免疫治疗的短期疗效评估及基于PD⁃L1/TMB的个体化预测模型构建[J].中国临床研究,2025,38(11):1751-1755，1760.