晚期非小细胞肺癌患者帕博利珠单抗疗效的影响因素分析及预测模型构建

Analysis of Factors Influencing Pembrolizumab Efficacy and Construction of a Prediction Model in Patients with Advanced Non-Small Cell Lung Cancer

摘要

目的 探讨影响晚期非小细胞肺癌（non-small cell lung cancer，NSCLC）患者帕博利珠单抗治疗反应的临床及生物学因素，建立列线图预测模型以辅助个体化治疗决策。方法 采用多中心回顾性队列设计，纳入 2020 年 1 月至 2024 年 12 月常州市第一人民医院、苏州大学附属第一医院及苏州大学附属第二医院收治的 247 例ⅢB~Ⅳ期 NSCLC 患者作为开发队列，收集人口学特征、肿瘤分子生物学指标[程序性死亡配体 1 （programmed death-ligand 1，PD-L1）和肿瘤突变负荷（tumor mutational burden，TMB）]、临床病理参数及炎症指标[中性粒细胞与淋巴细胞比值（neutrophil-to-lymphocyte ratio，NLR）、血小板与淋巴细胞比值（platelet-to-lymphocyte ratio，PLR）及淋巴细胞与单核细胞比值（lymphocyte-to-monocyte ratio，LMR）]。通过单因素及多因素 Logistic 回归筛选疗效相关因素并构建列线图模型，采用受试者工作特征曲线（receiver operating characteristic curve，ROC 曲线）、Bootstrap 法及校准曲线评估模型性能。进一步纳入 2025 年 1 月至 10 月于常州市第一人民医院收治的 112 例独立前瞻性患者作为外部验证队列，采用 ROC 曲线、校准曲线及 Bootstrap 法评估模型性能。结果 单因素分析显示，转移部位 ≥ 3 个、PD-L1 阴性、TMB<10 mut·Mb^-1、单药治疗、NLR ≥ 5、PLR ≥ 150 及 LMR ≤ 1.5 与疗效不佳显著相关（P< 0.05）。多因素分析显示，转移部位 ≥ 3 个、PD-L1 阴性、TMB < 10 mut·Mb^-1、单药治疗、NLR ≥ 5、PLR ≥ 150 及 LMR ≤ 1.5 是帕博利珠单抗疗效不佳的独立危险因素（P< 0.05）。列线图模型 ROC 曲线的曲线下面积（area under the curve，AUC）为 0.771 （95% CI：0.713 ~ 0.830），一致性指数（concordance index，C-index）为 0.753 （95% CI：0.698 ~ 0.808），校准曲线显示预测与实际值一致性良好（Hosmer-Lemeshow 拟合优度检验结果为χ² = 7.820，P= 0.452）。外部验证该模型的 AUC 为 0.744 （95% CI：0.653 ~ 0.835）。校准曲线斜率为 0.921 （95% CI：0.852 ~ 0.990），Hosmer-Lemeshow 拟合优度检验结果为 χ² = 5.210，P= 0.767，提示模型预测概率与实际发生率一致性良好。结论 转移部位数量、PD-L1 表达状态、TMB 水平、治疗模式及炎症指标是帕博利珠单抗疗效的关键预测因子。本研究构建的列线图模型具有中等以上的预测效能，可为临床筛选获益人群及优化治疗策略提供参考。

Abstract

Objective To explore the clinical and biological factors influencing the therapeutic response to pembrolizumab in patients with advanced non-small cell lung cancer (NSCLC), and to establish a nomogram prediction model to assist individualized treatment decision-making. Methods A multicenter retrospective cohort design was adopted. A total of 247 patients with stage IIIB-IV NSCLC admitted to The First People’s Hospital of Changzhou, The First Affiliated Hospital of Soochow University and The Second Affiliated Hospital of Soochow University from January 2020 to December 2024 were enrolled as the development cohort. Demographic characteristics, tumor molecular biomarkers [programmed death-ligand 1 (PD-L1) and tumor mutational burden (TMB)], clinicopathological parameters, and inflammatory indicators [neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR)] were collected. Univariate and multivariate logistic regression were performed to screen factors associated with treatment efficacy and construct the nomogram model. The model performance was evaluated using receiver operating characteristic (ROC) curve, Bootstrap method and calibration curve. Meanwhile, 112 independently and prospectively recruited patients from The First People,s Hospital of Changzhou between January 2025 and October 2025 were enrolled as an external validation cohort, and the model performance was verified using ROC curve, calibration curve and Bootstrap method.Results Univariate analysis revealed that ≥ 3 metastatic sites, negative PD-L1 expression, TMB < 10 mut·Mb ^-1, monotherapy, NLR ≥ 5, PLR ≥ 150 and LMR ≤ 1.5 were significantly associated with poor efficacy (P< 0.05). Multivariate analysis showed that ≥ 3 metastatic sites, negative PD-L1 expression, TMB <10 mut·Mb^-1, monotherapy, NLR ≥ 5, PLR ≥ 150, and LMR ≤ 1.5 were independent risk factors for poor efficacy of pembrolizumab (P< 0.05). For the nomogram model, the area under the curve (AUC) for the ROC curve was 0.771 (95% CI: 0.713 - 0.830), and the concordance index (C-index) was 0.753 (95% CI: 0.698 - 0.808). The calibration curve showed good consistency between the predicted probabilities and actual observations (Hosmer-Lemeshow goodness-of-fit test: χ² = 7.820, P= 0.452). In the external validation, the model achieved an AUC of 0.744 (95% CI: 0.653 - 0.835). The calibration curve had a slope of 0.921 (95% CI: 0.852 - 0.990), and the Hosmer-Lemeshow goodness-of-fit test yieldedχ² = 5.210, P= 0.767, indicating good consistency between predicted probabilities and actual incidence rates. Conclusion The number of metastatic sites, PD-L1 expression status, TMB level, treatment regimen and inflammatory indicators are key predictors of pembrolizumab efficacy. The nomogram model constructed in this study has moderate-to-good predictive performance, and can provide a reference for clinical screening of patients who may benefit from pembrolizumab and optimizing treatment strategies.

关键词

帕博利珠单抗 / 非小细胞肺癌 / 疗效预测 / 列线图模型 / 炎症微环境

Key words

pembrolizumab / non-small cell lung cancer / efficacy prediction / nomogram model / inflammatory microenvironment

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基金资助

常州市科技计划项目(CE20235057)