Ramos-Guerra Ana D, Farina Benito, Rubio Pérez Jaime, Vilalta-Lacarra Anna, Zugazagoitia Jon, Peces-Barba Germán, Seijo Luis M, Paz-Ares Luis, Gil-Bazo Ignacio, Dómine Gómez Manuel, Ledesma-Carbayo María J
Biomedical Image Technologies, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain.
Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain.
Cancer Immunol Immunother. 2025 Feb 25;74(4):120. doi: 10.1007/s00262-025-03966-9.
The identification of non-small cell lung cancer (NSCLC) patients who will benefit from immunotherapy remains a clinical challenge. Monitoring real-world data (RWD) in the first cycles of therapy may provide a more accurate representation of response patterns in a real-world setting. We propose a multivariate Bayesian joint model using generalized linear mixed effects, trained and validated on RWD from 424 advanced NSCLC patients retrospectively collected from three clinical centers. Center1 was used as training ( ), while Center2 and Center3 were used as independent testing sets ( and , respectively). Peripheral blood data (PBD) were collected at baseline and at three follow-up time points, alongside demographic and epidemiologic features. Six models were trained to predict progression-free survival at 6 months, PFS(6), using different number of longitudinal samples (baseline, two, or four time points) of the neutrophil-to-lymphocyte ratio (NLR) or a multivariate feature selection. Long-term predictions at 12 and 24 months were also evaluated. Prediction accuracy was measured using the area under the receiver operating characteristic curve (AUC). The proposed model significantly improved prediction performance, achieving AUCs of 0.870, 0.804 and 0.827 at 6, 12 and 24 months for Center2, and 0.824, 0.822 and 0.667 for Center3. There was also a significant difference in PFS and overall survival (OS) between predicted response groups, defined by a 6-month PFS cutoff (log-rank test ). Our study suggests that the integration of multiple biomarkers and monitored PBD in an RWD-based Bayesian joint model framework significantly improves immunotherapy response prediction in advanced NSCLC compared to conventional approaches involving biomarker data at baseline only.
确定哪些非小细胞肺癌（NSCLC）患者将从免疫治疗中获益仍然是一项临床挑战。在治疗的最初周期监测真实世界数据（RWD）可能会在真实世界环境中更准确地反映反应模式。我们提出了一种使用广义线性混合效应的多变量贝叶斯联合模型，该模型在从三个临床中心回顾性收集的424例晚期NSCLC患者的RWD上进行训练和验证。中心1用作训练集（ ），而中心2和中心3用作独立测试集（分别为 和 ）。在基线和三个随访时间点收集外周血数据（PBD），以及人口统计学和流行病学特征。训练了六个模型，使用不同数量的中性粒细胞与淋巴细胞比率（NLR）纵向样本（基线、两个或四个时间点）或多变量特征选择来预测6个月时的无进展生存期，即PFS(6)。还评估了12个月和24个月时的长期预测。使用受试者工作特征曲线下面积（AUC）来衡量预测准确性。所提出的模型显著提高了预测性能，中心2在6、12和24个月时的AUC分别为0.870、0.804和0.827，中心3分别为0.824、0.822和0.667。由6个月PFS临界值定义的预测反应组之间的PFS和总生存期（OS）也存在显著差异（对数秩检验 ）。我们的研究表明，与仅涉及基线生物标志物数据的传统方法相比，在基于RWD的贝叶斯联合模型框架中整合多种生物标志物和监测的PBD可显著改善晚期NSCLC的免疫治疗反应预测。
