In oncology, longitudinal biomarkers reflecting the patient’s status and disease evolution can offer reliable predictions of the patient’s response to treatment and prognosis. By leveraging clinical data in patients with advanced non-small-cell lung cancer receiving first-line chemotherapy, we aimed to develop a framework combining anticancer drug exposure, tumor dynamics (RECIST criteria), and C-reactive protein (CRP) concentrations, using nonlinear mixed-effects models, to evaluate and quantify by means of parametric time-to-event models the significance of early longitudinal predictors of progression-free survival (PFS) and overall survival (OS). Tumor dynamics was characterized by a tumor size (TS) model accounting for anticancer drug exposure and development of drug resistance. CRP concentrations over time were characterized by a turnover model. An x-fold change in TS from baseline linearly affected CRP production. CRP concentration at treatment cycle 3 (day 42) and the difference between CRP concentration at treatment cycles 3 and 2 were the strongest predictors of PFS and OS. Measuring longitudinal CRP allows for the monitoring of inflammatory levels and, along with its reduction across treatment cycles, presents a promising prognostic marker. This framework could be applied to other treatment modalities such as immunotherapies or targeted therapies allowing the timely identification of patients at risk of early progression and/or short survival to spare them unnecessary toxicities and provide alternative treatment decisions.
在肿瘤学领域，反映患者状态及疾病进展的纵向生物标志物可为患者治疗反应及预后提供可靠预测。本研究基于接受一线化疗的晚期非小细胞肺癌患者临床数据，通过非线性混合效应模型构建了整合抗癌药物暴露量、肿瘤动态变化（RECIST标准）与C反应蛋白浓度的分析框架，并采用参数化事件时间模型评估和量化无进展生存期与总生存期的早期纵向预测因子意义。肿瘤动态变化通过包含抗癌药物暴露量与耐药性发展的肿瘤大小模型进行表征，C反应蛋白浓度随时间变化则通过周转模型描述。肿瘤大小较基线的倍数变化对C反应蛋白生成呈线性影响。治疗第3周期（第42天）的C反应蛋白浓度及其与第2周期的浓度差值，被证实是无进展生存期与总生存期的最强预测因子。纵向监测C反应蛋白可有效追踪炎症水平，其随治疗周期递减的趋势展现出作为预后标志物的潜力。该分析框架可拓展至免疫疗法或靶向治疗等其他治疗模式，有助于及时识别早期进展和/或短期生存风险的患者，避免不必要的毒副作用，并为临床决策提供替代治疗方案。
肿瘤(癌症)患者之家