PO.PR01.05 · 预防研究
为预测接受免疫检查点抑制剂治疗的癌症患者大型前瞻性队列中的内分泌毒性而设计的整合性理论框架
Designing an integrative theoretical framework for predicting endocrine toxicity in a large prospective cohort of cancer patients treated with immune checkpoint inhibitor therapy
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
免疫检查点抑制剂（ICI）与累及内分泌系统的免疫相关不良事件（irAE）相关。内分泌irAE的发生率因所用药物不同而差异显著，所有级别的平均发生率为23%，重度级别的发生率为19%。与其他irAE不同，内分泌病变常在ICI治疗停止后持续存在，这凸显了对风险分层和预测性监测的需求。目前对内分泌irAE的认识有限，尚无可靠方法能够前瞻性识别最有可能发生这些并发症的个体，或在治疗开始时进行风险分层。本研究提出了一个用于系统研究内分泌irAE潜在机制和风险因素的理论框架。其主要目的是生成假设并推进方法学方法，以支持后续的预测建模和临床试验，从而支持针对这些irAE早期检测和管理的个体化策略的开发。在该框架内，我们提出内分泌irAE具有复杂性，是多种临床、生物学和生活方式决定因素之间多维交互作用的结果。我们通过多方面的方法考察内分泌irAE风险，包括：（1）临床和治疗因素（ICI药物选择、给药方案、治疗顺序），（2）免疫失调的生物标志物（源自CBC的指标以及淋巴细胞/中性粒细胞计数），（3）炎症介质（IL-6、IFN-gamma、TNF-alpha、IL-8、TGF-beta），（4）遗传易感性，以及（5）行为和环境暴露（营养、体力活动、烟草使用、共病）。我们还假设性别是一个关键的效应修饰因素，特别是影响特定CBC指标对免疫失调参数在irAE易感性方面的预测效用。我们采用一个由机制数据和现有实证证据支撑的系统生物学框架，开发出一种整合性方法，将定量生物标志物与患者报告的行为和环境暴露评估相结合。我们提出，稳健的预测模型需要整合临床、生物学、可改变风险和环境因素，并按性别进行分层。这一方法力求转向基于系统的方法而非单纯的生物标志物评估，为后续实证验证及纳入临床试验方法学提供结构化基础。通过整合临床、生物学和生活方式因素，该框架支持开发可在未来临床试验和肿瘤学实践中实施的预测模型和个体化监测方案。
查看英文原文 English abstract
Immune checkpoint inhibitors (ICI) are associated with immune-related adverse events (irAEs) involving the endocrine system. The rate of endocrine irAEs can vary significantly depending on the agent used, with an average all-grade occurrence of 23% and a severe-grade occurrence of 19%. Unlike other irAEs, endocrinopathies frequently persist after ICI treatment discontinuation, highlighting the need for risk stratification and predictive monitoring. The current understanding of endocrine irAEs is limited, and there are no reliable methods to identify individuals who are most at risk of developing these complications prospectively or to stratify risk at the initiation of treatment. The current study presents an theoretical framework for systematically investigating underlying mechanisms and risk factors for endocrine irAEs. The primary objective is to generate hypotheses and advance methodological approaches for subsequent predictive modeling and clinical trials, thereby supporting the development of tailored strategies for the early detection and management of these irAEs. Within this framework, we propose that endocrine irAEs are complex and result from multidimensional interactions among various clinical, biological, and lifestyle determinants. We examine endocrine irAE risk through a multifaceted approach encompassing: (1) clinical and treatment factors (ICI agent selection, dosing schedules, treatment sequencing), (2) biomarkers of immune dysregulation (CBC-derived indices and lymphocyte/neutrophil counts), (3) inflammatory mediators (IL-6, IFN-gamma, TNF-alpha, IL-8, TGF-beta), (4) genetic predisposition, and (5) behavioral and environmental exposures (nutrition, physical activity, tobacco use, comorbid diseases). Sex is also hypothesized to be a critical effect modifier, specifically influencing the predictive utility of specific CBC indices for immune dysregulation parameters for susceptibility to irAEs. Using a systems biology framework informed by mechanistic data and current empirical evidence, we develop an integrative approach that combines quantitative biomarkers with patient-reported assessments of behavioral and environmental exposures. Robust predictive models, we propose, require integration of clinical, biological, modifiable risk, and environmental factors, stratified by sex. This approach seeks to move toward a systems-based approach rather than biomarker assessment, providing a structured foundation for subsequent empirical validation and integration into clinical trial methodology. By integrating clinical, biological, and lifestyle factors, this framework supports the development of predictive models and tailored surveillance protocols that can be implemented in future clinical trials and oncology practice.
利益披露 Disclosure
H. Awad, None..
M. Mohamed, None..
L. Danish, None.