Background: A major challenge in cancer treatment is the ability of tumor cells to adapt to immunotherapy through immune escape, often mediated by the PD-1/PD-L1 pathway. To investigate this, we adapted an ordinary differential equation model of combination therapy, incorporating the dynamics of the immune checkpoint inhibitor Avelumab and the immunostimulant NHS-muIL12.Methods: Using literature-derived parameter values, we refitted a single parameter across therapies, which showed that PD-L1 expression increased with immunotherapy, while Avelumab blocked its functional signaling, preventing PD-L1 from suppressing T-cell activity. Incorporating therapy-dependent, dynamically regulated PD-L1 expression enabled a biologically grounded mechanism to reproduce experimental observations, leading us to formulate PD-L1 tumor expression as a dynamic variable (ϵ) and providing a mechanistic basis for both therapeutic synergy and treatment failure.Results: We validated this mechanistic framework by showing that the distinct outcomes observed in two independent cancer datasets (EMT-6 and MC38) can be captured by the same model structure, differing only in the parameterization of tumor-specific parameters and PD-L1 regulatory dynamics. Our results indicate that tumor resistance is linked to dose-dependent upregulation of PD-L1 following NHS-muIL12 treatment, explaining treatment failure, while PD-1/PD-L1 blockade in combination therapy enables effective antitumor immune responses.Conclusions: This work provides a validated mechanistic framework for adaptive resistance in combination immunotherapy. Quantified parameter differences between responder and non-responder phenotypes enable clearer biological interpretation and support the development of predictive tools for optimizing treatment strategies.
背景：癌症治疗面临的主要挑战在于肿瘤细胞能够通过免疫逃逸机制适应免疫治疗，其中PD-1/PD-L1通路常介导这一过程。为探究该机制，我们改进了一个联合疗法的常微分方程模型，整合了免疫检查点抑制剂Avelumab与免疫刺激剂NHS-muIL12的动态作用。 方法：基于文献参数值，我们重新拟合了跨疗法的单一参数。结果显示，免疫治疗会促使PD-L1表达上调，而Avelumab能阻断其功能性信号传导，从而阻止PD-L1抑制T细胞活性。通过纳入治疗依赖性、动态调控的PD-L1表达机制，我们建立了符合生物学原理的模型以复现实验观测结果。这促使我们将肿瘤PD-L1表达量化为动态变量（ϵ），并为治疗协同效应及治疗失败提供了机制性解释。 结果：我们通过两个独立癌症数据集（EMT-6和MC38）验证了该机制框架。研究表明，虽然两组数据呈现不同治疗结果，但可通过同一模型结构进行描述，差异仅体现在肿瘤特异性参数与PD-L1调控动力学的参数化设定上。结果显示，肿瘤耐药性与NHS-muIL12治疗后PD-L1的剂量依赖性上调相关，这解释了治疗失败的原因；而联合疗法中PD-1/PD-L1阻断则能激活有效的抗肿瘤免疫应答。 结论：本研究为联合免疫治疗中的适应性耐药机制提供了经过验证的理论框架。通过量化应答者与非应答者表型间的参数差异，该模型有助于更清晰地阐释生物学机制，并为开发优化治疗策略的预测工具提供支持。
Dynamic PD-L1 Regulation Shapes Tumor Immune Escape and Response to Immunotherapy
肿瘤(癌症)患者之家