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动态PD-L1调节塑造瘤免疫逃生和对免疫治疗的反应

Bruce Pell¹, Aigerim Kalizhanova², Aisha Tursynkozha³

¹Department of Mathematics and Computer Science, Lawrence Technological University, Southfield, MI 48075, USA.

bioRxiv : the preprint server for biology

|November 24, 2025

概括

瘤细胞通过PD-1/PD-L1通路适应逃避癌症免疫治疗. 与Avelumab和NHS-muIL12联合治疗显示动态PD-L1调节解释了治疗成功和失败,使有效的抗瘤免疫力.

科学领域:

免疫学免疫学免疫学
计算生物学计算生物学
癌症研究癌症研究

背景情况:

癌症免疫疗法面临的挑战是由于瘤的免疫逃生机制.
PD-1/PD-L1通路是瘤微环境中免疫抑制的关键调解者.
组合疗法旨在克服耐药性,但需要机械的理解.

研究的目的:

为了研究动态PD-L1表达对组合免疫疗法的反应中的作用.
为了模拟Avelumab (抗PD-L1) 和NHS-muIL12 (免疫刺激剂) 之间的相互作用.
识别治疗协同作用和治疗失败背后的机制.

主要方法:

调整了一个普通微分方程模型用于组合疗法.
作为一个关键变量,内置了动态PD-L1表达式.
使用文献衍生值和实验数据重新调整模型参数.

主要成果:

免疫疗法增加了PD-L1表达,但Avelumab阻止了其抑制信号传递.
动态PD-L1调节为观察到的实验结果提供了机制基础.
瘤耐药性与NHS-muIL12诱导的,剂量依赖的PD-L1上调有关.

结论:

建模动态PD-L1表达对于理解免疫治疗反应至关重要.
用PD-1/PD-L1阻断剂和免疫刺激剂进行组合治疗可以引起有效的抗瘤免疫力.
了解PD-L1动态可以预测并潜在地克服治疗失败.

关键词:

PD-L1,自适应性免疫反应癌症免疫疗法免疫疗法不同方程微分方程微分方程免疫刺激剂是一种免疫刺激剂.数学模型数学模型数学模型建模建模的模型是什么

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