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放射治疗与免疫治疗协同效应的数学建模

Mathematical modeling of the synergetic effect between radiotherapy and immunotherapy.

作者信息

Xing Yixun, Moore Casey, Saha Debabrata, Nguyen Dan, Bleile MaryLena, Jia Xun, Timmerman Robert, Peng Hao, Jiang Steve

机构信息

Medical Artificial Intelligence and Automation Laboratory, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.

Department of Advanced Data Analytics, University of North Texas, Denton, TX 76205, USA.

出版信息

Math Biosci Eng. 2025 Apr 17;22(5):1206-1225. doi: 10.3934/mbe.2025044.

Abstract

The synergy between radiotherapy and immunotherapy plays a pivotal role in enhancing tumor control and treatment outcomes. To explore the underlying mechanisms of synergy, we investigated a novel treatment approach known as personalized ultra-fractionated stereotactic adaptive radiation (PULSAR) therapy, which emphasizes the impact of radiation timing. Unlike conventional daily treatments, PULSAR delivers high-dose radiation in spaced intervals over weeks or months, enabling tumors to adapt and potentially enhancing synergy with immunotherapy. Drawing on insights from small-animal radiation studies, we developed a discrete-time model based on multiple difference equations to elucidate the temporal dynamics of tumor control driven by both radiation and the adaptive immune response. By accounting for the migration and infiltration of T cells within the tumor microenvironment, we established a quantitative link between radiation therapy and immunotherapy. Model parameters were estimated using a simulated annealing algorithm applied to training data, and our model achieved high accuracy for the test data with a root mean square error of 287 mm. Notably, our framework replicated the PULSAR effect observed in animal studies, revealing that longer intervals between radiation treatments in the context of immunotherapy yielded enhanced tumor control. Specifically, mice receiving immunotherapy alongside radiation pulses delivered at extended intervals, ten days, showed markedly improved tumor responses, whereas those treated with shorter intervals did not achieve comparable benefits. Moreover, our model offers an in-silico tool for designing future personalized ultra-fractionated stereotactic adaptive radiation trials. Overall, these findings underscore the critical importance of treatment timing in harnessing the synergy between radiotherapy and immunotherapy and highlight the potential of our model to optimize and individualize treatment protocols.

摘要

放射治疗与免疫治疗之间的协同作用在增强肿瘤控制和治疗效果方面起着关键作用。为了探索协同作用的潜在机制,我们研究了一种名为个性化超分割立体定向自适应放疗(PULSAR)的新型治疗方法,该方法强调了放疗时间的影响。与传统的每日治疗不同,PULSAR在数周或数月内以间隔的方式给予高剂量辐射,使肿瘤能够适应并可能增强与免疫治疗的协同作用。借鉴小动物辐射研究的见解,我们基于多个差分方程开发了一个离散时间模型,以阐明由辐射和适应性免疫反应驱动的肿瘤控制的时间动态。通过考虑肿瘤微环境中T细胞的迁移和浸润,我们建立了放射治疗与免疫治疗之间的定量联系。使用应用于训练数据的模拟退火算法估计模型参数,我们的模型对测试数据实现了高精度,均方根误差为287毫米。值得注意的是,我们的框架复制了在动物研究中观察到的PULSAR效应,揭示了在免疫治疗背景下延长放疗间隔可增强肿瘤控制。具体而言,接受免疫治疗并同时接受延长间隔(十天)的放射脉冲治疗的小鼠,肿瘤反应明显改善,而接受较短间隔治疗的小鼠则未获得类似的益处。此外,我们的模型为设计未来的个性化超分割立体定向自适应放疗试验提供了一种计算机模拟工具。总体而言,这些发现强调了治疗时间在利用放射治疗与免疫治疗之间协同作用方面的至关重要性,并突出了我们的模型在优化和个性化治疗方案方面的潜力。

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