标准放化疗联合CAR-T细胞疗法治疗恶性胶质瘤：基于脉冲数学框架与虚拟试验的洞见

中文摘要 AI 辅助

恶性胶质瘤的极端治疗耐药性推动了多模式治疗策略的发展。我们提出了一个机制性数学框架，以研究放疗、替莫唑胺和嵌合抗原受体（CAR）T细胞疗法的耦合动力学。该模型被构建为一个九维脉冲动力系统，捕捉增殖-静息肿瘤状态、治疗特异性耐药、组织损伤、TMZ药代动力学以及常规治疗方案对工程化T细胞施加的淋巴毒性干扰。数学分析确立了生物学有意义解的存在性，描绘了不变结构，并确定了持续治疗局部稳定无肿瘤平衡点的阈值条件。在与包括标准Stupp方案在内的临床试验进行研究匹配基准测试后，我们部署了异质性虚拟患者队列，以评估五种CAR-T与Stupp联合序列。在群体水平上，免疫治疗辅助手段带来了一致但适度的生存期延长，中位总生存期增加了0.8至0.9个月，且对时间顺序或日程扰动敏感性极低。至关重要的是，这些群体水平的中位数掩盖了深刻的个体异质性：在联合方案下，19%至22%的虚拟患者生存期延长超过60天，约3%的患者获益超过一年。低内在肿瘤增殖率r1成为反应的主要决定因素，并得到稳健T细胞扩增和减弱微环境抑制的增强。这些发现表明，多模式整合的临床前景取决于识别响应性肿瘤表型以进行个性化治疗，而非寻找单一普遍最优的时间表。

英文摘要

The extreme therapeutic resistance of malignant gliomas motivates the development of multimodal treatment strategies. We present a mechanistic mathematical framework to investigate the coupled dynamics of radiotherapy, temozolomide, and chimeric antigen receptor (CAR) T-cell therapy. The model is formulated as a nine-dimensional impulsive dynamical system that captures proliferative-quiescent tumor states, treatment-specific resistance, tissue damage, TMZ pharmacokinetics, and the lymphotoxic interferences exerted by conventional regimens on engineered T cells. Mathematical analysis establishes the existence of biologically meaningful solutions, maps invariant structures, and identifies threshold conditions under which sustained therapy locally stabilizes the tumor-free equilibrium. After study-matched benchmarking against clinical trials, including the standard Stupp protocol, we deploy heterogeneous virtual patient cohorts to evaluate five combined CAR-T--Stupp sequences. At the population level, the immunotherapeutic adjunct yields a consistent but modest survival extension, increasing median overall survival by $0.8$--$0.9$ months with minimal sensitivity to temporal ordering or scheduling perturbations. Crucially, these population-level medians mask profound individual heterogeneity: under combined protocols, $19\%$--$22\%$ of virtual patients extend survival by more than $60$ days, and $\sim 3\%$ gain over a year. A low intrinsic tumor proliferation rate $r_1$ emerges as the primary determinant of response, bolstered by robust T-cell expansion and attenuated microenvironmental suppression. These findings suggest that the clinical promise of multimodal integration depends on identifying responsive tumor phenotypes for personalized treatments rather than searching for a single universally optimal timeline.

发表机构

• Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country（巴斯克大学生物物理研究所）
• Ikerbasque, Basque Foundation for Science（伊卡巴斯基奎基金会）
• Mathematical Oncology Laboratory (MOLAB), Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, University of Castilla-La Mancha（卡斯蒂利亚-拉曼恰大学应用数学与工程学研究所）
• Department of Mathematics, Escuela Técnica Superior de Ingeniería Industrial, University of Castilla-La Mancha（卡斯蒂利亚-拉曼恰大学高等工业工程学院数学系）
• Laboratorio de Oncología Matemática, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM)（卡斯蒂利亚-拉曼恰卫生研究所）
• University of Castilla-La Mancha（卡斯蒂利亚-拉曼恰大学）

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