Combining standard chemoradiotherapy with CAR-T cell therapy in malignant gliomas: Insights from an impulsive mathematical framework and virtual trials

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.

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Authors

Dmitry Sinelshchikov; Nikols Amaru Mora Millán; Miguel Perales-Patón; Juan Belmonte-Beitia; Matteo Italia

Published

Sep 30, 2026

Paper type

preprint

Source

Arxiv