Optimizing chemoradiotherapy for malignant gliomas: A validated mathematical approach

Malignant gliomas (MGs), particularly glioblastoma, are among the most aggressive brain tumors, with limited treatment options and a poor prognosis. After maximal safe resection, radiotherapy with concomitant and adjuvant temozolomide, commonly referred to as the Stupp protocol, remains the standard first-line treatment. However, despite intensive combined chemoradiotherapy (CRT), survival benefits remain modest, highlighting the need for alternative therapeutic strategies. Emerging evidence suggests that alternative dosing schedules, such as less aggressive regimens with extended intervals between consecutive treatment applications, may improve outcomes, enhancing survival, delaying the emergence of resistance, and minimizing side effects. In this study, we develop, calibrate, and validate in mouse models a novel ordinary differential equation-based mathematical model, using in vivo data to describe MG dynamics under combined CRT. The proposed model incorporates key biological processes, including cancer cell dormancy, phenotypic switching, drug resistance through persister cells, and treatment-induced effects. Through in silico trials, we identified optimized CRT protocols that may outperform the standard Stupp protocol. Finally, we computationally extrapolated the results obtained from the in vivo model to humans, showing up to a four-fold increase in median survival with protracted administration protocols in silico . Although further experimental and clinical validation is required, our framework provides a computational foundation to optimize and personalize treatment strategies for MG and potentially other cancers with similar biological mechanisms.