Speakers
Description
More than 50% of cancer patients undergo radiotherapy. However, radioresistance too often leads to tumor recurrence and poor prognosis. Recent studies (for reviews, see [1,2]) have highlighted the key role of glycolytic reprogramming in modulating tumor cell responses to radiation. In tumors, under hypoxic and nutrient-limited conditions, cells enhance glycolysis and lactate production in order to maintain high proliferation rates. This metabolic adaptation enhances resistance to radiotherapy by modulating oxidative stress and facilitating DNA repair pathways.
Beyond intracellular mechanisms, radiotherapy responses are characterized by strong inter-patient heterogeneity and complex tumor volume dynamics over time, often observed under clinically realistic right-censored follow-up conditions. Accurately modeling these longitudinal tumor trajectories is therefore essential to better understand treatment response, resistance emergence, and to guide personalized therapeutic strategies.
In this minisymposium, we will explore recent advances in mathematical and data-driven modeling approaches that capture the multiscale interplay between cellular metabolism, radiation response, and tumor growth dynamics. In particular, we will discuss hybrid mechanistic models and geometric adversarial learning frameworks for reconstructing censored tumor evolution directly from clinical data, offering new perspectives for uncertainty-aware prediction of radiotherapy outcomes.
Bibliography
[1] Gao, S., Liu, X., Chen, S., & Zhou, P. (2025). Glucose Metabolism Modulation as a Strategy to Enhance Cancer Radiotherapy. Metabolites, 15(12), 793. [2] Bimbenet D., Badoual M., Powathil G., Stéphanou A. (2026) Exploring How Metabolism comes into play with Radiations: A Review, International Journal of Radiation Biology
