Fitness advantages, prediction of resistances and protocol design: exploiting the synergy between mathematical modelling and statistics

Abstract

Tumour evolution and treatment resistance are driven by the interplay between clonal heterogeneity, selective pressures, and therapeutic interventions. In this work, we propose an integrated mathematical and statistical framework to investigate clonal selection, predict radioresistance, and optimize radiotherapy protocols. We develop a multi-clonal dynamical model incorporating proliferation, resource competition, epigenetic instability, and clone-specific radiosensitivity. Through the statistical analysis of 255,150 in silico simulations, we identify key biological determinants of treatment response and characterize the effects of evolutionary competition and radiotherapy on clonal composition. Our results show that evolutionary dynamics favour highly proliferative clones, whereas radiotherapy selects radioresistant populations and reduces tumour heterogeneity. We further investigate how tumour characteristics influence resistance trajectories and identify trade-offs between tumour control, toxicity, and resistance selection across different treatment schedules. Optimal protocols are determined for distinct patient profiles, accounting for dose, fractionation, and timing. Finally, we demonstrate how treatment history affects the efficacy of second-line therapies. Overall, our findings highlight the potential of combining mechanistic modelling and statistical inference to understand cancer evolution and support personalized radiotherapy design.