Speaker
Description
Tumors exhibit phenotypic heterogeneity in proliferation rates, affecting growth, therapy response, and relapse risk. Faster-proliferating cells dominate untreated growth but face fitness costs like increased susceptibility to DNA damage or metabolic stress. Proliferation capacity is a core tumor characteristic, and capturing its heterogeneity is key to understanding tumor evolution and treatment outcomes.
We introduce a novel mathematical framework using a continuous phenotype-structured PDE to model proliferation heterogeneity after radiotherapy. Parameterized with experimental data from irradiated murine lung tumors, the model captures tumor growth, phenotypic drift, and selection pressures. Radiotherapy is modeled with a trait-dependent killing term, reflecting the biological premise that slower-proliferating cells are more resistant to radiation-induced damage.
Simulations show radiotherapy exerts strong selection pressure, enriching slow-proliferating, radioresistant cells, rather than merely reducing tumor volume. This compositional shift reduces the population-wide mean proliferation rate, slowing post-treatment regrowth. Depending on dose, residual tumors show distinct phenotypic distributions, such as bimodal structures with coexisting slow- and fast-proliferating subpopulations, revealing divergent evolutionary paths. Our findings emphasize the importance of integrating continuous heterogeneity for accurate modeling and designing evolutionarily informed therapies.
