Speaker
Description
Glioblastoma is an aggressive brain tumor characterized by infiltrative growth, frequent recurrence after treatment, and intratumoral and microenvironmental heterogeneity. A key driver of this heterogeneity is the tumor microenvironment, particularly oxygen gradients that arise as tumor expansion outpaces vascular development. These gradients can induce phenotypic plasticity in tumor cells, including the “go-or-grow” switch between migratory and proliferative phenotypes. Experimental studies link hypoxia to a more migratory phenotype, but the regulatory response to hypoxia and its implications are poorly understood. To explore how oxygen-dependent phenotypic switching influences glioblastoma growth and evolution, we develop a hybrid discrete-continuous mathematical model. The model extends a previous cellular automaton model by adding an oxygen-dependent phenotypic switch function and coupling partial differential equations governing oxygen dynamics. By exploring the parameter space governing the oxygen-dependent switch function, we investigate whether nontrivial regulatory regimes optimize tumor growth and promote spatial heterogeneity. Incorporating oxygen dynamics also enables a detailed description of radiotherapy, which is more effective in normoxic regions, allowing analysis of how oxygen-dependent phenotypic plasticity influences post-treatment tumor recurrence.
