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A Theoretical Study of Evolutionary Therapy for Neuroblastoma
Evolutionary therapy exploits the evolutionary dynamics within a tumour comprising subpopulations with different combinations of mutations. The most famous example is adaptive therapy, which uses treatment-sensitive cancer cells to suppress their resistant peers. This project aims to investigate evolutionary therapy in the context of neuroblastoma.
After conceiving the project, I established an international collaboration with Dr Sabine Taschner-Mandl from St. Anna Children's Cancer Research Institute. Dr Matishalin Patel, an expert in evolutionary biology and a departmental colleague at the University of Hull, and I secured a DAIM PhD studentship through a competitive process to fund this project. In January 2024, Francesca Covell became our first PhD candidate (AI and Data Science).
Fran is interested in neuroblastoma's phenotypic plasticity in this context. She has already built a model describing neuroblastoma cells' interconversion between the noradrenergic, intermediate, and mesenchymal identities with ordinary differential equations. Using the model, she is now investigating the effects of inhibiting different interconversion pathways on clonal evolution during treatment. Typically, noradrenergic neuroblastoma cells are more sensitive to treatment, but Dr Taschner-Mandl is developing mesenchymal phenotype–specific inhibitors. The goal is to manipulate a tumour's interconversion dynamics to steer its evolution towards a state where it is vulnerable to certain drugs.
Other phenomena await explanation too. Most if not all of the existing models of adaptive therapy consider two drugs only, but the rapid COJEC protocol for induction chemotherapy alone requires five drugs. Multidrug adaptive therapy can certainly benefit from a more general theoretical framework. Spatial and stochastic effects, as well as various non-cancer cells in the tumour microenvironment, complicate the dynamics further.
