A revised mathematical model of pre-diagnostic glioma growth incorporating vascularization and tumor mutational burden

Gliomas, a type of brain tumor, have become increasingly important in oncology as they are difficult to treat due to their location deep in the brain. While some research has been done on spatiotemporal prediction of future glioma growth--something that can aid in surgical resections of gliomas once a patient has been diagnosed--modeling efforts for pre-diagnostic gliomas remain limited. The lack of retrospective, pre-diagnostic data makes this a challenging task; yet, pre-symptomatic serum glucose levels in patients have been shown to have a relationship with the emergence of gliomas, motivating this area of research. In 2015, Sturrock et al. presented an ordinary differential equation model of pre-diagnostic glioma growth that describes glioma-glucose-immune interactions. This report reproduces the major findings of Sturrock et al., revising their model to incorporate more biological phenomena--namely vascularization and mutational burden--testing additional medically relevant patient scenarios, and providing an extended discussion on the implications of the model. In-silico simulations performed in this report provide further insight into models describing glioma-glucose-immune interactions, and how they can be expanded to incorporate physiologically relevant features. Future work is necessary to refine model parameters and validate predictions with the limited, albeit steadily growing, amount of longitudinal patient data.