Insights from a multiscale framework on metabolic rate variation driving glioblastoma multiforme growth and invasion

Abstract

Non-physiological levels of oxygen and nutrients within the tumors result in heterogeneous cell populations that exhibit distinct necrotic, hypoxic, and proliferative zones. Among these zonal cellular properties, metabolic rates strongly affect the overall growth and invasion of tumors. Here, we report on a hybrid discrete-continuum (HDC) mathematical framework that uses metabolic data from a biomimetic two-dimensional (2D) in-vitro cancer model to predict three-dimensional (3D) behaviour of in-vitro human glioblastoma (hGB). The mathematical model integrates modules of continuum, discrete, and neurons. Results indicated that the HDC model is capable of quantitatively predicting growth, invasion length, and the asymmetric finger-type invasion pattern in in-vitro hGB tumors. Additionally, the model could predict the reduction in invasion length of hGB tumoroids in response to temozolomide (TMZ). This model has the potential to incorporate additional modules, including immune cells and signaling pathways governing cancer/immune cell interactions, and can be used to investigate targeted therapies. Meitham Amereh and colleagues report a hybrid discrete-continuum model to predict the cancerous growth, invasion, and treatment response of glioblastoma tumours. Their in-silico model uses metabolic data from a biomimetic two-dimensional in-vitro cancer model to predict three-dimensional behaviour of in-vitro human glioblastoma.