Speaker
Description
Efforts to suppress glioblastoma (GBM) recurrence include targeting peritumoral regions by increasing extent of tumor resection and volume/dose of radiotherapy. Due to their broad application, these methods include risks of radiation toxicity and excess healthy tissue excision. Techniques to better predict where GBM recur could circumvent these limitations. Recent evidence indicates that recurrence location depends on unique metabolic programs of residual peritumoral GBM cells along with selective tumor microenvironment (TME) factors including biophysical characteristics of the migration zone and interactions with immune cell populations. This study implements a continuum-scale model to simulate GBM recurrence post-resection. The model represents tumor growth and simulation of resection with the goal to predict likely location(s) of recurrence based on local TME factors and metabolic characteristics. The model simulates GBM interactions with the TME, including vascularization and immune species. We recently analyzed patient tumor core (contrast enhancing) and peritumoral (T2/FLAIR hyperintense) samples via metabolomics, finding key metabolic signature differences between these tumor regions. The model results show that GBM recurrence is biased towards locations of higher vascularization and disrupted TME, modulated by dysregulated metabolism in peritumoral tissue. This work could facilitate spatially targeted approaches that prevent recurrence and improve outcomes.
