TMET-46. Investigating metabolic dependencies during the evolution of glioblastoma

Glioblastoma (GBM) is the most common and aggressive form of brain cancer in adults, with a five-year survival rate of less than 7%. GBMs are difficult to treat and often recur within a year of diagnosis. Despite many attempts, treatment options have remained stagnant for decades. Clinical trials have yet to reveal an effective therapy for GBMs. This stems, in part, from an incomplete understanding of tumour biology and a lack of appropriate preclinical models to capture patient-specific tumour heterogeneity. Patient-derived organoids (PDOs) have emerged as powerful models that preserve the genetic and phenotypic features of the original tumours, making them valuable for studying disease progression and testing personalized therapies. In this study, we developed and characterized matched sets (n=4) of PDOs from newly diagnosed GBM (ndGBM) and recurrent GBM (rGBM), with a particular focus on investigating the phenotypic, growth, and metabolic changes found upon recurrence. We observed that nd-rGBM PDOs retained key features of the original tumours, including expression of cycling (Ki-67), stem (NES, SOX2), oligodendrocytic (OLIG2), and astrocytic (GFAP) cell markers. Additionally, GBMs exhibited distinct, patient-specific changes in growth phenotype upon recurrence. By assessing mitochondrial and metabolic profiles alongside proliferation, we linked phenotypic growth dynamics with underlying bioenergetics. We found that slow-proliferating GBM samples consistently exhibited characteristics associated with higher oxidative phosphorylation (OxPhos) activity, such as increased membrane polarization and dense cristae, while faster-proliferating tumours relied on glycolysis. These findings suggest that metabolic reprogramming occurs in a patient-specific manner upon recurrence and may drive differential tumor growth. Given the critical role of metabolism in tumour progression and its correlation with prognosis, investigating the metabolic adaptations of nd-rGBM is essential for identifying tumour-specific vulnerabilities. Ultimately, our work may provide insights into GBM tumour evolution, identifying new prognostic markers, and guiding personalized treatment strategies for GBM.