Cell Lines as Potential Drivers of Therapy Resistance in Glioblastoma

<p><strong>Glioblastoma (GBM) is the most aggressive and malignant brain tumour, characterised by a rapid growth rate, highly infiltrative nature, and cellular heterogeneity. Despite standard treatment, which includes surgical resection followed by radiotherapy and chemotherapy, GBM remains associated with poor prognosis and low survival rates, with a median survival of approximately 12–15 months.</strong></p><p>A major challenge in treating GBM is its extreme heterogeneity, which drives therapy resistance and tumour recurrence. This heterogeneity arises from genetic, epigenetic, and cellular diversity within the tumour, leading to varied treatment responses across different subpopulations. Additionally, the lack of preclinical models that accurately mimic the GBM TME, including interactions with stromal and immune components, has hindered the development of effective therapies. This study utilised a co-culture model incorporating a Genetically Engineered Mouse Cell Line Model (GEM-CLeM) to examine responses to chemotherapy and radiotherapy. Results demonstrated that co-culture conditions influence therapy response in a context-dependent manner. Specifically, IDH1 GEM-CLeMs exhibited a survival advantage in co-culture primarily at higher radiation doses, whereas RAS GEM-CLeMs showed a more consistent benefit from co-culture at lower doses. 3T3 fibroblast cells generally showed a preference for monoculture, and co-culture appeared to enhance survival under DOX treatment. This finding suggests that specific therapeutic stresses may alter the interactions between tumour and stromal cells, potentially leading to adaptive resistance mechanisms.</p><p>While these preliminary experiments provided valuable insights, analysing an assay that has not been previously explored presents challenges, particularly in standardising experimental conditions and establishing reliable methodologies. The dynamic nature of GEM-CLeMs further complicated therapy response assessments. Future work will optimise experimental conditions, increase biological replicates, and improve analytical methodologies to enhance reproducibility and reliability. Developing advanced, accessible, and ethically viable models will be crucial in bridging the gap between preclinical research and clinically relevant GBM treatment strategies.</p>