Glioblastoma avatars for preclinical evaluation of treatment response

Glioblastoma (GBM) is the most common primary brain cancer in adults and one of the most aggressive cancers. Despite extensive research and progress in molecular profiling of GBM subtypes, standard treatment has not been updated since almost 20 years. Among the reasons for the tumor aggressiveness and lack of therapeutic efficacy are the very high inter- and intratumoral heterogeneity as well as the extensive cellular plasticity of cells, which can be induced by treatment itself. Current in vitro models utilized for evaluating treatment response in GBM often lack the complexity and representation of the tumor microenvironment, limiting their predictive accuracy. Therefore, there is a critical need to develop advanced in vitro models that better mimic the intricacies of GBM and its response to therapeutic interventions.

In this project, new in vitro (e.g. brain organoid/tumor spheroid assembloids), ex vivo (e.g. patient-derived organotypic spheroids on chip) and in vivo (e.g. patient-derived orthotopic xenografts) models (together "avatars") of glioblastoma will be developed that mimic tumors in their natural environment (i.e. the brain). These advanced models will be thoroughly characterized using molecular and imaging techniques to assess their resemblance to GBM tumors in vivo. We aim to evaluate in these models the tumor treatment response in terms of cell death, migration/invasion, cellular plasticity and (acquired) treatment resistance. Furthermore, these models will be used to evaluate genetic/pharmacological interference with drivers of treatment-induced cellular plasticity. Together, we will determine their predictive value in assessing drug efficacy and possible resistance mechanisms. As such, we wish to prevent GBM cells from becoming more resistant to therapy. Future treatments based on this principle should eventually guide personalized therapeutic strategies to improve outcome for patients suffering from this devastating disease.

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