Glioblastoma (GBM) remains one of the most lethal brain cancers, largely due to its treatment-resistant cellular heterogeneity.
Building on pilot data where 80% of eight repurposed drugs—including anti-epileptics and anti-inflammatories—successfully inhibited growth in patient-derived neurosphere (PDN) models, this project will explore how GBM responds to these drugs and others at the single-cell level.
We will treat PDNs with selected compounds and generate single-cell RNA-seq data to capture dose-dependent, cell state-specific responses. Using AI-based methods, we will analyse these datasets to predict synergistic drug combinations tailored to each model.
This project integrates wet-lab experimentation (drug screening, single-cell sequencing) with dry-lab analysis (AI-driven synergy prediction), aiming to uncover non-traditional, personalised therapeutic strategies for GBM and build a pipeline that connects functional data to precision drug discovery.
