New Molecular Approaches to Glioblastoma Therapy

Glioblastoma (GBM) is the most common high-grade brain tumor diagnosed in patients who are more than 50 years of age. The standard of care treatment is surgery, followed by radiotherapy and chemotherapy. The median life expectancy of patients is only between 12 to 15 months after receiving current treatment regimes. Hence, identification of new therapeutic compounds and gene targets are highly warranted. This thesis describes four interlinked studies to attain this goal. In study 1, we explored drug combination effects in a material of 41 patient-derived GBM cell (GC) cultures. Synergies between three compounds, pterostilbene, gefitinib, and sertraline, resulted in effective killing of GC and can be predicted by biomarkers. In study 2, we performed a large-scale screening of FDA approved compounds (n=1544) in a larger panel of GCs (n=106). By combining the large-scale drug response data with GCs genomics data, we built a novel computational model to predict the sensitivity of each compound for a given GC. A notable finding was that GCs respond very differently to proteasome inhibitors in both in-vitro and in-vivo. In study 3, we explored new gene targets by RNAi (n=1112) in a panel of GC cells. We found that loss of transcription factor ZBTB16/PLZF inhibits GC cell viability, proliferation, migration, and invasion. These effects were due to downregulation of c-MYC and Cyclin B1 after the treatment. In study 4, we tested the genomic stability of three GCs upon multiple passaging. Using molecular and mathematical analyses, we showed that the GCs undergo both systematic adaptations and sequential clonal takeovers. Such changes tend to affect a broad spectrum of pathways. Therefore, a systematic analysis of cell culture stability will be essential to make use of primary cells for translational oncology. Taken together, these studies deepen our knowledge of the weak points of GBM and provide several targets and biomarkers for further investigation. The work in this thesis can potentially facilitate the development of targeted therapies and result in more accurate tools for patient diagnostics and stratification.