Despite a tremendous effort in basic science, clinical trials, drug development, and technical advances in
surgery and radiation oncology, glioblastoma remains incurable and improvements in overall survival have
been marginal. While radiotherapy is still one of the most effective treatment options for glioblastoma, it cannot
control the disease over time. This suggests that novel combination therapies are desperately needed to
improve radiation treatment outcome for patients suffering from this disease. The studies outlined in this
proposal are based on a hypothesis that is backed by our extensive preliminary data and rigorous published
data in the literature. The overall hypothesis is that biomarker-based drug selection predicts synergistic lethality
of combination therapies in GICs and glioblastoma bulk tumor cell populations, prevents radiation-induced
GBM phenotype conversion and allows for individualized optimization of radiotherapy. The three aims of this
study will address this aspect of glioma biology using an innovative tool to track GICs and their progeny, while
leveraging the unique resources and expertise available at UCLA and the NIH/NCI CTEP portfolio of drugs.
Aim 1 will identify compounds in the NCI CTEP portfolio that interfere with radiation-induced phenotype
conversion in glioblastoma and develop biomarker profiles predictive of synergistic lethality in combination with
radiation. Studies in Aim 2 will optimize combination therapies in vivo. Finally, Aim 3, will use patient avatar
studies to validate biomarker-based drug selection in PDX models of glioblastoma.