Glioblastoma is the most common and aggressive brain tumor. Current treatments include surgical resection, radiation therapy, and/or chemotherapy, yet only serve to extend patient lifespan a few months. Relapse is thought to occur as a result of ?persisters?; a slow cycling drug resistant population that provides a latent reservoir of cells with the capacity to mutate over time and propagate tumors. Persisters arise either from genetic or reversible (presumably epigenetic) causes and are a prime target for therapeutic intervention. Here, I describe a system of reversible persister generation in glioblastoma cell lines through the inhibition of platelet derived growth factor receptor tyrosine kinase.
I aim to characterize these persisters and define whether resistance potential is induced by drug treatment or a pre-existing quality. I will generate a persister gene signature and visualize expression of these genes through antibody staining and fluorescent reporters. Next I aim to identify persister dependencies, by knocking out persister signature genes and chromatin regulators. I will explore these dependent genes further in an organoid model system. And finally, to understand the limitations and therapeutic opportunities of this work, I will test persister dependencies in other persister models. This work will inform our understanding of reversible drug resistant mechanisms and potentially lead to the development of new therapeutics in glioblastoma and other drug resistant cancers.
Drug resistance is a devastating and elusive problem in cancer treatment. This resistance is often attributed to genetic mutations that endow cancer cells with the ability to survive and proliferate. Less studied, though equally important, are the reversible (non-genetic) mechanisms that enable cancer cells to evade drug treatment and eventually give rise to tumors. Here, I present a strategy to characterize reversible drug resistant cancer cells and discover how they could be effectively targeted for therapy.
| Najm, Fadi J; Strand, Christine; Donovan, Katherine F et al. (2018) Orthologous CRISPR-Cas9 enzymes for combinatorial genetic screens. Nat Biotechnol 36:179-189 |
