Abstract
Glioblastoma (GBM) remains the most common adult brain cancer, with a dismal average patient survival of less than two years. No new treatments have been approved for GBM since the introduction of the alkylating agent temozolomide in 2005. Even then, temozolomide treatment only increases the average survival of GBM patients by a few months. Thus, novel therapeutic options are direly needed. The aurora kinases A and B are targetable and overexpressed in GBM, and their expression is highly correlated with patient survival outcomes. Our lab has found that small molecule aurora kinase inhibition reduces GBM tumor growth in vitro and in vivo, however, eventually tumors still grow. Computational analysis integrating compound transcriptional response signatures from the LINCS L1000 dataset with the single-cell RNA-sequencing data of patient GBM tumors resected at the University of Miami predicts that aurora inhibition targets a subset of cells present within any GBM tumor. Results of in vivo single-cell perturbation experiments with the aurora kinase inhibitor alisertib coincide with our predictions and reveal a cellular transcriptional phenotype resistant to aurora kinase inhibition, characterized by a mesenchymal expression program. We find that small molecules that are predicted to target different cell populations from alisertib, including this resistant mesenchymal population, synergize with alisertib to kill GBM cells. As a whole, we have identified the cellular population resistant to aurora kinase inhibition and have developed an analytical framework that identifies synergistic small molecule combinations by identifying compounds that target transcriptionally distinct cellular populations within GBM tumors.
Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through ‘reverse phenotyping’