Abstract
Glioblastoma (GBM) remains the most common and lethal adult malignant primary brain cancer with few treatment options. A most significant issue hindering GBM therapeutic development is intratumor heterogeneity. GBM tumors contain neoplastic cells within a spectrum of different transcriptional states. The identification of effective therapeutics requires a platform that predicts the differential sensitivity and resistance of these states to different treatments. Here, we developed a novel framework, ISOSCELES (Inferred cell Sensitivity Operating on the integration of Single-Cell Expression and L1000 Expression Signatures), to quantify the cellular drug sensitivity and resistance landscape. Using single-cell RNA sequencing of newly diagnosed and recurrent GBM tumors, we identified compounds from the LINCS L1000 database with transcriptional response signatures selectively discordant with distinct GBM cell states. We validated the significance of these findings through in vitro, ex vivo, and in vivo use-cases, including the identification of a novel combination of an OLIG2 inhibitor and Depatux-M which synergizes in vivo. Our studies suggest that ISOSCELES identifies cell states sensitive and resistant to targeted therapies in GBM, and can be applied to identify new synergistic combinations.