Integration of Proteomics, Phosphoproteomics and Loss-of-function Screen Identifies WEE1 Inhibition as a Combination Therapy with Dasatinib against Proneural Glioblastoma.

Background: Glioblastoma (GB) is the most common primary malignancy of the central nervous system. It can be classified into proneural (PN), mesenchymal (MES) and classical (CL) based on transcriptomics. Due to increased resistance against targeted therapies, such as the PN subtype and dasatinib, identification of combination therapies is of great interest. Methods: Proteomics and phosphoproteomics data were collected from dasatinib inhibited glioblastoma stem cells (GSCs). Additionally, a pooled shRNA loss-of-function viability screen was utilized to identify genes whose knockdown sensitizes GSCs to dasatinib. These data, along with existing transcriptomics data, were computationally integrated using a novel modification of the SamNet algorithm (SamNet 2.0) for network flow learning to identify potential combination therapies. In vitro viability assays were used to verify synergy of potential combinations. Results: Using omics data and the pooled shRNA screen, the cell cycle protein WEE1 was identified as a potential combination therapy target for PN GSCs. Validation experiments showed a robust synergistic effect through combination of dasatinib and the WEE1 inhibitor, MK-1775, in PN GSCs. Combined inhibition using dasatinib and MK-1775 propagated DNA damage in PN GCSs, with GCSs showing a differential subtype-driven pattern of expression of cell cycle genes in TCGA RNA-Seq data.