Integration of transcriptomics, proteomics and loss-of-function screening reveals WEE1 as a target for combination with dasatinib against proneural glioblastoma.

Glioblastoma is characterized by a pronounced resistance to therapy with dismal prognosis. Transcriptomics classify glioblastoma into proneural (PN), mesenchymal (MES) and classical (CL) subtypes that show differential resistance to targeted therapies. The aim of this study was to provide a viable approach for identifying combination therapies in glioblastoma subtypes. Proteomics and phosphoproteomics were performed on dasatinib inhibited glioblastoma stem cells (GSCs) and complemented by an shRNA loss-of-function screen to identify genes whose knockdown sensitizes GSCs to dasatinib. Proteomics and screen data were computationally integrated with transcriptomic data using the SamNet 2.0 algorithm for network flow learning to reveal potential combination therapies in PN GSCs. In vitro viability assays and tumor spheroid models were used to verify the synergy of identified therapy. Further in vitro and TCGA RNA-Seq data analyses were utilized to provide a mechanistic explanation of these effects. Integration of data revealed the cell cycle protein WEE1 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. The integration of proteomics, loss-of-function screens and transcriptomics confirmed WEE1 as a target for combination with dasatinib against PN GSCs. Utilizing this integrative approach could be of interest for studying resistance mechanisms and revealing combination therapy targets in further tumor entities.

Genetic Associations Between Smoking- and Glaucoma-Related Traits.

Purpose: The purpose of this study was to describe the genetic relationship between smoking and glaucoma. Methods: We used summary-level genetic data for smoking initiation, smoking intensity (cigarettes per day [CPD]), intraocular pressure (IOP), vertical cup-disc ratio, and open-angle glaucoma (OAG) to estimate global genetic correlations (rg) and perform two-sample Mendelian randomization (MR) experiments that explored relations between traits. Finally, we examined associations between smoking genetic risk scores (GRS) and smoking traits with measured IOP and OAG in Rotterdam Study participants. Results: We identified weak inverse rg between smoking- and glaucoma-related traits that were insignificant after Bonferroni correction. However, MR analysis revealed that genetically predicted smoking initiation was associated with lower IOP (-0.18 mm Hg per SD, 95% confidence interval [CI] = -0.30 to -0.06, P = 0.003). Furthermore, genetically predicted smoking intensity was associated with decreased OAG risk (odds ratio [OR] = 0.74 per SD, 95% CI = 0.61 to 0.90, P = 0.002). In the Rotterdam Study, the smoking initiation GRS was associated with lower IOP (-0.09 mm Hg per SD, 95% CI = -0.17 to -0.01, P = 0.04) and lower odds of OAG (OR = 0.84 per SD, 95% CI = 0.73 to 0.98, P = 0.02) in multivariable-adjusted analyses. In contrast, neither smoking history nor CPD was associated with IOP (P ≥ 0.38) or OAG (P ≥ 0.54). Associations between the smoking intensity GRS and glaucoma traits were null (P ≥ 0.13). Conclusions: MR experiments and GRS generated from Rotterdam Study participants support an inverse relationship between smoking and glaucoma. Translational Relevance: Understanding the genetic drivers of the inverse relationship between smoking and glaucoma could yield new insights into glaucoma pathophysiology.