ArticleCancer letters2024
Integration of transcriptomics, proteomics and loss-of-function screening reveals WEE1 as a target for combination with dasatinib against proneural glioblastoma.
Article in Cancer letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Protein kinase WEE1 promotes cardiomyocytes ferroptosis and cardiac hypertrophy via the NF-κB/LCN2 axis.Acta pharmacologica Sinica · 2026Article
- Programmed death 1 antibody drug conjugates (PD 1 ADCs): Innovations in overcoming resistance and enhancing immune mediated antitumor activity.Translational oncology · 2026Review
- A novel TRP channel-related prognostic model of glioma based on transcriptomics and single cell sequencing analysis.Discover oncology · 2025Article
- The HMGB1-RAGE Axis Drives the Proneural-to-Mesenchymal Transition and Aggressiveness in Glioblastoma.International journal of molecular sciences · 2025Article
- Cancer network pharmacology: multi-network regulatory mechanisms and future directions.Medical oncology (Northwood, London, England) · 2025Review
- Applications of artificial intelligence in cancer immunotherapy: a frontier review on enhancing treatment efficacy and safety.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
23 authors.
Funding
Abstract
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.
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