Proteomics

Dataset Information

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


ABSTRACT: 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.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Brain, Cell Suspension Culture, Stem Cell, Cell Culture

DISEASE(S): Brain Glioblastoma Multiforme

SUBMITTER: Obada Alhalabi  

LAB HEAD: Violaine Goidts

PROVIDER: PXD039264 | Pride | 2026-01-12

REPOSITORIES: Pride

Dataset's files

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Action DRS
UP000005640_9606.fasta Fasta
UP000005640_9606_additional.fasta Fasta
qecf1-OA0015-01.raw Raw
qecf1-OA0015-02.raw Raw
qecf1-OA0015-03.raw Raw
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Publications


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 shR  ...[more]

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