{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Alhalabi OT"],"funding":["Deutsche Forschungsgemeinschaft","Foundation for the National Institutes of Health","NCI NIH HHS","National Institutes of Health","NIGMS NIH HHS"],"pagination":["217265"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12768573"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["605"],"pubmed_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 viabi"],"journal":["Cancer letters"],"pubmed_title":["Integration of transcriptomics, proteomics and loss-of-function screening reveals WEE1 as a target for combination with dasatinib against proneural glioblastoma."],"pmcid":["PMC12768573"],"funding_grant_id":["T32 GM087237","U01 CA253547","404521405","U01CA253547","SFB 1389"],"pubmed_authors":["Jabali A","Hielscher T","Phillips E","Lokumcu T","Herold-Mende C","Gold MP","Fraenkel E","Iskar M","Alhalabi OT","Baumgartner U","Day BW","Heßling B","Sahm F","Goidts V","Cousins CC","Hai L","Gottmann M","Wick W","Schlue S","Kessler T","Horschitz S","Koch P","Krieg SM"],"additional_accession":[]},"is_claimable":false,"name":"Integration of transcriptomics, proteomics and loss-of-function screening reveals WEE1 as a target for combination with dasatinib against proneural glioblastoma.","description":"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 viabi","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2026-06-06T10:57:06.597Z","creation":"2026-05-29T03:12:22.602Z"},"accession":"S-EPMC12768573","cross_references":{"pubmed":["39332586"],"doi":["10.1016/j.canlet.2024.217265"]}}