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@ARTICLE{Alhalabi:293795,
      author       = {O. Alhalabi$^*$ and M. Göttmann$^*$ and M. P. Gold and S.
                      Schlue$^*$ and T. Hielscher$^*$ and M. Iskar$^*$ and T.
                      Kessler$^*$ and L. Hai and T. Lokumcu$^*$ and C. C. Cousins
                      and C. Herold-Mende and B. Heßling$^*$ and S. Horschitz and
                      A. Jabali and P. Koch and U. Baumgartner and B. W. Day and
                      W. Wick$^*$ and F. Sahm$^*$ and S. M. Krieg and E. Fraenkel
                      and E. Phillips$^*$ and V. Goidts$^*$},
      title        = {{I}ntegration of {T}ranscriptomics, {P}roteomics and
                      {L}oss-of-function {S}creening {R}eveals {WEE}1 as a
                      {T}arget for {C}ombination with {D}asatinib against
                      {P}roneural {G}lioblastoma.},
      journal      = {Cancer letters},
      volume       = {605},
      issn         = {0304-3835},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2024-01953},
      pages        = {217265},
      year         = {2024},
      note         = {#EA:B067#LA:B067# / 2024 Sep 25:605:217265},
      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.},
      keywords     = {Loss-of-function shRNA screen (Other) / Phosphoproteomics
                      (Other) / WEE1 (Other) / computational integration (Other) /
                      dasatinib (Other)},
      cin          = {B067 / C060 / B060 / HD01 / B320 / W120 / B300},
      ddc          = {570},
      cid          = {I:(DE-He78)B067-20160331 / I:(DE-He78)C060-20160331 /
                      I:(DE-He78)B060-20160331 / I:(DE-He78)HD01-20160331 /
                      I:(DE-He78)B320-20160331 / I:(DE-He78)W120-20160331 /
                      I:(DE-He78)B300-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39332586},
      doi          = {10.1016/j.canlet.2024.217265},
      url          = {https://inrepo02.dkfz.de/record/293795},
}