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@ARTICLE{Korshunov:275602,
      author       = {A. Korshunov$^*$ and K. Okonechnikov$^*$ and D.
                      Schrimpf$^*$ and S. Tonn and M. Mynarek and J. Koster and P.
                      Sievers$^*$ and T. Milde$^*$ and F. Sahm$^*$ and D.
                      Jones$^*$ and A. von Deimling$^*$ and S. Pfister$^*$ and M.
                      Kool$^*$},
      title        = {{T}ranscriptome analysis stratifies second-generation
                      non-{WNT}/non-{SHH} medulloblastoma subgroups into
                      clinically tractable subtypes.},
      journal      = {Acta neuropathologica},
      volume       = {145},
      number       = {6},
      issn         = {0001-6322},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DKFZ-2023-00812},
      pages        = {829-842},
      year         = {2023},
      note         = {#EA:B300#LA:B062# / 2023 Jun;145(6):829-842},
      abstract     = {Medulloblastoma (MB), one of the most common malignant
                      pediatric brain tumor, is a heterogenous disease comprised
                      of four distinct molecular groups (WNT, SHH, Group 3, Group
                      4). Each of these groups can be further subdivided into
                      second-generation MB (SGS MB) molecular subgroups, each with
                      distinct genetic and clinical characteristics. For instance,
                      non-WNT/non-SHH MB (Group 3/4) can be subdivided molecularly
                      into eight distinct and clinically relevant tumor subgroups.
                      A further molecular stratification/summarization of these
                      SGS MB would allow for the assignment of patients to
                      risk-associated treatment protocols. Here, we performed DNA-
                      and RNA-based analysis of 574 non-WNT/non-SHH MB and
                      analyzed the clinical significance of various molecular
                      patterns within the entire cohort and the eight SGS MB, with
                      the aim to develop an optimal risk stratification of these
                      tumors. Multigene analysis disclosed several
                      survival-associated genes highly specific for each molecular
                      subgroup within this non-WNT/non-SHH MB cohort with minimal
                      inter-subgroup overlap. These subgroup-specific and
                      prognostically relevant genes were associated with pathways
                      that could underlie SGS MB clinical-molecular diversity and
                      tumor-driving mechanisms. By combining survival-associated
                      genes within each SGS MB, distinct metagene sets being
                      appropriate for their optimal risk stratification were
                      identified. Defined subgroup-specific metagene sets were
                      independent variables in the multivariate models generated
                      for each SGS MB and their prognostic value was confirmed in
                      a completely non-overlapping validation cohort of
                      non-WNT/non-SHH MB (n = 377). In summary, the current
                      results indicate that the integration of transcriptome data
                      in risk stratification models may improve outcome prediction
                      for each non-WNT/non-SHH SGS MB. Identified
                      subgroup-specific gene expression signatures could be
                      relevant for clinical implementation and survival-associated
                      metagene sets could be adopted for further SGS MB risk
                      stratification. Future studies should aim at validating the
                      prognostic role of these transcriptome-based SGS MB subtypes
                      in prospective clinical trials.},
      keywords     = {Medulloblastoma (Other) / Non-WNT/non-SHH (Other) /
                      Prognosis (Other) / Subgroups (Other) / Transcriptomic
                      (Other)},
      cin          = {B300 / HD01 / B062 / B360 / B310},
      ddc          = {610},
      cid          = {I:(DE-He78)B300-20160331 / I:(DE-He78)HD01-20160331 /
                      I:(DE-He78)B062-20160331 / I:(DE-He78)B360-20160331 /
                      I:(DE-He78)B310-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37093271},
      doi          = {10.1007/s00401-023-02575-z},
      url          = {https://inrepo02.dkfz.de/record/275602},
}