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@ARTICLE{Arora:305575,
      author       = {S. Arora and N. Nuechterlein and M. Jensen and G. Glatzer
                      and P. Sievers$^*$ and S. Varadharajan and A. Korshunov$^*$
                      and F. Sahm$^*$ and S. C. Mack and M. D. Taylor and T.
                      Gujral and E. C. Holland},
      title        = {{I}ntegrated transcriptomic landscape of medulloblastoma
                      and ependymoma reveals novel tumor subtype-specific
                      biology.},
      journal      = {Neuro-Oncology},
      volume       = {nn},
      issn         = {1522-8517},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DKFZ-2025-02237},
      pages        = {nn},
      year         = {2025},
      note         = {epub},
      abstract     = {Medulloblastoma and ependymoma are common pediatric central
                      nervous system tumors with significant molecular and
                      clinical heterogeneity. While molecular subgrouping has
                      enabled classification into molecular subtypes, the extent
                      of heterogeneity within these subgroups remains poorly
                      defined.We collected bulk RNA sequencing data from 888
                      medulloblastoma and 370 ependymoma tumors to establish a
                      comprehensive reference landscape. After rigorous batch
                      effect correction, normalization, and dimensionality
                      reduction, we generated a unified landscape to explore gene
                      expression, signaling pathways, RNA fusions, and copy number
                      variations.Our transcriptional analysis revealed distinct
                      clustering patterns, including two primary ependymoma
                      compartments, EPN-E1 and EPN-E2, each with specific RNA
                      fusions and molecular signatures. In medulloblastoma, we
                      observed precise stratification of Group 3/4 tumors by
                      subtype and in SHH tumors by patient age. We also identified
                      subtype-specific pathways and gene fusions, enriched in each
                      group.This transcriptomic landscape serves as a resource for
                      biomarker discovery, diagnostic refinement, and prediction
                      of tumor biology and outcome. By enabling projection of new
                      patients' bulk RNA-seq data onto the reference map using
                      nearest neighbor analysis, the framework supports accurate
                      subtype classification. The landscape is publicly available
                      via Oncoscape, an interactive platform for global
                      exploration and application.},
      keywords     = {RNA-Seq (Other) / copy number (Other) / gene fusions
                      (Other) / pathways (Other) / subtyping (Other)},
      cin          = {B300 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)B300-20160331 / I:(DE-He78)HD01-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41159380},
      doi          = {10.1093/neuonc/noaf251},
      url          = {https://inrepo02.dkfz.de/record/305575},
}