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@ARTICLE{Gmez:132739,
      author       = {S. Gómez and A. Garrido-Garcia and L. Garcia-Gerique and
                      I. Lemos and M. Suñol and C. de Torres and M. Kulis and S.
                      Pérez-Jaume and Á. M. Carcaboso and B. Luu and M. W.
                      Kieran and N. Jabado and A. Kozlenkov and S. Dracheva and V.
                      Ramaswamy and V. Hovestadt$^*$ and P. Johann$^*$ and D.
                      Jones$^*$ and S. Pfister$^*$ and A. Morales La Madrid and O.
                      Cruz and M. D. Taylor and J.-I. Martin-Subero and J. Mora
                      and C. Lavarino},
      title        = {{A} {N}ovel {M}ethod for {R}apid {M}olecular {S}ubgrouping
                      of {M}edulloblastoma.},
      journal      = {Clinical cancer research},
      volume       = {24},
      number       = {6},
      issn         = {1557-3265},
      address      = {Philadelphia, Pa. [u.a.]},
      publisher    = {AACR},
      reportid     = {DKFZ-2018-00393},
      pages        = {1355 - 1363},
      year         = {2018},
      abstract     = {Purpose: The classification of medulloblastoma into WNT,
                      SHH, group 3, and group 4 subgroups has become of critical
                      importance for patient risk stratification and
                      subgroup-tailored clinical trials. Here, we aimed to develop
                      a simplified, clinically applicable classification approach
                      that can be implemented in the majority of centers treating
                      patients with medulloblastoma.Experimental Design: We
                      analyzed 1,577 samples comprising previously published DNA
                      methylation microarray data (913 medulloblastomas, 457
                      non-medulloblastoma tumors, 85 normal tissues), and 122
                      frozen and formalin-fixed paraffin-embedded medulloblastoma
                      samples. Biomarkers were identified applying stringent
                      selection filters and Linear Discriminant Analysis (LDA)
                      method, and validated using DNA methylation microarray data,
                      bisulfite pyrosequencing, and direct-bisulfite
                      sequencing.Results: Using a LDA-based approach, we developed
                      and validated a prediction method (EpiWNT-SHH classifier)
                      based on six epigenetic biomarkers that allowed for rapid
                      classification of medulloblastoma into the clinically
                      relevant subgroups WNT, SHH, and non-WNT/non-SHH with
                      excellent concordance $(>99\%)$ with current gold-standard
                      methods, DNA methylation microarray, and gene signature
                      profiling analysis. The EpiWNT-SHH classifier showed high
                      prediction capacity using both frozen and formalin-fixed
                      material, as well as diverse DNA methylation detection
                      methods. Similarly, we developed a classifier specific for
                      group 3 and group 4 tumors, based on five biomarkers
                      (EpiG3-G4) with good discriminatory capacity, allowing for
                      correct assignment of more than $92\%$ of tumors. EpiWNT-SHH
                      and EpiG3-G4 methylation profiles remained stable across
                      tumor primary, metastasis, and relapse samples.Conclusions:
                      The EpiWNT-SHH and EpiG3-G4 classifiers represent a new
                      simplified approach for accurate, rapid, and cost-effective
                      molecular classification of single medulloblastoma DNA
                      samples, using clinically applicable DNA methylation
                      detection methods. Clin Cancer Res; 24(6); 1355-63. ©2018
                      AACR.},
      cin          = {B060 / B062},
      ddc          = {610},
      cid          = {I:(DE-He78)B060-20160331 / I:(DE-He78)B062-20160331},
      pnm          = {312 - Functional and structural genomics (POF3-312)},
      pid          = {G:(DE-HGF)POF3-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29351917},
      doi          = {10.1158/1078-0432.CCR-17-2243},
      url          = {https://inrepo02.dkfz.de/record/132739},
}