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@ARTICLE{Cruzeiro:143213,
      author       = {G. A. V. Cruzeiro and K. B. Salomão and C. A. O. de Biagi
                      and M. Baumgartner and D. Sturm$^*$ and R. C. P. Lira and T.
                      de Almeida Magalhães and M. Baroni Milan and V. da Silva
                      Silveira and F. P. Saggioro and R. S. de Oliveira and P. H.
                      Dos Santos Klinger and A. L. Seidinger and J. A. Yunes and
                      R. G. de Paula Queiroz and S. M. Oba-Shinjo and C. A.
                      Scrideli and S. M. K. Nagahashi and L. G. Tone and E. T.
                      Valera},
      title        = {{A} simplified approach using {T}aqman low-density array
                      for medulloblastoma subgrouping.},
      journal      = {Acta Neuropathologica Communications},
      volume       = {7},
      number       = {1},
      issn         = {2051-5960},
      address      = {London},
      publisher    = {Biomed Central},
      reportid     = {DKFZ-2019-00812},
      pages        = {33},
      year         = {2019},
      abstract     = {Next-generation sequencing platforms are routinely used for
                      molecular assignment due to their high impact for risk
                      stratification and prognosis in medulloblastomas. Yet, low
                      and middle-income countries still lack an accurate
                      cost-effective platform to perform this allocation. TaqMan
                      Low Density array (TLDA) assay was performed using a set of
                      20 genes in 92 medulloblastoma samples. The same methodology
                      was assessed in silico using microarray data for 763
                      medulloblastoma samples from the GSE85217 study, which
                      performed MB classification by a robust integrative method
                      (Transcriptional, Methylation and cytogenetic profile).
                      Furthermore, we validated in 11 MBs samples our proposed
                      method by Methylation Array 450 K to assess methylation
                      profile along with 390 MB samples (GSE109381) and copy
                      number variations. TLDA with only 20 genes accurately
                      assigned MB samples into WNT, SHH, Group 3 and Group 4 using
                      Pearson distance with the average-linkage algorithm and
                      showed concordance with molecular assignment provided by
                      Methylation Array 450 k. Similarly, we tested this
                      simplified set of gene signatures in 763 MB samples and
                      we were able to recapitulate molecular assignment with an
                      accuracy of $99.1\%$ (SHH), $94.29\%$ (WNT), $92.36\%$
                      (Group 3) and $95.40\%$ (Group 4), against 97.31, 97.14,
                      88.89 and $97.24\%$ (respectively) with the Ward.D2
                      algorithm. t-SNE analysis revealed a high level of
                      concordance (k = 4) with minor overlapping features
                      between Group 3 and Group 4. Finally, we condensed the
                      number of genes to 6 without significantly losing accuracy
                      in classifying samples into SHH, WNT and non-SHH/non-WNT
                      subgroups. Additionally, we found a relatively high
                      frequency of WNT subgroup in our cohort, which requires
                      further epidemiological studies. TLDA is a rapid, simple and
                      cost-effective assay for classifying MB in low/middle income
                      countries. A simplified method using six genes and
                      restricting the final stratification into SHH, WNT and
                      non-SHH/non-WNT appears to be a very interesting approach
                      for rapid clinical decision-making.},
      cin          = {B360},
      ddc          = {610},
      cid          = {I:(DE-He78)B360-20160331},
      pnm          = {312 - Functional and structural genomics (POF3-312)},
      pid          = {G:(DE-HGF)POF3-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30832734},
      pmc          = {pmc:PMC6398239},
      doi          = {10.1186/s40478-019-0681-y},
      url          = {https://inrepo02.dkfz.de/record/143213},
}