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@ARTICLE{Lyskjaer:168739,
      author       = {I. Lyskjaer and S. De Noon and R. Tirabosco and A. M. Rocha
                      and D. Lindsay and F. Amary and H. Ye and D. Schrimpf$^*$
                      and D. Stichel$^*$ and M. Sill$^*$ and C. Koelsche and N.
                      Pillay$^*$ and A. Von Deimling$^*$ and S. Beck and A. M.
                      Flanagan},
      title        = {{DNA} methylation-based profiling of bone and soft tissue
                      tumours: a validation study of the '{DKFZ} {S}arcoma
                      {C}lassifier'.},
      journal      = {The journal of pathology: clinical research},
      volume       = {7},
      number       = {4},
      issn         = {2056-4538},
      address      = {Chichester},
      publisher    = {Wiley},
      reportid     = {DKFZ-2021-01033},
      pages        = {350-360},
      year         = {2021},
      note         = {2021 Jul;7(4):350-360},
      abstract     = {Diagnosing bone and soft tissue neoplasms remains
                      challenging because of the large number of subtypes, many of
                      which lack diagnostic biomarkers. DNA methylation profiles
                      have proven to be a reliable basis for the classification of
                      brain tumours and, following this success, a DNA
                      methylation-based sarcoma classification tool from the
                      Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg has
                      been developed. In this study, we assessed the performance
                      of their classifier on DNA methylation profiles of an
                      independent data set of 986 bone and soft tissue tumours and
                      controls. We found that the 'DKFZ Sarcoma Classifier' was
                      able to produce a diagnostic prediction for $55\%$ of the
                      986 samples, with $83\%$ of these predictions concordant
                      with the histological diagnosis. On limiting the validation
                      to the 820 cases with histological diagnoses for which the
                      DKFZ Classifier was trained, $61\%$ of cases received a
                      prediction, and the histological diagnosis was concordant
                      with the predicted methylation class in $88\%$ of these
                      cases, findings comparable to those reported in the DKFZ
                      Classifier paper. The classifier performed best when
                      diagnosing mesenchymal chondrosarcomas (CHSs, $88\%$
                      sensitivity), chordomas $(85\%$ sensitivity), and fibrous
                      dysplasia $(83\%$ sensitivity). Amongst the subtypes least
                      often classified correctly were clear cell CHSs $(14\%$
                      sensitivity), malignant peripheral nerve sheath tumours
                      $(27\%$ sensitivity), and pleomorphic liposarcomas $(29\%$
                      sensitivity). The classifier predictions resulted in
                      revision of the histological diagnosis in six of our cases.
                      We observed that, although a higher tumour purity resulted
                      in a greater likelihood of a prediction being made, it did
                      not correlate with classifier accuracy. Our results show
                      that the DKFZ Classifier represents a powerful research tool
                      for exploring the pathogenesis of sarcoma; with refinement,
                      it has the potential to be a valuable diagnostic tool.},
      keywords     = {bone (Other) / classifier (Other) / methylation profiling
                      (Other) / sarcoma (Other) / soft tissue (Other)},
      cin          = {B300 / HD01 / B062},
      ddc          = {610},
      cid          = {I:(DE-He78)B300-20160331 / I:(DE-He78)HD01-20160331 /
                      I:(DE-He78)B062-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33949149},
      doi          = {10.1002/cjp2.215},
      url          = {https://inrepo02.dkfz.de/record/168739},
}