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@ARTICLE{Jurmeister:182807,
      author       = {P. Jurmeister$^*$ and S. Glöß and R. F. Roller$^*$ and M.
                      Leitheiser and S. Schmid$^*$ and L. H. Mochmann and E. Payá
                      Capilla and R. Fritz and C. Dittmayer and C. Friedrich$^*$
                      and A. Thieme$^*$ and P. Keyl and A. Jarosch and S.
                      Schallenberg and H. Bläker and I. Hoffmann and C.
                      Vollbrecht$^*$ and A. Lehmann and M. Hummel$^*$ and D. Heim
                      and M. Haji and P. Harter$^*$ and B. Englert and S. Frank
                      and J. Hench and W. Paulus and M. Hasselblatt and W.
                      Hartmann and H. Dohmen and U. Keber and P. Jank and C.
                      Denkert and C. Stadelmann and F. Bremmer and A. Richter and
                      A. Wefers$^*$ and J. Ribbat-Idel and S. Perner and C. Idel
                      and L. Chiariotti and R. Della Monica and A. Marinelli and
                      U. Schüller and M. Bockmayr and J. Liu and V. J. Lund and
                      M. Forster and M. Lechner and S. L. Lorenzo-Guerra and M.
                      Hermsen and P. D. Johann and A. Agaimy and P. Seegerer and
                      A. Koch and F. Heppner$^*$ and S. Pfister$^*$ and D.
                      Jones$^*$ and M. Sill and A. von Deimling$^*$ and M. Snuderl
                      and K.-R. Müller and E. Forgó and B. E. Howitt and P.
                      Mertins and F. Klauschen$^*$ and D. Capper$^*$},
      title        = {{DNA} methylation-based classification of sinonasal
                      tumors.},
      journal      = {Nature Communications},
      volume       = {13},
      number       = {1},
      issn         = {2041-1723},
      address      = {[London]},
      publisher    = {Nature Publishing Group UK},
      reportid     = {DKFZ-2022-02939},
      pages        = {7148},
      year         = {2022},
      abstract     = {The diagnosis of sinonasal tumors is challenging due to a
                      heterogeneous spectrum of various differential diagnoses as
                      well as poorly defined, disputed entities such as sinonasal
                      undifferentiated carcinomas (SNUCs). In this study, we apply
                      a machine learning algorithm based on DNA methylation
                      patterns to classify sinonasal tumors with clinical-grade
                      reliability. We further show that sinonasal tumors with SNUC
                      morphology are not as undifferentiated as their current
                      terminology suggests but rather reassigned to four distinct
                      molecular classes defined by epigenetic, mutational and
                      proteomic profiles. This includes two classes with
                      neuroendocrine differentiation, characterized by IDH2 or
                      SMARCA4/ARID1A mutations with an overall favorable clinical
                      course, one class composed of highly aggressive
                      SMARCB1-deficient carcinomas and another class with tumors
                      that represent potentially previously misclassified adenoid
                      cystic carcinomas. Our findings can aid in improving the
                      diagnostic classification of sinonasal tumors and could help
                      to change the current perception of SNUCs.},
      cin          = {BE01 / MU01 / FM01 / HD01 / B300 / B062 / B360},
      ddc          = {500},
      cid          = {I:(DE-He78)BE01-20160331 / I:(DE-He78)MU01-20160331 /
                      I:(DE-He78)FM01-20160331 / I:(DE-He78)HD01-20160331 /
                      I:(DE-He78)B300-20160331 / I:(DE-He78)B062-20160331 /
                      I:(DE-He78)B360-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36443295},
      doi          = {10.1038/s41467-022-34815-3},
      url          = {https://inrepo02.dkfz.de/record/182807},
}