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Jurmeister, Philipp; Glöß, Stefanie; Roller, Renée; Leitheiser, Maximilian; Schmid, Simone; Mochmann, Liliana H.; Payá Capilla, Emma; Fritz, Rebecca; Dittmayer, Carsten; Friedrich, Corinna; Thieme, Anne; Keyl, Philipp; Jarosch, Armin; Schallenberg, Simon; Bläker, Hendrik; Hoffmann, Inga; Vollbrecht, Claudia; Lehmann, Annika; Hummel, Michael; Heim, Daniel; Haji, Mohamed; Harter, Patrick; Englert, Benjamin; Frank, Stephan; [...]

DNA methylation-based classification of sinonasal tumors

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  • Medientyp: E-Artikel
  • Titel: DNA methylation-based classification of sinonasal tumors
  • Beteiligte: Jurmeister, Philipp; Glöß, Stefanie; Roller, Renée; Leitheiser, Maximilian; Schmid, Simone; Mochmann, Liliana H.; Payá Capilla, Emma; Fritz, Rebecca; Dittmayer, Carsten; Friedrich, Corinna; Thieme, Anne; Keyl, Philipp; Jarosch, Armin; Schallenberg, Simon; Bläker, Hendrik; Hoffmann, Inga; Vollbrecht, Claudia; Lehmann, Annika; Hummel, Michael; Heim, Daniel; Haji, Mohamed; Harter, Patrick; Englert, Benjamin; Frank, Stephan; [...]
  • Erschienen: Springer Science and Business Media LLC, 2022
  • Erschienen in: Nature Communications
  • Sprache: Englisch
  • DOI: 10.1038/s41467-022-34815-3
  • ISSN: 2041-1723
  • Schlagwörter: General Physics and Astronomy ; General Biochemistry, Genetics and Molecular Biology ; General Chemistry ; Multidisciplinary
  • Entstehung:
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  • Beschreibung: <jats:title>Abstract</jats:title><jats:p>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 <jats:italic>IDH2</jats:italic> or <jats:italic>SMARCA4/ARID1A</jats:italic> 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.</jats:p>
  • Zugangsstatus: Freier Zugang