Nature Communications (Nov 2022)

DNA methylation-based classification of sinonasal tumors

  • Philipp Jurmeister,
  • Stefanie Glöß,
  • Renée Roller,
  • Maximilian Leitheiser,
  • Simone Schmid,
  • Liliana H. Mochmann,
  • Emma Payá Capilla,
  • Rebecca Fritz,
  • Carsten Dittmayer,
  • Corinna Friedrich,
  • Anne Thieme,
  • Philipp Keyl,
  • Armin Jarosch,
  • Simon Schallenberg,
  • Hendrik Bläker,
  • Inga Hoffmann,
  • Claudia Vollbrecht,
  • Annika Lehmann,
  • Michael Hummel,
  • Daniel Heim,
  • Mohamed Haji,
  • Patrick Harter,
  • Benjamin Englert,
  • Stephan Frank,
  • Jürgen Hench,
  • Werner Paulus,
  • Martin Hasselblatt,
  • Wolfgang Hartmann,
  • Hildegard Dohmen,
  • Ursula Keber,
  • Paul Jank,
  • Carsten Denkert,
  • Christine Stadelmann,
  • Felix Bremmer,
  • Annika Richter,
  • Annika Wefers,
  • Julika Ribbat-Idel,
  • Sven Perner,
  • Christian Idel,
  • Lorenzo Chiariotti,
  • Rosa Della Monica,
  • Alfredo Marinelli,
  • Ulrich Schüller,
  • Michael Bockmayr,
  • Jacklyn Liu,
  • Valerie J. Lund,
  • Martin Forster,
  • Matt Lechner,
  • Sara L. Lorenzo-Guerra,
  • Mario Hermsen,
  • Pascal D. Johann,
  • Abbas Agaimy,
  • Philipp Seegerer,
  • Arend Koch,
  • Frank Heppner,
  • Stefan M. Pfister,
  • David T. W. Jones,
  • Martin Sill,
  • Andreas von Deimling,
  • Matija Snuderl,
  • Klaus-Robert Müller,
  • Erna Forgó,
  • Brooke E. Howitt,
  • Philipp Mertins,
  • Frederick Klauschen,
  • David Capper

DOI
https://doi.org/10.1038/s41467-022-34815-3
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 14

Abstract

Read online

Sinonasal tumour diagnosis can be complicated by the heterogeneity of disease and classification systems. Here, the authors use machine learning to classify sinonasal undifferentiated carcinomas into 4 molecular classe with differences in differentiation state and clinical outcome.