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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

November 28, 2022

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.