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
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...
November 28, 2022: Nature Communications