DNA methylation-based classification of sinonasal tumors
Philipp Jurmeister, Stefanie Glöß, Renée F. 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 N. 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 K. 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 Förster, Matt Lechner, Sara Lucila Lorenzo‐Guerra, Mario Hermsen, Pascal D. Johann, Abbas Agaimy, Philipp Seegerer, Arend Koch, Frank L. Heppner, Stefan M. Pfister, David Jones, Martin Sill, Andreas von Deimling, Matija Snuderl, Klaus‐Robert Müller, Erna Forgó, Brooke E. Howitt, Philipp Mertins, Frederick Klauschen, David Capper
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.