학술논문
DNA methylation-based classification of sinonasal tumors
Document Type
Original Paper
Author
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; Hench, Jürgen; Paulus, Werner; Hasselblatt, Martin; Hartmann, Wolfgang; Dohmen, Hildegard; Keber, Ursula; Jank, Paul; Denkert, Carsten; Stadelmann, Christine; Bremmer, Felix; Richter, Annika; Wefers, Annika; Ribbat-Idel, Julika; Perner, Sven; Idel, Christian; Chiariotti, Lorenzo; Della Monica, Rosa; Marinelli, Alfredo; Schüller, Ulrich; Bockmayr, Michael; Liu, Jacklyn; Lund, Valerie J.; Forster, Martin; Lechner, Matt; Lorenzo-Guerra, Sara L.; Hermsen, Mario; Johann, Pascal D.; Agaimy, Abbas; Seegerer, Philipp; Koch, Arend; Heppner, Frank; Pfister, Stefan M.; Jones, David T. W.; Sill, Martin; von Deimling, Andreas; Snuderl, Matija; Müller, Klaus-Robert; Forgó, Erna; Howitt, Brooke E.; Mertins, Philipp; Klauschen, Frederick; Capper, David
Source
Nature Communications. 13(1)
Subject
Language
English
ISSN
2041-1723
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.
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.
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.