DNA methylation-based classification of sinonasal tumors

Jurmeister P, Glöß S, Roller R, Leitheiser M, Schmid S, Mochmann LH, Payá Capilla E, Fritz R, Dittmayer C, Friedrich C, Thieme A, Keyl P, Jarosch A, Schallenberg S, Bläker H, Hoffmann I, Vollbrecht C, Lehmann A, Hummel M, Heim D, Haji M, Harter P, Englert B, Frank S, Hench J, Paulus W, Hasselblatt M, Hartmann W, Dohmen H, Keber U, Jank P, Denkert C, Stadelmann C, Bremmer F, Richter A, Wefers A, Ribbat-Idel J, Perner S, Idel C, Chiariotti L, Della Monica R, Marinelli A, Schüller U, Bockmayr M, Liu J, Lund VJ, Forster M, Lechner M, Lorenzo-Guerra SL, Hermsen M, Johann PD, Agaimy A, Seegerer P, Koch A, Heppner F, Pfister SM, Jones DT, Sill M, von Deimling A, Snuderl M, Müller KR, Forgó E, Howitt BE, Mertins P, Klauschen F, Capper D (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Book Volume: 13

Article Number: 7148

Journal Issue: 1

DOI: 10.1038/s41467-022-34815-3

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.

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Involved external institutions

Charité - Universitätsmedizin Berlin Germany (DE) Klinikum der Universität München Germany (DE) Deutsches Krebsforschungszentrum (DKFZ) Germany (DE) Universitätsklinikum Göttingen Germany (DE) Philipps-Universität Marburg Germany (DE) Universitätsklinikum Heidelberg Germany (DE) Universitätsklinikum Schleswig-Holstein (UKSH) Germany (DE) University College London (UCL) United Kingdom (GB) Schwäbisches Kinderkrebszentrum Germany (DE) New York University (NYU) United States (USA) (US) Universitätsklinikum Hamburg-Eppendorf (UKE) Germany (DE) Stanford University United States (USA) (US) ISPA Instituto de Investigación Sanitaria del Principado de Asturias Spain (ES) Universitätsspital Basel Switzerland (CH) Universitätsklinikum Münster Germany (DE) Max-Delbrück-Centrum für Molekulare Medizin / Max Delbrück Center for Molecular Medicine (MDC) Berlin-Buch Germany (DE) Hopp-Kindertumorzentrum Heidelberg - KiTZ Germany (DE) CEINGE-Biotecnologie Avanzate Franco Salvatore Italy (IT) Justus-Liebig-Universität Gießen Germany (DE) Università degli Studi di Napoli Federico II Italy (IT) Universitätsklinikum Leipzig Germany (DE) Technische Universität Berlin Germany (DE)

How to cite

APA:

Jurmeister, P., Glöß, S., Roller, R., Leitheiser, M., Schmid, S., Mochmann, L.H.,... Capper, D. (2022). DNA methylation-based classification of sinonasal tumors. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-34815-3

MLA:

Jurmeister, Philipp, et al. "DNA methylation-based classification of sinonasal tumors." Nature Communications 13.1 (2022).

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