Nature communications · 2022

DNA methylation-based classification of sinonasal tumors.

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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

Аннотация

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.

Предметные рубрики MeSH

Transcription Factors Proteomics Maxillary Sinus Neoplasms DNA Helicases Carcinoma Reproducibility of Results DNA Methylation Humans Nuclear Proteins

DNA methylation-based classification of sinonasal tumors.

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Аннотация

Предметные рубрики MeSH