DNA methylation-based classification of sinonasal tumors
Standard
DNA methylation-based classification of sinonasal tumors. / 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.
in: NAT COMMUN, Jahrgang 13, Nr. 1, 7148, 28.11.2022.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - JOUR
T1 - DNA methylation-based classification of sinonasal tumors
AU - Jurmeister, Philipp
AU - Glöß, Stefanie
AU - Roller, Renée
AU - Leitheiser, Maximilian
AU - Schmid, Simone
AU - Mochmann, Liliana H
AU - Payá Capilla, Emma
AU - Fritz, Rebecca
AU - Dittmayer, Carsten
AU - Friedrich, Corinna
AU - Thieme, Anne
AU - Keyl, Philipp
AU - Jarosch, Armin
AU - Schallenberg, Simon
AU - Bläker, Hendrik
AU - Hoffmann, Inga
AU - Vollbrecht, Claudia
AU - Lehmann, Annika
AU - Hummel, Michael
AU - Heim, Daniel
AU - Haji, Mohamed
AU - Harter, Patrick
AU - Englert, Benjamin
AU - Frank, Stephan
AU - Hench, Jürgen
AU - Paulus, Werner
AU - Hasselblatt, Martin
AU - Hartmann, Wolfgang
AU - Dohmen, Hildegard
AU - Keber, Ursula
AU - Jank, Paul
AU - Denkert, Carsten
AU - Stadelmann, Christine
AU - Bremmer, Felix
AU - Richter, Annika
AU - Wefers, Annika
AU - Ribbat-Idel, Julika
AU - Perner, Sven
AU - Idel, Christian
AU - Chiariotti, Lorenzo
AU - Della Monica, Rosa
AU - Marinelli, Alfredo
AU - Schüller, Ulrich
AU - Bockmayr, Michael
AU - Liu, Jacklyn
AU - Lund, Valerie J
AU - Forster, Martin
AU - Lechner, Matt
AU - Lorenzo-Guerra, Sara L
AU - Hermsen, Mario
AU - Johann, Pascal D
AU - Agaimy, Abbas
AU - Seegerer, Philipp
AU - Koch, Arend
AU - Heppner, Frank
AU - Pfister, Stefan M
AU - Jones, David T W
AU - Sill, Martin
AU - von Deimling, Andreas
AU - Snuderl, Matija
AU - Müller, Klaus-Robert
AU - Forgó, Erna
AU - Howitt, Brooke E
AU - Mertins, Philipp
AU - Klauschen, Frederick
AU - Capper, David
N1 - © 2022. The Author(s).
PY - 2022/11/28
Y1 - 2022/11/28
N2 - 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.
AB - 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.
KW - Humans
KW - DNA Methylation/genetics
KW - Proteomics
KW - Reproducibility of Results
KW - Carcinoma
KW - DNA Helicases/genetics
KW - Nuclear Proteins/genetics
KW - Transcription Factors
U2 - 10.1038/s41467-022-34815-3
DO - 10.1038/s41467-022-34815-3
M3 - SCORING: Journal article
C2 - 36443295
VL - 13
JO - NAT COMMUN
JF - NAT COMMUN
SN - 2041-1723
IS - 1
M1 - 7148
ER -