DNA methylation-based classification of sinonasal tumors.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
28 11 2022
28 11 2022
Historique:
received:
31
03
2022
accepted:
07
11
2022
entrez:
28
11
2022
pubmed:
29
11
2022
medline:
1
12
2022
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 36443295
doi: 10.1038/s41467-022-34815-3
pii: 10.1038/s41467-022-34815-3
pmc: PMC9705411
doi:
Substances chimiques
SMARCA4 protein, human
EC 3.6.1.-
DNA Helicases
EC 3.6.4.-
Nuclear Proteins
0
Transcription Factors
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
7148Informations de copyright
© 2022. The Author(s).
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