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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., (© 2022. The Author(s).)

IDH2 R172 mutations occur in sinonasal undifferentiated carcinoma (SNUC), large-cell neuroendocrine carcinoma (LCNEC), sinonasal adenocarcinomas, and olfactory neuroblastoma (ONB). We performed a clinical, pathologic, and genetic/epigenetic analysis of a large IDH2-mutated sinonasal tumor cohort to explore their distinct features. A total 165 sinonasal/skull base tumors included 40 IDH2 mutants studied by light microscopy, immunohistochemistry, and genome-wide DNA methylation, and 125 IDH2 wild-type tumors used for comparison. Methylation profiles were analyzed by unsupervised hierarchical clustering, t-distributed stochastic neighbor embedding dimensionality reduction and assessed for copy number alterations (CNA). Thirty-nine histologically assessable cases included 25 (64.1%) SNUC, 8 (20.5%) LCNEC, 2 (5.1%) poorly differentiated adenocarcinomas, 1 (2.7%) ONB, and 3 (7.7%) IDH2-mutated tumors with ONB features. All cases were high-grade showing necrosis (82.4%), prominent nucleoli (88.9%), and median 21 mitoses/10 HPFs. AE1/AE3 and/or CAM 5.2 were positive in all and insulinoma-associated protein 1 (INSM1) in 80% cases. All IDH2 mutants formed one distinct group by t-distributed stochastic neighbor embedding dimensionality reduction separating from all IDH2 wild-type tumors. There was no correlation between methylation clusters and histopathologic diagnoses. Recurrent CNA included 1q gain (79.3%), 17p loss (75.9%), and 17q gain (58.6%). No CNA differences were observed between SNUC and LCNEC. IDH2 mutants showed better disease-specific survival than SMARCB1-deficient (P=0.027) and IDH2 wild-type carcinomas overall (P=0.042). IDH2-mutated sinonasal tumors are remarkably homogeneous at the molecular level and distinct from IDH2 wild-type sinonasal malignancies. Biology of IDH2-mutated sinonasal tumors might be primarily defined by their unique molecular fingerprint rather than by their respective histopathologic diagnoses., Competing Interests: Conflicts of Interest and Source of Funding: Supported by the Cancer Center Support Grant of the National Institutes of Health/National Cancer Institute under award number P30CA008748 (to MSKCC/S.D. and D.G.P.). Additional funding was provided by the German Cancer Consortium (DKTK), partner site Berlin (to D.C.). Berlin Institute of Health (BIH) (to S.G.). Charité Universitätsmedizin Berlin, BIH and the German Research Foundation (to P.J.). Friedberg Charitable Foundation (to M.S.), and German Cancer Aid and the Fördergemeinschaft Kinderkrebs-Zentrum, Hamburg (to U.S.). For the remaining authors none were declared., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

In the original publication, the second name of the twentieth author was incorrect. It should read as 'Miguel Sáinz-Jaspeado'. The original publication of the article has been updated to reflect the change. This correction was authored by Ulrich Schüller on behalf of all authors of the original publication.

Olfactory neuroblastoma/esthesioneuroblastoma (ONB) is an uncommon neuroectodermal neoplasm thought to arise from the olfactory epithelium. Little is known about its molecular pathogenesis. For this study, a retrospective cohort of n = 66 tumor samples with the institutional diagnosis of ONB was analyzed by immunohistochemistry, genome-wide DNA methylation profiling, copy number analysis, and in a subset, next-generation panel sequencing of 560 tumor-associated genes. DNA methylation profiles were compared to those of relevant differential diagnoses of ONB. Unsupervised hierarchical clustering analysis of DNA methylation data revealed four subgroups among institutionally diagnosed ONB. The largest group (n = 42, 64%, Core ONB) presented with classical ONB histology and no overlap with other classes upon methylation profiling-based t-distributed stochastic neighbor embedding (t-SNE) analysis. A second DNA methylation group (n = 7, 11%) with CpG island methylator phenotype (CIMP) consisted of cases with strong expression of cytokeratin, no or scarce chromogranin A expression and IDH2 hotspot mutation in all cases. T-SNE analysis clustered these cases together with sinonasal carcinoma with IDH2 mutation. Four cases (6%) formed a small group characterized by an overall high level of DNA methylation, but without CIMP. The fourth group consisted of 13 cases that had heterogeneous DNA methylation profiles and strong cytokeratin expression in most cases. In t-SNE analysis, these cases mostly grouped among sinonasal adenocarcinoma, squamous cell carcinoma, and undifferentiated carcinoma. Copy number analysis indicated highly recurrent chromosomal changes among Core ONB with a high frequency of combined loss of chromosome 1-4, 8-10, and 12. NGS sequencing did not reveal highly recurrent mutations in ONB, with the only recurrently mutated genes being TP53 and DNMT3A. In conclusion, we demonstrate that institutionally diagnosed ONB are a heterogeneous group of tumors. Expression of cytokeratin, chromogranin A, the mutational status of IDH2 as well as DNA methylation patterns may greatly aid in the precise classification of ONB.