Ontology highlight
ABSTRACT:
PROVIDER: PRJNA644026 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12145000.fastq.gz | Fastqsanger.gz | |||
| SRR12145001.fastq.gz | Fastqsanger.gz | |||
| SRR12145002.fastq.gz | Fastqsanger.gz | |||
| SRR12145003.fastq.gz | Fastqsanger.gz | |||
| SRR12145004.fastq.gz | Fastqsanger.gz |
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Project description:The histopathological diagnosis of sinonasal tumors is challenging as it encompasses a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we show that a machine learning algorithm based on DNA methylation is able to classify sinonasal tumors with clinical-grade reliability. We further show that tumors with SNUC morphology are not as undifferentiated as their current terminology suggests, but can be assigned to four molecular classes defined by distinct epigenic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SWI/SNF chromatin remodeling complex mutations and overall favorable clinical course, highly aggressive tumors that are driven by SMARCB1-deficiency and tumors that represent previously misclassified adenoid-cystic carcinomas. Our findings have the potential to dramatically improve the diagnostic of challenging sinonasal tumors and could fundamentally change the current perception of SNUCs.
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