Project description: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 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, whereas tumors that are driven by SMARCB1-deficiency and tumors that represent previously misclassified adenoid cystic carcinomas are highly aggressive. Our findings have the potential to dramatically improve the diagnostic classification of sinonasal tumors and will fundamentally change the current perception of SNUCs.
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.
2022-09-25 | GSE189778 | GEO
Project description:Genomic Profiling of Anaplastic/Undifferentiated Thyroid Carcinoma
Project description:Human papillomavirus (HPV)-related multiphenotypic sinonasal carcinoma (HMSC) is a recently described rare tumor that is morphologically similar to high grade adenoid cystic carcinoma (ACC), but clinically distinct. We utilized single-cell RNA sequencing (scRNA-seq) to characterize transcriptional heterogeneity in a human HMSC tumor and compared it to published ACC and oropharyngeal squamous cell carcinoma (OPSCC) scRNA-seq datasets.
Project description:Phenotypic and genomic characterization of Early Stage Breast Carcinoma using Training set (n=109) Validation set (n=105) of SNP6 arrays
Project description:Characterization of copy number alterations and unbalanced breakpoints in human esophageal squamous cell carcinoma cell lines by array-based comparative genomic hybridization.
Project description:This dataset contains raw exome sequencing data from nine sinonasal undifferentiated carcinoma FFPE samples and matched normal tissue that were assigned to a shared epigenetic class using DNA methylation-based classification. They were analyzed using the Twist Human Core Exome Plus Kit (Twist Bioscience) on a NovaSeq 6000 sequencer.
| EGAD00001009668 | EGA
Project description:Genomic characterization of Mucoepidermoid Carcinoma
Project description:The LC-MS/MS raw data (.mzxml) of nasal polyps and sinonasal squamous cell carcinoma patients.The study utilized 90 datasets from 30 individual nasal tissue samples across three independent experiments (Nasal polyps (NP): 48 datasets, SNSCC: 42 datasets).