Similar Datasets
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.
2022-09-25 | GSE196227 | GEO
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:This SuperSeries is composed of the SubSeries listed below.
2018-01-18 | GSE109381 | GEO
Project description:DNA methylation-based classification of sinonasal tumors
| PRJNA803926 | ENA
Project description:Sinonasal undifferentiated carcinoma (SNUC) is an aggressive malignancy harboring IDH2 R172 mutations in >80% cases. We explored the potential of genome-wide DNA methylation profiling to elucidate tumor biology and improve the diagnosis of sinonasal undifferentiated carcinoma and its histologic mimics. Forty-two cases, including sinonasal undifferentiated, large cell neuroendocrine, small cell neuroendocrine, and SMARCB1-deficient carcinomas and olfactory neuroblastoma, were profiled by Illumina Infinium Methylation EPIC array interrogating >850,000 CpG sites. The data were analyzed using a custom bioinformatics pipeline. IDH2 mutation status was determined by the targeted exome sequencing (MSK-IMPACTTM) in most cases. H3K27 methylation level was assessed by the immunohistochemistry-based H-score. DNA methylation-based semi-supervised hierarchical clustering analysis segregated IDH2 mutants, mostly sinonasal undifferentiated (n = 10) and large cell neuroendocrine carcinomas (n = 4), from other sinonasal tumors, and formed a single cluster irrespective of the histologic type. t-distributed stochastic neighbor embedding dimensionality reduction analysis showed no overlap between IDH2 mutants, SMARCB1-deficient carcinoma and olfactory neuroblastoma. IDH2 mutants demonstrated a global methylation phenotype and an increase in repressive trimethylation of H3K27 in comparison to IDH2 wild-type tumors (p < 0.001). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed no difference in pathway activation between IDH2-mutated sinonasal undifferentiated and large cell neuroendocrine carcinomas. In comparison to SMARCB1-deficient, IDH2-mutated carcinomas were associated with better disease-free survival (p = 0.034) and lower propensity for lung metastasis (p = 0.002). ARID1A mutations were common in small cell neuroendocrine carcinoma but not among IDH2 mutants (3/3 versus 0/18 and p < 0.001). IDH2 mutations in sinonasal carcinomas induce a hypermethylator phenotype and define a molecular subgroup of tumors arising in this location. IDH2-mutated sinonasal undifferentiated carcinoma and large cell neuroendocrine carcinoma likely represent a phenotypic spectrum of the same entity, which is distinct from small cell neuroendocrine and SMARCB1-deficient sinonasal carcinomas. DNA methylation-based analysis of the sinonasal tumors has potential to improve the diagnostic accuracy and classification of tumors arising in this location.
| S-EPMC7391258 | biostudies-literature
Project description:DNA methylation-based classification of sinonasal tumors [test set]
| PRJNA803928 | ENA
Project description:DNA methylation-based classification of sinonasal tumors [reference set]
| PRJNA784516 | ENA
Project description:In present work one of our main objectives was to collect enough DNA methylation profiles of diverse tumor types in renal cancer. So, we used a collection of newly processed samples on DNA methylation data to have a variety of diverse kidney tumors from various sources. We processed a subset of samples (n=156) profiled as a part of our routine clinical methylation testing for kidney cancer at NIH. We collected one subset of samples (n =173) from our collaborators at UCSF and another subset of samples (n=82) from NYU.
2024-02-15 | GSE253101 | GEO
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 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 previously misclassified adenoid cystic carcinomas. This repository includes the results from DNA sequencing and mass spectrometry-based proteomics.
| EGAS00001006713 | EGA
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 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 previously misclassified adenoid cystic carcinomas. The repository includes the raw mass spectrometry-based proteomics data
| EGAS00001006712 | EGA