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DNA methylation-based classification of sinonasal tumors [Proteomics data]

ABSTRACT: 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

PROVIDER: EGAS00001006712 | EGA |

REPOSITORIES: EGA

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DNA methylation-based classification of sinonasal tumors [test set]

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

DNA methylation-based classification of sinonasal tumors [reference set]

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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DNA methylation-based classification of sinonasal tumors [DNA sequencing]

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.

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Gene expression profiling in sinonasal adenocarcinoma.

Project description:Transcriptional profiling of ethmoïd tumors samples comparing normal samples from the controlateral sinus. RNA were extracted from biopsies. Sinonasal adenocarcinomas are uncommon tumors developping in ethmoid sinus after wood dust exposure. Although the etiology of these tumors is well defined very little is known regarding the molecular basis of these tumors. In an attempt to identify genes involved in this disease we proceed to a gene expression profiling using cancer-dedicated microarrays, on matched samples of nine sinonasal adenocarcinomas and non-tumoral sinusal tissue. Among the genes with significant differential expression we selected: LGALS4, ACS5, CLU, BAX, PDGFRa, SRI and CCT5 for further exploration by quantitative real-time reverse-transcription-PCR on a larger set of tumors and confirmed the microarray data. Protein expression alterations were shown for LGALS4, ACS5, and CLU by immunohistochemistry. Our results suggest that two genes might be involved in the pathogenesis of these tumors: LGALS4 highly up-regulated, particularly in the most differentiated tumors, and CLU, whose expression was lost. After further evaluation these genes could be used as markers for a better characterization of these tumors and will potentially help to an earlier detection of cancer in woodworkers, who have high risk of developing sinonasal adenocarcinomas.

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Project description:Comparative analysis of gene expression in murine sinonasal mucosa in wild-type and CC10-knockout littermates with allergic eosinophilic chronic rhinosinusitis. The data provide a comprehensive overview of genes expressed in the mouse sinonasal mucosa and show that the expression of several known and unidentified genes is modified by disruption of the CC10 gene. Total RNA isolated from sinonasal mucosae of 6- to 8-week-old mice, C57BL/6 strain, was used for this comparison. Three groups: wild-type control, wild-type with allergic eosinophilic chronic rhinosinusitis, and CC10-knockout with allergic eosinophilic chronic rhinosinusitis.

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