Proteotranscriptomic classification and characterization of pancreatic neuroendocrine neoplasms
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ABSTRACT:
PROVIDER: EGAS00001005024 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Pancreatic neuroendocrine neoplasms (PNENs) are biologically and clinically heterogeneous neoplasms. We used quantitative global proteomic analysis on 40 PNENs to compliment paired transcriptome data.
2021-11-30 | PXD024175 | Pride
Project description:Neuroendocrine neoplasms of the gallbladder and liver occur rarely in dogs and humans. A recent reclassification of human neuroendocrine neoplasms by the World Health Organization has refined categorization of these tumors by morphology, replicative indices, and molecular signatures. In humans, these factors correlate with survival outcomes. Improved characterization of these tumors is needed in dogs to identify diagnostic biomarkers and determine therapeutic strategies. To achieve this objective, the proteome of 3 canine hepatobiliary neoplasms was compared to normal canine adrenal and liver tissue from formalin-fixed paraffin-embedded samples. Thirty-two upregulated and 121 downregulated differentially expressed proteins were identified in the hepatobiliary neuroendocrine neoplasm samples. Among the upregulated proteins is galectin-1, a multivalent carbohydrate binding protein known to play a role in lung and pancreatic neuroendocrine neoplasia development and progression in humans. Drugs targeting the galectin family have shown promise as anticancer therapeutics in cervical cancer, prostate cancer, lung and pancreatic neuroendocrine neoplasia in human medicine. Galectin-1 may represent a novel treatment target in hepatobiliary neuroendocrine neoplasia in both humans and dogs.
2023-03-11 | PXD028822 | Pride
Project description:The newest WHO classification suggests eliminating cases with BRAF and NRAS mutations from the categories of Spitz tumors (ST) and Spitz melanoma (SM). We aimed to better characterize the genomics of Spitz neoplasms and assess whether integrating genomic data with morphologic diagnosis improves classification and prognostication. We performed DNA and RNA sequencing on 80 STs, 26 SMs, and 22 melanomas with Spitzoid features (MSF). NGS data was used to reclassify tumors by moving BRAF/NRAS-mutated cases to MSF. Eighty-one percent of STs harbored kinase fusions/truncations. Of SMs, 77% had fusions/truncations, 8 involving MAP3K8. Novel fusions identified were MYO5A-FGFR1, MYO5A-ERBB4, and PRKDC-CTNNB1. The majority of MSFs (84%) had BRAF, NRAS, or NF1 mutations, and 62% had TERT promoter mutations. Only after reclassification, the following was observed: 1) mRNA expression showed distinct clustering of MSF; 2) 6/7 cases with recurrence and all distant metastases were MSFs; 3) RFS was worse in MSF than ST and SM groups (p=0.0073); 4) classification incorporating genomic data was highly predictive of recurrence (OR 13.20, p=0.0197). The majority of STs and SMs have kinase fusions as primary initiating genomic events. Eliminating BRAF/NRAS-mutated neoplasms from these categories results in improved classification and prognostication of melanocytic neoplasms with Spitzoid cytomorphology.
2020-02-26 | GSE142441 | GEO
Project description:The newest WHO classification suggests eliminating cases with BRAF and NRAS mutations from the categories of Spitz tumors (ST) and Spitz melanoma (SM). We aimed to better characterize the genomics of Spitz neoplasms and assess whether integrating genomic data with morphologic diagnosis improves classification and prognostication. We performed DNA and RNA sequencing on 80 STs, 26 SMs, and 22 melanomas with Spitzoid features (MSF). NGS data was used to reclassify tumors by moving BRAF/NRAS-mutated cases to MSF. Eighty-one percent of STs harbored kinase fusions/truncations. Of SMs, 77% had fusions/truncations, 8 involving MAP3K8. Novel fusions identified were MYO5A-FGFR1, MYO5A-ERBB4, and PRKDC-CTNNB1. The majority of MSFs (84%) had BRAF, NRAS, or NF1 mutations, and 62% had TERT promoter mutations. Only after reclassification, the following was observed: 1) mRNA expression showed distinct clustering of MSF; 2) 6/7 cases with recurrence and all distant metastases were MSFs; 3) RFS was worse in MSF than ST and SM groups (p=0.0073); 4) classification incorporating genomic data was highly predictive of recurrence (OR 13.20, p=0.0197). The majority of STs and SMs have kinase fusions as primary initiating genomic events. Eliminating BRAF/NRAS-mutated neoplasms from these categories results in improved classification and prognostication of melanocytic neoplasms with Spitzoid cytomorphology.
2020-08-27 | GSE142442 | GEO
Project description:Comprehensive Sequencing of High Grade Neuroendocrine Neoplasms
| PRJNA648441 | ENA
Project description:Comprehensive Sequencing of High Grade Neuroendocrine Neoplasms
| PRJNA648442 | ENA
Project description: Not available
| S-EPMC4653242 | biostudies-literature
Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors. A total of 191 RCC FFPE samples are analyzed including 63 clear cell RCC (ccRCC), 57 papillary RCC (pRCC), 35 chromophobe RCC (chrRCC) and 36 oncocytoma (OC). Two-color array-comparative genomic hybdrization on custom designed using RCC DNA as test and normal sex-matched DNA as reference.
2015-04-29 | E-GEOD-57162 | biostudies-arrayexpress
Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors.
2015-04-29 | GSE57162 | GEO
Project description:Pancreatic neuroendocrine tumors (PanNETs) account for 1-3% of all neoplasms of the pancreas. Epigenetic changes seem to be the main drivers of PanNET tumorigenesis. Indeed, the most frequent mutated genes in PanNETs are DAXX (Death domain associated protein 6), ATRX (Transcriptional Regulator ATRX) and MEN1 (menin 1), which are all involved in epigenetic regulation. The objective of the project is to study the epigenetic changes undergoing in PanNETs. Specifically, we aimed at investigating whether do exist biologically important epigenetic differences in PanNET, useful for a clinically relevant classification.
2022-11-30 | E-MTAB-11962 | biostudies-arrayexpress