Project description:We report the application of single-molecule-based sequencing technology for high-throughput profiling of RNAs in human pancreatic neuroendocrine tumor (PNET) tissues. We analyzed the transcriptome maps of primary tumor sites from patients that presented metastasis or not (localized). This analysis revelaed that the immune cell and pathway-related molecular signatures were enriched in metastatic compared to localized tumors. Given the wide change of gene expression in metastatic PNETs, we analyzed the RNA-seq data using a L1000 drug-induced signature to identify pharmacologic agents that might preferentially target metastatic disease. In a separate study, we analyzed our RNA-seq data to understand the molecular basis of sexual dimorphisms in PNETs. We found that PNETs are associated with the emergence of unique sex-specific transcriptomic differences that are not observed in non-neoplastic pancreatic islet tissues.
Project description:Pancreatic neuroendocrine tumor (PanNET) is relatively infrequent but is nevertheless metastatic. Seeking to extend a new paradigm of personalized medicine, we performed an integrative analysis of transcriptomic (mRNA and microRNA) and mutational profiles and defined three clinically relevant human PanNET subtypes. Importantly, cross-species analysis revealed two of these three subtypes in a well-characterized, genetically engineered mouse model (RIP1-Tag2) of PanNET and its cell lines. Each subtype share similarities to distinct cell types in pancreatic neuroendocrine development, features are reflected in their metabolic profiles. Subtype-specific molecular signatures metabolites are proposed to identify these subtypes. Gene expression data from different stages of RIP1-TAG2 genetically engineered PanNET mouse model RT2 mouse PanNET tumors, liver metastases, normal, hyperplastic, and angiogenic islets were dissected out or isolated. RNA was extracted and hybridized on Affymetrix GeneChip Mouse Gene 1.0 ST arrays. The CEL files were processed using aroma.affymetrix.
Project description:We report the application of single-cell RNA sequencing (scRNA-seq) technology for single-cell level profiling of the transcriptional features of cells from two primary lesions, one liver metastasis, one normal liver tissue, and peripheral blood monocuclear cells in a patient with metastatic pNET. The transcriptomic profiles of a total of 24.544 cells were captured.
Project description:Pancreatic neuroendocrine tumor (PanNET) is relatively infrequent but is nevertheless metastatic. Seeking to extend a new paradigm of personalized medicine, we performed an integrative analysis of transcriptomic (mRNA and microRNA) and mutational profiles and defined three clinically relevant human PanNET subtypes. Importantly, cross-species analysis revealed two of these three subtypes in a well-characterized, genetically engineered mouse model (RIP1-Tag2) of PanNET and its cell lines. Each subtype share similarities to distinct cell types in pancreatic neuroendocrine development, features are reflected in their metabolic profiles. Subtype-specific molecular signatures metabolites are proposed to identify these subtypes. Gene expression data from different stages of RIP1-TAG2 genetically engineered PanNET mouse model
Project description:Pancreatic neuroendocrine tumor (PanNET) is relatively infrequent but is nevertheless metastatic. Seeking to extend a new paradigm of personalized medicine, we performed an integrative analysis of transcriptomic (mRNA and microRNA) and mutational profiles and defined three clinically relevant human PanNET subtypes. Importantly, cross-species analysis revealed two of these three subtypes in a well-characterized, genetically engineered mouse model (RIP1-Tag2) of PanNET and its cell lines. Each subtype share similarities to distinct cell types in pancreatic neuroendocrine development, features are reflected in their metabolic profiles. Subtype-specific molecular signatures metabolites are proposed to identify these subtypes. RNA was extracted from fresh frozen archival patient PanNET samples and hybridized on Affymetrix GeneChip human Gene 1.0 ST arrays. The CEL files were processed using R based bioconductor and normalized values were obtained using RMA.
Project description:The single-cell RNA profiles of dissociated 10 pancreatic primary tumors and 6 metastatic biopsies were obtained using the 10x Genomics Chromium platform