Developmental enhancer signatures stratify and predict outcomes of non-functional pancreatic neuroendocrine tumors
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PROVIDER: PRJNA478244 | ENA |
REPOSITORIES: ENA
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Project description:Enhancer signatures stratify and predict outcomes of non-functional pancreatic neuroendocrine tumors
| PRJNA575773 | ENA
Project description:Enhancer signatures stratify and predict outcomes of non-functional pancreatic neuroendocrine tumors
| PRJNA575774 | ENA
Project description:Most pancreatic neuroendocrine tumors (PNETs) do not produce symptoms of hormonal excess and are hence considered ‘non-functional’. Their clinical behaviors vary widely, emphasizing the need for a robust classification with prognostic power. Using enhancer maps to infer regulatory programs, we find that the large majority of non-functional PNETs fall into two major sub-types –A and B– that reflect alpha and beta endocrine cell ontogeny, respectively. A and B tumors have similar clinical presentations and histology, but express distinct lineage-specifying transcription factors, ARX or PDX1. In immunohistochemistry, 84% of 142 non-functional PNETs expressed one or the other factor, rarely both. Longitudinal data on 104 cases revealed markedly different outcomes, irrespective of MEN1 mutation status: relapse occurred almost exclusively in patients with type A tumors. These findings reveal a robust molecular stratification that provides insight into cellular origins of non-functional PNETs, accurately predicts disease course, and informs clinical decisions and future trial design.
2019-06-12 | GSE116356 | GEO
Project description:Most pancreatic neuroendocrine tumors (PNETs) do not produce excess hormones and are therefore considered 'non-functional'1-3. As clinical behaviors vary widely and distant metastases are eventually lethal2,4, biological classifications might guide treatment. Using enhancer maps to infer gene regulatory programs, we find that non-functional PNETs fall into two major subtypes, with epigenomes and transcriptomes that partially resemble islet α- and β-cells. Transcription factors ARX and PDX1 specify these normal cells, respectively5,6, and 84% of 142 non-functional PNETs expressed one or the other factor, occasionally both. Among 103 cases, distant relapses occurred almost exclusively in patients with ARX+PDX1- tumors and, within this subtype, in cases with alternative lengthening of telomeres. These markedly different outcomes belied similar clinical presentations and histology and, in one cohort, occurred irrespective of MEN1 mutation. This robust molecular stratification provides insight into cell lineage correlates of non-functional PNETs, accurately predicts disease course and can inform postoperative clinical decisions.
| S-EPMC6919319 | biostudies-literature
Project description:Cancer-associated fibroblasts (CAFs) gene signatures predict outcomes in breast and prostate tumor patients
| PRJNA1124516 | ENA
Project description:Over the last two decades, tumor-derived RNA expression signatures have been developed for the two most commonly diagnosed tumors worldwide, namely prostate and breast tumors, in order to improve both outcome prediction and treatment decision-making. In this context, molecular signatures gained by main components of the tumor microenvironment, such as cancer-associated fibroblasts (CAFs), have been explored as prognostic and therapeutic tools. Nevertheless, a deeper understanding of the significance of CAFs-related gene signatures in breast and prostate cancers still remains to be disclosed. RNA sequencing technology (RNA-seq) was employed to profile and compare the transcriptome of CAFs isolated from patients affected by breast and prostate tumors. The differentially expressed genes (DEGs) characterizing breast and prostate CAFs were intersected with data from public datasets derived from bulk RNA-seq profiles of breast and prostate tumor patients. Pathway enrichment analyses allowed us to appreciate the biological significance of the DEGs. K-means clustering was applied to construct CAFs-related gene signatures specific for breast and prostate cancer and to stratify independent cohorts of patients into high and low gene expression clusters. Kaplan-Meier survival curves and log-rank tests were employed to predict differences in the outcome parameters of the clusters of patients. Decision-tree analysis was used to validate the clustering results and boosting calculations were then employed to improve the results obtained by the decision-tree algorithm. Data obtained in breast CAFs allowed us to assess a signature that includes 8 genes (ITGA11, THBS1, FN1, EMP1, ITGA2, FYN, SPP1, and EMP2) belonging to pro-metastatic signaling routes, such as the focal adhesion pathway. Survival analyses indicated that the cluster of breast cancer patients showing a high expression of the aforementioned genes displays worse clinical outcomes. Next, we identified a prostate CAFs-related signature that includes 11 genes (IL13RA2, GDF7, IL33, CXCL1, TNFRSF19, CXCL6, LIFR, CXCL5, IL7, TSLP, and TNFSF15) associated with immune responses. A low expression of these genes was predictive of poor survival rates in prostate cancer patients. The results obtained were significantly validated through a two-step approach, based on unsupervised (clustering) and supervised (classification) learning techniques, showing a high prediction accuracy (≥ 90%) in independent RNA-seq cohorts. We identified a huge heterogeneity in the transcriptional profile of CAFs derived from breast and prostate tumors. Of note, the two novel CAFs-related gene signatures might be considered as reliable prognostic indicators and valuable biomarkers for a better management of breast and prostate cancer patients.
2024-06-21 | GSE269968 | GEO
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:CHARACTERIZING TOXICITY PATHWAYS OF FLUOXETINE TO PREDICT ADVERSE OUTCOMES IN ADULT PIMEPHALES PROMELAS
| PRJNA758289 | ENA
Project description:This SuperSeries is composed of the following subset Series: GSE6871: Gene expression signatures that predict radiation exposure (human) GSE6873: Gene expression signatures that predict radiation exposure (mouse) Keywords: SuperSeries Refer to individual Series
2010-06-12 | E-GEOD-6874 | biostudies-arrayexpress
Project description:Neuroendocrine bladder cancer is an aggressive variant of bladder cancer with significant metastatic potential and high risk of mortality. Diagnosis of these tumors is currently dependent on morphological criteria and staining for neuroendocrine markers. Using machine learning and multiple validation cohorts in different clinical settings, we developed a model that identifies tumors with transcriptomic profiles consistent with NE bladder cancers with an absence of NE features by morphological criteria. Early and accurate identification of these patients by genomic analysis may improve outcomes through treatment intensification and adaptation of standard treatment regimens.
2019-03-23 | GSE128702 | GEO