Dataset Information

Immunophenotypes of pancreatic ductal adenocarcinoma: Meta-analysis of transcriptional subtypes.

ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) is the most common malignancy of the pancreas and has one of the highest mortality rates of any cancer type with a 5-year survival rate of <5%. Recent studies of PDAC have provided several transcriptomic classifications based on separate analyses of individual patient cohorts. There is a need to provide a unified transcriptomic PDAC classification driven by therapeutically relevant biologic rationale to inform future treatment strategies. Here, we used an integrative meta-analysis of 353 patients from four different studies to derive a PDAC classification based on immunologic parameters. This consensus clustering approach indicated transcriptomic signatures based on immune infiltrate classified as adaptive, innate and immune-exclusion subtypes. This reveals the existence of microenvironmental interpatient heterogeneity within PDAC and could serve to drive novel therapeutic strategies in PDAC including immune modulation approaches to treating this disease.

SUBMITTER: de Santiago I

PROVIDER: S-EPMC6767191 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Immunophenotypes of pancreatic ductal adenocarcinoma: Meta-analysis of transcriptional subtypes.

de Santiago Ines I Yau Christopher C Heij Lara L Middleton Mark R MR Markowetz Florian F Grabsch Heike I HI Dustin Michael L ML Sivakumar Shivan S

International journal of cancer 20190318 4

Pancreatic ductal adenocarcinoma (PDAC) is the most common malignancy of the pancreas and has one of the highest mortality rates of any cancer type with a 5-year survival rate of <5%. Recent studies of PDAC have provided several transcriptomic classifications based on separate analyses of individual patient cohorts. There is a need to provide a unified transcriptomic PDAC classification driven by therapeutically relevant biologic rationale to inform future treatment strategies. Here, we used an ...[more]

PMID: 30720864

Similar Datasets

Project description:A comprehensive meta-analysis of publicly available gene expression microarray data obtained from human-derived pancreatic ductal adenocarcinoma (PDAC) tissues and their histologically matched adjacent tissue samples was performed to provide diagnostic and prognostic biomarkers, and molecular targets for PDAC. An integrative meta-analysis of four submissions (GSE62452, GSE15471, GSE62165, and GSE56560) containing 105 eligible tumor-adjacent tissue pairs revealed 344 differentially over-expressed and 168 repressed genes in PDAC compared to the adjacent-to-tumor samples. The validation analysis using TCGA combined GTEx data confirmed 98.24% of the identified up-regulated and 73.88% of the down-regulated protein-coding genes in PDAC. Pathway enrichment analysis showed that "ECM-receptor interaction", "PI3K-Akt signaling pathway", and "focal adhesion" are the most enriched KEGG pathways in PDAC. Protein-protein interaction analysis identified FN1, TIMP1, and MSLN as the most highly ranked hub genes among the DEGs. Transcription factor enrichment analysis revealed that TCF7, CTNNB1, SMAD3, and JUN are significantly activated in PDAC, while SMAD7 is inhibited. The prognostic significance of the identified and validated differentially expressed genes in PDAC was evaluated via survival analysis of TCGA Pan-Cancer pancreatic ductal adenocarcinoma data. The identified candidate prognostic biomarkers were then validated in four external validation datasets (GSE21501, GSE50827, GSE57495, and GSE71729) to further improve reliability. A total of 28 up-regulated genes were found to be significantly correlated with worse overall survival in patients with PDAC. Twenty-one of the identified prognostic genes (ITGB6, LAMC2, KRT7, SERPINB5, IGF2BP3, IL1RN, MPZL2, SFTA2, MET, LAMA3, ARNTL2, SLC2A1, LAMB3, COL17A1, EPSTI1, IL1RAP, AK4, ANXA2, S100A16, KRT19, and GPRC5A) were also found to be significantly correlated with the pathological stages of the disease. The results of this study provided promising prognostic biomarkers that have the potential to differentiate PDAC from both healthy and adjacent-to-tumor pancreatic tissues. Several novel dysregulated genes merit further study as potentially promising candidates for the development of more effective treatment strategies for PDAC.

Project description:Comprehensive characterization of signaling in pancreatic ductal adenocarcinoma (PDAC) promises to enhance our understanding of the molecular aberrations driving this devastating disease, and may identify novel therapeutic targets as well as biomarkers that enable stratification of patients for optimal therapy. Here, we use immunoaffinity-coupled high-resolution mass spectrometry to characterize global tyrosine phosphorylation patterns across two large panels of human PDAC cell lines: the ATCC series (19 cell lines) and TKCC series (17 cell lines). This resulted in the identification and quantification of over 1800 class 1 tyrosine phosphorylation sites and the consistent segregation of both PDAC cell line series into three subtypes with distinct tyrosine phosphorylation profiles. Subtype-selective signaling networks were characterized by identification of subtype-enriched phosphosites together with pathway and network analyses. This revealed that the three subtypes characteristic of the ATCC series were associated with perturbations in signaling networks associated with cell-cell adhesion and epithelial-mesenchyme transition, mRNA metabolism, and receptor tyrosine kinase (RTK) signaling, respectively. Specifically, the third subtype exhibited enhanced tyrosine phosphorylation of multiple RTKs including the EGFR, ERBB3 and MET. Interestingly, a similar RTK-enriched subtype was identified in the TKCC series, and 'classifier' sites for each series identified using Random Forest models were able to predict the subtypes of the alternate series with high accuracy, highlighting the conservation of the three subtypes across the two series. Finally, RTK-enriched cell lines from both series exhibited enhanced sensitivity to the small molecule EGFR inhibitor erlotinib, indicating that their phosphosignature may provide a predictive biomarker for response to this targeted therapy. These studies highlight how resolution of subtype-selective signaling networks can provide a novel taxonomy for particular cancers, and provide insights into PDAC biology that can be exploited for improved patient management.

Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer related death in the world with a five-year survival rate of less than 5%. Not all PDAC are the same, because there exist intra-tumoral heterogeneity between PDAC, which poses a great challenge to personalized treatments for PDAC.MethodsTo dissect the molecular heterogeneity of PDAC, we performed a retrospective meta-analysis on whole transcriptome data from more than 1200 PDAC patients. Subtypes were identified based on non-negative matrix factorization (NMF) biclustering method. We used the gene set enrichment analysis (GSEA) and survival analysis to conduct the molecular and clinical characterization of the identified subtypes, respectively.ResultsSix molecular and clinical distinct subtypes of PDAC: L1-L6, are identified and grouped into tumor-specific (L1, L2 and L6) and stroma-specific subtypes (L3, L4 and L5). For tumor-specific subtypes, L1 (~?22%) has enriched carbohydrate metabolism-related gene sets and has intermediate survival. L2 (~?22%) has the worst clinical outcomes, and is enriched for cell proliferation-related gene sets. About 23% patients can be classified into L6, which leads to intermediate survival and is enriched for lipid and protein metabolism-related gene sets. Stroma-specific subtypes may contain high non-epithelial contents such as collagen, immune and islet cells, respectively. For instance, L3 (~?12%) has poor survival and is enriched for collagen-associated gene sets. L4 (~?14%) is enriched for various immune-related gene sets and has relatively good survival. And L5 (~?7%) has good clinical outcomes and is enriched for neurotransmitter and insulin secretion related gene sets. In the meantime, we identified 160 subtype-specific markers and built a deep learning-based classifier for PDAC. We also applied our classification system on validation datasets and observed much similar molecular and clinical characteristics between subtypes.ConclusionsOur study is the largest cohort of PDAC gene expression profiles investigated so far, which greatly increased the statistical power and provided more robust results. We identified six molecular and clinical distinct subtypes to describe a more complete picture of the PDAC heterogeneity. The 160 subtype-specific markers and a deep learning based classification system may be used to better stratify PDAC patients for personalized treatments.

Project description:Adenocarcinomas of the pancreatic duct (PDAC) are characteristically aggressive tumors that are extremely challenging to treat as curative surgical resection, the definitive treatment, is seldom possible. Regretably, most patients are diagnosed with metastatic disease at the time of initial presentation. In addition, current chemotherapeutic concepts that are used for advanced disease stages show frustrating results. Thus, there is an urgent need to identify novel therapeutic molecular targets that are associated with PDAC disease. Recently, the chemokine receptor CXCR4 has been demonstrated to be highly expressed in metastatic PDAC. However, the results of the published data on CXCR4 and its association with clinicopathological variables and prognosis in PDAC seem to be heterogeneous. Consequently, to clarify the relevance of CXCR4 as a biomarker in PDAC we performed a comprehensive literature search by using PubMed and Web of Science databases to identify articles that focused on the expression of CXCR4 in PDAC by using immunohistochemistry. Subsequently, data from nine relevant studies, encompassing 1183 patients were extracted, qualitatively assessed, and entered into a meta-analysis. By using a random effects model, the pooled hazard ratio of the seven studies that reported on patients overall survival revealed a correlation between expression of CXCR4 and poor prognosis (HR 1.49; 95% CI: 1.04-2.14; P = 0.03; I2 = 74%). Although heterogeneity became evident, subgroup analyses confirmed the prognostic value of CXCR4 in PDAC, especially in high-quality studies that performed multivariate analysis. In addition, meta-analysis revealed a strong association of CXCR4 expression with the UICC stage (OR: 3.40; 95% CI: 1.67-6.92; P = 0.0007; I2 = 0%) and metastatic disease (N-status: OR: 2.55; 95% CI: 1.56-4.15; P = 0.0002; I2 = 26%; recurrence to the liver: OR: 2.80; 95% CI: 1.48-5.29; P = 0.001; I2 = 0%). Taken together, our meta-analysis suggests that CXCR4 represents a useful prognostic biomarker in PDAC and might therefore be evaluated as a potential therapeutic target in the treatment of metastatic cancer disease of the pancreas.

Dataset Information

Immunophenotypes of pancreatic ductal adenocarcinoma: Meta-analysis of transcriptional subtypes.

Publications

Immunophenotypes of pancreatic ductal adenocarcinoma: Meta-analysis of transcriptional subtypes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets