Dataset Information

Transcriptional and immunohistological assessment of immune infiltration in pancreatic cancer.

ABSTRACT: Pancreatic adenocarcinoma is characterized by a complex tumor environment with a wide diversity of infiltrating stromal and immune cell types that impact the tumor response to conventional treatments. However, even in this poorly responsive tumor the extent of T cell infiltration as determined by quantitative immunohistology is a candidate prognostic factor for patient outcome. As such, even more comprehensive immunophenotyping of the tumor environment, such as immune cell type deconvolution via inference models based on gene expression profiling, holds significant promise. We hypothesized that RNA-Seq can provide a comprehensive alternative to quantitative immunohistology for immunophenotyping pancreatic cancer. We performed RNA-Seq on a prospective cohort of pancreatic tumor specimens and compared multiple approaches for gene expression-based immunophenotyping analysis compared to quantitative immunohistology. Our analyses demonstrated that while gene expression analyses provide additional information on the complexity of the tumor immune environment, they are limited in sensitivity by the low overall immune infiltrate in pancreatic cancer. As an alternative approach, we identified a set of genes that were enriched in highly T cell infiltrated pancreatic tumors, and demonstrate that these can identify patients with improved outcome in a reference population. These data demonstrate that the poor immune infiltrate in pancreatic cancer can present problems for analyses that use gene expression-based tools; however, there remains enormous potential in using these approaches to understand the relationships between diverse patterns of infiltrating cells and their impact on patient treatment outcomes.

SUBMITTER: Bernard B

PROVIDER: S-EPMC7458344 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Transcriptional and immunohistological assessment of immune infiltration in pancreatic cancer.

Bernard Brady B Rajamanickam Venkatesh V Dubay Christopher C Piening Brian B Alonso Emilio E Jutric Zeljka Z Tang Ephraim E Newell Pippa P Hansen Paul P Medler Terry T Gunderson Andrew A Young Kristina K Bifulco Carlo C Pucliowska Joanna J Crittenden Marka R MR Gough Michael J MJ

PloS one 20200831 8

Pancreatic adenocarcinoma is characterized by a complex tumor environment with a wide diversity of infiltrating stromal and immune cell types that impact the tumor response to conventional treatments. However, even in this poorly responsive tumor the extent of T cell infiltration as determined by quantitative immunohistology is a candidate prognostic factor for patient outcome. As such, even more comprehensive immunophenotyping of the tumor environment, such as immune cell type deconvolution via ...[more]

PMID: 32866185

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Project description:Background: Pancreatic cancer (PC) is an aggressive cancer with worse survival in the world. Emerging evidence suggested that the imbalance of alternative splicing (AS) is a hallmark of cancer and indicated poor prognosis of patients. Genes-derived splicing events can produce neoepitopes for immunotherapy. However, the profound study of splicing profiling in PC is still elusive. We aimed to identification of novel prognostic signature across a comprehensive splicing landscape and reveal their relationship with tumor-infiltrating immune cells in pancreatic cancer microenvironment. Methods: Based on integrated analysis of splicing profiling and clinical data, differentially splicing events were filtered out. Then, stepwise Cox regression analysis was applied to identify survival-related splicing events and construct prognostic signature. Functional enrichment analysis was performed to explore biology function. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were performed to validate the predictive effect of predictive signature. We also verified the clinical value of prognostic signature under the influence of different clinical parameters. For deeper analysis, we evaluated the correlation between prognostic signature and infiltrating immune cells by CIBERSORT. Results: According to systematic analyzing, a final six splicing events were identified and validated the good prognostic capability in entire TCGA dataset, validation set 1 and validation set 2 by Kaplan-Meier curves (P < 0.0001). The area under the curve (AUC) of ROC curves were also confirmed the high predictive efficiency of the prognostic signature in these three cohorts (AUC = 0.857, 0.895 and 0.788). In order to validate whether prognostic signature highlights a correlation between AS and immune contexture, CIBERSORT was performed to analyze the proportion of tumor-infiltrating immune cells in PC. Based on prognostic signature, we identified survival-related immune cells including CD8 T cells (P = 0.0111), activated CD4 memory T cells (P = 0.0329) and resting mast cells (P = 0.0352). Conclusion: In conclusion, our study contribute to provide a promising prognostic signature based on six splicing events and revealed prognosis-related immune cells which indeed represented novel tumor drivers and provide potential targets for personalized therapeutic.

Project description:Pancreatic cancer (PC) is a stubborn malignancy with high lethality and a low 5-year overall survival (OS) rate. Collagen type VII α1 chain (COL7A1), a major component of the extracellular matrix, serves important roles in numerous physiological processes and various illnesses. COL7A1 protein acts as an anchoring fibril between the external epithelial cells and the underlying stroma, and mutation of COL7A1 could cause recessive dystrophic epidermolysis bullosa. Raw data for PC were acquired from The Cancer Genome Atlas and the Gene Expression Omnibus database, and raw data for the normal pancreas were obtained from the Genotype-Tissue Expression database. COL7A1 mRNA expression in PC tissues was compared with that in either paired (GSE15471 dataset) or unpaired (all other data) normal pancreas tissues. The association between COL7A1 mRNA expression and clinicopathological factors was assessed using logistic regression analysis. Cox analysis and Kaplan-Meier analysis were used to evaluate the role of COL7A1 mRNA expression in prognosis and nomograms were constructed. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis, Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were performed to evaluate the relevant functions of COL7A1 and correlation with immune cell infiltration. Furthermore, reverse transcription-quantitative PCR was used to assess the mRNA expression levels of COL7A1 in PC. The present study demonstrated that COL7A1 mRNA expression was higher in PC tissues compared with in normal pancreas tissues. The Kaplan-Meier survival analysis indicated that patients with PC with high COL7A1 mRNA expression had shorter overall survival (OS), disease-specific survival (DSS) and progression-free interval (PFI) times compared with patients with PC with low COL7A1 mRNA expression. Multivariate analysis demonstrated that COL7A1 mRNA expression was an independent risk factor for OS, DSS and PFI. Nomogram and calibration plots were constructed to predict the prognosis of patients with PC. GSEA demonstrated that high mRNA expression levels of COL7A1 were associated with multiple cancer-related pathways. ssGSEA analysis indicated that COL7A1 expression was positively associated with natural killer CD56bright cells and T helper (Th)2 cells, and negatively associated with Th17 cells and eosinophils. The results of the present study suggested that COL7A1 could be an independent biomarker and an influential moderator of immune infiltration in PC.

Dataset Information

Transcriptional and immunohistological assessment of immune infiltration in pancreatic cancer.

Publications

Transcriptional and immunohistological assessment of immune infiltration in pancreatic cancer.

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