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Alternative polyadenylation drives oncogenic gene expression in pancreatic ductal adenocarcinoma.

ABSTRACT: Alternative polyadenylation (APA) is a gene regulatory process that dictates mRNA 3'-UTR length, resulting in changes in mRNA stability and localization. APA is frequently disrupted in cancer and promotes tumorigenesis through altered expression of oncogenes and tumor suppressors. Pan-cancer analyses have revealed common APA events across the tumor landscape; however, little is known about tumor type-specific alterations that may uncover novel events and vulnerabilities. Here, we integrate RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project and The Cancer Genome Atlas (TCGA) to comprehensively analyze APA events in 148 pancreatic ductal adenocarcinomas (PDACs). We report widespread, recurrent, and functionally relevant 3'-UTR alterations associated with gene expression changes of known and newly identified PDAC growth-promoting genes and experimentally validate the effects of these APA events on protein expression. We find enrichment for APA events in genes associated with known PDAC pathways, loss of tumor-suppressive miRNA binding sites, and increased heterogeneity in 3'-UTR forms of metabolic genes. Survival analyses reveal a subset of 3'-UTR alterations that independently characterize a poor prognostic cohort among PDAC patients. Finally, we identify and validate the casein kinase CSNK1A1 (also known as CK1alpha or CK1a) as an APA-regulated therapeutic target in PDAC. Knockdown or pharmacological inhibition of CSNK1A1 attenuates PDAC cell proliferation and clonogenic growth. Our single-cancer analysis reveals APA as an underappreciated driver of protumorigenic gene expression in PDAC via the loss of miRNA regulation.

SUBMITTER: Venkat S

PROVIDER: S-EPMC7111527 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Alternative polyadenylation drives oncogenic gene expression in pancreatic ductal adenocarcinoma.

Venkat Swati S Tisdale Arwen A AA Schwarz Johann R JR Alahmari Abdulrahman A AA Maurer H Carlo HC Olive Kenneth P KP Eng Kevin H KH Feigin Michael E ME

Genome research 20200206 3

Alternative polyadenylation (APA) is a gene regulatory process that dictates mRNA 3'-UTR length, resulting in changes in mRNA stability and localization. APA is frequently disrupted in cancer and promotes tumorigenesis through altered expression of oncogenes and tumor suppressors. Pan-cancer analyses have revealed common APA events across the tumor landscape; however, little is known about tumor type-specific alterations that may uncover novel events and vulnerabilities. Here, we integrate RNA-s ...[more]

PMID: 32029502

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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.

Dataset Information

Alternative polyadenylation drives oncogenic gene expression in pancreatic ductal adenocarcinoma.

Publications

Alternative polyadenylation drives oncogenic gene expression in pancreatic ductal adenocarcinoma.

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