Objective Pancreatic ductal adenocarcinoma (PDA) is a highly metastatic disease with limited therapeutic options. Genome and transcriptome analyses have identified signalling pathways and cancer driver genes with implications in patient stratification and targeted therapy. However, these analyses were performed in bulk samples and focused on coding genes, which represent a small fraction of the genome.
Design We developed a computational framework to reconstruct the non-coding transcriptome from cross-sectional RNA-Seq, integrating somatic copy number alterations (SCNA), common germline variants associated to PDA risk and clinical outcome. We validated the results in an independent cohort of paired epithelial and stromal RNA-Seq derived from laser capture microdissected human pancreatic tumours, allowing us to annotate the compartment specificity of their expression. We employed systems and experimental biology approaches to interrogate the function of epithelial long non-coding RNAs (lncRNAs) associated with genetic traits and clinical outcome in PDA.
Results We generated a catalogue of PDA-associated lncRNAs. We showed that lncRNAs define molecular subtypes with biological and clinical significance. We identified lncRNAs in genomic regions with SCNA and single nucleotide polymorphisms associated with lifetime risk of PDA and associated with clinical outcome using genomic and clinical data in PDA. Systems biology and experimental functional analysis of two epithelial lncRNAs (LINC00673 and FAM83H-AS1) suggest they regulate the transcriptional profile of pancreatic tumour samples and PDA cell lines.
Conclusions Our findings indicate that lncRNAs are associated with genetic marks of pancreatic cancer risk, contribute to the transcriptional regulation of neoplastic cells and provide an important resource to design functional studies of lncRNAs in PDA.
- pancreatic cancer
- gene regulation
- epithelial cells
- cancer genetics
- RNA expression
Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.
LA, ZL and JW contributed equally.
Contributors Conceptualisation: LA. Computational analysis: ZL and JW. Software: NB. Investigation: LA, ZL, HCM, IS, MSM, DCG and DAB. Resources: LA, KPO and RR. Visualisation: LA and ZL. Funding acquisition: LA. Project oversight and management: LA, KPO and RR. LA wrote the manuscript with feedback from KPO and RR. All authors discussed the results and commented on the manuscript.
Funding This work was funded by the IRIS (LA) and CaST (LA) programmes at Columbia University, the Juvenile Diabetes Research Foundation APF-2014-197-A-N and FAC-2018-541-A-N (LA) and NIH R21CA188059 (LS). These studies used the resources of the Herbert Irving Comprehensive Cancer Center (Center Grant P30CA013696) and the Diabetes and Endocrinology Research Center (Center Grant 5P30DK063608). RR and ZL were funded by the NIH U54 CA193313. NB was funded by an NIH-NHLBI T35 training grant. JW was funded byN_HKUST606/17 and C6002-17G
Competing interests None declared.
Ethics approval Columbia University Institutional Review Board.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement All data used in this manuscript will be available through GEO Express GSE96931 after publication.