Introduction
Recent studies using single-cell transcriptomic analysis have reported several distinct clusters of neoplastic epithelial cells and cancer-associated fibroblasts in the pancreatic cancer tumor microenvironment. However, their molecular characteristics and biological significance have not been clearly elucidated due to intra- and inter-tumoral heterogeneity.
Aims & Methods
We performed single cell RNA sequencing using enriched epithelial cells and fibroblasts from 17 pancreatic cancer tissues. Then, to resolve the spatial information of heterogeneous cancer subclusters and cancer-associated niches in human pancreatic cancer tissues, we performed RNA in situ hybridization and spatial transcriptome analysis using 10X Visium technique in paired tumor tissues.
Results
We identified five distinct functional subclusters of pancreatic cancer cells and six distinct cancer-associated fibroblast subclusters. We deeply profiled their characteristics, and we found that these subclusters successfully deconvoluted most of the features suggested in bulk transcriptome analysis of pancreatic cancer. Among those subclusters, we identified a novel cancer cell subcluster, Ep_VGLL1, showing intermediate characteristics between the extremities of basal-like and classical dichotomy, despite its prognostic value. Molecular features of Ep_VGLL1 suggest its transitional properties between basal-like and classical subtypes. In the spatial transcriptome analysis, we constructed an adjacency network graph reflecting the neighborhood enrichment relationships across diverse cell types and draw a putative cancer progression axis from Ep_FXYD2, ADM population, to Ep_TRIM54, Ep_VGLL1, and Ep_KRT6A, supporting our model of pancreatic cancer cell dynamics. In addition, we also discovered that the spatial distributions of Fb_LRRC15, representing the myCAF population, were highly correlated with pancreatic cancer cell distributions, whereas Fb_SFRP1, representing the iCAF population, were located distal to the cancer-associated niche.
Conclusion
This integrative analysis not only provides a comprehensive landscape of pancreatic cancer and fibroblast population, but also suggests a novel insight to the dynamic states of pancreatic cancer cells and unveils potential therapeutic targets.
References
Collisson, E. A. et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat. Med. 17, 500-503, (2011).
Moffitt, R. A. et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat Genet 47, 1168-1178, (2015).
Collisson, E. A. et al. Molecular subtypes of pancreatic cancer. Nat. Rev. Gastroenterol. Hepatol. 16, 207-220, doi:10.1038/s41575-019-0109-y (2019).
Chan-Seng-Yue, M. et al. Transcription phenotypes of pancreatic cancer are driven by genomic events during tumor evolution. Nat Genet 52, 231-240, (2020).
Porter, R. L. et al. Epithelial to mesenchymal plasticity and differential response to therapies in pancreatic ductal adenocarcinoma. Proc. Natl. Acad. Sci. U. S. A., doi:10.1073/pnas.1914915116 (2019).
Guo, J. A. et al. Refining the Molecular Framework for Pancreatic Cancer with Single-cell and Spatial Technologies. Clin Cancer Res 27, 3825-3833, doi:10.1158/1078-0432.CCR-20-4712 (2021).
Hannah L. W, et al. Spatially Resolved Single-Cell Assessment of Pancreatic Cancer Expression Subtypes Reveals Co-expressor Phenotypes and Extensive Intratumoral Heterogeneity. Cancer Res 83, 441-455 (2023).
Liu, X. et al. Conditional reprogramming and long-term expansion of normal and tumor cells from human biospecimens. Nat Protoc 12, 439-451, doi:10.1038/nprot.2016.174 (2017).