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. 2020 Sep 29;12(1):80.
doi: 10.1186/s13073-020-00776-9.

Single-cell transcriptome analysis of tumor and stromal compartments of pancreatic ductal adenocarcinoma primary tumors and metastatic lesions

Wei Lin  1 , Pawan Noel  1 , Erkut H Borazanci  1   2 , Jeeyun Lee  3 , Albert Amini  2 , In Woong Han  3 , Jin Seok Heo  3 , Gayle S Jameson  2 , Cory Fraser  2 , Margaux Steinbach  2 , Yanghee Woo  4 , Yuman Fong  4 , Derek Cridebring  1 , Daniel D Von Hoff  1   2 , Joon Oh Park  5 , Haiyong Han  6
Affiliations

Single-cell transcriptome analysis of tumor and stromal compartments of pancreatic ductal adenocarcinoma primary tumors and metastatic lesions

Wei Lin et al. Genome Med. .

Abstract

Background: Solid tumors such as pancreatic ductal adenocarcinoma (PDAC) comprise not just tumor cells but also a microenvironment with which the tumor cells constantly interact. Detailed characterization of the cellular composition of the tumor microenvironment is critical to the understanding of the disease and treatment of the patient. Single-cell transcriptomics has been used to study the cellular composition of different solid tumor types including PDAC. However, almost all of those studies used primary tumor tissues.

Methods: In this study, we employed a single-cell RNA sequencing technology to profile the transcriptomes of individual cells from dissociated primary tumors or metastatic biopsies obtained from patients with PDAC. Unsupervised clustering analysis as well as a new supervised classification algorithm, SuperCT, was used to identify the different cell types within the tumor tissues. The expression signatures of the different cell types were then compared between primary tumors and metastatic biopsies. The expressions of the cell type-specific signature genes were also correlated with patient survival using public datasets.

Results: Our single-cell RNA sequencing analysis revealed distinct cell types in primary and metastatic PDAC tissues including tumor cells, endothelial cells, cancer-associated fibroblasts (CAFs), and immune cells. The cancer cells showed high inter-patient heterogeneity, whereas the stromal cells were more homogenous across patients. Immune infiltration varies significantly from patient to patient with majority of the immune cells being macrophages and exhausted lymphocytes. We found that the tumor cellular composition was an important factor in defining the PDAC subtypes. Furthermore, the expression levels of cell type-specific markers for EMT+ cancer cells, activated CAFs, and endothelial cells significantly associated with patient survival.

Conclusions: Taken together, our work identifies significant heterogeneity in cellular compositions of PDAC tumors and between primary tumors and metastatic lesions. Furthermore, the cellular composition was an important factor in defining PDAC subtypes and significantly correlated with patient outcome. These findings provide valuable insights on the PDAC microenvironment and could potentially inform the management of PDAC patients.

Keywords: Cellular heterogeneity; Pancreatic cancer; Pancreatic cancer subtypes; Single-cell RNA sequencing.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Multiple cell types were identified in PDAC primary tumors and metastatic lesions by single-cell RNA sequencing (scRNA-Seq). The cells from PDAC primary tumors (a, c) or metastatic lesions (b, d) were analyzed using unsupervised clustering and visualized using a UMAP plot. The clusters in a and b are color-coded based on cell types identified using known cell type-specific markers. The clusters in c and d are color-coded based on the patients. e A box plot showing the distribution of each cell type in the primary tumors and metastatic biopsies (MET)
Fig. 2
Fig. 2
Unsupervised clustering analysis of tumor cells and cancer-associated fibroblasts (CAFs) in PDAC primary tumors and metastatic lesions. a Tumor cells in the primary tumors are mostly segregated by patients. b Tumor cells in the metastatic lesions also cluster by patients. c Three major clusters are formed by CAFs from primary tumors. d CAFs from different patients are mixed in the different clusters
Fig. 3
Fig. 3
Unsupervised clustering analysis of immune cells in PDAC primary tumors and metastatic lesions. Tumor-infiltrating lymphocytes (TILs) from primary tumors and metastatic lesions are mixed together (a) and form two main clusters (b). One of the clusters (c0) showed higher expression of genes associated with T cell exhaustion (c) and those cells also express a higher level of Ki67 gene (d). The tumor-associated macrophages (TAMs) from primary tumors and metastatic lesions form separate clusters (e). Heatmap shows distinct gene expression patterns between the two TAM populations (f) and the genes specifically express in the TAMs associated with the primary tumors are enriched in processes related to extracellular matrix (left panel in g) and wound healing (right panel in g). The expression level (Y-axis) in c and d is the logarithm-transformed ratio of the UMI counts of the gene(s) of interest over the total UMI counts in each individual cell. GO Gene Ontology
Fig. 4
Fig. 4
Expression of PDAC subtype signature genes in different cell types identified by single-cell transcriptomics. Violin plots are used to show the modular expression scores of the signature genes that define subtypes described previously: the classic subtype described by Collisson et al. (a) and Moffitt et al. (b), the progenitor (c) and the squamous subtypes by Bailey et al. (d), the QM subtype by Collisson et al. (e), and the basal subtype by Moffitt et al. (f). Red boxes indicate cell types that have higher expression scores than the other cell types
Fig. 5
Fig. 5
Kaplan–Meier survival curves for PDAC patients in the ICGC database by expression levels of cell type-specific gene signatures derived from the single-cell transcriptomics analysis. a EMT cell gene signature. b ETC cell gene signature. c Endothelial cell gene signature. d CAF gene signature. e CAF cluster 0 gene signature. f CAF cluster 1 gene signature. g CAF cluster 2 gene signature. h TIL gene signature. i TAM gene signature. j Dendritic cell gene signature
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