Single-cell profiling to explore pancreatic cancer heterogeneity, plasticity and response to therapy

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer entity characterized by a heterogeneous genetic landscape and an immunosuppressive tumor microenvironment. Recent advances in high-resolution single-cell sequencing and spatial transcriptomics technologies have enabled an in-depth characterization of both malignant and host cell types and increased our understanding of the heterogeneity and plasticity of PDAC in the steady state and under therapeutic perturbation. In this Review we outline single-cell analyses in PDAC, discuss their implications on our understanding of the disease and present future perspectives of multimodal approaches to elucidate its biology and response to therapy at the single-cell level.

Figure 1. Single-cell profiling to uncover pancreatic cancer heterogeneity and complexity.

Left, single-cell profiling approaches, such as scRNA-seq (transcriptomics), scDNA-seq (genomics) and scATAC-seq (epigenetics), provide detailed insights into pancreatic cancer heterogeneity at various levels. Right, depicted are hallmarks of pancreatic cancer heterogeneity: (1) intratumor heterogeneity – tumors are composed of clonal tumor cell subpopulations, for which multiple tumor cell subtypes can co-occur within the same tumor, as well as diverse subTMEs, such as the deserted and reactive one; (2) stromal heterogeneity – the pancreatic cancer TME is characterized by a high abundance of CAF subsets (myoCAFs, iCAFs and apCAFs) with distinct functional phenotypes, for example, tumor-restraining (αSMA+ CAFs) and tumor-promoting (FAP+ CAFs) fibroblasts and (3) an immunosuppressive microenvironment – pancreatic cancer shows an exclusion of T cells or presence of exhausted, dysfunctional T cells, and tumors are highly infiltrated by immunosuppressive myeloid cells. LAG3, lymphocyte-activation gene 3; PD-1, programmed cell death protein 1; TIM3, T cell immunoglobulin and mucin domain-containing protein 3; UMAP, uniform manifold approximation and projection.

Figure 2. scRNA-seq approaches to decode cell-cell interaction and communication, as well as the spatial architecture of PDAC subtypes.

Left, using cellular indexing of transcriptomics and epitopes by sequencing (CITE-seq), the simultaneous expression of cell-surface protein markers, as well as gene expression, can be assessed. INs-seq enables the profiling of intracellular protein expression to infer signaling activity, transcription factor expression and metabolic states within single cells. Both approaches uncover distinct cellular phenotypic states. Middle, sequencing of physically interacting cells (PIC-seq) entails cell-cell communication analysis between two directly interacting cell subpopulations. Complex cell-cell communication networks are inferred by the expression of ligand-receptor interaction pairs of different cell types in the pancreatic cancer TME. Right, integration of scRNA-seq with spatial transcriptomics data allows analysis of spatial distribution and spatially resolved TME communities within a tumor. PIC, physically interacting cells.

Figure 3. Multimodal spatial single-cell profiling technologies.

Overview of spatial profiling technologies encompassing spatial genomics, spatial transcriptomics, spatial proteomics and spatial epigenetics approaches to assess inter- and intratumor heterogeneity and the spatial organization of pancreatic cancer. Spatial profiling methods are applied to analyze intact tissue sections without the need to dissociate the tissue, thereby preserving the spatial architecture of the tumor. Spatial genomics (spatial DNA-seq) identify tumor cell subclones and reveal differences in spatially defined regions within the tumor. Using spatial transcriptomics and proteomics, different cell types and functional cell states can be assessed. Besides, cell-cell communication channels and networks between neighboring cell types can be analyzed in a spatially resolved manner. Spatial epigenetics approaches allow the profiling of chromatin accessibility and chromatin modifications in distinct tumor regions. TF, transcription factor.

Figure 4. Timeline of subtyping classification advances in pancreatic cancer.

Overview of selected pancreatic cancer subtyping studies that used RNA-seq (top) and scRNA-seq (bottom) datasets for subtype classification. Collisson et al., Moffitt et al. and Bailey et al. profiled large cohorts of patients with PDAC using RNA-seq and identified discrete tumor cell subtype states, as well as stroma subtypes. Mueller et al. revealed that the two major PDAC subtypes (classical and basal-like) show differences in the dosage of mutationally active KRAS (KRAS-mut), with the basal-like subtype displaying the highest dosage and expression levels of mutated KRAS. Single-cell profiling enabled the identification of intermediate tumor cell states between the classical and basal-like subtypes, as well as extended and refined existing subtyping classifications. Moreover, through scRNA-seq analysis, distinct tumor cell states can be linked to subtype-specific TME communities characterized by enrichment of different CAF and immune cell populations^,,. C1QC, complement C1qC chain; SPP1, secreted phosphoprotein 1.