Spatial determination and prognostic impact of the fibroblast transcriptome in pancreatic ductal adenocarcinoma
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eLife assessment:
This manuscript uses an innovative combination of spatial profiling with single-cell transcriptomics to define expression profiles of stromal components in proximal tumor regions compared to those in distal regions in pancreatic ductal adenocarcinoma (PDAC). Based on this, the authors claim that the presence of a proximal fibroblast population predicts worse outcomes for PDAC patients than the presence of a distal fibroblast population. While the work provides valuable insight into how different types of tumor stromal fibroblasts may affect PDAC outcomes, the work is currently incomplete and will benefit from more extended use of fibroblast and myeloid cell markers and efforts to better define the transcriptomic data generated.
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Abstract
Pancreatic ductal adenocarcinoma has a poor clinical outcome and responses to immunotherapy are suboptimal. Stromal fibroblasts are a dominant but heterogenous population within the tumor microenvironment and therapeutic targeting of stromal subsets may have therapeutic utility. Here, we combine spatial transcriptomics and scRNA-Seq datasets to define the transcriptome of tumor-proximal and tumor-distal cancer-associated fibroblasts (CAFs) and link this to clinical outcome. Tumor-proximal fibroblasts comprise large populations of myofibroblasts, strongly expressed podoplanin, and were enriched for Wnt ligand signaling. In contrast, inflammatory CAFs were dominant within tumor-distal subsets and expressed complement components and the Wnt-inhibitor SFRP2. Poor clinical outcome was correlated with elevated HIF-1α and podoplanin expression whilst expression of inflammatory and complement genes was predictive of extended survival. These findings demonstrate the extreme transcriptional heterogeneity of CAFs and its determination by apposition to tumor. Selective targeting of tumor-proximal subsets, potentially combined with HIF-1α inhibition and immune stimulation, may offer a multi-modal therapeutic approach for this disease.
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eLife assessment:
This manuscript uses an innovative combination of spatial profiling with single-cell transcriptomics to define expression profiles of stromal components in proximal tumor regions compared to those in distal regions in pancreatic ductal adenocarcinoma (PDAC). Based on this, the authors claim that the presence of a proximal fibroblast population predicts worse outcomes for PDAC patients than the presence of a distal fibroblast population. While the work provides valuable insight into how different types of tumor stromal fibroblasts may affect PDAC outcomes, the work is currently incomplete and will benefit from more extended use of fibroblast and myeloid cell markers and efforts to better define the transcriptomic data generated.
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Reviewer #1 (Public Review):
Croft, Pearce, and colleagues used a combination of spatial transcriptomics and analysis of publicly available scRNA-sequencing data of patients with PDAC to assess differences in the transcriptome of tumor proximal and distal stromal cells and associated these differences with survival data. Focusing on aSMA+ cancer-associated fibroblasts (CAFs), they find that tumor-proximal CAFs were defined by high expression of PDPN and Wnt ligands, and tumor-distal CAFs expressed inflammatory, complement, and Wnt inhibitor genes. While gene expression in relation to tumor distance did not per se correlate with clinical outcome, the authors find that individual genes within the tumor proximal and distal subsets were associated with increased or decreased survival. Based on this, the authors suggest a combination of …
Reviewer #1 (Public Review):
Croft, Pearce, and colleagues used a combination of spatial transcriptomics and analysis of publicly available scRNA-sequencing data of patients with PDAC to assess differences in the transcriptome of tumor proximal and distal stromal cells and associated these differences with survival data. Focusing on aSMA+ cancer-associated fibroblasts (CAFs), they find that tumor-proximal CAFs were defined by high expression of PDPN and Wnt ligands, and tumor-distal CAFs expressed inflammatory, complement, and Wnt inhibitor genes. While gene expression in relation to tumor distance did not per se correlate with clinical outcome, the authors find that individual genes within the tumor proximal and distal subsets were associated with increased or decreased survival. Based on this, the authors suggest a combination of targeting tumor-proximal CAFs defined by PDPN expression and inhibition of HIF-1a in the tumor distal stroma. Using an innovative approach to combine their spatial transcriptomics with single-cell transcriptomics data, the authors further identify an association between the expression of proximal CAF marker genes with elevated expression of complement and retinoic acid metabolism genes, and that complement genes were associated with increased survival.
While spatial differences in the abundance of different CAF subsets have been suggested before, the spatial transcriptomic data presented in this study provide a fresh look at CAF heterogeneity in PDAC and will be a useful resource for hypothesis generation and testing on the spatial regulation of CAF heterogeneity. However, there are major concerns associated with the interpretation of the data given the markers used to generate it, the association with single-cell data, and an overinterpretation of transcriptomic data, as detailed below.
The spatial transcriptomic data on fibroblasts relies on aSMA as a fibroblast marker. However, several studies in PDAC and other tumors have shown that there are subtypes of CAFs that do not (or only weakly) express aSMA, including inflammatory CAFs (iCAFs) and antigen-presenting CAFs (apCAFs) which therefore might have been missed by the authors. While there is no universal CAF marker, more recently the pancreatic cancer field has been using PDPN as a pan-CAF marker. aSMA is a classical myofibroblast marker across tissues and tumor types and the authors should state that they focused on myofibroblasts in their study.
While the association of the spatial data and single-cell data is innovative, it is flawed by the use of a single marker gene (DCN) to define the fibroblast population and the small number of cells (229) within this population. The authors need to corroborate their findings using a combination of fibroblast marker genes as well as other studies comprising a larger number of cells of fibroblast origin.
The authors find HIF1a expression to be associated with poor survival and enriched within the tumor-distal stroma. They interpret this as a reflection of the hypoxic environment of PDAC. However, the authors only ever look at HIF1a mRNA, but hypoxic regulation of HIF-1a occurs post-transcriptionally. Transcriptional regulation of HIF1a has been reported through pro-inflammatory cytokines and NFkB signaling. Therefore, the authors should either stain for HIF1a to confirm enrichment on the protein level or adjust their discussion to reflect the important biology behind HIF1a regulation.
The authors often misinterpret the correlations and associations they observe as causation and explanation - they should either adjust their language or perform experiments to show causation.
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Reviewer #2 (Public Review):
In the current manuscript, the authors select 24 surgically resected pancreatic cancer samples from patients who had a poor outcome (survival of less than one year) or better outcome (survival of at least 3 years). They use a Nanostring Geomx Digital Spatial profiler using a panel of 94 probes. The authors identify a proximal fibroblast population that expresses high levels of PDPN, while a distal fibroblast population expresses high levels of inflammatory genes such as IL6 and IL11, as well as complement genes. Using single-cell RNA sequencing, the authors are able to identify fibroblast populations reflecting those identified in the spatial data and identify other pathways that distinguish the two populations, and that define better or poorer outcomes (for instance, Hif signaling is associated with a …
Reviewer #2 (Public Review):
In the current manuscript, the authors select 24 surgically resected pancreatic cancer samples from patients who had a poor outcome (survival of less than one year) or better outcome (survival of at least 3 years). They use a Nanostring Geomx Digital Spatial profiler using a panel of 94 probes. The authors identify a proximal fibroblast population that expresses high levels of PDPN, while a distal fibroblast population expresses high levels of inflammatory genes such as IL6 and IL11, as well as complement genes. Using single-cell RNA sequencing, the authors are able to identify fibroblast populations reflecting those identified in the spatial data and identify other pathways that distinguish the two populations, and that define better or poorer outcomes (for instance, Hif signaling is associated with a poorer prognosis while markers of T cell activation are associated with better prognosis).
The manuscript addresses an important topic, namely whether fibroblasts, a heterogenous and relatively poorly understood cell population within the pancreatic cancer microenvironment, predict poor response. Further, the manuscript integrates spatial and single-cell data, in the quest to identify how the tissue composition of a tumor affects the overall prognosis. Some weaknesses are also noted and should be addressed. Most notably, the prognostic predictions are based on a relatively small number of samples. Further, as spatial transcriptomics is not a single cell-level technology, the authors could use co-immunofluorescence to validate their cell populations and specifically prove that the signatures correspond to genes expressed by fibroblasts, rather than infiltrating immune cells. Finally, the author shows that my-CAF-like fibroblasts correlate with worse prognosis, while inflammatory CAFs predict better prognosis: this finding should be discussed in the context of other CAF literature, some indicating that iCAFs are a negative prognostic predictor.
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