Spatially discrete signalling niches regulate fibroblast heterogeneity in human lung cancer

Abstract

Fibroblasts are functionally heterogeneous cells, capable of promoting and suppressing tumour progression. Across cancer types, the extent and cause of this phenotypic diversity remains unknown. We used single-cell RNA sequencing and multiplexed immunohistochemistry to examine fibroblast heterogeneity in human lung and non-small cell lung cancer (NSCLC) samples. This identified seven fibroblast subpopulations: including inflammatory fibroblasts and myofibroblasts (representing terminal differentiation states), quiescent fibroblasts, proto-myofibroblasts (x2) and proto-inflammatory fibroblasts (x2). Fibroblast subpopulations were variably distributed throughout tissues but accumulated at discrete niches associated with differentiation status. Bioinformatics analyses suggested TGF-β1 and IL-1 as key regulators of myofibroblastic and inflammatory differentiation respectively. However, in vitro analyses showed that whilst TGF-β1 stimulation in combination with increased tissue tension could induce myofibroblast marker expression, it failed to fully re-capitulate ex-vivo phenotypes. Similarly, IL-1β treatment only induced upregulation of a subset of inflammatory fibroblast marker genes. In silico modelling of ligand-receptor signalling identified additional pathways and cell interactions likely to be involved in fibroblast activation, which can be examined using publicly available R shiny applications (at the following links: myofibroblast activation and inflammatory fibroblast activation). This highlighted a potential role for IL-11 and IL-6 (among other ligands) in myofibroblast and inflammatory fibroblast activation respectively. This analysis provides valuable insight into fibroblast subtypes and differentiation mechanisms in NSCLC.

Characterising cancer-associated fibroblast heterogeneity in non-small cell lung cancer: a systematic review and meta-analysis

“…An additional approach as previously mentioned is the use of scRNA sequencing experiments to identify transcriptomically-different subpopulations of CAFs. Such experiments are already yielding exciting results 22,23 and the combination of these analyses whilst also assessing the prognostic effect of any identified proteins, as in this study, has the potential to further our understanding of CAF biology and in particular, its heterogeneity.…”

“…CAFs are increasingly recognised as a heterogeneous cell type. Recent studies have described transcriptomically-distinct CAF phenotypes in NSCLC, which may correspond to discrete functional subsets 22,23 . Our aim was to therefore determine whether a set of protein markers, grouped together by function, would show prognostic differences.…”

“…Unsurprisingly, this population correlates with poor prognosis in a range of malignancies 9,[19][20][21] . However, there is increasing evidence that CAFs are in fact a heterogeneous cell type, with a range of distinct phenotypes and functions [22][23][24] . For example, an inflammatory CAF sub-group has been described in a number of different tumours including pancreatic cancer 25 .…”

“…CAFs are an attractive therapeutic target, but despite promising data from pre-clinical models, the results of clinical trials targeting CAFs have been mixed 33,34 . Characterisation of CAF phenotypes and their impact on outcomes has gained increasing interest in recent years, and there are now multiple studies profiling CAF heterogeneity at single-cell resolution [22][23][24] . To date, there have been many individual reports describing the prognostic effect of single CAF markers in NSCLC.…”

Remodeling of Stromal Cells and Immune Landscape in Microenvironment During Tumor Progression

“…Myofibroblasts exclusively originate from tumor tissues and are mostly enriched in collagens and low elastins ( 24 ). Myofibroblast subtypes have different activation mechanisms such as TGF-β1, IL-11 stimulation, and IL-1β, IL-6 treatment that induces upregulation of inflammatory fibroblast marker genes (iCAFs) ( 25 ). CAF specific markers of identification include α smooth muscle actin (αSMA) (also known as ACTA2), desmin, S100A4, Fibroblast activated protein (FAP), express pro-inflammatory cytokine arrays like IL-1β, IL-6, IL-8, TGF-β, CXCL12, and collagen ( 18 ).…”