Distinct fibroblast subpopulations associated with bone, brain or intrapulmonary metastasis in advanced non-small-cell lung cancer

Abstract

Background: Bone or brain metastases may develop in 20-40% of individuals with late-stage non-small-cell lung cancer (NSCLC), resulting in a median overall survival of only 4-6 months. However, the primary lung cancer tissue's distinctions between bone, brain and intrapulmonary metastases of NSCLC at the single-cell level have not been underexplored.

Methods: We conducted a comprehensive analysis of 14 tissue biopsy samples obtained from treatment-naïve advanced NSCLC patients with bone (n = 4), brain (n = 6) or intrapulmonary (n = 4) metastasis using single-cell sequencing originating from the lungs. Following quality control and the removal of doublets, a total of 80 084 cells were successfully captured.

Results: The most significant inter-group differences were observed in the fraction and function of fibroblasts. We identified three distinct cancer-associated fibroblast (CAF) subpopulations: myofibroblastic CAF (myCAF), inflammatory CAF (iCAF) and antigen-presenting CAF (apCAF). Notably, apCAF was prevalent in NSCLC with bone metastasis, while iCAF dominated in NSCLC with brain metastasis. Intercellular signalling network analysis revealed that apCAF may play a role in bone metastasis by activating signalling pathways associated with cancer stemness, such as SPP1-CD44 and SPP1-PTGER4. Conversely, iCAF was found to promote brain metastasis by activating invasion and metastasis-related molecules, such as MET hepatocyte growth factor. Furthermore, the interaction between CAFs and tumour cells influenced T-cell exhaustion and signalling pathways within the tumour microenvironment.

Conclusions: This study unveils the direct interplay between tumour cells and CAFs in NSCLC with bone or brain metastasis and identifies potential therapeutic targets for inhibiting metastasis by disrupting these critical cell-cell interactions.

Keywords: CAF; fibroblast; non-small-cell lung cancer; single-cell RNA-seq; tumour microenvironment.

FIGURE 1

Single‐cell transcriptomic landscape of advanced NSCLC with different metastatic sites originating from the lungs. (A) Schematic graph showing the study design and major cell type annotation. (B) Violin plot showing the expression of representative genes in major cell types. (C) The fraction of major cell types in advanced NSCLC with bone, brain or intrapulmonary metastases. (D) The number of differentially expressed genes (DEGs) among major cell types in advanced NSCLC. (Bone: bone metastasis, Brain: brain metastasis, Lung: intrapulmonary metastasis).

FIGURE 2

Fibroblast landscape in NSCLC with different metastatic sites originating from the lungs. (A) UMAP plot showing fibroblast subpopulations. (B) Heatmap showing the five most variable genes across each fibroblast subset. (C) DEGs of fibroblasts at different metastatic sites of NSCLC originating from the lungs. (D) Kaplan–Meier curve of overall survival of GAL and SPP1 in NSCLC. (E) Immunohistochemistry analysis of GAL expression in primary NSCLC with intrapulmonary, brain or bone metastases. (F) Immunohistochemistry analysis of SPP1 expression in primary NSCLC with intrapulmonary, brain or bone metastases. (Bone M: bone metastasis, Brain M: brain metastasis, Lung M: intrapulmonary metastasis).

FIGURE 3

Normal and tumour epithelial cell landscape in NSCLC with different metastatic sites originating from the lungs. (A) Heatmap showing large‐scale CNVs of epithelial cells. Red indicates genomic amplifications and blue reveals genomic deletions. The expression values for B cells and myeloid cells are plotted in the top heatmap, and the epithelial cells are plotted in the bottom heatmap. Genes are ordered from left to right across the chromosomes. (B) UMAP plot of epithelial cells. Data are colour coded by subtypes (left), metastatic sites (upper‐right), patient origins (middle‐right) and tissue origins (lower‐right). (C) Association between the gene modules of epithelial cells and NSCLC with different metastatic sites (top), and association between the gene modules of epithelial cells and subtypes of epithelial cells (bottom). (D) UMAP plot and DEGs of the green module (upper), grey60 module (middle) and light cyan module (lower). Two‐sided unpaired Wilcoxon test was performed to compare between the groups. (E) The GO analysis of the green module, light cyan module and salmon module. (F) Heatmap of cell ratio correlations between CAFs subsets and epithelial subsets. (G) Circos plot showing intercellular interactions between CAFs subsets and epithelial cell subsets in NSCLC with bone, brain or intrapulmonary metastasis. (H) Dot plot showing the mean expression level and percentage of selected interaction pairs involved in bone, brain or intrapulmonary metastasis of NSCLC between epithelial cells and CAFs subtypes. (Bone: bone metastasis, Brain: brain metastasis, Lung: intrapulmonary metastasis).

FIGURE 4

IHC analysis of communication between cancer epithelial cells and fibroblast. (A) IHC detection of continuous tissue sections surrounding MET+ cancer epithelial cells and HGF+ fibroblasts. (B) IHC detection of continuous tissue sections surrounding SPP1+ fibroblast and CD44+ cancer epithelial cells.

FIGURE 5

Tumour microenvironment landscape in NSCLC with different metastatic sites originating from the lungs. (A) Heatmap of proportional correlation among subsets of CAFs and epithelial and T cells. (B) Ligand–receptor (L‐R) interaction showing epithelial subsets and T cell subsets involved in bone, brain or intrapulmonary metastasis of NSCLC. (C) Circos plots showing details of L‐R pairs among different cells in NSCLC with bone, brain or intrapulmonary metastasis. (Bone: bone metastasis, Brain: brain metastasis, Lung: intrapulmonary metastasis).