CAF-macrophage crosstalk in tumour microenvironments governs the response to immune checkpoint blockade in gastric cancer peritoneal metastases

Abstract

Background: Peritoneal metastasis is the most common metastasis pattern of gastric cancer. Patients with gastric cancer peritoneal metastasis (GCPM) have a poor prognosis and respond poorly to conventional treatments. Recently, immune checkpoint blockade (ICB) has demonstrated favourable efficacy in the treatment of GCPM. Stratification of best responders and elucidation of resistance mechanisms of ICB therapies are highly important and remain major clinical challenges.

Design: We performed a phase II trial involving patients with GCPM treated with ICB (sintilimab) combined with chemotherapy. The samples of primary tumours, GCPMs and peripheral blood from patients were collected for single-cell sequencing to comprehensively interpret the tumour microenvironment of GCPM and its impacts on immunotherapy efficacy.

Results: The GCPM ecosystem coordinates a unique immunosuppressive pattern distinct from that of primary GC, which is dominated by a stroma-myeloid niche composed of SPP1+tumour-associated macrophages (TAMs) and Thrombospondin 2 (THBS2)+matrix cancer-associated fibroblasts (mCAFs). Consequently, this stroma-myeloid crosstalk is the major mediator of ICB resistance in patients with GCPM. Mechanistically, the accumulated THBS2+mCAFs facilitate the recruitment of peritoneum-specific tissue-resident macrophages and their transformation into SPP1+TAMs via the complement C3 and its receptor C3a receptor 1 (C3AR1), thereby forming a protumoral stroma-myeloid niche. Blocking the C3-C3AR1 axis disrupts the stroma-myeloid crosstalk and thereby significantly improves the benefits of ICB in in vivo models.

Conclusion: Our findings provide a new molecular portrait of cell compositions associated with ICB resistance in patients with GCPM and aid in the prioritisation of therapeutic candidates to potentiate immunotherapy.

Keywords: complement; drug resistance; gastric cancer; immunotherapy; macrophages.

Figure 1. Study design and single-cell transcriptional atlas of peritoneal metastases of gastric cancer (GC). (A) Overview of the study design. (B) Uniform Manifold Approximation and Projection (UMAP) embedding of single-cell RNA sequencing (scRNA-seq) data for all 410 612 cells. (C) Heatmap showing the RNA expression of marker genes used to define the 15 major cell types. (D) UMAP embedding and cell abundance of scRNA-seq data for gastric normal (GN) tissues, primary GC tissues, gastric cancer peritoneal metastases (GCPM) tissues, peritoneal (PE) tissues and peripheral blood mononuclear cell (PBMC). (E) Heatmap displaying the distribution of 16 cell types across different tissue types (GN, GC, GCPM, PE and PBMC), as estimated by Ro/e. (F) Box plot comparing the abundances of plasma cells, mast cells, macrophages and endothelial cells across different tissue types, including GN (n=14), GC (n=31), GCPM (n=24), PE (n=1) and PBMC (n=4). The statistical significance was tested via Student’s t-test. (G) Dot plot showing the results of Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the differentially expressed genes between the GCPM and GC groups. CAF, cancer-associated fibroblasts; DC, dendritic cell; EC, endothelial cell; ICB, immune checkpoint blockade; NK, natural killer cell; NKT, natural killer T cell; PC, pericyte.

Figure 2. Lymphocyte landscape. (A) Uniform Manifold Approximation and Projection (UMAP) embedding of 10 CD4+ T-cell subclusters. (B) UMAP embedding of 14 cytotoxic lymphocyte (CTL) subclusters (9 CD8+ T-cell subclusters, 3 natural killer (NK) cell subclusters, 1 natural killer T (NKT) cell subcluster and 1 Tgd (γδT) cell subcluster). (C) UMAP embedding of nine B/plasma cell subclusters. (D) Heatmap displaying the distribution of lymphocytes across different immunotherapy responses, including partial response (PR), stable disease (SD) and progressive disease (PD), as estimated by Ro/e. (E) Ridgeline plot displaying the key markers used to define KLRD1+NKT cells, including T-cell markers (CD3D, CD8A and CD8B) and NK markers (GNLY, KLRD1 and FCGR3A). (F) Multiplex immunofluorescence of DAPI, CD3, CD8 and CD56 in the gastric cancer peritoneal metastases (GCPM). The upper and the lower panels are images of two representative patients. (G) Stack plot displaying the abundance of CTL cells in response to immunotherapy (PR vs PD). (H) Violin plot comparing the expression of killer-like markers (KLRD1 and FGFBP2) and killer receptors (FCGR3A) in KLRD1+NKT cells in response to immunotherapy (PR vs PD). The statistical significance was tested via Wilcoxon test. (I) Overlap of T-cell receptors of KLRD1+NKT cells across matched gastric normal (GN) tissues, primary gastric cancer (GC) tissues, GCPM tissues and peripheral blood mononuclear cell (PBMC) samples from patients treated with immunotherapy (n=4). (J) UMAP plot comparing the clonal expansion of CTLs in the GCPM and PBMC samples between PR patients and patients with PD. (K) Stack plot comparing the clonal expansion of CTLs in the GCPM and PBMC samples between PR patients and patients with PD. ICB, immune checkpoint blockade.

Figure 3. Landscape and cell trajectory of myeloid cells. (A) Uniform Manifold Approximation and Projection (UMAP) embedding of 13 myeloid cell subclusters (4 dendritic cell (DC) subclusters, 1 neutrophil subcluster, 3 monocyte subclusters and 5 macrophage subclusters). (B) Dot plot showing the RNA expression of marker genes used to define the myeloid cell types. (C) UMAP embedding and cell abundance of myeloid cells for gastric normal (GN) tissues, primary gastric cancer (GC) tissues and gastric cancer peritoneal metastases (GCPM) tissues and peritoneum (PE) tissues. (D) Pseudotime and (E) cell trajectories of monocytes/macrophages inferred by Monocle3. Two independent cell trajectories were identified: branch 1 represented monocyte-derived macrophages, and branch 2 represented macrophages derived from tissue-resident macrophages (TRMs). (F) Cell trajectories of monocytes/macrophages inferred by Monocle3 in five tissue types. (G) Pseudotime and (H) cell trajectories of TRM-derived macrophages inferred by Monocle2. (I) Cell trajectories of TRM-derived macrophages inferred by Monocle2 in the GN, GC, PE and GCPM groups. (J) Differential transcription factor (TF) activity between F13A1+TRM cells, CXCL9+macrophages and SPP1+tumour-associated macrophages (TAMs). (K) Dynamic changes in TF activity over time. (L) Dynamic change in corresponding TF expression over time.

Figure 4. Relationship of tumour-associated macrophages with immunotherapy efficiency. (A) Box plot comparing the abundance of myeloid cell subclusters across gastric normal (GN) tissues (n=14), primary gastric cancer (GC) tissues (n=31) and gastric cancer peritoneal metastases (GCPM) tissues (n=24). The statistical significance was tested via Student’s t-test. (B) Violin plot comparing the expression levels of SPP1, CD81, TGFB1, MSR1, TREM2 and HAVCR2 in the GCPM and GC samples. The statistical significance was tested via Wilcoxon test. (C) Multiplex immunofluorescence of DAPI, CD68 and TREM2 in GC and GCPM samples. The left and right panels are images of two representative patients. (D) Heatmap displaying the distribution of myeloid cells across different immunotherapy responses, including partial response (PR) (n=5), stable disease (SD) (n=4) and progressive disease (PD) (n=5), as estimated by Ro/e. (E) Uniform Manifold Approximation and Projection (UMAP) graph displaying the distribution of myeloid cells across different immunotherapy responses (PR vs PD). (F) Volcano plot showing the differentially expressed genes (DEGs) of monocytes/macrophages according to immunotherapy response (PR vs PD) in GCPMs. (G) Violin plot showing the expression of SPP1, GPNMB, CD9 and TREM2 in monocytes/macrophages according to immunotherapy response (PR vs PD) in GCPM. The statistical significance was tested via Wilcoxon test. (H) UMAP plot showing the expression of SPP1 in monocytes/macrophages according to immunotherapy response (PR vs PD) in GCPM. (I) Dot plot showing the cell-cell interactions of macrophages with other cell types, comparing PRs and PDs. (J) Violin plot showing the expression of SPP1 and CD44 in all cell types according to immunotherapy response (PR vs PD) in GCPM. The statistical significance was tested via Wilcoxon test. (K) Multiplex immunofluorescence of DAPI, SPP1 and CD8 in GCPM. Top panel: low SPP1 level; bottom panel: high SPP1 level. (L) Multiplex immunofluorescence of DAPI, SPP1 and CD56 in GCPM. Top panel: low SPP1 level; bottom panel: high SPP1 level. Significance: ns, not significant; *p<0.05; **p<0.01; ***p<0.001. ICB, immune checkpoint blockade.

Figure 5. Landscape of stromal cells and relationship of stromal cells with immunotherapy efficiency. (A) Uniform Manifold Approximation and Projection (UMAP) embedding of 10 stromal cell subclusters, including 4 endothelial cell (EC) subclusters, 3 cancer-associated fibroblast (CAF) subclusters and 2 pericyte (PC) subclusters. (B) Dot plot showing the RNA expression of marker genes used to define the stromal cell types. (C) Violin plot showing the expression of CXCL14, CCL11, ACTA2 (encoding αSMA), COL1A1, FAP, THBS2 and IGF1 according to stromal cell type. (D) UMAP embedding of single-cell RNA sequencing (scRNA-seq) data for gastric normal (GN) tissues, primary gastric cancer (GC) tissues, gastric cancer peritoneal metastases (GCPM) tissues and peritoneum (PE) tissues. (E) Graph representation of Nhoods identified by Milo in stromal cells. Nodes are Nhoods, coloured by their log2 fold change (FC) between GCPMs (n=24) and GCs (n=31). Non-differentially abundant neighbourhoods (false discovery rate ≥0.1) are coloured white, and sizes correspond to the number of cells in a neighbourhood. The graph edges depict the number of cells shared between adjacent Nhoods. (F) Beeswarm plot showing the distribution of adjusted log2 FC in abundance between GCPMs (n=24) and GCs (n=31) in Nhoods according to stromal cell type. (G) Box plot comparing the abundance of stomal cell subclusters across GNs (n=14), GCs (n=31) and GCPMs (n=24). The statistical significance was tested via the Wilcoxon test. (H) Multiplex immunofluorescence of DAPI, FAP, αSMA and THBS2 in the GC and GCPM samples. The left and right panels are images of two representative patients. (I) Box plot comparing the RNA expression of THBS2, FAP and IGF1 in 12 pairs of matched GN, GC and GCPM samples. The statistical significance was tested via paired Wilcoxon test. (J) Overall survival (OS) and progression-free survival (PFS) in the TCGA-STAD cohort stratified by the number of THBS2+CAFs. The numbers of THBS2+CAFs were inferred via the single-sample gene set variation analysis (ssGSVA) method. The statistical significance was tested via log-rank test. (K) Heatmap displaying the distribution of stromal cells across different immunotherapy responses, including partial response (PR) (n=5), stable disease (SD) (n=4) and progressive disease (PD) (n=5), as estimated by Ro/e. (L) UMAP graph displaying the distribution of stromal cells across different immunotherapy response groups (PR vs PD). (M) Volcano plot showing the differentially expressed genes (DEGs) of CAFs according to immunotherapy response (PR vs PD) in GCPM. (N) Violin plot showing the expression of THBS2, LUM, DCN and IGF1 in CAFs according to immunotherapy response (PR vs PD) in GCPM. The statistical significance was tested via Wilcoxon test. (O) UMAP plot showing the expression of THBS2 in CAFs according to immunotherapy response (PR vs PD) in GCPM. αSMA, alpha smooth muscle actin; DAPI, 4',6-diamidino-2-phenylindole; FAP, fibroblast activation protein alpha; ICB, immune checkpoint blockade.

Figure 6. Crosstalk between fibroblasts and macrophages in peritoneal metastases of gastric cancer. (A) Heatmap displaying the correlations across all cell types in the single-cell data. Gastric normal (GN) tissues (n=14), primary gastric cancer (GC) tissues (n=31) and gastric cancer peritoneal metastases (GCPM) tissues (n=24) were included in the analyses. (B) Correlations between cancer-associated fibroblasts (CAFs) and macrophages in the single-cell data. (C) Dot plot showing the cell-cell interactions of all cell types on macrophages, comparing the GCPM and GC samples. (D) Uniform Manifold Approximation and Projection (UMAP) plot showing the expression of C3 and C3AR1 in all cell types. (E) Violin plot showing the expression of C3 and C3AR1 in all cell types. (F) Heatmap displaying the correlations between CAFs and macrophages in the single-cell data. (G) Rideline plot showing the expression of C3 and C3AR1 in CAFs and macrophages. (H) Correlations between THBS2+matrix CAFs (mCAFs) and macrophage subclusters in the single-cell data. (I) Dot plot showing the function of the C3-C3AR1 axis in THBS2+mCAFs in macrophage subclusters. (J) Box plot comparing the levels of SPP1+tumour-associated macrophages (TAMs) and THBS2+mCAFs in 12 pairs of matched GN, GC and GCPM samples. The statistical significance was tested via paired Student’s t-test. (K) Correlations between SPP1+TAMs and THBS2+mCAFs in 12 pairs of matched GN, GC and GCPM RNA sequencing (RNA-seq) data. (L) Correlations between the expression of C3 and C3AR1 in 12 pairs of matched GN, GC and GCPM RNA-seq data. (M) Multiplex immunofluorescence of DAPI, C3 and C3AR1 to show the interaction of THBS2+mCAFs and SPP1+TAMs in GCPM. The left and right panels are images of two representative patients. Significance of correlation: *p<0.05; **p<0.01; ***p<0.001. DC, dendritic cell; EC, endothelial cell; NK, natural killer cell; NKT, natural killer T cell; PC, pericyte.

Figure 7. The crosstalk of fibroblasts and macrophages dominates the immunotherapy response in peritoneal metastases of gastric cancer. (A) Heatmap comparing the expression of cytokines in 12 pairs of matched gastric normal (GN), primary gastric cancer (GC) and gastric cancer peritoneal metastases (GCPM) samples. (B) Violin plot comparing the level of C3 in cancer-associated fibroblasts (CAFs) and the level of C3AR1 in macrophages in the single-cell RNA sequencing (scRNA-seq) data of GN, GC and GCPM samples. The statistical significance was tested via Wilcoxon test. (C) Box plot comparing the levels of C3 and C3AR1 in 12 pairs of matched GN, GC and GCPM samples. The statistical significance was tested via paired Wilcoxon test. (D) Box plot comparing the levels of C3 in the peritoneal flushing fluid of patients without ascites, the non-malignant ascites, the malignant ascites of patients with gastric cancer and the malignant ascites of patients with other cancers. The statistical significance was tested via Wilcoxon test. (E) Migration assay showing the capacity of RAW264.7 macrophages to migrate in response to different concentrations of C3 (stratified C3 concentrations: 0 ng/mL, 5 ng/mL, 10 ng/mL and 20 ng/mL). (F) Bar plot showing the number of migrated RAW264.7 macrophages as a function of the concentration of C3 (mean±SE). The statistical significance was tested via Student’s t-test. (G) Migration assay showing the migratory capacity of THP-1-derived macrophages at different concentrations of C3 (stratified C3 concentrations: 0 ng/mL, 5 ng/mL, 10 ng/mL and 20 ng/mL). (H) Bar plot showing the number of migrated THP-1-derived macrophages according to the concentration of C3 (mean±SE). The statistical significance was tested via Student’s t-test. (I) Multiplex immunofluorescence of DAPI, THBS2 and F4/80 to show the relationships between THBS2+matrix CAFs (mCAFs) and macrophages in GCPM. (J) Multiplex immunofluorescence of DAPI, THBS2 and CD8 to show the relationships between THBS2+mCAFs and CD8+ T cells in GCPM. (K) Dot plot showing the cell-cell interactions of macrophages with other cell types, comparing immune checkpoint blockade (ICB)-responsive and non-responsive samples. (L) Violin plot comparing the level of C3 in CAFs and the level of C3AR1 in macrophages in the scRNA-seq data of ICB-responsive and non-responsive samples. The statistical significance was tested via Wilcoxon test. (M–O) Subcutaneous tumour models of EO771 cells were constructed to evaluate the treatment efficacy of SB290157 (an antagonist of C3aR) and its combination with anti-PD1 antibody (α-PD1). Five groups were compared: dimethylsulfoxide (DMSO) (n=6), C3aR antagonist (n=6), IgG (n=6), α-PD1 (n=6), C3AR antagonist plus α-PD1 (n=6). (M) Growth curves of subcutaneous tumours derived from EO771 cells in different treatment groups. (N) Terminal tumour weights of subcutaneous tumours derived from EO771 cells in different treatment groups. The values are expressed as mean±SE. The statistical significance was tested via Student’s t-test. (O) Multiplex immunofluorescence assays were used to explore the abundance of macrophages (F4/80) and CD8+ T cells (CD8) in subcutaneous tumours derived from EO771 cells in different treatment groups. (P) Proposed working model based on this study. The model shows that THBS2+mCAFs can release complement C3 to recruit tissue-resident macrophage-derived (TRM-derived) SPP1+tumour-associated macrophages (TAMs), retraining the antitumour immune response via the C3-C3AR1 axis. In ICB-resistant tumours, the extensive accumulation of THBS2+mCAFs and TRM-derived SPP1+TAMs facilitates immunotherapy resistance, and the blockade of the C3-C3AR1 axis might disrupt this process and enhance immunotherapy efficiency.