Takatsugu Ishimoto

Cancer‐associated fibroblasts (CAFs) are major stromal components of the tumor microenvironment (TME) and play diverse roles in gastrointestinal (GI) cancer progression, immunity, and therapeutic resistance. However, the heterogeneity, tissue specificity, and clinical relevance of CAFs across GI cancers remain incompletely defined. We integrated 14 independent single‐cell RNA‐sequencing datasets comprising 239 GI adenocarcinoma samples to define the subtypes of CAFs. Machine learning–based deconvolution was applied to 18 bulk transcriptomic cohorts (3396 patients) to estimate CAF subtype abundance and assess prognostic associations by meta‐analysis. Functional states, differentiation trajectories, cell–cell communication, and spatial organization were analyzed using gene‐set enrichment, transcription factor activity inference, pseudotime modeling, ligand–receptor analysis, and spatial transcriptomics. We identified eight CAF subtypes with distinct transcriptional programs and organ‐specific distributions. Two myofibroblastic CAF subtypes (myCAF1 and myCAF2) were consistently associated with poor prognosis, characterized by extracellular matrix remodeling, TGF‐β signaling, hypoxia adaptation, and close crosstalk with immunosuppressive macrophages, as well as tumor cells displaying epithelial‐mesenchymal transition and hypoxia signatures. In contrast, inflammatory CAF (iCAF) 1 was associated with a favorable prognosis and increased infiltration of antitumor immune cells. Spatial transcriptomic analyses further revealed that distinct CAF subtypes preferentially occupied discrete spatial domains within the GI TME. Our study demonstrated that the functional state and spatial context of CAFs jointly affect tumor progression and outcomes in patients with GI cancers. myCAF‐driven stromal niches promote immune suppression and poor prognosis, whereas a balanced inflammatory CAF program may support antitumor immunity.

Background A fibrotic tumor microenvironment (TME) promotes tumor progression by the interactions between tumor cells and cancer-associated fibroblasts (CAFs) in the extracellular matrix (ECM), which enhance tumor cell survival and growth, and by suppressing antitumor immunity. However, how characteristic gene expression and amplification in cancer cells drive the formation of a fibrotic TME in patients with gastric cancer (GC) is unclear. Methods We performed genomic and transcriptomic analyses via datasets from The Cancer Genome Atlas and the Asian Cancer Research Group to identify amplified and overexpressed genes associated with the presence of a fibrotic TME in tumors. Syngeneic mouse models of GC and multiplexed immunohistochemistry (IHC) were used to validate the findings of the transcriptomic analysis and investigate the underlying mechanisms. Results The Frizzled class receptor 1 (FZD1) gene was frequently amplified and highly expressed in GC patients with fibrotic tumors, and FZD1 expression was related to a poor prognosis. The overexpression of Fzd1 in murine GC tumor cells was significantly associated with enhanced tumor fibrosis and growth and reduced infiltration of CD3⁺ lymphocytes and CD8⁺ T cells into tumors. SLIT2 secretion was increased in Fzd1-overexpressing tumor cells via the canonical WNT–β-catenin pathway, and SLIT2 activated CAFs to produce more ECM through the SLIT2‒ROBO1 axis. Conclusions This study highlights the potential of FZD1 as a biomarker for predicting fibrotic status in patients with GC and the SLIT2‒ROBO1 axis as a therapeutic target to reverse a fibrotic and immunosuppressive TME.

ScRNA-seq enables identification of distinct gastric cancer fibroblast subtypes and INHBA–FAP axis as a CAF regulator. A, Bubble plots demonstrating stage-dependent increases in the proportion of fibroblast cells with STF3 being the dominant subcluster. The size of the circle represents the proportion of cells expressing subcluster-specific genes. B, Bubble plots showing fibroblast subclusters (STF1–3) expressing distinct CAF canonical markers (FAP, CSPG4, ACTA2, and TAGLN). The size of the circle represents the proportion of cells expressing different genes. C, Violin plot showing the expression of INHBA in STF2 and STF3 fibroblast clusters with negligible expression in the STF1 fibroblast cluster. D, Fibroblast ROIs captured by DSP analysis based on immunofluorescence staining for Pan-CK (epithelial, green), CD138 (plasma, pink), SMA (fibroblast, cyan), and DAPI (blue). The circular ROI is 300 μm in diameter. E, Bee swarm plot showing differential expression of FAP in fibroblast ROIs of normal and tumor samples by DSP (n = 13). F, Bee swarm plot showing differential expression of INHBA in fibroblast ROIs of normal and tumor samples by DSP (n = 13). G, Pearson correlation graph demonstrating strong positive correlations between INHBA and FAP gene expression in fibroblast ROIs using DSP. H, Bar graph showing significant expression of FAP and INHBA genes in flow-sorted tumor fibroblasts compared with matched normal fibroblasts (n = 10 each). I, Bar graph showing significant reduction in FAP gene expression after siRNA-mediated INHBA knockdown in tumor fibroblast lines. Two independent siRNAs were used. J, Bar graph showing significant increases in FAP gene expression in two normal fibroblast lines after treatment with recombinant INHBA (rINHBA) for 48 and 96 hours, respectively. K, Bubble plot depicting stage-dependent increases of FAP+ and INHBA+ cells in fibroblast cluster STF3 (P = 0.041). The circle sizes represent the relative proportion of cells expressing these genes. P values were computed using Kendall's τ method. L, Kaplan–Meier survival curves of TCGA-STAD data showing significant differences in overall survival between INHBA-high and INHBA-low samples. P values were computed using log-rank tests.

Differential TME analysis between histologic subtypes identifies increased plasma cells in diffuse-type tumors. A, Density plot of UMAP representation comparing diffuse and intestinal gastric cancer samples after random downsampling to approximately 25,000 cells each. Each dot represents a single cell. Dashed lines highlight a higher proportion of plasma cells in diffuse gastric cancer. B, IHC staining of IRF4 expression in diffuse (n = 5) and intestinal (n = 12) gastric cancer samples (scale bar, 400 μm). Bar graph showing significantly higher average IRF4 IHC score in diffuse compared with intestinal gastric cancer. C, Plasma cell proportions deconvoluted by CIBERSORTX in diffuse and intestinal gastric cancer in the TCGA data set (diffuse n = 63; intestinal n = 167). D, Bar graph showing enrichment of plasma cell proportions in PL4 and PL5 plasma subclusters in diffuse versus intestinal gastric cancer single-cell samples. E, Pearson correlation plot showing significant positive correlation of plasma cell proportion to average KLF2 expression in the epithelial metacluster. F, Bee swarm plot showing increased KLF2 expression in diffuse versus intestinal gastric cancer samples in the bulk RNA-seq TCGA-STAD data set. G, Bar graph showing increased KLF2 expression in Pan-CK+ epithelial morphologic regions of diffuse versus intestinal gastric cancer by DSP (n = 10). H, DSP analysis depicting epithelial ROIs proximal (top) and distal to plasma cells (bottom). Analysis is based on immunofluorescence staining for Pan-CK (epithelial, green), CD138 (plasma, pink), smooth muscle actin (SMA; fibroblast, cyan), and DAPI (blue). Each circular ROI is 300 μm in diameter. I, Bar graph showing expression of KLF2, IRF4, and SLAMF7 genes in GSU humanized and nonhumanized mice against SNU1750 humanized and nonhumanized mice. *, P < 0.05; **, P < 0.01. J, Western blot (top) showing stable knockdown of KLF2 in gastric cancer cell line GSU (shKLF2) compared with shNT (nontargeting) control. Loss of KLF2 in GSU significantly reduces migration of plasma cells derived from peripheral blood mononuclear cells (PBMC) and multiple myeloma cell line KMS-11 (N = 17; bottom). K, Upregulation of immunoglobulin genes in diffuse versus intestinal epithelial metacluster (top). Tree map shows the overlap of upregulated pathways in epithelial metacluster versus subclusters, with the EpiC cluster showing the greatest overlap (bottom).

ScRNA-seq of gastric tumor and normal samples defines 34 cell states including rare cell populations. A, Schematic representation of experimental design and techniques used in this study. Thirty-one unique patients with gastric cancer undergoing surgical resection or endoscopy had tumor samples (n = 31) and adjacent normal samples (n = 11) harvested for analysis. Tumors ranged from stage I to IV and included samples of both primary tumors, distant (peritoneal) metastases, and matched normal gastric tissues. Twenty-nine tumors had scRNA-seq performed using the 10× platform (along with 11 adjacent normal tissues). Four patients had PDOs generated from their tumors (4 tumors + 4 adjacent normal), which were also sequenced by 10× scRNA-seq. A subset of 13 samples also had DSP performed using the NanoString GeoMx platform (10 tumor + 3 normal). In total, more than 200,000 cells were sequenced in this study. B, Uniform Manifold Approximation and Projection (UMAP) of 152,423 cells representing 34 unique cell states color-coded by their corresponding cell lineage or subtype. Each dot in the UMAP represents a single cell. C, Cell-lineage compositions of gastric cancer and normal samples inferred by scRNA-seq data. Middle (bubble plot), cell subclusters (rows) by tumor versus normal, stage, and gastric cancer histologic subtype (diffuse vs. intestinal). The size of the circle represents the cell proportion of each specific cell lineage/type. The circles are color-coded by defined cell lineages/types as shown in B. The stacked bar graph on the top shows the number of cells in each meta-cluster for each category. The histogram on the right shows the absolute cell numbers in each subcluster. D, Cluster–cluster heat map of gene-expression data of all 34 cell states across all samples using Pearson correlation matrix. Darker colors correspond to higher correlation. E, Pseudotime analysis of plasma metacluster generated using Monocle. The trajectory was rooted against the plasmablasts. Pseudotime analysis demonstrates different stages of plasma cell differentiation and maturation including plasmablasts, short-lived plasma cells, and long-lived plasma cells. F, Expression of PLVAP and RGS5 in endothelial (STE2) and fibroblast (STF2 and STF4) clusters. Doublets were identified and filtered out using DoubletFinder. PLVAP+RGS5− cells are predominantly present in the endothelial cluster (STE2). PLVAP−RGS5+ cells are predominant in the fibroblast cluster (STF2). The STF4 cluster shows cells expressing PLVAP+RGS5+, suggestive of a rare mixed-lineage population.

ScRNA-seq deconvolutes gastric tumor programs associated with distinct cell states. A, Density plot of UMAP representation comparing normal and gastric tumor samples after random downsampling to approximately 30,000 cells each to allow statistical equivalence. Each dot represents a single cell. Dashed lines highlight higher proportions of epithelial cells in normal samples and myeloid cells in tumor samples. B, Split violin plot of EMT oncogenic gene signature score in normal and tumor cells, showing a significantly higher score in tumor cells. C, Bubble plot depicting the expression of gastric cancer oncogenes in tumor epithelial cell clusters. The size of the circle represents the percentage of cells expressing the gene in that specific epithelial cell cluster, whereas the color represents the average expression of the gene. D, Box plot depicting CNV scores for epithelial cells (green) and macrophage cells (blue) in normal and tumor samples. CNV scores were computed using InferCNV. P values were computed using Wilcoxon rank-sum test. E, Bar graph depicting differences in transcriptomic profiles between tumor and normal tissue by number of upregulated and downregulated genes in epithelial cell clusters. F, Heat map of LIPF and PGA3 gene expression (classic chief cell marker genes) in tumor versus normal samples. Darker color signifies higher expression. G, Gene expression of LIPF by DSP analysis, in tumor epithelial cells (Pan-CK+) compared with normal samples (n = 13). H, Bubble plot depicting sublineage-specific expression of CAF marker genes FAP, CSPG4, PDGFA, ASPN, S100A4, COL8A1, THBS2, and CTHRC1 in fibroblast clusters. STF1 and STF3 are LUM-associated fibroblasts, whereas STF2 comprises proangiogenic pericytes. The size of the circle represents the percentage of cells expressing the gene in that specific fibroblast cell cluster, whereas the color represents the average expression of the gene. I, Bubble plot depicting significant log fold differences in expression of genes between tumor and normal by metacluster mapped against the bulk RNA-seq data (five genes per metacluster are shown). The size of the circle represents the log fold change in the expression of specific genes.

Comparative analysis of primary and organoid samples. A, UMAP representation of approximately 200,000 cells (∼48,000 cells from tumor PDOs with matched normal PDOs, combined with primary samples; ∼152,000 cells) recapitulating the major five metaclusters color-coded by their cell types. Each dot in the UMAP represents a single cell. B, Violin plot showing the expression of gastric cancer gene module scores in tumor PDOs compared with matched normal samples. C, Trajectory plot analysis of epithelial cells from tumor and normal PDOs demonstrating the expression of cellular differentiation gene programs in tumor PDOs depicted by long multiple branches. D, Density plot of UMAP representation comparing PDO and primary gastric samples demonstrating enrichment of lymphoid and plasma metaclusters in primary samples compared with PDOs. E, Graph showing the number of upregulated and downregulated genes in PDOs versus primary samples in the five metaclusters. The plasma meta-cluster shows the highest number of differentially expressed genes as compared with other metaclusters. F, Volcano plot of upregulated and downregulated genes in the plasma metacluster between PDOs and primary samples, showing significant downregulation of antibody-mediated complement factor genes in PDOs. x-axis shows the −log10 adjusted P value and y-axis log2 fold change in gene expression. G, Top common upregulated pathways in PDOs versus primary samples across all metaclusters.