Abstract
Introduction:
Cancer-associated fibroblasts (CAFs) are integral components of the tumor microenvironment that modulate the response to immune checkpoint inhibitors, particularly in breast cancer. However, the specific roles of CAF subtypes in regulating the efficacy of anti-PD-1 therapy remain poorly elucidated.
Methods:
In this study, we reanalyzed single-cell RNA sequencing data from breast cancer patients treated with anti-PD-1 inhibitors to identify CAF subtypes and characterize their molecular signatures. Identified subtypes were further validated using spatial transcriptomics mapping to assess their anatomical niches.
Results:
Four distinct CAF subtypes were identified: vascular CAFs (vCAF), myofibroblastic CAFs (myCAF), inflammatory CAFs (iCAF), and antigen-presenting CAF-like (apCAF-like) cells. MyCAFs were localized to fibrotic stromal regions, while iCAFs were found within immune-rich, inflamed areas. In responders, stromal remodeling occurs, characterized by the functional re-education of iCAFs—transitioning to a pro-inflammatory CXCL9-CXCR3 axis—and the concurrent disarmament of vCAF and myCAF populations. Conversely, resistance in non-responders is linked to stromal fortification, driven by the apCAF-like-derived THBS2-CD47 axis and the pathological intensification of the vCAF-derived CXCL12-CXCR4 axis, leading to dysfunctional lymphoid sequestration.
Discussion:
Collectively, these findings highlight the critical role of CAF heterogeneity and spatial organization in modulating the response to anti-PD-1 therapy. Targeting subtype-specific stromal modules may represent a promising therapeutic strategy to enhance the efficacy of immunotherapy in breast cancer.
1 Introduction
Breast cancer remains one of the leading causes of cancer-related mortality worldwide, and resistance to systemic therapies—including immunotherapy—continues to pose major clinical challenges (1, 2). The tumor microenvironment (TME) plays a crucial role in shaping therapeutic outcomes, with stromal components increasingly recognized as key modulators of treatment response (3). Among these, cancer-associated fibroblasts (CAFs) represent the most abundant stromal cell population and exert profound influence on tumor progression, immune regulation, and drug resistance (4).
Unlike normal fibroblasts, which maintain tissue homeostasis and support wound healing, CAFs adopt tumor-promoting phenotypes that remodel the extracellular matrix (ECM), secrete immunomodulatory cytokines, and orchestrate a pro-tumorigenic microenvironment (5). Advances in single-cell RNA sequencing (scRNA-seq) have revealed that CAFs are not a uniform population but comprise transcriptionally and functionally distinct subtypes (6). These include myofibroblastic CAFs (myCAFs) associated with ECM deposition, inflammatory CAFs (iCAFs) characterized by cytokine signaling, and antigen-presenting CAFs (apCAFs) expressing MHC (Major histocompatibility complex) class II molecules (7). This heterogeneity underlies their diverse and context-dependent effects on tumor immunity.
Despite the transformative potential of immune checkpoint inhibitors (ICIs), such as anti–PD-1 antibodies, only a fraction of breast cancer patients experience durable clinical benefit (8). Emerging evidence indicates that cancer-associated fibroblasts play a critical role in modulating resistance to ICIs. They may contribute to immune evasion by sequestering immune cells from the tumor core or by secreting immunosuppressive factors that inhibit cytotoxic T-cell function (1). However, the precise CAF subtypes involved and the underlying molecular pathways that influence the anti–PD-1 response in breast cancer remain insufficiently understood (7). A deeper understanding of these mechanisms is essential for developing strategies to overcome CAF-mediated resistance and improve ICI efficacy.
To address this gap, we reanalyzed a publicly available single-cell RNA-seq dataset of breast cancer patients treated with anti–PD-1 immunotherapy. Our aim was to systematically define CAF subtypes, delineate their molecular and functional programs, and evaluate their associations with therapeutic response. By focusing on the stromal compartment rather than immune or epithelial cells, this study provides an integrated view of CAF remodeling under checkpoint blockade, highlighting CAF-derived signaling pathways as potential targets to improve immunotherapy efficacy in breast cancer.
2 Materials and methods
To investigate stromal heterogeneity in breast cancer, we reanalyzed scRNA-seq data from a publicly available dataset, which includes paired pre-treatment and on-treatment tumor biopsies from patients treated with anti–PD-1 immunotherapy (9). For this study, we focused on 31 treatment-naive patients from the first cohort with operable, non-metastatic breast tumors. The cohort comprised three tumor subtypes—Estrogen receptor-positive (ER+), Human epidermal growth factor receptor 2-positive (HER2+), and Triple-negative breast cancer (TNBC)—and spanned three distinct age groups: young adults, middle-aged adults, and elderly individuals (Supplementary Figure S1). Among these patients, 15 harbored ER+ tumors, primarily in middle-aged and elderly adults; 3 had HER2+ tumors, mostly in middle-aged and elderly adults; and the remaining 13 presented with TNBC, distributed across young adults (1 patient), middle-aged adults (5 patients), and elderly adults (7 patients). This distribution highlights the diversity of tumor subtypes and age groups, providing a representative foundation for downstream single-cell analyses.
The cohort was specifically selected to avoid confounding effects from prior chemotherapy and to ensure the availability of high-quality paired biopsies suitable for single-cell analysis. Each patient received a single dose of pembrolizumab prior to surgery, and tumor tissues were collected both before and shortly after treatment. While the original study primarily emphasized immune cell dynamics and malignant epithelial programs, our analysis concentrated on the stromal compartment, with particular focus on fibroblast populations. The analytical workflow comprised three main steps: first, raw scRNA-seq data were processed and subjected to stringent quality control to ensure reliable resolution at both the cell and gene levels; second, dimensionality reduction and unsupervised clustering were applied to identify transcriptionally distinct fibroblast subsets, which were further characterized via differential expression and pathway enrichment analyses to define molecular programs and subtype-specific signatures; third, the functional relevance and generalizability of the identified CAF subtypes were evaluated through spatial transcriptomics validation (Figure 1). All major visualizations, including dimensionality reduction plots, heatmaps, pathway enrichment summaries, and spatial projection figures, were generated and refined using BioVinci (BioTuring Inc.) (10).
Figure 1
2.1 Clustering and initial identification of fibroblast subtypes
Fibroblast cell clusters were identified using the Louvain algorithm at a resolution of r = 0.5, implemented through the “Clustering” function of BBrowserX (BioTuring Inc., California, USA) (RRID: SCR 025984) (11), which initially yielded four distinct fibroblast clusters. To capture disease-associated gene expression changes, differential expression (DEG) analysis was performed between relevant cell groups within each cluster. The thresholds applied for DEG analysis included an average log2 fold change (log2 FC) > 0.5 and a false discovery rate (FDR) < 0.05, both of which are widely accepted criteria for identifying significantly regulated genes. Volcano plots were generated to visualize these results, enabling rapid assessment of genes that were significantly up- or downregulated and providing a basis for selecting candidate genes implicated in pathogenesis.
To refine classification, marker gene prioritization first incorporated coverage-based metrics—Within-cluster Coverage, Outside-cluster Coverage, and weighted log2 FC (Wlog2 FC)—to better capture both the prevalence and discriminatory power of candidate genes. We then applied stringent differential marker selection (highlog2 FC, low FDR), ranked candidates by specificity and magnitude of upregulation, and assessed their biological relevance. This integrative framework enabled the systematic annotation of CAF clusters and highlighted ambiguous populations requiring cautious interpretation.
Each fibroblast cluster was further characterized by applying the “Marker Genes” function, which facilitated the assignment of putative CAF subtypes through comparison with established or novel cell-type markers. To confirm the robustness and specificity of these assignments, fibroblasts were separated from other cell types using the “Sub-Cluster” function. Enrichment analyses were subsequently performed on the DEG sets of each fibroblast cluster under both conditions (NC – Normal Control, BC – Breast Cancer) to refine subtype classification.
2.2 Downstream functional and interaction characterization of CAF subtypes
After defining fibroblast clusters and assigning putative subtype identities, we next performed molecular and functional profiling to delineate their biological programs and potential interactions within the tumor microenvironment. Two complementary analyses were conducted: pathway enrichment analysis of subtype- specific DEGs and inference of intercellular communication networks.
For pathway enrichment analysis, DEGs of each CAF cluster were submitted to the Enrichr platform (RRID: SCR 001575) (12), with functional annotations derived from multiple curated resources including Reactome (RRID: SCR 003485) (13), Wikipathways (RRID: SCR 002134) (14), and the Gene Ontology (GO) biological processes database (RRID: SCR 002811) (15). Significance was assessed using adjusted p-values with FDR < 0.05. This approach enabled the systematic identification of pathways enriched in each CAF subtype, providing insight into distinct transcriptional programs underlying extracellular matrix remodeling, angiogenesis, metabolism, protein synthesis, and immune modulation.
To evaluate potential cell–cell interactions, ligand–receptor pairing was analyzed using CellPhoneDB (v2.1.7) (RRID: SCR 017054) (16) and cross-validated with BBrowserX’s built-in cell-cell communication inference tool. Only statistically significant interactions (p-values < 0.05, permutation test) were retained. We specifically examined pathways with known relevance to tumor-immune crosstalk. Interaction networks were visualized to highlight both outgoing (CAF-derived ligands) and incoming (CAF-expressed receptors) signaling axes, enabling comparative mapping across CAF subtypes. This integrated molecular and functional profiling strategy provided the foundation for subsequent interpretation of CAF subtype-specific roles in shaping the tumor microenvironment and modulating responses to immune checkpoint blockade.
2.3 Data validation by comparison with previous studies
To examine the spatial organization of CAF programs identified in our scRNA-seq analysis, we analyzed a publicly available breast cancer spatial transcriptomics dataset generated using the 10x Genomics Visium FFPE platform (2021) (RRID: SCR 023571) (17). The dataset was accessed via Talk2Data (18) and processed using the SpatialX platform (BioTuring Inc.) (19). The analyzed tissue corresponds to an FFPE section (Block 738811QB, Section 1) from a grade II breast carcinoma of a 73-year-old Asian female, encompassing regions of ductal carcinoma in situ and invasive carcinoma. As no information on treatment status or clinical response was available, the analysis was restricted to baseline spatial organization. Spatial spots were clustered using the Louvain algorithm (resolution = 5). CAF subtypes were assigned based on marker gene signatures derived from our scRNA-seq analysis and projected onto tissue coordinates to assess their spatial distribution.
3 Results
3.1 CAF subtypes with distinct roles in the tumor microenvironment: vCAF, myCAF, iCAF, and apCAF-like phenotypes
To investigate the functional heterogeneity of CAFs within the tumor microenvironment, we identified four distinct CAF subtypes based on their gene expression profiles: CAF1 as vascular CAFs (vCAF), CAF2 as myofibroblastic CAFs (myCAF), CAF3 as inflammatory CAFs (iCAF), and CAF4 as antigen-presenting CAF-like (apCAF-like) CAFs. Each subtype exhibited unique molecular signatures and pathway enrichments, suggesting distinct roles in tumor progression and therapy resistance.
CAF1 exhibited a robust gene expression profile associated with vascular functions, with key markers including Notch Receptor 3 (NOTCH3), Melanoma Cell Adhesion Molecule (MCAM), Cytochrome C Oxidase Subunit 4I2 (COX4L2), HIG1 Hypoxia Inducible Domain Family Member 1B (HIGD1B), and Cadherin 6 (CDH6) (Figure 2A). NOTCH3, involved in angiogenesis and endothelial cell signaling, reinforced the vascular phenotype of CAF1 (20). MCAM and CDH6, adhesion molecules critical for cell-cell interactions, suggest CAF1’s role in vessel formation and stabilization (21). The NOTCH Regulated Ankyrin Repeat Protein (NRARP) gene, regulating NOTCH signaling, further supports the angiogenic profile (Supplementary Figure S2A; Supplementary Data 2) (22). Additionally, the expression of Gap Junction Protein Alpha 4 (GJA4) and G Protein-Coupled Receptor 4 (GPR4), along with Potassium Voltage-Gated Channel Subfamily A Member 5 (KCNA5), RAS Like Glutamate Rich (RERGL), and Calsequestrin 2 (CASQ2), highlights CAF1’s potential role in modulating endothelial function and vascular responses to tumor growth (23–25). Pathway analysis further enriched CAF1 in mitochondrial energy metabolism pathways, including the Electron Transport Chain, Mitochondrial ATP Synthesis, and the TCA cycle (Citric Acid cycle), underscoring its energetic support for tumor progression. This, combined with pathways related to RNA processing, splicing, and VEGFA–VEGFR2 signaling, indicates CAF1’s active involvement in angiogenesis, vessel stabilization, and possibly immune exclusion via vascular-mediated mechanisms (Figure 2B; Supplementary Data 3).
Figure 2
Exhibiting a transcriptional signature indicative of myofibroblastic differentiation and ECM remodeling, CAF2 expresses key markers such as Maternally Expressed 3 (MEG3), KCNQ1 Opposite Strand/Antisense Transcript 1 (KCNQ1OT1), Integrin Subunit Alpha 11 (ITGA11), Ankyrin Repeat Domain 36C (ANKRD36C), Myocardial Infarction Associated Transcript (MIAT), and ADAM Metallopeptidase With Thrombospondin Type 1 Motif 6 (ADAMTS6) (Figure 2A). ITGA11, an integrin involved in ECM attachment and fibroblast migration, highlights CAF2’s role in promoting tissue stiffness (26). The long noncoding RNAs Nuclear Paraspeckle Assembly Transcript 1 (NEAT1), MIAT, Xist Ribonucleoprotein (XIST), and KCNQ1OT1 suggest transcriptional reprogramming, typical of fibroblast activation and fibrosis (Supplementary Figure S2B; Supplementary Data 2) (27). ADAMTS6, a metalloproteinase, plays a critical role in ECM turnover, reinforcing CAF2’s function in ECM remodeling (28). Pathway analysis reveals strong enrichment of ECM-related pathways, such as Collagen-Containing Extracellular Matrix, Extracellular Matrix Organization, and Collagen Formation, confirming CAF2’s role in desmoplasia, mechanotransduction, and stromal stiffening (Figure 2B; Supplementary Data 3).
A gene signature enriched in inflammatory and immunomodulatory pathways characterizes CAF3, with key markers such as Microfibril Associated Protein 4 (MFAP4), Platelet Derived Growth Factor Receptor Like (PDGFRL), Complement C3 (C3), Insulin Like Growth Factor 1 (IGF1), and MAF BZIP Transcription Factor B (MAFB) (Figure 2A). C3, a complement system component, indicates CAF3’s role in immune modulation through inflammation and immune cell recruitment (29). PDGFRL, a receptor involved in stromal-immune interactions, and IGF1, promoting tumor survival, further support this function (30, 31). Pathway analysis revealed a significant intensification of secretory programs within this subtype (Figure 2B, Supplementary Data 3). The Complement and Coagulation Cascades (WP558) pathway, featuring SERPING1, C1S, C1R, and Complement Factor D (CFD), establishes iCAFs as a primary source of innate immune modulators. Concurrently, the Cytokine-Cytokine Receptor Interaction (WP5473) axis, involving IL6ST, CXCL12, CXCL14, and CCL2, positions iCAFs as a central signaling hub. Additionally, iCAFs exhibit high metabolic plasticity via the NRF2 Pathway (WP2884), characterized by antioxidant genes such as Superoxide Dismutase 3 (SOD3), Glutathione Peroxidase 3 (GPX3), and Hepatocyte Growth Factor (HGF). Beyond immunomodulation, pro-angiogenic factors like Retinoic Acid Receptor Responder 1 (RARRES1) and Vascular Endothelial Growth Factor D (VEGFD) suggest that iCAFs foster metastasis through vascular remodeling (Supplementary Figure S2C; Supplementary Data 2) (32, 33). These findings establish iCAFs as essential orchestrators of an inflammatory, tumor-supportive stroma.
CAF4, designated as an apCAF-like subtype, exhibited a mosaic transcriptional signature with features of stromal, epithelial, and immune-related cells. Key markers such as C-X-C Motif Chemokine Ligand 14 (CXCL14), Keratin 19 (KRT19), Mucin Like 1 (MUCL1), CD74 Molecule (CD74), and Major Histocompatibility Complex, Class II, DP Alpha 1 (HLA-DPA1) suggest a hybrid phenotype, with CXCL14 involved in immune cell recruitment and KRT19 marking epithelial-like features (Figure 2A). CD74, an antigen-presenting molecule, supports CAF4’s potential role in immune modulation, similar to that of antigen-presenting CAFs (apCAF) (34). To ensure cellular identity and exclude potential doublets or technical artifacts, we performed stringent quality control and expression profiling. CAF4 cells consistently displayed gene counts within a normal range (Number of genes < 3,000), arguing against technical artifacts combining multiple cell types (Supplementary Figure S2E). Furthermore, while low-level expression of epithelial-associated markers and other transcripts such as HLA-DPA1, MUCL1, and Trefoil Factor 3 (TFF3) were detectable (Supplementary Figure S2D; Supplementary Data 2) (35, 36), CAF4 cells maintained high expression of core fibroblast markers, including Collagen Type I Alpha 1 Chain (COL1A1), Collagen Type III Alpha 1 Chain (COL3A1), Decorin (DCN), and Lumican (LUM), confirming their lineage as bona fide fibroblasts (Supplementary Figure S2F). These findings, distinct clustering observed in UMAP embedding, confirm that CAF4 represents a specialized fibroblast state adapted to the immune-rich niche rather than epithelial contamination (Figure 2C) (37). Pathway analysis further reveals that CAF4 is enriched in mitochondrial energy metabolism (e.g., Electron Transport Chain and Mitochondrial ATP Synthesis) and translation-related programs (e.g., Cap-Dependent Translation Initiation and Ribosomal Subunit Joining) (Figure 2B), indicating elevated energetic and biosynthetic activity. Importantly, CAF4 also shows significant enrichment of antigen processing and presentation pathways, including cross-presentation of exogenous antigens via endosomal compartments and MHC class I–mediated antigen presentation, consistent with an apCAF-like functional phenotype (Supplementary Data 3).
3.2 Modelling of CAF subpopulations heterogeneity in breast cancer
We assessed the association between the characterized CAF subtypes and the anti-PD-1 response (using Welch’s t-test) (Figure 3A). The vCAF, iCAF, and apCAF-like subtypes were significantly enriched in non-responders compared to responders during treatment. In contrast, the myCAF subtype remained at low, unchanged levels. At the pre-treatment baseline, only the apCAF-like subtype showed modest enrichment in non-responders, suggesting a potential baseline predictive value. Upon treatment, both the vCAF and iCAF subtypes were elevated in non-responders, reflecting their dynamic roles in sustaining immunosuppressive signaling and driving adaptive therapy resistance. The apCAF-like subtype remained higher in non-responders at both pre-treatment and on-treatment stages, reinforcing its stable contribution to a resistant stromal niche. Conversely, responders consistently showed low levels of these three CAF subtypes, indicating a less suppressive stromal environment that may facilitate T cell infiltration and anti-tumor immunity. Collectively, these results suggest that the apCAF-like/admixture subtype serves as a baseline predictor, while the vCAF and iCAF subtypes are numerically associated with adaptive resistance; the myCAF subtype appears to have minimal impact on the treatment outcome.
Figure 3
To explore context-specific roles, we examined the vCAF, iCAF, and apCAF-like subtypes within different breast cancer types (Figure 3B). The vCAF and apCAF-like subtypes were found to be enriched in TNBC. Crucially, the iCAF subtype showed a selective increase in ER+ tumors during treatment, highlighting a differential response mechanism. HER2+ samples were excluded from comprehensive statistical analysis due to limited sample size (n = 3). However, preliminary cross-subtype comparisons (Figure 3C) indicated that the vCAF subtype appeared higher in HER2+ compared to TNBC, and the iCAF subtype was significantly enriched in ER+ relative to TNBC. The myCAF and apCAF-like subtypes showed no significant differences across these breast cancer subtypes. These findings underscore the heterogeneity of CAF distribution and function across breast cancer subtypes, confirming the apCAF-like subtype as a baseline predictor and the vCAF and iCAF subtypes as adaptive drivers of resistance, with the iCAF subtype showing a distinct, selective enrichment in ER+ tumors during therapy. The observations related to HER2+ tumors remain inconclusive due to sample limitations.
3.3 Anti-PD-1 therapy orchestrates dual stromal reprogramming: re-education of iCAFs and functional disarmament of vCAFs and myCAFs
To decode the cellular determinants governing therapeutic efficacy, we quantified the global landscape of intercellular communication within the tumor microenvironment, revealing a profound bifurcation in interaction trajectories contingent upon treatment outcomes (Figure 4A; Supplementary Data 4). In non-responders, disease progression was characterized by Stromal Fortification, where vCAFs and iCAFs intensified direct supportive signaling to cancer cells, effectively shielding the tumor niche. In sharp contrast, responders exhibited a distinctive Stromal Remodeling, marked by the synchronous attenuation of tumor-supportive interactions. Notably, while CAF lineages underwent extensive reconfiguration—specifically with iCAFs redirecting signals toward T cells and myeloid cells—the intrinsic interaction repertoire of T cells remained remarkably stable (Δ ≈ 0 to −1). This signaling stasis in the T-cell compartment serves as a critical baseline, demonstrating that the observed therapeutic shift is not driven by an autonomous expansion of immune signaling repertoires, but is instead orchestrated by CAFs actively rewriting the intercellular script. Analysis of high-intensity interaction networks (≥ 42 interactions; Figure 4B) corroborated that the CAF signaling hierarchy in responders shifted decisively from a tumor-supporting to an immune-promoting profile.
Figure 4
To elucidate the molecular mechanisms driving these macroscopic shifts, we interrogated specific ligand–receptor pairs (Figure 4C; Supplementary Data 5), identifying a sophisticated two-pronged reprogramming strategy induced by anti-PD-1 therapy. First, the phenotypic re-education of the iCAF lineage. Analysis of pre-treatment baselines revealed that iCAFs initially engaged T cells via the Amyloid Precursor Protein (APP)–CD74 axis. In responders, this specific iCAF-derived signal was significantly attenuated, replaced by the effective activation of the CXCL9–CXCR3 axis. This molecular switch transforms iCAFs from a physical barrier into an immune recruitment hub, facilitating the infiltration of CD8+ and Th1 T cells. Crucially, while iCAF-derived APP signaling diminishes, the APP pathway itself is not extinguished but rather functionally reallocated to distinct stromal subsets (vCAF and apCAF) to sustain antigen presentation, as elucidated in the pathway analysis (Section 3.5). Second, the functional disarmament of protective vCAF and myCAF populations.
In non-responders, vCAFs strongly expressed signals associated with cancer stemness (JAG1–NOTCH2) and basement membrane reinforcement (COL4A2–SDC1). In responders, this specific malignant signalling axis targeting cancer cells was effectively dismantled, thereby removing key survival inputs. Similarly, myCAFs in responders displayed an attenuation of chemical defense mechanisms; interactions involving the complement-regulatory protein CD55 (CD55–ADGRE5) and the anti-phagocytic signal (COMP–CD47) were significantly diminished. Furthermore, apCAF-like cells in the responder group exhibited upregulated MHC Class I molecules (HLA-B/C), directly contributing to antigen presentation. Collectively, these data demonstrate that effective anti-PD-1 therapy necessitates a coordinated disruption of stromal defense systems alongside immune recruitment.
3.4 Subtype-specific signaling dynamics: from matrix reinforcement to immune engagement and the context-dependent role of CXCL12
The detailed ligand-receptor mapping of CAF-mediated cell–cell interactions and signaling dynamics (Figures 5A, B; Supplementary Data 6) unveils subtype-specific reprogramming with distinct functional consequences. In the case of vCAF, reprogramming is characterized by the upregulation of the Thrombospondin-1–CD47 (THBS1–CD47) signaling pathways. These alterations translate into moderate-strength interactions with endothelial and myeloid populations within the responder group (Figure 5B). This enhancement suggests a functional shift toward immune-modulatory activity and immune cell recruitment (38, 39). Concurrently, tumor-promoting signaling axes, such as Midkine–Nucleolin (MDK–NCL) and Fibronectin 1–Integrin (FN1–integrin), are downregulated, which indicates a reduction in ECM-mediated support for tumor growth (40, 41).
Figure 5
In contrast, myCAF exhibits selective functional remodeling rather than a universal attenuation of activity. The downregulation of structural signals like MDK–NCL and COMP–CD47 (Figure 5A) suggests a dismantling of the physical barriers that typically exclude immune cells from the tumor microenvironment (42). Crucially, myCAFs within responders maintain strong, high-affinity interactions with T cells and endothelial cells (Figure 5B). This stark contrast suggests a phenotypic switch from a barrier-forming phenotype to an immune-permissive state, which actively supports T-cell trafficking and vascular normalization. Similarly, iCAF demonstrates a clear shift towards immune-facing signaling. iCAFs emerge as a central hub of communication in responders, marked by peak interaction strengths with myeloid cells and T cells (Figure 5B). This shift is driven by heightened THBS1–CD47 signaling and a reduction in integrin-mediated support, suggesting that iCAFs orchestrate a permissive microenvironment that facilitates robust immune engagement.
The CXCL12–CXCR4 axis, however, exhibited a profound functional divergence between pre-treatment and on-treatment stages, characterized by opposing dynamics in vCAF and apCAF-like populations. (Figures 5C, D). In non-responders, therapeutic intervention induced a pathological intensification of vCAF-derived signaling toward plasmacytoid dendritic cells, a feature markedly attenuated in responders. Notably, while vCAF-to-T cell communication was elevated at the pre-treatment baseline in the resistance group, anti-PD-1 therapy triggered a secondary, broad-spectrum surge in apCAF-mediated CXCL12 signaling toward T cells, pDCs, and B cells. Although this apCAF-driven axis was detectable across all patients post-treatment, its magnitude was significantly more pronounced in non-responders. These dynamics suggest that in the context of therapeutic failure, the CXCL12–CXCR4 axis does not facilitate productive immune recruitment but instead orchestrates an immunosuppressive niche characterized by pDC sequestration and dysfunctional lymphoid entrapment.
3.5 Divergent signaling architectures: responder-specific APP, NOTCH, and midkine modules versus THBS2–CD47 dominance in resistance
To define the molecular determinants underlying these divergent trajectories, we performed a detailed pathway analysis of ligand–receptor pairs exclusive to each response group (Table 1). This analysis revealed a stark dichotomy in signaling programs that was strictly compartmentalized by CAF subtype. Specifically, Responders were characterized by the subtype-restricted activation of Amyloid Precursor Protein (APP), NOTCH, and Midkine (MK) pathways, whereas the Non-response group was dominated by the Thrombospondin-2 (THBS2) axis (43–45). This indicates that therapeutic distinctness is driven by precise functional modules within specific CAF populations rather than ubiquitous stromal activation.
Table 1
| Num | Sig. | Response type* | Ligand | Receptor | Predicted interactions |
|---|---|---|---|---|---|
| 1 | APP | Autocrine dominant – Increase in Response group | APP | TNFRSF21 | vCAF → AXL–SIGLEC6 DC, vCAF, Langerhans cell iCAF → vCAF apCAF-like → vCAF, pDC |
| SORL1 | vCAF →γδ T cell apCAF-like → Myeloid cell | ||||
| CD74 | vCAF → vCAF, myCAF, iCAF, apCAF-like, Malignant epithelial cell iCAF → iCAF apCAF-like → B cell, Endothelial cell, Mast cell, Myeloid cell | ||||
| TREM2 + TYROBP | apCAF-like → Myeloid cell | ||||
| 2 | NOTCH | Paracrine dominant – Increase in Response group | JAG1 | NOTCH1 | myCAF → Endothelial cell |
| NOTCH2 | myCAF → myCAF, iCAF, Langerhans cell, Myeloid cell | ||||
| NOTCH3 | myCAF → vCAF, myCAF, iCAF | ||||
| NOTCH4 | myCAF → Endothelial cell | ||||
| 3 | MK | Autocrine dominant – Increase in Response group | MDK (MK) | LRP1 | apCAF-like → apCAF-like |
| ITGA4 + ITGB1 | apCAF-like → B cell | ||||
| SDC1 | apCAF-like → B cell | ||||
| NCL | apCAF-like → myCAF, Mast cell, Myeloid cell | ||||
| 4 | THBS2 | Paracrine dominant – Non-response group | THBS2 | CD47 | apCAF-like → B cell, iCAF, Malignant epithelial cell, Myeloid cell, pDC, T cell |
| SDC1 | apCAF-like → myCAF, iCAF | ||||
| SDC4 | apCAF-like → Malignant epithelial cell | ||||
| ITGA3 + ITGB1 | apCAF-like → vCAF | ||||
| CD36 | apCAF-like → Endothelial cell, pDC |
Predicted cell–cell communications mediated by CAF subtypes through key signaling pathways.
*The ‘Response Type’ column indicates the signaling context and functional impact of each pathway.
Specifically, in the responder group, the APP pathway operated as a prominent autocrine and paracrine immune-supportive module, predominantly orchestrated by vCAF and apCAF-like populations. Here, vCAF and apCAF-like cells served as primary ligand sources, interacting with CD74 receptors on a diverse range of targets—including B cells, endothelial cells, mast cells, and myeloid cells. Additionally, vCAF-derived APP engaged SORL1 on γδ T cells, while apCAF-like-derived APP targeted TREM2+TYROBP on myeloid lineages. This specific connectivity network promotes antigen cross-presentation and fosters an immune-permissive microenvironment. Similarly, NOTCH signaling in responders was identified as a specific stromal-vascular crosstalk axis driven exclusively by myCAF-derived JAG1. This ligand engaged NOTCH1/4 on endothelial cells and NOTCH2/3 on stromal and myeloid subsets, potentially supporting vascular normalization and stromal remodeling without triggering immunosuppression. Furthermore, the MK pathway emerged as a critical lymphoid-niche organizing mechanism, where apCAF-like cells specifically targeted B cells via MDK–ITGA4+ITGB1 and MDK–SDC1 interactions. This mechanism likely favors B-cell recruitment and tertiary lymphoid structure (TLS) formation.
Conversely, the Non-response group was dominated by a unique broad-spectrum suppressive broadcast driven by apCAF-like cells via the THBS2 pathway. Unlike the spatially coordinated signaling in responders, this resistance-associated module involved apCAF-like-derived THBS2 engaging CD47 on a broad spectrum of targets (B cells, iCAFs, malignant cells, myeloid cells, pDCs, and T cells), as well as NCL, SDC1/4, and CD36 on stromal and endothelial compartments. This specific apCAF-like–THBS2–CD47 axis likely contributes to therapeutic resistance by sustaining an immunosuppressive and exclusionary tumor microenvironment. Collectively, the identification of these response-specific ligand–receptor pairs highlights APP, NOTCH, and MK as potential biomarkers for effective anti-PD-1 therapy, while positing the apCAF-driven THBS2–CD47 axis as a critical target to overcome therapeutic resistance.
3.6 Spatial validation of myCAF and iCAF identities and their immune-associated niches
To examine the spatial distribution of CAF subtypes within an intact tumor architecture, we analyzed an independent breast cancer spatial transcriptomics dataset (17). Using a two-step spatial mapping strategy, we first assessed whether marker genes derived from our scRNA-seq analysis delineated distinct CAF populations in situ, and subsequently evaluated their anatomical localization within the tumor microenvironment. Established reference markers, including ANTXR1 for myCAFs and PI16 for iCAFs, together with the pan-fibroblast marker FAP (46), were used to contextualize CAF identity. In parallel, subtype-specific markers identified in our scRNA-seq analysis, ITGA11 for myCAFs and IGF1 for iCAFs (Figure 2A), were spatially mapped alongside these reference markers (Figure 6A). This approach delineated two spatially separable CAF populations, defined as ITGA11+ANTXR1+FAP+ myCAFs and IGF1+PI16+FAP+ iCAFs, consistent with their transcriptomic identities.
Figure 6
We next examined the spatial neighborhoods associated with each CAF subtype. ITGA11+ANTXR1+ myCAFs were predominantly localized within fibrotic stromal regions and were spatially segregated from immune cell–enriched areas (Figure 6B). This spatial distribution is consistent with an immune-excluded stromal architecture and suggests a structural association between myCAF-enriched stroma and limited immune cell accessibility (26, 47). In contrast, the IGF1+PI16+ iCAF population exhibited a distinct spatial organization, preferentially localizing to immune-associated regions of the tumor microenvironment (Figure 6C). iCAFs were frequently observed in proximity to macrophages and T cells, indicating a baseline anatomical configuration permissive for close stromal–immune spatial association (31, 48). Although spatial transcriptomics captures a static snapshot, this organization provides the spatial context required for the subtype-specific ligand–receptor interactions identified in our network analyses.
4 Discussion
Our study has expanded the understanding of the role of cancer-associated fibroblasts (CAFs) in modulating the response to anti-PD-1 therapy. The context of this research arises from previous works, which primarily focused on immune cells and unfortunately overlooked the role of stromal cells. By filling this gap and focusing on the heterogeneity of CAFs, we have identified key subsets that play a crucial role in creating an immunosuppressive tumor microenvironment. These findings reinforce recent evidence, suggesting that the diversity of the tumor stroma is a critical determinant of treatment efficacy, especially in immunotherapy.
In this context, the CAF clusters identified here show strong correspondence with previously defined fibroblast phenotypes across diverse tumor types. This correspondence is supported by cross-dataset validation using shared upregulated genes with published datasets, highlighting the robustness and cross-cancer generalizability of these CAF subtypes (Supplementary Table S1). vCAFs, as reflected by the expression of Protein Phosphatase 1 Regulatory Inhibitor Subunit 14A (PPP1R14A), Regulator Of G Protein Signaling 5 (RGS5), HIGD1B, and MCAM, exhibited profiles consistent with vascular programs reported in ovarian and breast cancer as well as in normal heart tissue (49, 50). Notably, overlap with NOTCH3, Collagen Type XVIII Alpha 1 Chain (COL18A1), and Myosin Heavy Chain 11 (MYH11) further confirmed its vascular identity while distinguishing it from pericytes, in line with the vCAF cluster described (51). myCAFs, sharing Collagen Type X Alpha 1 Chain (COL10A1), Collagen Type XI Alpha 1 Chain (COL11A1), Thrombospondin 2 (THBS2), Syndecan 1 (SDC1), and Podocan Like 1 (PODNL1) with fibroblast clusters reported in breast, pancreatic, and colorectal tumors, displayed hallmark collagen- and ECM-remodeling signatures (51–54). iCAFs, characterized by CXCL12, Phospholipase A2 Group IIA (PLA2G2A), Scavenger Receptor Class A Member 5 (SCARA5), and CFD, showed consistency with iCAF signatures across breast, thyroid, and colorectal cancers, supporting their role in immune modulation and paracrine signaling (48, 51, 55). By contrast, apCAFs, sharing CD74 and HLA-DPA1 but also displaying epithelial and immune admixture signatures, suggest that this population represents a hybrid or context-dependent state rather than a canonical CAF lineage (56–59).
A primary finding of this study is the association of vCAFs with resistance to anti-PD-1 therapy through a mechanism of stromal fortification. In non-responders, disease progression is characterized by intensified supportive signaling from vCAFs and iCAFs to malignant cells, effectively establishing a protective niche. Mechanistically, our intercellular communication analysis identifies the CXCL12–CXCR4 axis as a primary driver of this resistance, predicated on highly context-dependent target specificity. In non-responding patients, therapeutic intervention triggers a pathological intensification of vCAF-derived signaling specifically toward plasmacytoid dendritic cells. This phenomenon suggests a stromal-pDC trap that reinforces immune evasion, potentially by impairing IFN-α production and fostering a tolerogenic environment (47, 60). Notably, while apCAF-mediated CXCL12 signaling toward lymphoid subsets increased post-treatment across the entire cohort, its magnitude was significantly more pronounced in the non-response group. This suggests that excessive CXCL12 may orchestrate dysfunctional immune cell sequestration rather than productive recruitment. These observations provide a compelling rationale for utilizing CXCR4 blockade to dismantle these exclusionary barriers (61).
In sharp contrast, responders exhibit a distinctive stromal dismantling, characterized by the functional disarmament of vCAF and myCAF populations. This process effectively abrogates key survival inputs and immune-evasive signals directed at cancer cells. Specifically, the attenuation of the JAG1–NOTCH2 axis in responders validates recent reports identifying this pathway as a central oncogenic driver in breast cancer, where JAG1 facilitates metastasis and diminishes survival by sustaining tumor stemness (62). Furthermore, the downregulation of the CD55–ADGRE5 axis and concomitant stromal defense mechanisms aligns with emerging evidence that cancer-associated fibroblasts establish an exclusionary shield. Recent studies emphasize that CAF-derived interactions, particularly those involving CD55 and extracellular matrix remodeling, are critical for maintaining an immunosuppressive environment and driving therapeutic resistance (63). By dismantling these specific circuits, responders transition from a state of stromal-mediated protection to an immune-permissive environment, thereby facilitating effective anti-PD-1 activity.
Our study further identifies a two-pronged reprogramming strategy induced by anti-PD-1 therapy. First, the re-education of the iCAF lineage represents a pivotal therapeutic shift, characterized by a molecular switch from the inhibitory APP–CD74 baseline toward the immune-recruiting CXCL9–CXCR3 axis. This transformation effectively converts iCAFs from a physical barrier into an immune recruitment hub, facilitating Th1 and CD8+ T-cell infiltration. Such functional plasticity aligns with recent high-resolution dissections demonstrating that iCAF subsets can transition from pro-tumorigenic to immune-supportive states under therapeutic pressure (64). Critically, the emergence of iCAF-derived CXCL9 in responders validates recent findings identifying CXCL9 as a fundamental orchestrator of the T-cell inflamed phenotype and a primary determinant of immunotherapy success in breast cancer (65). By redirecting signaling toward this recruitment axis, iCAFs in responders actively prime the microenvironment, confirming that stromal-mediated CXCL9 production is a prerequisite for effective anti-PD-1 activity. Second, responders leverage subtype-specific modules for microenvironmental normalization, including myCAF-mediated JAG1–NOTCH signaling for vascular normalization (66) and apCAF-driven Midkine signaling supporting tertiary lymphoid structure formation. Conversely, the resistance-associated landscape is dominated by a broad-spectrum suppressive broadcast via the apCAF–THBS2–CD47 axis, which sustains a systemic exclusionary environment (67, 68). Collectively, these findings demonstrate that CAF subsets orchestrate divergent stromal programs based on clinical context, establishing the stromal compartment as a highly regulated gatekeeper of immunotherapy success.
While our study primarily focuses on stromal resistance mechanisms in the context of anti–PD-1 therapy, the potential conservation of CAF-mediated immune barriers across other ICIs, including anti–PD-L1 and anti-CTLA-4, is of clear clinical relevance. We anticipate substantial mechanistic overlap with anti–PD-L1 therapies, as both agents target the same inhibitory axis and act predominantly during the effector phase within the tumor microenvironment. Notably, this is the compartment in which vCAFs and apCAF-like populations exert immunosuppressive functions through CXCL12–CXCR4 and THBS2–CD47 signaling, respectively, thereby reinforcing immune exclusion. In contrast, anti-CTLA-4 therapy primarily enhances T-cell priming in secondary lymphoid organs. In this setting, CAFs are likely to function as a downstream resistance bottleneck (60). Even if CTLA-4 blockade effectively expands the peripheral T-cell repertoire, vCAF-mediated angiogenic remodeling and CAF-associated immunosuppressive signaling may still impede effector T-cell infiltration and function within the tumor bed. This concept is consistent with prior reports demonstrating that TGF-β–driven stromal programs attenuate therapeutic responses to both anti–PD-L1 and anti-CTLA-4 agents (69–71). Collectively, these observations suggest that targeting specific CAF subtypes—particularly through disruption of stromal TGF-β or CXCL12 signaling—may represent a rational combinatorial strategy to overcome resistance across multiple ICI modalities.
This study highlights the central role of the stromal compartment in shaping tumor–immune organization with potential relevance to immunotherapy. Through the integration of stringent quality control, unsupervised clustering, pathway enrichment, and intercellular communication analyses, we provide a high-resolution framework describing CAF heterogeneity and its association with immune architecture. Moving beyond an immune-centric perspective, our findings position stromal–immune crosstalk as an important dimension contributing to therapeutic sensitivity and resistance. Several considerations merit discussion. The overall sample size and the clinical diversity of the cohort, particularly across breast cancer subtypes, may influence the extent to which these observations can be generalized. Notably, the limited representation of HER2-positive tumors constrains subtype-resolved analyses. While the CAF programs identified here may reflect conserved principles of stromal organization and immune modulation, comprehensive pan-cancer validation will require future studies incorporating larger, clinically stratified datasets. With respect to spatial analyses, the available spatial transcriptomics data enabled assessment of the anatomical distribution of major CAF subtypes and their immune-associated niches. However, evaluation of treatment-dependent remodeling and response-linked spatial dynamics will necessitate spatial datasets explicitly annotated with therapeutic exposure and clinical outcome. In addition, the current spatial data provide spot-level representations of tissue architecture rather than true single-cell resolution, limiting the ability to resolve transitional CAF states and dynamic phenotypic plasticity. Finally, as our analyses are primarily transcriptome-based, they do not directly capture post-transcriptional regulation, proteomic dynamics, or the long-term functional stability of CAF programs, which will be important areas for future investigation.
Future studies incorporating longitudinal sampling and multi-omics spatial profiling, including high-resolution proteogenomic approaches, will be essential to clarify CAF plasticity and establish causal relationships. Functional perturbation models, such as organoid co-cultures or in vivo systems, will further be required to determine whether specific signaling programs—such as the apCAF-associated THBS2–CD47 axis—directly contribute to immune modulation. Together, our results position CAF heterogeneity as a key stromal dimension associated with immunotherapy response and highlight context-specific CAF signaling pathways as potential candidates for rational combination strategies in breast cancer and other solid tumors.
Statements
Data availability statement
Processed single-cell RNA-seq data generated and analyzed in this study are available in Figshare: Pham et al. (2025), Processed single-cell RNA-seq dataset of cancer-associated fibroblasts in breast cancer patients receiving anti–PD-1 therapy (https://doi.org/10.6084/m9.figshare.30663536.v1). This study reanalyzes raw data from the original BioKey study. Raw sequencing reads (scRNA-seq, scTCR-seq, CITE-seq, exome, and low-coverage WGS) are available under controlled access at the European Genome-phenome Archive (EGA) (study no. EGAS00001004809, accession EGAD00001006608). Public read count matrices are accessible at https://lambrechtslab.sites.vib.be/en. Public spatial transcriptomics data used for validation were obtained from 10x Genomics Visium platform: Human Breast Cancer: Ductal Carcinoma In Situ, Invasive Carcinoma (FFPE).
Author contributions
KD: Project administration, Supervision, Conceptualization, Methodology, Writing – review & editing, Validation, Investigation, Writing – original draft, Funding acquisition, Resources. AT: Methodology, Visualization, Writing – original draft, Formal analysis. AP: Resources, Formal analysis, Visualization, Writing – original draft, Methodology. TM: Formal analysis, Writing – original draft, Methodology. TP: Writing – review & editing, Supervision, Investigation, Software, Writing – original draft, Methodology. HD: Conceptualization, Writing – review & editing, Data curation, Investigation, Writing – original draft, Resources, Software.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Acknowledgments
The authors gratefully acknowledge BioTuring Inc. for providing access to the suite of computational platforms that facilitated this study. Specifically, we utilized BBrowserX for large-scale single-cell data processing and exploration, Talk2Data for the retrieval and querying of public single-cell and spatial transcriptomics datasets, SpatialX for spatial mapping and neighborhood analysis of CAF subtypes, and BioVinci for data visualization and figure generation. Collectively, these tools were essential for the comprehensive analyses presented herein.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2026.1754311/full#supplementary-material
References
1
DvirKGiordanoSLeoneJP. Immunotherapy in breast cancer. Int J Mol Sci. (2024) 25:7517. doi: 10.3390/ijms25147517
2
World Health Organization. Breast cancer – fact sheet (2025). Available online at: https://www.who.int/news-room/fact-sheets/detail/breast-cancer (Accessed November 12, 2025).
3
KunduMButtiRPandaVKMalhotraDDasSMitraTet al. Modulation of the tumor microenvironment and mechanism of immunotherapy-based drug resistance in breast cancer. Mol Cancer. (2024) 23:92. doi: 10.1186/s12943-024-01990-4
4
BiffiGTuvesonDA. Diversity and biology of cancer-associated fibroblasts. Physiol Rev. (2020) 101:147–76. doi: 10.1152/physrev.00048.2019
5
PrakashJShakedY. The interplay between extracellular matrix remodeling and cancer therapeutics. Cancer Discov. (2024) 14:1375–88. doi: 10.1158/2159-8290.CD-24-0002
6
KiefferYHocineHRGentricGPelonFBernardCBourachotBet al. Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer. Cancer Discov. (2020) 10:1330–51. doi: 10.1158/2159-8290.CD-19-1384
7
YangJXuQLuY. Decoding epithelial–fibroblast interactions in lung adenocarcinoma through single-cell and spatial transcriptomics. J Cancer Res Clin Oncol. (2025) 151:1–12. doi: 10.1007/s00432-025-06250-6
8
EmensLA. Breast cancer immunotherapy: facts and hopes. Clin Cancer Res. (2018) 24:511–20. doi: 10.1158/1078-0432.CCR-16-3001
9
BassezAVosHVan DyckLFlorisGArijsIDesmedtCet al. A single-cell map of intratumoral changes during anti-pd1 treatment of patients with breast cancer. Nat Med. (2021) 27:820–32. doi: 10.1038/s41591-021-01323-8
10
BioTuring Inc. Biovinci: A versatile data visualization platform for life scientists (2025). Available online at: https://app.bioturing.com/xplot (Accessed November 12, 2025).
11
BioTuring Inc. Bbrowserx: Single-cell analysis platform (2025). Available online at: https://app.bioturing.com/bbrowserx/ (Accessed November 12, 2025).
12
Ma’ayan Laboratory. Enrichr – interactive gene list enrichment analysis tool (2025). Available online at: https://maayanlab.cloud/Enrichr/ (Accessed November 11, 2025).
13
Reactome Consortium. Reactome – pathway database of human biological processes (2025). Available online at: https://reactome.org/ (Accessed November 11, 2025).
14
WikiPathways Consortium. Wikipathways – community-curated biological pathway database (2025). Available online at: https://www.wikipathways.org/ (Accessed November 11, 2025).
15
Gene Ontology Consortium. Gene ontology – a resource for gene product annotation (2025). Available online at: https://geneontology.org/ (Accessed November 11, 2025).
16
CellPhoneDB Consortium. Cellphonedb – interactive database for ligand-receptor mediated cell-cell communication (2025). Available online at: https://www.cellphonedb.org/ (Accessed November 12, 2025).
17
10x Genomics. Human breast cancer: Ductal carcinoma in situ, invasive carcinoma (FFPE) (2021). Available online at: https://www.10xgenomics.com/datasets/human-breast-cancer-ductal-carcinoma-in-situ-invasive-carcinoma-ffpe-1-standard-1-3-0 (Accessed December 18, 2025)
18
BioTuring Inc. Talk2data: Large-scale single-cell data platform (2025). Available online at: https://app.bioturing.com/talk2data/ (Accessed November 12, 2025).
19
BioTuring Inc. Spatialx: High-performance spatial transcriptomics analysis platform (2025). Available online at: https://bioturing.com/spatialx (Accessed December 19, 2025).
20
KayamoriKKatsubeKISakamotoKOhyamaYHiraiHYukimoriAet al. Notch3 is induced in cancer-associated fibroblasts and promotes angiogenesis in oral squamous cell carcinoma. PloS One. (2016) 11:e0154112. doi: 10.1371/journal.pone.0154112
21
BrechbuhlHMBarrettASKopinEHagenJCHanALGillenAEet al. Fibroblast subtypes define a metastatic matrisome in breast cancer. JCI Insight. (2020) 5:e130751. doi: 10.1172/jci.insight.130751
22
ImaokaTOkutaniTDainoKIizukaDNishimuraMShimadaY. Overexpression of notch-regulated ankyrin repeat protein is associated with breast cancer cell proliferation. Anticancer Res. (2014) 34:2165–71.
23
YeQWLiuYJLiJQHanMBianZRChenTYet al. Gja4 expressed on cancer associated fibroblasts (cafs)—a ‘promoter’of the mesenchymal phenotype. Trans Oncol. (2024) 46:102009. doi: 10.1016/j.tranon.2024.102009
24
ZhuHGuoSZhangYYinJYinWTaoSet al. Proton-sensing gpcr-yap signalling promotes cancer-associated fibroblast activation of mesenchymal stem cells. Int J Biol Sci. (2016) 12:389. doi: 10.7150/ijbs.13688
25
ForsthuberAAschenbrennerBKorosecAJacobTAnnusverKKrajicNet al. Cancer-associated fibroblast subtypes modulate the tumor-immune microenvironment and are associated with skin cancer Malignancy. Nat Commun. (2024) 15:9678. doi: 10.1038/s41467-024-53908-9
26
IwaiMTulafuMTogoSKawajiHKadoyaKNambaYet al. Cancer-associated fibroblast migration in non-small cell lung cancers is modulated by increased integrin α11 expression. Mol Oncol. (2021) 15:1507–27. doi: 10.1002/1878-0261.12937
27
Al-MnaseerZAM. Investigation into the role of the long non-coding RNAs NEAT1 and MIAT in breast cancer. Ph.D. thesis Keele Univ. (2018).
28
CainSMularczykESinghMMassam-WuTKieltyC. Adamts-10 and-6 differentially regulate cell-cell junctions and focal adhesions. Sci Rep. (2016) 6:35956. doi: 10.1038/srep35956
29
DavidsonSColesMThomasTKolliasGLudewigBTurleySet al. Fibroblasts as immune regulators in infection, inflammation and cancer. Nat Rev Immunol. (2021) 21:704–17. doi: 10.1038/s41577-021-00540-z
30
JooEHKimSParkDLeeTParkWYHanKYet al. Migratory tumor cells cooperate with cancer associated fibroblasts in hormone receptor-positive and her2-negative breast cancer. Int J Mol Sci. (2024) 25:5876. doi: 10.3390/ijms25115876
31
DaubriacJHanSGrahovacJSmithEHoseinABuchananMet al. The crosstalk between breast carcinoma-associated fibroblasts and cancer cells promotes rhoa-dependent invasion via igf-1 and pai-1. Oncotarget. (2017) 9:10375. doi: 10.18632/oncotarget.23735
32
YuZLiuHYeJLiuYXinLLiuQet al. Integrative analysis identifies cancer cell-intrinsic rarres1 as a predictor of prognosis and immune response in triple-negative breast cancer. Front Genet. (2024) 15:1360507. doi: 10.3389/fgene.2024.1360507
33
WangHYnZZhangSLiuKHuangRLiZet al. Transcriptome-wide analysis reveals potential roles of cfd and angptl4 in fibroblasts regulating b cell lineage for extracellular matrix-driven clustering and novel avenues for immunotherapy in breast cancer. Mol Med. (2025) 31:179. doi: 10.1186/s10020-025-01237-y
34
ThomasMEJieEKimAMMayberryTGCowanBCLuechtefeldHDet al. Exploring the role of antigen-presenting cancer-associated fibroblasts and cd74 on the pancreatic ductal adenocarcinoma tumor microenvironment. Med Oncol. (2024) 42:15. doi: 10.1007/s12032-024-02564-6
35
ChenXChenFJiaSLuQZhaoM. Antigen-presenting fibroblasts: emerging players in immune modulation and therapeutic targets. Theranostics. (2025) 15:3332. doi: 10.7150/thno.104900
36
GaoZJFangHSunSLiuSQFangZLiuZet al. Single-cell analyses reveal evolution mimicry during the specification of breast cancer subtype. Theranostics. (2024) 14:3104. doi: 10.7150/thno.96163
37
Lujano OlazabaOFarrowJMonkkonenT. Fibroblast heterogeneity and functions: insights from single-cell sequencing in wound healing, breast cancer, ovarian cancer and melanoma. Front Genet. (2024) 15:1304853. doi: 10.3389/fgene.2024.1304853
38
AhmedMSULordBDAdu AddaiBSinghalSKGardnerKSalamABet al. Immune profile of exosomes in african american breast cancer patients is mediated by kaiso/thbs1/cd47 signaling. Cancers. (2023) 15:2282. doi: 10.3390/cancers15082282
39
ZielińskaKAKatanaevVL. The signaling duo cxcl12 and cxcr4: chemokine fuel for breast cancer tumorigenesis. Cancers. (2020) 12:3071. doi: 10.3390/cancers12103071
40
AllerEJNairHBVadlamudiRKViswanadhapalliS. Significance of midkine signaling in women’s cancers: Novel biomarker and therapeutic target. Int J Mol Sci. (2025) 26:4809. doi: 10.3390/ijms26104809
41
ChenWJiangMZouXChenZShenLHuJet al. Fibroblast activation protein (fap)+ cancer-associated fibroblasts induce macrophage m2-like polarization via the fibronectin 1-integrin α5β1 axis in breast cancer. Oncogene. (2025) 44:2396–412. doi: 10.1038/s41388-025-03359-3
42
RockMJHoldenPHortonWACohnDH. Cartilage oligomeric matrix protein promotes cell attachment via two independent mechanisms involving cd47 and. αvβ3 integrin. Mol Cell Biochem. (2010) 338:215–24. doi: 10.1007/s11010-009-0355-3
43
LimSYooBKKimHSGilmoreHLLeeYHpLet al. Amyloid-β precursor protein promotes cell proliferation and motility of advanced breast cancer. BMC Cancer. (2014) 14:928. doi: 10.1186/1471-2407-14-928
44
PupoMPisanoAAbonanteSMaggioliniMMustiAM. Gper activates notch signaling in breast cancer cells and cancer-associated fibroblasts (cafs). Int J Biochem Cell Biol. (2014) 46:56–67. doi: 10.1016/j.biocel.2013.11.011
45
CohenSShacharI. Midkine as a regulator of b cell survival in health and disease. Br J Pharmacol. (2014) 171:888–95. doi: 10.1111/bph.12419
46
CroizerHMhaidlyRKiefferYGentricGDjerroudiLLeclereRet al. Deciphering the spatial landscape and plasticity of immunosuppressive fibroblasts in breast cancer. Nat Commun. (2024) 15:2806. doi: 10.1038/s41467-024-47068-z
47
GroutJASirvenPLeaderAMMaskeySHectorEPuisieuxIet al. Spatial positioning and matrix programs of cancer-associated fibroblasts promote t-cell exclusion in human lung tumors. Cancer Discov. (2022) 12:2606–25. doi: 10.1158/2159-8290.CD-21-1714
48
WuSZAl-EryaniGRodenDLJunankarSHarveyKAnderssonAet al. A single-cell and spatially resolved atlas of human breast cancers. Nat Genet. (2021) 53:1334–47. doi: 10.1038/s41588-021-00911-1
49
LoretNVandammeNDe ConinckJTaminauJDe ClercqKBlanckeGet al. Distinct transcriptional programs in ascitic and solid cancer cells induce different responses to chemotherapy in high-grade serous ovarian cancer. Mol Cancer Res. (2022) 20:1532–47. doi: 10.1158/1541-7786.MCR-21-0565
50
AspMGiacomelloSLarssonLWuCFürthDQianXet al. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell. (2019) 179:1647–60. doi: 10.1016/j.cell.2019.11.025
51
CordsLTietscherSAnzenederTLangwiederCReesMde SouzaNet al. Cancer-associated fibroblast classification in single-cell and spatial proteomics data. Nat Commun. (2023) 14:4294. doi: 10.1038/s41467-023-39762-1
52
WuSZRodenDLWangCHollidayHHarveyKCazetASet al. Stromal cell diversity associated with immune evasion in human triple-negative breast cancer. EMBO J. (2020) 39:e104063. doi: 10.15252/embj.2019104063
53
LeeHOHongYEtliogluHEChoYBPomellaVVan den BoschBet al. Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer. Nat Genet. (2020) 52:594–603. doi: 10.1038/s41588-020-0636-z
54
StorrsEPChatiPUsmaniASloanIKrasnickBABabbraRet al. High-dimensional deconstruction of pancreatic cancer identifies tumor microenvironmental and developmental stemness features that predict survival. NPJ Precis Oncol. (2023) 7:105. doi: 10.1038/s41698-023-00455-z
55
LuLWangJRHendersonYCBaiSYangJHuMet al. Anaplastic transformation in thyroid cancer revealed by single-cell transcriptomics. J Clin Invest 133. (2023) 133:e169653. doi: 10.1172/JCI169653
56
QianJOlbrechtSBoeckxBVosHLaouiDEtliogluEet al. A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling. Cell Res. (2020) 30:745–62. doi: 10.1038/s41422-020-0355-0
57
LeeJJBernardVSemaanAMonbergMEHuangJStephensBMet al. Elucidation of tumor-stromal heterogeneity and the ligand-receptor interactome by single-cell transcriptomics in real-world pancreatic cancer biopsies. Clin Cancer Res. (2021) 27:5912–21. doi: 10.1158/1078-0432.CCR-20-3925
58
ZilionisREngblomCPfirschkeCSavovaVZemmourDSaatciogluHDet al. Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. Immunity. (2019) 50:1317–34. doi: 10.1016/j.immuni.2019.03.009
59
QiZLiuYMintsMMullinsRSampleRLawTet al. Single-cell deconvolution of head and neck squamous cell carcinoma. Cancers. (2021) 13:1230. doi: 10.3390/cancers13061230
60
FeigCJonesJOKramanMWellsRJDeonarineAChanDSet al. Targeting cxcl12 from fap-expressing carcinoma-associated fibroblasts synergizes with anti–pd-l1 immunotherapy in pancreatic cancer. Proc Natl Acad Sci. (2013) 110:20212–7. doi: 10.1073/pnas.1320318110
61
HanleyCJ. Thomas GJ. T-cell tumour exclusion and immunotherapy resistance: a role for caf targeting. Br J Cancer. (2020) 123:1353–5. doi: 10.1038/s41416-020-1020-6
62
WangMYuFZhangYLiP. Novel insights into notch signaling in tumor immunity: potential targets for cancer immunotherapy. Front Immunol. (2024) 15:1352484. doi: 10.3389/fimmu.2024.1352484
63
ZhangXZhangXYangQHanRFadhulWSachdevaAet al. Comprehensive analysis of adgre5 gene in human tumors: clinical relevance, prognostic implications, and potential for personalized immunotherapy. Heliyon. (2024) 10:e27459. doi: 10.1016/j.heliyon.2024.e27459
64
BonninERodrigo RiestraMMarzialiFMena OsunaRDenizeauJMaurinMet al. Cd74 supports accumulation and function of regulatory t cells in tumors. Nat Commun. (2024) 15:3749. doi: 10.1038/s41467-024-47981-3
65
HongLHuangFHuZDongQKongYZhengXet al. Role of pd-1 in modulating ifn-γ-cxcl9/10-cxcr3 signaling in breast cancer. J Biochem Mol Toxicol. (2024) 38:e23842. doi: 10.1002/jbt.23842
66
GhoshAMitraAK. Metastasis and cancer associated fibroblasts: taking it up a notch. Front Cell Dev Biol. (2024) 11:1277076. doi: 10.3389/fcell.2023.1277076
67
LiuZBaYShanDZhouXZuoAZhangYet al. Thbs2-producing matrix cafs promote colorectal cancer progression and link to poor prognosis via the cd47-mapk axis. Cell Rep. (2025) 44:115555. doi: 10.1016/j.celrep.2025.115555
68
ZhaoYLiYWangPZhuMWangJXieBet al. The cancer-associated fibroblasts interact with Malignant t cells in mycosis fungoides and promote the disease progression. Front Immunol. (2025) 15:1474564. doi: 10.3389/fimmu.2024.1474564
69
MariathasanSTurleySJNicklesDCastiglioniAYuenKWangYet al. Tgfβ attenuates tumour response to pd-l1 blockade by contributing to exclusion of t cells. Nature. (2018) 554:544–8. doi: 10.1038/nature25501
70
ChakravarthyAKhanLBenslerNPBosePDe CarvalhoDD. Tgf-β-associated extracellular matrix genes link cancer-associated fibroblasts to immune evasion and immunotherapy failure. Nat Commun. (2018) 9:4692. doi: 10.1038/s41467-018-06654-8
71
TaurielloDVPalomo-PonceSStorkDBerenguer-LlergoABadia-RamentolJIglesiasMet al. Tgfβ drives immune evasion in genetically reconstituted colon cancer metastasis. Nature. (2018) 554:538–43. doi: 10.1038/nature25492
Summary
Keywords
anti-PD-1 therapy, breast cancer, CAF subtypes, cancer-associated fibroblasts (CAF), immune checkpoint inhibitors, immune resistance, single-cell RNA sequencing, tumor microenvironment
Citation
Do KV, Tran AV, Pham AD, Mac TT, Pham TL and Do HN (2026) Molecular remodeling of cancer-associated fibroblasts in breast cancer patients receiving anti–PD-1 immunotherapy. Front. Oncol. 16:1754311. doi: 10.3389/fonc.2026.1754311
Received
25 November 2025
Revised
27 December 2025
Accepted
30 January 2026
Published
24 February 2026
Volume
16 - 2026
Edited by
Federica Papaccio, University of Salerno, Italy
Reviewed by
Caterina De Rosa, University of Campania Luigi Vanvitelli, Italy
Manuel Cabeza-Segura, INCLIVA Biomedical Research Institute, Spain
Updates
Copyright
© 2026 Do, Tran, Pham, Mac, Pham and Do.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Khanh Van Do, khanh.dovan@phenikaa-uni.edu.vn
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.