YAP1 Inhibition Induces Phenotype Switching of Cancer-Associated Fibroblasts to Tumor Suppressive in Prostate Cancer

Immune-checkpoint blockade (ICB) therapies have made great breakthroughs in the field of advanced cancer treatment (1). Programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA4) promote antitumor immunity and confer a durable benefit in patients with cancer (2, 3). Prostate cancer, unfortunately, is characterized by an immunologically “cold” tumor microenvironment (TME) with a relative dearth of infiltrating immune cells (4, 5). Clinical trials have revealed that anti-PD-1 therapy alone has a minimal effect on prostate cancer (6, 7). The inability of ICB therapies to induce durable clinical responses has highlighted the complex immunosuppressive nature of its TME (8). Therefore, understanding immunosuppressive mechanisms and identifying new immunotherapy targets are prerequisites to increasing the response rate of prostate cancer to ICB.

Cancer-associated fibroblasts (CAF) are essential components of the TME. They are primarily associated with ICB therapy resistance through diverse mechanisms, including supporting tumor cell growth by the secretion of growth factors and chemokines and by extracellular matrix (ECM) remodeling (9–11). Furthermore, CAFs act as barriers preventing drug delivery, attenuating the effect of immunotherapies on several solid tumors (12, 13). Therefore, CAFs are considered promising targets to enhance the immunotherapy response. However, therapeutic strategies targeting CAFs are not potent, partly owing to the complexity and plasticity of CAFs. Therefore, further in-depth study on CAFs is required. Notably, most studies on CAFs have described their protumorigenic functions, but also with accumulating evidence suggesting that some CAF subtypes also fulfill antitumor functions (14), implying that CAF-targeting approaches must be selective and individualized.

Recent single-cell RNA sequencing (scRNA-seq) techniques have corroborated the previously unappreciated complexity and plasticity of CAFs in multiple cancer types (15). Certain CAF subtypes are formed in response to specific signals (16, 17). For instance, in pancreatic ductal adenocarcinoma, IL1 reportedly induces a CAF subtype via inflammatory chemokines, and TGFβ antagonizes this process by promoting ECM remodeling (18). Another study revealed that two distinct CAF subtypes reversed their phenotype when cultured in different models (19). These studies indicated that CAFs switch between phenotypes to cope with the dynamic and complex TME. In the past few decades, many studies in which CAFs were targeted for therapy revealed that they had antitumor functions, challenging the long-held belief that they are specifically protumorigenic (20–23). CAFs respond to proinflammatory signals, producing various chemokines and cytokines to recruit and activate leukocytes (24). This process involves genes encoding chemokines and cytokines, such as chemokine (C–X–C motif) ligand 9 (CXCL9) and CXCL10 (25–27). However, the mechanisms governing this phenotypic shift remain largely unknown. Therefore, the mechanisms and uses of this plasticity of CAFs to suppress tumor progression should be clarified.

Therefore, we integrated four prostate cancer scRNA-seq datasets from 37 samples to analyze the heterogeneity and plasticity of CAFs. We identified the pro- and antitumorigenic phenotypes of two different CAF subtypes and investigated the mechanisms of the phenotypic switch. Using in vivo and in vitro experiments, we discovered a therapeutic strategy of targeting a special CAF subtype to switch from a protumorigenic to an antitumor phenotype, enhancing the ICB in prostate cancer.

The human prostate cancer samples were obtained from patients who underwent radical prostatectomy for the treatment of prostate cancer at Xijing Hospital, Fourth Military Medical University. We obtained written informed consent from all the patients and the study protocol was approved by the Ethics Committees of Xijing Hospital (No. KY20223431-1) that followed the Declaration of Helsinki. The human prostate cancer tissue microarray (TMA) was purchased from SuperBiotec (#PRC1601). The clinical information of patients and information on the TMA are listed in Supplementary Table S1.

All animals used in this study received human care in compliance with the principles stated in the Guide for the Care and Use of Laboratory Animals. The animal experiments in this study were approved by the Laboratory Animals Welfare Ethics Committee of Fourth Military Medical University (No. 20220801). Mice were housed in the Laboratory Animal Center of the Fourth Military Medical University. Room temperature was maintained at 22°C ± 3°C and the humidity at 55% ± 15%. Animals were kept at a 12/12 hours dark/light cycle with free access to food and water. The C57BL/6 and NOD-Prkdc^em26Cd52Il2rg^em26Cd22/Gpt (NCG) mice (6–8 weeks old, male, body weight 18–23 g) were purchased from GemPharmatech LLC. Cd248-CreER^T2, Rosa26-LSL-tdTomato, and Yap1^flox/flox mice were purchased from the Shanghai Model Organisms Center. All mice were on the C57BL/6 background. Mice were genotyped by PCR using mouse genomic DNA from tail biopsy specimens. Genotyping primers and expected band sizes are listed in Supplementary Table S2.

The HEK293T cell line, human prostate cancer cell line 22Rv1, mouse prostate cancer cell line RM-1, and Tramp-C1 were purchased from Zqxzbio Co., Ltd. HEK293T and Tramp-C1 cells were cultured in DMEM (Gibco) containing 10% FBS (Gibco). 22Rv1 and RM-1 cells were cultured in RPMI-1640 (Gibco) containing 10% FBS. Human CD3⁺ T cells were isolated from peripheral blood mononuclear cells (PBMC) by using the Human CD3⁺ T-cell Isolation Kit (BioLegend, #480022) and cultured in RPMI-1640 containing 10% FBS. Human primary CAFs were dissociated from tumor samples obtained from patients with prostate cancer at Xijing Hospital, Fourth Military Medical University. Mouse primary stromal cells were dissociated from 8- to 12-week-old C57BL/6 mouse prostate tissues as described previously (28). The primary stromal cells were cultured in DMEM/F12 (Gibco) containing 10% FBS. All the experiments in this study used fresh primary stromal cells within three passages after single-cell dissociation from prostates. All cells were incubated at 37°C with 5% CO₂. Mycoplasma contamination was tested monthly by PCR. Cell lines were authenticated and were not passaged more than 10 times.

For inducing ECM-CAF, we used TGFβ (20 ng/mL) stimulation and cultured in DMEM/F12 containing 10% FBS for 48 hours. For inducing Lym-CAF, we used TNFα (20 ng/mL) combined with IFNγ (20 ng/mL) stimulation and cultured in DMEM/F12 containing 10% FBS for 48 hours. Cells were washed twice in phosphate-buffered saline (PBS) and cultured in DMEM/F12 containing 10% FBS without exogenous cytokines.

For subcutaneous tumor syngeneic graft, 1 × 10⁵ RM-1 cells mixed with 3 × 10⁵ mouse prostate stromal cells in 100-μL PBS were injected into 6- to 8-week-old male C57BL/6 mice. For subcutaneous tumor xenograft, 5 × 10⁵ 22Rv1 cells mixed with 1.5 × 10⁶ human primary CAFs in 100-μL PBS were injected into 6- to 8-week-old male NCG hosts. CAFs had been infected with lentiviruses expressing shCON (5′-TTCTCCGAACGTGTCACGT-3′) or shYAP1 (5′-CTCAGGAATTGAGAACAATGA-3′). For the study of selective Yes-associated protein 1 (YAP1) depletion, after intraperitoneal injections (120 mg/kg/day for five consecutive days) of tamoxifen (MCE, #HY13757), 1 × 10⁵ RM-1 cells or 1 × 10⁶ Tramp-C1 cells in 100-μL PBS were injected subcutaneously into Cd248-CreER^T2; Yap1 ^flox/flox and age-matched littermate control Cd248-CreER^T2 mice. For bioluminescence imaging, D-luciferin (75 mg/kg; MCE, #HY12591) was intraperitoneally administered and an In Vivo Imaging Systems (PerkinElmer) was used to acquire images of mice in the prone position 15 minutes after injection. For the study of ICB treatment, mice were inoculated with 10-mg/kg antimouse PD-1 antibodies (Bio X Cell, #BE0146). Control mice were inoculated with 10-mg/kg respective antimouse IgG₁ kappa isotype control antibodies (Bio X Cell, #BE0083). Antibodies were administered via intraperitoneal injection on days 7, 10, 13, 16, and 19 after tumor cell implantation. All experiments were terminated as planned or once the diameter of the tumor exceeded 1.5 cm.

Human prostate cancer specimens or mouse tumor cells were dissociated into single-cell suspensions according to the manufacturer’s protocol (Miltenyi Biotec). The samples were filtered through a 70-μm filter and removed supernatant. Cells were subsequently incubated with fluorescent-conjugated antibodies at 4°C for 30 minutes. For analysis of the infiltration and activation of CD8⁺ T cells, single-cell suspensions were stained with Fixable Viability Dye (BioLegend, #423105) at 4°C for 20 minutes. Cells were subsequently stained with antimouse CD45, CD8, and other antibody panels. Information on the antibodies used for flow cytometry analyses is listed in Supplementary Table S3.

For fluorescence-activated cell sorting (FACS), we first applied a selection to exclude epithelial (EPCAM⁺), hematopoietic (CD45⁺), and endothelial (CD31⁺) cells. Thereafter, we used ECM-CAF marker CD248 or Lym-CAF marker TSG6. To do so, cells were stained with an antibody mix containing anti-EPCAM-APC (Invitrogen, #17579182), anti-CD31-PE (BioLegend, #303105), anti-CD45-Pacific Blue (BioLegend, #304021), anti-CD248 primary antibody (BioLegend, #949902), or anti-TSG6 primary antibody (Abcam, #ab267469). Anti-CD248 and anti-TSG6 antibodies were conjugated with the secondary antibody FITC (Abcam, #ab6785 and #ab6717). Data analysis was performed using FlowJo software (Tree Star Inc.). Gates were set using appropriate fluorescence minus on controls.

Human prostate cancer specimens were obtained from Xijing Hospital, Fourth Military Medical University. The corresponding organoids model was established according to the protocol (29). Specimens were cut into small pieces and cultured in a 15-mL centrifuge tube containing digestive liquid (2.5-mg/mL type II collagenase and 10-µmol/L Y27632 basic medium) for 1 hour. The digestive products were filtered through a 70 μm filter and removed supernatant. The cells were cultured with 30 μL of matrix glue in a 24-well plate (1 × 10⁵ cells/well) for 15 minutes; 500 μL of organoids culture medium was added after the matrix gel solidified as previously described (29). When the diameters of the organoids were approximately 50 to 100 μm, 1 × 10⁵ human primary CAFs per well were mixed with organoids to investigate the functions of CAFs.

For the small interfering RNA (siRNA) experiment, 1 × 10⁵ CAFs were transfected with 10-nmol/L YAP1/NF-κB p65 siRNA or negative control siRNA. YAP1 silencing was performed with two distinct siRNA targeting YAP1 (5′-CUG​CCA​CCA​AGC​UAG​AUA​ATT-3′ and 5′-GGU​GAU​ACU​AUC​AAC​CAA​ATT-3′). NF-κB p65 silencing was performed with two different siRNA targeting NF-κB p65 (5′-GAU​UGA​GGA​GAA​ACG​UAA​A-3′ and 5′-AAU​ACA​CCU​CAA​UGU​CCU​C-3′). Efficient YAP1 and p65 silencing were observed after 48 hours and maintained during the coculture experiment. For the overexpression experiment, YAP1 or IKKα plasmids were transfected into CAFs. These experiments were performed using Lipofectamine 2000 transfection reagent (Invitrogen, #11668019), following the provided instructions. For the coculture model of CAFs with CD3⁺ lymphocytes, 1 × 10⁵ CAFs (preinduced to ECM-CAF or Lym-CAF) were plated in the lower chamber of six-well plate (LABSELECT, #14111) in DMEM/F12 containing 10% FBS. After CAFs were silencing YAP1 or NF-κB p65, the medium was replaced with fresh DMEM/F12 containing 1% FBS just before adding 5 × 10⁵ CD3⁺ lymphocytes in the upper chamber. The CD3⁺ lymphocytes were activated prior to coculturing with CAFs by using CD3/CD28 Dynabeads (Gibco, #11161). After being cocultured for 48 hours, CD3⁺ lymphocytes and CAFs were collected and analyzed separately.

Protein samples were separated by 10% SDS-PAGE and transferred onto a polyvinylidene difluoride membrane (Thermo Fisher Scientific). The membrane was blocked in 5% skimmed milk, incubated with the primary antibodies listed in Supplementary Table S4 at 4°C overnight, incubated with horseradish peroxidase–conjugated secondary antibodies, and visualized via chemiluminescence. The human CXCL9 ELISA Kit (Abcam, #ab219047), CXCL10 ELISA Kit (Abcam, #ab83700) and CXCL11 ELISA Kit (Abcam, #ab289695) were used to determine CXCL9/CXCL10/CXCL11 levels in the cell culture media according to the manufacturer’s instructions.

Total RNA was isolated with TRIzol, and reverse transcription (RT) was performed using PrimeScript RT Master Mix (TaKaRa, #RR036A). Then, quantitative PCR was performed using a SYBR Green Master Mix Kit (YEASEN, #11201ES). All quantitative PCR data are shown as the means ± SD. Primer sequences are listed in Supplementary Table S5.

Total RNA was isolated with TRIzol as mentioned above. Human primary ECM-CAF and Lym-CAF were obtained by FACS and performed RNA-seq. After being cocultured with CD3⁺ lymphocytes for 48 hours, CAFs were collected and performed RNA-seq. Libraries were sequenced using the Illumina HiSeq sequencing platform. Differential expression was determined using DESeq2 v3.10 in R v4.1.0.

ScRNA-seq data for prostate cancer were obtained from 37 samples of 33 patients (30–33). The clinical information of these patients and the numbers of cells in each sample are listed in Supplementary Table S6. scRNA-seq data analysis was performed using the Seurat package (v4.0.5) in R v4.1.0 (34). Seurat Objects were filtered to exclude cells that expressed fewer than 200 genes, more than 6,000 or 8,000 genes, higher than 20% mitochondrial genes, and higher than 0.1% or 1% hemoglobin genes; genes expressed in fewer than three cells were also excluded. All Seurat Objects were merged to generate a combined Seurat Object, and the merged Seurat Object was normalized and scaled separately using the NormalizeData and ScaleData functions. Based on the variable genes, principal component analysis (PCA) was conducted using the RunPCA function. After the PCA, we applied Harmony Package (v0.1.0) to integrate the merged Seurat Objects and correct batch effects from different samples (35). Visualization was performed via Uniform Manifold Approximation and Projection (UMAP). Marker genes of cell clusters were identified using the Find All Markers function via Wilcoxon rank-sum tests. To infer the pseudotime trajectory on UMAP, user-selected cell embeddings were fed to the Monocle algorithm in BBrowser (36).

HEK293T cells were transfected with NF-κB luciferase reporter plasmid together with thymidine kinase promoter-Renilla luciferase vectors as reference controls using Lipofectamine 2000 transfection reagent. Cells were subjected to luciferase activity measurement as described in dual-luciferase reporter assay kit (Promega).

Coimmunoprecipitation was performed as described previously (37) using antibodies anti-YAP1 (Proteintech, #13584-1-AP), and anti-IKKα (Abcam, #ab32041) antibodies. Human IgG (Proteintech, #30000-0-AP) was used as a negative control.

Prostate cancer tissues were fixed in 10% buffered formalin and paraffin-embedded. Hematoxylin and eosin staining and immunofluorescence staining were performed with 5-μm sections, as previously described (38). The information on primary antibodies is listed in Supplementary Table S4. Immunofluorescence staining was visualized using a ZEISS laser scanning microscope 900 (ZEISS). Images were processed using the image-processing package Fiji (https://fiji.sc/). For quantification of CD8⁺ T cells, αSMA⁺ cells, and YAP1⁺ αSMA⁺ cells, three representative fields of 0.1 mm² for each section were evaluated. The number of these cells was evaluated in each field. The proportion of YAP1⁺ of αSMA⁺ cells was calculated by the number of YAP1⁺ αSMA⁺ cells/αSMA⁺ cells. Thresholds were set manually for each channel and kept consistent for each image.

Statistical tests were performed using GraphPad Prism 8. Unless otherwise indicated, data are presented as ± standard errors of the mean, and comparisons between two groups were made using a two-tailed Student t test. If more than two groups were compared, one-way ANOVA analysis was performed with the Dunnett test for multiple comparisons. For all correlation analyses, R² values were calculated from the Pearson correlation coefficient. For survival analysis, data were analyzed with a long-rank test, comparing only two groups at a time. P values are reported as follows: nonsignificant (ns), P ≥ 0.05; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

The scRNA-seq data analyzed in this study were obtained from the Genome Sequence Archive for Human (accession number: HRA000823) and the Gene Expression Omnibus (accession number: GSE176031, GSE141445, and GSE172316). The Cancer Genome Atlas (TCGA)-prostate adenocarcinoma bulk RNA-seq datasets and clinical data were obtained from the UCSC Xena platform (39). The RNA-seq data of prostate cancer primary CAFs are available through NCBI BioProject (accession number: PRJNA1113297). All other raw data generated in this study are available upon request from the corresponding author.

We investigated the immunosuppressive nature of the prostate cancer TME by combining the scRNA-seq datasets of 37 prostate cancer samples from 33 patients (Fig. 1A; Supplementary Fig. S1A and S1B; refs. 30–33). Further analysis revealed that the CAFs were divided into six clusters (Fig. 1B; Supplementary Fig. S1C and S1D). We performed differential gene expression analysis and identified the characteristic markers of each CAF subtype (Fig. 1C). We investigated the differences in CAF function among the six clusters via Gene Ontology analysis and found that two clusters exerted different functions: c3CAFs, which were highly associated with collagen fibril organization, and c5CAFs, which were associated with leukocyte migration and inflammatory response regulation (Fig. 1D). We further investigated these two subtypes by dissociating c3CAFs with the characteristic marker CD248 (also called endosialin) and c5CAFs with the marker TSG6 [tumor necrosis factor alpha-induced protein 6 (TNFAIP6)] from human prostate cancer via FACS and performing RNA-seq analysis (Fig. 1E; Supplementary Fig. S1E and S1F). These analyses demonstrated that c3CAFs were highly enriched in CD248, collagen type I alpha 1 chain (COL1A1), COL1A2, calponin 1 (CNN1), and cellular communication network factor 2 (CCN2), whereas c5CAFs were highly enriched in TNFAIP6, IL33, CXCL8, CXCL9, CXCL10, and CXCL11 (Fig. 1F; Supplementary Fig. S1G and S1H). Based on their functions and characteristic genes, we termed c3CAFs “ECM-associated CAF” (ECM-CAF) and c5CAFs “lymphocyte-associated CAF” (Lym-CAF).

We compared the major functions of ECM-CAF and Lym-CAF by using Gene Ontology analysis of the RNA-seq data. We discovered that ECM-CAF was associated with ECM organization, whereas Lym-CAF was associated with immune response and leukocyte activation, consistent with the scRNA-seq database results (Fig. 1G and H). We sought to gain insight into how ECM-CAF and Lym-CAF were induced by enriching the pathways of each subtype. The results of gene set enrichment analysis indicated that ECM-CAF was enriched on TGFβ signaling pathway, whereas Lym-CAF was enriched on TNFα and IFNγ signaling response (Supplementary Fig. S1I and S1J). We speculated that ECM-CAF was induced by TGFβ stimulation and Lym-CAF was induced by the combined stimulation of TNFα/IFNγ. Therefore, we dissociated human primary CAFs from patients with prostate cancer and cultured them in a medium with exogenous cytokines for 48 hours, we used quantitative PCR to detect the characteristic markers of ECM-CAF and Lym-CAF. The results confirmed that TGFβ stimulation increased the expression of ECM-CAF-associated markers, so we called “induced ECM-CAF” (i-ECM-CAF). TNFα/IFNγ stimulation increased the expression of Lym-CAF-associated markers, and we called “induced Lym-CAF” (i-Lym-CAF; Fig. 1I and J). These results revealed the functions of ECM-CAF and Lym-CAF and identified their respective characteristic markers.

To validate the functions of ECM-CAF and Lym-CAF in vivo, we designed a tumor syngeneic graft experiment for coinjection of RM-1 and preinduced CAFs (Fig. 2A). First, we dissociated mouse prostate stromal cells and cultured them in TGFβ or TNFα/IFNγ stimulation to induce the phenotype of ECM-CAF or Lym-CAF. Then, we implanted tumor syngeneic graft subcutaneously with prostate cancer cells RM-1 mixed preinduced CAFs. Notably, the two CAF subtypes exerted opposite biologic effects. RM-1 cells mixed with i-ECM-CAF significantly promoted prostate cancer growth. However, i-Lym-CAF suppressed tumor growth compared with Control CAF (Fig. 2B and C; Supplementary Fig. S2A). We used flow cytometry to determine the infiltration of CD8⁺ T cells and evaluated the activation of CD8⁺ T cells by CD25, CD69, granzyme B (Gra B), and IFNγ. The results revealed that i-Lym-CAF increased the infiltration and activation of CD8⁺ T cells (Fig. 2D–H; Supplementary Fig. S2B). We further analyzed the clinical significance of Lym-CAF in TCGA datasets. The results emphasized that a higher Lym-CAF proportion yields a higher ESTIMATE immune score, and progression-free interval survival analysis confirmed that a high proportion of Lym-CAF was associated with a better prognosis (Supplementary Fig. S2C and S2D).

TNFα and IFNγ are mainly secreted by the immune cells in the TME (40, 41). Therefore, we cocultured CAFs with CD3⁺ lymphocytes obtained from human PBMCs. After with/without coculturing for 48 hours, CAFs were collected and analyzed by RNA-seq. The results revealed that the characteristic markers of Lym-CAF, such as TNFAIP6, IL33, CXCL10, and CXCL11 were upregulated after being cocultured with CD3⁺ lymphocytes, implying that Lym-CAF phenotype was induced (Fig. 2I). Gene Ontology analysis indicated that after cocultured with CD3⁺ lymphocytes, the function of immune response in CAFs was enrichment (Supplementary Fig. S2E and S2F). To investigate the mechanisms of Lym-CAF induction, we used transcription factor enrichment analysis. We revealed that RELA (NF-κB p65) was the most strongly upregulated transcription factor in Lym-CAF after coculturing with CD3⁺ lymphocytes (Fig. 2J). Similarly, the scRNA-seq data indicated that RELA was specifically highly expressed in Lym-CAF (Supplementary Fig. S2G). Accordingly, we examined the features of Lym-CAF in pathologic sections of human prostate cancer by using immunofluorescence staining, which revealed good colocalization of TSG6, p65, and αSMA (Fig. 2K). Furthermore, we verified the nuclear translocation of p65 and our results indicated that p65 was activated in i-Lym-CAF (Fig. 2L), which implies that Lym-CAF exerts its activation effects on CD8⁺ T cells through the NF-κB p65 signaling pathway.

We used siRNA to knock down p65 expression in i-Lym-CAF before coculturing them with CD3⁺ lymphocytes. We evaluated the p65 knockdown efficiency in i-Lym-CAF and detected the p65 in CD3⁺ lymphocytes to exclude inadvertent silencing (Supplementary Fig. S2H and S2I). After coculturing for 48 hours, CD3⁺ lymphocytes were collected and analyzed by flow cytometry (Supplementary Fig. S2J). We evaluated the activation of CD8⁺ T cells by Gra B and CD25, and the results demonstrated that p65 exerted an immune-activation effect in Lym-CAF (Fig. 2M–O). We also used quantitative PCR to detect the expression of CXCL9/10/11 in i-Lym-CAF, and the results indicated that NF-κB p65 was essential to secret CXCL9/10/11 (Supplementary Fig. S2K). Based on these results, we verified that Lym-CAF exhibited an antitumor phenotype and may activate the CD8⁺ T cells through the NF-κB p65 signaling pathway.

The hypersecretion and deposition of collagen and ECM by CAFs reportedly contribute to the immunologically “cold” TME (9). Our study similarly revealed and identified a CAF subtype with a signature of ECM organization, ECM-CAF. As mentioned above, RM-1 cells mixed with i-ECM-CAF significantly promoted prostate cancer growth. Therefore, we further demonstrated that ECM-CAF significantly promoted collagen deposition by using Masson staining and morphologic maps of atomic force microscopy (AFM; Fig. 3A and B). TCGA datasets analysis indicated that ECM-CAF was highly positive correlated with ECM-score, and progression-free interval survival analysis revealed that high ECM-CAF proportion yielded a poor prognosis (Supplementary Fig. S3A and S3B). Similarly, the scRNA-seq data indicated that ECM-CAF highly expressed ECM-associated genes (Supplementary Fig. S3C).

YAP1 reportedly exerts transcriptional control over hepatic stellate cell activation (42, 43). The activation of hepatic stellate cells caused fibrogenesis and promoted ECM-associated genes, exhibiting ECM-CAF phenotype to some extent. Therefore, we investigated the contribution and mechanisms of YAP1 in ECM-CAF in prostate cancer. First, the scRNA-seq data indicated that YAP1 was highly expressed in ECM-CAF (Supplementary Fig. S3D). Similarly, we examined ECM-CAF features in pathologic sections of human prostate cancer samples by using immunofluorescence staining, which revealed good colocalization of CD248, YAP1, and αSMA (Fig. 3C). Second, we verified the nuclear translocation of YAP1 and results indicated that YAP1 was activated in i-ECM-CAF (Fig. 3D). Furthermore, we evaluated the functions of YAP1 in ECM-CAF in vitro by dissociating and culturing prostate cancer organoids. We cocultured the organoids with human-induced ECM-CAF and demonstrated that CAFs, especially i-ECM-CAF, significantly promote the growth of such organoids. Notably, i-ECM-CAF enhanced the tumor-promoting effect, whereas YAP1 knockdown reversed the tumor-promoting effect of i-ECM-CAF (Fig. 3E). We also investigated the ECM-associated genes in CAFs by using quantitative PCR and demonstrated that YAP1 silencing significantly suppressed ECM-associated genes, suggesting that YAP1 exerted ECM-hypersecretory functions in ECM-CAF (Fig. 3F).

We performed multiplex IHC staining by using TMA from patients with prostate cancer to investigate the clinical significance of high YAP1 expression in CAFs. The results indicated that high YAP1 expression in αSMA⁺ CAFs yielded a worse overall survival prognosis and lower CD8⁺ T-cell infiltration (Fig. 3G and H). In addition, patients with high YAP1 expression in αSMA⁺ CAFs had a higher T stage, N stage, tumor grade, and prostate-specific antigen concentration (Fig. 3I–L; Supplementary Fig. S3E), suggesting that YAP1 in CAFs is a clinically meaningful target.

We investigated the connection between the two CAF subtypes owing to the attractive plasticity of CAFs. First, we performed a pseudotime analysis of both six CAF clusters and discovered that Lym-CAF could be differentiated into ECM-CAF (Supplementary Fig. S4A). Then, we analyzed the pseudotime trajectory of ECM-CAF and Lym-CAF, our results confirmed this trajectory and indicated that the YAP1 trajectory was consistent with ECM-CAF transformation, suggesting that YAP1 is the gatekeeper that regulates the switching between these two CAF subtypes (Fig. 4A; Supplementary Fig. S4B).

We demonstrated the role of YAP1 in this transformation by overexpressing constitutively activated YAP1^S127A or silencing YAP1 in preinduced Lym-CAF. As expected, the ECM-CAF phenotype was enhanced when YAP1 was overexpressed, whereas the Lym-CAF phenotype was enhanced by YAP1 silencing in i-Lym-CAF (Fig. 4B and C; Supplementary Fig. S4C and S4D). To convert ECM-CAF to Lym-CAF, we used siRNA to knock down YAP1 in preinduced ECM-CAF before coculturing them with CD3⁺ T cells. The results revealed that YAP1 silencing in ECM-CAF increased the expression of Lym-CAF-associated markers, and most importantly, enhanced the activation of CD8⁺ T cells (Fig. 4D–F). Furthermore, we verified the secretion of CXCL9/10/11 by ELISA, and the results demonstrated that YAP1 depletion promoted the secretion of CXCL9/10/11 (Supplementary Fig. S4E–S4G), implying that targeting YAP1 depletion in ECM-CAF may promote the activation of T cells.

We further investigated the molecular mechanisms of phenotype switching between ECM-CAF and Lym-CAF, and YAP1 and NF-κB p65 were the core transcription factors, respectively. We detected the YAP1 and p65 and their phosphorylated forms under the stimulation of TGFβ or TNFα/IFNγ. Western blotting and immunofluorescence analysis confirmed that YAP1 and p65 were mutually exclusive and dominated in terms of nuclear translocation in the ECM- and Lym-CAF phenotypes, respectively (Fig. 4G and H). We also noticed that the NF-κB promoter was more highly activated after YAP1 silencing by using a dual-luciferase reporter assay (Fig. 4I), suggesting that YAP1 inhibits activation of the NF-κB pathway. We detected an inhibitor of nuclear factor kappa B (IκB) kinase alpha (IKKα), a classic inhibitor upstream of NF-κB p65, to further investigate YAP1 and NF-κB interaction. The results revealed that YAP1 significantly inhibited IKKα-activated NF-κB activity, suggesting that YAP1 may act by inhibiting IKKα rather than by directly inhibiting p65 (Fig. 4J and K). Therefore, we used primary CAFs for coimmunoprecipitation, which revealed that YAP1 can directly bind to IKKα (Fig. 4L). YAP1 knockdown significantly activated phospho-IKKα/β. Notably, the gradient overexpression of YAP1 also attenuated phospho-IKKα/β expression. Furthermore, phospho-IκB was downregulated so that p65 would not be detached from the IκB complex, thus inhibiting nuclear translocation (Fig. 4M and N; Supplementary Fig. S4H). These results indicate that YAP1 inhibited NF-κB p65 activation through a direct interaction with IKKα, inhibiting IKKα phosphorylation. Therefore, we demonstrated that YAP1 is the gatekeeper in switching between Lym-CAF and ECM-CAF (Fig. 4O).

Next, we demonstrated that selective YAP1 depletion targeting ECM-CAF could switch the phenotype of ECM-CAF into antitumor Lym-CAF in vivo. First, we chose CD248 as a characteristic marker of ECM-CAF and constructed Cd248-CreER^T2; Rosa26-LSL-tdTomato mice to evaluate the specificity of CD248. We verified good colocalization of CD248 with COL1A1 and αSMA by using immunofluorescence staining, implying that CD248 is specifically expressed and is a potential target in ECM-CAF (Supplementary Fig. S5A and S5B). Furthermore, we constructed Cd248-CreER^T2; Yap1^flox/flox (cKO) mice, which we could use to achieve selective YAP1 depletion in ECM-CAF by using tamoxifen (Fig. 5A). After implantation of the RM-1 tumor syngeneic graft, we discovered that selective YAP1 depletion targeting ECM-CAF inhibited prostate cancer progression (Fig. 5B and C). Flow cytometry analysis demonstrated that the infiltration of CD8⁺ T cells was increased (Fig. 5D). We also observed that the exhaustion markers, PD-1 and CTLA4, were reduced in cKO mice (Fig. 5E). Meanwhile, the activated markers, CD25, CD69, Gra B, and IFNγ, were upregulated (Fig. 5F and G), implying that selective YAP1 depletion in ECM-CAF has a powerful tumor-suppressive effect and could active the antitumor functions of CD8⁺ T cells. In addition, we performed Masson staining and atomic force microscopic morphologic mapping and discovered that collagen deposition in prostate cancer was reduced after selective YAP1 depletion (Fig. 5H and I). We also implanted Tramp-C1 tumor syngeneic graft on Cd248-CreER^T2; Yap1^flox/flox mice, and the results demonstrated that selective YAP1 depletion in ECM-CAF increased the infiltration and the activation of CD8⁺ T cells and exhibited tumor-suppressive effect (Supplementary Fig. S5C–S5E). To exclude the immune changes introduced by tamoxifen, we separately treated Cd248-CreER^T2 mice with vehicle or tamoxifen. The results showed no significant changes after tamoxifen treatment (Supplementary Fig. S5F–S5H).

We also created a humanized immunoreconstruction model on NCG mice to thoroughly validate the tumor-suppressive effects of CAF-selective YAP1 depletion. Immunodeficient NCG mice were engrafted with preactivated human PBMCs as previously described (44). Furthermore, we used the 22Rv1 cell line mixed with human primary, preinduced ECM-CAF and discovered that knockdown of YAP1 in ECM-CAF can inhibit tumor progression (Fig. 5J and K), as in the abovementioned animal models, as well as an increasing infiltration of CD8⁺ T cells (Fig. 5L). These animal models demonstrated that selective YAP1 depletion has a tumor-suppressive effect and activates the antitumor functions of CD8⁺ T cells.

We hypothesized that selective YAP1 depletion targeting ECM-CAF would improve ICB efficacy in prostate cancer and reverse the dilemma of a cold TME to some extent, based on its antitumor effects. Therefore, we conducted an anti-PD-1 combination therapy trial. We used cKO mice to simulate selective YAP1 depletion. We administered the anti-PD-1 antibody 7 days after implantation of RM-1 tumor xenografts (Fig. 6A). Consequently, combined cKO and anti-PD-1 antibody therapy yielded better therapeutic effects than cKO or anti-PD-1 antibody therapy alone (Fig. 6B and C). Furthermore, the combination therapy promoted CD8⁺ T-cell infiltration, decreased the expression of exhausted marker PD-1, and promoted activated markers Gra B and IFNγ (Fig. 6D–F), thus demonstrating a potent tumor-suppressive function and enhancing immune-activation response. These results indicate that selective YAP1 depletion targeting ECM-CAF is a promising strategy that may significantly promote the therapeutic effect of anti-PD-1 antibodies and may be effective in clinical application.

In summary, our work revealed and identified the characteristic markers of two CAF subtypes: ECM-CAF was induced by TGFβ stimulation and hypersecreted ECM, exhibiting a protumorigenic phenotype, whereas Lym-CAF was induced by TNFα/IFNγ secreted by lymphocytes, exhibiting an antitumor phenotype with high cytokine secretion. YAP1 and NF-κB p65 were their respective core transcription factors, and the interaction of YAP1 and p65 led to phenotype switching between ECM-CAF and Lym-CAF. Selective YAP1 depletion targeting ECM-CAF may switch protumorigenic CAFs into antitumor CAFs and enhance the therapeutic effects of anti-PD-1 antibodies on prostate cancer.

CAFs do not represent a single, homogenous population but a group of heterogeneous cells in the TME exhibiting different activation patterns (45). The remarkable plasticity of CAFs allows them to shift phenotypically and functionally in response to environmental changes, thereby providing a rationale for inducing CAF phenotypic switching as a strategy in the development of antitumor therapies. First, the identification of the antitumor CAF subtype had to be further investigated. The first evidence of antitumor CAFs demonstrated that an αSMA⁺ myofibroblast subtype was required to suppress the regulatory T cells and maintain the function of CD8⁺ T cells (46). The loss of type I collagen in αSMA⁺ CAFs reportedly accelerates tumor progression increases the infiltration of myeloid-derived suppressor cells, and decreases T-cell infiltration, partly explaining the tumor-restrictive function of αSMA⁺ CAFs (22). Notably, more antitumor CAF subtypes have been identified with the rapid development of scRNA-seq techniques. A new antitumor CAF subtype, ilCAFs reportedly augments T-cell infiltration and activation after TGFβ blockade is performed (47). Our study revealed a special antitumor CAF subtype, Lym-CAF, in integrated prostate cancer scRNA-seq datasets and identified its characteristic marker genes. We also demonstrated that it promotes CD8⁺ T-cell infiltration and activation, confirming its functions in immune activation and its potential to reverse the immunologically “cold” TME.

Another challenge is to understand the generation of antitumor CAFs. CAF plasticity can be induced by numerous tumor cell-derived growth factors and chemokines; for example, TGFβ can induce a myofibroblastic phenotype, whereas IL1 particularly induces an immune-modulating phenotype, usually as a protumorigenic function (9). A recent spatial analysis revealed that immune cells and CAFs accumulated primarily around the tumor border in the margin areas, indicating a distinct immune microenvironment around the border. Notably, cytokines such as TGFβ, TNFα, and IFNγ were dramatically different in the tumor-adjacent sections of the margin areas compared with those in tumor tissues (48). Our study demonstrated that Lym-CAF was induced via combined stimulation with TNFα and IFNγ, resulting in an immune-activation phenotype through the NF-κB p65 signal pathway. An assay of primary CAFs cocultured with T cells revealed that antitumor CAFs were probably domesticated by T–cell secreting cytokines in the complex TME. Therefore, we speculate that the heterogeneity of CAFs may result from the “domestication” of different cytokines within the TME. This would partly explain that a “cold” TME with a relative dearth of infiltrating immune cells usually represents the protumor CAF phenotype. We look forward to further studies in which more effective antitumor CAF subtypes are discovered and in which subtypes are more accurately defined based on diverse T–cell produced cytokines.

YAP1 acts as an important hub for CAF activation and exerts a crucial regulatory role in shifting their phenotypes by the convergence of intrinsic and extrinsic signaling pathways (49, 50). When YAP1 is translocated to the nucleus with subsequent activation of target genes, triggered by mechanical stimuli, such as matrix stiffness, cytoskeletal tension, and extracellular mechanical tension, protumorigenic CAF functions are activated (51). In this study, we partly investigated the mechanisms of the shifting of CAF phenotypes. However, phenotype shifting is a complex epigenetic process involving multiple transcription factors. Phase separation was widely observed in transcription regulation (52), and the mechanisms of YAP1 and NF-κB p65 in phenotype switching warrants closer inspection.

Few clinical trials targeting YAP1 are ongoing. An antisense oligonucleotide against YAP1 is undergoing a phase I trial for advanced solid tumors (NCT04659096). Furthermore, an inhibitor targeting YAP1-mediated transcription is in phase I clinical trial for advanced mesothelioma and other solid cancers (NCT04857372; ref. 49). Recently, more evidence has suggested that targeting YAP1 may turn immunologically “cold” tumors into “hot” ones (43). This scenario would incentivize the testing of combinations of YAP1 inhibitors and ICB therapy in clinical settings. However, the principal challenge is identifying tumor-specific biomarkers for YAP1 inhibitors or depletions targeting CAF.

Our team previously revealed that CD248 is mainly expressed in the stroma, especially in CAFs in most solid tumors, and was considered an ideal target for cancer treatment (53). In this study, selective YAP1 depletion mice undergoing anti-PD-1 antibody therapy revealed an encouraging tumor-restrictive effect in prostate cancer. Our team will continue its efforts in YAP1 depletion therapeutic strategies targeting CAFs, such as antibody–drug conjugates and CD248-targeting nanodrug delivery.

No disclosures were reported.

H. Song: Data curation, investigation, methodology, writing–original draft. T. Lu: Formal analysis, investigation, writing–original draft. D. Han: Resources, data curation. J. Zhang: Formal analysis. L. Gan: Data curation, formal analysis. C. Xu: Investigation. S. Liu: Resources. P. Li: Investigation. K. Zhang: Methodology. Z. Hu: Resources. H. Li: Software, Validation. Y. Li: Resources. X. Zhao: Data curation. J. Zhang: Data curation, software. N. Xing: Resources. C. Shi: Supervision, visualization, methodology. W. Wen: Conceptualization, funding acquisition, writing–review and editing. F. Yang: Conceptualization, writing–review and editing. W. Qin: Funding acquisition, project administration, writing–review and editing.

This work was supported by the National Natural Science Foundation of China (nos. 82220108004, 82173204, 82203633, and 82202933), Innovation Capability Support Program of Shaanxi (2023-CX-TD72, 2021TD39, and 2020PT021), Natural Science Basic Research Program of Shaanxi (2022JZ62), Key Research and Development Program in Shaanxi (2023-YBSF251), and Joint Innovation Fund of Innovation Research Institute program in Xijing Hospital (LHJJ24JH19). We thank Igenebook Biotechnology (Wuhan, China) for technical support. We thank Jintao Hu (Department of Immunology, Fourth Military Medical University) for assistance with flow cytometry analysis. We thank Zhengxuan Li, Yike Zhou, and Tiantian Shi (Department of Urology, Xijing Hospital) for assistance with laboratory management. We thank Caiqin Zhang, Ya Zhao, Jing Qin, Pengpeng Wu, and Minli Huang (Laboratory Animal Center) for assistance with the animal study.