Spatial heterogeneity of MDSCs mediated by ANXA1-FPRs signaling drives immune suppression in OSCC progression

Domain: chemistry
Published: 2026-03-18
Editorial outcome: major_revision

Abstract
Limited efficacy of immunotherapy in oral squamous cell carcinoma (OSCC) is driven by an immunosuppressive tumor microenvironment, yet the role of intratumoral spatial heterogeneity in immune responses remains unclear. Here, we employ single-cell and spatial transcriptomics to dissect the cellular composition and spatial organization of OSCC. We find CD8+ T cells are spatially localized yet functionally suppressed in late-stage OSCC, while myeloid-derived suppressor cells (MDSCs) transition from tumor core infiltration in early-stage OSCC to marginal localization with CD8+ T cells in advanced stages. ANXA1-FPR2 signaling mediates tumor-MDSCs communications, sustaining MDSCs recruitment and immune suppression. Disrupting ANXA1-FPR2 with an antagonist enhances the efficacy of immune checkpoint blockade therapy in OSCC mouse models. These findings reveal the spatial dynamics of MDSCs as key modulators of immune suppression and therapeutic resistance, offering a promising target to improve immunotherapy outcomes in OSCC.

Introduction
Oral squamous cell carcinoma (OSCC) is a prevalent malignancy with poor prognostic outcomes, accounting for approximately 377,713 new cases and 177,757 deaths globally in 20201. Despite early-stage OSCC being associated with lower mortality risk and often amenable to surgical intervention, traditional therapies, including surgery and chemotherapy, show limited efficacy in advanced disease stages. Alarmingly, nearly 60% of OSCC cases are diagnosed at an advanced stage2, which is not suitable for surgery and underscoring the urgent need for more effective treatment strategies.

Immunotherapy, which harnesses innate and adaptive immune cells to target and eliminate tumor cells, has emerged as a promising therapeutic approach, frequently outperforming conventional radiotherapy and chemotherapy3. However, the efficacy of immunotherapy in OSCC remains highly variable among patients, largely due to the profound heterogeneity of the tumor microenvironment (TME). The TME plays a pivotal role in shaping immune responses4, and its dynamic changes during OSCC progression remain poorly understood.

Emerging evidence highlights significant alterations in cellular composition and spatial organization within the TME, even during the precancerous and early stages of OSCC5. Myeloid-derived suppressor cells (MDSCs) are key players in modulating the immune landscape of the TME6. By expressing immunosuppressive factors such as arginase-1 and PD-L1, MDSCs suppress the cytotoxic activity of CD8+ T cells7,8. Furthermore, MDSCs exacerbate immune suppression by recruiting other immunosuppressive cell types, including M2 macrophages and regulatory T cells (Tregs)9, thereby amplifying tumor immune evasion. Tumor cells actively recruit MDSCs to the TME, fostering a suppressive immune milieu that promotes tumor progression10.

Despite these insights, our understanding of the intricate cellular interactions within the OSCC immune microenvironment remains incomplete. Key gaps persist regarding the mechanisms of cell-cell communication among tumor cells, immunosuppressive cells, effector immune cells, and stromal components, as well as the spatial and temporal dynamics of these interactions during OSCC progression. Addressing these gaps is critical for developing effective therapeutic strategies. A deeper understanding of the interplay between immunosuppressive and effector cells, and their spatial organization, may unlock novel approaches to reprogram the immune landscape and enhance anti-tumor immunity. Annexin A1 (ANXA1), a recently discovered secretory calcium-dependent phospholipid-binding protein, plays a significant role in the regulation of the tumor microenvironment. Studies have shown that ANXA1 exhibits high affinity for the formyl peptide receptor family (FPR1/2/3), and the specific binding between them plays an important role in regulatory processes such as the recruitment of anti-inflammatory monocytes, directional migration of neutrophils, and polarization of immunosuppressive macrophages11–13. FPR2, belongs to the FPR family, plays a dual role in immunoregulation14. Recent study reveals that FPR2 leads exhaustion of T cells and drives immune escape tumor environment15.

In this study, to investigate the dynamic development of TME with OSCC progression and its underlying events contributing to immune tolerance, we performed single-cell transcriptome and spatial transcriptome analyses on primary OSCC specimens, including early-stage (stage I-II) and late-stage (stage III-IV) tumors. Our findings reveal that in early-stage OSCC, MDSCs predominantly accumulate in the tumor region, driven by Annexin A1 (ANXA1) expressed on tumor cells. As OSCC progresses, tumor cell ANXA1 expression decreases, leading to MDSCs redistribution to tumor margin regions enriched with CD8+ T cells. This redistribution enhances immune suppression by directly inhibiting CD8+ T cell cytotoxicity, thereby exacerbating immune evasion. ANXA1-FPR2 signaling as a critical axis mediating MDSCs recruitment and immunosuppressive activity. This study elucidates a novel ANXA1-FPR2-mediated mechanism of immune suppression in OSCC, offering a promising therapeutic target.

Results
Single-cell transcriptome analysis reveals the immunosuppressive tumor microenvironment in advanced OSCC
To investigate the cellular composition and dynamics of the TME in OSCC, we performed single-cell RNA sequencing (SC-seq) on stage I–II (n=7 patients) and stage III–IV (n=9 patients) OSCC specimens (Fig. 1a). The characteristics of the OSCC specimen were shown in Supplementary Table 1. Pathological staging was validated through hematoxylin and eosin (H&E) staining (Fig. 1b). High-quality single cells were defined by mitochondrial RNA content of less than 10% and fewer than 2500 detected RNA features. Batch effects were mitigated using the harmony algorithm. Dimensionality reduction and clustering via UMAP revealed 10 distinct cell clusters within OSCC tumors (Fig. 1c). Heatmap analysis of the top 10 highly expressed genes in each cluster confirmed unique transcriptional profiles across clusters (Supplementary Fig. 1a).

Cluster-specific markers identified various cell types: squamous epithelial cells (KRT5, KRT14), CD8+ T cells (CD3D, CD8A), CD4+ T cells (CD3D, CD4), B cells (CD79A, CD19), myeloid cells (CD14, LYZ), fibroblasts (COL1A1, TAGLN, DCN), pericytes/smooth muscle cells (RGS5, TAGLN), mast cells (KIT, TPSAB1), and nerve-like cells (CNTNAP2) (Fig. 1d). Early-stage OSCC exhibited fewer pericytes but more CD8+ T cells with significance change comparing to late-stage OSCC (Fig. 1e) and Supplementary Fig. 1b), indicating that the number of T cells and their activity changed during tumor progression.

To further assess T-cell activity in OSCC, gene set variation analysis (GSVA) was performed on T-cell receptor (TCR) signaling pathways. Both signatures of TCR signaling showed diminished enrichment in late-stage tumors (Fig. 1f, g). Interestingly, immunosuppressive signaling in myeloid cells increased across tumor stages (Fig. 1h). Antigen presentation pathways were significantly reduced in myeloid cells from late-stage OSCC, indicating an impaired immune response as tumors progress (Fig. 1i).

We further evaluated the chromosomal alternation of individual cell clusters using inferCNV analysis, employing CD8+ T cells, CD4+ T cells, and pericytes as reference normal cells. Chromosomal abnormalities were predominantly observed in epithelial cells, affecting loci across multiple chromosomes (Supplementary Fig. 1c). Epithelial cells exhibited significantly higher inferCNV scores compared to other cell types (Supplementary Fig. 1d), with malignancy increasing in late-stage OSCC (Supplementary Fig. 1e) by using the GSVA to examine the enrichment of EMT signaling16. These findings suggest that late-stage OSCC is characterized by an immunosuppressive microenvironment.

Cellular communication between epithelial tumor cells and MDSCs declines in advanced OSCC
To elucidate the mechanisms underlying the formation of an immunosuppressive tumor microenvironment in OSCC, we analyzed cell-cell communications among the 10 cell types identified via SC-seq. Interactions between epithelial tumor cells and myeloid cells were notably elevated in early-stage OSCC compared to advanced OSCC (Fig. 2a and Supplementary Fig. 2a). Moreover, epithelial tumor cells exhibited reduced communication with other cell clusters, particularly endothelial cells, in late-stage OSCC (Supplementary Fig. 2a). This observation aligns with the notion that epithelial tumor cell junctions deteriorate during OSCC progression17.

In early-stage OSCC, epithelial tumor cell-to-myeloid cell communication was predominantly mediated by ANXA1-FPR1 and ANXA1-FPR3 ligand-receptor pairs, which diminished in advanced disease stages (Fig. 2b, c and Supplementary Fig. 2b). Interestingly, ANXA1-FPR2 pairs defined as significant interactions emerged in 5/9 late-stage OSCC samples, suggesting a compensatory role for ANXA1-FPR2 in maintaining communication between epithelial tumor cells and myeloid cells when ANXA1-FPR1 and ANXA1-FPR3 interactions were diminished (Fig. 2d and Supplementary Fig. 2c). ANXA1 was expressed across multiple cell types, whereas FPR1, FPR2, and FPR3 expression was restricted to myeloid cells (Supplementary Fig. 2d-e).

The myeloid cell compartment consisted of tumor-associated macrophages (TAM) and MDSCs, distinguished by their respective marker expression profiles (Fig. 2e, f). These subpopulations exhibited distinct transcriptional patterns (Supplementary Fig. 2f). And the cell portion of these two subclusters in all cells had no significant change with OSCC progression (Supplementary Fig. 2g).

Notably, FPR1/2/3 expression was heterogeneous among these myeloid subclusters: FPR3 was highly expressed in TAM, while FPR1 and FPR2 were predominantly expressed in MDSCs (Fig. 2g). FPR2 expression, however, was limited to approximately 10% of myeloid cells, and was higher expressed in myeloid cells from late-stage OSCC (Fig. 2g and Supplementary Fig. 2h). To identify specific myeloid cells expressing FPR2, we focused on myeloid cells with FPR2 expression levels exceeding a defined threshold (average expression level). Within this subset, both TAM and MDSCs were present (Fig. 2h, i).

Remarkably, in FPR1high/FPR2high/FPR3high myeloid cells, the expression of these three receptors was significantly higher in MDSCs than TAM (Fig. 2j-l). This suggests that MDSCs are the principal mediators of ANXA1-FPRs communication with epithelial tumor cells in OSCC. The expression of FPR3 in MDSCs was polarized, with many showing low expression. Therefore, when viewed overall, FPR3 appeared to have higher expression in TAMs. However, when filtering by mean values was applied, it effectively excluded those with low expression, resulting in the appearance of higher expression in MDSCs. Importantly, FPR1 and FPR3 expression in MDSCs and TAM, respectively, was independent of FPR2 expression (Supplementary Fig. 2i-j). This indicates that ANXA1-FPR1-mediated communication predominantly involves MDSCs, whereas ANXA1-FPR3 interactions are mainly facilitated by TAM.

In advanced OSCC, FPR1 and FPR2 expression were significantly increased, while FPR3 expression in myeloid cells had no significant difference across disease progression (Fig. 2m-o). Furthermore, the proportion of FPR2high MDSCs increased in late-stage OSCC compared to early-stage disease, whereas the proportion of FPR2high TAM had no significant change (Fig. 2p). In contrast, the ratios of MDSCs and TAM in total or FPR1high/FPR2low/FPR3high myeloid cells remained unchanged between early and late-stage OSCC (Supplementary Fig. 2k-m). In addition, the communications between epithelial cells and myeloid cells had no significant difference in the groups with different age or gender (Supplementary Fig. 2n). Thus, the change in early- and late-stage was demonstrated as the major factor to cause the different phenomenon. These findings underscore the compensatory role of FPR2high MDSCs in sustaining the immunosuppressive tumor microenvironment in advanced OSCC.

MDSCs predominantly reside in non-tumor regions in advanced OSCC
SC-seq analysis revealed exacerbation of immunosuppressive features and dynamic changes in cell-cell communication during OSCC progression. However, spatial context, which provides critical insights into the tumor microenvironment, was absent in SC-seq. To address this, spatial RNA-seq (SP-seq) was performed on tissues from three early-stage OSCC and three late-stage OSCC, in conjunction with SC-seq (Fig. 1b). A total of 3591 spots (early-stage OSCC) and 5864 spots (late-stage OSCC) were analyzed, with an average of 46,677 reads and a median of 7132 genes per spot (Supplementary Fig. 3a, b). SP-seq identified cell types consistent with SC-seq (Fig. 3a).

Using Robust Cell Type Decomposition (RCTD) for deconvolution, cell type distributions across spatial spots were quantified (Fig. 3b). The tissues were divided into two regions: epithelial tumor cell-poor regions (region I, outside the ROI with black line) and epithelial tumor cell-rich regions (region II, within the ROI with black line) (Fig. 3b). In early-stage OSCC, myeloid cells were enriched in region II, while in late-stage OSCC, they predominantly resided in region I (Fig. 3b). Notably, this spatial redistribution was specific to myeloid cells, as other cell types did not exhibit similar changes.

Using the neighborhood analysis of spatial spots with reference to epithelial cells, we found that myeloid cells were enriched in intra-spots in early-stage OSCC, while in late-stage OSCC, they predominantly resided in inter- and distal-spots (Fig. 4a–b). Similarly to the results from RCTD, this spatial redistribution was specific to myeloid cells, as other cell types did not exhibit similar changes (Fig. 4b and Supplementary Fig. 3c, d). Additionally, CD8+ T cells infiltrated to intra-, inter- or distal-spots of epithelial tumor cells with similar degree in early-stage OSCC, but CD8+ T cells rarely infiltrated to intra-spots while dominantly located in inter- and distal-spots of epithelial tumor cells in late-stage OSCC (Fig. 4c and Supplementary Fig. 3d).

Neighborhood analysis of spatial spots revealed distinct interactions in early and late-stage OSCC. In early-stage OSCC, intra-spot ratios of myeloid cells had the highest ratio, which was decreased in inter- and distal-spots (Fig. 4d-f). In late-stage OSCC, fibroblasts dominated intra-spot regions, while endothelial cells and fibroblasts were more prominent in inter- and distal-spots (Fig. 4g-i), accompanied by increased myeloid cell ratios (Fig. 4b and Supplementary Fig. 3d). Further analysis demonstrated that MDSCs from early-stage group preferred to locate in tumor cells enrich region (Fig. 4k). However, MDSCs from late-stage group and TAM from the both groups mainly distributed in tumor cells poor region (Fig. 4k), suggesting that MDSCs were the dominant component of myeloid cells mediating immune-suppressive tumor environment in OSCC. Although ANXA1 was expressed across most cell types in SC-seq, SP-seq revealed its elevated expression in epithelial tumor cell-enriched regions (Fig. 4j).

Immunofluorescence (IF) validated SP-seq findings, showing CD11b+ MDSCs predominantly localized to tumor regions in early-stage OSCC but distributed mainly in non-tumor regions in late-stage OSCC, with a small subset still present in tumor regions (Fig. 5a-c). These spatial patterns were corroborated in a transgenic OSCC mouse model (Tgfbr1f/f;Ptenf/f;K14-CreERT2) (Fig. 5d, e).

Multiplex immunohistochemistry (mIHC) analysis of human OSCC tissues further confirmed that MDSCs were preferentially localized to epithelial tumor cell-enriched regions in stage I OSCC but not in stage IV OSCC (Fig. 5f). The percentage overlap of MDSCs and epithelial tumor cell regions was significantly higher in stage I OSCC than in stage IV OSCC (Fig. 5g). Importantly, lower overlap between MDSCs and epithelial tumor cell regions correlated with poor overall survival (Fig. 5h).

These findings strongly suggest that the redistribution of MDSCs to non-tumor regions in advanced OSCC is closely associated with disease prognosis, underscoring the critical role of spatial MDSCs dynamics in shaping the tumor microenvironment.

FPR1+ or FPR2+ myeloid cells have alternative contact with ANXA1+ epithelial cells across progression
Above SC-seq results demonstrated that the cell-cell communication pair of ANXA1-FPR1 between epithelial tumor cells and myeloid cells was elevated in early-stage OSCC, while ANXA1-FPR2 was increased in late-stage OSCC. To further validate this observation, we used mIHC to assess the three proteins in corresponding samples to SC-seq. As shown in Fig. 6a, b and Supplementary Fig. 4a, b, FPR1 positive myeloid cells, which were near to the ANXA1 positively expressed epithelial tumor cells, significantly reduced in the late-stage OSCC, while FPR2 positive myeloid cells significantly increased. Notably, the protein levels of ANXA1 decreased in late-stage OSCC, whereas protein levels of FPR2 increased with no significant change of FPR1 across disease progression (Fig. 6c, d), which was consistent to the results from SC-seq. These results demonstrated that the decreased expression of ANXA1 may contributed to the reduced interactions with FPR1 between epithelial tumor cells and myeloid cells, and increased expression of FPR2 attempted to remedy these cell-cell communications.

ANXA1high epithelial tumor cells are identified as the major sub-cluster communicated to MDSCs
To delineate the epithelial tumor cell sub-clusters involved in communication with MDSCs via ANXA1-FPR1/2/3 signaling, epithelial tumor cells were subjected to dimensionality reduction clustering. Two distinct sub-clusters were identified: ANXA1 highly expressing epithelial tumor cells (ANXA1high_Epi) and ANXA1 lowly expressing epithelial tumor cells (ANXA1low_Epi) (Fig. 7a and Supplementary Fig. 5a, b). Notably, ANXA1 expression was enriched in ANXA1high_Epi but nearly absent in ANXA1low_Epi (Fig. 7b), suggesting that ANXA1high_Epi predominantly engages MDSCs through ANXA1-FPR interactions. Notably, the expression of ANXA1 from late-stage OSCC in both subclusters significantly reduced (Fig. 7c). However, the cell portion of these two subclusters of epithelial tumor cells had no significant difference (Fig. 7d). Interestingly, although ANXA1 was predominantly expressed in ANXA1high_Epi, its expression decreased during OSCC progression (Fig. 7c). This reduction likely contributed to the weakened interaction between epithelial tumor cells and MDSCs via ANXA1-FPRs in advanced OSCC.

The stemness of the two subclusters was further analyzed using CytoTRACE. ANXA1high_Epi subcluster exhibited higher stemness than ANXA1low_Epi subcluster (Fig. 7e, f), and the tumor stemness signaling was also enriched in epithelial tumor cells from early-stage OSCC (Fig. 7g), indicating that ANXA1-FPRs-mediated communication occurs primarily during early tumorigenesis. Further analysis revealed a dramatic downregulation of interferon (IFN-γ and IFN-α) response signaling and antigen presentation in epithelial tumor cells from late-stage OSCC (Fig. 7h and Supplementary Fig. 5c-e).

Gene enrichment analysis highlighted distinct functional profiles of these sub-clusters. ANXA1high_Epi cells showed reduced Myc targets, G2M checkpoints and E2F targets pathways (Supplementary Fig. 5f), indicating that ANXA1high_Epi cells had lower proliferation activity than ANXA1low_Epi cells.

Together, these results demonstrated that OSCC epithelial tumor cells exhibited suppressed IFN response signaling, facilitating immune evasion with disease progression. The progressive reduction of ANXA1 expression in epithelial tumor cells underscored its critical role in tumor-immune cell communication and highlighted a potential mechanism for diminished immune engagement during OSCC progression.

Treatment of FPR2 antagonist enhances the response to anti-PD-1 antibody in OSCC mice
As the above data suggested, cell communication among epithelial tumor cells and MDSCs through ANXA1-FPR2 played a complementary signaling for decreased ANXA1-FPR1/3 signaling, keeping the recruitment of MDSCs by epithelial tumor cells in late-stage OSCC. Therefore, the disruption of FPR2 from MDSCs may enhance the effect of ICB treatment. To establish the OSCC model, we injected the advanced OSCC cell line SCC7 into C3H/HeJ mice subcutaneously. Once the tumor volume reached 50 mm3, the mice were randomly divided into four groups, receiving IgG isotype plus placebo, IgG isotype plus WRW4 (FPR2 inhibitor)18, anti-PD-1 antibody plus placebo and anti-PD-1 antibody plus WRW4, respectively (Fig. 8a). The tumor growth was significantly inhibited by combination therapy of WRW4 with anti-PD-1 antibody, while the single therapy groups had little effect on the tumor growth (Fig. 8b).

Survival analysis demonstrated that single treatment of anti-PD-1 or WRW4 barely affected the survival period, whereas combination treatment of anti-PD-1 with WRW4 significantly extended the survival prognosis (Fig. 8c). In addition, anti-PD-1 or WRW4 treatment alone hardly inhibited tumor growth, which was significantly inhibited by combination treatment (Fig. 8d).

Furthermore, tumors from mice received vehicle and isotype control IgG were infiltrated with large amount of CD11b+ MDSCs, but the CD8+ T cells were hardly distributed in the PanCK abundant region (Fig. 8e-g). Even though anti-PD-1 single treatment could increase the infiltration of CD8+ T cells within the squamous tumor cells enriched region, extensive CD11b+ MDSCs distributed in the whole microenvironment (Fig. 8e-g). Although CD11b+ MDSCs were eliminated and the CD8+ T cells were abundant in tumor microenvironment by WRW4, the growth of tumors was not inhibited (Fig. 8e-g). Combination of anti-PD-1 with WRW4 treatment led to significantly decreased CD11b+ MDSCs infiltration and increased CD8+ T cells infiltration, which had impressive inhibitory effect on tumor growth (Fig. 8d). Although both inhibition of FPR2 and PD-1 increased the anti-tumor effect in the mice, exhausted signaling in CD8+ T cells reduced in late-stage OSCC (Supplementary Fig. 4c, d), which aligns with the opinion that persistent T cell receptor (TCR) stimulation can effectively induce T cell exhaustion19 as our current findings showing a significant enhancement of TCR signaling in early-stage OSCC (Fig. 1f, g).

These results demonstrated that recruitment of CD11b+ MDSCs was predominantly maintained by FPR2 signaling. Both FPR2 inhibitor and ICB were necessary to inhibit immunosuppressive actions within the tumor microenvironment of advanced OSCC. Altogether, ANXA1 is a predominant ligand to FPRs which is responsible for cell communications among epithelial tumor cells and MDSCs. Enhanced communication of epithelial tumor cells ANXA1 and MDSCs FPR1 contributes to recruitment of MDSCs, which is partially compensated by ANXA1-FPR2 pairing accompanied with reduced expression of ANXA1 in advanced OSCC tumor tissues. Weakened cell talking among epithelial tumor cells and MDSCs via ANXA1-FPRs with tumor progression leads to diffuse of MDSCs to cytotoxic T cells, enhancing the immunosuppressive tumor microenvironment. Blocking of ANXA1-FPR2 by FPR2 inhibitor promoted the anti-tumor effect with anti-PD-1 antibody.

Methods
Ethics statement and collection of patient samples
To investigate the cellular composition and dynamics of the TME in OSCC, primary human OSCC tumor specimens were collected from patients who underwent surgical resection at the First Affiliated Hospital of Chengdu Medical college. The procedures were reviewed and approved by the Institutional Review Board (IRB) at the First Affiliated Hospital of Chengdu Medical college (Ethics Approval No. 2022CYFYIRB-BA-Aug11). Samples were collected according to the institutional regulation of the Institutional Review Board (IRB) and were examined by pathologists. All procedures were performed with signed informed consent from the patients, and the donation of tumor tissues was entirely voluntary and uncompensated.

Samples preparation for single-cell transcriptome analysis
Fresh OSCC tissue samples were collected and divided into two portions: one for single-cell dissociation and the other stored temporarily in tissue preservation solution (MACS, 130-100-008, Miltenyi Biotec) at 4°C for nuclear preparation. To synchronize the preparation of single-cell and nuclear suspensions, single-cell dissociation was performed first. For single-cell dissociation, tissues were washed twice with pre-cooled DMEM medium (11965092, Gibco) supplemented with 0.04% BSA (30066575, Gibco), minced into small pieces, and enzymatically digested in a solution containing DMEM, 0.04% BSA, 0.2% collagenase II (17101015, Gibco), and 100 μg/L DNase I (A3778, AppliChem) at 37°C with gentle agitation (22 rpm) for ~30 min. This was followed by the addition of 0.25% trypsin (2062476, Gibco) for an additional 5 min of digestion. The resulting cell suspension was passed through a 40 μm cell strainer, centrifuged at 4°C at 300g for 7 min, and treated with Red Blood Cell Lysis Solution (130-094-183, Miltenyi Biotec) to remove erythrocytes. After washing with medium, the cell pellet was resuspended in PBS containing 1% BSA, and cell count and viability were assessed using a Luna Fluorometer (LUNA-FLTM, Logos Biosystems). The final cell concentration was adjusted to 700–1200 cells/μL.

In parallel, nuclear suspension preparation was initiated once single cells were centrifuged post-erythrocyte lysis. Tissue for nuclear isolation was washed twice with pre-cooled medium, minced into pieces ~millet-sized, and lysed in 1 mL pre-cooled NST buffer (0.1% NP-40, 10 mM Tris-HCl, 146 mM NaCl, 1 mM CaCl₂, 21 mM MgCl₂, and 1 U/μL RNase inhibitor) for 3–5 min on ice. The nuclear suspension was filtered through a 40 μm cell strainer, centrifuged at 4°C at 500g for 10 min, and washed with 2 mL wash buffer (10 mM Tris-HCl, 146 mM NaCl, 1 mM CaCl₂, 21 mM MgCl₂, 0.01% BSA, and 40 U/mL RNase inhibitor). After a second centrifugation at 4°C at 500g for 5 min, the nuclei were resuspended in PBS containing 1% BSA and 1 U/μL RNase inhibitor. Nuclei were stained with Trypan blue and counted under a microscope. The final nuclear concentration was adjusted to 700–1200 nuclei/μL and maintained on ice until use.

Single-cell RNA-seq and analysis
SC-seq was performed using the MobiCube High-throughput Single Cell 3’ Transcriptome Set V2.1 (PN-S050200301) and the MobiNova-100 microfluidic platform at OE biotech company. Single-cell suspensions were adjusted to a concentration of 700–1200 cells/μL and immediately loaded into a chip for micro-droplet formation on the MobiNova-100 system33. Reverse transcription, cDNA amplification, and DNA library construction were performed following the manufacturer’s protocol. High-throughput sequencing was conducted using paired-end 150bp (PE-150) mode.

Sample quality control was performed using MobiVision, which incorporates the STAR Solo software for data processing. Sequencing reads were aligned to the reference genome (GRCh38), and metrics such as cell count, gene count, and genome alignment rate were obtained from the raw data to evaluate sample quality. For downstream analysis, the ‘Seurat’ R package (version 5.1.0)34 was used to create analysis objects. Cells with mitochondrial RNA content below 10%, gene counts (nFeature_RNA) between 200 and 2500, and high-quality transcriptomes were retained for further analysis. Batch effects were corrected using the ‘Harmony’ R package to ensure robust integration across samples.

Copy number variations analysis
To investigate copy number variations (CNVs) across different cell populations within OSCC tissue, we performed inferCNV analysis35, a computational approach designed to detect large-scale chromosomal alterations in SC-seq data. SC-seq data from OSCC tissue samples were preprocessed to remove low-quality cells, normalize gene expression levels, and annotate cells by type using clustering or marker gene-based methods. A reference set comprising CD8+ T cells, CD4+ T cells, and pericytes was included to serve as a baseline for CNV comparison. CNV analysis was conducted using the inferCNV R package on a high-performance computing cluster. The processed SC-seq gene expression matrix was input into the inferCNV pipeline, which calculates relative gene expression changes to infer chromosomal alterations. The results were visualized using the ggplot2 R package, generating heatmaps and plots to illustrate CNV patterns across cell populations.

Cell-cell communication analysis
To investigate the interactions between different cell types in the tissue microenvironment, we performed cell-cell communication analysis using the CellPhoneDB method36. CellPhoneDB analysis was carried out using the CellPhoneDB Python package, which was run on a computational cluster. The gene expression data was processed and formatted as required by the tool, and ligand-receptor interaction predictions were made using default parameters unless otherwise specified. The resulting interaction networks were visualized using ggplot2 in R for heatmaps and plots.

Spatial transcriptome analysis
Freshly collected OSCC tissue samples were divided into appropriately sized pieces and fixed in 4% formaldehyde, followed by paraffin embedding. Tissue sections (5 μm thick) were prepared from the embedded samples and processed for hematoxylin-eosin (HE) staining, imaging, and destaining, following the protocol (CG000614) recommended by 10x Genomics. Subsequent steps, including probe hybridization, probe release, and transfer to the 10x Visium CytAssist slide, were performed according to the 10x Genomics workflow (CG000495). Library construction was carried out using the Visium CytAssist Spatial Gene Expression for FFPE kit (PN-1000520, 10x Genomics). The resulting DNA libraries were subjected to high-throughput sequencing using paired-end 150bp (PE-150) mode to ensure comprehensive spatial transcriptome profiling.

SP-seq data preprocessing
FASTQ files generated from spatial RNA sequencing were processed and aligned to the GRCh38 human reference genome using Space Ranger software (version 2.0.1) from 10x Genomics37. Unique molecular identifier (UMI) counts were summarized for each barcode. Tissue-overlaying spots were identified based on the accompanying histological images to distinguish tissue-associated signals from background noise. The filtered UMI count matrix was analyzed using the Seurat R package (version 5.1.0). Data normalization and identification of the top 3000 highly variable genes (HVGs) were performed using the SCTransform function38. Dimensionality reduction was conducted using principal component analysis (PCA) on the log-transformed gene-barcode matrices of the HVGs. Graph-based clustering was applied to classify cells based on their gene expression profiles using the FindClusters function. Data visualization was achieved with the Uniform Manifold Approximation and Projection (UMAP) algorithm, implemented through the RunUMAP function. Marker genes for each cluster were identified using the FindAllMarkers function with the test.use = “bimod” parameter to ensure robust detection of cluster-specific genes.

Deconvolution of spatial transcriptome data
To investigate the cellular composition of each spatial transcriptomics spot, we employed the Robust Cell-Type Deconvolution (RCTD, version 1.1.0) method39. RCTD is a computational framework designed to infer the proportions of different cell types in complex tissues based on gene expression data. Spatial transcriptomics data, providing gene expression profiles for individual tissue locations, were paired with a reference cell-type gene expression matrix derived from SC-seq data of corresponding OSCC samples (early-stage and late-stage respectively). The reference matrix encompassed gene expression profiles for various cell types identified within the tissue, serving as signatures for the deconvolution process. This approach enabled the precise estimation of cell-type proportions across spatial locations, facilitating the exploration of spatial cellular heterogeneity and its role in OSCC progression.

Neighborhood analysis of epithelial tumor cells
Neighborhood analysis40 was conducted to examine the spatial interactions of epithelial tumor cells with surrounding cell types, based on the results from the RCTD deconvolution assay applied to spatial transcriptomics data. Epithelial tumor cells were identified as intra-spots, while other cell types within two layers of proximity were categorized as inter-spots. Cells located beyond this range were classified as distal-spots. The relative proportions of each cell type in intra, inter, and distal spots were calculated for both early-stage and late-stage OSCC samples. The results were visualized through comparative plots to illustrate the spatial organization and dynamics of cellular neighborhoods around epithelial tumor cells during OSCC progression. The code for this analysis, which was developed by a company and author, is available from the corresponding author on reasonable request.

Multiplex immunohistochemical (mIHC) assay
Paraffin-embedded tissue sections were heated in an oven at 65°C for 2 h, and the subsequent dewaxing process included two rounds of 10-min xylene treatment. Next, the sections were rehydrated in 100% (for twice), 95%, and 70% alcohol in sequence, followed by heat-induced antigen retrieval with Tris-EDTA buffer (pH 9.0), followed by natural cooling to room temperature. 3% H2O2 was added for endogenous peroxidase blocking. 3% BSA was used to block unspecific binding at room temperature for 1 h. After the removal of the 3% BSA, the sections were incubated rabbit anti-pan-Keration (1: 200, 26411-1-AP, Proteintech) at 4°C for overnight. These sections were then washed with PBS three times, and incubated with HRP-labeled secondary antibodies for 10 min at room temperature. Signal amplification was performed using Cy3-labled tyramide signal amplification working solution (1: 500, G1223, Servicebio) and incubated at room temperature for 10 min, followed by the treatment of microwave heat. Repeat with rabbit anti-CD8 (1:200, ab316778, Abcam), rabbit anti-CD11b (1:200, ab133357, Abcam) combined with FITC-labeled tyramide (1:200, G1231, Servicebio) or Cy5-labled tyramide (1:200, G1232, Servicebio) signal amplification working solution, respectively. Nuclei were then stained with DAPI (G1012, Servicebio). Antibodies against to ANXA1(1: 200, ab214486, Abcam), FRP1 (1:200, ab113531, Abcam) and FRP2 (1:200, ab203129, Abcam) were obtained from Abcam. The stained slides were scanned with Olympus VS200 and analyzed with OlyVIA analysis software (Version 3.3).

Cell culture
SCC7 cells were acquired from WANWUSHENGWU (Delf-17084) and authenticated via short tandem repeat (STR) profiling, and mycoplasma contamination was routinely tested and confirmed to be negative. SCC7 cells were cultured in RPMI 1640 (Gibco, Shanghai, China) supplemented with 10% FBS (10099141, Gibco), penicillin (100 U/mL) and streptomycin (100 mg/mL) (15140122, Gibco) at 37°C in a humidified incubator under 5% CO2.

Animal care and mouse models
Animal care and experiments in this study were approved by the Institutional Animal Care and Use Committee (IACUC) of Chengdu Medical College (approval No. CMC-IACUC-2024088), and were conducted in accordance with the institutional guidelines under the official animal license (SYXK(Sichuan) 2020-0196) issued by the Sichuan Provincial Department of Science and Technology. All the mice were bred and maintained in a specific pathogen-free (SPF) barrier facility at Laboratory Animal Center of Chengdu Medical College. The mice were maintained on a 12/12-h light/dark cycle (lights on at 09:00 a.m.), 22±2°C with a standard and sterile chow diet and water ad libitum.

For generation of spontaneous OSCC genetic mice model, Pten/Tgfbr1 double conditional knockout (2cKO) mice are prone to spontaneously developing OSCC41. Ptenflox/flox, Tgfbr1flox/flox and K14-CreERT2 mouse line (genetic strain FVB/N) were purchased from The Jackson Laboratory and crossed to generate Tgfbr1/Pten 2cKO mice (Tgfbr1f/f; Ptenf/f; K14-CreERT2). Littermates were genotyped at 1 week of age and grouped based on genotypes for the experiments. Tamoxifen (200 μL of 10 μg/μL in corn oil) was administrated in the oral cavity of 3-month-old Tgfbr1/Pten 2cKO mice for 5 consecutive days to induce deletion of Tgfbr1 and Pten in epithelia to trigger the initiation of OSCC. When tumors initiated in the oral cavity, mice were switched to soft food and monitored daily. Histological slides were prepared to identify primary tumors.

For in vivo tumor growth, syngeneic mouse model was generated. Six-week-old male C3H/HeJ mice were purchased from Charles River (Beijing, China). The 1×106 SCC7 oral cancer cells were subcutaneously injected into flank of mice. Tumor size was measured with a caliper every three days. Tumor volume was calculated by width2×length×1/2. Once the tumor volume reached to 50 mm3, 20 mice were randomly divided into four groups (5 mice per group), receiving IgG (A2123, Selleck, China), IgG plus WRW4 (S9818, Selleck, China), anti-PD-1 antibody (A2122, Selleck, China) or WRW4 plus anti-PD-1 antibody respectively by intraperitoneal injection. The dose of IgG isotype control was 10 μg/injection diluted in 100 μL PBS. The dose of anti-PD-1 antibody was 200 μg/injection diluted in 100 μL PBS. The dose of WRW4 was 10 μg/injection diluted in 20 μL saline. Treatment was administrated once every two days for 6 times, then the tumors were dissociated and the weight as well as volume of tumors were measured. The maximal tumor size permitted by the IACUC of Chengdu Medical College is 20 mm at the largest diameter in mice. The maximal tumor size in our animal experiments did not exceed the permitted maximal tumor size. The mice were euthanized by using carbon dioxide when tumor volume reached 1500 mm3 or on day 20, whichever came first. 3 days after fixed in 4 % paraformaldehyde, tumors were embedded with paraffin form IHC assay.

Statistics analysis
For experiments containing multiple groups, one/two-way ANOVA was used to statistical analysis following appropriate methods to compare two groups (Tukey/Bonferroni’s multiple comparison test). Unpaired two-tailed student’s t-test was employed for comparing two groups. Studies involving animals displayed data from specified sample sizes. More detail information for statistics analysis can be found in the legend of each panel of figures. Statistical analyses were carried out using GraphPad Prism V8 software. Significance levels were denoted as follows: * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001, and ‘ns’ indicating nonsignificant results.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this work, the authors we use single-cell RNA-seq of 5 oral squamous cell carcinomas (OSCC) to identify immune and\ntumor cell populations. Spatial transcriptomic assay was used on 2 samples to further localize their positions and determine\nwhich immune cell types are enriched in the tumor regions. Specific focus was given to the ANXA1-FPR2 signaling axis.\nLastly, a mouse model was used to demonstrate that combined inhibition of FPR2 and PD-1 improved outcomes better than\neither treatment alone. While this work reveals new hypotheses related to signaling between MDSCs and tumor cells in\nOSCC, there are several limitations include lack of statistical rigor for several analyses and an overall low sample size with\nminimal validation in larger cohorts.\nMajor comments:\n1. The overall number of samples used was small including 5 for single cell RNA-seq and 2 for Visium spatial transcriptomic\nprofiling. Therefore, it is not clear how often these interactions will occur in larger population of patients. Validation on more\nindividual tumors would be needed to determine how frequent these interactions are.\n2. Many observations were qualitative in nature but lacked the backing of statistical tests. I line 96, a statistical test needs to\nbe performed to determine if the differences in abundances are significant and worth reporting.\n3. Additionally, related to the findings in Figure 3, appropriate statistical tests for proportional data (e.g. sccomp) should be\napplied to make statistically rigorous claims. In the cell type deconvolution analysis (line 176): “intra-spot ratios of CD4+ T\ncells, CD8+ T cells, endothelial cells, and myeloid cells were high … In distal spots, myeloid cells were less prevalent\ncompared to CD8+ T cells, endothelial cells, and pericytes…For MDSCs were more abundant in region II of early-stage\nOSCC, while TAMs were predominant in region I across both stages… Although ANXA1 was expressed across most cell\ntypes in SC-seq, SP-seq revealed its elevated expression in epithelial tumor cell-enriched regions”. All of these results\nshould have quantitative comparisons to substantiate their significance.\n4. The clinical characteristics for the patient samples used for IF and mIHC validation were not described. How were these\nsamples obtained and are there other clinical characteristics different between high and low-stage disease?\n5. While staining was performed for one marker of MDSCs, the staining and spatial interactions between cells expressing\nANXA1 or FPR1 were unfortunately not further validated in human samples. It has been previously shown that MDSCs can\nincrease in OSCC (e.g. PMID: 32092078). However, the ANXA1-FPR1 cross talk is the most novel aspect of the study.\n6. The monocle/trajectory analysis does not seem to add anything important to the results. While the previous clustering\nanalysis in figure 5 indicates 2 clusters, the trajectory analysis shows many more branches.\n7. For results related to figure 5, it is not clear if these cells areas from the SC-seq data or if these are spots with\npredominantly cancer cells from the Visium data. If the data being analyzed is from Visium, can it be ruled out that the\ndifference between these two clusters are just due to infiltration of different cell types? If the data is from the SC-seq, how are\nthe 2 clusters distributed across patients and stages?\n8. In the mouse model, both inhibition of FPR2 and PD-1 was needed to see an increased effect. Was there evidence of a T-\ncell exhaustion phenotype in the SC-seq or spatial datasets?\n9. The fastq files, count data, and clustering results should be submitted to a public repository to enable reproducibility.\n10. Similarly, the code from the single cell and spatial analyses could be made available to enhance reproducibility.\nMinor Comments:\nLine 101: “Both early and late-stage OSCC showed diminished enrichment of TCR signaling in late-stage tumors.” – The\nwording seems incorrect as the enrichment in in late-stage tumors, not “both early and late stage”.\nLine 110: “Epithelial cells exhibited significantly higher inferCNV scores compared to other cell types (Figure S1c), with\nmalignancy increasing in late-stage OSCC (Figure S1d).” – In line 110-11: The phrase “malignancy increasing in late-stage\nOSCC” is not clear. There could be more copy number changes or aneuploidy.\nThe axis labels for Figure 2a are not clear “e.g. Cluster 1 produces molecule 1”.", "reviewer_2": "Head and neck tumors, including OSCC, are characterized by a high degree of immunosuppression, which complicates\nimmunotherapeutic treatments. Therefore, the authors investigate a very important problem. Through multiple in silico, in\nvitro and in vivo studies, the authors were able to demonstrate spatial dynamic changes of MDCS that inhibit the activity of\nCD8+ through the interaction of ANXA1A and FRP2. If this communication is inhibited, this leads to a significant\nimprovement in immune checkpoint therapy.\nThe manuscript is excellently written, of great importance and interest to a wide readership working in this field.\nNevertheless, there are a few points to work on:\n1. In the introduction, the molecules ANXA1 and FPR2 should be introduced in the context of cancer, but also their\nphysiological functions\n2. Neither the positive ethics vote for the analysis of the human data nor the animal testing license is indicated\n3. The patient characteristics should be presented (age, gender, stage, noxious agents, etc.)\n4. The data availability statement is not sufficient. The RNAseq data should be made accessible to all researchers and\nuploaded to a public-available server\n5. Mouse experiments: the SCC VII- C3H/HeJ mouse model is a syngeneic mouse model, not a xenograft model\n6. the mouse models are not described clearly enough: how many animals were initially taken into the experiment per group,\nhow long did the experiment last and when were the animals taken out of the experiment? For example, “endpoint” is written\nin Fig 6e. Which day after tumor induction was the endpoint? It would be good to include a graph showing the tumor growth\nover time in the individual groups\n7. What was the reasoning behind using two different OSCC mouse models?", "reviewer_3": "The study aims to profile single cell transcriptome and spatial transcriptome profiling of the tumour microenvironment in oral\nsquamous cell carcinoma.\nSpecific comments:\n1. line 67 - what do the authors mean by 'the central tumour region'?\n2. the sample size is very small (lines 85 - n=3 early, n=2 late) - was this TNM staging according which version of the\nguidelines? I\n3. the approach used is not true spatial analysis (using the 10x genomics visium approach) - this felt misleading in the\nintroduction and the authors should clarify this. it's multicellular readout.\n4. Figure 3a - can the H&E's be included here to visualise these structures? E.g B-cells in the TME?\n5. the sample size remains a worrying feature of this study and it remains descriptive.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We sincerely appreciate you for the constructive suggestions. In deed, the\nsample amount in the primary manuscript was too small. Therefore, we collected more\nsamples with same criteria (without any therapy and primarily occurred in oral cavity).\nThe early-stage and late-stage groups include 7 or 9 samples respectively from 16\nindividuals for SC-seq, where 3 samples from each group (n=3/group) were used for\nspatial transcriptome analysis. Then we obtained similar results demonstrating the core\ndiscovery in the initial manuscript.\nMajor comments:\n1. The overall number of samples used was small including 5 for single cell RNA-seq\nand 2 for Visium spatial transcriptomic profiling. Therefore, it is not clear how often\nthese interactions will occur in larger population of patients. Validation on more\nindividual tumors would be needed to determine how frequent these interactions are.\nResponse: We sincerely appreciate you for the constructive suggestions. In the revised\nmanuscript (revised figure 2), we collected more samples to SC-seq (n=7 in early-stage\ngroup and n=9 in late-stage group) and spatial transcriptome analysis (n=3 in early-\nstage group and n=3 in late-stage group). As presented in revised figure 2, the\ninteraction strength between epithelial tumor cells and myeloid cells were significantly\nreduced in late-stage OSCC. Besides, the interaction pairs of ANXA1 and FPR1/3 were\nsignificantly reduced in late-stage OSCC, but the pairs of ANXA1-FPR2 were emerged\nin 55% (5 of 9) of late-stage OSCC as defined as significant interaction using the\nCellphoneDB analysis.\n2. Many observations were qualitative in nature but lacked the backing of statistical\ntests. I line 96, a statistical test needs to be performed to determine if the differences in\nabundances are significant and worth reporting.\nResponse: We sincerely appreciate you for the advice. In the revised manuscript\n(revised figure 2), the all comparing studies were performed statistical tests. Through\nmore samples included in the study, we found that the interactions number between\ntumor cells and MDSC were significantly reduced in late-stage OSCC. Furthermore,\nthe interaction pairs of ANXA1-FPR1/3 significantly decreased in late-stage. The\nANXA1-FPR2 was emerged in 55% (5 of 9) patients from late-OSCC, but 0 of 7 in\nearly-stage OSCC, which had statistical significance (p = 0.0151).\n3. Additionally, related to the findings in Figure 3, appropriate statistical tests for\nproportional data (e.g. sccomp) should be applied to make statistically rigorous claims.\nIn the cell type deconvolution analysis (line 176): “intra-spot ratios of CD4+ T cells,\nCD8+ T cells, endothelial cells, and myeloid cells were high … In distal spots, myeloid\ncells were less prevalent compared to CD8+ T cells, endothelial cells, and\npericytes…For MDSCs were more abundant in region II of early-stage OSCC, while\nTAMs were predominant in region I across both stages… Although ANXA1 was\nexpressed across most cell types in SC-seq, SP-seq revealed its elevated expression in\nepithelial tumor cell-enriched regions”. All of these results should have quantitative\ncomparisons to substantiate their significance.\nResponse: We sincerely appreciate you for the suggestions. After the collection of more\nsamples for spatial transcriptome analysis (n=3 in early-stage; n=3 in late-stage), the\ncell distribution was re-analyzed and the statistical tests were performed (revised Figure\n3a-j). The results demonstrated that the ratio of myeloid cells reduced in inter- and\ndistal-spots compared to intra-spots of epithelial tumor cells in early-stage OSCC with\nsignificant test (revised Figure S3c), but increased in inter- and distal-spots compared\nto intra-spots of epithelial tumor cells in late-stage OSCC with significant test (revised\nFigure S3d). The expression of ANXA1 elevated in tumor region than non-tumor region\nsignificantly from SP-seq (p < 0.0001, revised Figure 3k).\n4. The clinical characteristics for the patient samples used for IF and mIHC validation\nwere not described. How were these samples obtained and are there other clinical\ncharacteristics different between high and low-stage disease?\nResponse: We sincerely appreciate you for the advice. The samples were collected from\nsurgical therapy and the fresh tissues were divided into two parts, one of which was\nsent to SC-seq and the other one was fixed by formalin, followed by paraffin embed\nand SP-seq. The characteristics of the OSCC specimen were shown in Table S1. Except\nfor the difference in stage, the different gender and age distributed in the two group. As\nshown in Figure R1, the communications between epithelial cells and myeloid cells had\nno significant difference in the groups with different age or gender. Thus, the change in\nearly- and late- stage was demonstrated the major factor to cause the different\nphenomenon.\nFigure R1. The cell-cell communications between epithelial cells and myeloid cells\nwas not associated with age and gender. (a) The communication between epithelial\nand myeloid cells showed no significant change between the groups below or above the\nmedian age (55). (b) The communication between epithelial and myeloid cells showed\nno significant change between the male and female groups.\n5. While staining was performed for one marker of MDSCs, the staining and spatial\ninteractions between cells expressing ANXA1 or FPR1 were unfortunately not further\nvalidated in human samples. It has been previously shown that MDSCs can increase in\nOSCC (e.g. PMID: 32092078). However, the ANXA1-FPR1 cross talk is the most\nnovel aspect of the study.\nResponse: We sincerely appreciate you for this comment. Being consistent to the\nprevious study (PMID: 32092078), the number of FPR2high MDSC was increased in\nlate-stage OSCC by SC-seq analysis (revised Figure 2p). The expression of ANXA1,\nFPR1 and FPR2 was further examined in the OSCC samples, suggesting a significant\ndecreased of FPR1 positive cells in the border region between epithelial tumor cells and\nmyeloid cells determined by SP-seq, while a significant increase of FPR2 positive cells\nin late-stage OSCC was observed (revised Figures 5a-b). Moreover, the expression of\nANXA1 significantly reduced in late-stage OSCC compared to early-stage OSCC\n(revised Figures 5c-d). Despite that the FPR1 positive cells reduced but the protein\nlevels of FPR1 had no significant change across OSCC progression, while the protein\nlevels of FPR2 was also increased in late-stage OSCC (revised Figures 5c-d). These\nresults emphasized the conclusion that reduced communications between epithelial\ntumor cells and myeloid cells via ANXA1-FPR1 was compensated by ANXA1-FPR2.\n6. The monocle/trajectory analysis does not seem to add anything important to the\nresults. While the previous clustering analysis in figure 5 indicates 2 clusters, the\ntrajectory analysis shows many more branches.\nResponse: We sincerely appreciate you for this comment. With considering that the\nmonocle/trajectory analysis was not meaningful enough for the major results in the\npresent study, we removed the monocle/trajectory analysis results. However, stemness\nanalysis was performed using CytoTRACE and GSVA assay as the results suggesting\nthat ANXA1high_Epi cells had higher stemness property than ANXA1low_Epi cells\n(revised Figures 6e-g), indicating that ANXA1-FPRs-mediated communication occurs\nprimarily during early tumorigenesis.\n7. For results related to figure 5, it is not clear if these cells areas from the SC-seq data\nor if these are spots with predominantly cancer cells from the Visium data. If the data\nbeing analyzed is from Visium, can it be ruled out that the difference between these two\nclusters are just due to infiltration of different cell types? If the data is from the SC-seq,\nhow are the 2 clusters distributed across patients and stages?\nResponse: We sincerely appreciate you for these questions. The subclusters of\nepithelial tumor cells were from SC-seq data. The cell portion of these two subclusters\nhad no significant difference across the patients (revised Figure 6d). However, the\nexpression of ANXA1 in the two subclusters reduced in late-stage OSCC compared to\nearly-stage OSCC (revised Figure 6c).\n8. In the mouse model, both inhibition of FPR2 and PD-1 was needed to see an\nincreased effect. Was there evidence of a T-cell exhaustion phenotype in the SC-seq or\nspatial datasets?\nResponse: We sincerely appreciate you for the question. GSVA analysis revealed a\nreduction in CD8+ T cell exhaustion signaling in late-stage oral squamous cell\ncarcinoma (OSCC) samples (rebuttal Figure 1a–b). Previous studies have demonstrated\nthat persistent T cell receptor (TCR) stimulation can effectively induce T cell\nexhaustion (PMID: 41034580), which aligns with our current findings showing a\nsignificant enhancement of TCR signaling in early-stage OSCC (revised Figure 1f–g).\nTogether, these results suggest that CD8+ T cell activity is suppressed in late-stage\nOSCC, rather than being driven by an upregulation of the exhausted phenotype.\nFigure R2. GSVA analysis of exhausted signatures in CD8+ T cells. (a) The\nenrichment of signaling pathways up-regulated in exhausted CD8⁺ T cells compared to\nmemory CD8⁺ T cells were decreased in late-stage CD8⁺ T cells. (b) The enrichment of\nsignaling pathways down-regulated in memory CD8⁺ T cells relative to exhausted CD8⁺\nT cells were also reduced in late-stage CD8⁺ T cells.\n9. The fastq files, count data, and clustering results should be submitted to a public\nrepository to enable reproducibility.\nResponse: We are grateful to the reviewer for raising this important point. The\nraw sequence files in this study were uploaded on the Sequence Read Archiv\ne (SRA) database with the PRJNA1348242 accession number (https://dataview.n\ncbi.nlm.nih.gov/object/PRJNA1348242?reviewer=4krqnvr1mdto2v6253e4rrh3kh). T\nhe expression count data and clustering results were uploaded in GEO dataset\n(GSE310797, reviewer link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=\nGSE310797. Token: wrahiqsoxdedzop).\n10. Similarly, the code from the single cell and spatial analyses could be made available\nto enhance reproducibility.\nResponse: This is a valid point. We have uploaded the script code used to analyze the\nsingle cell RNA-seq and spatial transcriptome data on the GitHub database\n(https://github.com/zwqnwxpkgc-web/Script-for-SC-seq-and-SP-seq.git).\nMinor Comments:\nLine 101: “Both early and late-stage OSCC showed diminished enrichment of TCR\nsignaling in late-stage tumors.” – The wording seems incorrect as the enrichment in in\nlate-stage tumors, not “both early and late stage”.\nResponse: Thanks for the reviewer’s kindly point. We corrected the description for the\nresults as “Both signatures of TCR signaling showed diminished enrichment in late-\nstage tumors (Figures 1f-g)” in the revised manuscript. (line 107 in revised manuscript)\nLine 110: “Epithelial cells exhibited significantly higher inferCNV scores compared to\nother cell types (Figure S1c), with malignancy increasing in late-stage OSCC (Figure\nS1d).” – In line 110-11: The phrase “malignancy increasing in late-stage OSCC” is not\nclear. There could be more copy number changes or aneuploidy.\nResponse: Thanks for the reviewer’s kindly point. We agree with this comment. Thus,\nwe further analyzed the malignancy signaling in epithelial tumor cells, suggesting a\nsignificant increased enrichment of malignant related EMT pathway (PMID:37258682)\nin late-OSCC (revised Figure S1e, line 116 in revised manuscript).\nThe axis labels for Figure 2a are not clear “e.g. Cluster 1 produces molecule 1”.\nResponse: Thanks for the reviewer’s kindly point. The labels referred to molecules\ninvolved in cell-cell communications were shown clearly in revised format (revised\nfigure S2b).\nReviewer #2 (Remarks to the Author):\nHead and neck tumors, including OSCC, are characterized by a high degree of\nimmunosuppression, which complicates immunotherapeutic treatments. Therefore, the\nauthors investigate a very important problem. Through multiple in silico, in vitro and\nin vivo studies, the authors were able to demonstrate spatial dynamic changes of MDCS\nthat inhibit the activity of CD8+ through the interaction of ANXA1A and FRP2. If this\ncommunication is inhibited, this leads to a significant improvement in immune\ncheckpoint therapy.\nThe manuscript is excellently written, of great importance and interest to a wide\nreadership working in this field.\nResponse: We sincerely appreciate the encouraged comments from the reviewer.\nNevertheless, there are a few points to work on:\n1. In the introduction, the molecules ANXA1 and FPR2 should be introduced in the\ncontext of cancer, but also their physiological functions\nResponse: We sincerely appreciate you for the suggestion. We investigated literatures\nabout ANXA1 and FPR2 carefully, and added the description of the major functions of\nANXA1 and FPR2 in the cancer as well as physiological role in the introduction part\n(line 63-70 in revised manuscript).\n2. Neither the positive ethics vote for the analysis of the human data nor the animal\ntesting license is indicated\nResponse: Thank you for this important comment. We sincerely apologize for the lack\nof clarity in the initial submission. To maintain the blinding required for the peer-review\nprocess, the specific approval numbers for both the human ethics and the animal license\nwere deliberately placed in the cover letter addressed to the editor, as is standard\npractice for our journal. They were omitted from the main manuscript text for this\nreason.\n3. The patient characteristics should be presented (age, gender, stage, noxious agents,)\nResponse: We sincerely appreciate the suggestion. The characteristics of the OSCC\nspecimen were provided in revised Table S1, containing the age, gender, stage and\nagents’ history information.\n4. The data availability statement is not sufficient. The RNAseq data should be made\naccessible to all researchers and uploaded to a public-available server\nResponse: We thank the reviewer for highlighting the importance of data avail\nability. As noted in our manuscript, the raw sequence files are publicly accessi\nble in the Sequence Read Archive (SRA) under accession number PRJNA1348\n242 (reviewer link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1348242?revie\nwer=4krqnvr1mdto2v6253e4rrh3kh), and the processed expression count data alo\nngside the clustering results are provided in GEO dataset (GSE310797, reviewe\nr link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE310797. Token: w\nrahiqsoxdedzop).\n5. Mouse experiments: the SCC VII- C3H/HeJ mouse model is a syngeneic mouse\nmodel, not a xenograft model\nResponse: Thank you for pointing this out. You are correct. The SCC VII cell line is\nderived from C3H/HeJ mice, and implanting these cells back into immunocompetent\nC3H/HeJ mice constitutes a syngeneic model, not a xenograft model. We apologize for\nthe error in terminology. We have carefully revised the manuscript to replace \"xenograft\nmodel\" with \"syngeneic mouse model\" throughout the text to accurately describe our\nexperimental approach.\n6. the mouse models are not described clearly enough: how many animals were initially\ntaken into the experiment per group, how long did the experiment last and when were\nthe animals taken out of the experiment? For example, “endpoint” is written in Fig 6e.\nWhich day after tumor induction was the endpoint? It would be good to include a graph\nshowing the tumor growth over time in the individual groups\nResponse: Thank you for this valuable feedback. We agree that a clearer description of\nthe mouse experimental design is essential and apologize for any lack of clarity in the\noriginal manuscript.\nWe have now revised the “Materials and Methods” section to explicitly state the\nfollowing details for the syngeneic model (SCC VII / C3H/HeJ) used in “Syngeneic\nmouse model”: Once the tumor volume reached to 50 mm3, the 20 mice were randomly\ndivided into four groups (5 mice per group). The predefined endpoint for the experiment\nand for animal sacrifice was either when the tumor volume reached 1500 mm³ or on\nday 20, whichever came first. This is the \"endpoint\" indicated in Fig. 7b, which\ncorresponds to day 20. Furthermore, we have included a graph showing the tumor\ngrowth over time. This data is now presented in the new Figure 7b.\n7. What was the reasoning behind using two different OSCC mouse models?\nResponse: Thank you for this insightful question. The use of two distinct OSCC mouse\nmodels was a strategic decision to leverage the complementary strengths of each system,\nthereby strengthening the translational relevance of our findings.\nThe Genetically Engineered Mouse Model (Tgfbr1f/f; Ptenf/f; K14-CreERT2):\nWe employed this autochthonous model because it recapitulates the spontaneous\ndevelopment of OSCC driven by specific genetic alterations in the oral epithelium. This\nmodel is excellent for validating the phenomena we identified in human OSCC samples,\nas the tumors arise de novo within their native, immunocompetent microenvironment,\nclosely mimicking human tumorigenesis. However, as the reviewer may note, a\nlimitation of this model is the inherent variability in tumor onset, which makes it less\nideal for synchronized, large-scale intervention studies.\nThe Syngeneic Graft Model (SCC VII-C3H/HeJ): Precisely to address the limitation\nabove, we utilized the SCC VII syngeneic model for our functional intervention\nexperiments. By implanting the SCC VII cell line (derived from a C3H/HeJ mouse)\ninto immunocompetent C3H/HeJ hosts, this model provides a consistent and\nsynchronized tumor growth platform. Crucially, it retains an intact and functional\nimmune system, allowing us to rigorously investigate treatment effects on the tumor\nimmune microenvironment, a central focus of our study that would not be feasible in\nan immunodeficient xenograft setting.\nReviewer #3 (Remarks to the Author):\nThe study aims to profile single cell transcriptome and spatial transcriptome profiling\nof the tumour microenvironment in oral squamous cell carcinoma.\nSpecific comments:\n1. line 67 - what do the authors mean by 'the central tumour region'?\nResponse: We sincerely appreciate you for the question. In the initial manuscript, ‘the\ncentral tumor region’ was used to emphasize the tumor cell enriched area. Considering\nthat the words ‘central’ may mislead readers, we corrected the sentence to ‘the tumor\nregion’. (line 73, in revised manuscript)\n2. the sample size is very small (lines 85 - n=3 early, n=2 late) - was this TNM staging\naccording which version of the guidelines? I\nResponse: We sincerely appreciate you for the constructive suggestion. The sample\nsize was expanded (n = 7 in early-stage; n =9 in late-stage). The stage classification was\naccording to the NCCN guideline Head and Neck Cancers - version 4.2025 (ST-2).\n3. the approach used is not true spatial analysis (using the 10x genomics visium\napproach) - this felt misleading in the introduction and the authors should clarify this.\nit's multicellular readout.\nResponse: We sincerely appreciate you for the advice. The spatial analysis was\nprocessed following standard protocols as described in the methods part. Indeed, the\nwords ‘10x visium spatial RNA-seq data preprocessing’ was not suitable in the initial\nmanuscript, thus we corrected it to ‘SP-seq data preprocessing’ to avoid misleading\n(line 426 in revised manuscript).\n4. Figure 3a - can the H&E's be included here to visualise these structures? E.g B-cells\nin the TME?\nResponse: We sincerely appreciate you for the suggestion. The pictures of the OSCC\nspecimen stained by H&E were included in revised figure 3a. The structures, such as\nsquamous tumor cells, fibroblasts and non-tumor region could be visualized.\n5. the sample size remains a worrying feature of this study and it remains descriptive.\nResponse: We sincerely appreciate you for the concern. The samples used for SC-seq\nand SP-seq were expanded. There were 7 and 9 samples in early-stage and late-stage\ngroup respectively for SC-seq. For SP-seq, 3 samples in early-stage and 3 samples in\nlate-stage group were included. The information of clinical specimen was provided in\nTable S1.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nResponse: We sincerely appreciate you for the reviewer’s comment."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed my comments and questions. The additional samples and statistical analyses have greatly improved the manuscript.", "reviewer_2": "The authors thoroughly revised the manuscript based on the reviewers' comments. All my concerns were satisfactorily addressed and incorporated into the revised version.", "reviewer_3": "The revised manuscript remains descriptive and of a small sample size to draw meaningful conclusions."}, "author_response": "Response: Thank you for your positive feedback and for acknowledging the\nimprovements made to the manuscript. We greatly appreciate the time and effort you\ndedicated to reviewing our work.\nResponse: Thank you for your kind and constructive feedback. We are very pleased\nthat our revisions have satisfactorily addressed your concerns, and we greatly\nappreciate the time and expertise you have contributed to improving the manuscript.\nResponse: Thank you for your comment. We acknowledge that the current study is\ndescriptive and utilizes a limited sample size. As an initial, exploratory investigation, it\nwas designed to identify key patterns and generate hypotheses. While the sample size\nof 16 was constrained by practical resources, it aligns with similar preliminary studies\nin the field and provides a necessary foundation. Importantly, as stated therein, the\nobserved associations will require validation in future, larger-scale studies specifically\ndesigned to test causality. We appreciate this feedback, which underscores the\nimportance of our planned follow-up work."}]

Decision letter