VISTA drives pancreatic tumor progression through modulation of the tumor-associated macrophage polarity

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

Abstract
Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies due to its highly immunosuppressive tumor microenvironment (TME), which limits effective therapeutic interventions. Here, we demonstrate that V-domain immunoglobulin suppressor of T cell activation (VISTA) plays a crucial role in orchestrating macrophage polarity within the PDAC TME. Using murine PDAC models, we show that VISTA deficiency markedly impairs tumor growth, leading to prolonged survival. Functionally, VISTA deficiency is linked to a shift in tumor-associated macrophages (TAMs) from an immunosuppressive phenotype marked by secreted phosphoprotein 1 (SPP1), to one enriched for C-X-C motif chemokine ligand 9 (CXCL9), indicative of a pro-inflammatory state. This shift is accompanied by enhanced recruitment of CXCR3⁺ CD8⁺ T cells with sustained cytotoxic potential, among which terminal exhaustion-like CD8+ T cell states are less prevalent. Additionally, VISTA-deficient TAMs exhibit increased antigen cross-presentation, further amplifying CD8+ T cell response against tumors. These findings are corroborated by human PDAC data, which reflect similar immune reprogramming trends. By defining the role of VISTA in controlling Cxcl9:Spp1 ratio and modulating CD8⁺ T cell dynamics, this study positions VISTA inhibition as a promising strategy to reshape the TME and potentiate anti-tumor immunity in PDAC.

Introduction
Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest malignancies, with a five-year survival rate below 5% and a rising incidence projected to become the second leading cause of cancer-related deaths in the United States by 2030 1, 2. Current therapeutic options remain largely ineffective, with only 15–20% of patients eligible for curative surgery 2, 3. Most patients present with unresectable or metastatic disease, and even those who undergo surgery often experience recurrence 3. Gemcitabine, the standard chemotherapeutic agent for PDAC, provides only marginal benefits due to rapid resistance, leaving patients with limited options and poor prognosis 4.

Recent breakthroughs in immunotherapy, particularly immune checkpoint blockade (ICB) therapies targeting Programmed cell Death protein 1 (PD-1), PD-Ligand 1 (PD-L1), and Cytotoxic T-Lymphocyte-Associated protein 4 (CTLA-4), have transformed the treatment landscape for several cancers 5, 6, 7. However, PDAC remains resistant to these approaches due to its unique tumor microenvironment (TME) 8, 9, 10. The TME in PDAC is characterized by dense stroma, low tumor mutational burden, and a highly immunosuppressive milieu, which together limit ICB effectiveness 5, 11, 12. Thus, there is an urgent need to explore novel immune targets and mechanisms to overcome resistance in PDAC and foster tumor regression.

V-domain immunoglobulin suppressor of T cell activation (VISTA), also known as Vsir, c10orf54, GI 24, Dies-1, PD-1H, and DD1α, has emerged as a promising target in this context 13, 14, 15, 16. Unlike PD-1 and CTLA-4, VISTA regulates immune homeostasis during early T-cell activation 17. VISTA is predominantly expressed on myeloid-derived suppressor cells (MDSCs) and tumor-infiltrating leukocytes, playing a pivotal role in promoting immune tolerance and dampening anti-tumor immunity 18.

While tumor-associated macrophages (TAMs) are abundant in many cancers, most studies on inhibitory receptors have focused on T cells 18. This oversight is significant, as the high presence of TAMs has the potential to amplify inhibitory receptor signaling. Recent studies have shown that elevated VISTA expression in TAMs correlates with poor prognosis in multiple cancers, including PDAC 19, 20. Despite extensive research on VISTA in other malignancies, its role in regulating macrophage activity and shaping cancer immune responses in PDAC remains poorly understood.

In this study, we define the role of VISTA in regulating anti-tumor immunity by controlling macrophage polarization in PDAC. We show that VISTA deficiency suppresses tumor growth by shifting tumor-associated macrophages toward a pro-inflammatory state and enhancing CD8⁺ T cell recruitment and function. Consistent patterns observed in human PDAC datasets support the relevance of this immune reprogramming. Collectively, our findings identify VISTA as a key regulator of the PDAC immune microenvironment and highlight its potential as a therapeutic target to improve responses to immunotherapy.

Results
VISTA Deficiency Attenuates Pancreatic Tumor Growth by Enhancing CD8+ T cell and Macrophage Infiltration

To investigate how VISTA deficiency impacts the immune-oncologic landscape of PDAC, we utilized syngeneic orthotopic models to compare tumor growth between wild-type (WT) and VISTA-deficient (Vsir–/–) mice. Pan02 and KPC tumor cells (5  105) were orthotopically inoculated into the pancreatic tail of each group. VISTA deficiency significantly impaired tumor growth in both models, resulting in a 2.5-fold reduction in tumor size in the immunogenic Pan02 model and a 1.8-fold reduction in the less immunogenic KPC model at endpoint (Fig. 1a, Supplementary Fig. 1a). These findings were further supported by pharmacological blockade of VISTA in WT mice, where treatment with an anti-VISTA antibody (VISTA) similarly suppressed tumor growth in the Pan02 model (Fig. 1b, Supplementary Fig. 2).

VISTA deficiency significantly prolonged survival, with Vsir–/– mice surviving approximately 20 days longer than WT controls (Fig. 1c). In human PDAC, lower VSIR expression was similarly associated with improved patient survival by Kaplan-Meier analysis of The Cancer Genome Atlas Pancreatic Adenocarcinoma (TCGA-PAAD) cohort (Supplementary Fig. 3a). VSIR expression was significantly elevated in pancreatic cancer tissues (n = 179) compared to non-malignant pancreatic tissues (n = 171) (Supplementary Fig. 3b). Interestingly, VSIR expression increased with advancing histological grade (Supplementary Fig. 3c), a trend that was independently validated at the protein level by immunohistochemistry and immunofluorescence analysis of a PDAC tissue microarray (Supplementary Fig. 3d–f). Together, these clinical observations support the relevance of VISTA as a therapeutic target in PDAC.

To characterize the immunological consequences of VISTA deficiency, we conducted flow cytometry and immunohistochemistry analyses in orthotopic tumor models. Vsir–/– mice exhibited a 1.5-fold increase in F4/80+CD11b+ TAMs and a 1.8-fold (Pan02) to 2.0-fold (KPC) increase in CD8+ T cells compared to WT controls (Fig. 1d, e, Supplementary Fig. 1b, c, and Supplementary Fig. 4a). Immunofluorescence staining corroborated these findings, revealing increased infiltration of CD8+ T cells (green/red) and F4/80+ (green) or CD11b+ myeloid cells (red) in both Vsir–/– and VISTA-treated tumors compared to WT controls (Supplementary Fig. 5a and b). In contrast, no significant changes were observed in Ly6G+ neutrophils, CD11c+MHCII+ dendritic cells, CD19+ B cells, or FoxP3+CD25+CD4+ Treg cells, underscoring the selective immunomodulatory effects of VISTA (Supplementary Fig. 6a).

Critically, TAMs in Vsir–/– tumors acquired a pro-inflammatory phenotype, characterized by increased iNOS+ macrophages (Pan02) and I-A/I-E+CD206‒ macrophages (KPC) (Fig. 1f, Supplementary Fig. 1d), accompanied by reduced Arginase-1+ macrophages (Fig. 1g). Concurrently, Vsir–/– tumors exhibited increased CD8+ T cell infiltration and a shift towards effector or effector memory phenotypes (CD44+CD62L–) (Fig. 1h), along with increased production of IFN- and higher frequencies of TNF-–IFN-+ and TNF-+IFN-+ CD8+ T cells (Supplementary Fig. 6b).

To explore clinical relevance of these findings, we performed multivariate logistic regression on the TCGA-PAAD dataset. Elevated CD8B expression was associated with 71.6% lower odds of high VSIR expression (Supplementary Table 1). Similarly, high expression of ADGRE1 and CCR2‒markers of myeloid lineage‒were associated with 60.66% and 81.70% reduced odds of elevated VSIR expression, respectively. These patterns recapitulate those observed in murine models.

Collectively, these findings position VISTA as a key immunoregulatory checkpoint associated with immunosuppressive macrophage states and impaired CD8+ T cell function in PDAC. Targeting VISTA represents a promising immunotherapeutic strategy to overcome the immunosuppressive TME and enhance anti-tumor immunity in PDAC.

VISTAhighF4/80highCD11bhigh Tumor-Associated Macrophages Modulate CD8+ T Cell Dynamics

To delineate the role of VISTA in shaping the PDAC TME, we focused on TAMs and CD8+ T cells. In Vsir–/– tumors, we observed a pronounced reduction in the F4/80highCD11bhigh TAM subset, indicating that VISTA is required to maintain this tissue-resident, immunosuppressive macrophage population (Fig. 2a) 21. Consistent with this, VISTA expression was higher in F4/80highCD11bhigh TAMs than F4/80intCD11bint TAMs in WT tumors (Fig. 2b). To test whether selectively depleting these VISTAhigh TAMs could recapitulate the Vsir–/– phenotype, we blocked CSF1R – a key survival signal for tissue-resident macrophages 21. Mice received 1 mg of anti-CSF1R antibody on day 1, followed by 0.5 mg intraperitoneally for four consecutive days (Supplementary Fig. 7a). By day 28, CSF1R blockade reduced tumor mass significantly (Fig. 2c, Supplementary Fig. 7b), despite leaving the overall frequency of F4/80+CD11b+ TAMs unchanged (Supplementary Fig. 7c). Crucially, the proportion of the F4/80highCD11bhigh TAM subpopulation within the TAM compartment fell sharply (Fig. 2d), mirroring the shift seen in Vsir–/– mice. VISTA expression was highest in these F4/80highCD11bhigh TAMs (Fig. 2e), underscoring their functional convergence.

Depletion of VISTAhighF4/80highCD11bhigh TAMs promoted a marked influx of CD8+ T cells (Fig. 2f) and skewed them toward an effector/effector memory phenotype (CD44+CD62L–; Fig. 2g), while reducing the proportion of exhausted PD-1+TIM-3+ CD8+ T cells (Supplementary Fig. 7d), which are typically associated with impaired cytotoxicity 22, 23. Collectively, these data show that VISTAhigh macrophages actively suppress CD8+ T cell activation in PDAC. Targeting either VISTA or CSF1R disrupts this immunosuppressive axis, leading to depletion of VISTAhighF4/80highCD11bhigh TAMs and reinvigoration of anti-tumor CD8+ T cell responses.

Tumor-Associated Macrophage Characteristics in VISTA Deficiency: Chemokine Signaling and Pro-Inflammatory Shifts

To uncover the molecular mechanisms by which VISTA regulates tumor immune microenvironment, we performed single-cell RNA sequencing (scRNA-seq) on CD45+ immune cells isolated from WT and Vsir–/– Pan02 tumors 28 days post-inoculation. Following quality filtering, we applied uniform manifold approximation and projection (UMAP) to analyze 20, 415 single-cell transcriptome profiles, revealing 23 clusters (Supplementary Fig. 8a) corresponding to nine major immune cell types, including tumor-associated neutrophils (TAN), monocytes/macrophages (Mono/MΦ), dendritic cells (DC), CD8+ T cells, proliferating CD8+ T cells, CD4+ T cells, natural killer (NK) cells, B cells, and mast cells (Supplementary Fig. 8b and c). To ensure balanced comparison between WT and Vsir–/– conditions, we downsampled macrophages to 1,365 cells per sample.

Sub-clustering revealed eight macrophage subsets (Fig. 3a and b, Supplementary Fig. 8d): (i) Ccr2+Arg1+ Mac (MΦ_cluster0), (ii) Vegfa+Spp1+ Mac (MΦ_cluster1), (iii) Cxcl9highPfnlo Mac (MΦ_cluster2), (iv) Cxcl9lowCxcl10+Ch25h+ Mac (MΦ_cluster3), (v) Sell+Ly6c2+ Mono (Mono4), (vi) Tox+Ikzf3+ Mac (MΦ_cluster5), (vii) Lyve1+Hes+ Mac (MΦ_cluster6), and (viii) Cxcr2+Mmp2+ Mac (MΦ_cluster7). Notably, Vsir was enriched in MΦ clusters 1, 3, 5, 6, 7 and Mono4, with the highest levels in MΦ_cluster1 (Supplementary Fig. 9a, left). This cluster also showed elevated Adgre1 expression, identifying MΦ_cluster1 as the previously described VISTAhighF4/80highCD11bhigh TAMs (Supplementary Fig. 9a, right).

MΦ_cluster1 exhibited transcriptional signatures associated with immunosuppressive function, including elevated expression of Vegfa, Spp1, Havcr2, Apoe, Trem2, Abca1, and Mertk (Supplementary Fig. 8d and 9b). In contrast, Vsir–/– macrophages in MΦ_cluster2 showed prominent upregulation of IFN signaling genes (Stat1, Stat2, Irf7, Irf8, Socs1, Pim1, and Cxcl9) and chemokines (Ccl5, Ccl7, Cxcl9, and Cxcl10), suggesting a shift toward a pro-inflammatory phenotype (Supplementary Fig. 9c). Downregulation of Nfkb1 and Socs3, key regulators of tumor-promoting and anti-inflammatory signaling respectively 24, 25, further supports this phenotype in Vsir–/– TAM.

Macrophage cluster distribution differed between WT and Vsir–/– tumors, with significant transcriptional distinctions between Vsir–/– (red) and WT (blue) macrophages. MΦ_cluster 2 was almost exclusively present in Vsir–/– tumor, whereas MΦ_clusters 4, 0, 1, and 3 were prominent in WT control, with MΦ_cluster1 and MΦ_cluster3 as dominant populations (Fig. 3b and c). Ccr2 expression was highest in Mono4 and MΦ_cluster0, while Cx3cr1 was elevated in MΦ_clusters 1, 2, and 3 (Supplementary Fig. 8d), suggesting that TAMs in both WT and Vsir–/– tumors originated from circulating monocytes. However, WT TAMs differentiated predominantly into MΦ_clusters 1 and 3, while Vsir–/– TAMs favored differentiation into MΦ_cluster2.

We next examined the balance of Cxcl9 and Spp1 expression across clusters (Fig. 3d). CXCL9 recruits and activates CD8+ T cells, while SPP1 (osteopontin) is associated with immunosuppressive function 26, 27. The Cxcl9:Spp1 ratio has recently emerged as a predictor of favorable immune responses 28, 29. In our data, Cxcl9 expression was significantly upregulated in Vsir–/– TAMs, likely contributing to enhanced CD8+ T cell infiltration. Specifically, Cxcl9+Spp1+ TAMs were more prevalent in WT tumor (35.5%) compared to Vsir–/– tumors (19.0%), while Cxcl9+Spp1– cells were 2.45-fold more frequent in Vsir–/– TAMs (Fig. 3e). The Cxcl9:Spp1 ratio was 2.51 in MΦ_cluster2 versus 0.69 in MΦ_cluster1 (Fig. 3f, Supplementary Fig. 9d), indicating a shift toward a more chemotactic, immunostimulatory TAM phenotype in Vsir–/– tumors.

These results aligned with human PDAC scRNA-seq data from Bill et al. 28, showing similarity between MΦ_cluster1 and immunosuppressive Spp1+ TAMs, and between MΦ_cluster2 and Cxcl9+ TAMs. Gene Set Enrichment Analysis (GSEA) confirmed that Spp1+ TAMs were enriched in MΦ_cluster1 (NES = 1.84, P < 0.001), whereas Cxcl9+ TAMs were significantly enriched in MΦ_cluster2 (NES = –2.51, P < 0.001) (Supplementary Fig. 9e, top). KEGG pathway analysis further showed that MΦ_cluster2 was enriched for cytokine–cytokine receptor interaction and antigen processing and presentation pathways (Supplementary Fig. 9e, bottom), supporting these TAMs may promote CD8+ T cell activation and function.

To validate these findings in human samples, we analyzed the HTAN WUSTL cohort. VSIRhigh tumors were enriched for anti-inflammatory TAMs (hMФ.0; CCL2+MAF+ and hMФ.4; CSTB+SPP1+), while VSIRlow tumors were dominated by pro-inflammatory monocyte-like TAMs (RhMФ.1; S100A8/A9) (Supplementary Fig. 10a‒d). These findings suggest that VSIR expression in human PDAC is associated with macrophage states enriched for immunosuppressive gene programs, consistent with the immune regulatory patterns observed in murine models.

Functional Enhancement of TAMs in VISTA Deficiency: Antigen Cross-Presentation and CD8+ T Cell Activation

To assess the functional impact of VISTA deficiency on macrophages, we analyzed several characteristics. While WT and Vsir–/– BMDMs showed comparable phagocytic ability (Supplementary Fig. 11a), transcriptomic and pathway analyses revealed notable changes in immunomodulatory capacity. Consistent with these transcriptional differences, phospho-protein profiling of WT and Vsir–/– BMDMs under matched stimulation conditions revealed distinct phosphorylation patterns across multiple signaling nodes (Supplementary Fig. 12a and b), indicating altered intracellular signaling states in the absence of VISTA. Principal Component Analysis (PCA) highlighted distinct transcriptional profiles among MΦ_clusters 6, 0, and 4, while MΦ_clusters 1, 2 and 3 were closely related, with MΦ_clusters 2 and 3 particularly similar (Supplementary Fig. 13a). The largest variance (PC1) separated Mono4 and MΦ_cluster6 from other clusters. Given the distinct profiles between MΦ_cluster2 and MΦ_cluster3 in Vsir–/– tumor versus WT control, we focused on their functional characteristics and spatial proximity to CD8+ T cells.

MΦ_cluster2 showed high expression of Ccl8, Il18bp, Vcam1, Vdr, Tapbp, Hspa8, and Calr, while MΦ_cluster3 expressed Ccl3, Ccl7, Tnf, Irf1, Irf7, and Cxcl10. Both clusters shared expression of Slamf7, Cxcl9, Ccl5, and Itgax (Fig. 4a, Supplementary Fig. 13b). KEGG pathway analysis revealed that MΦ_cluster2 was enriched in pathways related to antigen processing and presentation, protein processing in the endoplasmic reticulum, and MAPK and Ras signaling. In contrast, MΦ_cluster3 was enriched in NF-B signaling, cytokine-cytokine receptor interactions, arginine and proline metabolism, apoptosis, and TNF signaling (Fig. 4b and c). These findings suggest that MΦ_cluster2 is more specialized for antigen presentation and may play a key role in activating CD8+ T cells in VISTA-deficient tumors.

Module score analysis confirmed that MΦ_cluster2 had significantly higher expression of antigen presentation-related genes compared to MΦ_clusters 1 and 3 (Supplementary Fig. 13c). Transcriptomic profiling further showed increased expression of MHC class I pathway components, including Tapbp, Tap1, Tap2, and Hsp70, in MΦ_cluster2 from Vsir–/– TAMs (Fig. 4b). These findings were validated by RT-qPCR, which showed upregulated Tapbp and Tap1 in Vsir–/– BMDMs following OVA and LPS stimulation (Supplementary Fig. 11b and c).

To directly test antigen presentation capacity, we performed an antigen cross-presentation assay by measuring SIINFEKL–H2-Kb complex on WT and Vsir–/– BMDMs. Upon SIINFEKL peptide loading, no difference in MFI were observed (Supplementary Fig. 11d). However, Vsir–/– BMDMs exposed to full-length OVA protein displayed significantly higher SIINFEKL–H-2Kᵇ levels, especially at higher antigen concentrations (Fig. 4d), indicating enhanced antigen processing and cross-presentation in the absence of VISTA. Notably, this occurred without changes in CD80, CD86, or I-A/I-E expression (Supplementary Fig. 14), suggesting that VISTA loss selectively enhances antigen-presenting function without broadly activating macrophages.

In functional co-culture assays, Vsir⁻/⁻ BMDMs stimulated robust proliferation of OT-I CD8⁺ T cells by day 3 when loaded with full-length OVA, underscoring their superior antigen-presenting function in physiologically relevant conditions (Fig. 4e, top). These co-cultures also showed higher IFN-γ, TNF-α, and perforin production in OT-I T cells (Fig. 4f). Notably, enhanced IFN-γ production in the Vsir⁻/⁻ BMDM co-cultures was accompanied by increased CXCL9 output (Supplementary Fig. 15), consistent with activation of an IFN-γ–CXCL9 chemokine axis in this setting. In contrast, SIINFEKL peptide stimulation produced similar CD8⁺ T cell proliferation regardless of macrophage genotype (Fig. 4e, bottom), emphasizing the advantage of Vsir⁻/⁻ macrophages in antigen processing rather than presentation alone.

Importantly, no differences were observed with WT versus Vsir–/– splenic DCs (Supplementary Fig. 11e), indicating VISTA’s modulatory role is specific to macrophages.

Spatial analysis revealed CD8+ T cells were closer proximity to TAMs in Vsir–/– tumors (median distance: 3–4 μm) compared to WT tumors ( 10 m), suggesting enhanced macrophage–T cell crosstalk (Fig. 4g). This was further supported by increased CD3+ T cell–CD11b+ myeloid interactions in Vsir–/– tumor cores. To functionally validate these findings in vivo, we used a co-reconstitution model in Rag1–/– mice. Co-injection of Vsir–/– BMDMs and OT-I CD8+ T cells with Pan02-OVA cells significantly reduced tumor growth compared to WT BMDMs (Fig. 4h). No difference was observed with polyclonal CD3+ T cells (Fig. 4i), underscoring the antigen-specific nature of this response. Collectively, VISTA deficiency enhances TAM antigen processing/presentation, driving CD8+ T cell activation and potent anti-tumor immunity.

Reduced Exhaustion and Enhanced Effector Function of CD8+ T Cells in VISTA-Deficient Tumors

To explore how VISTA deficiency influences CD8+ T cell function within the TME, we examined the balance between effector function and exhaustion in CD8+ T cells. This balance is crucial for effective anti-tumor immunity, and understanding it may offer insights into enhancing immune responses. Using unsupervised network-based clustering, we identified six distinct CD8+ T cell subclusters (Fig. 5a–c): (i) Cx3cr1highKlrc1lo CD8+ T cells (Exhausted Intermediate, CD8+ T_cluster0), (ii) Cx3cr1loKlrc1high CD8+ T cells (Exhausted Terminal, CD8+ T_cluster1), (iii) Cx3cr1highKlrc1high CD8+ T (Exhausted-KLR, CD8+ T_cluster2), (iv) Cxcr3+Slamf7+ CD8+ T cells (CD8+ T_cluster3), (v) Fos+Gab2+ CD8+ T cells (CD8+ T_cluster4), and (vi) Maf+Il2ra+ CD8+ T cells (CD8+ T_cluster5). By day 28 post-tumor cell inoculation, no naïve CD8+ T cells (identified by Lef1, Ccr7, Tcf7, and Sell) were detected, suggesting extensive tumor-driven T cell differentiation. Most CD8+ T cells exhibited a resident phenotype (Cxcr6, Cd69, and Runx3). Moreover, CD8+ T_clusters 0, 1, and 2 expressed genes associated with cytotoxicity (Gzmb, Ifng, Cst7, Prf1, and Nkg7) as well as exhaustion markers (Ctla4, Tigit, Pdcd1, Lag3, and Havcr2) (Supplementary Fig. 16a).

CD8+ T_clusters 0 and 3 were predominantly derived from Vsir–/– tumor, whereas clusters 1 and 2 were enriched in CD8+ T cells from WT tumor (Fig. 5c and d). To further characterize their functional states, we quantified exhaustion and effector phenotypes using established gene signatures. CD8+ T_clusters 1 and 2 contained higher proportions of Cd38+Eomes+ cells (22.5% and 44.0%) and Cd226–Pdcd1+ cells 30 (41.0% and 44.3%), respectively, compared to CD8+ T_clusters 0 and 3 (Cd38+Eomes+: 14.0% and 5.0%; Cd226–Pdcd1+: 31.5% and 25.3%) (Fig. 5e, Supplementary Fig. 17a–c). Flow cytometric analysis demonstrated that WT tumors contained a higher cumulative burden of exhaustion-associated inhibitory receptors on CD8⁺ T cells, as assessed by the co-expression of PD-1, TIM-3, LAG-3, and TIGIT (Supplementary Fig. 17d). In particular, CD8⁺ T cells expressing three or more exhaustion markers were enriched in WT tumors compared with Vsir–/– tumors, consistent with a greater prevalence of terminal exhaustion–like CD8⁺ T cell states. In contrast, Vsir–/– tumors exhibited a relative reduction in these highly exhausted populations, accompanied by increased TNF-α and IFN-γ production (Supplementary Fig. 6b).

Consistently, Vsir expression was significantly correlated with key exhaustion markers (Havcr2, Tigit, Lag3, Pdcd1, Cxcl13, and Layn) in both TCGA-PAAD tumor/normal samples and GTEx normal pancreas samples (Supplementary Fig. 16b), suggesting a potential association between VISTA and T cell exhaustion within the TME. GSEA showed enrichment of oxidative phosphorylation and cytokine-cytokine interaction pathways in CD8+ T_cluster3 (Supplementary Fig. 18). Given that oxidative phosphorylation is often reduced in chronically exhausted T cells, this suggests that CD8+ T cells in Vsir–/– tumor exhibit a unique metabolic adaptation that supports their effector function 31, 32. In contrast, exhausted CD8+ T cells in WT tumor likely suffer from impaired mitochondrial function and reduced oxidative phosphorylation, further contributing to their exhausted phenotype.

Flow cytometric analysis further characterized the altered state of CD8+ T cells in Vsir–/– tumor. Vsir–/– tumor had significantly increased expression of CXCR3, with 28.3% of CD8+ T cells co-expressing CXCR3 and CX3CR1 compared to 8.07% in WT tumor (Fig. 5f). Additionally, Vsir–/– tumor had a higher proportion of TCF-1+PD-1+ CD8+ T cells and fewer terminally exhausted CXCR3+CX3CR1– CD8+ (Texterm) cells compared to WT control (Fig. 5g and h). These findings suggest that VISTA deficiency promotes a CD8+ T cell phenotype with reduced exhaustion and improved effector function within the TME.

To extend these findings to human disease, multiplex immunofluorescence analysis of a limited number of PDAC specimens revealed that, although overall CD8⁺ T cell infiltration showed an increasing trend with advancing tumor grade, the fraction of CXCR3⁺ cells within the CD8⁺ T cell compartment was significantly reduced in grade 2 and grade 3 tumors compared with grade 1 tumors (Supplementary Fig. 19a and b), demonstrating an inverse relationship between tumor grade and CXCR3 expression. Notably, spatial analysis further showed a decreased nearest-neighbor distance between VISTA⁺CD68⁺ macrophages and CD8⁺ T cells in grade 2 and grade 3 tumors, indicating increased spatial proximity in higher-grade disease (Supplementary Fig. 19c).

Finally, we validated these observations in the HTAN WUSTL. The VSIRlow group exhibited increased frequencies of effector (hCD8.1; CCL4high) and effector-like (hCD8.2; KCNQ1OT1+) CD8+ T cells, while the VSIRhigh group was enriched for pre-effector (hCD8.0; GZMK+GPR183+) and exhausted (hCD8.7; CXCL13high) CD8+ T cell subsets (Supplementary Fig. 10a and Fig. 20a–c). Together, these results confirm that low VISTA expression is associated with enhanced CD8⁺ T cell effector function and reduced exhaustion in human pancreatic tumors.

Cxcl9-Cxcr3 Axis in VISTA Deficiency: Enhanced CD8+ T Cell Interaction with TAMs

To explore how VISTA-deficient macrophages and CD8+ T cells communicate within the TME, we conducted an intercellular communication network analysis using CellChat 33. Mono/Mac played a central role, especially in Vsir–/– tumor, where they demonstrated enhanced interactions with CD8+ T cells (Fig. 6a). The increased node size representing Mono/Mac highlighted their importance in driving anti-tumor immune responses in pancreatic tumors.

Using CellChatDB, we analyzed ligand-receptor interactions, focusing on Mono/Mac as the primary signal sender. The out-degree magnitude represented the cumulative probability for signals sent by Mono/Mac, while the in-degree represented signals received by CD8+ T cells. Unique ligand-receptor interactions were observed in Vsir–/– tumor. The MΦ_cluster2 macrophage subtype, exclusive to VISTA deficiency, showed increased expression of Cxcl9 and Cxcl4 (Pf4), which are likely to interact with Cxcr3 on CD8+ T_cluster3, predominantly found in Vsir–/– condition (Fig. 6b, left and d). This interaction suggests that MΦ_cluster2 macrophages in Vsir–/– tumor create an inflammatory environment, enhancing CD8+ T cell recruitment and activation, which strengthens the anti-tumor response. Specifically, the Cxcl9-Cxcr3 axis facilitates CD8+ T cell infiltration and activation, supporting efficient tumor clearance 34.

We evaluated whether the anti-tumor effect of Vsir deletion was mediated by IFN-γ–CXCL9–CXCR3 axis by administering a neutralizing anti–IFN-γ antibody or anti-CXCR3 antibody to VISTA-KO mice. Anti-IFN-γ treatment abolished the reduced tumor growth seen in Vsir–/– mice. Likewise, blockade of CXCR3 also reversed the enhanced tumor control in Vsir–/– hosts. These results indicate that the protective effect of VISTA loss depends on the IFN-γ–CXCL9–CXCR3 pathway (Fig. 6c, Supplementary Fig. 21). In addition, both WT and Vsir–/– tumors exhibited Cxcl16-Cxcr6 and Ccl8-Ccr5 interactions, supporting tissue repair and immune regulation, respectively (Fig. 6b) 35, 36.

We quantified the interaction probability from macrophages to CD8+ T cells by converting the total sum of interaction probabilities between WT and Vsir–/– tumors to 100%. In Vsir–/– tumor, Mono/Mac exhibited 100% relative interaction probability via the Cxcl9-Cxcr3 and Pf4-Cxcr3 ligand-receptor pairs, and over 50% for Il18-Il18r1-Il18rap and Lgals9-Ptprc interactions (Fig. 6b, right and e). These interactions are critical for immune activation and chemokine-mediated recruitment of effector T cells 34, 37, 38, 39. In contrast, WT Mono/Mac were predicted to interact through ligand-receptor pairs such as Il15-Il15ra-Il2rb, Ptn-Ncl, Ebi3-Il27ra-Il6st, Spp1-Itga4-Itgb1, Spp1-Itgav-Itgb1 and Tnf-Tnfrsf1a, suggesting a more immunosuppressive role 40. Both WT and Vsir–/– tumors also exhibited predicted interactions through Cxcl16-Cxcr6, Ccl8-Ccr5, and Lglas9-Havcr2.

Interestingly, unique interactions were observed in WT tumor, where MΦ_cluster1 was predicted to interact with CD8+ T_cluster1 through Spp1 and its potential partners Itga4-Itgb1 or Itgav-Itgb1 on CD8+ T cells. This interaction, absent in MΦ_cluster3, highlights the role of Spp1 in maintaining an immunosuppressive TME.

We also identified reciprocal interactions, with CD8+ T cells acting as the source and Mono/Mac as the target (Fig. 6d). In both WT and Vsir–/– tumors, CD8+ T cells expressed Ccl3 and Ccl5, which potentially interact with Ccr5 on macrophages, forming a feedback loop that amplifies immune activation (Fig. 6d, left and f). Additionally, CD8+ T cells expressed Ifng, potentially interacting with Ifngr1 and Ifngr2 on macrophages, establishing a pro-inflammatory feedback loop that activates macrophages in both tumor types. However, WT CD8+ T cells uniquely expressed interactions involving Sema3c-Nrp1-Plxna4, Sema3c-Nrp2-Plxna4, and Spp1-Itga8-Itgb1, suggesting distinct suppressive signaling.

Vsir–/– CD8+ T cells demonstrated 100% interaction probability through the Spp1-Itga9-Itgb1 pair, with strong interactions predicted via Ccl5-Ccr1, Lgals9-Ighm, Mif-Cd74-Cxcr4, and Ccl5-Ccr5 (Fig. 6d, right and f). These interactions indicate a pro-inflammatory environment facilitated by VISTA deficiency. The shift toward pro-inflammatory signaling aligns with the enhanced immune response in Vsir–/– tumor, where CD8+ T cells exhibit robust anti-tumor activity.

VISTA Blockade Synergize with Gemcitabine to Remodel the Pancreatic Tumor Immune Landscape

To determine whether these VISTA-dependent signaling network we identified is therapeutically exploitable, we turned to the KPC orthotopic model and compared anti-VISTA monotherapy, gemcitabine, and their combination (Fig. 7a). Both monotherapies produced a modest but significant reduction in tumor burden; however, the combination elicited a markedly greater suppression, indicating at least an additive—and potentially synergistic—anti-tumor effect (Fig. 7b).

Immune profiling revealed that anti-VISTA monotherapy alone increased total TAMs and increased CD8+ T cells (Supplementary Fig. 22a). Importantly, the antibody skewed TAMs toward an iNOS⁺ phenotype while producing only minor changes in Arg1⁺ TAMs (Supplementary Fig. 22b). Consistent with our CellChat predictions, CXCL9⁺/SPP1⁺ TAMs were enriched in the anti-VISTA treatment group (Supplementary Fig. 22c), both anti-VISTA and the combination regimen boosted infiltration of CXCR3+ CD8+ T cells (Supplementary Fig. 22d) —hallmarks of the CXCL9–CXCR3 chemotactic axis we mapped in Vsir⁻/⁻ tumors. These findings validate our mechanistic model and demonstrate that pharmacologic VISTA blockade can re-program the myeloid–T-cell circuit even in the presence of cytotoxic chemotherapy.

Taken together, our data position VISTA as a central architect of immunosuppressive niches in pancreatic tumors. In WT tumors, TAMs co-express Cxcl9 and Spp1: engagement of SPP1 with integrins Itga4/Itgb1 or Itgav/Itgb1 on CD8⁺ T cells likely dampens T cell recruitment and activation. By contrast, VISTA deficiency—or therapeutic blockade—tilts this balance toward CXCL9 dominance, amplifying the CXCL9–CXCR3 axis and fostering a pro-inflammatory TME that supports robust CD8⁺ T cell activity (Fig. 7c). Thus, combining VISTA inhibition with standard chemotherapy offers a rational strategy to overcome immune suppression and improve anti-tumor effect.

Methods
In vivo animal studies
All experiments and procedures were performed according to protocols approved by the Institutional Animal Care and Use Committee (IACUC No. 220222-8-11) at Seoul National University animal facilities. C57BL/6J (Stock No. 000664), B6.Rag1em10Lutzy/J (Stock No. 034159), B6.Tg (TcraTrcb)1100Mjb/J (OT-I; Stock No. 003831) mice were purchased from the Jackson Laboratory. B6.Vsir–/– mice were established and bred using a cryopreserved sperm (KOMP Repository, UCD, Davis, CA, USA). Mice were 7–9 weeks old at the start of experiments. Only male mice were used to minimize potential variability associated with sex hormones. All mice were housed in a specific pathogen-free (SPF) barrier facility at Seoul National University under controlled conditions (12-h reverse light/dark cycle, 23 ± 2°C, and 40–60% humidity). Experimental and control mice were co-housed whenever possible. Mice were euthanized by CO inhalation followed by cervical dislocation.

Cell Lines
The Pan02 cell line from National Cancer Institute (NCI, 0509770), and KPC001 cell line isolated from the tumor of a 5– to 6–month-old genetically engineered mouse model of KrasG12Dp53R172H/+Pdx-1-Cre mice were used 64, 65. Pan02-OVA variant was generated by cloning chicken ovalbumin (OVA) into the pCDH-CMV-EF1-RFP-T2A-puro plasmid, followed by transfection into the HEK293T cell line using PromoFectin. Lentiviral particles were harvested and transduced into the Pan02 cell line. The transduced cells were selected by positive expression level of RFP validated by flow cytometry and maintained with puromycin (4 g/mL). Cell lines were cultured in RPMI-1640 medium (Gibco, Waltham, MA, USA) supplemented with 10% FBS (Gibco) and 10 μM ciprofloxacin. Cells were maintained at 37°C in a humidified 5% CO₂ atmosphere. All cell lines were regularly tested for mycoplasma contamination and were authenticated prior to use.

Orthotopic tumor models
To establish orthotopic models, 7– to 9–week-old male mice were anaesthetized with rompun®-ketamine mixture and subjected to surgical procedure. After upper left abdominal incision, pancreatic tails were exposed and injected with either 5  105 Pan02 or KPC001 cells resuspended in cold complete RPMI medium mixed at 1:1 dilution with Matrigel (18 mg/mL, Corning Incorporated, Corning, NY, USA) in a final volume of 20 L. After injection, the pancreas was returned to the abdomen, and the incision was closed using 3-0 Vicryl™ (Ethicon, Inc., Somerville, NJ, USA) sutures. Experiments were terminated when tumor reached a size of 1,000 mm3, as per the IACUC limit.

In vivo treatments
To validate the VISTA model, 400 g per mouse of anti-VISTA antibody (Clone 13F3, InVivoMab, BioXCell, Lebanon, NH, USA) or an Armenian Hamster IgG isotype control (Polyclonal, InVivoMab, BioXCell) was administered via intraperitoneal injection on days 5, 8, and 11 after cancer cell inoculation. For the macrophage depletion model, mice were intraperitoneally injected with 1 mg per mouse of anti-CSF1R antibody (Clone AFS98, InVivoMab, BioXCell) or Rat IgG2, isotype control (Clone 2A3, InVivoMab, BioXCell) 13 days prior to tumor inoculation, followed by three consecutive daily injections of 0.5 mg. For IFN- and CXCR3 neutralization, 200 g of anti-IFN- (Clone XMG1.2, InVivoMab, BioXCell) or anti-CXCR3 neutralizing antibody (Clone CXCR3-173, InVivoMab, BioXCell) were administered intraperitoneally 5 days post KPC001 cell inoculation every 3 days until tumor harvest. For combination therapy, 50 mg/kg gemcitabine (Selleckchem, Houston, TX, USA) and/or anti-VISTA antibody (Clone 13F3, InVivoMab, BioXCell, Lebanon, NH, USA) were administered intraperitoneally, starting from day 6 after cancer cell injection. Treatments were given every 3 days.

Tissue dissociation
Mouse pancreatic tumors were manually minced in small pieces. Tissues were suspended in RPMI-1640 supplemented with DNase I (1 mg/mL, Roche, Basel, Switzerland) and Collagenase IV (1 mg/mL, Roche) and incubated at 37°C for 20 min, with frequent agitation. The digested cell suspensions were then filtered through a 70-m nylon cell strainer and treated with 1 RBC lysis buffer (BioLegend, San Diego, CA, USA) for 15 min on ice and resuspended in the appropriate buffer for cell counting and downstream application.

Flow cytometry
Single-cell suspensions (1  106 cells) from the tumor were incubated with mouse FcγIII/II receptor (CD16/CD32) blocking antibody for 15 min on ice and pelleted by centrifugation. Surface staining was then performed with fluorophore-conjugated primary antibodies for 30 min at 4°C to detect different populations of tumor-associated leukocytes (gated as CD45+) that included TAMs (gated as CD45+CD11b+Ly6G–F4/80+), monocytes (CD45+CD11b+Ly6C+), granulocytes/PMN (CD45+CD11b+Gr-1+), dendritic cells (DC, CD45+CD11c+F4/80–), CD4 Th (CD45+CD3+CD4+), CD8 Tc (CD45+CD3+CD8+), and B cells (CD45+CD19+). iNOS+ TAMs and Arg1+ TAMs were gated from F4/80+CD11b+ TAMs; Effector memory T cells within the CD8 Tc population were gated as CD44+CD62L–; Exhausted CD8 Tc populations were gated as TIM3+PD-1+; CXCR3+CX3CR1+ T cells, TCF-1+PD-1– T cells were gated on CD3+CD8+ T cells; CXCR3+CX3CR1– CD3+ T cells were gated on TCF-1–PD-1+ T cells. Dead cells were excluded by either FVS780 (BD Horizon) or FVS510 (BD Horizon) staining.

The antibodies against CD3 (BV711, 17A2, BioLegend; 1:500), CD4 (BV605, GK1.5, BioLegend; 1:500), CD4 (FITC, RM45, BD Pharmingen; 1:1000), CD8 (BV650, 53-6.7, BioLegend; 1:500), CD8 (APC, 53-6.7, BioLegend; 1:1000), CD45.2 (PerCPCy5.5, 104, BioLegend; 1:500), I-A/I-E (BV605, M5/11, BD Horizon; 1:500), F4/80 (BV711, T45-2342, BD Horizon; 1:500), CD11b (BV785, M1/70, BioLegend; 1:3000), CD11c (APC-Cy7, N418, BioLegend; 1:1000), Ly6G (APC, 1A8-Ly6g, Invitrogen; 1:500), Ly6C (PE-Cy7, HK1.4, BioLegend; 1:500), Gr-1 (BV510, RB6-8C5, BD Horizon; 1:1000), CD62L (BV605, MEL-14, BioLegend; 1:1000), CD44 (BV510, IM7, BioLegend; 1:500), TIM3 (PE, B8.2C12, BioLegend; 1:500), PD-1 (FITC, 29F.1A12, BioLegend; 1:100), VISTA (APC, MIH63, BioLegend; 1:200), CX3CR1 (Pacific Blue, SA011F11, BioLegend; 1:500), CXCR3 (BV421, CXCR3-173, BioLegend; 1:200), CCR2 (BV421, SA203G11, BioLegend; 1:400), CD206 (BV650, C068C2, BioLegend; 1:500), CD206 (BV605, CE068C2, BioLegend; 1:500), CD80 (PE, 16-10A1, BioLegend; 1:500), and CD86 (FITCP, GL-1, BioLegend; 1:200) were used for surface staining.

For intracellular staining, samples were fixed with 4% paraformaldehyde (PFA) after surface staining, followed by intracellular staining of CXCL9 (PE, MIG-2F5.5, BioLegend; 1:200), OPN (AlexaFluor647, LFMb-14, SantaCruz; 1:200), IFN- (APC, XMG1.2, BioLegend; 1:500), TNF- (PE, Mab11, BioLegend; 1:500), Granzyme B (FITC, GB11, BioLegend; 1:500), and Perforin (PE, S16009A, BioLegend; 1:500) in Triton buffer. For intracellular cytokine detection, immune cells were stimulated with Phorbol 12-myristate 13-acetate (PMA, 50 ng/mL, Sigma-Aldrich), ionomycin (1 g/mL, Sigma-Aldrich), and Golgi-Stop (2 M, BD Biosciences) for 4 h.

Cells were washed with staining buffer and analyzed by flow cytometry on LSR Fortessa X-20 flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA). Data were analyzed using FlowJo v10 software (BD Biosciences). Gating strategies of flow cytometry analysis are reported in Supplementary Fig. 4.

Immunohistochemistry (IHC) and Immunofluorescence (IF) staining
Tumor tissues were fixed in 4% PFA, embedded in paraffin, and sectioned to 4 m of thickness. For IHC, slides were first deparaffinized in xylene and rehydrated through a graded ethanol series. Antigen retrieval was performed by immersing the slides in pH 6 Citrate Antigen Retrieval Solution (Sigma-Aldrich, St. Louis, MO, USA) and heating for 10 min. Endogenous peroxidase activity was blocked by incubating the slides with 10% hydrogen peroxide for 10 min. Slides were then blocked for 1 h at RT in a solution containing 0.1 M Tris, 0.1% Triton X-100, 10% normal goat/donkey serum, and 1% bovine serum albumin to reduce nonspecific binding.

Primary antibodies, including CD3 (SP162, Abcam, Cambridge, UK; 1:300), CD8 (EPR21916, Abcam; 1:1000), CD11b (EPR1344, Abcam; 1:4000), and F4/80 (EPR26545-166, Abcam; 1:5000), were diluted as per the manufacturer’s instructions and incubated with the slides overnight at 4oC. The slides were then washed with PBS and incubated with biotinylated secondary antibodies for 1 h at RT, followed by detection using a DAB chromogen system. Slides were counterstained with hematoxylin, dehydrated, and mounted for imaging. Images were acquired using a Leica DMi8 microscope (Leica Microsystems, Wetzlar, Germany). IHC was performed to visualize immune cell infiltration and tumor architecture.

For IF staining, the following primary antibodies were used: CD8 (4SM15, Novus Biologicals, Centennial, Co, USA; 1:300), F4/80 (A3-1, Abcam; 5 g/mL) and CD11b (EPR1344, Abcam; 1:2000).The secondary antibodies included Goat anti-rat IgG H&L AF488 (Thermo Fisher Scientific, Waltham, MA, USA; 1:500), goat anti-rabbit IgG H&L AF647 (Thermo Fisher Scientific; 1:500), goat anti-rat IgG H&L AF488 (Thermo Fisher Scientific; 1:500), and goat anti-rabbit IgG H&L AF647 (Thermo Fisher Scientific; 1:500). Nuclei were counterstained with DAPI and images were captured using a laser scanning confocal NIKON Eclipse Ti microscope (Nikon Instruments Inc., Tokyo, Japan). Contact analysis between macrophages and infiltrated T cells was conducted using the Imaris program (version 9.3.1; Bitplane).

For human tissue microarray, PA1921c (TissueArray, Derwood, MD, USA) was stained with anti-VISTA (D1L2G, Cell Signaling Technology, Danvers, MA, USA) antibody diluted as per manufacturer’s instructions and incubated. The image was scanned using the Aperio AT2 digital whole slide image scanner (Leica Biosystems, Wetzlar, Germany), and tumor areas (tumor and stroma) were annotated by pathologist. Tumor cells were annotated for the measurement of staining intensity of the tumor cells, and artifact, pigment or necrotic area were excluded. Counting of positive cells was performed using an image analyzer, and density was calculated by pathologist (Density = Number of positive cells / Annotated are in square mm).

Opal multiplex immunofluorescence staining was performed on pancreatic cancer tissue microarrays (PA2082b, PA2081-L64, PA961-L87, and PA483-L97). Sections were stained with antibodies against CD68, VISTA, CXCR3, and CD8α, which were visualized using Opal 620, Opal 690, Opal 520, and Opal 480 fluorophores, respectively.

Human pancreatic ductal adenocarcinoma tissue microarrays were purchased from TissueArray. According to the vendor, all specimens were collected with written informed consent under Institutional Review Board approval and were fully de-identified prior to purchase. The use of de-identified human tissue specimens in this study does not involve interaction with human subjects.

BMDM cell culture and treatment
Bone marrow-derived macrophages (BMDMs) were generated from femurs and tibias of either WT or Vsir–/– mice, filtered, and red blood cells were lysed. Cells were washed and plated at 2  10⁶ cells/mL in non-treated six-well plates containing DMEM (Gibco) supplemented with 10% FBS, 1% Antibiotic/Antimycotic, and 10% L929 supernatant. On day 3, cells were refed with fresh media. At day 6, the differentiated BMDMs were detached, counted, and reseeded to the cell culture plate for subsequent experiments.

To conduct CD8+ T-cell proliferation assay, BMDMs were cultured overnight in media containing 100 ng/mL lipopolysaccharide (LPS) ( Sigma-Aldrich) and OVA peptide or protein according to the experiment. Maturation was confirmed via flow cytometry using anti-mouse CD11b (PE, M1/70, BioLegend) and anti-mouse F4/80 (BV711, BM8, BioLegend) antibodies.

Splenic dendritic cell culture
Spleens were harvested from WT or Vsir–/– mice, minced into small pieces, and agitated in Hank’s Balanced Salt Solution (HBSS) containing Collagenase IV (1 mg/mL, Roche), DNase I (50 g/mL, Roche), and 1% FBS for 20 min at 37oC. The enzymatic reaction was stopped by adding 1 mM EDTA for 5 min at RT. The cell suspension was filtered through a 70-m cell strainer. CD11c+ DCs were enriched using magnetic-activated cell sorting (MACS Microbeads and Separation Unit, Miltenyi Biotec, Bergisch Gladbach, Germany).

In vitro Macrophage phagocytosis assay
BMDMs from WT or Vsir–/– mice cultured in 24-well plates (2  105/well) were co-incubated with fluorescently stained 1  106 apoptotic thymocytes. Thymocytes were added to the BMDMs at 1:4 ratio. Phagocytosis was allowed to proceed for 2 h at 37 °C. After coculture, thymocytes were washed away extensively and macrophages were detached by trypsinization. Percentage of macrophages engulfing dead cells was determined on a flow cytometer.

In vitro CD8+ T cell proliferation assay
BMDMs from WT or Vsir–/– mice were differentiated and on day 6, BMDMs were treated with different concentrations of OVA (Sigma-Aldrich): 3 g/mL, 10 g/mL, 30 g/mL, 100 g/mL, 300 μg/mL and 100 ng/mL LPS (Sigma-Aldrich) were added to the culture. CD8+ T cells were isolated from the lymph nodes of OT-I transgenic mice by negative selection using MACS (Miltenyi Biotec). To test T cell proliferation and cytotoxicity, cultured BMDMs were co-incubated with freshly prepared CD8+ T cells (> 95% viability) at a ratio of 1:4. At 48 h and 72 h post-co-culture, IFN-, TNF-, and perforin-producing CD8+ T cells were analyzed after stimulation with PMA (50 ng/mL) and ionomycin (1 g/mL) and Golgi-Stop (2 M) for 4 h. T cell proliferation and cytotoxicity were verified by flow cytometry.

Real-time qPCR
Total cellular RNA was extracted using Trizol reagent (Thermo Fisher Scientific, Waltham, MA, USA). RNA was reversely transcribed using the PrimeScript RT Reagent Kit (TaKaRa Bio Inc., Shiga, Japan). Gene expression was evaluated by real-time reverse-transcriptase PCR using iQ SYBR Green Supermix (Bio-Rad Laboratories, Hercules, CA, USA) on a PCR amplification and detection instrument (CFX Connect Real-Time PCR Detection System; Bio-Rad). Gene expression was normalized to Gapdh and the mean relative gene expression was calculated using the 2–ΔΔCt method. The primers used are listed in Supplementary Table 2.

Single-cell RNA sequencing
Samples were collected 28 days after cancer cell inoculation and dissociated as described above. Live CD45+ cells were enriched using FACS on an AriaIII sorter (BD Biosciences). ScRNA-seq libraries were generated using the Chromium Single Cell 3’ Reagent Kit v3.1 (10X Genomics, Pleasanton, CA, USA) following the manufacturer’s instructions. Briefly, single cells were encapsulated in gel beads in emulsion (GEMs), lysed, and subjected to RNA barcoding, reverse transcription, and PCR amplification. Library quality control was performed with an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) at the Genomic Medicine Institute Research Service Center, and sequencing was conducted on an Illumina NovaSeq 6000 instrument.

Single cell curation and high-dimensional data reduction
FASTQ files were aligned to mouse genome reference (mm10-2020-A) and converted into gene counts with Cell Ranger (v 4.0.0) with default parameters. Gene counts were imported into the R environment (v 4.4.0) and processed with Seurat (v 5.1.0). During Seurat object creation, cells and genes satisfying any of the criteria were removed: 1) genes expressed in fewer than three cells, 2) cells with more than 100, 000 RNA counts, fewer than 200 genes, or more than 9, 000 genes, or 3) cells with a mitochondrial gene expression exceeding 10%. Gene counts for each cell were normalized using the SCTransfrom (SCT) normalization method and scaled with the ‘ScaleData’ function. The top 3, 000 highly variable genes were identified using the ‘FindVariableFeatures’ function using the ‘vst’ method in the Seurat R-package. Principal component analysis (PCA) was performed using the ‘RunPCA’ function with default parameters in Seurat R-package.

Cluster annotation and detection of differential genes between genotypes
A shared nearest neighbor (SNN) graph was computed using the ‘FindNeighbors’ function, a Seurat R-package, based on the first 20 principal components to calculate the distance between cells. Cell clusters were defined using the Louvain algorithm with the ‘FindCluster’ function in Seurat R-package. For visualizing high dimensional components into two dimensions, uniform manifold approximation and projection (UMAP) was adopted using the ‘RunUMAP’ function in Seurat R-package. Cluster-specific genes were identified using the ‘FindAllMarkers’ and ‘FindMarker’ functions in Seurat R-package with following options including ‘pos = TRUE and min.pct = 0.1’ and ‘log fold-change (logfc.threshold) cut-off of < 0.25’. The ‘FindConservedMarkers’ function in Seurat R-package was used for each cluster to identify conserved markers, and top features were selected based on a minimum cutoff of 10% of cells with the lowest expression of the gene to ensure adequate cell cluster annotation. We downsampled Mono/Macrophages to 1, 365 cells, and CD8+ T cells to 966 cells so that the number of cells were identical between WT and Vsir–/– genotypes. For all downstream analyses, CD45+ cancer-associated fibroblasts (CAF) were neglected and downsampled files were used to study macrophages and CD8+ T cells.

Trajectory analysis in Macrophages
To assess monocytes/macrophages trajectory, we used the R package Monocle 3 66. The macrophage subset was randomly downsampled to 1, 365 cells and imported into Monocle 3. Non-linear dimensionality reduction was performed using UMAP. Cell trajectory was assessed using the simplePPT method which learns tree-like trajectories and further reduces dimensionality, implemented by the “learn_graph” function. The resolution for clustering was set to 1e-2. Data was visualized with UMAP embeddings and trajectories derived within Monocle and overlaid with clusters annotated from the Seurat analysis.

Gene set enrichment analysis
To analyze the potential biological pathways involved in the pathogenesis of pancreatic cancer, we performed gene set enrichment analysis (GSEA, v4.3.2), comparing Macrophage and CD8+ T cell subclusters between WT and Vsir–/– samples. Three gene set databases were used: Hallmark gene sets (h.all.v2024.1.Hs.symbols.gmt), Kyoto Encyclopedia of Genes and Genomes (KEGG, c2.Rcp.kegg_medicus.v2024.1.Hs.symbols.gmt), and the Immunologic Signature gene sets (c7.immunesigdb.v2024.1.Hs.symbols.gmt). The number of permutations was set to 1, 000, and a p-value less than 0.05 was considered statistically significant. Significant pathways differentially expressed between WT and Vsir–/– samples were visualized using the ggplot2 based on their normalized enrichment scores (NES) and -log 10 (P-value).

Cell-cell interaction
Cell-cell interactions were analyzed using CellChat v.2.1 33 to explore interaction patterns among clusters in pancreatic tumor. Overall interactions between cell type pairs were first calculated, followed by an analysis of communication probabilities between macrophages and CD8+ T cell clusters in WT and Vsir–/– mice. A minimum of 10 cells per cluster was required to infer communication. Communication probabilities were computed using the triMean to determine average gene expression per group. Differences in interaction strength and ligand-receptor (L-R) pairs between the two cell populations were subsequently identified.

Overall survival analysis on the VSIR gene signature
TCGA patient data were stratified into “high” (top 25%) and “low” (bottom 25%) VISTA expression groups based on quartile cutoffs. Survival differences between the two groups were analyzed using the Kaplan-Meier estimator and compared with a log-rank test. Hazard ratios (HR) were calculated using Cox proportional hazards regression. Visualization was performed using the R packages ‘survival’ and ‘survminer’ function.

Logistic Regression of Dichotomized Gene Expression Profiles in TCGA PAAD
The STAR counts from the TCGA PAAD cohort for the following genes, or the ratio of STAR counts between genes, were dichotomized into LOW and HIGH groups based on their median values: VSIR, CD8B, ADGRE1, CD68, CCR2, CX3CR1, and CXCL9/SPP1. The associations between the LOW and HIGH groups for VSIR and those for other genes were evaluated using multiple logistic regression. Groups based on genes other than VSIR were included as independent variables, with the VSIR group as the dependent variable, and analyses were adjusted for age and sex. Missing values were handled by listwise deletion. The significance threshold was set at 0.05.

Processing and analysis of human PDAC single-cell RNA-Seq data
Single-cell RNA-seq data from 25 pancreatic samples in the HTAN WUSTL cohort (pancreatobiliary-type carcinoma, 10x Genomics) were accessed via the HTAN data portal (https://humantumoratlas.org) through Synapse 67 and processed using Seurat R-package. Raw 10X matrices were imported using ‘Read10X()’ and converted into Seurat objects with ‘CreateSeuratObject()’, retaining only PTPRC-expressing cells.

Samples were merged using ‘merge()’ and integrated via ‘JoinLayers()’. Mitochondrial content was calculated with ‘PercentageFeatureSet()’ (pattern ‘^MT-’), and cells were filtered based using strict QC criteria: < 50% mitochondrial reads, 100–5,000 genes, and 100–20,000 UMIs. Data were normalized using ‘SCTransform()’, with regression on transcripts and gene counts. Dimensionality reduction was performed using PCA (top 30 components), followed by UMAP ‘RunUMAP()’. Clusters were identified using Louvain algorithm with a resolution parameter set to 0.8 for the TIL population and 0.5 for macrophage and CD8+ T cell subsets. Cluster markers were identified using FindAllMarkers() (expressed in > 25% of cells, log2 FC > 1.5 and adjusted-P < 0.05). To stratify patients by immune checkpoint expression, aggregate VSIR expression within the TIL compartment was computed. Based on ranked expression values, 21 patients were stratified into VSIRhigh (top 10; n = 10) and VSIRlow (bottom 10; n = 10) groups. Group comparisons were conducted using the one-sided Mann–Whitney U test.

Quantification of gene co-expression and differential expression analysis
To assess gene co-expression patterns at single-cell resolution, two-dimensional kernel density contour plots were generated using the geom_density_2d() function from the ggplot2 R package 68. Normalized expression values were extracted from Seurat objects using the FetchData() function. To mitigate issues arising from zero expression values that can cause fragmented contours, small random noise (i.e. jitter) was added to each expression value, resulting in smoother and more interpretable density estimates.

Gene pairs were selected based on relevant biological context, and for each gene, the mean expression level across all cells was used as a threshold to define vertical and horizontal cutoff lines, dividing the expression space into four quadrants. Cells were classified as positive if their expression exceeded the gene-specific mean, and the proportion of cells in each quadrant was quantified to evaluate co-expression dynamics across conditions.

Differential gene expression analysis between clusters and genotypes
For differential expression analysis between selected clusters irrespective of genotype, we used the full set of cells without downsampling to preserve native cell distributions. DEGs were identified using the FindMarkers() function in Seurat R-package, with consistent filtering criteria applied: > 25% expression frequency, fold change > log 2 (1.5), and adjusted p-value < 0.05. In a separate analysis focusing on genotype-specific differences, we selected clusters enriched in either wild-type or Vsir-deficient conditions. To control for unequal cell numbers across clusters, 300 cells were randomly sampled per cluster prior to DEG analysis. Resulting gene sets were intersected or unioned to identify genotype-specific or shared transcriptional signatures.

Quantification and statistical analyses
Results were expressed as mean ± standard error of the mean (SEM). Differences between groups were evaluated using appropriate statistical tests. Repeated Measures (RM) ANOVA with Geisser-Greenhouse correction was used for tumor growth curves. One-way ANOVA with Sidak’s post hoc test was used for immune cell frequency comparisons over time. Pairwise group comparisons were performed using unpaired two-sided Student’s t-test or Mann-Whitney U test, as appropriate. Survival data were analyzed using the log-rank test. For scRNA-seq data, adjusted p-values (p_adj) for multiple comparisons were calculated using the FindAllMarkers function, with all DEGs meeting the criterion p_adj < 0.05. For the analysis of human scRNA-seq data, which was conducted to validate findings from murine models, the one-sided Mann-Whitney U test was employed.

Statistical analyses were conducted using GraphPad Prism v9.0 (GraphPad Software) or R v3.4.1 (R project). Details regarding statistical tests, exact n values, and their definitions are provided in the figure legends. Exact P values are reported in the figures wherever possible.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Shin et al. explore the role of VISTA in shaping the TME of PDAC, a malignancy characterized by poor prognosis and\nresistance to immunotherapy. Using murine models and corroborated with human datasets, the authors demonstrate that\nVISTA deficiency shifts TAMs from an immunosuppressive phenotype to a pro-inflammatory state, enhancing CD8+ T cell\nrecruitment and activation. Specifically, it identifies a novel axis where VISTA deficiency shifts TAMs from an\nimmunosuppressive (SPP1-high) phenotype to a pro-inflammatory (CXCL9-high) state, enhancing CD8+ T cell recruitment,\nactivation, and cytotoxicity. This reprogramming reduces tumor growth, improves survival, and is a reshaped TME more\nconducive to anti-tumor immunity. The findings align with the growing understanding that macrophage-driven\nreprogramming can synergize with adaptive immune responses to overcome the resistance of PDAC to immune checkpoint\nblockade therapies. This manuscript highlights the therapeutic potential of targeting VISTA to overcome the\nimmunosuppressive barriers in PDAC.\nThe study lacks sufficient human experimental validation to establish translational relevance. Furthermore, the study would\nbenefit from a deeper exploration of the molecular pathways regulating VISTA expression and its upstream and downstream\neffects since others have already established CXCL9-SPP1 (ex. Bill et al. Science 2023). Despite these limitations, the\nresearch offers significant contributions to understanding VISTA’s role in the PDAC TME and lays a strong foundation for\nconsidering VISTA inhibition as a promising therapeutic strategy.\nMajor Weaknesses:\n1. While human scRNA-seq datasets are used, more direct experimental validation in human PDAC samples is required to\nstrengthen translational relevance.\n2. The role of upstream regulators of VISTA expression and downstream signaling pathways influencing TAM polarization\nand mechanism remains insufficiently addressed.\n3. The study does not compare the efficacy of VISTA inhibition with standard treatments (ex., gemcitabine plus paclitaxel or\nFOLFIRINOX) or other immune checkpoint inhibitors, limiting its positioning within the broader therapeutic landscape.\nMinor Weaknesses:\n1. Functional validation of macrophage-T cell interactions beyond proximity measures (e.g., cytokine secretion profiles)\nwould be beneficial.\n2. Sentences in the introduction and discussion sections occasionally overstate claims (e.g., significantly reprogram TAMs).\n3. Figure 2: The role of CSF1R inhibition in TAM modulation is compelling but needs additional experimental replication to\nconfirm its rigor.\n4. Figure 3: Comparative gene expression heatmaps could better illustrate shifts in Cxcl9:Spp1 ratios.\n5. Clarify ambiguities in the discussion, such as “VISTA inhibition could synergize with ICB” (specify conditions).\n6. The depletion of F4/80highCD11bhigh TAMs via CSF1R inhibition in Figure 2 is an important mechanistic result, but the\ntext focuses more on TAM dynamics than on connecting these changes to downstream CD8+ T cell activation (Figure 2g–h).\n7. The importance of macrophage cluster2’s higher Cxcl9:Spp1 ratio (Figure 3f) is briefly stated in the text but not adequately\ncontextualized regarding CD8+ T cell activation and tumor regression.\n8. The results text emphasizes the reduced exhaustion and enhanced effector function of CD8+ T cells in VISTA-deficient\ntumors (Figure 5), but the exhaustion marker analysis (e.g., module scores) lacks sufficient explanation to clarify the\ndistinction between effector and terminally exhausted phenotypes.\nSupplementary figures:\n1. S3: Immunofluorescence staining of CD8+ T cells and CD11b+ macrophages is crucial to visualize immune infiltration but\nis only superficially discussed in the context of Figure 1.\n2. Supplementary Figure S5 contains important flow cytometric data on CSF1R-mediated macrophage depletion and VISTA\nexpression, but its relationship to Figures 2 and 3 is not explicitly stated. For instance, the text does not sufficiently highlight\nhow the functional phenotypes of TAMs (e.g., changes in iNOS and Arginase-1 expression in S5a–c) connect to their pro-\ninflammatory or immunosuppressive states.\n3. S7 supports Figure 3 but is insufficiently explained in the results, particularly regarding the functional differences between\nSpp1+ and Cxcl9+ macrophages.\n4. Supplementary Figure S8 discusses the lack of difference in phagocytosis capacity between WT and VISTA-deficient\nmacrophages (S8a), which could provide a contrasting point to emphasize functional gains in antigen presentation.", "reviewer_2": "This manuscript by Shin et al. investigates the role of the inhibitory immune receptor VISTA in macrophage polarization and\nT cell differentiation in a mouse model of pancreatic cancer. The authors find a clear tumor growth phenotype when VISTA is\ngenetically or therapeutically targeted, and ultimately use single cell RNA sequencing to associate this effect with\ntranscriptional changes in tumor macrophages associated with suppressive-to-inflammatory macrophage conversion\n(Spp1:CXCL9 axis) and a shift in CD8 T cell differentiation away from terminal exhaustion. The phenotypes are convincing\nand the data is novel, relevant, and clearly presented. Key weaknesses to be addressed include its heavy reliance on an\nestablished, but genetically irrelevant and immunologically receptive PDAC model, potential concerns related to the number\nof experimental replicates across in vivo experiments, and additional validation experiments needed to confirm mechanistic\ninsights that are established only through correlation. In summary, the manuscript provides valuable new insights into the\nrole of VISTA in PDAC and has room for improvement through validation experiments that seem feasible and have a high\nchance of success. Please see my specific comments below:\nMajor comments:\n1. Most in vivo experiments seem to utilize 3-5 mice per group with no statement of replicates, unless I have missed a\nstatement of experiment replication somewhere in the manuscript. Please clarify in methods and/or figure legends how many\nsamples per condition were used, whether any samples were removed from consideration and by what criteria, and how\nmany independent experiments were performed for each experiment.\n2. The findings of this paper are highly reliant on a relatively immunogenic and genetically irrelevant PancO2 PDAC model.\nNumerous, more genetically relevant and poorly immunogenic PDAC models exist at this time; can the key results of this\nstudy be replicated in a more rigorous KPC-type model?\n3. It remains unclear whether (i) VISTA deficiency primarily affects macrophage polarization leading to secondary effects on\nT cell differentiation; (ii) VISTA deficiency primarily affects T cell differentiation/exhaustion leading to secondary effects on\nmacrophage polarization; or (iii) these effects occur independently. Can the authors provide any data that indicates whether\nVISTA deficiency on macrophages or T cells alone is necessary or sufficient for their phenotypes?\n4. Can the authors explain why VISTA-KO BMDMs show a functional phenotype as compared to VISTA WT BMDMs in vitro\npresumably in the absence of VISTA ligand? What is acting as the VISTA ligand in the WT BMDMs?\n5. Key points of the proposed mechanism could benefit from in vivo validation. Is the CXCL9/CXCR3 axis required for the\nrobust antitumor phenotype in the VISTA-KO mice? Are macrophages derived from tumors grown in VISTA-KO versus WT\nmice differentially able to mediate T cell chemotaxis, priming, and/or function? The validation only in BMDCs does not\nrecapitulate the key, complex biology of the TME.\nMinor comments:\n1. Fig 2B: What is the rationale for your anti-CSF1R regimen starting 2 weeks before tumor implantation? Why not apply the\nantibody during/after implantation, in context of tumor progression? What macrophages are you depleting with the D-14 anti-\nCSF1R regimen?\n2. I would recommend removing the artificial \"gates\" on the scRNA UMAP plots; let the clusters speak for themselves. Lines\ndrawn in UMAP space have no inherent meaning\n3. Figure 3F: specifically what do you mean by cumulative frequency at the far right? Cluster 2 shows 100% frequency in\nboth genotype conditions? What does this mean? Also, how did you determine the cutoff for CXCL9 positivity versus Spp1\npositivity at the gene expression level?\n4. Figure 4i: the % positive gate seems to bias the results because it excludes non-proliferating T cells, of which there are\nmore in the WT BMDM group. If you only assess only the proliferating cells in both groups, is there a difference in cytokine\nproduction btw groups?\n5. Figure 4H: Is there any difference in costimulatory ligand expression (CD80/CD86, etc.) in BMDMs from WT versus\nVISTA-KO mice? Or is the difference in cross-presentation capacity solely due to higher levels of pMHC?\n6. Figure 6: CXCR3 and CX3CR1 expression are likely insufficient to conclusively infer levels of T cell exhaustion. Could\nyou show a difference in expression of canonical markers such as PD1/Tim-3, PD-1/CD39, or PD-1/TOX?", "reviewer_3": "The article investigates the role of VISTA in PDAC and its regulatory effects on TAM polarization and the immune\nmicroenvironment. This topic is highly relevant, given the advancement of immunotherapy. The study’s use of single-cell\nRNA sequencing and the novel discovery of TAM polarity (using the Cxcl9/Spp1 ratio) hold substantial scientific value.\nVISTA is proposed as a potential immunotherapeutic target, complementary to existing PD-1/PD-L1 or CTLA-4 immune\ncheckpoint inhibitors, underscoring the clear clinical relevance of this research.\nThe study employs a broad array of modern techniques, including single-cell RNA sequencing, immunohistochemistry, flow\ncytometry, and in vivo/in vitro functional assays. The methodology is comprehensive and robust, with the syngeneic\northotopic model providing a realistic simulation of the immune microenvironment and strong data support. However, certain\nareas of the study warrant further refinement to maximize its scientific and translational impact.\n1. Deeper Mechanistic Research Needed\n• When the study reveals the regulatory effects of VISTA deficiency on TAM polarization, it lacks a deeper exploration of\nupstream signaling pathways or regulators. Including more mechanistic studies could enhance its scientific impact.\n• The mechanisms underlying the enhanced CD8+ T cell functon(e.g., metabolic states or epigenetic regulation) are\nunderexplored. Please adding more data on these aspects would strengthen this part.\n2. Optimizing Data Integration and Presentation\n• The article relies heavily on supplementary materials (e.g., many critical findings are presented in supplementary figures).\nThis might hinder reviewers’ ability to grasp the study’s significance quickly.\n• Although the single-cell RNA sequencing analysis is comprehensive, the data could be better integrated to simplify\nconclusions and improve readability.\n3. Strengthening the Discussion on Clinical Relevance\n• The discussion on the clinical feasibility of VISTA-targeted therapy is brief. For example, more information on the\ndevelopment and challenges of existing VISTA inhibitors would be valuable. High-impact journals often emphasize\ntranslational relevance.\n4. Addressing the Lack of Comparative Analysis\n• The study does not compare VISTA’s function and utility with other immune checkpoint molecules (e.g., PD-1, TIM-3).\nSuch comparisons could better highlight VISTA’s uniqueness.\nIn general, the manuscript requires major revisions to achieve its full potential."}, "author_response": "Responses to Reviewers’ Comments\nWe are deeply grateful for your thoughtful comments and invaluable suggestions regarding our\nmanuscript. Your feedback has provided us with an excellent opportunity to improve the quality\nand clarity of our work. We have carefully revised the manuscript in accordance with the\nreviewers’ recommendations and your editorial guidance. Below, we respectfully addressed\neach of the reviewers’ comments point-by-point and described the corresponding revisions\nmade to the manuscript. All changes are clearly marked in red for your convenience. Please\nnote that, as we have reorganized several figures and added new ones, we made minor\nadjustments to a few words in the text. These changes were made solely for consistency and\nclarity, and do not affect the meaning of the manuscript. Thank you once again for your time\nand consideration.\nReviewer #1’s Comments and Responses\nMajor Weaknesses:\n1.While human scRNA-seq datasets are used, more direct experimental validation in human\nPDAC samples is required to strengthen translational relevance.\n(Author’s Response/Action)\nWe sincerely appreciate your suggestion to strengthen the translational relevance of our\nfindings. In response, we performed multi-layered analyses using publicly available human\nPDAC datasets and primary tumor tissue samples to assess whether key observations from our\nmurine studies extend to human PDAC. Specifically, we sought to determine whether:\n(i) VSIR expression increases with tumor progression,\n(ii) CD8+ T cell- macrophage-associated gene signatures (e.g., CD8B, ADGRE1, and\nCCR2) are enriched in pancreatic cancer patients with lower VSIR expression in bulk\nRNA-seq, and\n(iii) patterns of macrophage polarization and T cell exhaustion differ between VSIRhigh and\nVSIRlow pancreatic cancer patients, consistent with our observation that VISTA-\n-1-\ndeficient mouse tumors exhibit fewer SPP1+ TAMs and a greater abundance of\nfunctional effector-like CD8+ T cells.\nTo address the first question, we analyzed VSIR expression in the TCGA-PAAD cohort.\nAfter adjusting the effects of age and sex, Spearman rank partial correlation analysis revealed\na positive correlation between VSIR transcript levels and advancing tumor grade (ρ = 0.1507,\np = 0.0041), and analysis-of-covariance analysis revealed the overall mean difference in VSIR\ntranscript levels across tumor grades (F = 20.1097, p < 0.0001; post-hoc tests were used,\nsuggesting a role in PDAC progression (Supplementary Fig. S3c). This trend was mirrored at\nthe protein level: immunohistochemistry of pancreatic tissue microarray demonstrated elevated\nVISTA expression in grade 2 and grade 3 tumors compared to grade 1 tumors (Supplementary\nFig. S3d and e). While these samples lacked co-staining for VISTA and pan-macrophage\nmarker CD68, the observed pattern aligns with prior reports of VISTA+CD68+ macrophage\nenrichment in PDAC stroma relative to melanoma (Blando et al., PNAS, 2019).\nNew Supplementary Figure S3\nSupplementary Figure S3. High VSIR (c10orf54) expression level correlates with poor\noverall survival in PAAD patients. a Kaplan-Meier curve for the VSIR signature in The\n-2-\nCancer Genome Atlas (TCGA) pancreatic adenocarcinoma (PAAD) cohort. Statistical\nsignificance was determined by the log-rank (Mantel-Cox) test. b VSIR expression levels in\ntumor (n = 179) versus normal pancreas (n = 171) samples from the TCGA PAAD cohort,\ntransformed as –log(TPM+1). c VSIR expression levels in tumor stratified by tumor grade from\nthe TCGA PAAD cohort. d Quantification of VISTA+ cells in pancreatic tumors stratified by\ntumor grade. e Representative immunohistochemical images from a tumor microarray are\nshown. n.s., not significant.\nIn Results (Page 5, Lines 113-119)\nOriginal:\n“This finding was further supported by clinical data from the TCGA dataset, where higher\nVISTA expression correlated with poorer overall survival across multiple cancer types (n = 33),\nincluding pancreatic adenocarcinoma (Supplementary Fig. S1a). In the PAAD cohort, elevated\nVISTA expression was observed in both basal (n = 65) and classical (n = 85) subtypes compared\nto normal tissues (Supplementary Fig. S1b). These clinical correlations underscore the potential\nof targeting VISTA as a therapeutic strategy for PDAC.”\nRevised:\n→ “In human PDAC, lower VSIR expression was similarly associated with improved patient\nsurvival by Kaplan-Meier analysis of The Cancer Genome Atlas Pancreatic Adenocarcinoma\n(TCGA-PAAD) cohort (Supplementary Fig. S3a). VSIR expression was significantly elevated\nin pancreatic cancer tissues (n = 179) compared to non-malignant pancreatic tissues (n = 171)\n(Supplementary Fig. S3b). Interestingly, VSIR expression increased with advancing\nhistological grade (Supplementary Fig. S3c), a trend also confirmed at the protein level by\nimmunohistochemistry using a PDAC tissue microarray (Supplementary Fig. S3d and e).\nTogether, these clinical observations highlight the potential relevance of VISTA as a therapeutic\ntarget in PDAC.”\nSecond, we stratified TCGA-PAAD patients into VSIRhigh and VSIRlow groups. Tumors in\nthe VSIR low group exhibited relatively higher expression of CD8B, ADGRE1, and CCR2\n-3-\nconsistent with a more immune-infiltrated phenotype similar to that observed in VISTA KO\nmouse tumors. Although associations with CXCL9 and SPP1 did not reach statistical\nsignificance, they showed directionally consistent trends, supporting the hypothesis that VSIR\nexpression may influence myeloid chemokine signaling.\nNew Supplementary Table S1\nSupplementary Table S1. Multivariate Logistic Regression Analysis of Immune-Related\nGene Associations with VSIR Expression (High versus Low), Adjusted for Age and Sex\n95% Confidence Interval\nOdds ratio P-value\nLower bound Upper bound\nCD8B 0.0803 0.0343 0.1878 < 0.0001\nADGRE1 0.1254 0.0567 0.2773 < 0.0001\nCD68 0.4612 0.2240 0.9495 0.0340\nCCR2 0.0610 0.0257 0.1447 < 0.0001\nCX3CR1 0.2622 0.1265 0.5433 0.0003\nCXCL9/SPP1 0.3893 0.1892 0.8009 0.0097\nIn Results (Page 6, Lines 134-138)\nOriginal:\n“Beyond macrophage reprogramming, Vsir–/– tumor exhibited a significant increase in CD8+ T\ncell infiltration, with a shift towards an effector or effector memory (CD44+CD62L–) phenotype\n(Fig. 1i). This was accompanied by elevated IFN- levels and increased frequency of TNF-\n+IFN-+ and TNF-–IFN-+ CD8+ T cells (Supplementary Fig. S4b), indicating enhanced\nimmune activation and cytotoxic potential.\nTaken together, our findings suggest that VISTA serves as a key regulator of immune\nevasion within the PDAC microenvironment, and targeting VISTA represents a promising\nstrategy to overcome the immunosuppressive barriers in PDAC and enhance anti-tumor\nimmunity.”\nRevised:\n-4-\n→ “… TNF-+IFN-+ and TNF-–IFN-+ CD8+ T cells (Supplementary Fig. S6b), indicating\nenhanced immune activation and cytotoxic potential.\nTo explore clinical relevance of these findings, we performed multivariate logistic\nregression on the TCGA-PAAD dataset. Elevated CD8B expression was associated with 71.6%\nlower odds of high VSIR expression (Supplementary Table S1). Similarly, high expression of\nADGRE1 and CCR2‒markers of myeloid lineage‒were associated with 60.66% and 81.70%\nreduced odds of elevated VSIR expression, respectively. These patterns recapitulate those\nobserved in murine models.\nCollectively, these findings identify VISTA as a central regulator of immune suppression\nin PDAC, orchestrating TAM-mediated immunosuppression and dampening CD8⁺ T cell\nresponses. Targeting VISTA represents a promising immunotherapeutic strategy to overcome\nthe immunosuppressive TME and enhance anti-tumor immunity in PDAC.”\nThird, we analyzed a publicly available single-cell RNA-seq dataset from the HTAN\nWUSTL cohort to examine immune cell composition and gene expression at higher resolution.\nPatients were stratified into VSIRhigh and VSIRlow groups based on aggregate VSIR expression\nin tumor-infiltrating lymphocytes (TILs). In the myeloid compartment, VSIRhigh tumors were\nenriched for anti-inflammatory, TAM-like subclusters (e.g., CCL2+MAF+ and CSTB+SPP1+),\nwhereas VSIRlow tumors contained a greater proportion of monocyte-like macrophages\nexpressing S100A8/9. In the CD8+ T-cell compartment, VSIRhigh tumors were dominated by\npre-effector (GZMK+GPR183+) and cycling-exhausted (TOX+MKI67+) subclusters with\nreduced expression of cytotoxic effector molecules. Conversely, VSIRlow tumors exhibited\nincreased frequencies of effector-like CD8+ T cells, including CCL4high subsets, consistent with\nthe CD44+CD62L‒ effector phenotypes seen in VISTA KO mice.\n-5-\nNew Supplementary Figure S10\nSupplementary Figure S10. Validation of VSIR expression in human scRNA-seq datasets.\na Schematic workflow of human scRNA-seq analysis from the HTAN WUSTL cohort of\npancreatobiliary-type carcinoma patients. b UMAP visualization of macrophage subsets\nfollowing subclustering. c Dot plot showing gene expression patterns across macrophage\nsubsets. d Proportion of pro-inflammatory-like and anti-inflammatory-like genes in\nmacrophages from VSIRhigh (n = 10) versus VSIRlow (n = 10) patients. Patients were stratified\nby immune checkpoint expression: aggregate VSIR expression within the TIL compartment\nwas calculated for each patient, and the top and bottom 10 ranked patients were assigned to\n-6-\nVSIRhigh and VSIRlow groups, respectively. Group comparisons were performed using the one-\nsided Mann-Whitney U test.\nIn Results (Pages 10-11 , Lines 237-246)\nOriginal:\n“Our findings align with human scRNA-seq data on SPP1+ and CXCL9+ TAMs reported by\nBill et al. 28, with similarities noted between MΦ_clusters 1 and 2. Gene set enrichment analysis\n(GSEA) showed that Cxcl9+ TAMs were consistently enriched in MΦ_cluster2 compared to\nMΦ_cluster1, whereas Spp1+ TAMs were predominantly in MΦ_cluster1 (Supplementary Fig.\nS7d, top). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis further\ndemonstrated that MΦ_cluster2 was enriched in cytokine-cytokine receptor interactions and\nantigen processing/presentation pathways (Supplementary Fig. S7d, bottom).\nThese findings suggest that targeting VISTA may modulate the immune microenvironment\nby shifting TAMs from an immunosuppressive to an immunostimulatory phenotype, thereby\nenhancing anti-tumor immunity.”\nRevised:\n→ “These results aligned with human PDAC scRNA-seq data from Bill et al. 28, showing\nsimilarity between MΦ_cluster1 and immunosuppressive Spp1+ TAMs, and between\nMΦ_cluster2 and Cxcl9+ TAMs. Gene set enrichment analysis (GSEA) confirmed that\nSpp1+ TAMs were enriched in MΦ_cluster1 (NES = 1.84, P < 0.001), whereas Cxcl9+ TAMs\nwere significantly enriched in MΦ_cluster2 (NES = –2.51, P < 0.001) (Supplementary Fig.\nS9e, top). KEGG pathway analysis further showed that MΦ_cluster2 was enriched for\ncytokine–cytokine receptor interaction and antigen processing and presentation pathways\n(Supplementary Fig. S9e, bottom), supporting these TAMs may promote CD8+ T cell\nactivation and function.\nTo validate these findings in human samples, we analyzed the HTAN WUSTL cohort.\nVSIRhigh tumors were enriched for anti-inflammatory TAMs (hMФ.0; CCL2+MAF+ and hMФ.4;\nCSTB+SPP1+), while VSIRlow tumors were dominated by pro-inflammatory monocyte-like\nTAMs (hMФ.1; S100A8/A9) (Supplementary Fig. S10a‒d). These findings suggest that VISTA\n-7-\nshapes the macrophage landscape to favor immune suppression, and that its inhibition\nreprograms TAMs toward a phenotype that promotes anti-tumor immunity.”\nNew Supplementary Figure S17\nSupplementary Figure S17. Validation of VSIR expression in human scRNA-seq data. a\nUMAP visualization of CD8+ T-cell subsets following subclustering. b Dot plot showing gene\nexpression profiles across identified CD8+ T-cell subsets. c Proportion of effector-like genes in\n-8-\nVSIRhigh versus VSIRlow patients. Patient groups were stratified using the same criteria as in\nSupplementary Fig. S10: aggregate VSIR expression within the TIL compartment was\ncalculated for each patient from the HTAN WUSTL cohort, with the top and bottom 10 patients\nassigned to VSIRhigh versus VSIRlow groups, respectively. Statistical significance was assessed\nusing the one-sided Mann-Whitney U test.\nIn Results (Pages 14-15, Lines 336-342)\nOriginal:\n“Flow cytometric analysis further characterized the altered state of CD8+ T cells in Vsir–/–\ntumor.\nVsir–/– tumor had significantly increased expression of CXCR3, with 28.3% of CD8+ T cells\nco-expressing CXCR3 and CX3CR1 compared to 8.07% in WT tumor (Fig. 5h). Additionally,\nVsir–/– tumor had a higher proportion of TCF-1+PD-1+ CD8+ T cells and fewer\nCXCR3+CX CR1– CD8+ Texterm cells compared to WT control (Fig. 5i, j). These findings\n3\nsuggest that VISTA deficiency promotes a CD8+ T cell phenotype with reduced exhaustion and\nimproved effector function within the TME.”\nRevised:\n→ “…improved effector function within the TME.\nFinally, we validated these observations in the HTAN WUSTL cohort. The VSIRlow group\nexhibited increased frequencies of effector (hCD8.1; CCL4high) and effector-like (hCD8.2;\nKCNQ1OT1+) CD8+ T cells, while the VSIRhigh group was enriched for pre-effector (hCD8.0;\nGZMK+GPR183+) and exhausted (hCD8.7; CXCL13high) CD8+ T-cell subsets (Supplementary\nFig. S10a and Fig. S17a–c). Together, these results confirm that low VISTA expression is\nassociated with enhanced CD8⁺ T cell effector function and reduced exhaustion in human\npancreatic tumors.”\nIn Methods\n→ “Logistic Regression of Dichotomized Gene Expression Profiles in TCGA PAAD\nThe STAR counts from the TCGA PAAD cohort for the following genes, or the ratio of STAR\ncounts between genes, were dichotomized into LOW and HIGH groups based on their median\n-9-\nvalues: VSIR, CD8B, ADGRE1, CD68, CCR2, CX3CR1, and CXCL9/SPP1. The associations\nbetween the LOW and HIGH groups for VSIR and those for other genes were evaluated using\nmultiple logistic regression. Groups based on genes other than VSIR were included as\nindependent variables, with the VSIR group as the dependent variable, and analyses were\nadjusted for age and sex. Missing values were handled by listwise deletion. The significance\nthreshold was set at 0.05.\nProcessing and analysis of human PDAC single-cell RNA-Seq data\nSingle-cell RNA-seq data from 25 pancreatic samples in the HTAN WUSTL cohort\n(pancreatobiliary-type carcinoma, 10x Genomics) were accessed via the HTAN data portal\n(https://humantumoratlas.org) through Synapse 69 and processed using Seurat R-package. Raw\n10X matrices were imported using ‘Read10X()’ and converted into Seurat objects with\n‘CreateSeuratObject()’, retaining only PTPRC-expressing cells.\nSamples were merged using ‘merge()’ and integrated via ‘JoinLayers()’. Mitochondrial\ncontent was calculated with ‘PercentageFeatureSet()’ (pattern ‘^MT-’), and cells were filtered\nbased using strict QC criteria: < 50% mitochondrial reads, 100–5,000 genes, and 100–20,000\nUMIs. Data were normalized using ‘SCTransform()’, with regression on transcripts and gene\ncounts. Dimensionality reduction was performed using PCA (top 30 components), followed by\nUMAP ‘RunUMAP()’. Clusters were identified using Louvain algorithm with a resolution\nparameter set to 0.8 for the TIL population and 0.5 for macrophage and CD8+ T-cell subsets.\nCluster markers were identified using FindAllMarkers() (expressed in > 25% of cells, log2\nFC > 1.5 and adjusted-P < 0.05). To stratify patients by immune checkpoint expression,\naggregate VSIR expression within the TIL compartment was computed. Based on ranked\nexpression values, 21 patients were stratified into VSIRhigh (top 10; n = 10) and VSIRlow (bottom\n10; n = 10) groups. Group comparisons were conducted using the one-sided Mann–Whitney U\ntest.\nQuantification of gene co-expression and differential expression analysis\nTo assess gene co-expression patterns at single-cell resolution, two-dimensional kernel\ndensity contour plots were generated using the geom_density_2d() function from the ggplot2\n-10-\nR package 70. Normalized expression values were extracted from Seurat objects using the\nFetchData() function. To mitigate issues arising from zero expression values that can cause\nfragmented contours, small random noise (i.e. jitter) was added to each expression value,\nresulting in smoother and more interpretable density estimates.\nGene pairs were selected based on relevant biological context, and for each gene, the mean\nexpression level across all cells was used as a threshold to define vertical and horizontal cutoff\nlines, dividing the expression space into four quadrants. Cells were classified as positive if\ntheir expression exceeded the gene-specific mean, and the proportion of cells in each quadrant\nwas quantified to evaluate co-expression dynamics across conditions.\nDifferential gene expression analysis between clusters and genotypes\nFor differential expression analysis between selected clusters irrespective of genotype, we\nused the full set of cells without downsampling to preserve native cell distributions. DEGs\nwere identified using the FindMarkers() function in Seurat R-package, with consistent\nfiltering criteria applied: > 25% expression frequency, fold change > log (1.5), and adjusted\n2\np-value < 0.05. In a separate analysis focusing on genotype-specific differences, we selected\nclusters enriched in either wild-type or Vsir-deficient conditions. To control for unequal cell\nnumbers across clusters, 300 cells were randomly sampled per cluster prior to DEG analysis.\nResulting gene sets were intersected or unioned to identify genotype-specific or shared\ntranscriptional signatures.”\nWhile we recognize that human and mouse datasets differ in resolution, sampling depth,\nand immunological context, these cross-platforms and cross-species findings are directionally\naligned and conceptually supportive of our model. Collectively, they suggest that VSIR\nexpression is associated with an immunosuppressive landscape in human PDAC, and may\nrepresent a clinically relevant axis for immune stratification and therapeutic targeting.\n-11-\n2.The role of upstream regulators of VISTA expression and downstream signaling pathways\ninfluencing TAM polarization and mechanism remains insufficiently addressed.\n(Author’s Response/Action)\nWe thank you for this valuable suggestion. In light of your comment, we have (1) expanded\nthe Discussion to incorporate relevant mechanistic insights to better contextualize our findings,\nand (2) conducted a phospho-protein kinase array analysis to explore signaling molecules\npotentially involved in the VISTA pathway.\nFirst, upstream regulation of VISTA (VSIR) expression has been linked to various\nimmunological and microenvironmental cues. Hypoxia-induced signaling through HIF-1α has\nbeen shown to enhance VISTA expression in tumor-infiltrating myeloid cells, consistent with\nthe role of metabolic stress in shaping the immune landscape (Deng et al., Cancer Immunol\nRes, 2020). Although type I and II interferons are known to induce broad immune checkpoint\nprograms, direct transcriptional regulation of VISTA by interferons remains incompletely\ncharacterized and requires further investigation.\nDownstream of VISTA, several studies have described immunoregulatory functions that\ninfluence macrophage behavior. Zhang et al. (Cell Rep, 2024) reported that VISTA deficiency\nreduces phosphorylation of ERK and STAT3 in tumor-associated myeloid-derived suppressor\ncells, suggesting that VISTA positively regulates ERK/STAT3 signaling. Given that STAT3 is\na known transcriptional activator of SPP1 (Yu et al., Dev Cell, 2025), these findings support\nour findings in which VISTA promotes SPP1 expression. Whether this is through STAT3-\ndependent signaling will be necessary to delineate the full signaling cascade.\nSecond, our own experimental data support additional downstream signaling effects of\nVISTA in macrophages during early activation. Using LPS and IFN--stimulated BMDMs,\nwe\nfound that VISTA deficiency leads to increased phosphorylation of GSK-3/β at Ser21/Ser9—\nan established inactivating modification. We also observed decreased phosphorylation of p53\n(S15/S45) in VISTA KO BMDMs. As p53 activity has been associated with M2 polarization\n(Li et al., Cell Death Differ, 2014), this result further supports a shift away from an\nimmunosuppressive phenotype. Additionally, phosphorylation of STAT3 at S727, which\n-12-\ncontributes to M2-like macrophage function (Zhang et al., Front Immunol, 2023), was similarly\nreduced in VISTA KO BMDMs.\nWhile further studies will be required to determine whether this regulation is direct or\nmediated via intermediate signaling nodes, these findings offer a mechanistic basis for the\nobserved immunophenotypic shifts.\nThese findings are now contextualized in the revised Discussion. We have opted not to\ninclude the phospho-proteome array data in the main manuscript, as this analysis remains\nongoing. We appreciate your suggestion, which has helped refine and deepen the mechanistic\nframework of our study. Based on our previous experience studying VISTA in kidney disease\nmodel, we plan to pursue these signaling pathways in future mechanistic investigations.\n-13-\nIn Discussion (Page 18, Line 412)\nOriginal:\n“Our study highlights VISTA, a B7 checkpoint molecule, as a pivotal regulator of TAM\npolarization and the immune landscape in PDAC 46, 47.”\nRevised:\n→ “Our study highlights VISTA as a modulator of macrophage polarization and immune\ndynamics in PDAC, although further studies are needed to define its mechanistic role 46, 47.”\n3.The study does not compare the efficacy of VISTA inhibition with standard treatments (ex.,\ngemcitabine plus paclitaxel or FOLFIRINOX) or other immune checkpoint inhibitors,\nlimiting its positioning within the broader therapeutic landscape.\n(Author’s Response/Action)\nWe appreciate this important comment regarding the need to compare VISTA inhibition with\nstandard PDAC therapies. In response, and in alignment with your concerns regarding model\nrelevance, we selected the KPC001 syngeneic PDAC model for combination therapy studies.\nWhile our prior mechanistic work was performed in the more immunogenic Pan02 model,\nwhich robustly highlights differences between WT and Vsir KO tumors, the KPC001 model\nwas chosen for its closer resemblance to the immune-excluded microenvironment of human\nPDAC.\nAccordingly, we conducted in vivo experiments using the KPC001 model, comparing\ngemcitabine monotherapy, anti-VISTA monotherapy, and their combination (Fig. 7a). Both\nmonotherapies moderately reduced tumor growth, while the combination therapy resulted in a\nsignificantly enhanced antitumor effect, suggesting additive or synergistic efficacy (Fig. 7b).\nMechanistically, anti-VISTA treatment did not lead to an overall increase in macrophage\npercentage in the KPC001 model, in contrast to findings in the Pan02 model, where VISTA\ndeficiency increased macrophage abundance (Supplementary Fig. S18b). However, we\nobserved a selective increase in iNOS⁺ macrophages, with only a modest change in Arg1⁺\n-14-\nmacrophages, indicating a phenotypic shift toward inflammatory macrophages despite a stable\noverall frequency (Supplementary Fig. S18c). These results suggest that anti-VISTA modulates\nmacrophage polarization rather than abundance in this PDAC context. The mechanism of\naction in the combination group appears to differ and remains to be elucidated.\nWe also observed an increased CXCL9/SPP1 ratio in the anti-VISTA monotherapy group,\nconsistent with a shift toward a proinflammatory chemokine profile. Although this ratio was\nonly modestly elevated in the combination group, the trend supports the notion that VISTA\nblockade alone can induce IFN-associated chemokine expression. The more limited effect\nobserved in the combination group may reflect the immunosuppressive influence of\ngemcitabine, underscoring the importance of dosing and timing in combination strategies.\nIn addition, anti-VISTA monotherapy led to increased infiltration of CD8⁺ T cells. In the\ncombination group, CD8⁺ T cell levels remained relatively stable, likely reflecting\ncounterbalancing effects between gemcitabine-induced depletion and VISTA blockade–\nmediated recruitment. This finding further supports the need for optimized scheduling and\ndosing in combinatorial regimens, which we plan to evaluate in future studies.\nBeyond CD8+ T cell dynamics, we found that both anti-VISTA monotherapy and the\ncombination regimen increased infiltration of CXCR3+ immune cells while reducing CX CR1+\n3\npopulations relative to control and gemcitabine-treated groups. This increase in CXCR3+ cells\nmirrors observations in the Pan02 tumor model following VISTA deficiency, suggesting a\nconserved immunologic response across PDAC models. These shifts are consistent with\nenhanced effector T cell recruitment and attenuation of immunosuppressive myeloid subsets,\nrespectively, reinforcing the concept that VISTA inhibition reprograms the tumor immune\nmicroenvironment in a manner complementary to cytotoxic chemotherapy. We hope these\nresults, now presented in the revised manuscript, enhance the positioning of VISTA-targeted\ntherapy within the current PDAC treatment landscape.\n-15-\nNew Figure 7\nFigure 7. Effect of anti-VISTA antibody in combination with gemcitabine therapy. a\nSchematic of the treatment regimen for combination therapy with anti-VISTA antibody and\ngemcitabine. b Tumor volumes of mice treated with isotype control, anti-VISTA antibody,\ngemcitabine, or combination therapy (n = 10 per group). Data are presented as mean ± SEM.\nStatistical significance was determined using multiple t-tests, with p-values corrected for\nmultiple comparisons using the Sidak method. n.s., not significant. Data are representative of\nat least three independent experiments. c Schematic summary of key ligand-receptor\ninteractions and immune cell communication pathways in WT versus VISTA-deficient tumors,\nhighlighting the enhanced crosstalk in the absence of VISTA.\n-16-\nNew Supplementary Figure S18\nSupplementary Figure S18. Immune cell dynamics following combination therapy. a Flow\ncytometric quantification of F4/80+CD11b+ TAMs (left) and CD8+ T cells (right) at days\n10 and 15 post-treatment (n = 5), expressed as a percentage of CD45+ cells b Flow cytometric\nanalysis of iNOS+ Arg-1‒ (left) and iNOS- Arg-1+ (right) TAM subsets in tumor, shown as a\npercentage of CD45+ cells (n = 10). c Representative flow cytometry plots (left) and\nquantification bar graphs (right) of CXCL9+/SPP1+ TAMs in tumor, expressed as a percentage\nof CD45+ cells (n = 7). d Representative flow cytometry plots (left) and quantification bar\ngraphs (right) of CXCR3+CX CR1‒ CD8+ T cells and CXCR3‒CX CR1+ CD8+ T-cell subsets\n3 3\nin tumor, shown as a percentage of CD45+ cells (n = 5). Data are mean ± SEM. Statistical\nanalysis for panels a‒d was performed using one-way ANOVA with Sidak’s multiple\ncomparisons post hoc test. n.s., not significant. Data represent at least three independent\nexperiments.\n-17-\nIn Results (Page 17, Lines 400-402)\nOriginal:\n“These findings reveal how VISTA deficiency reprograms the TME from an\nimmunosuppressive to an immune-stimulatory state, highlighting the potential for VISTA as a\ntherapeutic target to boost immune responses against pancreatic cancer.”\nRevised:\n→ “VISTA Blockade Synergizes with Gemcitabine to Remodel the Pancreatic Tumor\nImmune Landscape\nTo determine whether these VISTA-dependent signaling network we identified is\ntherapeutically exploitable, we turned to the KPC orthotopic model and compared anti-VISTA\nmonotherapy, gemcitabine, and their combination (Fig. 7a). Both monotherapies produced a\nmodest but significant reduction in tumor burden; however, the combination elicited a\nmarkedly greater suppression, indicating at least an additive—and potentially synergistic—\nanti-tumor effect (Fig. 7b).\nImmune profiling revealed that anti-VISTA monotherapy alone increased total TAMs and\nincreased CD8+ T cells (Supplementary Fig. S18a). Importantly, the antibody skewed TAMs\ntoward an iNOS⁺ phenotype while producing only minor changes in Arg1⁺ TAMs\n(Supplementary Fig. S18b). Consistent with our CellChat predictions, CXCL9⁺/SPP1⁺ TAMs\nwere enriched in the anti-VISTA treatment group (Supplementary Fig. S18c), both anti-VISTA\nand the combination regimen boosted infiltration of CXCR3+ CD8+ T cells (Supplementary\nFig. S18d) —hallmarks of the CXCL9–CXCR3 chemotactic axis we mapped in Vsir⁻/⁻ tumors.\nThese findings validate our mechanistic model and demonstrate that pharmacologic VISTA\nblockade can re-program the myeloid–T-cell circuit even in the presence of cytotoxic\nchemotherapy.\nTaken together, our data position VISTA as a central architect of immunosuppressive\nniches in pancreatic tumors. In WT tumors, TAMs co-express Cxcl9 and Spp1: engagement\nof SPP1 with integrins Itga4/Itgb1 or Itgav/Itgb1 on CD8⁺ T cells likely dampens T cell\nrecruitment and activation. By contrast, VISTA deficiency—or therapeutic blockade—tilts\nthis balance toward CXCL9 dominance, amplifying the CXCL9–CXCR3 axis and fostering a\n-18-\npro-inflammatory TME that supports robust CD8⁺ T cell activity (Fig. 7c). Thus, combining\nVISTA inhibition with standard chemotherapy offers a rational strategy to overcome immune\nsuppression and improve anti-tumor effect.”\nIn Methods (Page 21, Line 477)\nOriginal:\n“The Pan02 cell line, derived from a methylcholanthrene-induced PDAC, was a generous gift\nfrom Dr. Keehoon Jung (Seoul National University) 65.”\nRevised:\n→ “The Pan02 cell line from National Cancer Institute (NCI, 0509770), and KPC001 cell line\nisolated from the tumor of a 5– to 6–month-old genetically engineered mouse model of\nKrasG12Dp53R172H/+Pdx-1-Cre mice were used 66, 67. ”\nIn Methods (Pages 21-22, Lines 489-490)\nOriginal:\n“After upper left abdominal incision, pancreatic tails were exposed and injected with 5  105\nPan02 cells resuspended in cold…”\nRevised:\n→ “After upper left abdominal incision, pancreatic tails were exposed and injected with either\n5  105 Pan02 or KPC001 cells…”\nIn Methods (Page 22, Line 503-504)\nOriginal:\n“or Rat IgG2, isotype control (Clone 2A3, InVivoMab, BioXCell) 13 days prior to tumor\ninoculation, followed by three consecutive daily injections of 0.5 mg.”\nRevised:\n-19-\n→ “…0.5 mg. For combination therapy, 50 mg/kg gemcitabine (Selleckchem, Houston, TX,\nUSA) and/or anti-VISTA antibody (Clone 13F3, InVivoMab, BioXCell, Lebanon, NH, USA)\nwere administered intraperitoneally, starting from day 6 after cancer cell injection. Treatments\nwere given every 3 days.”\nMinor Weaknesses:\n1.Functional validation of macrophage-T cell interactions beyond proximity measures (e.g.,\ncytokine secretion profiles) would be beneficial.\n(Author’s Response/Action)\nWe thank you for this valuable suggestion. We fully agree that proximity-based spatial analyses\nbenefit from complementary functional validation. In our original submission, we addressed\nthis by performing a co-culture assay of BMDMs and CD8+ T cells. As shown in Figure 4,\nVISTA KO macrophages significantly enhanced CD8+ T cell secretion of IFN-, granzyme B\nand perforin, indicating increased effector function. This experiment was designed precisely\nvalidate the biological relevance of TAM-T cell proximity observed in the tumor\nmicroenvironment. We have now revised the Results to better highlight this connection by\nmoving the proximity measure result after cytokine secretion data (Fig. 4f).\n-20-\nIn Figure 4 (Page 44)\nOriginal:\n-21-\nRevised:\nFigure 4. Vsir deficiency enhances antigen presentation and stimulates CD8+ T cell\nresponses. a Differential gene expression analysis of macrophage subsets. Volcano plot\ncomparing MΦ_clusters 2 and 3 with MΦ_cluster1. b Schematic of the MHC class I antigen\nprocessing and presentation pathway. c KEGG pathway analysis of MΦ_cluster2 (light\ngreen), MΦ_cluster3 (green), and combined MΦ_clusters 2 and 3 (brown), showing\npathways satisfying p_adj < 0.05. d Flow cytometric analysis of SIINFEKL–H2-Kb complex\non WT and Vsir–/– BMDMs stimulated with OVA protein at varying concentrations. MFI\n-22-\nquantification shown on the right. e Proliferation of OT-I CD8+ T cells co-cultured with WT\nor Vsir–/– BMDMs loaded with OVA protein (top left) or SIINFEKL peptide (bottom right),\nshown as the percentage of divided CD8+ T cells. f Flow cytometric analysis of IFN-, TNF-\n, and perforin (PFN) production in OT-I CD8+ T cells co-cultured with WT or Vsir–/–\nBMDMs loaded with OVA protein. g Quantification of CD8+ T cells in proximity to\nmacrophages across varying distances (m). h Tumor growth curves following co-injection\nof BMDMs and CD3+ T cells (n = 6 per group). i Tumor growth curves following co-\ninjection Pan02 cells with OT-I CD8+ T cells and WT or Vsir–/– BMDMs (n = 5 per group).\nData are presented as mean ± SEM. Unpaired two-tailed Student’s t-tests were applied for\npanels d–f. Multiple t-tests with Holm-Sidak correction were used for h and i. n.s., not\nsignificant. All experiments were independently repeated at least twice; representative results\nare shown.\nIn Results (Page 13, Lines 295-299)\nOriginal:\n“…suggesting that VISTA uniquely modulates antigen cross-presentation in macrophages,\neven though DCs are typically more proficient in this function.\nCollectively, our results indicate that VISTA deficiency enhances the functional capacity of\nTAMs to process and present antigens, leading to increased CD8+ T cell activation and a more\npotent anti-tumor immune response.”\nRevised:\n→ “Spatial analysis revealed CD8+ T cells were closer proximity to TAMs in Vsir–/– tumors\n(median distance: 3–4 μm) compared to WT tumors ( 10 m), suggesting enhanced\nmacrophage–T cell crosstalk (Fig. 4g). This was further supported by increased CD3+ T cell–\nCD11b+ myeloid interactions in Vsir–/– tumor cores. ”\nIn addition, mouse cytokine array analysis of IFN- and LPS-stimulated BMDMs revealed\nelevated levels of Serpin E1 (Serpin1), leptin, DKK-1, GAS6, C1q, TNF-, Pref1, IL-27 p28,\nand IL-28A/B in VISTA KO BMDMs compared to WT BMDMs. The increase in Serpin E1\n-23-\n(Serpin 1) suggests enhanced M1 polarization and IFN--mediated stress responses in VISTA-\ndeficient macrophages. We anticipate conducting further studies in the future to investigate the\nfunctionality of VISTA; however, this will not be address in the present manuscript.\nAccordingly, the data below is not included in this submission.\n2.Sentences in the introduction and discussion sections occasionally overstate claims (e.g.,\nsignificantly reprogram TAMs).\n(Author’s Response/Action)\nWe appreciate this observation and have carefully revised the Introduction, Discussion, and\nAbstract sections to ensure that all claims are appropriately moderated and substantiated by the\ndata presented. In particular, potentially overstated phrases such as “significantly reprogram\nTAMs” have been revised to more evidence-aligned alternatives. Below, we provide edits made\nin the manuscript:\nIn Abstract (Page 2, Lines 38-43)\nOriginal:\n“Mechanistically, VISTA loss reprograms tumor-associated macrophages (TAMs) from an\nimmunosuppressive, secreted phosphoprotein 1 (SPP1) expressing phenotype to a pro-\ninflammatory, C-X-C motif chemokine ligand 9 (CXCL9) expressing subtype. This switch\nenhances the recruitment of C-X-C motif chemokine receptor 3 (CXCR3) positive CD8+ T\ncells, reducing their exhaustion and sustaining their cytotoxic activity.”\n-24-\nRevised:\n→ “Mechanistically, VISTA deficiency is linked to a shift in tumor-associated macrophages\n(TAMs) from an immunosuppressive phenotype marked by secreted phosphoprotein 1 (SPP1)\nto one enriched for C-X-C motif chemokine ligand 9 (CXCL9), indicative of a pro-\ninflammatory state. This shift was accompanied by enhanced recruitment of CXCR3⁺ CD8⁺ T\ncells, which showed reduced exhaustion and sustained cytotoxic potential. ”\nIn Introduction (Page 4, Line 87-89)\nOriginal:\n“Our findings demonstrate that VISTA is crucial for maintaining TAM polarization and\nimmune suppression in the TME of PDAC.”\nRevised:\n→ “Our findings demonstrate that VISTA deficiency impairs tumor growth by disrupting its\nrole in sustaining immunosuppressive TAM phenotypes and limiting CD8+ T cell activity\nwithin the PDAC microenvironment. ”\nIn Discussion (Page 18, Line 412)\nOriginal:\n“Our study highlights VISTA, a B7 checkpoint molecule, as a pivotal regulator of TAM\npolarization and the immune landscape in PDAC 46, 47.”\nRevised:\n→ “Our study highlights VISTA as a modulator of macrophage polarization and immune\ndynamics in PDAC, although further studies are needed to define its mechanistic role 46, 47.”\n-25-\n3.Figure 2: The role of CSF1R inhibition in TAM modulation is compelling but needs\nadditional experimental replication to confirm its rigor.\n(Author’s Response/Action)\nWe thank you for highlighting the importance of experimental reproducibility. In response, we\nperformed additional independent replicates of the CSF1R inhibition experiments across\nmultiple cohorts. These experiments confirmed the robustness of our initial findings,\ndemonstrating consistent TAM depletion and modulation of CD8⁺ T cell infiltration following\nCSF1R blockade. The results of these additional cohorts are now included in the revised\nmanuscript (Figure 2 and Supplementary Figure S7) and confirm the rigor and reproducibility\nof our conclusions.\n-26-\nIn Figure 2 (Page 41)\nOriginal:\nFigure 2. Depletion of VISTAhighF4/80highCD11bhigh TAMs enhances CD8+ T cell responses\nin Vsir–/– mice. a Flow cytometric analysis of Gr-1–F4/80+CD11b+ TAMs in WT and Vsir–/–\ntumors. Quantification of F4/80highCD11bhigh and F4/80intCD11bint TAMs as a percentage of\nCD45+ cells (n = 4 per group). b Anti-CSF1R antibody treatment scheme. c Tumor mass at day\n28 in WT (n = 8) and αCSF1R-treated (n = 9) mice. d Flow cytometric analysis of Gr-1‒\nF4/80+CD11b+ TAMs in isotype control and CSF1R-treated WT mice. Quantification of\nF4/80+CD11b+ TAMs as a percentage of CD45+ cells and normalized to tumor mass (n = 3 per\n-27-\ngroup). e Flow cytometric analysis of Gr-1–F4/80highCD11bhigh TAMs and Gr-1–\nF4/80intCD11bint TAMs in isotype control and CSF1R-treated WT mice. Quantification of\nGr-1–F4/80highCD11bhigh and F4/80intCD11bint TAMs as a percentage of CD45+ cells (n = 3 per\ngroup). f Flow cytometric analysis of CD4+ and CD8+ T cells in isotype control and αCSF1R-\ntreated WT mice. Quantification of CD8+ T cells as a percentage of CD3+ T cells and\nnormalized to tumor mass (n = 3 per group). g Flow cytometric analysis of effector memory\n(CD44+CD62L–) and central memory (CD44+CD62L+) CD8+ T cells in isotype control and\nCSF1R-treated tumors. Quantification is shown as a percentage of CD8+ T cells (n = 4 per\ngroup). h Flow cytometric analysis of PD-1 and TIM-3 expression on CD8+ T cells in isotype\ncontrol and CSF1R-treated tumors. Quantification is shown as a percentage of CD8+ T cells\n(n = 4 per group). i Quantification of CD8+ T cells in proximity to macrophages at varying\ndistances (m). j Immunofluorescence analysis of CD3 and CD11b in the tumor center and\nmargin in WT and Vsir–/– tumors. k Tumor growth curves of WT BMDM + OT-I T cells and\nVsir–/– BMDM + OT-I T cells following subcutaneous injection of Pan02 cells (n = 5 per group).\nData are presented as mean ± SEM and unpaired two-tailed Student’s t-test was used for panels\na, c–h and k.. n.s., not significant. All experiments were independently repeated at least twice;\nrepresentative results are shown.\n-28-\nRevised:\nFigure 2. Depletion of VISTAhighF4/80highCD11bhigh TAMs enhances CD8+ T cell\nresponses in Vsir–/– mice. a Flow cytometric analysis of Gr-1–F4/80+CD11b+ TAMs in WT\nand Vsir–/– tumors. Quantification of F4/80highCD11bhigh and F4/80intCD11bint TAM subsets\nshown as a percentage of CD45+ cells (n = 6 per group). b Representative flow cytometry\nplots (left) and MFI of VISTA expression (right) on F4/80highCD11bhigh (green) and\nF4/80intCD11b+ (blue) TAMs in WT mice. c Tumor mass at day 28 in WT (n = 8) and\nαCSF1R-treated (n = 9) mice. d Flow cytometric analysis of Gr-1–F4/80highCD11bhigh and Gr-\n1–F4/80intCD11bint TAMs in isotype control and CSF1R-treated WT mice (n = 6 per group).\ne Representative flow cytometry plots (left) and MFI of VISTA expression (right) on\nF4/80highCD11bhigh (green) and F4/80intCD11b+ (blue) TAMs in CSF1R-treated WT mice (n\n= 6 per group). f Flow cytometric analysis of CD4+ and CD8+ T cells in isotype control and\nαCSF1R-treated WT mice. Quantification of CD8+ T cells shown as a percentage of CD3+ T\ncells and normalized to tumor mass (n = 6 per group). g Flow cytometric analysis of effector\n-29-\nmemory (CD44+CD62L–) and central memory (CD44+CD62L+) CD8+ T cells in isotype\ncontrol and CSF1R-treated WT mice. Quantification shown as a percentage of CD8+ T cells\n(n = 5 per group). Data are mean ± SEM. Unpaired two-tailed Student’s t-tests were used for\nstatistical analysis in panels a‒g. n.s., not significant. Data for a‒f are representative of at\nleast three independent experiments; data for g are from two independent experiments with\nrepresentative results.\nSupplementary Figure S7. Effect of anti-CSF1R antibody treatment. a Anti-CSF1R\nantibody treatment scheme. b Representative flow cytometry plots showing effective\ndepletion of F4/80highCD11b+ tumor-associated macrophages (TAMs) in WT mice treated\nwith anti-CSF1R antibody on day 0 and day 21 after cancer cell inoculation. c Flow\ncytometric analysis of Gr-1–F4/80+CD11b+ TAMs in isotype control versus CSF1R-treated\nWT mice. Quantification is shown as the percentage of CD45+ cells and normalized to tumor\n-30-\nmass (n = 6 per group). d Flow cytometric analysis of PD-1 and TIM-3 expression on CD8+ T\ncells in isotype control versus CSF1R-treated tumors. Quantification is shown as the\npercentage of CD8+ T cells (n = 6 per group). Data are presented as mean ± SEM and\nunpaired two-tailed Student’s t-test was used for panels c and d. n.s., not significant. All\nexperiments were independently repeated at least twice; representative results are shown.\n4.Figure 3: Comparative gene expression heatmaps could better illustrate shifts in Cxcl9:Spp1\nratios.\n(Author’s Response/Action)\nWe thank you for this insightful suggestion. Instead of simply presenting the frequencies ratio\nof Cxcl9+ to Spp1+ cells as independent values, we visualized the ratio of Cxcl9+ to Spp1+ cells\nwithin each macrophage cluster. This approach was adopted because Cxcl9 exhibits globally\nhigher expression than Spp1 across macrophage clusters, which skews log₂(Cxcl9/Spp1) ratios\ntoward the positive range and may obscure biologically meaningful variation in Spp1+\nmacrophage clusters. As these values illustrate, the log-ratio approach tends to amplify Cxcl9-\ndominant patterns and underrepresent the contributions of Spp1+ populations (Table 1).\nBy focusing on the proportion of positive cells, our visualization minimizes distortion from\nexpression amplitude and more accurately reflects the relative enrichment of each macrophage\n-31-\nclusters. To ensure comparability across clusters, contour thresholds for each gene were derived\nfrom total macrophage cells, respectively.\nAdditionally, to further support the reviewer’s suggestion, we incorporated a quadrant-\nbased co-expression analysis of Cxcl9 and Spp1 for each cluster, which is now presented in\nSupplementary Figure S9d. We believe that this combined visualization strategy provides a\nmore nuanced and interpretable depiction of macrophage heterogeneity, as demonstrated in\nFigure 3f.\nNew Supplementary Figure S9d\nd Density contour plots of macrophage clusters based on Cxcl9 (x-axis) and Spp1 (y-axis). Each\nplot depicts a two-dimensional kernel density estimation of single macrophages within a cluster.\nRed dashed lines denote the average expression levels of each gene across all macrophages,\ndefining quadrant thresholds. Cell frequencies within each quadrant are reported based on\nrelative Cxcl9 and Spp1 expression.\n-32-\nIn Figure 3f (Page 41)\nOriginal:\nRevised:\nIn Figure 3f (Page 41)\nOriginal:\n-33-\nRevised:\nIn Results (Page 10, Lines 232-236)\nOriginal:\n“Specifically, Cxcl9+Spp1+ TAMs were more prevalent in WT tumor (35.5%) than in Vsir–/–\ntumors (12.6%) (Fig. 3e), while Cxcl9+Spp1– cells were 2.45-fold higher in Vsir–/– TAMs.\nFurther, MΦ_cluster2 had a Cxcl9:Spp1 ratio of 2.51, compared to a ratio of 0.69 in\nMΦ_cluster1, indicating a higher Cxcl9:Spp1 ratio in Vsir–/– TAMs could enhance anti-tumor\neffect (Fig. 3f).”\nRevised:\n→ “In our data, Cxcl9 expression was significantly upregulated in Vsir–/– TAMs, likely\ncontributing to enhanced CD8+ T cell infiltration. Specifically, Cxcl9+Spp1+ TAMs were more\nprevalent in WT tumor (35.5%) compared to Vsir–/– tumors (19.0%), while Cxcl9+Spp1– cells\nwere 2.45-fold more frequent in Vsir–/– TAMs (Fig. 3e).”\n5.Clarify ambiguities in the discussion, such as “VISTA inhibition could synergize with ICB”\n(specify conditions).\n(Author’s Response/Action)\nWe thank you for pointing out this ambiguity. In the originally submitted manuscript, we\nrefrained from specifying the conditions under which VISTA inhibition may synergize with\nimmune checkpoint blockade (ICB), as we had not yet performed combination treatment\nexperiments. As such, the statement was left intentionally broad. However, in the revised\nmanuscript, we have included new experimental data (Figure 7) that address this question more\ndirectly.\n-34-\nNew Figure 7\nFigure 7. Effect of anti-VISTA antibody in combination with gemcitabine therapy. a\nSchematic of the treatment regimen for combination therapy with anti-VISTA antibody and\ngemcitabine. b Tumor volumes of mice treated with isotype control, anti-VISTA antibody,\ngemcitabine, or combination therapy (n = 10 per group). Data are presented as mean ± SEM.\nStatistical significance was determined using multiple t-tests, with p-values corrected for\nmultiple comparisons using the Sidak method. n.s., not significant. Data are representative of\nat least three independent experiments. c Schematic summary of key ligand-receptor\ninteractions and immune cell communication pathways in WT versus VISTA-deficient tumors,\nhighlighting the enhanced crosstalk in the absence of VISTA.\nNew Supplementary Figure S18\n-35-\nSupplementary Figure S18. Immune cell dynamics following combination therapy. a\nFlow cytometric quantification of F4/80+CD11b+ TAMs (left) and CD8+ T cells (right) at days\n10 and 15 post-treatment (n = 5), expressed as a percentage of CD45+ cells b Flow cytometric\nanalysis of iNOS+Arg-1‒ (left) and iNOS–Arg-1+ (right) TAM subsets in tumor, shown as a\npercentage of CD45+ cells (n = 10). c Representative flow cytometry plots (left) and\nquantification bar graphs (right) of CXCL9+/SPP1+ TAMs in tumor, expressed as a\npercentage of CD45+ cells (n = 7). d Representative flow cytometry plots (left) and\nquantification bar graphs (right) of CXCR3+CX CR1‒ CD8+ T cells and CXCR3‒CX CR1+\n3 3\nCD8+ T-cell subsets in tumor, shown as a percentage of CD45+ cells (n = 5). Data are mean ±\nSEM. Statistical analysis for panels a‒d was performed using one-way ANOVA with Sidak’s\nmultiple comparisons post hoc test. n.s., not significant. Data represent at least three\nindependent experiments.\n-36-\nIn Discussion (Page 20, Lines 458-464)\nOriginal:\n“The findings from our study provide a strong rationale for targeting VISTA in PDAC.\nInhibiting VISTA has the potential to reprogram TAMs to adopt a pro-inflammatory,\nantitumor phenotype while enhancing recruitment and activation of stem-like CD8+ T cells.\nSuch an approach could synergize with existing immune checkpoint inhibitors to improve\ntheir efficacy. By simultaneously reprogramming TAMs and augmenting T cell function,\nVISTA inhibition represents a promising strategy to overcome the immunosuppressive\nenvironment of PDAC and improve patient outcomes 63, 64.”\nRevised:\n→ “ICB therapies targeting PD-1 and CTLA-4 have demonstrated limited efficacy in PDAC,\nunderscoring the need for alternative strategies that address multiple layers of immune\nsuppression. In this context, VISTA inhibition offers a promising therapeutic avenue. Notably,\nanti-VISTA monotherapy combined with gemcitabine elicited a synergistic anti-tumor\nresponse, characterized by an increased CXCL9/SPP1 ratio in TAMs and enhanced\ninfiltration of CXCR3⁺ CD8⁺ T cells. This additive effect is particularly relevant for immune-\nexcluded tumors like PDAC, where conventional ICB therapies have failed to yield substantial\nclinical benefit. Moreover, considering the well-documented toxicity profile of gemcitabine,\nour findings suggest that VISTA blockade could enhance therapeutic efficacy while\npotentially reducing dependence on cytotoxic agents.”\n6.The depletion of F4/80highCD11bhigh TAMs via CSF1R inhibition in Figure 2 is an\nimportant mechanistic result, but the text focuses more on TAM dynamics than on connecting\nthese changes to downstream CD8+ T cell activation (Figure 2g–h).\n(Author’s Response/Action)\nWe thank you for prompting this important point. To clarify the mechanistic link between\nF4/80high CD11bhigh TAM depletion and CD8+ T cell activation, we now emphasize the\nfollowing:\n-37-\n(1) Functional significance of F4/80high TAMs: F4/80high TAMs are tissue-resident,\nembryonically derived populations with immunosuppressive properties, whereas F4/80int\nmacrophages are typically monocyte-derived and infiltrating (Cheng et al, Signal transduction\nand target therapy, 2025). Our data suggest that VISTA sustains the F4/80high subset, which\ncorrelates with an immunosuppressive TME.\n(2) Restructured manuscript for clarity: Revised Figure 2 now focuses on macrophage\nphenotypes in VISTA-proficient vs. VISTA-deficient settings, while Figure 3 provides\ntranscriptional and functional characterization of macrophage subsets. We hope this\nreorganization clarifies the logic from TAM depletion to CD8+ T cell infiltration.\n-38-\nIn Figures (Page 41)\nOriginal:\n-39-\nRevised:\nIn Results (Page 7, Lines 147-149)\nOriginal:\nWe found that F4/80highCD11bhigh TAM subpopulation was significantly decreased in Vsir–/–\nmice compared to WT control (Fig. 2a), suggesting that VISTA supports a distinct\nF4/80highCD11bhigh TAM subpopulation that influences TME immune-cell composition.\nRevised:\n→ “In Vsir–/– tumors, we observed a pronounced reduction in the F4/80highCD11bhigh TAM\nsubset, indicating that VISTA is required to maintain this tissue-resident, immunosuppressive\nmacrophage population (Fig. 2a) 21. Consistent with this, VISTA expression was higher in\nF4/80highCD11bhigh TAMs than F4/80intCD11bint TAMs in WT tumors (Fig. 2b). To test whether\nselectively depleting these VISTAhigh TAMs could recapitulate the Vsir⁻/⁻ phenotype, we\nblocked CSF1R – a key survival signal for tissue-resident macrophages 21. ”\n-40-\n7.The importance of macrophage cluster2’s higher Cxcl9:Spp1 ratio (Figure 3f) is briefly stated in the text but not adequately\ncontextualized regarding CD8+ T cell activation and tumor regression.\n(Author’s Response/Action)\nThank you for commenting on this. In our original manuscript, we briefly stated that a higher\nCxcl9:Spp1 ratio in TAMs could enhance antitumor effects as we wanted to separate our topics\non macrophages and CD8+ T cells for better delivery, but we agree this statement lacked\nsufficient context and could be interpreted as an overreach. In the revised manuscript, we have\nrephrased this section to avoid making a causal interference and instead provide mechanistic\ncontext grounded in our data.\nIn Results (Page 12, Lines 235-237)\nOriginal:\n“Further, MΦ_cluster2 had a Cxcl9:Spp1 ratio of 2.51, compared to a ratio of 0.69 in\nMΦ_cluster1, indicating a higher Cxcl9:Spp1 ratio in Vsir–/– TAMs could enhance anti-tumor\neffect (Fig. 3f).”\nRevised:\n→ “The Cxcl9:Spp1 ratio was 2.51 in MΦ_cluster2 versus 0.69 in MΦ_cluster1 (Fig. 3f,\nSupplementary Fig. S9d), indicating a shift toward a more chemotactic, immunostimulatory\nTAM phenotype in Vsir–/– tumors.”\n8.The results text emphasizes the reduced exhaustion and enhanced effector function of CD8+\nT cells in VISTA-deficient tumors (Figure 5), but the exhaustion marker analysis (e.g., module\nscores) lacks sufficient explanation to clarify the distinction between effector and terminally\nexhausted phenotypes.\n(Author’s Response/Action)\n-41-\nWe appreciate your insightful comment regarding the limitations of module score–based\nanalysis in distinguishing between effector and exhausted CD8+ T cell phenotypes. As noted,\nwhile module scores are useful for summarizing gene set level expression trends, they may\nlack the resolution needed to capture co-expression patterns of key marker genes or to define\ndiscrete functional states.\nTo address this concern, we performed additional analyses beyond the initial module score\napproach. Specifically, we focused on CD8⁺ T-cell clusters exhibiting genotype-specific\ndifferences by conducting differential gene expression (DEG) analysis to identify key markers\nthat distinguish CD8⁺ T-cell subsets between tumors from WT and Vsir-deficient hosts. The\nresults are presented as scatter plots in the revised Supplementary Figure S15a, and cluster-\nspecific marker distributions are further clarified in Figure 5c. To complement these findings,\nwe also conducted a co-expression analysis, which replaced the original module score panel in\nFigure 5f. In this analysis, cell density was visualized using contour plots, and quadrant\nthresholds were defined based on the average expression level of each gene across the total\nCD8+ T-cell population. This approach was adopted to account for differences in baseline\nexpression levels of a gene across clusters. We also quantified the proportion of cells within\neach quadrant to facilitate interpretation of the co-expression patterns.\nIn detail, we focused on clusters with genotype-specific frequency biases—specifically,\nWT-enriched clusters 1 and 2, and clusters 0 and 3 enriched in tumors from Vsir-deficient\nhosts—to characterize the functional identity of each CD8⁺ T-cell population. Each cluster was\ncompared to a pooled set of clusters from the opposite genotype to identify DEGs. To minimize\nbias from unequal cell numbers, we downsampled each cluster to 300 cells prior to DEG\nanalysis and applied thresholds of log (fold change) > 1.5 and adjusted P-value < 0.05. This\n2\nanalysis revealed that clusters 1 and 2 from WT-specific clusters have consistently expressed\nmarkers that denote exhausted CD8+ T cells such as Eomes and associated markers Cd38, an\nNAD⁺-consuming ectoenzyme upregulated in dysfunctional CD8+ T cells, where it impairs\nmitochondrial fitness and mitophagy (Chen et al., Sci Adv, 2022). In tumors, high Cd38\nexpression marks terminally exhausted T cells with fixed chromatin states and limited\nreprogrammability to effector function (Philip et al., Nature, 2017). In contrast, clusters 0 and\n3 from Vsir-deficient host, uniquely expressed Cd226 (Supplementary Figure S15). Cd226\n-42-\nfunctions as a co-stimulatory receptor required for cytotoxicity and ICB responsiveness in\nCD8⁺ T cells; its loss marks a more terminally exhausted state and correlates with impaired\neffector function in tumor-infiltrating lymphocytes (Pichler et al., Front Immunol, 2022).\nCo-expression contour plots further showed that Eomes+Cd38+ double-positive cells\naccounted for over 22.6% of cells in clusters 1 and 2 whereas less than 14.0% of cells in cluster\n0 and 3, consistent with a terminally exhausted phenotype (revised Figure 5e left). In contrast,\nthe proportion of Pdcd1-Cd226+ single-positive cells were exceeded 20.3% in clusters 0 and 3,\nwhereas less than 12.3% of cells in cluster 1 and 2. Additionally, Pdcd1+Cd226+- double-\npositive cells were counted over 31.7%, whereas less than 18.0% of cells in cluster 1 and 2,\nindicative of a pre-exhausted phenotype (revised Figure 5e right). These findings are consistent\nwith previous studies of CD8⁺ T cell exhaustion in HIV patients and LCMV clone13-infected\nmice (Cella et al., Eur J Immunol, 2010)\nTogether, these analyses address the limitations of module score–based interpretation and\nestablish a multi-layered framework for more precisely resolving CD8+ T cell differentiation\nstates. These findings support the conclusion that CD8+ T cells from WT hosts are enriched for\nterminally exhausted phenotypes, whereas those from Vsir-deficient hosts predominantly\nexhibit less differentiated, partially exhausted, or effector-like states.\nWhile we acknowledge that these data are preliminary and derived from transcriptomic\ninference rather than direct functional assays, we believe they provide a coherent basis for the\nobserved phenotypic differences. Ongoing functional and epigenetic experiments will directly\ntest these hypotheses in future studies.\nIn addition, while revising Figure 5f, we have identified a typographical error in Figure 5e\nthat has now been corrected. We apologize for this oversight.\n-43-\nIn Figure 5f (Page 46)\nOriginal(left)/Revised(right):\ne Scatter plot of log fold changes: CD8+_cluster1 (x-axis) versus CD8+_cluster2 (y-axis), each\n2\ncompared with Vsir-deficient clusters (CD8+_cluster0 and cluster3).Genes upregulated only in\nCD8+_cluster1 (gold), only in CD8+_cluster2 (green) or in both WT-specific clusters (blue) are\nhighlighted.\n-44-\nSupplementary Figure S15. Transcriptional differences between WT- and Vsir‒/‒-specific\nCD8+ T-cell subclusters in the tumor microenvironment. a Scatter plot visualizing log fold\n2\nchanges in gene expression for CD8+_cluster1 (x-axis) and CD8+_cluster2 (y-axis), each\ncompared to the Vsir‒/‒ clusters (CD8+_cluster0 and cluster3), respectively. Each point\nrepresents a gene, color-coded by differential expression: gold for genes upregulated only in\nCD8+_cluster1, green for those upregulated only in CD8+_cluster2 , and blue for genes\nupregulated in both WT-specific clusters. b Differential expression analysis of Vsir‒/‒ CD8+ T-\ncell clusters relative to WT-specific subsets. The scatter plot shows log fold changes for\n(CD8+_cluster1 and cluster2), respectively. Genes are color-coded as follows: light red for\ngenes upregulated only in CD8+_cluster0, cyan for those upregulated only in CD8+_cluster3,\nand red for genes upregulated in both Vsir‒/‒ clusters. c Violin plots of normalized expression\nlevels of selected genes across CD8+ T-cell clusters. Clusters are grouped by genotype: WT-\nspecific clusters [CD8+_cluster1 (gold) and cluster2 (green)] and Vsir‒/‒-specific\n[CD8+_cluster0 (light red) and cluster3 (cyan)].\nIn Figure 5e\nOriginal:\nRevised:\n-45-\nSupplementary figures:\n1.S3: Immunofluorescence staining of CD8+ T cells and CD11b+ macrophages is crucial to\nvisualize immune infiltration but is only superficially discussed in the context of Figure 1.\n(Author’s Response/Action)\nWe thank you for highlighting the importance of our immunofluorescence data in visualizing\nthe spatial distribution of immune infiltrates. As we did not include F4/80 staining in our\nimmunofluorescence analysis, we presented these data in the Supplementary section. In\nresponse to the comment, we have revised the Results section to more clearly integrate and\ninterpret the results from Supplementary Fig. S3. Specifically, we now emphasize that IF\nanalysis of Pan02 tumors demonstrated a marked increase in CD8⁺ T cells (red) and F4/80⁺ or\nCD11b⁺ myeloid cells (green/red) infiltration in Vsir⁻/⁻ and αVISTA-treated tumors compared\nto WT controls. These findings visually confirm and complement the quantitative flow\ncytometric data presented in Fig. 1d, reinforcing that VISTA deficiency promotes selective\nrecruitment of CD8⁺ T cells and macrophages into the tumor microenvironment.\nThe revised text is now included in the Results to explicitly connect the IF data to the overall\nconclusion that VISTA regulates tumor immune infiltration. We thank you for encouraging a\nmore integrated presentation of this dataset.\nIn Results (Page 6, Lines 124-126)\nOriginal:\n“Immunofluorescence staining of Pan02 tumors further revealed increased infiltration of CD8+\nT cells (green) and CD11b+ myeloid cells (red) in VISTA-treated tumors compared to control\n(Supplementary Fig. S3a).”\nRevised:\n→ “Immunofluorescence staining corroborated these findings, revealing increased infiltration\nof CD8+ T cells (green/red) and F4/80+ (green) or CD11b+ myeloid cells (red) in both Vsir–/–\n-46-\nand VISTA-treated tumors compared to WT controls (Fig. 1d, Supplementary Fig. S5a and\nb).”\nIn Supplementary Fig. S3a\nOriginal:\nRevised:\n2.Supplementary Figure S5 contains important flow cytometric data on CSF1R-mediated\nmacrophage depletion and VISTA expression, but its relationship to Figures 2 and 3 is not\nexplicitly stated. For instance, the text does not sufficiently highlight how the functional\n-47-\nphenotypes of TAMs (e.g., changes in iNOS and Arginase-1 expression in S5a–c) connect to\ntheir pro-inflammatory or immunosuppressive states.\n(Author’s Response/Action)\nWe thank you for pointing this out. We agree that the connection between the flow cytometry\nfindings in Supplementary Figure S5 and the transcriptional profiling in Figure 2 and 3 was not\nsufficiently explained. In response, we have edited the Result section to bridge this gap and\nclarify the relationship between surface marker-defined subsets and their functional states.\nIn Figure 3, we aimed to further characterize TAM heterogeneity using scRNA-seq.\nAlthough we initially attempted to stratify macrophages by F4/80 expression levels, this was\nnot feasible due to limitations of transcript-level resolution for these surface markers. Therefore,\nwe performed unsupervised clustering and identified eight distinct monocyte/macrophage\nsubsets.\nThese clarifications are now reflected in the revised manuscript, and we hope this better\nintegrates our data to support a coherent model of VISTA-mediated macrophage regulation.\nIn Supplementary Figure S5\nOriginal:\nRevised:\n-48-\n3.S7 supports Figure 3 but is insufficiently explained in the results, particularly regarding the\nfunctional differences between Spp1+ and Cxcl9+ macrophages.\n(Author’s Response/Action)\nWe thank you for this insightful comment. In the revised manuscript, we have expanded the\nResults to provide a more explicit interpretation of Supplementary Figure S7, which details the\nfunctional differences between Spp1+ and Cxcl9+ TAMs. We now describe the differential\nenrichment of gene signatures and pathways associated with immunosuppressive versus pro-\ninflammatory programs, thereby clarifying the functional polarization of these TAM subsets.\nWe believe this addition directly addresses your concern and improves the interpretability of\nthe result.\nIn Results (Page 10-11, Lines 237-246)\n-49-\nOriginal:\n“Gene set enrichment analysis (GSEA) showed that Cxcl9+ TAMs were consistently enriched\nin MΦ_cluster2 compared to MΦ_cluster1, whereas Spp1+ TAMs were predominantly in\nMΦ_cluster1 (Supplementary Fig. S7d, top). Kyoto Encyclopedia of Genes and Genomes\n(KEGG) pathway analysis further demonstrated that MΦ_cluster2 was enriched in cytokine-\ncytokine-\ncytokine receptor interactions and antigen processing/presentation pathways (Supplementary\nFig. S7d, bottom).”\nRevised:\n→ “Gene set enrichment analysis (GSEA) confirmed that Spp1+ TAMs were enriched in\nMΦ_cluster1 (NES = 1.84, P < 0.001), whereas Cxcl9+ TAMs were significantly enriched in\nMΦ_cluster2 (NES = –2.51, P < 0.001) (Supplementary Fig. S9e, top). KEGG pathway\nanalysis further showed that MΦ_cluster2 was enriched for cytokine–cytokine receptor\ninteraction and antigen processing and presentation pathways (Supplementary Fig. S9e,\nbottom), supporting these TAMs may promote CD8+ T cell activation and function.”\n4.Supplementary Figure S8 discusses the lack of difference in phagocytosis capacity between\nWT and VISTA-deficient macrophages (S8a), which could provide a contrasting point to\nemphasize functional gains in antigen presentation.\n(Author’s Response/Action)\nWe thank you for this important point. To address this, we emphasize that VISTA deficiency\ndoes not result in a general upregulation of all macrophage functions. As shown in\nSupplementary Figure S8a, the phagocytosis capacity of VIST-deficient macrophages remains\ncomparable to that of WT controls, indicating that baseline innate macrophage functions are\npreserved. This lack of change in phagocytosis highlights that the observed increases in antigen\npresentation and T cell activation are likely function-specific gains, rather than a global\nenhancement of macrophage activity. We have added clarification to this point in the revised\nResults.\n-50-\nIn Results (Page 11, Lines 251-253)\nOriginal:\n“While WT and Vsir–/– BMDMs showed comparable phagocytic abilities (Supplementary Fig.\nS8a), transcriptomic and pathway analyses revealed notable changes in immunomodulatory\ncapacity.”\nRevised:\n→ “To directly test antigen presentation capacity, we performed an antigen cross-presentation\nassay by measuring SIINFEKL–H2-Kb complex on WT and Vsir–/– BMDMs. Upon\nSIINFEKL peptide loading, no difference in MFI were observed (Supplementary Fig. S11d).\nHowever, Vsir–/– BMDMs exposed to full-length OVA protein displayed significantly higher\nSIINFEKL–H2-Kᵇ levels, especially at higher antigen concentrations (Fig. 4d), indicating\nenhanced antigen processing and cross-presentation in the absence of VISTA. Notably, this\noccurred without changes in CD80, CD86, or I-A/I-E expression (Supplementary Fig. S13),\nsuggesting that VISTA loss selectively enhances antigen-presenting function without broadly\nactivating macrophages.”\nReviewer #2’s Comments and Responses\nMajor comments:\n1. Most in vivo experiments seem to utilize 3-5 mice per group with no statement of replicates,\nunless I have missed a statement of experiment replication somewhere in the manuscript.\nPlease clarify in methods and/or figure legends how many samples per condition were used,\nwhether any samples were removed from consideration and by what criteria, and how many\nindependent experiments were performed for each experiment.\n(Author’s Response/Action)\nWe deeply regret this significant oversight and take full responsibility for the lack of clarity\nregarding sample sizes and experimental replication in the initial submission. This was entirely\nmy mistake, as I failed to ensure that the necessary details were properly included in the\nmanuscript prior to submission.\n-51-\nTo address this issue, we have carefully revised the methods section and all figure legends\nto explicitly detail the exact number of mice used per condition and the number of independent\nexperiments for each study are revised in the following sections:\nFigure Legends\n→ Figure 1. VISTA deficiency attenuates pancreatic tumor growth in Vsir–/– mice. a Tumor\ngrowth curve of WT and Vsir–/– mice orthotopically implanted with Pan02 cells (WT: n = 35;\nVsir–/–: n = 35). Statistical significance was determined using multiple t-tests with Holm-Sidak\ncorrection for multiple comparisons. b Tumor mass at day 28 in WT (n = 17), Vsir–/– (n = 19),\nand VISTA-treated mice (n = 10) (left). Representative ex vivo tumor images at day 28 are\nshown on the right (a and b). c Kaplan-Meier survival analysis of WT and Vsir–/– mice (n = 10\nper group). P-value calculated using the log-rank (Mantel-Cox) test. d Flow cytometric\nquantification of F4/80+CD11b+ TAMs (left) and CD8+ T cells (right) in WT and Vsir–/– mice\nat days 14, 21, 28, and 35, shown as a percentage of CD45+ cells (n = 10 per group). e\nImmunohistochemical images of F4/80+ macrophages (top) and CD8+ T cells (bottom) in WT\nand Vsir–/– tumors (day 28). Quantification of CD8+ T cells per field (n = 3 per group). f Flow\ncytometric analysis of iNOS+ TAMs, shown as a percentage of CD45+ cells and normalized to\ntumor mass (n = 6 per group). g Flow cytometric analysis of Arg1+ TAMs, shown as mean\nfluorescence intensity (MFI) and as a percentage of CD45+ cells (n = 8 per group). h\nProportions of CD8+ T-cell subsets shown as a percentage of CD3+ T cells (n = 8 per group).\nData are mean ± SEM. Unpaired two-tailed Student’s t-tests were applied to panels d–h. n.s.,\nnot significant. Data represent at least three independent experiments.\nFigure 2. Depletion of VISTAhighF4/80highCD11bhigh TAMs enhances CD8+ T cell responses\nin Vsir–/– mice. a Flow cytometric analysis of Gr-1–F4/80+CD11b+ TAMs in WT and Vsir–/–\ntumors. Quantification of F4/80highCD11bhigh and F4/80intCD11bint TAM subsets shown as a\npercentage of CD45+ cells (n = 6 per group). b Representative flow cytometry plots (left) and\nMFI of VISTA expression (right) on F4/80highCD11bhigh (green) and F4/80intCD11b+ (blue)\nTAMs in WT mice. c Tumor mass at day 28 in WT (n = 10) and αCSF1R-treated (n = 10) mice.\nd Flow cytometric analysis of Gr-1–F4/80highCD11bhigh and Gr-1–F4/80intCD11bint TAMs in\n-52-\nisotype control and CSF1R-treated WT mice (n = 6 per group). e Representative flow\ncytometry plots (left) and MFI of VISTA expression (right) on F4/80highCD11bhigh (green) and\nF4/80intCD11b+ (blue) TAMs in CSF1R-treated WT mice (n = 6 per group). f Flow cytometric\nanalysis of CD4+ and CD8+ T cells in isotype control and αCSF1R-treated WT mice.\nQuantification of CD8+ T cells shown as a percentage of CD3+ T cells and normalized to tumor\nmass (n = 6 per group). g Flow cytometric analysis of effector memory (CD44+CD62L–) and\ncentral memory (CD44+CD62L+) CD8+ T cells in isotype control and CSF1R-treated WT\nmice. Quantification shown as a percentage of CD8+ T cells (n = 6 per group). Data are mean\n± SEM. Unpaired two-tailed Student’s t-tests were used for statistical analysis in panels a‒g.\nn.s., not significant. Data for a‒f are representative of at least three independent experiments;\ndata for g are from two independent experiments with representative results.\nFigure 4. Vsir deficiency enhances antigen presentation and stimulates CD8+ T cell\nresponses. a Differential gene expression analysis of macrophage subsets. Volcano plot\ncomparing MΦ_clusters 2 and 3 with MΦ_cluster1. b Schematic of the MHC class I antigen\nprocessing and presentation pathway. c KEGG pathway analysis of MΦ_cluster2 (light green),\nMΦ_cluster3 (green), and combined MΦ_clusters 2 and 3 (brown), showing pathways\nsatisfying p_adj < 0.05. d Flow cytometric analysis of H2-Kb–SIINFEKL complex on WT and\nVsir–/– BMDMs stimulated with OVA protein at varying concentrations. MFI quantification\nshown on the right. e Proliferation of OT-I CD8+ T cells co-cultured with WT or Vsir–/–\nBMDMs loaded with OVA protein (top left) or SIINFEKL peptide (bottom right), shown as\nthe percentage of divided CD8+ T cells. f Flow cytometric analysis of IFN-, TNF-, and\nperforin (PFN) production in OT-I CD8+ T cells co-cultured with WT or Vsir–/– BMDMs\nloaded with OVA protein. g Quantification of CD8+ T cells in proximity to macrophages across\nvarying distances (m). h Tumor growth curves following co-injection of BMDMs and CD3+\nT cells (n = 6 per group). i Tumor growth curves following co-injection Pan02 cells with OT-\nI CD8+ T cells and WT or Vsir–/– BMDMs (n = 5 per group). Data are presented as mean ±\nSEM. Unpaired two-tailed Student’s t-tests were applied for panels d–f. Multiple t-tests with\nHolm-Sidak correction were used for h and i. n.s., not significant. All experiments were\nindependently repeated at least twice; representative results are shown.\n-53-\nFigure 5. Vsir deficiency delays CD8+ T cell exhaustion with increased CXCR3 expression.\na UMAP of single-cell RNA-seq data showing CD8+ T-cell subsets in WT and Vsir–/– tumors;\nsubpopulations are color-coded. b Dot plot of differentially expressed genes across six CD8⁺\nT-cell clusters, with color and dot size representing expression level and cell proportion,\nrespectively. c Genotype-split UMAP depicting CD8+ T cells from WT (blue) and Vsir–/– (red)\ntumors. d Relative abundance of each CD8+ T-cell subsets in WT and Vsir–/– tumors. e Scatter\nplot of log fold changes: CD8+_cluster1 (x-axis) versus CD8+_cluster2 (y-axis), each\n2\ncompared with Vsir-deficient clusters (CD8+_cluster0 and cluster3).Genes upregulated only in\nCD8+_cluster1 (gold), only in CD8+_cluster2 (green) or in both WT-specific clusters (blue) are\nhighlighted. f Flow cytometry plots (left) and quantification (right) of CXCR3+CX CR1+ CD8+\n(right) of TCF-1+PD-1– CD8+ T cells in WT and Vsir–/– tumors (n = 7 per group). h Flow\ncytometry plots (left) and quantification (right) of terminally exhausted TCF-1–PD-\n1+CXCR3+CX CR1– CD8+ T cells (Texterm) in WT and Vsir–/– tumors (n = 7 per group). Data\n3\nare mean ± SEM and unpaired two-tailed Student’s t-tests were applied for panels f–h. n.s., not\nsignificant. Experiments were independently repeated at least twice; representative results are\nshown.\nThese revisions have been thoroughly reviewed and confirmed by Suk-Kyung Shin, Hang-\nRae Kim, and myself to ensure accuracy and transparency. I sincerely apologize again for the\nerror and thank you for bringing this to our attention.\n2. The findings of this paper are highly reliant on a relatively immunogenic and genetically\nirrelevant PancO2 PDAC model. Numerous, more genetically relevant and poorly\nimmunogenic PDAC models exist at this time; can the key results of this study be replicated in\na more rigorous KPC-type model?\n(Author’s Response/Action)\n-54-\nWe appreciate this critical observation and fully acknowledge the limitations of the Pan02\nmodel in terms of both immunogenicity and genetic relevance. In response, we performed\nadditional experiments using the KPC001 tumor model, which is derived from the KrasG12D/+;\nTrp53R172H/+; Pdx1-Cre (KPC) genetically engineered mouse model and recapitulates the\nmutational landscape and immunosuppressive tumor microenvironment of human PDAC\nbetter than Pan02 model.\nConsistent with our findings in the Pan02 model, VISTA deficiency significantly impaired\ntumor growth in KPC001-bearing mice, albeit to a lesser extent (Supplementary Fig. S1a).\nImmune profiling at multiple timepoints confirmed that KPC001 tumors harbored lower\nbaseline CD3+ T cell infiltration than Pan02, as expected for a poorly immunogenic model.\nNevertheless, VISTA deficiency in KPC001 tumors was associated with increased infiltration\nof CD8⁺ T cells and F4/80⁺ TAMs (Supplementary Fig. S1b and c), consistent with our\nobservations in the Pan02 model. Furthermore, TAMs in Vsir⁻/⁻ KPC001 tumors exhibited\nincreased I-A/I-E expression (Supplementary Fig. S1d), indicative of a more pro-inflammatory\nphenotype. Although we did not observe a reproducible increase in iNOS⁺ macrophages in this\nmodel, the shift in antigen presentation phenotype supports the overall conclusion that VISTA\nregulates macrophage polarization.\nNew Supplementary Figure S1\n-55-\nSupplementary Figure S1. VISTA deficiency reduces tumor growth in KPC001 Model. a\nTumor growth curve of WT and Vsir–/– mice orthotopically implanted with KPC001 cells\n(WT: n = 42, Vsir–/–: n = 42). Data are mean ± SEM. Significance was determined using\nmultiple t-tests, with p-values corrected for multiple comparisons using the Holm-Sidak\nmethod. b Flow cytometric quantification of F4/80+CD11b+ tumor-associated macrophages\n(TAMs) in WT (n = 19) and Vsir–/– (n = 17) mice at days 10, 15, and 21, shown as a\npercentage of CD45+ cells. c Flow cytometric quantification of CD8+ T cells in WT (n = 19)\nand Vsir–/– (n = 16) mice at days 10, 15, and 21, shown as a percentage of CD45+ cells. One-\nway ANOVA with Sidak’s post hoc test was applied for panels b and c. d Flow cytometric\nanalysis of I-A/I-E+ CD206‒ TAMs in tumor, expressed as a percentage of CD45+ cells (n =\n5). Data are mean ± SEM. Statistical analysis panels for b–d were performed using an\nunpaired two-tailed Student’s t-test. n.s., not significant. Data represent at least three\nindependent experiments.\nIn Results (Page 5, Lines 102-108)\nOriginal:\nTo investigate how VISTA deficiency impacts the immune-oncologic landscape of PDAC, we\nutilized a syngeneic orthotopic model comparing tumor growth between wild-type (WT) and\nVISTA-deficient (Vsir–/–) mice. Pan02 cells (5  105) were orthotopically inoculated into the\npancreas tail of both groups. VISTA deficiency significantly impaired pancreatic tumor growth,\nwith tumor volume notably reduced by day 21 post-inoculation, becoming even more\npronounced by day 28 (Fig. 1a). By day 28, tumor volumes in Vsir–/– mice were approximately\nthreefold smaller than those of WT control.\nRevised:\n→ “To investigate how VISTA deficiency impacts the immune-oncologic landscape of PDAC,\nwe utilized syngeneic orthotopic models to compare tumor growth between wild-type (WT)\nand VISTA-deficient (Vsir–/–) mice. Pan02 and KPC tumor cells (5  105) were orthotopically\ninoculated into the pancreatic tail of each group. VISTA deficiency significantly impaired\ntumor growth in both models, resulting in a 2.5-fold reduction in tumor size in the\n-56-\nimmunogenic Pan02 model and a 1.8-fold reduction in the less immunogenic KPC model at\nendpoint (Fig. 1a, Supplementary Fig. S1a). These findings were further supported by\npharmacological blockade of VISTA in WT mice, where treatment with an anti-VISTA\nantibody (VISTA) similarly suppressed tumor growth in the Pan02 model (Fig. 1b,\nSupplementary Fig. S2).”\nIn Results (Pages 5-6, Lines 120-124)\nOriginal:\nTo identify the underlying mechanisms driving these differences in tumor growth and survival,\nwe conducted flow cytometry and immunohistochemistry analyses. VISTA deficiency\nselectively promoted the infiltration of both tumor-associated macrophages (TAMs;\nF4/80+CD11b+) and CD8+ T cells into TME, peaking at day 28 (Fig. 1e, f, Supplementary Fig.\nS2a).\nRevised:\n→ “To characterize the immunological consequences of VISTA deficiency, we conducted flow\ncytometry and immunohistochemistry analyses in orthotopic tumor models. Vsir–/– mice\nexhibited a 1.5-fold increase in F4/80+CD11b+ TAMs and a 1.8-fold (Pan02) to 2.0-fold (KPC)\nincrease in CD8+ T cells compared to WT controls (Fig. 1d, Supplementary Fig. S1b, c, and\nS4a).”\nIn Results (Page 6, Lines 129-132)\nOriginal:\nImportantly, TAMs in Vsir–/– mice exhibited a pro-inflammatory phenotype with increased\niNOS expression (Fig. 1g) and decreased Arginase 1 expression (Fig. 1h), suggesting a shift\ntowards a more tumor-suppressive phenotype.\nRevised:\n-57-\n→ “Critically, TAMs in Vsir–/– tumors acquired a pro-inflammatory phenotype, characterized\nby increased iNOS+ macrophages (Pan02) and I-A/I-E+CD206‒ macrophages (KPC) (Fig. 1f,\nSupplementary Fig. S1d), accompanied by reduced Arginase-1+ macrophages (Fig. 1g).”\nAdditionally, we employed the KPC001 model to evaluate the therapeutic efficacy of\nVISTA targeting in combination with gemcitabine (Fig. 7a). Tumor reduction was observed\nfollowing anti-VISTA monotherapy, with a further synergistic or additive effect seen in the\ncombination treatment with gemcitabine (Fig. 7b). Key mechanistic findings were\nrecapitulated in this model: CXCL9 and SPP1 levels were elevated in the anti-VISTA group\ncompared to PBS controls (Supplementary Fig. S18c), and an increase in CXCR3⁺ CD8⁺ T\ncells was observed in both the anti-VISTA and combination therapy groups (Supplementary\nFig. S18d). These findings reinforce the mechanistic conclusions drawn from the Pan02 model\nand independently validate VISTA’s immunoregulatory role in a genetically and\nimmunologically relevant PDAC model, thereby strengthening the translational relevance of\nour findings. To further deepen the mechanistic insight in this clinically relevant setting, we\nplan to perform single-cell RNA sequencing of KPC001 tumors in future studies.\n-58-\nNew Figure 7\nFigure 7. Effect of anti-VISTA antibody in combination with gemcitabine therapy. a\nExperimental scheme for combination therapy. b Tumor volumes in mice treated with\ncombination therapy (n = 10 per group). Data are mean ± SEM. Significance was determined\nusing multiple t-tests, with p-values corrected for multiple comparisons using the Sidak method.\nn.s., not significant. Experiments were repeated at least three times independently. c Schematic\nsummarizing key intercellular communication pathways in wild-type versus VISTA-deficient\ntumors.\nIn Results (Page 17, Lines 400-402)\nOriginal:\n“These findings reveal how VISTA deficiency reprograms the TME from an\nimmunosuppressive to an immune-stimulatory state, highlighting the potential for VISTA as a\ntherapeutic target to boost immune responses against pancreatic cancer.”\n-59-\nRevised:\n→ “VISTA Blockade Synergizes with Gemcitabine to Remodel the Pancreatic Tumor\nImmune Landscape\nTo determine whether these VISTA-dependent signaling network we identified is\ntherapeutically exploitable, we turned to the KPC orthotopic model and compared anti-VISTA\nmonotherapy, gemcitabine, and their combination (Fig. 7a). Both monotherapies produced a\nmodest but significant reduction in tumor burden; however, the combination elicited a\nmarkedly greater suppression, indicating at least an additive—and potentially synergistic—\nanti-tumor effect (Fig. 7b).\nImmune profiling revealed that anti-VISTA monotherapy alone increased total TAMs and\nincreased CD8+ T cells (Supplementary Fig. S18a). Importantly, the antibody skewed TAMs\ntoward an iNOS⁺ phenotype while producing only minor changes in Arg1⁺ TAMs\n(Supplementary Fig. S18b). Consistent with our CellChat predictions, CXCL9⁺/SPP1⁺ TAMs\nwere enriched in the anti-VISTA treatment group (Supplementary Fig. S18c), both anti-VISTA\nand the combination regimen boosted infiltration of CXCR3+ CD8+ T cells (Supplementary\nFig. S18d) —hallmarks of the CXCL9–CXCR3 chemotactic axis we mapped in Vsir⁻/⁻ tumors.\nThese findings validate our mechanistic model and demonstrate that pharmacologic VISTA\nblockade can re-program the myeloid–T-cell circuit even in the presence of cytotoxic\nchemotherapy.”\nIn Methods (Page 21, Line 477)\nOriginal:\n“The Pan02 cell line, derived from a methylcholanthrene-induced PDAC, was a generous gift\nfrom Dr. Keehoon Jung (Seoul National University) 65.”\nRevised:\n→ “The Pan02 cell line from National Cancer Institute (NCI, 0509770), and KPC001 cell line\nisolated from the tumor of a 5– to 6–month-old genetically engineered mouse model of\nKrasG12Dp53R172H/+Pdx-1-Cre mice were used 66, 67. ”\n-60-\nIn Methods (Pages 21-22, Lines 489-490)\nOriginal:\n“After upper left abdominal incision, pancreatic tails were exposed and injected with 5  105\nPan02 cells resuspended in cold…”\nRevised:\n→ “After upper left abdominal incision, pancreatic tails were exposed and injected with either\n5  105 Pan02 or KPC001 cells…”\nIn Methods (Page 22, Line 503-504)\nOriginal:\n“or Rat IgG2, isotype control (Clone 2A3, InVivoMab, BioXCell) 13 days prior to tumor\ninoculation, followed by three consecutive daily injections of 0.5 mg.”\nRevised:\n→ “…0.5 mg. For combination therapy, 50 mg/kg gemcitabine (Selleckchem, Houston, TX,\nUSA) and/or anti-VISTA antibody (Clone 13F3, InVivoMab, BioXCell, Lebanon, NH, USA)\nwere administered intraperitoneally, starting from day 6 after cancer cell injection. Treatments\nwere given every 3 days.”\n3. It remains unclear whether (i) VISTA deficiency primarily affects macrophage polarization\nleading to secondary effects on T cell differentiation; (ii) VISTA deficiency primarily affects\nT cell differentiation/exhaustion leading to secondary effects on macrophage polarization;\nor (iii) these effects occur independently. Can the authors provide any data that indicates\nwhether VISTA deficiency on macrophages or T cells alone is necessary or sufficient for their\nphenotypes?\n(Author’s Response/Action)\nWe thank you for this insightful mechanistic question. While we have not yet performed\nexperiments involving cell type–specific deletion of VISTA or adoptive transfer models, our\n-61-\ncurrent data supports a model in which VISTA primarily regulates macrophage polarization,\nwhich then influences CD8⁺ T cell infiltration and activation.\n(i) Macrophage-centric mechanism:\nIn our in vitro co-culture system, M0 WT and VISTA KO BMDMs showed no\ndifferences in OT-I CD8+ T cell proliferation. However, when BMDMs were polarized\nwith LPS and IFN-, VISTA KO BMDMs combined with OVA proteins induced\nsignificantly greater OT-I T cell proliferation compared to unpolarized BMDMs.\nIn our in vivo system, when OT-I CD8+ T cells were co-injected with VISTA KO\nBMDMs, but not with WT BMDMs, tumor regression was observed in Rag1 KO mice,\nindicating a macrophage-centric mechanism (Relocated Figure 4i).\nProteome profiling (next page) further revealed decreased phospho-STAT3 in VISTA\nKO BMDMs even in the absence of T cells, indicating that the shift in macrophage\nphenotype begins prior to T cell interaction.\nIn vivo, VISTA deficiency led to increased expression of chemokines, such as CXCL9\nin TAMs, which are known to recruit and activate CD8+ T cells, further supporting a\nmodel where macrophage shift is the initiating event.\n(ii) Direct effects on T cells cannot be fully excluded, but our data do not support a primary\nrole for VISTA in directly regulating T cell differentiation or exhaustion in this context.\nInstead, the observed changes in T cell phenotype and function appear secondary to\nalterations in macrophage polarization and cytokine milieu.\nRelocated Figure 4i:\n-62-\nIn summary, while further investigation is needed to definitively dissect cell-intrinsic roles,\nour current findings indicate that VISTA deficiency predominantly affects macrophage\npolarization, which in turn shapes CD8+ T cell responses in the tumor microenvironment.\n4. Can the authors explain why VISTA-KO BMDMs show a functional phenotype as compared\nto VISTA WT BMDMs in vitro presumably in the absence of VISTA ligand? What is acting as\nthe VISTA ligand in the WT BMDMs?\n-63-\n(Author’s Response/Action)\nWe appreciate your thoughtful question regarding the ligand-receptor dynamics of VISTA in\nour in vitro system. While endogenous ligand(s) for VISTA remains incompletely\ncharacterized, several mechanisms may explain the functional differences between WT and\nVISTA-KO BMDMs:\n(i) Autocrine/Paracrine VISTA signaling: In WT BMDMs, VISTA may act as both a\nreceptor and a ligand in homotypic interactions (Yoon et al., Science, 2015). For example,\nVISTA on WT macrophages could engage with VISTA on neighboring macrophages,\ncreating an autocrine/paracrine immunosuppressive loop. This signaling would be absent\nin VISTA-KO macrophages, leading to enhanced pro-inflammatory responses.\n(ii) Known ligands in myeloid cells: PSGL-1, expressed majorly on T cells, binds to VISTA\nand suppresses T-cell activation (Johnston et al., Nature, 2019). In our system, WT\nBMDMs may engage PSGL-1 on neighboring CD8+ T cells, while VISTA-KO BMDMs\nlack this interaction. VSIG-3, a proposed ligand for VISTA (Wang et al., Immunology,\n2018), could mediate inhibitory signaling in WT BMDMs.\n(iii) MHC-I interaction: Recent studies suggest VISTA interacts with MHC-I on\nmacrophages, modulating antigen presentation and T cell activation. Loss of VISTA in\nKO BMDMs may disrupt this interaction, enhancing MHC-I-mediated antigen\npresentation.\nWe believe this phenotype may persist in vitro without exogenous ligand by either tonic\nsignaling, where VISTA could constitutively suppress pro-inflammatory pathways (e.g., NF-\nB) in WT macrophages, even in ligand-free conditions); or by cis-interactions, where VISTA\ninteracts with co-receptors (e.g., MHC-I) on the same membrane. While defining VISTA’s\nligand(s) in vitro requires further study, which we intend to address in the future, we believe\nour findings highlight its non-redundant role in myeloid cell self-regulation, independent of\nexogenous ligands.\n5. Key points of the proposed mechanism could benefit from in vivo validation. Is the\nCXCL9/CXCR3 axis required for the robust antitumor phenotype in the VISTA-KO mice? Are\n-64-\nmacrophages derived from tumors grown in VISTA-KO versus WT mice differentially able\nto mediate T cell chemotaxis, priming, and/or function? The validation only in BMDCs does\nnot recapitulate the key, complex biology of the TME.\n(Author’s Response/Action)\nWe thank you for this important comment. We agree that in vivo validation of CXCL9/CXCR3\naxis and macrophage-mediated T cell regulation would provide stronger mechanistic insight\ninto the role of VISTA. However, due to the extensive time required to develop and execute in\nvivo blockade studies (CXCR3 neutralization or macrophage adoptive transfer), we were\nunable to complete these experiments within the timeframe of our current study.\nWe are planning on designing follow-up studies to functionally interrogate the\nCXCL9/CXCR3 axis and the role of VISTA-deficient TAMs in T cell recruitment using in vivo\nmodels. We have added this limitation and future direction to the revised Discussion section.\nWe appreciate your suggestion in enlightening us with what could be done next on our ongoing\nresearch.\nIn Discussion (Page 19, Lines 439-444)\nOriginal:\nMacrophage secreting CXCL9/10 are thought to support anti-tumor effects of checkpoint\ninhibitors like anti-PD-1 and anti-CTLA-4 antibodies by recruiting stem-like CD8+ T cells 55,\n56. The elevated levels of IFN--producing CD8+ T cells observed in VISTA-deficient mice\nindicate enhanced immune activation. Therefore, targeting VISTA may delay CD8+ T cell\nexhaustion and promote durable anti-tumor responses in PDAC.\nRevised:\n→ “The elevated levels of IFN--producing CD8+ T cells observed in VISTA-deficient mice\nindicate enhanced immune activation. While these findings indicate that VISTA may influence\nCD8+ T cell recruitment and activation via the CXCL9/CXCR3 axis, further studies are\nrequired to establish whether this pathway is essential for these observed immune effects.”\n-65-\nMinor comments:\n1. Fig 2B: What is the rationale for your anti-CSF1R regimen starting 2 weeks before tumor\nimplantation? Why not apply the antibody during/after implantation, in context of tumor\nprogression? What macrophages are you depleting with the D-14 anti-CSF1R regimen?\n(Author’s Response/Action)\nWe thank you for this important question. Our rationale for initiating anti-CSF1R treatment at\nday –14 relative to tumor implantation was to systemically deplete CSF1R+ tissue-resident\nmacrophages and circulating monocytes prior to tumor implantation, thereby isolating their\nrole in early tumor seeding and immune priming. Though we are aware that temporal\nmodulation of CSF1R+ cells at later stages would provide critical insights into TAM\nreprogramming, day ‒14 timing targets embryonically derived, self-renewing macrophages\nand bone marrow-derived monocytes, which are critical for initial immune surveillance. By\ndepleting macrophages before TME establishment, we aimed to assess how their absence\nduring the initiation phase alters CD8+ T cell infiltration and spatial organization.\n2. I would recommend removing the artificial \"gates\" on the scRNA UMAP plots; let the\nclusters speak for themselves. Lines drawn in UMAP space have no inherent meaning\n(Author’s Response/Action)\nWe appreciate your thoughtful suggestion regarding the artificial contour lines surrounding\nUMAP clusters (Fig. 3b and c and Fig. 5b and c). We fully agree that such lines may impose\nmisleading boundaries that do not reflect the underlying structure of the high-dimensional data\nprojected in UMAP space. Accordingly, we have removed these gates from the figures to avoid\nthe implication of predefined cluster separations and to better represent the continuity and\nintrinsic relationships between clusters. We believe this revision allows the data-driven\nclustering results to be interpreted more objectively and aligns better with standard scRNA-seq\nvisualization practices.\n-66-\nIn Figure 3b, 3c (Page 43)\nOriginal:\nRevised:\nIn Figure 5b, c (Page 46)\nOriginal:\nRevised:\n-67-\n3. Figure 3F: specifically what do you mean by cumulative frequency at the far right? Cluster\n2 shows 100% frequency in both genotype conditions? What does this mean? Also, how did\nyou determine the cutoff for CXCL9 positivity versus Spp1 positivity at the gene expression\nlevel?\n(Author’s Response/Action)\nWe appreciate your thoughtful comment. As noted, the original Figure 3F was based on\ncumulative cell distributions along a single pseudotime trajectory line. This approach\ninadvertently resulted in misleading representations—such as cluster 2 appearing to reach 100%\nfrequency in both WT and Vsir-/- genotypes—due to the relative nature of cumulative plots.\nIn light of Figure 3c, where the trajectories for WT and Vsir-/- are clearly separated, the\ncumulative frequency plot in Figure 3f failed to accurately reflect the actual genotype composition of\nindividual clusters.\nTo address this issue, we revised Figure 3f to display the proportional distribution of WT\nand Vsir-/- cells within each cluster, rather than their cumulative progression along pseudotime.\nThis revised visualization more directly represents the genotype-specific distribution of\nmacrophage clusters and enables clearer interpretation of genotype-driven differences.\nIn addition, Cxcl9⁺ and Spp1⁺ cells were defined based on whether the expression level of\neach gene exceeded its average expression across the entire macrophage population. We chose\nthis gene-specific threshold because global expression levels vary widely across genes, and\napplying a uniform absolute cutoff could lead to biased or inaccurate classification of gene-\npositive cells. By using each gene’s average expression as a relative cutoff, we aimed to\npreserve interpretability while accounting for global expression differences across genes.\n-68-\nFinally, to complement the bar graph originally used to illustrate the frequency of Cxcl9⁺\nand Spp1⁺ cells across clusters, we have added a contour plot in Supplementary Figure S9d.\nThis revised figure offers a more intuitive and continuous visualization of the relative\nexpression patterns across clusters, facilitating clearer interpretation of Cxcl9 and Spp1\ndistributions.\nIn Figure 3f (Page 43)\nOriginal:\nRevised:\n-69-\nf Group frequencies and cell numbers of macrophage subtypes in WT and Vsir–/– tumors. The\nCxcl9:Spp1 ratio for each Mono/Mac cluster is calculated, with the highest ratio highlighted in\nred and the lowest in blue. Cumulative frequency of cells is compared between WT and Vsir–\n/– tumors.\nNew Supplementary Figure S9\nSupplementary Figure S9. Gene expression profiles from Mono/Macrophage subclusters.\nd Density contour plots of macrophage clusters based on Cxcl9 (x-axis) and Spp1 (y-axis). Each\nplot depicts a two-dimensional kernel density estimation of single macrophages within a cluster.\nRed dashed lines denote the average expression levels of each gene across all macrophages,\ndefining quadrant thresholds. Cell frequencies within each quadrant are reported based on\nrelative Cxcl9 and Spp1 expression.\nIn Results (Page 10, Lines 234-236)\nOriginal:\nFurther, MΦ_cluster2 had a Cxcl9:Spp1 ratio of 2.51, compared to a ratio of 0.69 in\nMΦ_cluster1, indicating a higher Cxcl9:Spp1 ratio in Vsir–/– TAMs could enhance anti-tumor\neffect (Fig. 3f).\n-70-\nRevised:\n→ “The Cxcl9:Spp1 ratio was 2.51 in MΦ_cluster2 versus 0.69 in MΦ_cluster1 (Fig. 3f,\nSupplementary Fig. S9d), indicating a shift toward a more chemotactic, immunostimulatory\nTAM phenotype in Vsir–/– tumors.”\n4. Figure 4i: the % positive gate seems to bias the results because it excludes non-proliferating\nT cells, of which there are more in the WT BMDM group. If you only assess only the\nproliferating cells in both groups, is there a difference in cytokine production btw groups?\n(Author’s Response/Action)\nThank you for pointing this out. We sincerely apologize for this oversight and appreciate your\ncareful review. We acknowledge that including non-proliferating T cells in the calculation of\ncytotoxic molecule frequencies may have introduced bias, particularly given the higher\nproportion of non-proliferating cells in WT BMDM group. To address this, we re-gated our\nflow cytometric data to focus exclusively on proliferating CD8+ T cells.\nThe revised analysis, presented in the update Figure 4f, demonstrates that VISTA KO\nBMDMs still elicit significantly higher frequencies of IFN-, TNF-, and perforin-producing\nCD8+ T cells compared to WT BMDM + CD8+ T cells, even when restricted to proliferating\npopulations. This correction hopefully ensures a more accurate representation of functional\ndifferences and confirms that the enhanced cytotoxic activity in VISTA KO BMDM co-cultures\nis not an artifact of gating strategy. We thank you once again for this critical insight.\nIn Figure 4i (Page 44)\n-71-\nOriginal(left)/Revised(right):\n5. Figure 4H: Is there any difference in costimulatory ligand expression (CD80/CD86, etc.) in\nBMDMs from WT versus VISTA-KO mice? Or is the difference in cross-presentation capacity\nsolely due to higher levels of pMHC?\n(Author’s Response/Action)\nWe thank you for this important mechanistic question. We examined the expression of key\ncostimulatory molecules, including CD80, CD86, and MHC-II (I-A/I-E), on BMDMs from WT\nand VISTA-KO mice. As shown in Supplementary Figure S13, there was no significant\ndifference in the expression of these surface markers between WT and VISTA-KO BMDMs.\nThese results suggest that the enhanced cross-presentation capacity observed in VISTA-KO\nmacrophages is not due to altered costimulatory ligand expression, but it is likely attributable\nto increased pMHC-I presentation or antigen-cross processing efficiency. This mechanistic\npoint has been clarified in the revised Results.\nIn Figure 4h (Page 44)\nRevised:\n-72-\nSupplementary Figure S13. Expression of CD80, CD86, and I-A/I-E in WT versus Vsir–/–\nBMDMs. Flow cytometric analysis of CD80, CD86, and I-A/I-E expression on WT and Vsir–\n/– BMDMs. Quantification is shown as mean fluorescence intensity (MFI) (n = 11). Data are\nmean ± SEM. Statistical significance was assessed using an unpaired two-tailed Student’s t-\ntest. n.s., not significant.\nIn Results (Page 12, Lines 276-283)\nOriginal:\n“To validate these findings, we performed an antigen cross-presentation assay by measuring\nSIINFEKL peptide bound to MHC class I (H2-Kb) on WT and Vsir–/– BMDMs. After treatment\nwith OVA peptide for 1 h or OVA protein for 24 h, we assessed antigen presentation efficiency.\nWhile SIINFEKL-loaded BMDMs showed no difference in H2-Kb SIINFEKL MFI between\nWT and Vsir–/– groups (Supplementary Fig. S8c), Vsir–/– BMDMs loaded with OVA protein\nexhibited significantly higher SIINFEKL peptide bound H2-Kb levels compared to WT\nBMDMs, especially at higher OVA protein concentrations (Fig. 4g). This suggests an enhanced\nantigen processing and presentation capacity in the absence of VISTA.”\nRevised:\n→ “However, Vsir–/– BMDMs exposed to full-length OVA protein displayed significantly\nhigher SIINFEKL-H2-Kᵇ levels, especially at higher antigen concentrations (Fig. 4d),\nindicating enhanced antigen processing and cross-presentation in the absence of VISTA.\n-73-\nNotably, this occurred without changes in CD80, CD86, or I-A/I-E expression (Supplementary\nFig. S13), suggesting that VISTA loss selectively enhances antigen-presenting function without\nbroadly activating macrophages.”\n6. Figure 6: CXCR3 and CX3CR1 expression are likely insufficient to conclusively infer levels\nof T cell exhaustion. Could you show a difference in expression of canonical markers such as\nPD1/Tim-3, PD-1/CD39, or PD-1/TOX?\n(Author’s Response/Action)\nWe sincerely appreciate the reviewer’s insightful comment. To more precisely evaluate the\nexhaustion states of CD8+ T cells, we updated Figure 5c to clarify the identities of CD8+ T-cell\nsubsets and expanded our analysis to include co-expression of canonical exhaustion marker\npairs including Pdcd1/Havcr2, Pdcd1/Entpd1, Pdcd1/Tox, and Tox/Entpd1, which have been\nwidely associated with terminal exhaustion (Jin et al., Proc Natl Acad Sci, 2010; Canale et al.,\nCancer Res, 2018; Scott et al., Nature, 2019). Gene expression patterns revealed that WT-\nspecific cluster 2 and Vsir-deficient Cluster 0 exhibited the highest frequencies of co-\nexpressing both markers among the genotype-specific clusters, suggesting that these\npopulations represent exhausted CD8+ T cells. Notably, Tox/Entpd1 expression was highest in\nClusters 1 and 2, both enriched in WT CD8+ T cells (Figure a below). In contrast, the\nIl7r/Cd226 pair showed the highest expression in Cluster 3, which may reflect a less exhausted\nor progenitor state (Figure b below). These results collectively indicate that Clusters 0, 1, and\n2 represent exhausted CD8⁺ T cells, whereas Cluster 3 retains features of an early exhausted\nphenotype in intratumoral CD8+ T cells.\n-74-\nDensity contour plots of CD8+ T cells using canonical exhaustion and effector-like\nmarkers\nEach contour plot depicts a two-dimensional kernel density estimation of single CD8+ T-cell\nclusters, based on normalized expression levels of paired exhaustion markers (a) and\nIl7r/Cd226 (b) derived from CD8+ T cells. Red dashed lines represent the average expression\nlevel of each gene across all CD8+ T cells and serve as quadrant thresholds for calculating the\nfrequency distribution within each cluster.\n-75-\nReviewer #3’s Comments and Responses\n1.Deeper Mechanistic Research Needed\n•When the study reveals the regulatory effects of VISTA deficiency on TAM polarization,\nit\nlacks a deeper exploration of upstream signaling pathways or regulators. Including more\nmechanistic studies could enhance its scientific impact.\n(Author’s Response/Action)\nWe thank you for this valuable comment. In response, we have strengthened the mechanistic\naspect of our study in two ways by: (1) expanding the Discussion to incorporate established\nknowledge regarding pathways involved in VISTA regulation, and (2) performing a phospho-\nprotein kinase array in WT and VISTA-deficient macrophages to identify differentially\nregulated signaling molecules.\nRegarding upstream regulators, HIF-1 has been shown to bind to a conserved hypoxia\nresponse element in the VISTA promoter, enhancing VISTA expression in myeloid cells and\ncontributing to an immunosuppressive tumor microenvironment (Deng et al., Cancer Immunol\nRes, 2020). While interferons are known to broadly regulate immune checkpoint molecules,\ndirect transcriptional control of VISTA by type I/II IFNs remains poorly defined and warrants\nfurther investigation.\nOn the downstream side, recent studies have linked VISTA to ERK and STAT3 signaling\ncascades in MDSCs (Zhang et al., Cell Rep, 2024). This aligns with our observation that\nVISTA KO TAMs exhibited reduced SPP1 expression (Supplementary Figure S18c), a gene\ntranscriptionally regulated by STAT3 (Yu et al., Dev Cell, 2025).\nTo investigate early signaling dynamics influenced by VISTA, we performed a phospho-\nproteome array in WT and VISTA KO BMDMs following LPS and IFN- stimulation for 15\nminutes. This revealed a marked reduction in GSK-3α/β phosphorylation at serine residues in\nVISTA KO BMDMs, implicating the GSK-3 pathway in VISTA-mediated macrophage\nregulation. We also observed decreased phosphorylation of p53 (S15/S45) in VISTA KO\nBMDMs. As p53 activity has been associated with M2 polarization (Li et al., Cell Death Differ,\n2014), this result further supports a shift away from an immunosuppressive phenotype. Additionally,\nphosphorylation of STAT3 at S727, which\n-76-\ncontributes to M2-like macrophage function (Zhang et al., Front Immunol, 2023), was similarly\nreduced in VISTA KO BMDMs.\nThese findings are now contextualized in the revised Discussion. We have opted not to\ninclude the phospho-proteome array data in the main manuscript, as this analysis remains\nongoing. We appreciate your suggestion, which has helped refine and deepen the mechanistic\nframework of our study. Based on our previous experience studying VISTA in kidney disease\nmodel, we plan to pursue these signaling pathways in future mechanistic investigations.\nIn Discussion (Page 18, Line 412)\nOriginal:\n“Our study highlights VISTA, a B7 checkpoint molecule, as a pivotal regulator of TAM\npolarization and the immune landscape in PDAC 46, 47.”\n-77-\nRevised:\n→ “Our study highlights VISTA as a modulator of macrophage polarization and immune\ndynamics in PDAC, although further studies are needed to define its mechanistic role 46,\n47. ”\n•The mechanisms underlying the enhanced CD8+ T cell function (e.g., metabolic states or\nepigenetic regulation) are underexplored. Please adding more data on these aspects would\nstrengthen this part.\n(Author’s Response/Action)\nThank you for this comment. To complement our phenotypic analysis of CD8+ T cell\nexhaustion, we examined the frequency of Eomes+Cd38+ double-positive cells across clusters.\nThis subset, which has been previously associated with terminal exhaustion under chronic\nstimulation (Philip et al., Nature, 2017, Chen et al., Sci Adv, 2022), was most abundant in WT-\nspecific cluster 2 (44.0%) and moderately enriched in cluster 1 (22.6%), but substantially\nreduced in Vsir-deficient clusters 0 (17.6%) and 3 (5.0%) (Figure 5f; revised to address\nReviewer 1’s comment). These data suggest that VISTA deficiency may attenuate terminal\ndifferentiation and promote less exhausted states among CD8+ TILs.\nIn Figure 5f (Page 46)\nOriginal (left) /Revised (right):\n-78-\nTo further investigate the underlying mechanisms, we evaluated module scores for key\nmetabolic pathways, including glycolysis, OXPHOS, FAO, mTORC1, MYC, and hypoxia,\nusing curated gene sets from the MSigDB Hallmark collection (Liberzon et al., Cell Syst, 2015).\nTo focus on biologically meaningful signals, we applied a visualization threshold (col.min =\n0.5). WT-specific cluster 2, which harbored the highest proportion of Eomes+Cd38+ cells,\nexhibited strong enrichment of glycolysis, MYC, and hypoxia—consistent with terminal\nexhaustion under metabolic stress. Cluster 1 showed high mTORC1 activity, supporting an\neffector-like state. In contrast, Vsir-deficient cluster 0 showed relatively low scores except for\nFAO, suggestive of a memory-like metabolic profile. Cluster 3 displayed elevated OXPHOS\nand mTORC1 despite low FAO, indicative of a metabolically active yet less terminally\nexhausted population.\nMetabolic module scores across intratumoral CD8+ T-cell clusters\nDot plot showing average expression and percent-expressing cells for key metabolic\nmodules—including mTORC1, MYC, hypoxia, glycolysis, FAO, and OXPHOS—across four\nCD8⁺ T-cell clusters in the tumor microenvironment.\nWe agree that validating these metabolic profiles through functional assays and\ncomplementary epigenetic interrogation would provide a deeper mechanistic insight. However,\nwe believe these experiments are beyond the scope of the current study. We appreciate your\ncomment and hope to pursue them in a future project focused specifically on delineating the\nmetabolic and epigenetic regulation of CD8+ T cell differentiation in the context of VISTA\nsignaling.\n-79-\n2.Optimizing Data Integration and Presentation\n•The article relies heavily on supplementary materials (e.g., many critical findings are\npresented in supplementary figures). This might hinder reviewers’ ability to grasp the study’s\nsignificance quickly.\n(Author’s Response/Action)\nWe acknowledge this concern and have restructured the manuscript to relocate key\nsupplementary figures to the main figure panels. This adjustment ensures that critical findings\nare more prominently presented and improves the overall readability and accessibility of the\nmanuscript.\n-80-\nIn Figure 2 (Page 41)\nOriginal:\n-81-\nRevised:\n-82-\nIn Figure 4 (Page 44)\nOriginal:\n-83-\nRevised:\n-84-\nIn Figure 5 (Page 46)\nOriginal:\n-85-\nRevised:\n-86-\nIn Figure 6 (Page 48)\nOriginal:\n-87-\nRevised:\n-88-\n•Although the single-cell RNA sequencing analysis is comprehensive, the data could be better\nintegrated to simplify conclusions and improve readability.\n(Author’s Response/Action)\nWe have streamlined the single-cell RNA sequencing analysis by consolidating key findings\ninto simplified visualizations (e.g., integrated UMAPs and pathway enrichment summaries).\nWe hope these revisions enhance data integration and make the conclusions more accessible to\nthe readers by removing:\n(1) artificial gating in scRNA-seq data in Figure 3 and 5, and\n(2) Figure 5a, as the text already includes information on CD8+ T-cell subsets analyzed in\nthe same manner as macrophages.\nIn Figure 3b, 3c (Page 43)\nOriginal:\nRevised:\n-89-\nIn Figure 5 (Page 46)\nOriginal:\nRevised:\n3.Strengthening the Discussion on Clinical Relevance\n•The discussion on the clinical feasibility of VISTA-targeted therapy is brief. For example,\nmore information on the development and challenges of existing VISTA inhibitors would be\nvaluable. High-impact journals often emphasize translational relevance.\n(Author’s Response/Action)\nWe appreciate this insightful suggestion. In the revised Discussion, we have expanded upon\nthe current challenges associated with VISTA inhibitors in clinical development, including\n-90-\ntoxicity, limited tumor specificity, and suboptimal therapeutic efficacy. We also emphasized the\nrationale for renewed focus on VISTA-targeted therapies and discussed potential strategies to\naddress these limitations. These additions were made to better underscore the translational\nrelevance of our findings.\nIn Discussion (Page 20, Lines 458- 464)\nOriginal:\n→ “The findings from our study provide a strong rationale for targeting VISTA in PDAC.\nInhibiting VISTA has the potential to reprogram TAMs to adopt a pro-inflammatory, antitumor\nphenotype while enhancing recruitment and activation of stem-like CD8+ T cells. Such an\napproach could synergize with existing immune checkpoint inhibitors to improve their efficacy.\nBy simultaneously reprogramming TAMs and augmenting T cell function, VISTA inhibition\nrepresents a promising strategy to overcome the immunosuppressive environment of PDAC\nand improve patient outcomes 63, 64.”\nRevised:\n→ “ICB therapies targeting PD-1 and CTLA-4 have demonstrated limited efficacy in PDAC,\nunderscoring the need for alternative strategies that address multiple layers of immune\nsuppression. In this context, VISTA inhibition offers a promising therapeutic avenue. Notably,\nanti-VISTA monotherapy combined with gemcitabine elicited a synergistic anti-tumor\nresponse, characterized by an increased CXCL9/SPP1 ratio in TAMs and enhanced\ninfiltration of CXCR3⁺ CD8⁺ T cells. This additive effect is particularly relevant for immune-\nexcluded tumors like PDAC, where conventional ICB therapies have failed to yield substantial\nclinical benefit. Moreover, considering the well-documented toxicity profile of gemcitabine,\nour findings suggest that VISTA blockade could enhance therapeutic efficacy while\npotentially reducing dependence on cytotoxic agents.”\n4.Addressing the Lack of Comparative Analysis\n•The study does not compare VISTA’s function and utility with other immune checkpoint\nmolecules (e.g., PD-1, TIM-3). Such comparisons could better highlight VISTA’s uniqueness.\n-91-\n(Author’s Response/Action)\nThank you for this insightful comment. Rather than positioning VISTA in direct comparison\nwith other immune checkpoint molecules such as PD-1 or TIM-3, we believe its therapeutic\nvalue lies in its complementary mechanism of action, which offers potential for combination\nstrategies with existing immune checkpoint blockade therapies.\nVISTA is predominantly expressed on myeloid cells, including TAMs, as shown below, and\nfunctions early in the immune response by modulating antigen presentation and naïve T cell\npriming. In contrast, PD-1 and TIM-3 are mainly expressed on T cells and regulate effector\nfunctions at later stages (Kumagai et al., Nat Immunol, 2020). These non-overlapping roles\nsuggest that VISTA blockade could enhance or restore anti-tumor immunity in settings where\ntraditional ICB shows limited efficacy, particularly in “cold” or myeloid-rich tumors like\npancreatic cancer.\nWe have now added an explanatory statement in the Discussion to clarify that macrophages\nexhibit high levels of VISTA expression.\nIn Discussion (Page 18, Lines 418-420)\nOriginal:\n“Cxcl9/10-engineered DCs have previously been shown to suppress tumor growth in NSCLC\nmurine models, emphasizing a crucial role of Cxcl9 in anti-tumor immune responses 48.”\nRevised:\n-92-\n“Cxcl9/10-engineered DCs have previously been shown to suppress tumor growth in NSCLC\nmurine models, emphasizing a crucial role of Cxcl9 in anti-tumor immune responses 49. In\nPDAC, however, VISTA’s predominant expression in TAMs creates distinct microenvironment\ndynamics. Our murine model revealed VISTA deficiency drives TAM polarization towards a\nCxcl9+ state while lowering Spp1+ state.”\n-93-\nResponses to Reviewer’s Comments\nWe are deeply grateful for your thoughtful comments and invaluable suggestions regarding our\nmanuscript. Your feedback has provided us with an excellent opportunity to improve the quality\nand clarity of our work. We have carefully revised the manuscript in accordance with the\nreviewers’ recommendations and your editorial guidance. Below, we respectfully addressed\neach of the reviewers’ comments point-by-point and described the corresponding revisions\nmade to the manuscript. All changes are clearly marked in red for your convenience. Thank\ny Thank\nyou once again for your time and consideration.\nReviewer #1 (Remarks to the Author):\nThe authors have significantly improved the manuscript by addressing previous critiques and\nenhancing both translational and mechanistic depth. The inclusion of human pancreatic ductal\nadenocarcinoma (PDAC) tissue and single cell RNA sequencing data from the HTAN cohort\nconnects findings from mouse models with human data. This demonstrates a consistent\nrelationship between VSIR expression, the phenotype of tumor-associated macrophages\n(TAMs), and the functionality of CD8⁺ T cells. The addition of rigorous multivariate analyses,\nsingle-cell stratification, and other co-expression profiling provides strong support for the\nmanuscript's central hypothesis: that VISTA orchestrates immunosuppression in PDAC by\nmodulating both the TAM and T-cell compartments.\nFinally, expanded the discussion of upstream and downstream VISTA signaling, and\nstrengthened the experimental data by including studies on CSF1R inhibition and combination\ntherapy with gemcitabine. The revised manuscript is comprehensive, data-driven, and well-\nintegrated across both murine and human systems.\n(Author’s Response)\nWe greatly appreciate your recognition of the improvements to our manuscript. Your\nconstructive feedback in the previous revision, particularly the suggestion to integrate human\nPDAC tissue data with single-cell RNA sequencing to bridge mouse and human findings, was\ninvaluable in enhancing the clarity and impact of our study.\nReviewer #2 (Remarks to the Author):\nThank you for your detailed responses to my concerns, which have been sufficiently addressed.\n(Author’s Response/Action)\nsufficiently addressed your concerns. We are confident that the revised manuscript has been\nsubstantially improved.\nReviewer #3 (Remarks to the Author):\nThe revised manuscript by authors investigates the immunomodulatory role of VISTA in\npancreatic ductal adenocarcinoma (PDAC), with a particular focus on tumor-associated\nmacrophage (TAM) polarization and the downstream effects on CD8⁺ T cell function. The study\naddresses an important and timely topic, given the poor responsiveness of PDAC to current\nimmunotherapies and the increasing interest in checkpoint blockade.\nMajor Concerns\n1.Mechanistic Evidence for VISTA Signaling Remains Incomplete:\nAlthough the authors state that phospho-protein array experiments were performed to explore\ndownstream signaling pathways, they have not presented any of these data. As a result, the\nclaim that VISTA regulates TAM polarization through specific pathways remains speculative.\n(Author’s Response/Action)\nThank you for this comment. To address your concern, we have carefully clarified the\nDiscussion and toned down statements that could be interpreted as overstatements, ensuring\nthat all conclusions are strictly supported by the data. Specifically, we now present a complete\nand coherent chain of evidence that links the in vivo VISTA KO phenotype to macrophage\nstate changes and a functionally required IFN-γ–CXCL9–CXCR3 axis, and we also provide\nthe phospho-protein array dataset that was previously referenced but not shown.\nWe first established the in vivo phenotype that motivated the mechanistic analyses.\nCompared with WT, VISTA KO tumors grew more slowly and exhibited a distinct immune\ncomposition by flow cytometry, including increased macrophage and CD8⁺ T cell infiltration\n(Fig. 1). To identify the cellular programs and interactions underlying these differences, we\nperformed tumor scRNA-seq. This analysis highlighted macrophage and CD8⁺ T cells as key\ncellular compartments linked to the VISTA KO immune contexture, and it nominated the\nCXCL9–CXCR3 ligand–receptor pair as a prominent interaction enriched in the VISTA KO\n2\nsetting (Fig. 6a, 6b). We validated this single-cell inference by flow cytometry, confirming\nthat CXCR3⁺ CD8⁺ T cells were increased in VISTA KO tumors relative to WT (Fig. 5f).\nIn parallel, scRNA-seq–based pathway analyses of macrophage subsets provided a\nmechanistic direction upstream of the CXCL9 program. When we compared WT and VISTA\nKO macrophage subsets, gene set enrichment analyses indicated that antigen-presentation\nrelated pathways were more prominent in VISTA KO macrophages (Fig. 4c), consistent with\nenhanced T cell stimulatory capacity. We tested this in a controlled antigen-specific system by\nco-culturing WT or VISTA KO BMDMs loaded with OVA peptide or protein, with OT-I CD8⁺\nT cells. OT-I T cells produced higher IFN-γ when cultured with VISTA KO BMDMs than with\nWT BMDMs (Fig. 4f), establishing that VISTA status in macrophages affects the magnitude\nof antigen-driven IFN-γ producing T cell responses. In the same co-culture setting, CXCL9\noutput was also higher in VISTA KO BMDM–OT-I co-cultures than in WT co-cultures (New\nSupplementary Fig. S15), aligning the enhanced IFN-γ response with a CXCL9-high\nmacrophage output state.\nNew Supplementary Figure S15\nwith OT-I CD8⁺ T cells at a 1:1 ratio. Quantification is shown as the percentage of\nCXCL9⁺SPP1⁺ cells. Data are pooled from two independent experiments.\nWe then directly tested whether this IFN-γ–CXCL9–CXCR3 axis is required for the tumor\ncontrol phenotype associated with VISTA deficiency. In longitudinal antibody blockade\nexperiments, neutralization of IFN-γ and blockade of CXCR3 each attenuated the tumor-\ngrowth phenotype observed in the VISTA KO setting (New Figure 6c). These in vivo\nperturbation experiments functionally validate that the IFN-γ–CXCL9–CXCR3 axis is\nnecessary for the VISTA KO associated anti-tumor effect, consistent with the interaction signal\ninferred from scRNA-seq and the CXCR3⁺ CD8⁺ T-cell increase confirmed by flow cytometry.\nNew Figure 6\nc Longitudinal tumor growth curves of tumor burden following anti-IFN- antibody (left) or\nanti-CXCR3 (right) treatment in WT and VISTA KO tumor-bearing mice.\nTo determine whether macrophage-intrinsic differences contribute to this phenotype beyond\ndifferences in T cell-derived IFN-γ, we performed macrophage only stimulation experiments.\nWT and VISTA KO BMDMs were stimulated with identical concentrations of LPS and IFN-γ\nacross a dose range, and CXCL9 secretion was quantified by ELISA. Under these matched\nmacrophage-only stimulation conditions, VISTA KO macrophages secreted higher CXCL9 in\n4\na dose-responsive manner (Figure 1 for Review), demonstrating increased CXCL9 output\ncapacity in the absence of VISTA even without T cell co-culture.\nFigure 1 for Review. CXCL9 production by WT and VISTA KO BMDMs after 24 h of LPS\nand IFN- and LPS stimulation.\nFinally, the phospho-protein array data referenced previously are now presented in full for\ntransparency, including complete membranes, densitometric quantification, and\ncomprehensive target-level tables. These membranes are provided as Source Data/Figure 2\nand Figure 3 for Review, and the bar graphs as New Supplementary Fig. S12, together with\nNew Supplementary Table S3 and S4. This unbiased screen identifies phosphorylation\nfeatures associated with VISTA status under defined stimulation conditions, including\ndifferences at inhibitory phosphorylation of GSK3α/β Ser21/Ser9 (Figure 4 for Review), and\nwe include the accompanying inhibitor/perturbation datasets as additional supporting material\nin the Supplementary section (New Supplementary Fig. S12a).\npatterns between experimental conditions in WT and Vsir KO BMDM groups.\nNew Supplementary Figures S12\n6\na. Integrated pixel density of phosphoproteins in WT and VISTA KO BMDMs following LPS\nand IFN- stimulation. Quantification is shown alongside phospho-protein array membranes.\nKO BMDMs under LPS and IFN- stimulation.\npatterns between experimental conditions in WT and Vsir KO BMDM groups.\nFigure 4 for Review. Flow cytometric analysis (left) and bar plot (right) of CD86+iNOS+ cells\ngated on F4/80+CD11b+ Ly6C+ macrophages in LPS and IFN- treated BMDMs. TWS119 was\ntreated 1 h prior to LPS+IFN- stimulation.\n60000\n40000\n20000\nJ\n10\n)0001\n(\nytisned\nlexip\ndetargetnI\nWT\nKO\nb. Integrated pixel density of phosphoproteins in WT and VISTA KO BMDMs following TGF-\n1 and IL-10 stimulation.\nSupplementary Table S4 Quantification of phospho-protein array results in WT and Vsir KO\nBMDMs under TGF-1 and IL-10 stimulation.\nOverall, the revised submission now provides direct, multi-level mechanistic support,\nspanning in vivo phenotype, single-cell interaction inference with orthogonal flow validation,\nantigen-specific macrophage–T cell functional assays, in vivo necessity tests by blockade, and\n11\nmacrophage-only intrinsic chemokine output, together with the complete phospho-protein\narray dataset that was previously referenced but not shown. We have refined the manuscript to\nimprove clarity and ensure closer alignment between the data and the conclusions, as detailed\nbelow.\nIn Abstract (Page 3, Lines 7-10)\nOriginal:\n“Mechanistically, VISTA deficiency is linked to a shift in tumor-associated macrophages\n(TAMs) from an immunosuppressive phenotype marked by secreted phosphoprotein 1 (SPP1)\nto one enriched for C-X-C motif chemokine ligand 9 (CXCL9), indicative of a pro-\ninflammatory state.”\nRevised:\n→ “Functionally, VISTA deficiency is linked to a shift in tumor-associated macrophages\n(TAMs) from an immunosuppressive phenotype marked by secreted phosphoprotein 1 (SPP1)\nto one enriched for C-X-C motif chemokine ligand 9 (CXCL9), indicative of a pro-\ninflammatory state.”\nIn Results (Page 8, Line 118-122)\nOriginal:\n“Collectively, these findings identify VISTA as a central regulator of immune suppression in\nPDAC, orchestrating TAM-mediated immunosuppression and dampening CD8⁺ T cell\nresponses. Targeting VISTA represents a promising immunotherapeutic strategy to overcome\nthe immunosuppressive TME and enhance anti-tumor immunity in PDAC.”\nRevised:\n→ “Collectively, these findings position VISTA as a key immunoregulatory checkpoint\nassociated with immunosuppressive macrophage states and impaired CD8+ T cell function in\nPDAC. Targeting VISTA represents a promising immunotherapeutic strategy to overcome the\n12\nimmunosuppressive TME and enhance anti-tumor immunity in PDAC.”\nIn Results (Page 11-12, Line 208-212)\nOriginal:\n“These findings suggest that VISTA shapes the macrophage landscape to favor immune\nsuppression, and that its inhibition reprograms TAMs toward a phenotype that promotes anti-\ntumor immunity.\nRevised:\n→ “These findings indicate that VSIR expression in human PDAC is associated with\nmacrophage states enriched for immunosuppressive gene programs, consistent with the\nimmune regulatory patterns observed in murine models.”\nIn Results (Page 12, Lines 217-225)\nOriginal:\n“To assess the functional impact of VISTA deficiency on macrophages, we analyzed several\ncharacteristics. While WT and Vsir–/– BMDMs showed comparable phagocytic ability\n(Supplementary Fig. S11a), transcriptomic and pathway analyses revealed notable changes in\nimmunomodulatory capacity. Principal Component Analysis (PCA) highlighted distinct\ntranscriptional profiles among MΦ_clusters 6, 0, and 4, while MΦ_clusters 1, 2 and 3 were\nclosely related, with MΦ_clusters 2 and 3 particularly similar (Supplementary Fig. S12a). The\nlargest variance (PC1) separated Mono4 and MΦ_cluster6 from other clusters. Given the\ndistinct profiles between MΦ_clusters 2 and 3 in Vsir–/– tumor versus WT control, we focused\non their functional characteristics and spatial proximity to CD8+ T cells.”\nRevised:\n→ “While WT and Vsir–/– BMDMs showed comparable phagocytic ability (Supplementary Fig.\nS11a), transcriptomic and pathway analyses revealed notable changes in immunomodulatory\ncapacity. Consistent with these transcriptional differences, phospho-protein profiling of WT\n13\nand Vsir–/– BMDMs under matched stimulation conditions revealed distinct phosphorylation\npatterns across multiple signaling nodes (Supplementary Fig. S), indicating altered intracellular\nsignaling states in the absence of VISTA. Principal Component Analysis (PCA) highlighted\ndistinct transcriptional profiles among MΦ_clusters 6, 0, and 4, while MΦ_clusters 1, 2, and 3\nwere closely related, with MΦ_clusters 2 and 3 particularly similar (Supplementary Fig. S12a).”\nIn Results (Page 14, Lines 258-261)\nOriginal:\n“These co-cultures also showed higher IFN-γ, TNF-α, and perforin production in OT-I T cells\n(Fig. 4f). In contrast, SIINFEKL peptide stimulation produced similar CD8⁺ T cell proliferation\nregardless of macrophage genotype (Fig. 4e, bottom), …”\nRevised:\n→ “…(Fig. 4f). Notably, enhanced IFN-γ production in the Vsir⁻/⁻ BMDM co-cultures was\naccompanied by increased CXCL9 output (Supplementary Fig. S15), consistent with activation\nof an IFN-γ–CXCL9 chemokine axis in this setting. In contrast, SIINFEKL peptide stimulation\nproduced comparable CD8⁺ T cell proliferation regardless of macrophage genotype (Fig. 4e,\nbottom), …”\nIn Discussion (Page 21, Lines 434-438)\nOriginal:\n“Our study highlights VISTA as a modulator of macrophage polarization and immune\ndynamics in PDAC, although further studies are needed to define its mechanistic role 47, 48.”\nRevised:\n“Our study highlights a close association between VISTA expression and macrophage state\ncomposition in PDAC; how this relationship is established within the tumor microenvironment\nwarrants further investigation.”\nIn Discussion (Page 23, Lines 479-483)\nOriginal:\n14\n“…Our single-cell analysis of the PDAC TME suggests that modulating the Cxcl9:Spp1 ratio\nvia VISTA could enhance anti-tumor responses by shifting the balance toward pro-\ninflammatory immune responses.”\nRevised:\n→ “…Our single-cell analysis of the PDAC TME suggests that modulating the Cxcl9:Spp1\nratio via VISTA could enhance anti-tumor responses by shifting the balance toward pro-\ninflammatory immune responses. The precise mechanism by which VISTA drives this shift in\nmacrophages remains to be determined.”\n2.Lack of Functional Validation for CXCL9/CXCR3 Axis:\nWhile the manuscript shows increased CXCL9 expression and CXCR3⁺ CD8⁺ T cell infiltration\nin VISTA-deficient tumors, it does not directly test whether this axis is necessary for the anti-\ntumor effects. Blocking experiments would be critical to establish causality. If not feasible, this\nlimitation should be explicitly stated in the Discussion.\n(Author’s Response/Action)\nThank you for this important comment regarding functional validation of the CXCL9/CXCR3\naxis. In response, we performed in vivo antibody blockade experiments and expanded our\npresentation to include longitudinal tumor growth trajectories, thereby directly testing whether\nthis axis is required for the VISTA KO associated tumor-control phenotype.\nWe treated orthotopic KPC001 tumor bearing mice with either anti–IFN-γ or anti–CXCR3\nantibodies (200 μg, i.p.) using a tumor burden matched initiation design. Specifically, the first\ndose was administered when tumors reached approximately 50–80 mm³, followed by dosing\nevery three days until day 20, and tumor volume was monitored serially over time. The\ncomplete longitudinal growth curves are provided (New Fig. 6c). Under these conditions, IFN-\nγ neutralization markedly attenuated the tumor control phenotype observed in the VISTA KO\nsetting, and CXCR3 blockade similarly reduced the phenotype. These results provide direct\nfunctional evidence that the IFN-γ–CXCL9–CXCR3 axis is required in vivo for the anti-tumor\neffect associated with VISTA deficiency.\nc Longitudinal tumor growth curves of tumor burden following anti-IFN- antibody (left) or\nanti-CXCR3 (right) treatment in WT and VISTA KO tumor-bearing mice.\nTo evaluate reproducibility across orthotopic PDAC models, we performed additional\nCXCR3 blockade experiments in a second orthotopic model (Pan02). In this independent\nsystem, anti-CXCR3 treatment again resulted in reduced intratumoral CD8⁺ T cell\naccumulation (normalized to tumor volume) in VISTA KO mice (New Supplementary Fig.\nS21a and b), consistent with CXCR3 blockade impairing CD8⁺ T-cell recruitment/retention in\nthe tumor microenvironment. Together, these data demonstrate that CXCR3-dependent CD8⁺\nT-cell recruitment is reproducibly linked to the VISTA KO anti-tumor phenotype across two\northotopic PDAC models.\na Tumor volume of WT and Vsir KO mice bearing Pan02 tumors on day 20 and 27 with or\nwithout anti-CXCR3 antibody administration. b Flow cytometric quantification of CD8 T cells\nnormalized to tumor volume (n = 4 per group) at day 21 following anti-CXCR3 antibody\nadministration.\nTaken together, these blockade experiments, supported by longitudinal growth analysis and\nreplication in an independent model, provide direct functional validation that the IFN-γ–\nCXCL9–CXCR3 axis is necessary for the VISTA KO associated anti-tumor immune\nphenotype. We have revised the Results section accordingly to reflect this strengthened causal\nevidence.\nIn Results (Page 18, Lines 358-370)\nOriginal:\n“This interaction suggests that MΦ_cluster2 macrophages in Vsir–/– tumor create an\ninflammatory environment, enhancing CD8+ T cell recruitment and activation, which\nstrengthens the anti-tumor response. Specifically, the Cxcl9-Cxcr3 axis facilitates CD8+ T cell\ninfiltration and activation, supporting efficient tumor clearance 34. Both WT and Vsir–/– tumors\nexhibited Cxcl16-Cxcr6 and Ccl8-Ccr5 interactions, supporting tissue repair and immune\nregulation, respectively 35, 36.”\nRevised:\n→ “This interaction suggests that MΦ_cluster2 macrophages in Vsir–/– tumor create an\n17\ninflammatory environment, enhancing CD8+ T cell recruitment and activation, which\nstrengthens the anti-tumor response. Specifically, the Cxcl9-Cxcr3 axis mediates CD8+ T cell\ninfiltration and activation, facilitating efficient tumor clearance 34.\nWe evaluated whether the anti-tumor effect of Vsir deletion was mediated by IFN-γ–\nCXCL9–CXCR3 axis by administering a neutralizing anti–IFN-γ antibody or anti-CXCR3\nantibody to VISTA-KO mice. Anti-IFN-γ treatment abolished the reduced tumor growth seen\nin Vsir–/– mice. Likewise, blockade of CXCR3 also reversed the enhanced tumor control in\nVsir–/– hosts. These results indicate that the protective effect of VISTA loss depends on the\nIFN-γ–CXCL9–CXCR3 pathway (Fig. 6c, Supplementary Fig. S21). In addition, both WT\nand Vsir–/– tumors exhibited Cxcl16-Cxcr6 and Ccl8-Ccr5 interactions, supporting tissue\nrepair and immune regulation, respectively (Fig. 6b) 35, 36.”\nIn Methods (Page 25-26, Lines 533-536)\n“For IFN- and CXCR3 neutralization, 200 g of anti-IFN- (Clone XMG1.2, InVivoMab,\nBioXCell) or anti-CXCR3 neutralizing antibody (Clone CXCR3-173, InVivoMab, BioXCell)\nwere administered intraperitoneally 5 days post KPC001 cell inoculation every 3 days until\ntumor harvest.”\n3.CD8⁺ T Cell Exhaustion Analysis Requires Further Support:\nThe conclusions regarding reduced T cell exhaustion rely mainly on module scoring from\nscRNA-seq. Without additional validation—such as flow cytometric co-expression of\nexhaustion markers or functional assays. The current interpretation may overstate the data.\nThe language should be moderated to reflect the correlative nature of the evidence.\n(Author’s Response/Action)\nWe appreciate this important point. We agree that our conclusion regarding reduced CD8 T\ncell exhaustion relied heavily on scRNA-seq module scoring and could be interpreted as\noverstating the evidence. To address this concern, we now added directed flow-cytometric\nvalidation in tumor-infiltrating CD8 T cells and revised the manuscript wording to reflect the\ncorrelative nature of the data.\nIn this analysis, the frequency of TIM-3+PD-1+ CD8 T cells alone did not differ\nsubstantially between WT and VISTA KO tumors (Figure 5 for Review). We explicitly report\nthis result because it underscores that single-marker or two-marker comparisons may not\ncapture differences along the exhaustion continuum, particularly when both groups contain\nactivated/exhaustion-prone T cells in an inflamed tumor microenvironment.\nFigure 5 for Review. Flow cytometric analysis of TIM+PD-1+ (top) and LAG3+PD-1+ (bottom)\nexpression gated on CD8+ T cells from WT and Vsir KO samples.\nMotivated by our scRNA-seq observation that multiple exhaustion-associated transcripts,\nincluding markers linked to more advanced or terminal exhaustion, were relatively enriched in\nWT tumors, we therefore stratified CD8 T cells along the exhaustion spectrum using a\ncumulative marker-burden approach. Rather than interpreting exhaustion from any single\nreceptor, we quantified the fraction of CD8 T cells expressing increasing numbers of inhibitor\nreceptors (0, 1, 2, and  3 markers; New Supplementary Figure S17d), which is conceptually\nconsistent with the view that more advanced exhaustion states are characterized by broader and\n19\nmore stable expression of multiple inhibitor receptors and reduced effector capacity (Wherry\nand Kurachi, Nat Rev Immunol, 2015; McLane et al., Nat Immunol, 2019).\nNew Supplementary Figure S17\nd Frequency distribution of intratumoral CD8+ T cells expressing 0, 1, 2, or  3 exhaustion\nmarkers (PD-1, TIM-3, LAG-3, and TIGIT) in KPC001 tumors.\nUsing this approach, we observed that WT tumors contained a higher proportion of CD8 T\ncells with high cumulative exhaustion marker co-expression (cells expressing 3 inhibitory\nreceptors), compared with VISTA KO tumors at day 32 post-KPC001 injection (WT vs KO:\n1.9 fold difference, New Supplementary Figure S17d). We interpret this as supportive\nevidence that, even if PD-1/TIM-3 alone does not differ, WT tumors may harbor a lager\nfraction of CD8 T cells occupying a more advanced or terminally exhausted state, considering\nthe overall inhibitory receptor burden. Importantly, we have revised the Results text to avoid\nclaiming definitive “reduced exhaustion” and instead describe this finding as evidence\ncompatible with a shift in exhaustion-state distribution.\nConsistent with this interpretation, our intracellular cytokine staining data (Supplementary\nFigure S6b) show that IFN-+TNF-+ CD8 T cells were more abundant in VISTA KO tumors\nthan in WT tumors. Because more exhausted CD8 T cells typically exhibit impaired\npolyfunctional cytokine production and reduced effector programs (Ahmed et al., Immunity,\n2007), this functional readout is compatible with (but does not on its own prove) a relative\nenrichment of better preserved effector-like CD8 T cells in the absence of VISTA.\nthe manuscript to emphasize that our exhaustion related conclusions are supported by a\ncombination of (i) scRNA-seq signatures and (ii) flow-cytometric marker burden patterns and\ncytokine production, and should be interpreted as correlative rather than definitive mechanistic\nproof of altered exhaustion programming.\nIn Abstract (Page 3, Lines 10-13)\nOriginal:\n“This shift was accompanied by enhanced recruitment of CXCR3⁺ CD8⁺ T cells, which showed\nreduced exhaustion and sustained cytotoxic potential.”\nRevised:\n→ “This shift was accompanied by enhanced recruitment of CXCR3⁺ CD8⁺ T cells, which\nshowed a lower prevalence of terminal exhaustion-like CD8 T cell states and sustained\ncytotoxic potential.”\nIn Results (Pages 15-16, Lines 300-309)\nOriginal:\n“CD8+ T_clusters 0 and 3 were predominantly derived from Vsir–/– tumor, whereas clusters 1\nand 2 were enriched in CD8+ T cells from WT tumor (Fig. 5c and d). To further characterize\ntheir functional states, we quantified exhaustion and effector phenotypes using established\ngene signatures. CD8+ T_clusters 1 and 2 contained higher proportions of Cd38+Eomes+ cells\n(22.5% and 44.0%) and Cd226–Pdcd1+ cells 30 (41.0% and 44.3%), respectively, compared to\nCD8+ T_clusters 0 and 3 (Cd38+Eomes+: 14.0% and 5.0%; Cd226–Pdcd1+: 31.5% and 25.3%)\n(Fig. 5e, Supplementary Fig. S15a and b). These data indicate that Vsir–/– tumors harbor CD8⁺\nT cells with reduced exhaustion and enhanced cytotoxic potential compared to their WT\ncounterparts.”\nRevised:\n→ “…CD8+ T_clusters 1 and 2 contained higher proportions of Cd38+Eomes+ cells (22.5% and\n44.0%) and Cd226–Pdcd1+ cells 30 (41.0% and 44.3%), respectively, compared to CD8+\n5e, Supplementary Fig. S15a and b). Flow cytometric analysis demonstrated that WT tumors\ncontained a higher cumulative burden of exhaustion-associated inhibitory receptors on CD8⁺ T\ncells, as assessed by the co-expression of PD-1, TIM-3, LAG-3, and TIGIT (Supplementary\nFig. S17d). In particular, CD8⁺ T cells expressing three or more exhaustion markers were\nenriched in WT tumors compared with Vsir–/– tumors, consistent with a greater prevalence of\nterminal exhaustion–like CD8⁺ T cell states. In contrast, Vsir–/– tumors exhibited a relative\nreduction in these highly exhausted populations, accompanied by increased TNF-α and IFN-γ\nproduction (Supplementary Fig. S6b).”\nIn Results (Pages 16, Lines 310-314)\nOriginal:\n“Consistently, Vsir expression was significantly correlated with key exhaustion markers\n(Havcr2, Tigit, Lag3, Pdcd1, Cxcl13, and Layn) in both TCGA-PAAD tumor/normal samples\nand GTEx normal pancreas samples (Supplementary Fig. S14b), highlighting VISTA’s role in\npromoting T cell exhaustion within the TME.”\nRevised:\n→ “Consistently, Vsir expression was significantly correlated with key exhaustion markers\n(Havcr2, Tigit, Lag3, Pdcd1, Cxcl13, and Layn) in both TCGA-PAAD tumor/normal samples\nand GTEx normal pancreas samples (Supplementary Fig. S16b), suggesting a potential\nassociation between VISTA and T cell exhaustion within the TME.”\n4.Insufficient Human Tissue Validation:\nAlthough the authors perform database analyses, they have not provided direct experimental\ndata on VISTA expression or macrophage phenotype in human PDAC tissue.\nImmunohistochemistry or multiplex immunofluorescence staining on a small patient cohort\ncould help validate the translational significance of the findings.\n(Author’s Response/Action)\nproviding direct experimental validation in human PDAC tissue. We agree that database-based\nanalyses alone are not sufficient. To address this concern, we performed multiplex\nimmunofluorescence (mIF) on human pancreatic cancer tissue microarrays (TMAs) to directly\nevaluate VISTA expression in the myeloid compartment and its immune spatial context.\nSpecifically, we conducted mIF staining for VISTA, CD68, CD8, and CXCR3 using four\ncommercially available PDAC TMAs and quantified both absolute cell counts and cell density\nper tissue core, alongside spatial proximity metrics between VISTA+ macrophages and CD8+\nT cells (New Supplementary Figure S19c). Across tumor grades, we observed an increase in\nVISTA+ macrophages and CD8+ T cells (New Supplementary Figure S19b). Across tumor\ngrades, we observed a clear increase in VISTA+CD68+ macrophages, both in absolute number\nand in density per core (New Supplementary Figure S19c). This direct human-tissue result\nsupports the translational consistency of our murine observation that a VISTA-associated\nmacrophage compartment expands with disease progression. Importantly, this pattern is\naligned with prior work reporting that VISTA is prominently expressed on CD68+ macrophages\nin human PDAC and enriched in the pancreatic tumor stroma (Blando et al., PNAS, 2019).\nNew Supplementary Figure S3\nf Percentage and cell density of VISTA⁺CD68⁺ co-expressing macrophages (left and middle),\nand the proportion of VISTA⁺ cells within the CD68⁺ macrophage population (right), across\ntumor grades in tissue microarrays.\nIn parallel, we quantified CD8 T cell infiltration and CXCR3 expression in the same TMAs.\nWhile CD8 T cell infiltration showed an overall increasing trend across grades, the fraction of\n2 and grade 1 versus grade 3 tumors (New Supplementary Figure S19a and b). Although we\ninterpret this finding cautiously given cohort size and the limitations of TMA-based snapshots,\nan inverse relationship between tumor grade and CXCR3 expression is biologically plausible\nin PDAC, where progressive immunosuppression can be accompanied by impaired effector\ntrafficking programs.\nNew Supplementary Figure S19\na Representative images from grade 1 and grade 3 patient samples (20×; left) and bar graph\nshowing the density of CD8⁺ T cells (cells/mm²; right). b Representative images from grade 1\nand grade 3 patient samples (20×; left) and bar graph showing the density of CXCR3⁺ cells\n(cells/mm²; right). CD68, Opal 620; VISTA, Opal 690; CXCR3, Opal 520; and CD8a, Opal\n480.\nNotably, we observed that the nearest-neighbor distance between VISTA+CD68+\nmacrophages and CD8+ T cells decreased from grade 2 and grade 3 tumors (New\nSupplementary Figures S19c), indicating increased spatial proximity in higher-grade disease.\ninterface in which VISTA expressing macrophages may influence CD8 T cell positioning and\nfunctional state in advanced PDAC.\nNew Supplementary Figure S19\nc Representative images from grade 2 and grade 3 patient samples (20×; left) and bar graph\nshowing the nearest-neighbor distance between CD68⁺VISTA⁺ macrophages and CD8⁺ T cells\n(right). CD68, Opal 620; VISTA, Opal 690; CXCR3, Opal 520; and CD8a, Opal 480.\nWe emphasize that the purpose of this human mIF experiment is to provide direct tissue-\nlevel validation of (i) VISTA localization to the macrophage compartment in PDAC and (ii)\nthe abundance and spatial context of VISTA+CD68+ macrophages is associated with tumor\ngrade. Accordingly, we present these data as translational validation of a grade-associated\nVISTA+ macrophage compartment and its immune spatial context. Collectively, these new\npatient tissue mIF data move our study beyond database inference and directly address your\nrequest for experimental validation in human samples.\nIn Results (Page 6-7, Lines 89-94)\nOriginal:\n“Interestingly, VSIR expression increased with advancing histological grade\n(Supplementary Fig. S3c), a trend that was independently validated at the protein level by\nimmunohistochemistry and immunofluorescence analysis of a PDAC tissue microarray\n(Supplementary Fig. S3d and e).”\nRevised:\n25\n→ “Interestingly, VSIR expression increased with advancing histological grade\n(Supplementary Fig. S3c), a trend that was independently validated at the protein level by\nimmunohistochemistry and immunofluorescence analysis of a PDAC tissue microarray\n(Supplementary Fig. S3d–f). Together, these clinical observations support the relevance of\nVISTA as a therapeutic target in PDAC.”\nIn Results (Page 16-17, Lines 321-336)\nOriginal:\n“Flow cytometric analysis further characterized the altered state of CD8+ T cells in Vsir–/–\ntumor. Vsir–/– tumor had significantly increased expression of CXCR3, with 28.3% of CD8+ T\ncells co-expressing CXCR3 and CX CR1 compared to 8.07% in WT tumor (Fig. 5f).\nterminally exhausted CXCR3+CX CR1– CD8+ (Texterm) cells compared to WT control (Fig.\n3\n5g and h). These findings suggest that VISTA deficiency promotes a CD8+ T cell phenotype\nwith reduced exhaustion and improved effector function within the TME.\nFinally, we validated these observations in the HTAN WUSTL…”\nRevised:\n→ “…within the TME. To extend these findings to human disease, multiplex\nimmunofluorescence analysis of a limited number of PDAC specimens revealed that that,\nalthough overall CD8⁺ T cell infiltration showed an increasing trend with advancing tumor\ngrade, the fraction of CXCR3⁺ cells within the CD8⁺ T cell compartment was significantly\nreduced in grade 2 and grade 3 tumors compared with grade 1 tumors (Supplementary Fig.\nS19a and b), demonstrating an inverse relationship between tumor grade and CXCR3\nexpression. Notably, spatial analysis further showed a decreased nearest-neighbor distance\nbetween VISTA⁺CD68⁺ macrophages and CD8⁺ T cells in grade 2 and grade 3 tumors,\nindicating increased spatial proximity in higher-grade disease (Supplementary Fig. S19c).”\nIn Methods (Page 29, Lines 623-626)\nRevised:\n26\n→ “Opal multiplex immunofluorescence staining was performed on pancreatic cancer tissue\nmicroarrays (PA2082b, PA2081-L64, PA961-L87, and PA483-L97). Sections were stained\nwith antibodies against CD68, VISTA, CXCR3, and CD8α, which were visualized using Opal\n620, Opal 690, Opal 520, and Opal 480 fluorophores, respectively.\n27"}]

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