αTIGIT-IL2 achieves tumor regression by promoting tumor-infiltrating regulatory T cell fragility in mouse models

Wang, Tianci; Xu, Yupu; Zhang, Zhengfeng; Wu, Yaqi; Chen, Long; Zheng, Xiaodong; Peng, Hui; Zou, Qiang; Sun, Rui; Ma, Hongdi; Sun, Haoyu; Tian, Zhigang; Zheng, Xiaohu

doi:10.1038/s41467-025-64296-z

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Published: 17 October 2025

αTIGIT-IL2 achieves tumor regression by promoting tumor-infiltrating regulatory T cell fragility in mouse models

Nature Communications volume 16, Article number: 9223 (2025) Cite this article

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Abstract

Administration of IL-2 may promote the suppressive function and proliferation of T_reg cells that cause immune tolerance in patients with cancer, which causes low-dose IL-2 to fail in achieving an optimal anti-tumor effect. Here, we designed an immunocytokine by fusing IL-2 and an anti-TIGIT monoclonal antibody, named αTIGIT-IL2, that targets T_reg cells and promotes their fragility in the tumor milieu. These fragile-like T_reg cells show impaired suppressive function and high IFN-γ production, triggering an immune-reactive tumor microenvironment. Such inflammation leads to the recruitment and functional reprogramming of intratumoral neutrophils, improving cross-talk between neutrophils and CD8⁺ T cells and enhancing the antitumor ability of CD8⁺ T cells. Combination therapy with αTIGIT-IL2 and PD-1 blocker could eliminate triple-negative breast cancer (TNBC) tumors resistant to immune checkpoint blockade (ICB) therapy. These findings provide the basis for developing a new generation of immunocytokines that target T_reg cells and promote their fragility in the tumor milieu, resulting in robust antitumor immunity.

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Introduction

Harnessing cytokines for the treatment of cancer has a long-standing history in clinical research¹. Interleukin-2 (IL-2), the first cloned cytokine, was originally used for culturing human T cells². Extensive clinical research has been conducted on low-dose IL-2 for the treatment of autoimmune diseases^3,4. IL-2 is also a bridge between adaptive and innate immunity, orchestrating the immune response against cancer⁵. In the context of immune checkpoint blockade (ICB) treatment-resistant tumors, clinical responses can still occur following high-dose IL-2 therapy, even in patients who have progressed after ICB treatment^6,7,8. Given that IL-2 preferentially expands regulatory T (T_reg) cells in-vivo, high-dose IL-2 administration is commonly employed in tumor immunotherapy to counteract immunosuppression and potentiate effective anti-tumor immune responses^9,10. Meanwhile, administration of high-dose IL-2 causes co-activation of innate and adaptive immunity, resulting in robust antitumor immunity but also posing a high risk of non-specific toxicities^9,11,12. Treatment with low-dose IL-2 could cause the proliferation and activation of regulatory T (T_reg) cells owing to the constitutively high expression of CD25, an indispensable component of the high-affinity IL-2 receptor on T_reg cells, resulting in impaired antitumor immunity^4,9,10,13.

Seeking to overcome this disadvantage of IL-2 caused by T_reg cells, numerous studies have explored molecular engineering of IL-2 for enhanced tumor immunotherapy¹⁴. While most of these studies have focused on disrupting the binding between IL-2 and CD25, some have examined the targeted delivery of IL-2 to the tumor microenvironment^14,15,16,17. Immunocytokines, antibody-conjugated cytokines, could mediate the targeted delivery of cytokines to specific cells, providing an innovative approach for cytokine delivery in immunotherapy^18,19,20. Immune checkpoint molecules are usually upregulated in tumor-infiltrating lymphocytes (TILs), representing a potentially suitable target for delivering cytokines^21,22,23,24. TIGIT, a new-generation immune checkpoint, is expressed on several TILs, especially in T_reg cells^25,26. TIGIT is an important receptor for the development and effector function of T_reg cells. TIGIT⁺ T_reg cells represent a highly suppressive T_reg cell subset and inhibit Th1 and Th17 cell responses²⁷. Activation of TIGIT signaling under inflammatory conditions is essential for maintaining Foxp3 expression and T_reg function through competition with CD226²⁸. A clinical trial of the combination of anti-TIGIT and anti-PD-1 antibodies revealed an effective response in patients with high-level intratumoral macrophages and T_reg cells²⁹, implying the crucial role of TIGIT in T_reg function.

Given the above findings, in this study, we generated an immunocytokine by fusing IL-2 and an anti-TIGIT monoclonal antibody (13G6), named αTIGIT-IL2, capable of targeting T_reg cells. αTIGIT-IL2 was shown to achieve effective tumor control in both ICB treatment-sensitive and -resistant mouse models. αTIGIT-IL2 impairs the suppressive effector function of T_reg cells and promotes their fragility, characterized by the expression of IFN-γ and the reduced suppressive capacity. The upregulated expression of IFN-γ in the tumor area triggers an inflammatory tumor microenvironment following αTIGIT-IL2 treatment. Neutrophils are recruited to the tumor site and reprogrammed to a high MHC-expressing state by intratumoral T_reg and CD4⁺ T_conv cells through chemokines and IFN-γ. Then, these reprogrammed neutrophils further activate intratumoral TAA-specific CD8⁺ TILs, generating efficient antitumor immunity. Our findings demonstrate that the combination of IL-2 signaling activation and TIGIT blockade against intratumoral T_reg cells could reprogram the tumor microenvironment from an immune-suppressive to an immune-activating state, resulting in improved effector antitumor immunity.

Results

αTIGIT-IL2 exhibits effective tumor control in mouse models

Here, we constructed αTIGIT-IL2 by linking IL-2 and anti-TIGIT single-chain variable fragment (scFv) to the human IgG1 Fc region via a (G₄S)₃ flexible linker. (Fig. 1a). The purity of the generated immunocytokine was up to 95% as detected by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and size-exclusion chromatography-high-performance liquid chromatography (SEC-HPLC) (Supplementary Fig. 1a, b). αTIGIT-IL2 exhibits an affinity for murine TIGIT comparable to that of 13G6, its parental antibody (Fig. 1b). The STAT5 phosphorylation-inducing activity of αTIGIT-IL2 was equivalent to that of recombinant IL-2, suggesting that αTIGIT-IL2 possesses the full bioactivity of IL-2 (Fig. 1c)²². Additionally, the ability of αTIGIT-IL2 to block the CD155-TIGIT axis is equivalent to that of 13G6 (Fig. 1d).

**Fig. 1: αTIGIT-IL2 exhibits effective tumor control even in the ICB treatment-resistant model.**

To determine the optimum dosage of αTIGIT-IL2, we treated MC38 subcutaneous tumors by administering three escalating dosage levels, 10 μg/dose, 20 μg/dose, and 30 μg/dose, with treatments given every three days, for a total of three rounds of administration. αTIGIT-IL2 exhibited effective tumor control at the dosage of 10 μg/dose without toxicity as evaluated by body weight (Fig. 1e, f). To further characterize the toxicity induced by αTIGIT-IL2 treatment, we performed blood tests for tumor-bearing mice 2 days after treatment, particularly alanine aminotransferase (ALT) as a liver damage marker and aspartate aminotransferase (AST) as a tissue damage marker. The serum levels of both ALT and AST were increased only in the 20 μg treatment group (Supplementary Fig. 2a, b). Hematoxylin-eosin (HE) staining of liver and lung tissues from mice treated with a single dose of αTIGIT-IL2 revealed normal tissue morphology at a dosage of 10 μg/dose (Fig. 1g). These data indicating that 10 μg per dose represents the optimal dosage for αTIGIT-IL2 treatment. We also produced a variant of αTIGIT-IL2, designated αTIGIT-IL2v, with its CD25 affinity abolished (Supplementary Fig. 1a–d)³⁰. Intriguingly, αTIGIT-IL2 exhibited a superior effect compared with αTIGIT-IL2v in the MC38 tumor model (Fig. 1h, i). To compare αTIGIT-IL2 with PD-1 targeted immunocytokine, αPD1-IL2 was produced, and its quality was confirmed (Supplementary Fig. 1a, b, e). αTIGIT-IL2 was found to exhibit better antitumor efficacy than αPD1-IL2 (Fig. 1j, k).

Fcγ receptors (FcγRs) are reported to play an indispensable role in the antitumor effects of anti-TIGIT antibodies²⁹. To assess the importance of Fc-mediated activation in αTIGIT-IL2, we produced an Fc function-abolished αTIGIT-IL2 mutant, named αTIGIT-IL2 (LALA-PG) (Supplementary Fig. 1a–e)³¹. αTIGIT-IL2 (LALA-PG) showed a similar effect as αTIGIT-IL2, suggesting that the activation of Fc receptors mediated by αTIGIT-IL2 plays a redundant role in its antitumor effects (Fig. 1l, m). Treatment with αTIGIT-IL2 demonstrated superior tumor control compared to an equivalent molar quantity of 13G6, αControl-IL2, or the combination of 13G6 with αControl-IL2. (Fig. 1n, o).

Furthermore, we assessed the function of αTIGIT-IL2 in B16F10 subcutaneous tumor model, a PD-1 inhibitor-resistant tumor model. In the B16F10 tumor model, administration of αTIGIT-IL2 led to significantly improved tumor control relative to both the control and αPD1-IL2 treatments (Fig. 1p, q). Taken together, these data show that αTIGIT-IL2 is a promising immunocytokine in tumor immunotherapy.

Targeted delivery of αTIGIT-IL2 to tumor-infiltrating T_reg cells rebuilds an immunoreactive tumor microenvironment

To determine the target cells of αTIGIT-IL2, mice were inoculated with MC38 tumor cells subcutaneously and given a single dose of αTIGIT-IL2 treatment 10 days after tumor inoculation. We analyzed the TILs 1 day after the administration of αTIGIT-IL2 (Fig. 2a). T_reg cells were the main cells to which αTIGIT-IL2 was bound in tumor tissue (Fig. 2b). TILs from MC38 tumor-bearing mice were isolated and incubated with αTIGIT-IL2 to evaluate the target cells of αTIGIT-IL2 in-vitro. The results of this in-vitro experiment were consistent with those observed in-vivo: T_reg cells were the predominant subset of tumor-infiltrating immune cells bound by αTIGIT-IL2 under αTIGIT-IL2-sufficient conditions (Fig. 2c, d). These data demonstrate that following αTIGIT-IL2 treatment, αTIGIT-IL2 mainly binds to T_reg cells.

**Fig. 2: Targeted delivery of αTIGIT-IL2 to T_reg cells was predominantly mediated by its constitutively high expression of CD25.**

To investigate which receptor plays a dominant role in mediating the targeted delivery of αTIGIT-IL2, we detected the expression of receptors related to αTIGIT-IL2 on TILs. Among TILs, intratumoral Treg cells exhibited the highest expression levels of TIGIT and CD25, whereas NK cells displayed the highest expression of CD122. (Fig. 2e, f and Supplementary Fig. 3). Correlation analysis of the expression pattern of these receptors, which might play crucial roles in the binding of αTIGIT-IL2, revealed that TIGIT and CD25 were positively correlated with the binding efficiency of αTIGIT-IL2 (Fig. 2g). Meanwhile, the expression of TIGIT was positively correlated with CD25 (Fig. 2g). TILs were pretreated with specific blockers targeting the binding receptors of αTIGIT-IL2 to reduce its binding efficiency. The binding efficiency was found to be significantly reduced only following treatment with the CD25 blocker (Fig. 2h–k). These data demonstrate that the targeted delivery of αTIGIT-IL2 to T_reg cells is predominantly mediated by its constitutively high expression of CD25.

Additionally, in contrast with αTIGIT-IL2, αPD1-IL2 exhibits a preference for PD-1⁺ TILs because of the high expression of PD-1 on PD-1⁺ TILs (Supplementary Fig. 4a–d). Furthermore, both αTIGIT-IL2 and αPD1-IL2 prefer T_reg cells over any other lymphocytes in spleen tissue (Supplementary Fig. 4d–f). These data suggest that, compared with αPD1-IL2, αTIGIT-IL2 more specifically favors intratumoral T_reg cells over any other TILs.

αTIGIT-IL2- and PBS-treated tumors were subjected to single-cell RNA sequencing (scRNA-seq). t-distributed stochastic neighbor embedding (tSNE)-based analysis of scRNA-seq data reveals differences in several subpopulations between the αTIGIT-IL2-treated group and the control group (Supplementary Fig. 5a and Supplementary Fig. 6a). tSNE-based clustering identified major clusters assigned to distinct cell populations by their gene expression profiles (Supplementary Fig. 5b)³². Flow cytometry confirmed the fold change in the proportion of several subpopulations— including NK cells, CD4⁺ T_conv cells, T_reg cells, and neutrophils—following the administration of αTIGIT-IL2 (Supplementary Fig. 6b–d). The cell counts of intratumoral NK and T_reg cells remained unchanged, while the amount of CD8⁺ T cells, CD4⁺ T_conv cells, and neutrophils increased (Supplementary Fig. 6e). CD8⁺ T cells and CD4⁺ T_conv cells also showed increased proliferative potential in tumors following αTIGIT-IL2 treatment than with control treatment, while the proliferative potential of NK and T_reg cells remained constant (Supplementary Fig. 6f). Notably, the ratio of CD8⁺ T to T_reg cells in tumor tissue was dramatically increased, demonstrating that αTIGIT-IL2 rebuilt an immunoreactive tumor microenvironment (Supplementary Fig. 6g)²².

αTIGIT-IL2 promotes tumor-infiltrating T_reg cell fragility

MC38 tumor-bearing mice were given one-dose treatment of αTIGIT-IL2 or PBS. Four days after treatment, lymphocytes from tumor and spleen tissues were isolated from tumor tissues to explore the phenotypic transformation of T_reg cells induced by αTIGIT-IL2 (Fig. 3a). The proportion of T_reg cells among CD4⁺ T cells was reduced in the tumor microenvironment following αTIGIT-IL2 treatment, in contrast to their proportion in splenic tissues (Fig. 3b). The immune-suppressive functionality of T_reg cells was considered to be impaired, with lower expression of several effective molecules including Foxp3, ICOS, CD44, CD25, CD39, CD73, LAG-3, TIGIT, Eos, Helios, and IL-10, as a result of the administration of αTIGIT-IL2 (Fig. 3c, Supplementary Fig. 7a and Supplementary Fig. 8b, d, e)^{27,28,33,34,35,36}. In contrast, αTIGIT-IL2 facilitated T_reg proliferation and immune-suppressive function in splenic tissues by upregulating ICOS, CD44, CTLA-4, LAG-3, TIGIT, CD39, Ki67, TGF-β, and IL-10, consistent with the effects of low dose IL-2 treatment reported in several studies (Fig. 3c and Supplementary Fig. 7a, b, d)^4,9,10. Remarkably, αTIGIT-IL2 induced a fragile-like phenotype among intratumoral T_reg cells, with higher expression of IFN-γ and TNF, which could promote antitumor immunity (Fig. 3c and Supplementary Fig. 7c, e)^37,38. Confusingly, IFN-γ was also upregulated in the spleen following αTIGIT-IL2 treatment (Fig. 3c). This could account for the toxicity observed with increasing concentrations of αTIGIT-IL2. We investigated the differential impact of αControl-IL2 and αTIGIT-IL2 on the immunosuppressive phenotype of tumor-infiltrating T_reg cells. Our findings indicate that αTIGIT-IL2 significantly attenuates the immunosuppressive phenotype of tumor-infiltrating T_reg cells, while αControl-IL2 fails to exert a comparable effect (Supplementary Fig. 8).

**Fig. 3: αTIGIT-IL2 reprograms tumor-infiltrating T_reg cells into a fragile-like phenotype.**

To identify the crucial factor underlying the two opposite phenotypes of T_reg cells in tumor and splenic tissues, we detected molecules that have an important role in deciding T_reg cell function. Ifng, which has been reported to drive Treg fragility, exhibited higher expression in tumors than in spleens, indicating that the tumor microenvironment may be more conducive to inducing fragile-like T_reg cells than the environment in splenic tissues (Supplementary Fig. 9a)³⁷. The tumor microenvironment exhibited significantly higher CD155 expression than the splenic tissue, which is indispensable for triggering TIGIT signaling in the tumor microenvironment (Supplementary Fig. 9a)³⁹. Total CD4⁺ splenocytes were isolated from MC38 tumor-bearing mice 11 days post-tumor inoculation, yielding a mixture with an approximate Treg-to-CD4⁺ Tconv cell ratio of 1:6, as determined by flow cytometry (Fig. 3a). This mixture was then activated by plate-bound anti-CD3/CD28 antibodies with or without CD155. αTIGIT-IL2 failed to impair the suppressive function of T_reg cells without the presence of CD155 (Supplementary Fig. 9b, c). To assess the contribution of T_reg cells to the antitumor effects of αTIGIT-IL2 treatment, we performed a T_reg cell depletion experiment. The experimental result demonstrates that the antitumor efficacy of αTIGIT-IL2 is entirely dependent on T_reg cells, highlighting their critical role in mediating the therapeutic effects of αTIGIT-IL2 (Fig. 3d, e). Because antibody–mediated T_reg depletion also removes a small fraction of early‑activated effector T cells, we isolated tumor‑infiltrating T_reg cells and assessed their immunosuppressive function in vitro (Fig. 3f). We observed that αTIGIT‑IL2 treatment significantly diminished the immunosuppressive activity of tumor‑infiltrating T_reg cells (Fig. 3g). These findings suggest that αTIGIT‑IL2 exerts its antitumor effects by modulating the immunosuppressive phenotype of T_regs.

The total CD4⁺ T cell population was reorganized into two subpopulations by Monocle 3 according to their gene expression profiles (Supplementary Fig. 9d, e). Then, T_reg cells were divided into four subpopulations by Monocle 3 based on their gene expression profiles (Fig. 3h). An increase in cluster 3 among total T_reg cells was observed following αTIGIT-IL2 treatment (Fig. 3i). The cluster 3 T_reg cells are indeed fragile-like T_reg cells expressing several inflammatory genes, including Il2, Tnf, and Ifng, according to the pseudotime analysis (Fig. 3j, k). T_reg cells presented an immune-activating phenotype, characterized by the enrichment of pathways that positively regulate lymphocyte activation and the production of pro-inflammatory cytokines and chemokines following αTIGIT-IL2 treatment (Fig. 3l).

In conclusion, αTIGIT-IL2 promotes T_reg cell fragility in the presence of CD155. The high expression of CD155 in the tumor region causes the tumor-specific bioactivity of αTIGIT-IL2 by impairing the immune suppressive function of T_reg cells.

IFN-γ triggers an inflammatory tumor microenvironment after administration of αTIGIT-IL2

To identify crucial cellular responders to αTIGIT-IL2, we evaluated the relative contributions of all subpopulations undergoing transformation following αTIGIT-IL2 treatment. The therapeutic efficiency of αTIGIT-IL2 was fully abolished with CD4⁺ T cell depletion and partially abolished with CD8⁺ T cell depletion (Fig. 4a, b and Supplementary Fig. 10a, b). NK cell depletion does not impair the antitumor efficacy of αTIGIT-IL2 (Fig. 4c and Supplementary Fig. 10c). Notably, neutrophil depletion did not produce synergistic effects with αTIGIT-IL2 treatment, implying that these increased amounts of neutrophils play a distinct role to that of MDSCs (Fig. 4d, Supplementary Fig. 6d, and Supplementary Fig. 10d).

Furthermore, the proportion and quantity of CD4⁺ T_conv and CD8⁺ T cells increased in tumor tissues 4 days post αTIGIT-IL2 treatment (Fig. 4e, f). The proliferative potential of tumor-infiltrating CD4⁺ T_conv and CD8⁺ T cells was enhanced by αTIGIT-IL2, as evidenced by increased Ki67 expression compared to the control treatment (Fig. 4g). Administration of αTIGIT-IL2 activated both tumor-infiltrating CD4⁺ T_conv and CD8⁺ T cells, leading to an increase in the number of IFN-γ⁺ and TNF⁺ T cells (Fig. 4h, i and Supplementary Fig. 10e–h). CD4⁺ T_conv and CD8⁺ T cells presented an immune-activating phenotype, characterized by the enrichment of pathways that positively regulate lymphocyte activation and the production of pro-inflammatory cytokines and chemokines after αTIGIT-IL2 treatment (Supplementary Fig. 10i, j).

Bulk-RNA sequencing showed a significant enrichment in the response to IFN-γ in tumor tissues, implying that IFN-γ plays important roles in αTIGIT-IL2-mediated tumor control (Fig. 4j and Supplementary Fig. 10k). The antitumoral effects of αTIGIT-IL2 were completely abolished when administered in combination with the neutralizing antibody of IFN-γ (Fig. 4k, l). Furthermore, the proportion of T_reg cells among total CD4⁺ T cells remained constant while the amount of T_reg cells increased in tumor tissues following the administration of αTIGIT-IL2 without the presence of IFN-γ (Fig. 4m–o). These results suggest that IFN-γ is crucial for αTIGIT-IL2 to trigger an inflammatory tumor microenvironment.

T_reg and CD4⁺ T_conv cells recruit neutrophils and facilitate their cross-presentation of tumor-associated antigens after αTIGIT-IL2 treatment

We observed a dramatic increase in intratumoral neutrophils following treatment with αTIGIT-IL2 (Supplementary Fig. 5a and Supplementary Fig. 6d, e). Bulk-RNA sequencing indicated significant enrichment in the neutrophil chemotaxis pathway in αTIGIT-IL2-treated tumors (Supplementary Fig. 10k). Recent research suggests that CD4⁺ T cells recruit neutrophils and reprogram intratumoral neutrophils from an immune-suppressive to an activated state, resulting in tumor elimination through chemokines and IFN-γ^40,41. Consistent with these findings, the gene expression profiles of T_reg and CD4⁺ T_conv cells were significantly enriched in multiple pathways associated with neutrophil recruitment following αTIGIT-IL2 treatment (Fig. 3f and Supplementary Fig. 6e). We identified a large number of differentially increased ligand-receptor interactions from T_reg and CD4⁺ T_conv cells to neutrophils in αTIGIT-IL2 vs. Control treated tumors by NicheNet, indicating the multiple roles of T_regs and CD4⁺ T_conv cells in reprogramming neutrophils in the tumor environment (Fig. 5a, b)⁴².

**Fig. 5: αTIGIT-IL2 activated CD4⁺ T cells enhance the antigen-presentation function in neutrophils and facilitate cross-talk between neutrophils and CD8⁺ T cells.**

Neutrophils underwent a shift into a pro-inflammatory and more mature phenotype in αTIGIT-IL2-treated tumors, with upregulation of CD14 and enrichment of pro-inflammation-related pathways (Fig. 5c and Supplementary Fig. 11a). αTIGIT-IL2 removed the tumor burden in MC38-OVA-GFP tumor-bearing mice and induced an increase in antigen-presenting neutrophils, which could, in turn, activate OVA-specific CD8⁺ T cells (Fig. 5d–f). Larger numbers of intratumoral Mpeg1⁺ neutrophils were induced following treatment with αTIGIT-IL2 than with the Control, suggesting enhanced cross-presenting ability of neutrophils (Supplementary Fig. 11b, c)⁴³. Spatial immunohistochemical analysis revealed a closer distance and high-frequency cross-talk between Ly6G⁺ neutrophils and CD8⁺ T cells in αTIGIT-IL2-treated tumors (Fig. 5g, h). Therefore, this MHC I high-expression state of intratumoral neutrophils and high-frequency cross-talk between neutrophils and CD8⁺ T cells in the tumor region could mediate effective activation of tumor-associated antigen (TAA)-specific CD8⁺ T cells. We performed an in-vitro assay to evaluate the antigen-presenting function of neutrophils and found that tumor-infiltrating neutrophils exhibited enhanced antigen-presenting capacity following T2 treatment (Fig. 5i). In MC38-OVA-GFP tumor-bearing mice, antigen-specific CD8⁺ T cells were increased and exhibited enhanced proliferation in tumor areas, following αTIGIT-IL2 treatment (Fig. 5j). These data suggest that the interaction between neutrophils and CD8⁺ T cells in tumor area was important for the activation of tumor-infiltrating cytotoxic T lymphocytes (CTLs). Using NicheNet, we found that the improved communication between neutrophils and CD8⁺ T cells induced by αTIGIT-IL2 may mainly mediated by CD80, IL-15, and MHC I molecules expressed on neutrophils, resulting in enhanced effector functions including cytotoxicity and cellular activation in CD8⁺ T cells (Supplementary Fig. 11a, d, e). The proportion of PD-1⁺Tim-3⁻ early effector and CD25⁺ potent memory CD8⁺ TILs was increased 4 days after αTIGIT-IL2 treatment (Supplementary Fig. 12a–c). PD-1⁻Tim-3⁻ naïve CD8⁺ TILs were the most proliferative cells among total CD8⁺ TILs at this early stage of αTIGIT-IL2 treatment (Supplementary Fig. 12d, e). At least 7 days after treatment, in αTIGIT-IL2-treated tumors, CD8⁺ TILs acquired improved cytotoxicity and the proportion of IFN-γ⁺TNF⁺ CD8⁺ TILs was significantly increased (Supplementary Fig. 12f–h). These results suggest that naïve antigen-specific CD8⁺ TILs were primed by neutrophils to acquire effector function in the αTIGIT-IL2-induced inflammatory tumor microenvironment.

TdLNs are reported to be the primary site for robust proliferation of antigen-specific CD8⁺ T cells following administration of immune checkpoint inhibitors^44,45. αTIGIT-IL2-mediated tumor control was not affected by the administration of FTY720, an S1PR inhibitor that inhibits lymphocyte migration (Supplementary Fig. 12i, j). In MC38-OVA-GFP tumor-bearing mice, the proportion of antigen-specific CD8⁺ T cells remains constant in tumor-draining lymph nodes (TdLNs) following αTIGIT-IL2 treatment (Supplementary Fig. 12k). However, neutrophils preferentially accumulate in tumor tissues rather than in tumor‑draining lymph nodes, suggesting that they may activate antigen‑specific CD8⁺ T cells during αTIGIT‑IL2–mediated tumor control. In addition, we also observed that neutrophil depletion did not affect the anti-tumor effect of αTIGIT-IL2, indicating that neutrophils are not the only pathway for αTIGIT-IL2-mediated anti-tumor immune response. Under neutrophil‑depleted conditions, other antigen‑presenting cell subsets may compensate for the absence of neutrophils in mediating the response to αTIGIT‑IL2.

To investigate the role of CD4⁺ T cells in recruiting and reprograming intratumoral neutrophils, we analyzed the phenotypic shifts in intratumoral neutrophils following αTIGIT-IL2 treatment in the context of CD4⁺ T cell depletion (Fig. 5k). The quantity of neutrophils in tumor are remain constant after αTIGIT-IL2 treatment in the context of CD4⁺ T cell depletion (Fig. 5l). CD4⁺ T cell depletion also abolished the enhanced antigen-presentation ability of neutrophils and the increased proportion of nAPCs induced by αTIGIT-IL2 (Fig. 5n and Supplementary Fig. 13a–e)^46,47. In conclusion, the increased number and pro-inflammatory reprogramming of neutrophils, mediated by T_reg and CD4⁺ T_conv cells following αTIGIT-IL2 treatment, enhance the activation of TAA-specific CD8⁺ T cells, thereby contributing to tumor control.

αTIGIT-IL2 synergizes with anti-PD1 treatment to eliminate tumors in an ICB-resistant TNBC mouse model

Triple-negative breast cancer (TNBC) demonstrates significant resistance to immunotherapy, is highly aggressive, and exhibits a high rate of recurrence^48,49. Tumor-infiltrating T_reg cells are positively correlated with breast cancer progression⁵⁰. High effector T_reg score is predictive of poor overall survival in patients with TNBC, suggesting an important role for T_reg cells in TNBC progression (Fig. 6a)⁵¹. To study the relevance of T_reg cells in human TNBCs, we reanalyzed intratumoral T_reg cells from human tumors using publicly available datasets that others have previously generated from 22 patients with TNBC treated with paclitaxel or its combination with atezolizumab⁵². UMAP analysis indicated that T_reg cells from ICB treatment responders were distinct from those from non-responders (Fig. 6b). T_reg cells from non-responders exhibited a more aggressive phenotype than those from responders post-ICB treatment (Fig. 6c). FOXP3 expression was upregulated in T_reg cells of non-responders post-ICB treatment (Fig. 6d). The T_reg functional score, calculated based on the mean expression of functional genes such as FOXP3, TIGIT, CTLA4, IL2RA, ENTPD1, ICOS, TNFRSF9, and LAG3 in T_reg cells, was higher in non-responders compared to responders (Fig. 6e)^33,34,53. The T_reg functional score was decreased in responders after ICB treatment but increased in non-responders (Fig. 6f). In addition, we examined the Treg cell functional markers identified in the mouse model and found that their expression levels were higher in T_reg cells from non-responders compared to responders (Supplementary Fig. 14a). These results suggest that T_reg cells are a promising immunotherapeutic target for eliminating resistance to ICB therapy in TNBC.

**Fig. 6: αTIGIT-IL2 eliminates resistance to ICB treatment in TBNC, resulting in tumor regression.**

To further explore whether targeting intratumoral T_reg cells by αTIGIT-IL2 could overcome ICB therapy resistance in TNBC, EO771 tumor-bearing mice were used. Although αTIGIT-IL2 exerts effective tumor control in EO771 tumor-bearing mice, it fails to synergize with PD-L1 blockade (Supplementary Fig. 14b, c). PD-L2, another ligand for PD-1 that could inhibit T cell activation, was found to be dramatically upregulated in αTIGIT-IL2-treated tumors, which may compensate for the inactivation of PD-L1 (Supplementary Fig. 14d, e)⁵⁴. Therefore, αTIGIT-IL2 was administered in combination with an anti-PD-1 antibody in EO771 tumor-bearing mice, resulting in tumor regression (Fig. 6g, i, h). These data demonstrate that anti-PD-1, rather than anti-PD-L1, antibodies are a more appropriate cooperator for αTIGIT-IL2 in clinical research. We performed a rechallenge tumor model to evaluate the long-term protection mediated by immune memory. We observed that the tumor growth was completely suppressed in 33 weeks old EO771 tumor-free mice treated with a combination of αTIGIT-IL2 and PD-1 inhibitor (Fig. 6j). Together these results demonstrated that the combination of αTIGIT-IL2 and PD-1 inhibitor effectively suppresses tumor growth and induces anti-tumor immune memory. We further employed the 4T1 subcutaneous tumor model to evaluate the efficacy of αTIGIT-IL2 in overcoming resistance to ICB therapy in TNBC. αTIGIT–IL2 demonstrated significant tumour control in 4T1 tumour-bearing mice, and its combination with a PD-1 blocker further enhanced antitumour efficacy, resulting in partial tumour regression (Fig. 6k, l). These data suggest that αTIGIT-IL2 may overcome resistance to ICB therapy in TNBCs and effectively reduce tumor burden when combined with PD-1 blockade.

Discussion

Here, we developed an immunocytokine with superior antitumor efficacy—namely, αTIGIT-IL2—by reprogramming intratumoral T_reg cells into a fragile-like phenotype. These fragile-like T_reg cells loss the suppressive function to CD4⁺ T_conv and CD8⁺ T cells and build an inflammatory tumor microenvironment. Neutrophils were found to accumulate in tumor regions and undergo phenotypic shifting to enhance their antigen-presenting capabilities via the MHC-dependent pathway following αTIGIT-IL2 treatment. CD8⁺ T cells were further activated immediately by reprogrammed neutrophils, resulting in tumor regression. IFN-γ plays a vital role in αTIGIT-IL2-mediated tumor control, especially in triggering T_reg cell phenotypic shifting. INF-γ and chemokines produced by CD4⁺ T cells were also reported to promote the engagement and reprogramming of neutrophils to eliminate tumors^40,41. In this study, the increased production of IFN-γ and chemokines induced by αTIGIT-IL2 in intratumoral T_reg and CD4⁺ T_comv cells promoted the recruitment of neutrophils to the tumor microenvironment and transformed them into an effectively cross-presenting state. The increased number of neutrophils and CD8⁺ T cells in tumor areas led to more frequent cross-talk between neutrophils and CD8⁺ T cells than that achieved with the control, resulting in effective antitumor immunity.

Targeting tumor-infiltrating T_reg cells is a crucial strategy in cancer immunotherapy. However, safe and feasible methods to implement this approach are lacking⁵³. αTIGIT-IL2 promotes intratumoral T_reg cell fragility but not the peripheral immune system, providing an approach to targeting tumor-infiltrating T_reg cells in cancer immunotherapy. The immunocytokine developed in this study showed effective antitumoral effects against both hot and cold tumors, representing a promising prospect for cancer immunotherapy. TIGIT was reported to promote T_reg stability by interfering with CD226²⁸. TIGIT⁺ T_reg cells are more suppressive than TIGIT⁻ T_reg cells²⁷. However, TIGIT could also perform its inhibitory function independently of CD226 through ligation-induced nanoclusters⁵⁵. It is unclear whether CD226 was essential for αTIGIT-IL2-mediated T_reg cell phenotypic shifting. Several studies have demonstrated that CD226 plays an important role in regulating T_reg function, and inactivation of CD226 on T_reg cells promotes immune tolerance^56,57. The high TIGIT to CD226 ratio of T_reg cells regulates their suppressive function and stability in melanoma⁵⁸. In our research, we only conclude that the high expression of CD155 in the tumor region was essential for the ability of αTIGIT-IL2 to induce the fragile-like phenotypic shifting of T_reg cells.

Importantly, such fragile-like shifting of T_reg cells should be limited to tumor tissues to prevent systematic toxicity caused by the activation of the peripheral immune system. It is unclear why the expression of IFN-γ was upregulated in T_reg cells in αTIGIT-IL2-treated spleens. We hypothesize that the simultaneous increase in production of IFN-γ and suppressive molecules by T_reg cells in spleens may represent an intermediate state between effector T_reg cells and fragile-like T_reg cells. To restrict the inflammatory reprogramming induced by αTIGIT-IL2 in the tumor microenvironment, the concept of pro-cytokines represents a potential strategy for reducing the systematic toxicity induced by high concentrations of αTIGIT-IL2⁵⁹.

In MC38 and EO771 subcutaneous tumor models, we observed that αTIGIT-IL2 treatment induces upregulation of PD-L2 expression, which might impair the therapeutic efficacy of PD-L1 blockade⁵⁴. IFN-γ is known to upregulate the expression of both PD-L1 and PD-L2 on tumor cells in murine models and human cancer patients⁶⁰. Based on our findings, we propose that αTIGIT-IL2 enhances the expression of PD-L1 and PD-L2 in tumor tissues by inducing IFN-γ expression within the tumor microenvironment. Therefore, we suggest that combining αTIGIT-IL2 with PD-1 blocking antibodies may be a more effective therapeutic strategy than combining it with PD-L1 blocking antibodies, both in preclinical mouse models and clinical applications.

In conclusion, our findings indicate a promising therapeutic strategy for tumor immunotherapy by inducing the shift of intratumoral T_reg cells into a fragile-like phenotype, promoting antigen-specific antitumor immunity. In combination with anti-PD-1 antibody, the administration of αTIGIT-IL2 represents a promising avenue to overcome the resistance of TNBCs to PD-1 inhibitors, thereby eliminating tumor burden.

Methods

Mice and tumor models

Female C57BL/6 J mice about 6-8 weeks old were purchased from the Shanghai Experimental Animal Center (Shanghai, China) and GemPharmatech Corporation Limited (Nanjing, China). 8–10 weeks old female Foxp3-YFP reporter mice were kindly provided by Professor Fengyin Li from University of Science and Technology of China. C57BL/6 J WT mice were subcutaneously inoculated with 2 × 10⁵ or 5 × 10⁵ MC38 cells, 2 × 10⁵ MCA205 cells, 5 × 10⁵ B16F10 cells, 1.4 × 10⁶ MC38-OVA-GFP, 2 × 10⁵ EO771, and 1 × 10⁵ 4T1 cells. Treatments were administered as described in the figure legend. Tumor volume was calculated by length × width × width / 2. All mice were maintained under specific pathogen-free conditions with 12/12 dark/light cycle, 20–26 °C, and 30–70% humidity. Animal care and experiments were carried out under institutional protocol and guidelines. All studies were approved by the ethics committee of the University of Science and Technology of China (USTCACUC25060124037). As approved by the Animal Ethics Committee, the maximum permitted tumour diameter is 17 mm. Experiments are terminated immediately once the average tumour diameter exceeds this limit. Mice were euthanized by exposure to carbon dioxide.

Cell lines

The CTLL-2 cytotoxic T lymphocyte cell line (TIB-214), hybridoma cells PK136 (HB-191), 293 T cell line (CRL-3216), and EO771 mammary gland carcinoma cell line (CRL-3461) were purchased from the American Type Culture Collection (ATCC). 4T1 cell lines (SCSP-5056) were purchased from the cell bank of the Chinese Academy of Sciences (Shanghai, China). Professor Yangxin Fu kindly provided the MC38 colon adenocarcinoma cell line from the University of Texas Southwestern Medical Center (Dallas, USA). The MCA205 fibrosarcoma cell line (BNCC339717) was purchased from BNCC (Beijing, China). The B16F10 cell line was kindly provided by Professor Qaing Zou from Shanghai Jiao Tong University School of Medicine. The 293T-mTIGIT cell line was constructed in our laboratory by expressing mouse TIGIT in 293 T cells. MC38-OVA-GFP was also constructed in our laboratory by co-expressing ovalbumin and GPF in the MC38 cell line. 293 T, MC38, B16F10, MCA205, 293T-mTIGIT, and MC38-OVA cell lines were cultured in complete DMEM, a DMEM (Gibco/Thermo Fisher Scientific, C11995500BT) supplemented with fetal bovine serum (FBS) (10% v/v, Sangon Biotech, E600001-0500) and penicillin/streptomycin (1% v/v, Solarbio, P1400). The CTLL2 and 4T1 cell lines was cultured in RPMI-1640 (Gibco/Thermo Fisher Scientific, C11875500BT) medium supplemented with FBS (10% v/v, Gibco/Thermo Fisher Scientific, 10091148), penicillin/streptomycin (1% v/v, Solarbio, P1400), and recombinant human IL-2 (100 U/ml).

Protein production and purification

αTIGIT-IL2, αTIGIT-IL2v, αTIGIT-IL2 (LALA-PG), αPD1-IL2, αControl-IL2, and mCD155-Fc were produced and purified in Biointron lnc (Taizhou, China) using the CHO-K1 expression system. The amino acid sequence of anti-mouse TIGIT scFv was derived from 13G6 generated in our laboratory. The amino acid sequence of anti-mouse PD-1 scFv (clone: RMP1-14) was from Biointron lnc. αControl-IL2 was composed of anti-human LUNX scFv, human IgG1 Fc region, and human IL-2⁶¹. Mouse CD155 extracellular domain was linked to human IgG1 Fc to generate mCD155-Fc fusion protein. The amino acid sequences of IL2v and human IgG1 Fc (LALA-PG) were in accordance with those reported in previous articles^22,31.

Antibody-mediated cellular depletion

For immunocytokine effector cell depletion, antibodies targeting CD25 (BioXCell, clone PC-61, BE0012, 300 μg), CD8β (BioXCell, clone 53-5.8, BE0223, 200 μg), CD4 (BioXCell, clone GK1.5, BE0003-1, 200 μg), NK1.1 (produced in our laboratory, clone PK136, 200 μg), and Ly6G (BioXCell, clone 1A8, BE0075-1, 200 μg) were administered intraperitoneally in conjunction with αTIGIT-IL2 treatment, as previously described⁵⁹.

In-vivo treatments

IFN-γ neutralizing antibody (BioXCell, clone XMG1.2, BE0055, 250 μg every 3 days) or rat IgG control immunoglobulin (Solarbio, SP032, 250 μg every 3 days) in 100 µl of PBS were injected intraperitoneally. To evaluate the effect of combination therapy, EO771 tumor-bearing mice were treated intraperitoneally with anti-PD1 (BioXCell, clone RMP1-14, BE0146, 250 μg), anti-PDL1 (BioXCell, clone 10 F.9G2™, BE0101, 250 μg), or rat IgG control immunoglobulin (Solarbio, SP032, 250 μg every 3 days) in combination with αTIGIT-IL2 treatment as described.

Toxicity studies

Serum was collected from MC38 tumor bearing mice at day 2 post initial treatment for AST and ALT quantification. Serum ALT (Solarbio, BC1555) and AST (Solarbio, BC1565) activities were subsequently measured using a commercial assay kit, following the manufacturer’s protocol.

Bioactivity assay

For ELISA assays, a Stripwell Microplate (Costar, 42592) was coated with recombinant mouse TIGIT at 0.2 µg/ml in 1 × PBS overnight at 4 °C. Subsequent washes were performed with PBST (PBS supplemented with 0.05% v:v Tween-20 (Millipore-Sigma, P9416)). Blocking was performed in PBSTA (PBS supplemented with 1% w:v bovine serum albumin (BSA; Sigma-Aldrich, 820476) and 0.05% v:v Tween-20) for 2 h at 37 °C. Biotinylated immunocytokines or 13G6 were diluted to relevant concentrations in PBSTA and detected using horseradish peroxidase (HRP)-conjugated streptavidin diluted 1:2,000 (Beyotime, A0305). Enhanced TMB Solution for ELISA (Beyotime, P0210) was added, allowed to develop for 15 min, and quenched with Stop Solution for TMB Substrate (Beyotime, P0215). Absorbance at 450 nm (A450) was measured for analysis.

For blockade efficiency assays, 1 × 10⁵ 293T-mTIGIT cells were incubated with different concentrations of immunocytokines or 13G6 for 1 h on ice. Subsequent washes were performed twice using 1× PBS. These pretreated 293T-mTIGIT cells were incubated with biotinylated mCD155-Fc (10 μg/ml) for 1 h on ice; then, the binding efficiency of mCD155-Fc was detected using APC-conjugated streptavidin (Biolegend, 405207, 1:100).

To assess IL-2 bioactivity, CTLL-2 cells were starved for 2 days in an incomplete medium without IL-2. In detail, 5 × 10⁴ starved CTLL-2 cells were seeded in a 96-well and cocultured with different concentrations of immunocytokines or recombinant human IL-2 (Novoprotein, C013) for 2 h at 37 °C. Immediately after treatment, cells were harvested for the detection of p-STAT5 using PE-conjugated Phospho-STAT5 (Tyr694) Monoclonal Antibody (Invitrogen, clone SRBCZX, 12-9010-42, 1:100) in accordance with the manufacturer’s instructions.

RNA isolation, reverse transcription, and quantitative RT-PCR

RNA was isolated from the tumor and splenic tissues of MC38 tumor-bearing mice with the RNA-Quick Purification Kit (ES Science, RN001). All procedures were performed according to the manufacturer’s instructions. A total of 1 μg of RNA was used for cDNA synthesis with the HiScript III RT SuperMix for qPCR (Vazyme, R323). qRT-PCR was performed using the ChamQ SYBR qPCR Master Mix (Vazyme, Q311). Primer sequences used for qRT–PCR are provided in Table 1.

Table 1 Primer sequences for quantitative RT–PCR

Full size table

Preparation of cell suspensions

TILs were isolated by dissociating tumor tissue in the presence of collagenase IV (1 mg/ml, Sigma-Aldrich, C5138) and DNase I (15 U/ml, Sigma-Aldrich, D5025) for 1 h before centrifugation on a discontinuous Percoll gradient (Cyvita, 17089109). Isolated cells were then used in various assays to evaluate the phenotype and function of TILs. Lymph nodes (LNs) and spleens were mechanically dissociated, passed through a 40 μm cell strainer, subjected to red blood cell lysis (Biosharp, BL503B), and then washed with cold 1× PBS and centrifuged.

Flow cytometry analysis

Single-cell suspensions of spleens, LNs, or tumors were blocked with rat serum (VivaCell, C2570) for 15 min on ice. Live/dead staining was performed with Zombie Aqua Fixable Viability Kit (Biolegend, 423102) for 30 min on ice. TAA-specific CD8⁺ T cells were stained with OVA-specific tetramer (T-Select H-2Kb OVA Tetramer-SIINFEKL-PE, MBL Life science, TS-5001-1C) for 30 min on ice, followed by staining of antibodies against surface antigens for 30 min on ice. Intracellular proteins were stained overnight on ice after fixation and permeabilization using the FoxP3 staining kit (Invitrogen, 00-5523-00) according to the manufacturer’s instructions. The following antibodies were used: CD45.2 (Biolegend, clone 104, 109824, 1:100), CD3 (BD, clone 145-2C11, 564378, 1:100), TCRβ (Biolegend, clone H57-597, 109230, 1:100), CD4 (BD, clone GK1.5, 565709, 1:100), CD8 (BD, clone 53-6.7, 564297, 1:100), CD8β (BD, clone H35-17.2, 550798, 1:100), NK1.1 (Biolegend, clone PK136, 108740, 1:100), CD11b (Biolegend, clone M1/70, 101251, 1:100), CD11c (Biolegend, clone N418, 117339, 1:100), Ly6G (Biolegend, clone 1A8, 127608, 1:100), F4/80 (Biolegend, clone BM8, 123116, 1:100), MHC II (Biolegend, clone M5/114.15.2, 107606, 1:100), MHC II (Biolegend, clone M5/114.15.2, 107625, 1:100), Foxp3 (eBioscience, clone FJK-16s, 45-5773, 1:100), IL15Rα (Biolegend, clone 6B4C88, 153505, 1:100), Ki67 (eBioscience, clone SolA15, 25-5698-80, 1:100), TIGIT (Biolegend, clone 1G9, 142106, 1:100), PD-1 (Biolegend, clone 29 F.1A12, 135225, 1:100), Tim-3 (Biolegend, clone RMT3-23, 119706, 1:100), CTLA-4 (Biolegend, clone UC10-4B9, 106309, 1:100), LAG-3 (BD, clone C9B7W, 740072, 1:100), ICOS (Biolegend, clone 7E.17G9, 117405, 1:100), CD44 (BD, clone IM7, 553133, 1:100), CD62L (BD, clone MEL-14, 562910, 1:100), CD25 (BD, clone PC61, 553866, 1:100), CD39 (eBioscience, clone 24DMS1, 12-0391, 1:100), CD73 (Biolegend, clone TY/11.8, 127220, 1:100), TNF (Biolegend, clone MP6-XT22, 506308, 1:100), IFN-γ (Biolegend, clone XMG1.2, 505826, 1:100), TGF-β (eBioscience, clone TW7-16B4, 12-9821-82, 1:100), Il-10 (Biolegend, clone JES5-16E3, 505022, 1:100), CD14 (Biolegend, clone Sa14-2, 123315, 1:100), CD80 (Biolegend, clone 16-10A1, 104733, 1:100), CD86 (Biolegend, clone GL-1, 105011, 1:100), CD274 (Biolegend, clone 10 F.9G2, 124333, 1:100), Eos (eBioscience, clone ESB7C2, 50-5758-80, 1:100), Helios (Biolegend, clone 22F6, 137214, 1:100), T-bet (Biolegend, clone 4B10, 644815, 1:100), EpCAM (Biolegend, clone G8.8, 118206, 1:100), H-2K^b bound to SIINFEKL (Biolegend, clone 25-D1.16, 141606, 1:100), Rat IgG1, κ (Biolegend, clone RTK2071, 400411, 1:100), Rat IgG1, κ (Biolegend, clone RTK2071, 400415, 1:100), Rat IgG2b, κ (Biolegend, clone RTK4530, 400639, 1:100), Rat IgG2b, κ (Biolegend, clone RTK4530, 400631, 1:100), Rat IgG2a, κ (Biolegend, clone RTK2758, 400521, 1:100), Rat IgG2a, κ (Biolegend, clone RTK2758, 400511, 1:100), Armenian Hamster IgG (Biolegend, clone HTK888, 400921, 1:100), Mouse IgG1, κ (Biolegend, clone MOPC-21, 981804, 1:100). Samples were analyzed on a BD LSR Fortessa or CytoFLEX LX (Beckman Coulter) and data was processed with the FlowJo v10.8.0 software and GraphPad Prism v8.

Staining with hematoxylin and eosin

Tissues of the liver and lung were fixed in 4% paraformaldehyde and embedded in paraffin. Tissue sections of 5 μm thickness were cut and stained with hematoxylin and eosin, which was carried out by Servicebio (Wuhan, China).

Biodistribution analysis

For in-vivo biodistribution of αTIGIT-IL2, MC38 tumor-bearing mice were given one dose of αTIGIT-IL2 treatment 10 days after tumor inoculation. Twenty-four h after treatment, mice were sacrificed and TILs were isolated for flow cytometry analysis. For ex vivo biodistribution analysis, single-cell suspensions of spleens or tumors were pretreated, according to their respective groups, for 30 min on ice. These pretreated cells were then incubated with αTIGIT-IL2 or αPD1-IL2 for 1 h on ice, followed process was the same with flow cytometry analysis.

T_reg cell suppression analysis

Total CD4⁺ splenocytes were isolated from MC38 tumor-bearing mice 11 days after tumor inoculation to obtain a mixture in which the ratio of T_reg to CD4⁺T_conv cells was approximately 1 to 6, as shown in Fig. 3b. Cell culture plates were precoated with anti-CD3 (2 μg/ml, BD, clone 145-2C11, 553057), anti-CD28 (2 μg/ml, BD, clone 37.51, 553294) antibodies, and either CD155-Fc (8 μg/ml) or equal control immunoglobulin (human IgG1 Fc, produced in our laboratory) 12 h prior to the experiment. Before use, the protein-coated plate was washed twice with 1× PBS. CD4⁺ splenocytes were labeled using the CellTrace™ Violet Cell Proliferation Kit (Invitrogen, C34571) according to the manufacturer’s instructions. These labeled cells were cultured in the protein-coated plate with αTIGIT-IL2 (100 ng/ml) or equimolar 13G6 plus αControl-IL2. After 3 days of culture, the proliferation efficiency of CD4⁺ T_conv cells was analyzed using flow cytometry. CD4⁺ splenocytes were cultured in Click’s medium (IrvineScientific, 9195) supplemented with FBS (10% v/v, Gibco/Thermo Fisher Scientific), penicillin/streptomycin (1% v/v, Solarbio), GlutaMAX Supplement (1% v/v, Gibco, 35050061), and 2-Mercaptoethanol (0.1% v/v, Gibco, 21985023).

For in vitro tumor-infiltrating T_reg cell suppression assays, tumor-infiltrating T_reg cell were isolated from MC38 tumor bearing Foxp3-YFP mice, 4 days after treatment. CD4⁺ Tconv cells from naïve mice spleen were labeled using the CellTrace Violet Cell Proliferation Kit (Invitrogen, C34571) according to the manufacturer’s instructions. CTV-labeled CD4⁺ Tconv cells were co-cultured with purified tumor-infiltrating T_reg cells at the indicated ratios (3:1) on 96-well round-bottom plates – maintaining continuous stimulation by anti-CD3/28 dynabeads (Gibco, 11452D). After 3 days of culture, the proliferation efficiency of CD4⁺ T_conv cells was analyzed using flow cytometry. T_reg and CD4⁺ T_conv cells were cultured in Click’s medium (IrvineScientific) supplemented with FBS (10% v/v, Gibco), penicillin/streptomycin (1% v/v, Solarbio), GlutaMAX Supplement (1% v/v, Gibco), and 2-Mercaptoethanol (0.1% v/v, Gibco).

In vitro antigen presentation assay

Tumor-infiltrating neutrophils were isolated from TIL suspensions using magnetic-activated cell sorting (MACS) experiment. In brief, tumor cells within the TIL suspension were further isolated using EasySep Mouse PE Positive Selection Kit Ⅱ (Stemcell, 17666), with EpCAM serving as a selection marker. Subsequently, neutrophils were enriched from the lymphocyte population using EasySep Mouse Neutrophil Enrichment Kit (Stemcell, 19762), and further purified based on Ly6G expression using EasySep Mouse PE Positive Selection Kit Ⅱ (Stemcell, 17666). MACS was performed following the manufacturer’s recommended procedures. CD8⁺ T cells were isolated from the spleens of OT-1 mice using Naïve CD8a⁺ T Cell Isolation Kit (Miltenyi Biotec, 130-096-543) following the manufacturer’s recommended procedures, mouse and subsequently labeled with CellTrace Violet Cell Proliferation Kit (Invitrogen, C34571) following the manufacturer’s recommended procedures. Neutrophils and CD8⁺ T cells were co-cultured in a 96-well plate at a 3:1 ratio with 10 ng/mL OVA (257–264) (GenScript, RP10611) peptide to evaluate antigen presentation capacity. The proliferation efficiency of CD8⁺ T_conv cells was analyzed using flow cytometry. Cells were cultured in Click’s medium (IrvineScientific) supplemented with FBS (10% v/v, Gibco), penicillin/streptomycin (1% v/v, Solarbio), GlutaMAX Supplement (1% v/v, Gibco), and 2-Mercaptoethanol (0.1% v/v, Gibco).

FTY720 preparation and dosing

FTY720 hydrochloride (Sigma-Aldrich, SML0700) was stored as stock solutions at 10 mg/ml in dimethyl sulfoxide. Before treatment, stock solutions were diluted to 10 µg in 50 µl of corn oil (Sangon, A449530). MC38 tumor-bearing mice were injected intraperitoneally with 20 μg FTY720 administered i.p. 1 day before treatment initiation and then 10 μg administered every other day for 2 weeks.

Multiplex immunohistochemistry staining

Multiplex immunohistochemistry (mIHC) was performed by staining 4-um-thick formalin-fixed, paraffin-embedded whole-tissue sections with standard, primary antibodies sequentially and paired with TSA 7-color kit (Absinbio, abs50015-100T). Then, samples were stained with DAPI. For example, deparaffinized slides were incubated with anti-Ly6G antibody (CST, 87048) overnight at 4°C and then treated with anti-rabbit/mouse horseradish peroxidase-conjugated (HRP) secondary antibody (Absinbio, abs50015-02) for 10 min. Then, labeling was developed for a strictly observed 10 min using TSA 520 according to the manufacturer’s instructions. Slides were washed in TBST buffer and then transferred to preheated citrate solution (90 °C) before being heat-treated using a microwave set at 20% of maximum power for 15 min. Slides were cooled in the same solution to room temperature. Between all steps, the slides were washed with Tris buffer. The slides were then incubated with anti-CD8 antibody (Abcam, 217344) for 1 h at room temperature, the same process as above was repeated following. The anti-CD8 antibody was labeled using TSA 620. Each slide was then treated with 2 drops of DAPI (Absinbio, abs47047616), washed in distilled water, and manually coverslipped. Slides were air-dried and images were acquired with Pannoramic MIDI II (3DHISTECH). Images were analyzed using Indica Halo software.

Bulk RNA-seq

Tumor tissues from MC38 tumor-bearing mice with or without αTIGIT-IL2 treatment were used for RNA sequencing. In detail, 1 μg total RNA was used for following library preparation and poly (A) mRNA isolation was performed using Oligo (dT) beads. mRNA fragmentation was performed using divalent cations and high temperatures. Priming was performed using Random Primers. First-strand cDNA and second-strand cDNA were synthesized. The purified double-stranded cDNA was then treated to repair both ends and add a dA-tail in a single reaction, followed by a T-A ligation to add adaptors to both ends. Size selection of adaptor-ligated DNA was then performed using DNA Clean Beads. Next, each sample was amplified by PCR using P5 and P7 primers and the PCR products were validated. Then, libraries with different indexes were multiplexed and loaded on an Illumina HiSeq/ Illumina Novaseq/ MGI2000 instrument for sequencing using a 2 × 150 paired-end (PE) configuration according to the manufacturer’s instructions. To remove technical sequences, including adapters, PCR primers or fragments thereof, and those with quality of bases lower than 20, pass filter data of fastq format were processed by Cutadapt (v1.9.1, phred cutoff: 20, error rate: 0.1, adapter overlap: 1 bp, min. length: 75, proportion of N: 0.1) to obtain high-quality clean data. First, reference genome sequences and gene model annotation files of relative species were downloaded from the genome website. Second, Hisat2 (v2.2.1) was used to index the reference genome sequence. Finally, clean data were aligned to reference genome via software Hisat2 (v2.2.1).

Initially, transcripts in fasta format were converted from known GFF annotation files and indexed properly. Then, with the file as a reference gene file, HTSeq (v0.6.1) was used to estimate gene and isoform expression levels from the pair-end clean data. Differential expression analysis was performed with the DESeq2 Bioconductor package, a model based on the negative binomial distribution. The estimates of dispersion and logarithmic fold changes incorporate data-driven prior distributions, and the adjusted p-value (Padj) of genes was set to ≤ 0.05 to detect differentially expressed genes (DEGs). GOSeq (v1.34.1) was used to identify Gene Ontology (GO) terms that annotate a list of enriched genes with a significant Padj less or equal to 0.05. Further, topGO was used to plot DAG. KEGG (Kyoto Encyclopedia of Genes and Genomes) is a collection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substances (http://en.wikipedia.org/wiki/KEGG). We used in-house scripts to enrich significant DEGs in KEGG pathways.

Single-cell RNA analysis of TILs

TILs were isolated from MC38 tumor-bearing mice 7 days following αTIGIT-IL2 treatment for single-cell RNA sequencing.

10× library preparation and sequencing

Beads with unique molecular identifiers (UMIs) and cell barcodes were loaded close to saturation so that each cell was paired with a bead in a Gel Beads-in emulsion (GEM). After exposure to cell lysis buffer, polyadenylated RNA molecules hybridized to the beads. Beads were retrieved into a single tube for reverse transcription. During cDNA synthesis, each cDNA molecule was tagged on the 5′ end (that is, the 3′ end of a mRNA transcript) with a UMI and cell label indicating the cell of origin. All the remaining procedures, including library construction, were performed according to the manufacturer’s standard protocol (CG000206 RevD). Sequencing libraries were quantified using a High Sensitivity DNA Chip (Agilent) on a Bioanalyzer 2100 and the Qubit High Sensitivity DNA Assay (Thermo Fisher Scientific). The libraries were sequenced on NovaSeq6000 (Illumina) using 2 × 150 chemistry. Library construction and sequencing were performed at Shanghai Biotechnology Corporation.

Single-cell RNA-seq data processing

Reads were processed using the CellRanger (v3.0.1) pipeline with default and recommended parameters. FASTQs generated from Illumina sequencing output were aligned to the mouse genome, version mm10, using the STAR algorithm. Next, gene-barcode matrices were generated for each sample by counting UMIs and filtering non-cell associated barcodes. Finally, we generated a gene-barcode matrix containing the barcoded cells and gene expression counts. This output was then imported into the Seurat (v3.0.2) R toolkit for quality control and downstream analysis of the single-cell RNAseq data. All functions were run with default parameters unless specified otherwise. We excluded cells with fewer than 200 or more than 6000 detected genes (where each gene had at least one UMI aligned in at least three cells). The expression of mitochondrial genes was calculated using the PercentageFeatureSet function of the Seurat package. To remove low-activity cells, cells with more than 10 percent expression of mitochondrial genes were excluded. The normalized data (NormalizeData function in the Seurat package) were used to extract a subset of variable genes. Variable genes were identified while controlling for the strong relationship between variability and average expression. Next, we integrated data from different samples after identifying ‘anchors’ between datasets using FindIntegrationAnchors and IntegrateData in the Seurat package. Then, we performed principal component analysis (PCA) and reduced the data to the top 30 PCA components after scaling the data. We visualized the clusters on a 2D map produced with t-distributed stochastic neighbor embedding (t-SNE).

Cells were clustered using graph-based clustering of the PCA-reduced data with the Louvain Method after computing a shared nearest-neighbor graph. For sub-clustering, we applied the same procedure of scaled, dimensionality reduction and clustering to the specific set of data (usually restricted to one type of cell.) We used the Wilcoxon Rank-Sum Test for each cluster to identify significant DEGs by comparing the remaining clusters. SingleR and known marker genes were used to identify cell types. Cells annotated as CD4⁺ T_conv and T_reg were extracted for further dimensionality reduction, projection, and higher-resolution clustering. The distinct cell subpopulations were identified based on Foxp3 expression. We then inferred developmental trajectories for all cells re-annotated as T_reg using Monocle 2⁶².

The FindMarkers function from the R package Seurat (v5.1.0) was used to identify DEGs between cells from the Control and αTIGIT-IL2 samples across different cell types. Genes expressed in more than 50% of the cells in a given sample, with at least a 1.5-fold upregulation and an adjusted p-value ≤ 0.05, were retained as DEGs for that sample. Subsequently, GO enrichment analysis was performed on these sets of DEGs using the clusterProfiler (v4.10.1) package. Cell-cell communication between different cell types was analyzed using the NicheNet R package (v2.1.5), with the ligand-target matrix referenced from previously published literature⁴².

Analysis of published scRNA-seq studies of human TNBC

Processed scRNA-seq data were obtained from previously published, publicly available datasets. scRNA-seq datasets from patients who underwent ICB treatment were used for further analysis. SCTransform was employed to eliminate batch effects. Following the previously described procedure, the dataset was reprocessed for dimensionality reduction and projection. Subsequently, T_reg cells were extracted based on the annotation information provided in the original publication. Differential expression analysis and GO enrichment analysis were then performed on these cells according to the aforementioned protocol.

Statistical analysis

Statistical analyses were performed in GraphPad Prism v8 using appropriate tests as indicated in the figure legends (unpaired two-tailed t-test, paired two-tailed t-test, one-way ANOVA followed by Tukey’s multiple comparisons test, two-way ANOVA, or Linear regression). No data/experiments were excluded unless there were technical issues with the experiment, and outliers were not excluded. P < 0.05 was considered to indicate statistical significance in all analyses.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The scRNA-seq data generated in this study have been deposited in the Genome Sequence Archive (GSA) database under accession code CRA019549. The Bulk RNA-seq data generated in this study have been deposited in the GSA database under accession code CRA019578. The statistical data generated in this study are provided in the Source Data file. The TNBC scRNA-seq data used in this study are available in the Gene Expression Omnibus (GEO) database under accession code GSE169246. All other data are available in the article and its Supplementary files or from the corresponding author upon request. Source data are provided with this paper.

Code availability

All code used in this study is archived on Zenodo with the (https://doi.org/10.5281/zenodo.16785532). The corresponding GitHub repository is available at (https://github.com/RicardoXuu/code_for_tigit-il2_reproducibility).

References

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2023YFA0915700); the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No.XDB0940302), the Natural Science Foundation of China (Reference Numbers: 82241216, 82388201, 81821001, 82122055, 82222053 and 32270963); Anhui key research and development plan (Reference Numbers: 2023z04020011); the Anhui Provincial Natural Science Foundation for Distinguished Young Scholars (Grant No. 2408085JX006); Research Funds of Center for Advanced Interdisciplinary Science and Biomedicine of IHM (QYZD20220008); Important Direction Project Cultivation Fund, Institute of Immunology and the CAS Key Laboratory of Innate Immunity and Chronic Disease (Reference Numbers: WK3520000017); the Fundamental Research Funds for the Central Universities (WK9100000089). The cartoon schematic in Fig. 3f was created in BioRender. Ma, H. (https://BioRender.com/bjbpp4y) is licensed under CC BY 4.0. The cartoon mouse in Figs. 1e, h, j, l, n, p, Fig. 2a, Fig. 3a, d, Fig. 4k, Fig. 5k, Fig. 6g, j, k, supplementary fig. 12i and supplementary fig. 14b were created in BioRender. Ma, H. (https://BioRender.com/j1zzwk5) is licensed under CC BY 4.0.

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Authors and Affiliations

State Key Laboratory of Immune Response and Immunotherapy, Institute of Immunology, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
Tianci Wang, Zhengfeng Zhang, Yaqi Wu, Long Chen, Xiaodong Zheng, Hui Peng, Rui Sun, Hongdi Ma, Zhigang Tian & Xiaohu Zheng
MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
Yupu Xu
Hongqiao International Institute of Medicine, Tongren Hospital & Shanghai Institute of Immunology, State Key Laboratory of Systems Medicine for Cancer, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Qiang Zou
Hefei TG ImmunoPharma Corporation Limited, Hefei, China
Rui Sun, Haoyu Sun, Zhigang Tian & Xiaohu Zheng
Department of Immunology, School of Basic Medical Sciences, Fudan University, Shanghai, China
Haoyu Sun
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
Haoyu Sun
Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
Haoyu Sun
Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Zhigang Tian & Xiaohu Zheng

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Tianci Wang
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Yupu Xu
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Zhengfeng Zhang
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Yaqi Wu
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Long Chen
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Hui Peng
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Hongdi Ma
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Haoyu Sun
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Zhigang Tian
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Contributions

T.W. conceived and designed the study. Y. X. completed all bioinformatic analyses in this study. T.W., Y.W., Z.Z., and L.C. performed the experiments. T.W. and Y.X. analyzed the data. Q.Z., X.Z., H.P., H.M., R.S., H.S., X.Z. and Z.T. contributed to discussions of results. T. W. wrote the manuscript. Z.T. and X.Z. supervised and conducted the project. All authors edited the manuscript.

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Correspondence to Hongdi Ma, Haoyu Sun, Zhigang Tian or Xiaohu Zheng.

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Wang, T., Xu, Y., Zhang, Z. et al. αTIGIT-IL2 achieves tumor regression by promoting tumor-infiltrating regulatory T cell fragility in mouse models. Nat Commun 16, 9223 (2025). https://doi.org/10.1038/s41467-025-64296-z

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Received: 15 January 2025
Accepted: 11 September 2025
Published: 17 October 2025
Version of record: 17 October 2025
DOI: https://doi.org/10.1038/s41467-025-64296-z