Reactivating exhausted tumor-infiltrating T cells by a bispecific DC-T cell engager in mice

Domain: biology
Published: 2026-03-17
Editorial outcome: major_revision

Abstract
Tumor infiltrating T cells (TIL) are key players in the anti-tumor immune response. However, chronic exposure to tumor-derived antigens drives the differentiation into ‘exhausted’ TILs. Whether intratumoral dendritic cells (DC) can mitigate TILs exhaustion and maintain function is unclear. Here, we develop a bispecific DC-T cell engager (BiDT), consisting of an anti-TIM3-IFN fusion protein, and demonstrate that, in preclinical mouse tumor models, this engager simultaneously targets TIM3 on exhausted TILs and activates DCs via the IFNAR receptor. Mechanistically, BiDT reactivates exhausted TIM3+TILs by preventing apoptosis through increased Bcl-2 expression and enhances DC function to reactivate T cells via IL-2 signalling and co-stimulatory B7/SCD28 interactions within the tumor microenvironment. Finally, to mitigate IFNα-induced toxicity, we engineer a Pro-BiDT engager featuring a pro-IFNα and report potent antitumor activity with reduced systemic toxicity. Thus, by bridging DC-T cells together, BiDT treatment enhances the critical communication pathways and cellular circuits necessary for effective anti-tumor immunity.

Introduction
Tumor-infiltrating lymphocytes (TIL), especially tumor-specific T cells, are essential components of anti-tumor immunity and serve as critical targets for cancer immune defense1. Within the tumor microenvironment (TME), chronic antigen exposure drives tumor-specific TILs to differentiate into the exhausted state2,3. Exhausted T cells are marked by upregulation of immune checkpoints molecules, diminished capacity for cytokine production and proliferation, and in some cases, apoptotic cell death. This dysfunctional state is frequently associated with ineffective tumor control4. Consequently, revitalizing exhausted T cells is a promising strategy for restoring anti-tumor immunity. T cell immunoglobulin and mucin domain-containing protein 3 (TIM3) is an immune checkpoint molecule that is essential for optimal effector T cell responses and contributes to T cell exhaustion5. Although in vivo blockade of TIM3 signaling can partially alleviate T cell exhaustion, its limited efficacy underscores that monotherapy targeting a single immune checkpoint often yields suboptimal outcomes6. Therefore, strategies to effectively reverse T cell exhaustio n need further investigation.

Dendritic cell (DC) in tumor-draining lymph nodes (TDLN) are critical for initiating naive T-cell priming7. During this process, CD28 engagement on T cells by CD80/86 expressed on DCs amplifies T-cell receptor (TCR) signaling by up to 100-fold8-10. Importantly, this co-stimulation increases IL-2 production and enhances the intrinsic capacity of T cells to resist apoptosis11. In contrast to the well-established role in TDLNs, the function of DCs within the TME in relation to T cell exhaustion or reactivation remains poorly defined. We hypothesize that productive crosstalk between intratumoral DCs and exhausted T cells could mitigate exhaustion or even re-activate T cells. However, such functional crosstalk requires fully activated DCs within the TME. Various immunosuppressive factors within the TME impair DC function by inhibiting their maturation and antigen presentation while upregulating checkpoint protein expression, thereby disrupting the DC-T cell interactions12,13. Type I interferons (IFN-I) are potent cytokines capable of rejuvenating intratumoral DCs, enhancing their antigen processing and presentation capacity to promote more effective T cell priming and re-activation14,15. Nevertheless, IFN-I expression in the TME is often limited or suppressed due to multiple mechanisms, including increased degradation of tumor-derived DNA and antigens, as well as defective cGAS-STING pathway signaling in certain tumor cells16,17. The clinical utilization of IFN-I is also limited due to severe systemic toxicity when delivered systemically18.

Here, we develop a bispecific DC-T cell engager (BiDT) based on an anti‑TIM3-IFNα fusion protein that enhances DC-T cell crosstalk in the TME. This molecule simultaneously activates DC and reactivates exhausted TILs. A tumor‑activated prodrug variant (Pro‑BiDT) retains potent anti‑tumor activity while reducing systemic toxicity. This approach provides a strategy to locally reprogram immunosuppressive niches and may help overcome resistance to current immunotherapies.

Results
Tumor-reactive T cells are exhausted inside advanced tumors
Tumor-reactive T cells within the TME frequently undergo exhaustion following prolonged exposure to tumor antigens, a state marked by impaired proliferative capacity and reduced effector functions19. To delineate the dynamic changes in T-cell states during tumor progression, we isolated TILs from MC38 tumor-bearing mice at an advanced stage (day 19 post-inoculation) and performed single-cell RNA sequencing (scRNA-seq) on CD8+ T cells. The MC38 murine colon adenocarcinoma cell line, established in 1975 from a chemically induced tumor in female C57BL/6 mice, is a widely used model in cancer research and therapy development 20. Our analysis revealed four molecular distinct states of intratumoral CD8+ T cells (Fig. 1A). A subset of proliferating CD8+ T cells was identified by high Ki67 expression and low levels of exhaustion markers including Pdcd1 and Havcr2. In contrast, CD8+ T cells in the exhausted state displayed elevated levels of Pdcd1 and Havcr2 without Ki67 expression, suggesting a loss of proliferative potential (Fig. 1B and C). To further demonstrate human relevance, we analyzed published scRNA-seq data from human colorectal cancer (CRC) samples 21. As shown below, TIM3+CD8+ T cells from human CRC tumors also showed higher expression of exhaustion-related genes (ENTPD1, PDCD1, HAVCR2) but lower function and proliferation-related genes (GZMK, GZMH, TNF, KLRG1, MKI67), indicating that these cells are exhausted (Supplementary Fig. 1A-C).

To directly assess whether tumor-specific CD8+ T cells predominantly adopt an exhausted state within the TME, we employed Tetramer-based tracking of tumor-specific T cells in the MC38-OVA tumor model. Our findings revealed that more than 80% of Tetramer-positive CD8+ T cells co-expressed TIM3, confirming that tumor-specific T cells are largely exhausted (Fig. 1D, Supplementary Fig. 2 and 4A). Further flow cytometry analysis confirmed that TIM3+ CD8+ T cells co-express established exhaustion markers such as PD-1, CD39 and Lag3, supporting their classification as exhausted T cells based on this multi-marker profile (Supplementary Fig. 3A-D). The scRNA-seq analysis revealed reduced Bcl2 expression in exhausted T cells (Fig. 1B and C). Consistently, flow cytometry analysis demonstrated decreased Bcl-2 levels in TIM3+CD8+ T cells compared to TIM3−CD8+ T cells (Fig. 1E and Supplementary Fig. 4B), indicating an enhanced susceptibility to apoptosis. These observations underscored the critical need to develop strategies to reactivate exhausted T cells.

To reactivate T cells, the cross presentation of DC cells is required. However, we found that tumor-infiltrating DC (TIDC) exhibited impaired ability to prime cytotoxic CD8+ T cells (Fig. 1F-G and Supplementary Fig. 4C). As is well known, IFNα has been shown to amplify DC activation, prompting us to investigate whether IFNα could enhance DC-T cell crosstalk in the TME to reactivate exhausted T cells. Analysis of clinical RNA-seq data from the KM plotter database (including lung cancer and colorectal adenocarcinoma cohorts) revealed that suppressed IFN-α signaling and high TIM3 expression are both associated with poor survival (Supplementary Fig. 4D-E). SGSEA analysis of interferon alpha/beta signaling genes in ICB responsive compared with ICB non-responsive melanoma patients, and found that interferon alpha/beta signaling genes are enriched in ICB responsive melanoma patients (Supplementary Fig. 4F), indicating that IFN-I signaling is important for the therapeutic effects of ICB 22,23.

Since monotherapies targeting TIM3 or IFNα showed limited efficacy in advanced tumors6,14,24, we proposed that targeting TIM3 positive cells with IFN could overcome such resistance by a combinatorial strategy. Strikingly, dual intraperitoneal administration of anti-TIM3 and Fc-IFNα additively improved therapeutic outcomes (Fig. 1H). However, even with this enhanced response, the combination failed to achieve complete eradication of established tumors but also caused severe toxicity (Fig. 1I), highlighting the need for further optimization. Collectively, our data suggested that combining IFN-I with TIM3 blockade additively enhanced control of advanced tumors.

BiDT elicits potent antitumor immunity in vivo
Given the superior antitumor efficacy of combined anti-TIM3 and IFNα therapy compared to monotherapies but severe IFNα-mediated toxicity (Fig. 1H and I), we need to reduce IFNα-associated toxicity while increasing tumor targeting. To achieve targeted delivery of IFNα to the TME, we adopted an antibody-based strategy, specifically using an anti-TIM3 antibody. Initial analysis in MC38 tumor-bearing mice confirmed that TIM3 expression was significantly higher on CD45⁺ immune cells within established tumors compared to those in the TDLNs of mice with advanced cancer (Supplementary Fig. 5A). This elevated and localized expression profile supported the rationale that a TIM-3-directed approach could facilitate the specific transport of IFN-α to the TME 25. Moreover, we further assessed the expression on multiple immune cells within the TME, finding the significantly highest level of TIM3 on CD8+ T cells, compared to it on DC and CD4+ T cells (Supplementary Fig. 5B). We proposed to use the high affinity of anti-TIM3 antibody to guide IFNα.

Concurrently, to enhance tumor-specific targeting and mitigate potential systemic toxicity, we needed to screen an IFNα fusion protein structure with lower IFNAR receptor affinity. This design aimed to ensure that the IFNα payload would be preferentially localized within the TME upon targeted delivery by the TIM3 antibody, rather than exerting effects in the periphery. To this end, we designed and compared two distinct architectures for the TIM3 antibody-IFNα fusion protein: a heterodimer format and a homodimer format (Fig. 2A). Subsequent evaluation aimed to identify the construct possessing the lower IFNα binding affinity for further development. In IFNAR-expressing TIM3⁻ cells, the heterodimer format exhibited significantly reduced binding of IFNα to IFNAR compared to the homodimer (Supplementary Fig. 5C). Based on these findings, the heterodimeric anti-TIM3-IFNα fusion protein (TIM3-IFNα) was selected as the lead construct, ensuring targeted delivery and localized activation of IFNα within the TME via the TIM3 antibody. This bispecific reagent is also engineered to concurrently target the immune checkpoint TIM3 on exhausted TILs and engage DC via the IFNAR, thereby facilitating the reactivation of exhausted tumor-reactive T cells through bispecific dendritic cell-T cell engagement (BiDT).

To evaluate the tumor-targeting efficacy of this designed BiDT, we conjugated it with a Cy5-maleimide tracer and administered it to tumor-bearing mice for whole-body imaging. Bioluminescence imaging revealed robust and specific accumulation of BiDT within tumor tissues (Supplementary Fig. 6A-B), confirming its targeted delivery to the TME and decreased the peripheral toxicity (Fig. 2B). Impressively, the systemic delivery of low dose BiDT in MC38 tumor-bearing mice enhances anti-tumor efficacy, resulting in complete tumor eradication among most murine subjects. In contrast, the combination of anti-TIM3 and Fc-IFNα failed to achieve comparable anti-tumor effects (Fig. 2C). Notably, BiDT treatment significantly improved overall survival compared to combination therapy (Fig. 2D). Together, these results suggest that BiDT’s efficacy has a synergistic effect for enhancing efficacy while reducing toxicity.

We wonder whether such DC-T cell interaction inside TME can also generate more tumor specific memory T cells for systemic protection. To test that, we harvested the splenocytes from mice 15 days after treatment. Indeed, we observed BiDT induced robust antigen-specific memory T-cell responses that move to the periphery, as evidenced by increased tumor-specific IFN-γ-producing cells in the spleens of tumor bearing mice (Fig. 2E and Supplementary Fig. 6C). Accordingly, we performed surface staining for memory markers on splenocytes. Our analysis revealed a significant increase in the proportion of central memory T cells within the splenocyte population following BiDT treatment (Supplementary Fig. 7A-B). Recognizing the importance of memory T-cell responses for long-term immunity26, we rechallenged BiDT-cured mice with a fourfold higher tumor cell dose 50 days post-treatment. The absence of tumorigenesis confirmed the induction of effective and prolonged memory responses (Fig. 2F). Furthermore, the adoptive transfer of splenocytes from tumor-free mice into Rag1⁻/⁻ recipients resulted in tumor rejection (Fig. 2G), further validating the role of memory T cells in enhancing tumor surveillance and efficiently recognizing and eliminating tumor cells. Lastly, to assess the impact on metastasis formation, a major cause of cancer mortality27, we utilized a pulmonary metastasis model. Mice bearing subcutaneous B16F10 tumors received an intravenous injection of additional B16F10 cells one day prior to low dose of BiDT treatment (Fig. 2H). Intratumoral BiDT administration significantly delayed local tumor growth (Fig. 2I) and suppressed lung metastasis formation (Fig. 2J and Supplementary Fig. 6D). Collectively, these findings indicate that BiDT induces a robust tumor-specific immune response that eradicates primary tumors, generates memory responses that are transferable, suppress metastasis, and prevent recurrence.

Pre-existing CD8 T cells and DC are essential and sufficient for the antitumor effect of BiDT.
TIM3 and IFNAR are widely expressed in immune cells, especially type I IFNs, which are versatile cytokines that bridge innate and adaptive immunity28,29. To fully understand the mechanism of BiDT, it is essential to identify which immune cells are involved in the treatment and determine the specific roles of TIM3 and IFNAR on these cells. Initial studies in Rag1-/- mice, which lack adaptive immunity but have enhanced NK cell activity 30, revealed a complete abrogation of BiDT’s antitumor effects, highlighting the pivotal contribution of the adaptive immunity in mediating therapeutic efficacy (Fig. 3A). To delineate the contribution of distinct T cell subsets to the therapeutic effect of BiDT, we performed depletion experiments using anti-CD4 or anti-CD8 antibodies. These studies revealed that the depletion of CD8+ T cells, but not CD4+ T cells, abrogated the therapeutic efficacy of BiDT (Fig. 3B). To assess the contributions of innate immune cells, we depleted NK cells by anti-NK1.1 and macrophages by anti-CSF1R in WT mice 31. Neither depletion impaired BiDT’s efficacy, indicating that these cell types are dispensable for its antitumor effects (Fig. 3C). Given the established role of DC in mediating type I IFN responses, we next evaluated their contribution to BiDT therapy. Using CD11c-DTR chimeric mice inoculated with MC38 tumors, we observed that DC depletion via diphtheria toxin (DT) completely abolished BiDT’s therapeutic effects, confirming the necessity of DC (Fig. 3D and E). These findings highlight the critical role of CD8+ T cells and DC in mediating the therapeutic effects of BiDT.

To investigate whether the therapeutic efficacy of BiDT relies on the continuous influx of T cells or preexisting T cells within the TME, we used FTY720, a compound that blocks T cell egress from lymph nodes, rapidly diminishing circulating T cells and halting their recruitment into tumors during treatment32. Results demonstrated that FTY720 did not compromise BiDT’s antitumor efficacy (Fig. 3F), indicating that preexisting T cells in the TME were sufficient to mediate tumor control. To further validate this conclusion, we conducted tumor transfer experiments: MC38 tumor tissues from wild-type mice were transplanted subcutaneously (s.c.) into Rag1-/- mice (which lack mature lymphocytes) and intratumoral injected with BiDT. In this model, all T cells present were derived exclusively from the transferred tumor tissue, excluding contributions from recipient lymph nodes. This approach corroborated that preexisting intratumoral T cells alone could drive BiDT’s therapeutic effect (Fig. 3G). Combined with the previously mentioned dominant roles of T cells and dendritic cells in the immune response, the data further suggest BiDT treatment relies on intratumoral DC and T cells. Collectively, these results pinpoint pre-existing CD8+ T cells, and DC are essential and sufficient for the antitumor effect of BiDT.

BiDT treatment enhances the crosstalk between DC and TIM3+ T cells
To dissect the contribution of IFNAR signaling in different cell populations to BiDT’s therapeutic efficacy33, we conducted a series of knockout (KO) experiments. Initially, to assess the impact of IFNAR signaling in tumor cells on BiDT-mediated antitumor activity, we evaluated its therapeutic efficacy in mice bearing Ifnar1-/- MC38 tumors. BiDT retained its antitumor activity in mice bearing Ifnar1-/- MC38 tumors, whereas its efficacy was completely abrogated in Ifnar1-/- mice harboring WT MC38 tumors (Fig. 4A and B). These findings indicated that IFNAR signaling in host cells, rather than tumor cells, is essential for the antitumor effects of BiDT. Next, we investigated the necessity of IFNAR signaling in CD8+ T cells for the efficacy of BiDT therapy. We adoptively transferred sorted WT or Ifnar1-/- CD8+ T cells into tumor-bearing Rag1-/- mice and treated them with BiDT. Notably, BiDT continued to inhibit tumor growth even in the absence of IFNAR signaling in CD8+ T cells, indicating that IFNAR expression on T cells is not required for the therapeutic effects of this combination therapy (Fig. 4C and D). To further clarify the role of IFNAR expression in DC within the TME, we conditionally deleted IFNAR in dendritic cells (Zbtb46-Cre Ifnar1 fl/fl). The therapeutic efficacy of BiDT was lost in mice with DC-specific IFNAR knockout (Fig. 4E). Together, our experiments highlight the importance of IFNAR on DC for the treatment of BiDT.

TIM3 is expressed on T cells as well as myeloid cells34. To further delineate the function of TIM3 in different immune cell populations, we specifically depleted TIM3 in DC (Zbtb46-Cre Havcr2fl/fl), macrophages (Lyz2-Cre Havcr2fl/fl), and T cells (Cd4-Cre Havcr2fl/fl). We observed that BiDT retained its therapeutic efficacy in mice lacking TIM3 on DC and macrophages but not in mice harboring T cell-specific TIM3 deletion (Fig. 4F and G), demonstrating that TIM3 engagement on T cells, but not myeloid cells, is critical for BiDT's antitumor activity. Further validating these findings, we investigated CD8+ T-cell dynamics in the MC38 tumor model. Treatment with BiDT resulted in a significant expansion of CD8+ T cells within the TME (Fig. 4H). Further analysis revealed that this increase was primarily driven by TIM3+CD8+ T cells, with no notable change in the TIM3-CD8+ T cell population (Fig. 4H and Supplementary Fig. 8A). Similar trends were observed in tumor-specific CD8+ T cells. In the MC38-OVA model, BiDT treatment led to a marked increase in Tetramer+CD8+ T cells within the TME (Fig. 4I). This expansion was predominantly attributed to TIM3+Tetramer+CD8+ T cells, while the TIM3-Tetramer+CD8+ T cell population remained largely unchanged (Fig. 4I and Supplementary Fig. 8B). These data confirmed the necessity of TIM3 expression on T cells for the antitumor effects of BiDT.

Collectively, these results establish that BiDT mediates its therapeutic effects primarily through IFNAR signaling in DC and TIM3 engagement on CD8+ T cells, providing a mechanistic basis of close DC-T interaction for reactivating TILs inside the TME, an important but previously difficult testing issue in cancer immunotherapy.

BiDT reactivates exhausted CD8+ TILs into a more effector-like state
To assess the functional impact of BiDT on TILs, we isolated intratumoral CD8+ T cells and performed single-cell RNA sequencing three days post-BiDT treatment. Our analysis revealed four molecularly distinct states of CD8+ T cells (Fig. 5A). Following BiDT monotherapy, the proportion of CD8+ T cells in the exhausted state was significantly reduced, while the proportion of CD8+ T cells in an effector-like exhausted state markedly increased (Fig. 5B, C). These phenotypically exhausted CD8+ T cells now exhibited elevated expression of genes associated with pro-inflammatory cytokines and cytokine receptors, including Ifng, Gzmb, Fasl, Il2ra, and Il10ra, as well as costimulatory molecules such as Cd28 and inhibitory receptors, compared to the exhausted counterparts (Fig. 5D, E). These findings suggest that BiDT reprogrammed exhausted CD8+ TILs into a more functional state. To further validate this hypothesis, we sorted and analyzed TIM3+ exhausted CD8+ T cells by flow cytometry 20 hours post-BiDT treatment. These cells displayed increased expression of pro-inflammatory cytokines and cytokine receptors, including IFN-γ, GZMB, IL-2, and CD25 (Fig. 5F and Supplementary Fig. 9A-D). Given the short timeframe for fusion protein moving into the TME to push the transcription and translation of those effector molecules, it is unlikely that these effector-like exhausted CD8+ T cells proliferated and differentiated from other subsets, supporting the notion that BiDT reactivates exhausted CD8+ TILs.

Gene set enrichment analysis (GSEA) showed the enrichment of the genes associated with the T cell receptor signaling pathway and the IL-2/STAT5 signaling pathway in effector-like exhausted CD8+ T cells (Fig. 5G), indicating that their enrichment is dependent on these signaling pathways. Collectively, these results suggest that BiDT reprograms exhausted CD8+ TILs into a more functional state, enhancing their capacity to mediate anti-tumor immunity.

Co-stimulatory and IL-2 signaling are essential for BiDT-mediated antitumor effects
To study if and how BiDT enhances DC function, we explored the potential key molecules on DC that are important for activating TILs. Our data showed that BiDT treatment significantly increased DC activation, as evidenced by elevated CD80 and CD86 expression (Fig. 6A, Supplementary Fig. 10A-C, and Supplementary Fig. 13A). We hypothesized that the therapeutic efficacy of BiDT relies on CD28-mediated T cell co-stimulation provided by CD80/86 interactions through DC-T cell crosstalk. To test this, we combined BiDT with anti-CD80/86 antibodies to block CD80/86-CD28 co-stimulation. Strikingly, this blockade completely abolished the anti-tumor effects of BiDT (Fig. 6B), underscoring the essential role of co-stimulatory signaling from DC to T cells in BiDT-mediated therapy.

Previous studies have demonstrated that CD28 co-stimulation enhances T-cell survival by inhibiting apoptosis and increasing the number of TILs11. We hypothesized that BiDT might similarly suppress apoptosis in exhausted T cells, thereby expanding TILs and tumor-specific T cells through CD80/86-CD28 co-stimulation. Single-cell RNA sequencing data revealed that BiDT upregulated Bcl2 expression in CD8+ T cells (Supplementary Fig. 11). Consistent with this, we observed a reduction in active caspase-3, a marker of apoptosis, and an increase in the anti-apoptotic protein Bcl-2 in exhausted T cells following BiDT treatment, indicating its anti-apoptotic effects (Fig. 6C, D and Supplementary Fig. 12A, B). Notably, the blockade of CD28 co-stimulation abrogated the anti-apoptotic function of BiDT (Fig. 6C, D and Supplementary Fig. 12A, B), highlighting the essential role of co-stimulatory signaling in BiDT-mediated therapy.

Analysis of the Cancer Genome Atlas (TCGA) database for clinical data revealed that DC infiltration and CD80/86 expression significantly correlated with the level of CD25 (Supplementary Fig.13D, E). As well known, CD25 is not only a marker of T cells but also a critical component of the IL-2 receptor (IL-2R)35,36. Further investigation into T cell activation and differentiation revealed that BiDT treatment induced higher expression of CD25 on TILs (Fig. 6E and Supplementary Fig. 12C). Blockade of CD80/86-CD28 co-stimulation reduced CD25 expression to baseline levels, indicating that BiDT-induced CD25 upregulation is dependent on co-stimulatory signaling (Fig. 6E). Higher CD25 expression correlates with the formation of high-affinity trimeric IL-2 receptor36,37, suggesting that BiDT might enhance the responsiveness of T cells to endogenous or exogenous IL-2. To test that, additional IL-2 overcame the resistance and achieved complete elimination of the more advanced tumors (Supplementary Fig. 13B, C), highlighting the synergistic role of IL-2 signaling in enhancing the antitumor efficacy of BiDT. Previous GSEA also highlighted the importance of IL-2 signaling in reprogramming exhausted CD8+ TILs into a more functional state. Therefore, we proposed that the antitumor effect of BiDT depended on the IL-2 signal pathway. BiDT restored IL-2 production in exhausted CD8+ T cells (Fig. 6F and Supplementary Fig. 12D). Blocking CD80/86-CD28 co-stimulation not only reduced CD25 expression but also decreased IL-2 production (Fig. 6E and F). Moreover, administration of anti-CD122 antibodies completely abolished BiDT's antitumor activity (Fig. 6H). This finding underscores the potential importance of DC-mediated T cell activation and IL-2 signaling in antitumor immunity. Thus, it opens avenues for more potent combinational strategies for more advanced cancer.

IL-2 stimulation could trigger CD8+ T cells to produce anti-tumor cytokines such as IFN-γ 19,38. BiDT monotherapy also enhanced the IFN-γ production of exhausted TILs (Fig. 6G and Supplementary Fig. 12E). Administration of an anti-CD80/86 blocking antibody in combination with BiDT resulted in a significant reduction in IFN-γ production (Fig. 6G). We proposed that BiDT reactivates TILs, leading to increased production of cytokines, particularly IFN-γ, by enhancing the IL-2 signaling pathway in TILs through CD80/86-CD28 co-stimulation. Notably, IFN-γ was highly produced in CD25+CD8+ T cells (Supplementary Fig. 12F and 13F), suggesting that IL-2 signaling reactivates exhausted TILs and enhances their cytokine production. To validate the role of IFN-γ, we administered an IFN-γ neutralizing antibody to BiDT-treated MC38 tumor-bearing mice, which completely abrogated the antitumor effects (Supplementary Fig.13G). This demonstrates that IFN-γ is essential for BiDT's efficacy.

In summary, BiDT facilitates physical and functional crosstalk between DC and TIM3⁺ exhausted CD8⁺ T cells inside the TME. IFN-I-mediated activation enhances DC’s expression of CD80/86, enabling them to deliver co-stimulatory signals to TIM3⁺ exhausted T cells via a physical bridge formed by BiDT. This interaction reactivates exhausted T cells, restores IL-2 signaling, and subsequently amplifies IFN-γ production. Ultimately, this cascade drives robust anti-tumor immunity.

BiDT overcomes immune checkpoint blockade resistance in cold tumors
Resistance to immune checkpoint blockade (ICB) in immunologically "cold" tumors, remains a major clinical challenge39,40. The B16F10 melanoma, a classic cold tumor model, exhibits poor responsiveness to ICB 41. Importantly, similar to the MC38 model, the B16F10 model is established in C57BL/6 mice, and tumor-infiltrating CD8⁺ T cells in this model also exhibit high TIM3 expression, consistent with the MC38 model (Supplementary Fig. 14A, B). We therefore investigated whether BiDT treatment could effectively eradicate cold tumors.

While BiDT monotherapy extended survival in the B16F10 model, it failed to achieve complete tumor eradication, with all tumors eventually recurring (Supplementary Fig. 14C). Analysis of the underlying mechanism revealed that PD-L1 expression was upregulated in DC within the TME following BiDT treatment (Fig. 6I and Supplementary Fig. 14D). This upregulation can be primarily attributed to the IFN-α component within the BiDT42,43. This IFNα-driven PD-L1 upregulation on DC indicated combining PD-L1 blockade with BiDT to counteract this adaptive resistance mechanism.

In the B16F10 model, anti-PD-L1 overcomes BiDT therapy resistance, achieving complete eradication in 40% cases (Fig. 6J-K). Critically, BiDT not only acted synergistically to eliminate tumors but also induced durable immune memory, preventing tumor recurrence upon rechallenge (Fig. 6L). These results position BiDT as a promising solution to the unmet clinical challenge of ICB resistance even in cold tumors, leveraging its dual ability to enhance T cell activation and reprogram immunologically inert tumor microenvironments.

Pro-BiDT maintained maximal antitumor activities with minimal toxicities
Despite FDA approval for melanoma and lymphoma treatment, the clinical use of type I IFN has been limited by severe systemic toxicity44. Acute side effects, such as flu-like symptoms, are driven by cytokine release (e.g., TNF, IL-1, IL-6), while subacute and chronic toxicities include hematological disorders such as leukopenia and thrombocytopenia45. To address these limitations, we engineered TIM3-ProIFNα (Pro-BiDT) (Fig. 7A), a tumor-activated prodrug derived from BiDT. Pro-BiDT incorporates a ProIFNα whereby the IFNα receptor is conjugated to matrix metalloproteinase (MMP) substrate, preventing IFNα from binding its receptor in peripheral tissues. MMPs, which are highly expressed in various tumor types and contribute to tumor angiogenesis and invasion, can cleave ProIFNα, ensuring activation of IFNα specifically within the TME 46,47.

Pro-BiDT demonstrated potent antitumor activity comparable to BiDT (Fig. 7B), even in the B16F10 cold tumor model (Fig. 7G). Importantly, Pro-BiDT significantly reduced systemic toxicity. While BiDT caused severe weight loss and elevated levels of pro-inflammatory cytokines (e.g., MCP-1, TNF, IFN-γ, IL-6), Pro-BiDT-treated mice showed no significant weight loss or cytokine elevation (Fig. 7C, D and Supplementary Fig.15A-C). Additionally, BiDT induced thrombocytopenia and leukopenia, whereas Pro-BiDT preserved normal platelet and leukocyte counts (Fig. 7E, F). In summary, Pro-BiDT maintains robust antitumor efficacy while minimizing systemic toxicity, offering a promising therapeutic strategy to overcome the limitations of conventional IFNα-based therapies.

Methods
Mice
C57BL/6 and Rag1-/- mice were obtained from GemPharmatech Co., Ltd (Stock Number: N000013 and T004753, respectively). Lyz2-Cre and Havcr2 fl/fl mice were provided by Gencheng Han (Beijing Institute of Basic Medical Sciences, Beijing). Cd4-Cre mice were a gift from Xiaohuan Guo (Tsinghua University, Beijing). Ifnar1-/- and Ifnar1fl/fl mice were sourced from Hua Peng (Institute of Biophysics, Chinese Academy of Sciences, China). Zbtb46-Cre mice were provided by Li Wu (Tsinghua University, Beijing). Female mice (7-8 weeks old) were selected based on their lower aggression, which facilitates group housing and reduces management requirements. All mice were maintained under specific pathogen-free conditions with 10/14 dark/light cycle, 20–26°C, and 30–70% humidity. Mice were euthanized by CO2 asphyxiation followed by cervical dislocation. Experimental animals and control animals were bred separately. All studies were approved by the Animal Care and Use Committee of Tsinghua University. The maximum tumor size allowed by the Animal Care and Use Committee of Tsinghua University is 1500 mm3, and we have adhered to this size limitation in our experiments.

Cell lines and reagents
MC38 cell line was purchased from Cytion (#305223). B16F10 cell line was obtained from the American Type Culture Collection (#CR6475). MC38-OVA was generated by selecting single-cell clones following lentiviral transfection with an OVA-expressing construct. Ifnar1-/- MC38 cell line was sourced from Mingzhao Zhu (Institute of Microbiology, Chinese Academy of Sciences, China). MC38, B16F10, and B16F10-OVA cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% heat-inactivated fetal bovine serum, penicillin (100 U/ml), and streptomycin (100 µg/ml) at 37°C under 5% CO2. Antibodies used in this study included anti-CD4 (GK1.5, Bioxcell #BE0003-1), anti-CD8 (53-5.8, Bioxcell #BE0004-1), anti-NK1.1 (PK136, Bioxcell #BE0036), anti-CSF1R (AFS98, Bioxcell #BE0213), anti-IFN-γ (XMG1.2, Bioxcell #BE0050), anti-CD80 (16-10A1, Bioxcell, #BE0024), anti-CD86 (GL-1, Bioxcell, #BE0025), and anti-IL-2Rβ (TM-β1, Bioxcell, #BE0298), all purchased from Bio X Cell. FTY720 was obtained from Sigma-Aldrich. A full list of the antibodies used in this study is provided in Supplementary Table 1.

Tumor inoculation and treatment
MC38 cells (5 × 105), MC38-OVA cells (1 × 106), Ifnar1-/- MC38 cells (1 × 106), or B16F10 cells (3 × 105) were subcutaneously injected into the right flank of mice. Tumor volume was measured twice weekly and calculated using the formula: length × width × height / 2. The criteria for early termination are 1500mm3 in tumor volume according to protocol of the Animal Care and Use Committee of Tsinghua University. For specific depletion of Macrophages, NK cells, CD4+ or CD8+ T cells, 500 μg anti-CSF1R, 200 μg anti-NK1.1, 200 μg anti-CD4, or 200 μg anti-CD8 was administered one day before treatment initiation and then twice weekly for two weeks. For anti-CD80 and anti-CD86 treatment, 200 μg anti-CD80 and CD86 were administered on the day of treatment initiation and then twice weekly for two weeks. For IL-2 or IFN-γ neutralization, 500 μg anti-IFN-γ was administered on the day of treatment initiation and then twice weekly for two weeks. To inhibit lymphocyte trafficking, FTY720 was administered at 20 μg one day prior to treatment initiation, then every other day for two weeks.

Production of BiDT and Pro-BiDT fusion protein.
On the basis of the heterodimeric Fc variant KiHss-AkKh technology50, the Fab fragment of anti-TIM3 was fused with the knob variant Fc region, and the IFNα or ProIFNα was fused with the hole variant Fc region. During the ProIFNα protein, the IFNα receptor and IFNα were joined with an MMP linker, which can be cleaved by MMP14. The entire sequence was then cloned into the pEE12.4 vector (Lonza). The anti-TIM3-knobs and IFNα-hole plasmids or ProIFNα-hole plasmids were transiently transfected into 293F cells at a ratio of 1:1. BiDT and Pro-BiDT were generated by transient co-transfection of two arms of plasmids into FreeStyle 293F cells. Supernatants were collected on day 7 after transfection. The supernatant containing bispecific antibodies was purified using Protein A affinity chromatography according to the manufacturer’s protocol.

Fluorescence imaging
TIM3-IFNα was labeled with Sulfo-Cy5 NHS ester (RuixiBiotech Co., Ltd) and purified to remove unbound Cy5. Cy5-labeled TIM3-IFNα or EGFR-IFNα was intravenously injected into MC38-bearing C57BL/6 mice. Tumor accumulation of the Cy5-labeled proteins was quantified using the IVIS Spectrum imaging system (PerkinElmer). Fluorescence imaging data were analyzed using Living Image software (PerkinElmer).

Flow cytometry
Mice were intraperitoneally (i.p.) injected with 250 μg of brefeldin A solution (BFA) six hours before being euthanizing. Tumor tissues were harvested, minced into small fragments, and enzymatically digested using 100 μg/ml DNase I (Roche) and 1 mg/ml collagenase IV (Roche) at 37°C for 40 minutes. The digested tissues were then filtered through a 70 μm cell strainer to generate single-cell suspensions. Cells were blocked with anti-CD16/32 antibody (clone 93) for 30 min and subsequently stained with fluorochrome-conjugated antibodies for 30 minutes at 4℃. For intracellular staining, cells were fixed, permeabilized, and incubated with specific antibodies for 1 hour on ice. All fluorescently labeled monoclonal antibodies were obtained from BioLegend or eBioscience. Samples were analyzed using a FACS Calibur or Fortessa flow cytometer, and data were processed using FlowJo software (TreeStar).

TCGA database analysis
Correlation of CD25 expression with DC infiltration or CD86 expression, and correlation of IFN-γ expression with CD25 expression were analysed using Tumor Immune Estimation Resource (TIMER; https://cistrome.shinyapps.io/timer/).

Sample preparation for scRNA-seq
Live CD45+ cells were sorted from MC38 tumors by FACS, and five biological replicates were amalgamated for library construction. Single-cell transcriptomic libraries were generated using the commercial workflow specified by 10x Genomics. Sequencing was executed on an Illumina NovaSeq 6000 platform (Singleron Biotechnologies, Beijing, China) employing paired-end sequencing with 150 bp read length.

Data analysis for scRNA-seq
Raw sequencing data were processed using Cell Ranger (v7.1.0), and downstream analysis was conducted via R (v4.2.2) and Seurat (v4.1.0). Cells with mitochondrial gene content below 10%, Hsp gene content below 3%, n_Feature RNA between 300 and 6000, n_Count RNA below 40000 were retained. Data normalization, scaling and identification of variable genes were performed via NormalizeData, ScaleData, and FindVariableFeatures respectively. For dimensionality reduction, 2,000 highly variable genes were selected for PCA reduction using RunPCA, followed by 2D visualization using FindNeighbors, FindClusters and RunTSNE or RunUMAP. CD8+ T cells were selected for further clustering and downstream phenotype analysis. Signature gene sets for GSEA analysis were downloaded from mouse MSigDB (the Molecular Signatures Database)51.

Statistical analysis
Data are presented as mean ± SEM. Statistical analyses were conducted using GraphPad Prism software. P values were calculated using unpaired two-tailed Student’s t-tests, paired t tests or One-way ANOVA with Tukey’s test. Survival curves were compared using the log-rank test. Significance levels are denoted as follows: *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The paper by Zhang et al presents the preclinical development and effects of a novel therapeutic approach called BiDT (Bispecific DC-T cell engager), designed to rejuvenate exhausted tumor-infiltrating T cells (TILs) and enhance anti-tumor immunity. BiDT and Pro-BiDT represent innovative strategies to improve DC-T cell interactions within the TME, rejuvenate exhausted T cells, and strengthen anti-tumor immunity.The design aims to enforce physical and functional interaction in the tumor microenvironment, specifically targeting functionally impaired tumor-resident exhausted CD8+ T cells and DCs.\nThe data show high tumor-specificity with tolerable toxicity of the engineered BiDT. Subsequently, the authors use a M38 tumor model system to show high anti-tumor efficacy of BiDT treatment in vivo. Furthermore, the BiDT induces robust memory responses and limits metastasis formation. Single cell RNA seq of CD8+ TILs 3 days post BiDT treatment revealed significant functional reinvigoration of exhausted T cells. In extensive in vivo knockout studies, the authors demonstrate that this effect can be lead back to the enforced co-stimulation via the B7-CD28 axis, which results in increased IL-2 signaling resulting in a downstream Bcl-2 Bcl-2-mediated apoptosis resistance in the respective T cells. Finally the authors further introduce a pro-drug format of the described BiDT, incorporating pro-IFNa, thus overcoming the associated toxicities. My main concern of the presented data is the definition of T cell exhaustion: most of the data is based on upregulation of inhibitory checkpoint molecules, however, these are markers that are upregulated in every activated T cell. The presented data is missing a more thorough phenotypic description of different types of exhausted T cells and importantly, at least some representative data in loss of function, e.g. cytotoxicity, cytokine secretion, T cell proliferation. Specifically, the following points need to be addresse:\nRecurring issues:\n-Figure 2-7: typo - tumor volume is spelled incorrectly (y-axis labeling)\n-Figure 1-7: inconsistent labeling of the control group (sometimes Ctrl, sometimes PBS)\n-Figure 1I: Can you comment on the depicted timepoints in relation to Fig1H? The graph depicts day 0-10 post tumor inoculation, however according to the figure legends (line 610) the indicated treatment is only administered after 9 days. This issue is recurring in multiple figures (see below), maybe just a labeling issue – days after treatment?\n-Figure 2B: coverage of graph (day 0-10, treatment initiation after 13 days (line 620))\n-Figure 7C: coverage of graph (day 0-10, treatment initiation after 13 days (line 765))\nMinor issues:\n-Line 81: they could briefly state the cell type of the used cell line MC38\n-Line 99: rephrase or cut – ‘markedly’ elevated compared to intermediate exhausted does not seem appropriate\n-Line 101 & Line 105: please include a relevant reference\n-Line 108-110 - Extended Data Fig. 1 D+E: Please rephrase, current phrasing is misleading, current form gives the impression of a total correlation between all three conditions in both referred datasets, however two independent correlations\n-Line 113 - Fig. 1H: I would claim that the described effect is rather additive than synergistic – can you provide adequate calculation to prove the claimed synergistic effect?\n-Fig 2E: The authors could include a surface staining for memory markers.\n-Fig 5B: I would suggest using the same color coding for the naïve/ stem-like population as in Fig 1\n-Line 246: typo - Fig. 5B\n-Fig 5F: I would suggest using the same color coding for the control group as in previous figures\n-Line 285: please rephrase or remove ‘completely’ as inappropriate for Fig. 6D\n-Line 297-298: syntax - please rephrase the sentence\n-Line (315-316) - Extended Data Fig. 4F: Indicated correlation is minor, maybe simply cut the sentence\n-Line 337: grammar - overcomes\n-Line 351: please introduce the abbreviation of MMP\n-Extended Data Fig. 3: Please include figure legend for subfigure B\n-Extended Data Fig. 3: Picture in panel A is hardly visible\nFurther remarks:\n-Figure 1&5: In general, I don’t fully agree with the authors’ definition for the distinct exhausted subsets (intermediate exhausted, terminal exhausted, functional terminal exhausted). To identify these subsets only based on their phenotype, especially when only looking at expression of Tim-3 and PD-1 seems overly simplified. Usually, we like to combine phenotyping with more functional data. Especially in the TME, it is known that the subset of tumor-resident T cells (TRM) but also activated tumor-reactive T cells might express checkpoint receptors as a result of their activation status.\n-Figure 1: Could you extend the expression profile with more precise markers, which are commonly used in literature to identify certain subsets of T cells? Examples would be CX3CR1 (for intermediate exhausted), CD39 and CD101 (for terminal exhausted) T cells. Furthermore, I would advise to check for markers of the TRM (CXCL13; CD103; …). It could also be interesting to look at NK-like markers, which could identify an intermediate exhausted subset of special interest due to its relatively high function.\nIn addition, I would like to see some ex vivo functional assessment (eg. Cytotoxicity or cytokine production) to have some functional evidence of exhaustion.\n-Figure 5: Per definition, terminally exhausted T cells are mostly unresponsive upon restimulation, therefore I do not agree with the term of ‘functional terminal exhausted’ T cells. Again, further markers might help to achieve a clearer distinction. While the authors show strong functional reinvigoration after 3 days, in order to speak of thorough ‘functional reprogramming’ and exclude a transient effect, I would like to see data from a later timepoint (eg. 14 days+ post treatment).", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "Review:\nZhang et al. report the development of a bispecific T-cell engager that combines an anti-TIM-3 antibody with an IFN-α fusion protein (BiDT) to explore the essential role of the interaction of tumor-infiltrating lymphocytes and dendritic cells (DC) in anti-tumor response. The authors demonstrate that BiDT selectively accumulates in the tumor microenvironment (TME) and reactivates TIM-3⁺ exhausted CD8⁺ TILs via interaction with intratumoral dendritic cells (DCs). This T cell–DC crosstalk is shown to depend on CD28 co-stimulation and IL-2 signaling, leading to enhanced anti-tumor immunity and the generation of memory T cell responses. The study presents a novel immunotherapeutic approach that combines immune checkpoint targeting with local cytokine release and has potential translational relevance. It is well structured, and the in vivo results are promising. However, several major conceptual, statistical, and methodological issues limit the strength of the submitted manuscript and its conclusion.\nMajor Comments:\n1. The statistical methodology is inaccurate and may lead to incorrect results. Throughout the manuscript, the authors apply unpaired t-tests to compare groups without considering whether data are paired or if more than two groups are compared. For example, Figure 1D/E contains paired measurements from the same animals, which requires a paired t-test or a Wilcoxon signed-rank test. Figures involving more than two groups (e.g., Fig. 1F/G, 2C, 6A) should use ANOVA with appropriate post hoc corrections (e.g., Bonferroni). Given the small sample sizes (n<10) in the presented data, assuming normality is unlikely, and the use of parametric tests without validation is questionable.\n2. Overall, the translational aspect is not sufficiently addressed. The origin of the project is based on the analysis of mouse samples. Although the authors have included analyses of public RNAseq datasets, these are limited to the expression of TIM-3 and IFNAR in colorectal and lung cancer, respectively, with only one of the markers tested in each entity. In vivo validation, on the other hand, was performed in mouse models for colorectal and melanoma cancer. In addition, there have been no direct tests or analyses of TIL composition in human cancer samples. A clearer link to patient data and validation in human samples, as well as further discussion of clinical applicability, is needed to demonstrate translational relevance.\n3. The rationale for switching between MC38 (colorectal carcinoma) and B16-F10 (melanoma) models is unclear. Moreover, it is not stated whether BiDT shows comparable efficacy in both models or whether TIM-3⁺ TIL populations differ between tumor types. A brief overview of tumor model characteristics should be added early in the results section, along with consistent use of abbreviations and proper figure labeling.\n4. The authors present body weight loss as a surrogate for treatment-associated toxicity, both in the initial experiments (Figure 1 and 2) and in the evaluation of the Pro-BiDT (Figure 7C). However, the body weight data are shown only up to day 10 post tumor inoculation, while treatment begins on day 11. This raises concerns about the relevance of the reported weight loss to treatment-related toxicity. Clarification or correction is needed.\n5. As the developed bispecific antibody is central to the study. However, aside from a schematic illustration in two figures, there is no detailed description of the antibody’s molecular design, production, or validation. Key information, such as the format, linker and expression system is entirely missing, and no methods section is provided. For transparency and reproducibility, a comprehensive description of the construct’s development is necessary.\nMinor comments:\n• Abbreviations are not defined consistently throughout the manuscript and figure legends.\n• Lines 89–93: TIM-3 alone is used to define exhausted T cells - was co-expression with other exhaustion markers assessed?\n• Lines 100–101: Reference is missing.\n• Lines 297–298: The Sentence is unclear and needs revision.\n• Line 309: Reference is missing.\n• Line 310: No figure reference is provided for the described result.\nFigure-specific comments:\nFigure 1:\n- Panel E: Consider adjusting the x-axis to start at 0 and end at 100 to better reflect relative proportions and align with panel D.\n- The main text mentions \"IFN alpha,\" while panels H and I refer to \"Fc-IFN alpha.\" Please clarify if these are the same or different reagents.\nFigure 2:\n- Inconsistent terminology: \"IFN alpha\" is used in panels B and C, but \"Fc-IFN alpha\" appears in the legend.\n- Line 627: The legend refers to panel D, but panel D contains a survival curve.\nFigure 6:\n- Panel H: The figure refers to \"anti-CD122,\" while the legend states \"anti–IL-2β receptor.\" Maybe consider a uniform naming.\nExtended Data Figure 3:\n- The figure contains panels A, B, and C, but only panels A and B are described in the legend. This should be corrected.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "RESPONSE TO REVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThe paper by Zhang et al presents the preclinical development and effects of a novel\ntherapeutic approach called BiDT (Bispecific DC-T cell engager), designed to\nrejuvenate exhausted tumor-infiltrating T cells (TILs) and enhance anti-tumor\nimmunity. BiDT and Pro-BiDT represent innovative strategies to improve DC-T cell\ninteractions within the TME, rejuvenate exhausted T cells, and strengthen anti-tumor\nimmunity. The design aims to enforce physical and functional interaction in the tumor\nmicroenvironment, specifically targeting functionally impaired tumor-resident\nexhausted CD8+ T cells and DCs.\nThe data show high tumor-specificity with tolerable toxicity of the engineered BiDT.\nSubsequently, the authors use M38 tumor model system to show high anti-tumor\nefficacy of BiDT treatment in vivo. Furthermore, the BiDT induces robust memory\nresponses and limits metastasis formation. Single cell RNA seq of CD8+ TILs 3 days\npost BiDT treatment revealed significant functional reinvigoration of exhausted T cells.\nIn extensive in vivo knockout studies, the authors demonstrate that this effect can be\nlead back to the enforced co-stimulation via the B7-CD28 axis, which results in\nincreased IL-2 signaling resulting in a downstream Bcl-2 Bcl-2-mediated apoptosis\nresistance in the respective T cells. Finally, the authors further introduce a pro-drug\nformat of the described BiDT, incorporating pro-IFNa, thus overcoming the associated\ntoxicities. My main concern of the presented data is the definition of T cell exhaustion:\nmost of the data is based on upregulation of inhibitory checkpoint molecules, however,\nthese are markers that are upregulated in every activated T cell. The presented data is\nmissing a more thorough phenotypic description of different types of exhausted T cells\nand importantly, at least some representative data in loss of function, e.g. cytotoxicity,\ncytokine secretion, T cell proliferation. Specifically, the following points need to be\naddresse:\nRecurring issues:\n-Figure 2-7: typo - tumor volume is spelled incorrectly (y-axis labeling).\nWe are grateful to the reviewer for pointing out these issues; they have been diligently\naddressed in the revised manuscript.\n-Figure 1-7: inconsistent labeling of the control group (sometimes Ctrl, sometimes PBS)\nWe thank the reviewer for the suggestion; the control groups have been revised to the\nuniform label \"Ctrl\".\n-Figure 1I: Can you comment on the depicted timepoints in relation to Fig1H? The\ngraph depicts day 0-10 post tumor inoculation, however according to the figure legends\n(line 610) the indicated treatment is only administered after 9 days. This issue is\nrecurring in multiple figures (see below), maybe just a labeling issue – days after\ntreatment?\n-Figure 2B: coverage of graph (day 0-10, treatment initiation after 13 days (line 620))\n-Figure 7C: coverage of graph (day 0-10, treatment initiation after 13 days (line 765))\nThank you to the reviewer for pointing out the mistake. We have corrected the errors,\nand the horizontal axis now represents days post-treatment. Therefore, the body weight\nchanges shown in the graph correspond to the changes at the relevant time points\nfollowing the administration of the treatment.\nFig R1. (A) The weight change after treatment for Figure 1I. (B) The weight change\nafter treatment for Figure 2B. (C) The weight change after treatment for Figure 7C.\nMinor issues:\n-Line 81: they could briefly state the cell type of the used cell line MC38.\nThank you for the reviewer’s suggestion. For clarity, we have added more information\nand references regarding the MC38 cell line in the article.\nThe MC38 murine colon adenocarcinoma cell line, established in 1975 from a\nchemically induced tumor in female C57BL/6 mice, is a widely used model in cancer\nresearch and therapy development (1).\n-Line 99: rephrase or cut – ‘markedly’ elevated compared to intermediate exhausted\ndoes not seem appropriate\nThank you for the reviewer’s comment. We have revised the manuscript and removed\nthe word \"markedly.\"\n-Line 101 & Line 105: please include a relevant reference\nThank you for the reviewer’s suggestion. We have added the relevant references for this\nparagraph.\n-Line 108-110 - Extended Data Fig. 1 D+E: Please rephrase, current phrasing is\nmisleading, current form gives the impression of a total correlation between all three\nconditions in both referred datasets, however two independent correlations.\nWe thank the reviewer for the suggestion and have modified the manuscript accordingly\nto state that poor patient survival is correlated with low IFN-α signaling and high TIM3\nexpression. Specifically, analysis of clinical RNA-seq data from the KM plotter\ndatabase (including lung cancer and colorectal adenocarcinoma cohorts) revealed that\nsuppressed IFN-α signaling and high TIM3 expression are both associated with poor\nsurvival.\n-Line 113 - Fig. 1H: I would claim that the described effect is rather additive than\nsynergistic – can you provide adequate calculation to prove the claimed synergistic\neffect?\nThank you for the review and advice. We calculated the results, which showed a\nCombination Index (CI) of 1.246, indicating that the two drugs have an additive effect\n(CI < 1) but not synergistic effect. We have modified the description in article. The\ncalculation process is outlined below:\nThe tumor growth inhibition for each treatment group was first calculated as the\nFraction Affected (Fa), where: Fa = 1 – (Mean Tumor Volume of Treatment Group /\nMean Tumor Volume of PBS Control Group)\nA Fa of 0 indicates no effect, while a Fa of 1 indicates 100% inhibition (complete tumor\neradication). The Combination Index (CI) was then calculated using the formula: CI =\n(Fa of Agent A / Fa of Combination) + (Fa of Agent B / Fa of Combination)\nA CI < 1 indicates synergy.\nBased on the mean tumor volumes at Day 30, the Fraction Affected (Fa) for each group\nwas calculated as follows:\nPBS Control (Fa_control): 0; ANTI-TIM3 alone (Fa_A): 1 – (526.55 / 805.63) = 0.346;\nFc-IFNα alone (Fa_B): 1 – (256.97 / 805.63) = 0.681; Combination (Fa_AB): 1 –\n(141.60 / 805.63) = 0.824; The Combination Index (CI) was then determined: CI =\n(0.346 / 0.824) + (0.681 / 0.824) = 0.420 + 0.826 = 1.246\n-Fig 2E: The authors could include a surface staining for memory markers.\nThank you for this suggestion. Accordingly, we performed surface staining for memory\nmarkers on splenocytes. Our analysis revealed a significant increase in the proportion\nof central memory T cells within the splenocyte population following BiDT treatment\n(Fig. R2A and B). This finding suggests that our intervention promotes the development\nof a long-lived memory pool, enhancing the durability of the anti-tumor immune\nresponse.\nFig R2. BiDT treatment promotes the development of central memory T cells in spleen.\nMC38-OVA tumor-bearing mice (n = 8/group) were i.p. treated with 25 μg BiDT on\nday 13, 16 and 19 post inoculation. At 15 day after treatment, splenocytes from different\ntreatment groups were isolated. The proportion of central memory T cells were detected\nby ELISpot assay. (A) The representative FACS images of CD44+CD62L+ in CD8+T\ncells. (B) The percentage of CD44+CD62L+ in CD8+T cells were measured by flow\ncytometric analysis. Data are shown as mean ± SEM and are representative of at least\ntwo independent experiments. P value was determined by unpaired two-tailed t tests\n(B).\n-Fig 5B: I would suggest using the same color coding for the naïve/ stem-like\npopulation as in Fig 1.\n-Line 246: typo - Fig. 5B\n-Fig 5F: I would suggest using the same color coding for the control group as in\nprevious figures\nWe thank the reviewer for this suggestion. We have now modified the color coding to\nensure that the same color is used consistently for the same cell population throughout\nthe figures.\n-Line 285: please rephrase or remove ‘completely’ as inappropriate for Fig. 6D\nWe thank the reviewer for the suggestion. We have revised the manuscript and removed\nthe word \"completely\".\n-Line 297-298: syntax - please rephrase the sentence\nWe thank the reviewer for pointing out the errors; the grammar issues in this sentence\nhave been corrected in the revised version:\nTo test that, additional IL-2 overcame the resistance and achieved complete elimination\nof the more advanced tumors (Fig. S4B, C), highlighting the synergistic role of IL-2\nsignaling in enhancing the antitumor efficacy of BiDT.\n-Line (315-316) - Extended Data Fig. 4F: Indicated correlation is minor, maybe simply\ncut the sentence.\nThanks for your suggestion. We have deleted this sentence.\n-Line 337: grammar - overcomes\nThank the reviewer for pointing out the errors; we have corrected the grammar issues\nin this sentence.\n-Line 351: please introduce the abbreviation of MMP.\nWe thank the reviewer for this suggestion. We have now introduced the abbreviation\n\"MMP\" and added a brief description of its function. The revised text reads as follows:\nPro-BiDT incorporates an IFNα receptor linked by matrix metalloproteinase (MMP)\nsubstrate to prevent IFNα from binding its receptor in peripheral tissues. MMPs, which\nare highly expressed in various tumor types and play a role in tumor angiogenesis and\ninvasion, can cleave ProIFNα, ensuring activation of IFNα specifically within the TME\n(2, 3).\n-Extended Data Fig. 3: Please include figure legend for subfigure B\nThank you for pointing out the error. We have made the necessary corrections and added\nthe description for Supplementary Figure 3.\nThe content has now been revised as follows:\nFigure S3. BiDT could accumulate within tumor tissue.\n(A-B) C57BL/6 mice (n=7) were inoculated with 5×105 MC38 cells. Tumor-bearing\nmice were i.v. treated with 50 μg of Cy5-labeled TIM3-IFNα or Cy5-labeled EGFR-\nIFNα as control group on day16. IVIS images of the tumor distribution of Cy5-labeled\nTIM3-IFNα or EGFR-IFNα (A) and imaging fluorescence intensity statistical results\n(B).\n(C) MC38-OVA tumor-bearing mice (n = 6/group) were i.p. treated with 25 μg BiDT\non day 13, 16 and 19 post inoculation. At 15 day after treatment, splenocytes from\ndifferent treatment groups were isolated and stimulated with either OT-I peptide or\nfreeze thawing MC38-OVA tumor cells. Antigen-specific T cells were detected by\nELISpot assay. The splenocyte representative spots are shown in (C).\n(D) C57BL/6 mice (n = 10/group) were subcutaneously inoculated with 3×105 B16F10\ntumor cells and injected with 2×106 B16F10 tumor cells through the tail vein on day\n10. The subcutaneous tumor was intratumorally treated with 5 μg BiDT on days 11, 14,\nand 17. The mice were sacrificed on day 21 post inoculation. The lungs were collected.\nThe five representative pictures of all ten samples in each group and number of B16F10\ntumor metastatic nodes in the lung (D).\nData are shown as mean ± SEM and are representative of at least two independent\nexperiments. P value was determined by unpaired two-tailed t tests (B).\n-Extended Data Fig. 3: Picture in panel A is hardly visible.\nWe thank the reviewer for this suggestion. We have readjusted the fluorescence contrast.\nIn the non-targeted control group (EGFR-IFNα), which shares the same scaffold\nstructure, the fluorescence intensity was low due to the lack of antibody-mediated\nfusion protein aggregation. Therefore, we have annotated the tumor regions with red\ncircles. This contrast further validates the superior tumor-targeting capability of TIM3-\nIFNα.\n、\nFig R3. C57BL/6 mice (n=7) were inoculated with 5×105 MC38 cells. Tumor-bearing\nmice were i.v. treated with 50 μg of Cy5-labeled TIM3-IFNα or Cy5-labeled EGFR-\nIFNα as control group on day16. IVIS images of the tumor distribution of Cy5-labeled\nTIM3-IFNα or EGFR-IFNα.\nFurther remarks:\n-Figure 1&5: In general, I don’t fully agree with the authors’ definition for the distinct\nexhausted subsets (intermediate exhausted, terminal exhausted, functional terminal\nexhausted). To identify these subsets only based on their phenotype, especially when\nonly looking at expression of Tim-3 and PD-1 seems overly simplified. Usually, we like\nto combine phenotyping with more functional data. Especially in the TME, it is known\nthat the subset of tumor-resident T cells (TRM) but also activated tumor-reactive T cells\nmight express checkpoint receptors as a result of their activation status.\n-Figure 1: Could you extend the expression profile with more precise markers, which\nare commonly used in literature to identify certain subsets of T cells? Examples would\nbe CX3CR1 (for intermediate exhausted), CD39 and CD101 (for terminal exhausted)\nT cells. Furthermore, I would advise to check for markers of the TRM (CXCL13; CD103; …). It could also be interesting to look at NK-like markers, which could\nidentify an intermediate exhausted subset of special interest due to its relatively high\nfunction.\nIn addition, I would like to see some ex vivo functional assessment (eg. Cytotoxicity or\ncytokine production) to have some functional evidence of exhaustion.\nThanks for your comments and suggestion. We have checked more markers to define\nthe cell subsets in scRNA-seq and have renamed the exhausted subsets (intermediate\nexhausted, exhausted, effector-like exhausted, Fig R4A, Fig R4C, Fig R5B and Fig\nR5D, related Fig1A, Fig1C, Fig.5B and Fig.5D in article). Intermediate exhausted\nCD8+ T cells expressed not only lower Pdcd1, Havcr2, Lag3 and Tigit but also higher\nTbx21 (usually be used to define Tex_int) and Tnf at scRNA-seq level, compared with\nexhausted CD8+ T cells. Intermediate exhausted CD8+ T cells also showed lower Gzmb\nand Gzmc expression, which are often highly expressed in late-stage differentiated\nexhausted CD8+ T cells (Fig R4C, Fig R5D). We have removed the word “terminal”\nsince these exhausted CD8+ T cells expressed cytotoxic molecules like Ifng and Gzmb\nat scRNA-seq level. Effector-like exhausted CD8+ T cells, which were induced by BiDT\ntreatment, expressed higher proliferation and functional markers like Mki67, Ccl3,\nCcl4,\nIfng, Gzmb, Fasl and so on.\nFig R4. Tumor-Reactive T Cells are exhausted in mouse MC38 tumors. (A-C)\nC57BL/6 mice (n = 3) were inoculated with 5×105 MC38 cells. CD8+ T cells were\nsorted for single-cell RNA sequencing 19 days after tumor inoculation. (A) UMAP\nplot of 2,199 CD8+ T cells exhibiting four cell subsets. (B) Expression of selected\nmarkers among CD8+ T cells by 2D UMAP visualization. (C) Heatmap plot showing\naverage relative expression of selected markers in four CD8+ T cell subsets. Related to\nFig.1 (A-C) in article.\nFig R5. (related Fig.5 in article) BiDT treatment reprogrammed exhausted CD8+ TILs\ninto effector-like exhausted CD8+ TILs. (B) UMAP plot of 5,856 CD8+ T cells\nshowing four cell subsets (2,169 cells from control group and 3,687 cells from BiDT\ntreated group). (C) Cell compositions of CD8+ T cell subsets from control and BiDT\ntreated group. (D) Heatmap plot showing average relative expression of selected\ngenes across four CD8+ T cell subsets depicted in (B).\nAs you suggested, we also checked the expression of Cx3cr1, Entpd1(CD39), Cd101,\nCxcl13, Itgae(CD103) among different CD8+ T cell subsets at scRNA-seq level.\nHowever, the expression of Cd101, Cxcl13 and Itgae are nearly undetectable, maybe\nbecause tissue resident CD8+T cells are rare in this MC38 subcutaneous tumor model.\nThe expression of Cx3cr1 and Entpd1 are comparable between intermediate exhausted\nCD8+ T cells and exhausted CD8+ T cells, so we did not use them as clustering markers\n(Fig1C). We also checked the relative transcription level of Slamf6, Tox and Cd69, but\nthe sequencing depth seems not enough to detect these genes. Therefore, we used Tbx21,\nTnf together with Pdcd1, Havcr2, Lag3 and Tigit to distinguish intermediate exhausted\nand exhausted CD8+ T cells.\nFig R6. Expression of selected markers among CD8+ T cells by 2D UMAP visualization.\nWe used anti-CD3 and anti-CD28 antibodies to stimulate CD8+ T cells for either 11 days\n(long-term stimulation) or 3 days (short-term stimulation), inducing TIM3+CD8+ T cells\nand non-exhausted TIM3- CD8+ T cells, respectively. When comparing the activity of the\ntwo cell groups, we found that the proportion of IFN-γ in TIM3+CD8+ T cells was\nsignificantly reduced, indicating a decline in cytokine production capacity.\nFig. R7 The expression of IFN-γ+ in TIM3- CD8+ T cells or TIM3+ CD8+ T cells in vitro.\nFig. R7(A) The representative FACS images of IFN-γ+ in TIM3- CD8+ T cells or TIM3+ CD8+ T cells. (B) The percentage of IFN-γ+ in TIM3- CD8+ T cells or TIM3+ CD8+ T cells were measured by flow cytometric analysis.\nData are shown as mean ± SEM and are representative of at least two independent\nexperiments. P value was determined by unpaired two-tailed t tests (B).\n-Figure 5: Per definition, terminally exhausted T cells are mostly unresponsive upon\nrestimulation, therefore I do not agree with the term of ‘functional terminal exhausted’\nT cells. Again, further markers might help to achieve a clearer distinction. While the\nauthors show strong functional reinvigoration after 3 days, in order to speak of thorough\n‘functional reprogramming’ and exclude a transient effect, I would like to see data from\na later timepoint (eg. 14 days+ post treatment).\nThanks for your comments. We have removed the word “terminal” since these\nexhausted CD8+ T cells actually expressed cytotoxic molecules like Ifng and Gzmb at\nscRNA-seq level. As mentioned above, we have renamed the exhausted subsets based\non more markers (intermediate exhausted, exhausted, effector-like exhausted, Fig R4A,\nFig R4C, Fig R5B and Fig R5D, related Fig1A, Fig1C, Fig.5B and Fig.5D in article).\nOn day 14 post-treatment, we analyzed the state of exhausted T cells in tumor tissue.\nWe found that, compared to the control group, TIM3+CD8+ T cells in the BiDT-treated\ngroup still maintained significantly higher levels of IFNγ production (Fig. R8 A-B).\nFig. R8 (A-B) MC38-bearing mice (n = 6/group) were treated with 25 µg BiDT on day\n16. The mice were sacrificed, and tumor tissues were collected 14 days after injection.\n(A) The representative FACS images of IFN-γ+ in TIM3+ CD8+ T cells. (B) The\npercentage of IFN-γ+ in TIM3+ CD8+ T cells were measured by flow cytometric analysis.\nData are shown as mean ± SEM and are representative of at least two independent\nexperiments. P value was determined by unpaired two-tailed t tests (B).\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nReviewer #3 (Remarks to the Author):\nReview:\nZhang et al. report the development of a bispecific T-cell engager that combines an anti-\nTIM-3 antibody with an IFN-α fusion protein (BiDT) to explore the essential role of\nthe interaction of tumor-infiltrating lymphocytes and dendritic cells (DC) in anti-\ntumor response. The authors demonstrate that BiDT selectively accumulates in the\ntumor microenvironment (TME) and reactivates TIM-3⁺ exhausted CD8⁺ TILs via interaction\nwith intratumoral dendritic cells (DCs). This T cell–DC crosstalk is shown to depend\non CD28 co-stimulation and IL-2 signaling, leading to enhanced anti-tumor immunity\nand the generation of memory T cell responses. The study presents a novel\nimmunotherapeutic approach that combines immune checkpoint targeting with local\ncytokine release and has potential translational relevance. It is well structured, and the\nin vivo results are promising. However, several major conceptual, statistical, and\nmethodological issues limit the strength of the submitted manuscript and its conclusion.\nMajor Comments:\n1. The statistical methodology is inaccurate and may lead to incorrect results.\nThroughout the manuscript, the authors apply unpaired t-tests to compare groups\nwithout considering whether data are paired or if more than two groups are compared.\nFor example, Figure 1D/E contains paired measurements from the same animals, which\nrequires a paired t-test or a Wilcoxon signed-rank test. Figures involving more than two\ngroups (e.g., Fig. 1F/G, 2C, 6A) should use ANOVA with appropriate post hoc\ncorrections (e.g., Bonferroni). Given the small sample sizes (n<10) in the presented\ndata, assuming normality is unlikely, and the use of parametric tests without validation\nis questionable.\nWe are grateful to the reviewer for their valuable comments. In response, we have\nmodified the data analysis methods and noted the corresponding revisions in the figure\nlegends.\n2. Overall, the translational aspect is not sufficiently addressed. The origin of the project\nis based on the analysis of mouse samples. Although the authors have included analyses\nof public RNAseq datasets, these are limited to the expression of TIM-3 and IFNAR in\ncolorectal and lung cancer, respectively, with only one of the markers tested in each\nentity. In vivo validation, on the other hand, was performed in mouse models for\ncolorectal and melanoma cancer. In addition, there have been no direct tests or analyses\nof TIL composition in human cancer samples. A clearer link to patient data and\nvalidation in human samples, as well as further discussion of clinical applicability, is\nneeded to demonstrate translational relevance.\nThanks for your comments. To further demonstrate human relevance, we analyzed\npublished scRNA-seq data from human colorectal cancer samples (4). As shown below,\nTIM3+ CD8+ T cells from human CRC tumors also showed higher expression of\nexhaustion-related genes (ENTPD1, PDCD1, HAVCR2) but lower function and\nproliferation-related genes (GZMK, GZMH, TNF, KLRG1, MKI67), indicating that\nthese cells are exhausted and therefore are potential targets of BiDT. In addition, we\nconducted GSEA analysis of interferon alpha/beta signaling genes in ICB responsive\ncompared with ICB non-responsive melanoma patients, and found that interferon\nalpha/beta signaling genes are enriched in ICB responsive melanoma patients,\nindicating that type I interferon signaling are important for therapeutic effects of ICB\nand therefore BiDT may work well on those ICB non-responsive melanoma patients\n(5).\nFig. R9 (related Fig. S1 in article) TIM3+ CD8+ T cells from human CRC tumors\nshowed higher expression of exhaustion-related genes but lower function and\nproliferation-related genes. (A) T-SNE plot of 5,466 CD8+ T cells from published\nhuman CRC scRNA-seq data (GSE132465). (B) Heatmap plot exhibiting relative\nexpression of selected markers in four CD8+ T cell subsets. (C) Expression of PDCD1,\nHAVCR2, GZMK, TNF and MKI67 among CD8+ T cells by 2D t-SNE visualization.\nFig. R10 (related Fig. S2F in article) Gene set enrichment analysis of interferon\nalpha/beta signaling genes in ICB responsive melanoma patients (n=49) compared with\nICB non-responsive melanoma patients (n=42).\n3. The rationale for switching between MC38 (colorectal carcinoma) and B16-F10\n(melanoma) models is unclear. Moreover, it is not stated whether BiDT shows\ncomparable efficacy in both models or whether TIM-3⁺ TIL populations differ between\ntumor types. A brief overview of tumor model characteristics should be added early in\nthe results section, along with consistent use of abbreviations and proper figure labeling.\nWe thank the reviewer for their suggestions. MC38 is a representative model for hot\ntumor while B16 is commonly used for cold tumor. MC38 tumor has large number of\nT cells express Tim3 high inside TME. This line tumor model responds to immune\ncheckpoint blockade (ICB). The B16F10 melanoma model is a well-established\nimmunologically \"cold\" tumor model and is commonly used for establishing lung\nmetastasis models. B16F10 tumors resist Resistance to ICB. Resistance to ICB,\nparticularly following PD-1/PD-L1-targeted therapy, remains a major clinical challenge\nin such cold tumors. Importantly, similar to the MC38 model, the B16F10 model is\nestablished in C57BL/6 mice, and tumor-infiltrating CD8⁺ T cells in this model also\nexhibit high TIM3 expression, consistent with the MC38 model (Fig. R11A and B).\nTherefore, the B16F10 melanoma model allows us to investigate whether and how\nBiDT can improve the tumor microenvironment (TME) of cold tumors.\nIn accordance with the reviewer’s advice, we have added detailed descriptions of both\nthe MC38 and B16F10 models in the manuscript, including relevant references, and\nunified all related annotations. Overall, by employing the B16F10 model in metastatic\nand combination therapy experiments, we effectively demonstrate BiDT’s ability to\nmodulate the TME in cold tumors and highlight the enhanced therapeutic efficacy\nachieved through combination treatment.\nFig. R11 The expression of TIM3 on CD8+ T cells in tumor from MC38 tumor model\nand B16F10 tumor model.\n(A-B) Tumor tissues were collected from B16F10 or MC38 tumor-bearing mice 14 days\npost-inoculation. Cells were isolated and stained for flow cytometric analysis. TIM3\nexpression was analyzed on CD8+ T cells in tumor samples from B16F10 or MC38\ntumor-bearing mice (n = 7/group). Fig. R11(A) is the representative FACS images of\nTIM3+ in CD8+T cells. The percentage of TIM3+ in CD8+T cells (B) were measured by\nflow cytometric analysis. Data are shown as mean ± SEM and are representative of at\nleast two independent experiments. P value was determined by unpaired two-tailed t\ntests (B).\n4. The authors present body weight loss as a surrogate for treatment-associated toxicity,\nboth in the initial experiments (Figure 1 and 2) and in the evaluation of the Pro-BiDT\n(Figure 7C). However, the body weight data are shown only up to day 10 post tumor\ninoculation, while treatment begins on day 11. This raises concerns about the relevance\nof the reported weight loss to treatment-related toxicity. Clarification or correction is\nneeded.\nThank you for pointing out this error. The horizontal axis represents the days post-\ntreatment. The body weight change is shown on different days after treatment, not after\ntumor inoculation. We have corrected this mistake in the figure.\nFig R12. (A) The weight change after treatment for Figure 1I. (B) The weight change\nafter treatment for Figure 2B. (C) The weight change after treatment for Figure 7C.\n5. As the developed bispecific antibody is central to the study. However, aside from a\nschematic illustration in two figures, there is no detailed description of the antibody’s\nmolecular design, production, or validation. Key information, such as the format,\nlinker and expression system is entirely missing, and no methods section is provided.\nFor transparency and reproducibility, a comprehensive description of the construct’s\ndevelopment is necessary.\nWe thank the reviewer for their suggestion. We have added a \"Methods\" paragraph\ndescribing the molecular design, format, linkers, and expression system. In addition,\nwe have modified the schematic illustration to depict the heterodimeric Fc variant\nemploying the KiHss-AkKh technology.\nProduction of BiDT and Pro-BiDT fusion protein. On the basis of the heterodimeric\nFc variant KiHss-AkKh technology (6), the Fab fragment of anti-TIM3 was fused with\nthe knob variant Fc region, and the IFNα or ProIFNα was fused with the hole variant\nFc region. During ProIFNα protein, the IFNα receptor and IFNα were joined with a\nMMP linker, which can be cleaved by MMP14. The entire sequence was then cloned\ninto the pEE12.4 vector (Lonza). The anti-TIM3-knobs and IFNα-hole plasmids or\nProIFNα-hole plasmids were transiently transfected into 293F cells at a ratio of 1:1.\nBiDT and Pro-BiDT were generated by transient co-transfection of two arms of\nplasmids into FreeStyle 293F cells. Supernatants were collected on day 7 after\ntransfection. The supernatant containing bispecific antibodies was purified using\nProtein A affinity chromatography according to the manufacturer’s protocol.\nMinor comments:\n• Abbreviations are not defined consistently throughout the manuscript and figure\nlegends.\nWe thank the reviewer for pointing out these errors. We have rechecked and reorganized\nthe manuscript and figures, and standardized all abbreviations throughout.\n• Lines 89–93: TIM-3 alone is used to define exhausted T cells - was co-expression with\nother exhaustion markers assessed?\nThanks for your comments and suggestion. We have checked more markers to define\nthe cell subsets in scRNA-seq. Exhausted CD8+ T cells expressed not only higher Pdcd1,\nHavcr2, Lag3 and Tigit but also lower Tbx21 (usually be used to define Tex_int) and\nTnf at scRNA-seq level, compared with intermediate exhausted CD8+ T cells.\nFig R13. Tumor-Reactive T Cells are exhausted in mouse MC38 tumors. (A-C)\nC57BL/6 mice (n = 3) were inoculated with 5×105 MC38 cells. CD8+ T cells were\nsorted for single-cell RNA sequencing 19 days after tumor inoculation. (A) UMAP plot\nof 2,199 CD8+ T cells exhibiting four cell subsets. (B) Expression of selected markers\namong CD8+ T cells by 2D UMAP visualization. (C) Heatmap plot showing average\nrelative expression of selected markers in four CD8+ T cell subsets.\nTo further demonstrate human relevance, we analyzed published scRNA-seq data from\nhuman colorectal cancer samples. As shown below, TIM3+ CD8+ T cells from human\nCRC tumors also showed higher expression of exhaustion-related genes (ENTPD1,\nPDCD1, HAVCR2) but lower function and proliferation-related genes (GZMK, GZMH,\nTNF, KLRG1, MKI67), indicating that these cells are exhausted.\nFig. R14 (related Fig. S1 in article) TIM3+ CD8+ T cells from human CRC tumors\nshowed higher expression of exhaustion-related genes but lower function and\nproliferation-related genes. (A) T-SNE plot of 5,466 CD8+ T cells from published\nhuman CRC scRNA-seq data (GSE132465). (B) Heatmap plot exhibiting relative\nexpression of selected markers in four CD8+ T cell subsets. (C) Expression of PDCD1,\nHAVCR2, GZMK, TNF and MKI67 among CD8+ T cells by 2D t-SNE visualization.\nWe stimulated CD8⁺ T cells with anti-CD3 and anti-CD28 antibodies for either 11 days\n(long-term stimulation) or 3 days (short-term stimulation), which induced TIM3⁺CD8⁺\nT cells and non-exhausted TIM3⁻CD8⁺ T cells, respectively. Comparison of the two\ngroups revealed a significantly reduced proportion of IFN-γ⁺ cells among TIM3⁺CD8⁺\nT cells, indicating impaired cytokine production capacity.\nFig. R15 The expression of IFN-γ+ in TIM3- CD8+ T cells or TIM3+ CD8+ T cells in\nvitro. Fig. R15(A) The representative FACS images of IFN-γ+ in TIM3- CD8+ T cells\nor TIM3+ CD8+ T cells. (B) The percentage of IFN-γ+ in TIM3- CD8+ T cells or TIM3+\nCD8+ T cells were measured by flow cytometric analysis. Data are shown as mean ±\nSEM and are representative of at least two independent experiments. P value was\ndetermined by unpaired two-tailed t tests (B).\nIn summary, our collective experimental evidence characterizes the exhausted T-cell\nsubset not only by the expression of surface markers such as TIM3 but also by\nfunctional impairments, including reduced proliferative capacity and diminished\ncytokine production.\n• Lines 100–101: Reference is missing.\nWe thank the reviewer for their suggestion. We have now added relevant references to\nsupport the content of this paragraph.\n• Lines 297–298: The Sentence is unclear and needs revision.\nWe thank the reviewer for identifying these errors; the grammatical issues in this\nsentence have been corrected. The revised text now reads as follows:\nTo test that, additional IL-2 overcame the resistance and achieved complete elimination\nof the more advanced tumors (Fig. S4B, C), highlighting the synergistic role of IL-2\nsignaling in enhancing the antitumor efficacy of BiDT.\n• Line 309: Reference is missing.\nWe thank the reviewer for their suggestion. In accordance with their recommendation,\nwe have added appropriate citations to support this sentence.\n• Line 310: No figure reference is provided for the described result.\nWe thank the reviewer for their suggestion. We have now added the relevant reference\nand the appropriate figure citation to this paragraph\nFigure-specific comments:\nFigure 1:\n- Panel E: Consider adjusting the x-axis to start at 0 and end at 100 to better reflect\nrelative proportions and align with panel D.\nThank you for the suggestion. We have revised Figure 1E as suggested, adjusting the\nx-axis range to run from 0 to 100.\n- The main text mentions \"IFN alpha,\" while panels H and I refer to \"Fc-IFN alpha.\"\nPlease clarify if these are the same or different reagents.\nFigure 2:\n- Inconsistent terminology: \"IFN alpha\" is used in panels B and C, but \"Fc-IFN alpha\"\nappears in the legend.\nWe thank the reviewer for highlighting the inaccurate terminology. The manuscript and\nfigures have been revised to uniformly use \"Fc-IFNα\" instead of \"IFNα\" in both the\ntext and Figures 1 & 2.\n- Line 627: The legend refers to panel D, but panel D contains a survival curve.\nWe thank the reviewer for identifying this issue. The representative image of splenocyte\nspots has been moved to Extended Data Figure 3B; however, the corresponding figure\nlegend was inadvertently omitted from the main text. This sentence has now been\nremoved.\nFigure 6:\n- Panel H: The figure refers to \"anti-CD122,\" while the legend states \"anti–IL-2β\nreceptor.\" Maybe consider an uniform naming.\nThanks for your suggestion. We have changed the term \" anti-IL-2β receptor \" in the\ntext to \" anti-CD122\".\nExtended Data Figure 3:\n- The figure contains panels A, B, and C, but only panels A and B are described in the\nlegend. This should be corrected.\nThank you for pointing out the error. We have made the necessary corrections and added\nthe description for this Supplementary Figure.\nFig. R16. BiDT could accumulate within tumor tissue.\n(A-B) C57BL/6 mice (n=7) were inoculated with 5×105 MC38 cells. Tumor-bearing\nmice were i.v. treated with 50 μg of Cy5-labeled TIM3-IFNα or Cy5-labeled EGFR-\nIFNα as control group on day16. IVIS images of the tumor distribution of Cy5-labeled\nTIM3-IFNα or EGFR-IFNα (A) and imaging fluorescence intensity statistical results\n(B).\n(C) MC38-OVA tumor-bearing mice (n = 6/group) were i.p. treated with 25 μg BiDT\non day 13, 16 and 19 post inoculation. At 15 day after treatment, splenocytes from\ndifferent treatment groups were isolated and stimulated with either OT-I peptide or\nfreeze thawing MC38-OVA tumor cells. Antigen-specific T cells were detected by\nELISpot assay. The splenocyte representative spots are shown in (C).\n(D) C57BL/6 mice (n = 10/group) were subcutaneously inoculated with 3×105 B16F10\ntumor cells and injected with 2×106 B16F10 tumor cells through the tail vein on day\n10. The subcutaneous tumor was intratumorally treated with 5 μg BiDT on days 11, 14,\nand 17. The mice were sacrificed on day 21 post inoculation. The lungs were collected.\nThe five representative pictures of all ten samples in each group and number of B16F10\ntumor metastatic nodes in the lung (D).\nData are shown as mean ± SEM and are representative of at least two independent\nexperiments. P value was determined by unpaired two-tailed t tests (B).\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nReference:\n1. T. H. Corbett, D. P. Griswold, Jr., B. J. Roberts, J. C. Peckham, F. M. Schabel,\nJr., Tumor induction relationships in development of transplantable cancers of\nthe colon in mice for chemotherapy assays, with a note on carcinogen structure.\nCancer Res 35, 2434-2439 (1975).\n2. E. I. Deryugina, J. P. Quigley, Matrix metalloproteinases and tumor metastasis.\nCancer Metastasis Rev 25, 9-34 (2006).\n3. E. Gobin et al., A pan-cancer perspective of matrix metalloproteases (MMP)\ngene expression profile and their diagnostic/prognostic potential. BMC Cancer\n19, 581 (2019).\n4. H. O. Lee et al., Lineage-dependent gene expression programs influence the\nimmune landscape of colorectal cancer. Nat Genet 52, 594-603 (2020).\n5. T. N. Gide et al., Distinct Immune Cell Populations Define Response to Anti-\nPD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy. Cancer\nCell 35, 238-255 e236 (2019).\n6. J. B. Ridgway, L. G. Presta, P. Carter, 'Knobs-into-holes' engineering of\nantibody CH3 domains for heavy chain heterodimerization. Protein Eng 9, 617-\n621 (1996)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "While the authors revised many aspects of the criticized points, there remain several issues that require further attention.\nRemaining issues:\n-The manuscript needs more stringent revision in terms of language grammar and style/ phrasing. There are several\nsentences not using proper English. Furthermore, especially for in the early results part (Figure 1 & Figure 2), whole\nparagraphs seem incoherent in writing logic/ storytelling. Examples include:\n- Line 66: type I interferons\n- Line 90/91: rephrase? Proliferating… identified by Ki67 and…?\n- Line 96: Abbreviation of CRC missing\n- Line 125: reference?\n- Line 131: “synergized” even though additive\n- Line 141: syntax\n- Line 142-144: syntax\n- Sentence 144-146: context? Missing introduction of IFNa hetero dimer vs monodimer?\n- Missing article before TME e.g. line 147, line 165, 349: “in TME”\n- Line 179: metatstasis formation? Same for 185\n- Line 208: syntax, we employed\n- Line 270: typo - reprogrammed\n- Line 349-351: needs rephrasing and articles\n-unprecise data description:\n- Line 349-351: states correlation of PD-L1 upregulation and IFNa treatment, however Figure 6I does not show any data on\nIFNa\n- Line 298-299: claims upregulation of Bcl-2 by treatment with BiDT, however Figure 5D does not show this clearly, different\nplot might necessary, eg. showing gene counts across the two treatments\n- Line 109-111: claims higher CD28 expression on exhausted T cells compared to proliferating/ interm. Exhausted T cells,\nbut difference based on heatmap is marginal\n-In our previous revision we criticized the use of the term ‘functional terminal exhausted’ T cells. In the rebuttal letter, the\nauthors claim to have removed this term of terminal exhausted T cells from their manuscript, however there are multiple\nexamples, where this has not been changed:\n- Line 104\n- -Line 277\n- -Line 316\n- -Graphical abstract\n-Our major criticism about the manuscript was the presented identification of exhausted T cells. While the authors revised\ntheir sequencing data, by incorporating further markers, all data beyond the sequencing data are only based on the\ndifferentiation between Tim-3+ and Tim-3-, which does not seem sufficient to identify exhausted T cells, but may also\ncomprise activated, responding T cells. (Figure 1D, Figure 5F, Figure 6)\nEspecially Figure 6F shows that Tim-3- CD8 T cells do not express CD25, and therefore seem to be simply inactive.\n-It would have been helpful to see the same set of markers used in Figure 5D for Figure 1C. With the current choice it\nremains unclear if the ‘proliferating’ subset of Fig1C is maybe represented in the ‘effector-like exhausted’ subset of Fig5D.\n-Furthermore, we asked the authors to provide additional readouts for T-cell function in order to be able to refer to\nexhaustion. While the authors provide data for 14-day stimulated T cells in the rebuttal letter, these data derive from in vitro\nbead-stimulated T cells, while a difference can be observed between Tim-3+ and Tim-3- T cells, the provided gating seems\nslightly biased and does not really provide information about the TILs in vivo. These are also provided by the authors in the\nrebuttal letter, but here differences in IFN production again seem marginal.\nOverall, the introduced BiDT antibody seems to achieve pretty clear in vivo function in terms of T-cell responses, tumor\nclearing and memory formation. However, the claimed mechanism of rejuvenating exhausted T cells does not seem fully\nconvincing. Rather it seems that while the PBS control group is unable to produce a proper T-cell response, this is well\nachieved upon treatment with the BiDt construct.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "The authors have satisfactorily addressed all major concerns raised in the initial review. The statistical analyses were\ncorrected throughout, resolving prior methodological issues. The characterization of exhausted CD8⁺ T-cell subsets has\nbeen expanded using additional phenotypic markers and supported by new functional data, which strengthens the\ninterpretation of T-cell exhaustion and BiDT-mediated reinvigoration. The translational relevance has been improved\nthrough the incorporation of human CRC scRNA-seq datasets and analyses of interferon signaling in ICB response.\nAlthough direct validation using primary human tumor samples is still lacking, the inclusion of additional human datasets\nsubstantially strengthens the translational aspect and is adequate within the scope of the current study. Clarifications\nregarding the use of MC38 and B16F10 models, corrections to toxicity time axes, and the addition of methodological details\ndescribing the BiDT construct further enhance rigor and transparency. Figure labeling and terminology inconsistencies have\nalso been resolved. Overall, the revisions substantially improve the clarity and robustness of the manuscript.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "RESPONSE TO REVIEWERS’ COMMENTS\nReviewer #1 (Remarks to the Author):\nWhile the authors revised many aspects of the criticized points, there remain several issues\nthat require further attention.\nRemaining issues:\n-The manuscript needs more stringent revision in terms of language grammar and style/\nphrasing. There are several sentences not using proper English. Furthermore, especially for in\nthe early results part (Figure 1 & Figure 2), whole paragraphs seem incoherent in writing\nlogic/ storytelling. Examples include:\nWe appreciate the suggestions and have revised the manuscript accordingly, addressing the\nreviewers' comments and refining the language for clarity and grammar. The modifications\nare highlighted in red.\n- Line 66: type I interferons\nWe thank the reviewer for pointing out this issue. We have performed a thorough check of the\nentire manuscript and corrected all similar errors we identified.\n- Line 90/91: rephrase? Proliferating… identified by Ki67 and…?\nThank you for your suggestion. We have reorganized the text as recommended. The updated\nversion is provided below: A subset of proliferating CD8+ T cells was identified by high Ki67\nexpression and low levels of exhaustion markers including Pdcd1 and Havcr2.\n- Line 96: Abbreviation of CRC missing\nWe thank the reviewer for the comment. We have added the abbreviation as suggested and\nhave checked the entire manuscript for similar issues.\n- Line 125: reference?\nWe thank the reviewer for this comment. As suggested, we have added the relevant citations.\n- Line 131: “synergized” even though additive\nWe thank the reviewer for this comment. In accordance with the suggestion, we have changed\n\"synergized\" to \"additive\" to be more precise.\n- Line 141: syntax\n- Line 142-144: syntax\n- Sentence 144-146: context? Missing introduction of IFNa hetero dimer vs monodimer?\nThank you for highlighting these issues. We have implemented the corrections in an integrated\nmanner as they pertained to adjacent paragraphs. We have also added a figure panel\nschematically illustrating the differences between the heterodimeric and monomeric fusion\nprotein structures. The modifications are detailed below:\nGiven the superior antitumor efficacy of combined anti-TIM3 and IFNα therapy compared to\nmonotherapies but severe IFNα-mediated toxicity (Fig. 1H and I), we need to reduce IFNα-\nassociated toxicity while increasing tumor targeting. To achieve targeted delivery of IFNα to\nthe TME, we adopted an antibody-based strategy, specifically employing anti-TIM3 antibody.\nInitial analysis in MC38 tumor-bearing mice confirmed that TIM3 expression was significantly\nhigher on CD45⁺ immune cells within established tumors compared to those in the TDLNs of\nmice with advanced cancer (Fig. S5A). This elevated and localized expression profile\nsupported the rationale that a TIM-3-directed approach could facilitate the specific transport of\nIFNα to the TME. Moreover, we further assessed the expression on multiple immune cells\nwithin the TME, finding the significantly highest level of TIM3 on CD8+ T cells, compared to\nit on DC and CD4+ T cells (Fig. S5B). We proposed to use the high affinity of anti-TIM3\nantibody to guide IFNα.\nConcurrently, to enhance tumor-specific targeting and mitigate potential systemic toxicity, we\nneeded to screen an IFNα fusion protein structure with lower IFNAR receptor affinity. This\ndesign aimed to ensure that the IFNα payload would be preferentially localized within the TME\nupon targeted delivery by the TIM3 antibody, rather than exerting effects in the periphery. To\nthis end, we designed and compared two distinct architectures for the TIM3 antibody-IFNα\nfusion protein: a heterodimer format and a homodimer format (Fig. 2A). Subsequent\nevaluation aimed to identify the construct possessing the lower IFNα binding affinity for\nfurther development. In IFNAR-expressing TIM3⁻ cells, the heterodimer format exhibited\nsignificantly reduced binding of IFNα to IFNAR compared to the homodimer (Fig. S5C).\nBased on these findings, the heterodimeric anti-TIM3-IFNα fusion protein (TIM3-IFNα) was\nselected as the lead construct, ensuring targeted delivery and localized activation of IFNα\nwithin the TME via the TIM3 antibody. This bispecific reagent is also engineered to\nconcurrently target the immune checkpoint TIM3 on exhausted TILs and engage DC via the\nIFNAR, thereby facilitating the reactivation of exhausted tumor-reactive T cells through\nbispecific dendritic cell-T cell engagement (BiDT).\nFig. R1 Schematic diagram of TIM3-IFNα fusion protein in homodimer or heterodimer\nformat.\n- Missing article before TME e.g. line 147, line 165, 349: “in TME”\nThank you for pointing out the mistake. We have searched the entire article for identical issues\nand corrected them all.\n- Line 179: metatstasis formation? Same for 185\nWe thank the reviewer for this comment. We have revised the wording throughout the\nmanuscript to be more accurate. A comprehensive check for similar issues has also been\nperformed.\n- Line 208: syntax, we employed\nWe thank the reviewer for the comment. As suggested, we have replaced \"employed\" with\n\"used\" in the relevant section.\n- Line 270: typo – reprogrammed\nThank you for pointing out the errors. We have corrected the identified errors in accordance\nwith your suggestions and performed a thorough revision of the manuscript to ensure\nconsistency.\n- Line 349-351: needs rephrasing and articles\nThank you for the reviewer's suggestion. We have revised this section accordingly. The\nfollowing changes have been made to this section:\nResistance to immune checkpoint blockade (ICB) in immunologically \"cold\" tumors remains\na major clinical challenge(1, 2). The B16F10 melanoma, a classic cold tumor model, exhibits\npoor responsiveness to ICB (3). Importantly, similar to the MC38 model, the B16F10 model is\nestablished in C57BL/6 mice, and tumor-infiltrating CD8⁺ T cells in this model also exhibit\nhigh TIM3 expression, consistent with the MC38 model (Fig. S14A, B). We therefore\ninvestigated whether BiDT treatment could effectively eradicate cold tumors.\nWhile BiDT monotherapy extended survival in the B16F10 model, it failed to achieve complete\ntumor eradication, with all tumors eventually recurring (Fig. S14C). Analysis of the underlying\nmechanism revealed that PD-L1 expression was upregulated in DC within the TME following\nBiDT treatment (Fig. 6I and Fig. S14D). This upregulation can be primarily attributed to the\nIFN-α component within the BiDT(4, 5). This IFNα-driven PD-L1 upregulation on DC\nindicated combining PD-L1 blockade with BiDT to counteract this adaptive resistance\nmechanism.\nIn the B16F10 model, anti-PD-L1 overcomes BiDT therapy resistance, achieving complete\neradication in 40% cases (Fig. 6J-K). Critically, BiDT not only acted synergistically to\neliminate tumors but also induced durable immune memory, preventing tumor recurrence upon\nrechallenge (Fig. 6L). These results position BiDT as a promising solution to the unmet clinical\nchallenge of ICB resistance even in cold tumors, leveraging its dual ability to enhance T cell\nactivation and reprogram immunologically inert tumor microenvironments.\n-unprecise data description:\n- Line 349-351: states correlation of PD-L1 upregulation and IFNa treatment, however Figure\n6I does not show any data on IFNa.\nWe thank the reviewer for this comment. We have revised the text to more precisely state. In\nfact, our analysis of post-BiDT treatment data revealed an increase in PD-L1 expression. This\nupregulation can be primarily attributed to the IFN-α component within the BiDT treatment.\nAccordingly, we have added a citation to support this explanation here. Furthermore, we have\nrevised this section together with the previous related issue. The specific modifications are as\nfollows:\nWhile BiDT monotherapy extended survival in the B16F10 model, it failed to achieve complete\ntumor eradication, with all tumors eventually recurring (Fig. S14C). Analysis of the underlying\nmechanism revealed that PD-L1 expression was upregulated in DC within the TME following\nBiDT treatment (Fig. 6I and Fig. S14D). This upregulation can be primarily attributed to the\nIFN-α component within the BiDT(4, 5). This IFNα-driven PD-L1 upregulation on DC\nindicated combining PD-L1 blockade with BiDT to counteract this adaptive resistance\nmechanism.\n- Line 298-299: claims upregulation of Bcl-2 by treatment with BiDT, however Figure 5D does\nnot show this clearly, different plot might necessary, eg. showing gene counts across the two\ntreatments.\nWe thank the reviewer for their valuable comments. In accordance with your suggestions, we\nperformed an analysis comparing control and post-treatment data, which confirmed that Bcl-2\ngene expression in tumor tissue was indeed upregulated after treatment. This finding is\nconsistent with the flow cytometry results (Fig. 6D) and validates our initial hypothesis.\nFig. R2 Single-cell violin plots compare Bcl-2 expression in CD8⁺ T cell subsets between\ncontrol and BiDT-treated groups.\n- Line 109-111: claims higher CD28 expression on exhausted T cells compared to proliferating/\ninterm. Exhausted T cells, but difference based on heatmap is marginal.\nThank you for the comment. We agree that the increased CD28 expression in exhausted T\ncells is limited and we have removed this point in the manuscript. In this part, we want to\nemphasize the dysfunction of DCs within the TME, indicating us to activate DCs by type I\nIFN.\n-In our previous revision we criticized the use of the term ‘functional terminal exhausted’\nT cells. In the rebuttal letter, the authors claim to have removed this term of terminal exhausted\nT cells from their manuscript, however there are multiple examples, where this has not been\nchanged:\n- Line 104\n- -Line 277\n- -Line 316\n- -Graphical abstract\nWe thank the reviewer for pointing out these issues. We have corrected the indicated errors and\nhave also conducted a thorough review of the entire manuscript to identify and rectify similar\nproblems.\n-Our major criticism about the manuscript was the presented identification of exhausted T cells.\nWhile the authors revised their sequencing data, by incorporating further markers, all data\nbeyond the sequencing data are only based on the differentiation between Tim-3+ and Tim-3-,\nwhich does not seem sufficient to identify exhausted T cells, but may also comprise activated,\nresponding T cells. (Figure 1D, Figure 5F, Figure 6)\nEspecially Figure 6F shows that Tim-3- CD8 T cells do not express CD25, and therefore seem\nto be simply inactive.\nWe thank the reviewer for this insightful comment. In response, we have incorporated additional\nmarkers to refine our sequencing data and performed an in-depth flow cytometry analysis of the TIM-\n3⁺ CD8⁺ T-cell subset. Our analysis revealed that this population not only exhibits high TIM-3\nexpression but also co-expresses other well-established exhaustion markers, including PD-1, CD39 and\nLag3. Based on this multi-marker profile, we have defined these cells as exhausted T cells in our revised\nmanuscript (Fig. S3).\nFurthermore, we fully agree with the reviewer's interpretation regarding T cell activation. After\ncareful consideration of the data showing elevated expression of CD25 and other activation\nmarkers on TIM-3⁺ T cells following treatment, we concur that the primary effect of the BiDT\ntreatment is the reactivation of intratumoral T cells, rather than the reversal of an exhausted\nphenotype. Our results indicate that the bispecific DC-T cell engager reactivates these T cells,\nparticularly the TIM-3⁺ subset, by engaging secondary signaling pathways. Specifically, the\nidentified involvement of the CD80/86-CD28 costimulatory axis and IL-2 signaling aligns with\nthe critical second and third signals required for robust T cell activation.\nWe have revised the manuscript accordingly to incorporate this perspective and appreciate the\nreviewer's suggestion, which has strengthened the interpretation of our findings.\nFig. R3.FACS analysis for the identification of exhausted T cells.\nTumor tissues were collected from MC38 tumor-bearing mice (female C57BL/6 mice) 14 days\npost-inoculation. Cells were isolated and stained for flow cytometric analysis. (A) The\nrepresentative FACS image of TIM3- and TIM3+ CD8+ T cells. (B-D) the PD-1, CD39 and\nLag3 expression were analyzed between TIM3- and TIM3+CD8+ T cells.\n-It would have been helpful to see the same set of markers used in Figure 5D for Figure 1C.\nWith the current choice it remains unclear if the ‘proliferating’ subset of Fig1C is maybe\nrepresented in the ‘effector-like exhausted’ subset of Fig5D.\nThanks for your suggestion. We checked the expression of markers used in Figure 5D on cells\nof Figure 1C (heatmap shown below). We think the ‘proliferating’ subset of Fig1C and the\n‘effector-like exhausted’ subset of Fig5D have some similarities but also some differences, so\nwe named them with different nomenclatures. Both subsets highly express Mki67 compared\nwith other subsets, but the expression level of some exhaustion markers (e.g. Pdcd1, Havcr2,\nLag3, ...) and effector markers (Gzmb, Fasl, ..) in the ‘proliferating’ subset are lower compared\nwith the ‘exhausted’ subset (heatmap shown below), indicating that the ‘proliferating’ subset\nof Fig1C is different from the ‘effector-like exhausted subset’ of Fig5D which highly express\nexhaustion and effector molecules. In contrast to the 'proliferating' subset, the 'effector-like\nexhausted' subset is generated following BiDT-mediated DC restimulation of the exhausted T-\ncell pool.\nFig. R4 Heatmap plot showing average relative expression of selected genes of Fig5D across\nfour CD8+ T cell subsets of Fig1C.\n-Furthermore, we asked the authors to provide additional readouts for T-cell function in order\nto be able to refer to exhaustion. While the authors provide data for 14-day stimulated T cells\nin the rebuttal letter, these data derive from in vitro bead-stimulated T cells, while a difference\ncan be observed between Tim-3+ and Tim-3- T cells, the provided gating seems slightly biased\nand does not really provide information about the TILs in vivo. These are also provided by the\nauthors in the rebuttal letter, but here differences in IFN production again seem marginal.\nThanks for your suggestion. Following your suggestions, we reanalyzed the FACS results of\ntumor tissues at 14 days post-treatment in animal models. In addition to changes in IFN-γ\nrelative frequencies, we also quantified the number of IFN-γ+ TIM3+ CD8+ T cells per\nmilligram of tumor tissue. The results demonstrated a threefold increase in IFN-γ+ TIM3+\nCD8+ T cell numbers per milligram in the treated group relative to the untreated controls at day\n14.\nIt should be noted that all analyses were conducted on freshly isolated tissues without\nrestimulation to preserve the in vivo profile. This approach, while ensuring biological relevance,\nyields relatively low baseline frequencies of IFN-γ⁺ cells, but it reflects the real production of\nintracellular cytokines. To address this, we administered brefeldin A (BFA) 6 hours prior to\neuthanizing the mice to allow sufficient accumulation of intracellular cytokines. This protocol\nis detailed in the study “Evaluating cytokine production by flow cytometry using brefeldin A\nin mice,” published in STAR Protocols, and we have also provided specific details in the\nMethods section. Mice were intraperitoneally injected with 50 μl of brefeldin A solution (BFA,\n5 mg/ml) six hours before being sacrificing.\nFig. R5 (A-B) MC38-bearing mice (n = 6/group) were treated with 25 µg BiDT on day 16. The\nmice were sacrificed, and tumor tissues were collected 14 days after injection. (A) The\nrepresentative FACS images of IFN-γ+ in TIM3+ CD8+ T cells. (B) The percentage of IFN-γ+\nin TIM3+ CD8+ T cells was measured by flow cytometric analysis. (C) The number of IFN-γ+\nin TIM3+ CD8+ T cells per mg was measured by flow cytometric analysis.\nData are shown as mean ± SEM and are representative of at least two independent experiments.\nP value was determined by unpaired two-tailed t tests (B and C).\nOverall, the introduced BiDT antibody seems to achieve pretty clear in vivo function in terms\nof T-cell responses, tumor clearing and memory formation. However, the claimed mechanism\nof rejuvenating exhausted T cells does not seem fully convincing. Rather it seems that while\nthe PBS control group is unable to produce a proper T-cell response, this is well achieved upon\ntreatment with the BiDT construct.\nWe thank the reviewer for their insightful comment. We have thoroughly examined the point\nraised, made corresponding corrections, and carefully re-edited the relevant sections of the\nmanuscript accordingly. We fully agree with the interpretation that BiDT treatment reactivates\nintratumoral T cells, particularly the TIM-3⁺ subset, by engaging secondary signaling pathways,\nand we have revised the text to incorporate this perspective. We also greatly appreciate your\npositive assessment of the in vivo efficacy of our approach. We believe these revisions have\nsignificantly strengthened the solidity of our manuscript, and we thank you for your valuable\nguidance in this process\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #3 (Remarks to the Author):\nThe authors have satisfactorily addressed all major concerns raised in the initial review. The\nstatistical analyses were corrected throughout, resolving prior methodological issues. The\ncharacterization of exhausted CD8⁺ T-cell subsets has been expanded using additional\nphenotypic markers and supported by new functional data, which strengthens the interpretation\nof T-cell exhaustion and BiDT-mediated reinvigoration. The translational relevance has been\nimproved through the incorporation of human CRC scRNA-seq datasets and analyses of\ninterferon signaling in ICB response. Although direct validation using primary human tumor\nsamples is still lacking, the inclusion of additional human datasets substantially strengthens the\ntranslational aspect and is adequate within the scope of the current study. Clarifications\nregarding the use of MC38 and B16F10 models, corrections to toxicity time axes, and the\naddition of methodological details describing the BiDT construct further enhance rigor and\ntransparency. Figure labeling and terminology inconsistencies have also been resolved. Overall,\nthe revisions substantially improve the clarity and robustness of the manuscript.\nWe sincerely thank the reviewer for their supportive and constructive assessment of our revised\nmanuscript. We are pleased to note that the additional experimental data, expanded phenotypic\nand functional characterization of exhausted CD8⁺ T cells, corrections to statistical analyses\nand figure presentations, and incorporation of human CRC scRNA-seq datasets have\nadequately addressed the key concerns raised during the initial review. The reviewer’s\nacknowledgment that these revisions have substantially improved the clarity and robustness of\nour work is greatly encouraging. We appreciate the time and thoughtful input provided\nthroughout the review process and hope that the manuscript now meets the journal's standards\nfor publication.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReferences\n1. J. Zhang, D. Huang, P. E. Saw, E. Song, Turning cold tumors hot: from molecular\nmechanisms to clinical applications. Trends Immunol43, 523-545 (2022).\n2. K. X. Lin et al., PD-1 and PD-L1 inhibitors in cold colorectal cancer: challenges and\nstrategies. Cancer Immunol Immunother72, 3875-3893 (2023).\n3. T. Zhang et al., Up-regulated PLA2G10 in cancer impairs T cell infiltration to dampen\nimmunity. Sci Immunol9, eadh2334 (2024).\n4. M. Muhlbauer et al., PD-L1 is induced in hepatocytes by viral infection and by interferon-\nalpha and -gamma and mediates T cell apoptosis. J Hepatol45, 520-528 (2006).\n5. T. Bald et al., Immune cell-poor melanomas benefit from PD-1 blockade after targeted\ntype I IFN activation. Cancer Discov4, 674-687 (2014)."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have responded carefully to the points raised in earlier rounds of review, and the key issues have been resolved.\nMost importantly, the revised mechanisms now claiming a functional re-activation of Tim-3+ CD8+ TILs rather than full reinvigoration of exhausted T cells seems to be more coherent.\nAdditional comments have been addressed adequately and complemented by additional readouts.\nMoreover, the language and overall presentation of the manuscript have improved substantially, with only minor language issues remaining, that can be handled in the final preparation of the article.\nAbstract, line 29: „To facility the crosstalk“\nAbstract, line 31: „we have demonstrated this BiDT simultaneously targeted“\nLine 137: „employing anti-TIM3 antibody“"}, "author_response": "We greatly appreciate your recognition of the revisions we made to address your\nconcerns. We have checked and modified the statements with language issues in the\nmanuscript."}]

Decision letter