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A trispecific antibody engaging T cells with tumour and myeloid cells augments antitumour immunity

Nature Biomedical Engineering (2025)Cite this article

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Abstract

Immunologically unresponsive tumours often resist immune checkpoint inhibitors due to the low abundance of tumour-specific T cells and an immunosuppressive microenvironment, despite pronounced infiltration of non-tumour-specific (bystander) T cells. Here we analysed single-cell RNA sequencing data from 300 patients across 17 tumour types, identifying abundant but functionally restrained bystander T cells in multiple malignancies, including ovarian and colorectal cancer. To enhance antitumour immunity in such contexts, we engineered B7H3xCD3xPDL1, a trispecific immunoglobulin-based T cell engager targeting B7H3, CD3 and PDL1, to redirect T cells while mitigating immunosuppression. Functional validation in co-culture systems, patient-derived tumour suspensions and fragments, and humanized mouse models showed T cell activation and tumour killing. Imaging cytometry and single-cell transcriptomics revealed IFNγ-dependent macrophage reprogramming and IL-15 secretion, establishing a feed-forward loop that augments T cell functionality. A machine learning model trained on ex vivo cytotoxicity and transcriptomic data predicted patient responsiveness, supporting data-driven clinical stratification for solid tumour immunotherapy.

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Fig. 1: Presence of potentially functional T cells in the microenvironment of immunologically inert tumours.
Fig. 2: Assessing the efficacy of Bi/Tri-TCEs across multiple primary human systems.
Fig. 3: Robust antitumour efficacy of B7H3xCD3xPDL1 in humanized mouse model.
Fig. 4: Myeloid-expressed PDL1 enhances the antitumour activity of B7H3xCD3xPDL1.
Fig. 5: B7H3xCD3xPDL1 promotes T cell proliferation and cytotoxic transition, and mediates tumour cell killing through IFNγ response.
Fig. 6: B7H3xCD3xPDL1 boosts antitumour efficacy by engaging PDL1+ macrophages to release IL-15, promoting sustained T cell functionality.
Fig. 7: A machine-learning model based on immune cell states for predicting responsiveness to B7H3xCD3xPDL1 therapy.

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Data availability

Bulk-seq and scRNA-seq data were deposited into GEO under the accession numbers GSE281102, GSE281646 and GSE282589. The main data supporting the findings of this study are available within the article and the Supplementary Information. Source data are provided with this paper. Other supporting data of this study are available from the corresponding authors on reasonable request. TCEs may be available upon request after signing a material transfer agreement with Lyvgen Biopharma.

Code availability

The code used to develop the predictive model for response is available via GitHub at https://github.com/Soulnature/Cancer_cls.

References

  1. Morad, G. et al. Hallmarks of response, resistance, and toxicity to immune checkpoint blockade. Cell 185, 576 (2022).

    Article  CAS  PubMed  Google Scholar 

  2. Ribas, A. & Wolchok, J. D. Cancer immunotherapy using checkpoint blockade. Science 359, 1350–1355 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Sharma, P. et al. Immune checkpoint therapy-current perspectives and future directions. Cell 186, 1652–1669 (2023).

    Article  CAS  PubMed  Google Scholar 

  4. Yarchoan, M., Hopkins, A. & Jaffee, E. M. Tumor mutational burden and response rate to PD-1 inhibition. N. Engl. J. Med. 377, 2500–2501 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  5. McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Zou, W. Immunosuppressive networks in the tumour environment and their therapeutic relevance. Nat. Rev. Cancer 5, 263–274 (2005).

    Article  CAS  PubMed  Google Scholar 

  7. Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Sade-Feldman, M. et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat. Commun. 8, 1136 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Meier, S. L., Satpathy, A. T. & Wells, D. K. Bystander T cells in cancer immunology and therapy. Nat. Cancer 3, 143–155 (2022).

    Article  PubMed  Google Scholar 

  10. Simoni, Y. et al. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557, 575–579 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Yang, B. et al. Spatial heterogeneity of infiltrating T cells in high-grade serous ovarian cancer revealed by multi-omics analysis. Cell Rep. Med. 3, 100856 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Scheper, W. et al. Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers. Nat. Med. 25, 89–94 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. van de Donk, N. W. C. J. & Zweegman, S. T-cell-engaging bispecific antibodies in cancer. Lancet 402, 142–158 (2023).

    Article  PubMed  Google Scholar 

  14. Labrijn, A. F. et al. Bispecific antibodies: a mechanistic review of the pipeline. Nat. Rev. Drug Discov. 18, 585–608 (2019).

    Article  CAS  PubMed  Google Scholar 

  15. Topp, M. S. et al. Safety and activity of blinatumomab for adult patients with relapsed or refractory B-precursor acute lymphoblastic leukaemia: a multicentre, single-arm, phase 2 study. Lancet Oncol. 16, 57–66 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Moreau, P. et al. Teclistamab in relapsed or refractory multiple myeloma. N. Engl. J. Med. 387, 495–505 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Ruffell, B. et al. Macrophage IL-10 blocks CD8+ T cell-dependent responses to chemotherapy by suppressing IL-12 expression in intratumoral dendritic cells. Cancer Cell 26, 623–637 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Lecker, L. S. M. et al. TGFBI production by macrophages contributes to an immunosuppressive microenvironment in ovarian cancer. Cancer Res. 81, 5706–5719 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Thumkeo, D., et al. PGE(2)-EP2/EP4 signaling elicits immunosuppression by driving the mregDC-Treg axis in inflammatory tumor microenvironment. Cell Rep. 39, 110914 (2022).

    Article  CAS  PubMed  Google Scholar 

  20. DeNardo, D. G. & Ruffell, B. Macrophages as regulators of tumour immunity and immunotherapy. Nat. Rev. Immunol. 19, 369–382 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Yang, F. et al. Synergistic immunotherapy of glioblastoma by dual targeting of IL-6 and CD40. Nat. Commun. 12, 3424 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Chamoto, K. et al. Insights from a 30-year journey: function, regulation and therapeutic modulation of PD1. Nat. Rev. Immunol. 23, 682–695 (2023).

    Article  CAS  PubMed  Google Scholar 

  23. Chow, A. et al. Clinical implications of T cell exhaustion for cancer immunotherapy. Nat. Rev. Clin. Oncol. 19, 775–790 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Zou, W., Wolchok, J. D. & Chen, L. PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: mechanisms, response biomarkers, and combinations. Sci. Transl. Med. 8, 328rv4 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Tang, H. et al. PD-L1 on host cells is essential for PD-L1 blockade-mediated tumor regression. J. Clin. Invest. 128, 580–588 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Xiong, H. et al. Anti-PD-L1 treatment results in functional remodeling of the macrophage compartment. Cancer Res. 79, 1493–1506 (2019).

    Article  CAS  PubMed  Google Scholar 

  27. Ivashkiv, L. B. IFNγ: signalling, epigenetics and roles in immunity, metabolism, disease and cancer immunotherapy. Nat. Rev. Immunol. 18, 545–558 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sun, C., Mezzadra, R. & Schumacher, T. N. Regulation and function of the PD-L1 checkpoint. Immunity 48, 434–452 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Oliveira, G. et al. Phenotype, specificity and avidity of antitumour CD8+ T cells in melanoma. Nature 596, 119–125 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ahmadzadeh, M. et al. Tumor antigen-specific CD8 T cells infiltrating the tumor express high levels of PD-1 and are functionally impaired. Blood 114, 1537–1544 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Yost, K. E. et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Hanada, K. I. et al. A phenotypic signature that identifies neoantigen-reactive T cells in fresh human lung cancers. Cancer Cell 40, 479–493.e6 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Prokhnevska, N. et al. CD8+ T cell activation in cancer comprises an initial activation phase in lymph nodes followed by effect or differentiation within the tumor. Immunity 56, 107–124.e5 (2023).

    Article  CAS  PubMed  Google Scholar 

  34. Seder, R. A., Darrah, P. A. & Roederer, M. T-cell quality in memory and protection: implications for vaccine design. Nat. Rev. Immunol. 8, 247–258 (2008).

    Article  CAS  PubMed  Google Scholar 

  35. Voabil, P. et al. An ex vivo tumor fragment platform to dissect response to PD-1 blockade in cancer. Nat. Med. 27, 1250–1261 (2021).

    Article  CAS  PubMed  Google Scholar 

  36. Musso, T. et al. Human monocytes constitutively express membrane-bound, biologically active, and interferon-gamma-upregulated interleukin-15. Blood 93, 3531–3539 (1999).

    Article  CAS  PubMed  Google Scholar 

  37. Kim, T. S., Rha, M. S. & Shin, E. C. IFN-γ Induces IL-15 trans-presentation by epithelial cells via IRF1. J. Immunol. 208, 338–346 (2022).

    Article  CAS  PubMed  Google Scholar 

  38. Bagaev, A. et al. Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell 39, 845–865.e7 (2021).

    Article  CAS  PubMed  Google Scholar 

  39. Luca, B. A. et al. Atlas of clinically distinct cell states and ecosystems across human solid tumors. Cell 184, 5482–5496.e28 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Lemaitre, L. et al. Spatial analysis reveals targetable macrophage-mediated mechanisms of immune evasion in hepatocellular carcinoma minimal residual disease. Nat. Cancer 5, 1534–1556 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Diskin, B. et al. PD-L1 engagement on T cells promotes self-tolerance and suppression of neighboring macrophages and effector T cells in cancer. Nat. Immunol. 21, 442–454 (2020).

    Article  CAS  PubMed  Google Scholar 

  42. Oh, S. A. et al. PD-L1 expression by dendritic cells is a key regulator of T-cell immunity in cancer. Nat. Cancer 1, 681–691 (2020).

    Article  CAS  PubMed  Google Scholar 

  43. Liu, L. et al. Rejuvenation of tumour-specific T cells through bispecific antibodies targeting PD-L1 on dendritic cells. Nat. Biomed. Eng. 5, 1261–1273 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Pyonteck, S. M. et al. CSF-1R inhibition alters macrophage polarization and blocks glioma progression. Nat. Med. 19, 1264–1272 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Poh, A. R. et al. Therapeutic inhibition of the SRC-kinase HCK facilitates T cell tumor infiltration and improves response to immunotherapy. Sci. Adv. 8, eabl7882 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Yang, F. et al. Small-molecule toosendanin reverses macrophage-mediated immunosuppression to overcome glioblastoma resistance to immunotherapy. Sci. Transl. Med. 15, eabq3558 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kaneda, M. M. et al. PI3Kγ is a molecular switch that controls immune suppression. Nature 539, 437–442 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Conlon, K. et al. Phase I study of single agent NIZ985, a recombinant heterodimeric IL-15 agonist, in adult patients with metastatic or unresectable solid tumors. J. Immunother. Cancer 9, e003388 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Klebanoff, C. A. et al. IL-15 enhances the in vivo antitumor activity of tumor-reactive CD8+ T cells. Proc. Natl Acad. Sci. USA 101, 1969–1974 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Mullard, A. First-in-class IL-15 receptor agonist nabs FDA approval for bladder cancer. Nat. Rev. Drug Discov. 23, 410 (2024).

    Article  CAS  PubMed  Google Scholar 

  51. Wang, K. et al. Biomimetic nanovaccine-mediated multivalent IL-15 self-transpresentation (MIST) for potent and safe cancer immunotherapy. Nat. Commun. 14, 6748 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Hotz, C. et al. Local delivery of mRNA-encoded cytokines promotes antitumor immunity and tumor eradication across multiple preclinical tumor models. Sci. Transl. Med. 13, eabc7804 (2021).

    Article  CAS  PubMed  Google Scholar 

  53. Jabri, B. & Abadie, V. IL-15 functions as a danger signal to regulate tissue-resident T cells and tissue destruction. Nat. Rev. Immunol. 15, 771–783 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Dubois, S. et al. IL-15Ralpha recycles and presents IL-15 In trans to neighboring cells. Immunity 17, 537–547 (2002).

    Article  CAS  PubMed  Google Scholar 

  55. Burkett, P. R. et al. Coordinate expression and trans presentation of interleukin (IL)-15Ralpha and IL-15 supports natural killer cell and memory CD8+ T cell homeostasis. J. Exp. Med. 200, 825–834 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Xu, Y. et al. Closely related T-memory stem cells correlate with in vivo expansion of CAR.CD19-T cells and are preserved by IL-7 and IL-15. Blood 123, 3750–3759 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Pilipow, K. et al. IL15 and T-cell stemness in T-cell-based cancer immunotherapy. Cancer Res. 75, 5187–5193 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Alizadeh, D. et al. IL15 enhances CAR-T cell antitumor activity by reducing mTORC1 activity and preserving their stem cell memory phenotype. Cancer Immunol. Res. 7, 759–772 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Goldman, M. J. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat. Biotechnol. 38, 675–678 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Mariathasan, S. et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Lin, F. et al. Multimodal targeting chimeras enable integrated immunotherapy leveraging tumor-immune microenvironment. Cell 187, 7470–7491 e32 (2024).

    Article  CAS  PubMed  Google Scholar 

  62. Cattaruzza, F. et al. Precision-activated T-cell engagers targeting HER2 or EGFR and CD3 mitigate on-target, off-tumor toxicity for immunotherapy in solid tumors. Nat. Cancer 4, 485–501 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank J. Liu from BD Biosciences for assistance with imaging flow cytometry and C. Wang (Shanghai Jiao Tong University) and L. Deng (Shanghai Jiao Tong University) for comments and editing on this paper. This work was supported by the National Natural Science Foundation of China (82473277 and 82522061 to F.Y.; 82373351 and 82573385 to G.Z.), Shanghai Pujiang Program (23PJ1407600 to F.Y.), Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (20240705 to F.Y.), Collaborative Innovation Centre for Clinical and Translational Science by Ministry of Education & Shanghai (CCTS-202502 to F.Y.), Science and Technology Commission of Shanghai Municipality (23JC1403000 to W.D.), innovative research team of high-level local universities in Shanghai (SHSMU-ZLCX20210200 to G.Z.), 111 project (number B21024 to G.Z.) and Guizhou Provincial Science and Technology Projects (grant number Qiankeherencai XKBF [2025]024 to G.Z.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Changrui Yang, Shanni Guo, Kaiyan Ye, Yizong Ding.

  2. These authors jointly supervised this work: Jieyi Wang, Jiwei Zhang, Wen Di, Guanglei Zhuang, Fan Yang.

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Changrui Yang, Shanni Guo, Kaiyan Ye, Bowen Sun, Shuyi Qian, Mei-Chun Cai, Xia Yin, Wen Di, Guanglei Zhuang & Fan Yang

  2. Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Changrui Yang, Shanni Guo, Kaiyan Ye, Bowen Sun, Shuyi Qian, Mei-Chun Cai, Xia Yin, Wen Di, Guanglei Zhuang & Fan Yang

  3. Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Yizong Ding

  4. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

    Xingzhong Zhao, Yucheng T. Yang & Xing-Ming Zhao

  5. MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China

    Xingzhong Zhao, Yucheng T. Yang & Xing-Ming Zhao

  6. Lyvgen Biopharma, Shanghai, China

    Luyan Liu & Jieyi Wang

  7. Department of Gynecologic Oncology, Zhongshan Hospital, Fudan University, Shanghai, China

    Libing Xiang

  8. Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China

    Jiwei Zhang

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  1. Changrui Yang
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Contributions

F.Y. and G.Z. designed and supervised the study. W.D., J.Z. and J.W. co-supervised the project. F.Y., G.Z. and J.W. conceived the ideas. F.Y. and G.Z. wrote the paper. C.Y., S.G., K.Y., Y.D., X.Y., S.Q., B.S. and M.-C.C. performed the experiments and analysed the data. X.Z., Y.T.Y. and X.-M.Z. developed the machine learning algorithms. L.L. and J.W. provided the TCE materials. Y.D., L.X. and X.Y. collected the sample materials. All authors read and approved the final version of the paper.

Corresponding authors

Correspondence to Jieyi Wang, Jiwei Zhang, Wen Di, Guanglei Zhuang or Fan Yang.

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Competing interests

L.L and J.W. are employees of Lyvgen Biopharma. The other authors declare no competing interests.

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Nature Biomedical Engineering thanks Pedro Berraondo, Christian Klein and Jeffrey Miller for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 B7H3 tumor expression, antigen-binding selectivity, and lack of off-target activation by B7H3xCD3xPDL1.

(a) Representative IHC staining of B7H3 was performed on formalin-fixed paraffin-embedded tumor sections from diverse human cancers, including: high-grade serous ovarian carcinoma (HGSOC), ovarian clear cell carcinoma (OCCC), mucinous ovarian carcinoma (MOC), adult granulosa cell tumor (AGCT), gastric carcinoma (GC), lung squamous cell carcinoma (LUSC), esophageal carcinoma (ESCA), hepatocellular carcinoma (HCC), colon adenocarcinoma (COAD), cholangiocarcinoma (CHOL). Images were acquired under 20× magnification. Scale bar 100 μm. (b) Binding assays showing the affinity of TCEs for B7H3, CD3, and PDL1. (c) Mean fluorescence intensity (MFI) of B7H3 expression across a panel of tumor cell lines. (d-g) Antigen-binding assay using SKOV3 wild-type (WT), SKOV3-B7H3 knockout (B7H3-KO), and murine ID8 cells treated with IFN-γ (50 ng/ml). Cells were incubated with B7H3xCD3xPDL1 (Tri-TCE) or control human IgG, followed by staining with FITC-conjugated anti-human H&L secondary antibody. Flow cytometry was used to assess antibody binding. (e) Surface expression of murine B7H3 and PDL1 on ID8 and IFNγ-treated ID8 cells was evaluated by flow cytometry. (f) Tri-TCE or control IgG binding to SKOV3-WT and ID8 cells. (g) Tri-TCE or control IgG binding to IFNγ-treated SKOV3-B7H3-KO and ID8 cells. (h–i) CHO cells expressing human B7H3 or PDL1 were co-cultured with CD3 (h) or PDL1 (i) luciferase reporter HEK293T cells. Tri-TCE (B7H3xCD3xPDL1), anti-CD3 or anti-PDL1 antibodies were treated at indicated doses for 24h, and luminescence was measured. (j-k) Flow cytometry analysis of Ki67 and CD25 expression in PBMCs (j) or whole blood (k) treated with control human IgG or B7H3xCD3xPDL1, or anti-CD3/CD28 as a positive control. Left, representative cell sorting results. Right, quantified data (n = 3 independent experiments, means ± SD, one-way ANOVA with Tukey’s multiple comparisons test or two-tailed Student’s t-test). (I) Flow cytometry analysis of B7H3 (top) and PDL1 (bottom) expression on CD11b+ cells from three healthy donors. Panel d created with BioRender.com.

Source data

Extended Data Fig. 2 B7H3xCD3xPDL1 treatment promotes T cell activation and tumor cell killing.

(a) Experimental procedure. (b) Flow cytometry analysis of cell viability, showing percentages of dead cells (Live/Dead-BV510+) gated on CD45- cells after B7H3xCD3xPDL1 treatment. Statistical significance was determined using two-tailed Student’s t-test (n = 3 patient tumors, means ± SD, two-tailed Student’s t-test). (c) Flow cytometry analysis of CD25, Ki67, GZMB and IFN-γ expression in CD8+ T cells after B7H3xCD3xPDL1 treatment. Statistical significance was determined using two-tailed Student’s t-test (n = 3 patient tumors, means ± SD, two-tailed Student’s t-test). (d) Analysis of naive, central memory (CM), and effector memory (EM) T cell populations after B7H3xCD3xPDL1 treatment. Representative flow cytometry plots and quantified data (n = 3 patient tumors, means ± SD, two-tailed Student’s t-test). Panel a created with BioRender.com.

Source data

Extended Data Fig. 3 B7H3 expression is required for Tri-TCE–mediated tumor killing.

(a-b,d) SKOV3-WT, SKOV3-B7H3-KO or SKOV3-PDL1-KO tumor cells were co-cultured with PBMCs and treated with B7H3xCD3xPDL1. (a) B7H3 knockout validated by flow cytometry. (b) Tumor growth kinetics (left) and representative images analyzed with ilastik software (right) (n=3 independent experiments, mean ± SD, two-way ANOVA test). PBMCs are indicated in green, and tumor cells in purple. Scale bar 200 μm. (c) SKOV3-WT and SKOV3-B7H3-KO cells were analyzed of PDL1 expression via flow cytometry. (d) Tumor growth kinetics of SKOV3-WT and SKOV3-PDL1-KO cells were monitored for 6 days using live-cell imaging (n = 3 independent experiments, mean ± SD, two-way ANOVA test).

Source data

Extended Data Fig. 4 Characterization of B7H3 and PDL1 expression across tumor and immune compartments.

(a) PBMCs were co-cultured with SKOV3 tumor cells in the presence or absence of B7H3xCD3xPDL1 (Tri-TCE). Flow cytometry was performed before and after treatment to assess the surface expression of B7H3 and PDL1 on SKOV3 tumor cells (left panels) and CD11b+ myeloid cells (right panels). Representative histograms from one of three independent experiments are shown. (b) SKOV3 cells were treated with or without IFNγ (50 ng/ml) for 24 h. PDL1 surface expression was then assessed using flow cytometry. (c-d) Patient-derived tumor suspensions were treated with control IgG or Tri-TCE (B7H3xCD3xPDL1) in PDTS samples. (c) Representative flow cytometry plots showing gating strategy, Histograms show surface expression of B7H3 and PDL1 on tumor cells (CD45-) and CD11b+ myeloid cells before and after treatment. (d) Quantitative results (n = 3 independent experiments, mean ± SD, one-way ANOVA with Tukey’s multiple comparisons test). (e) In the patient-derived tumor suspension (PDTS) model, tumor and immune compartments were distinguished by CD45 expression, and immune subsets were further gated into CD11b+ myeloid cells and CD3+ T cells. Flow cytometry was performed to assess B7H3 surface expression in each subset. (f) Quantification of B7H3+ cells among tumor and myeloid compartments in PDTS samples (n = 33 patient tumors, mean ± SD, paired two-tailed Student’s t-test).

Source data

Extended Data Fig. 5 Biochemical characterization and stability assessment of B7H3xCD3xPDL1.

B7H3xCD3xPDL1 was evaluated for stability under various conditions as a pre-drug. Time-dependent stability (Day 7, Day 14, Month 2) was assessed with SDS-PAGE (a), size exclusion chromatography profiles (b), ion exchange chromatography profiles (c), and binding affinity assays (d). (e-g) Stability following freeze-thaw cycles was evaluated through SDS-PAGE (e), chromatography profiles (f), and binding affinity assays (g). (h-j) Stability under varying pH and oxidation conditions was examined using SDS-PAGE (h), chromatography profiles (i), and binding affinity assays (j).

Source data

Extended Data Fig. 6 Pharmacokinetic study of B7H3xCD3xPDL1 in mice.

C57BL/6 mice were administered an intraperitoneal (i.p.) dose of 5 mg/kg of B7H3xCD3xPDL1. Blood samples were collected at specified time points, and the concentrations of B7H3, CD3, and PDL1 antibodies were measured using ELISA. The pharmacokinetic profiles were analyzed to assess the stability and distribution of the antibodies over time, shown as individual profiles (a) and average profiles (b).

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Extended Data Fig. 7 Assessment of B7H3xCD3xPDL1 treatment safety in vivo.

Human PBMCs were reconstituted in NOG mice followed by subcutaneous implantation of SKOV3 cells, and treatment with TCEs. (a) Flow cytometry analysis showing the percentage of hCD45+ cells in the peripheral blood of treated mice. Statistical significance was determined using one-way ANOVA with Tukey’s multiple comparisons test (means ± SD, n = 12). (b) Body weight measurements of mice in control, B7H3xCD3, and B7H3xCD3xPDL1 groups (n=12 mice per group). (c) Serum biochemical analysis for liver and kidney function markers, including ALT, AST, TBIL, ALB, ALP, γ-GT, BUN, CREA, and UA. Statistical significance was determined using one-way ANOVA with Tukey’s multiple comparisons test (means ± SD, n = 8 mice for control and B7H3xCD3-treated groups, n = 10 mice for B7H3xCD3xPDL1-treated group). (d) Histopathological analysis of major organs (liver, heart, lung, kidney, and spleen) from control, B7H3xCD3, and B7H3xCD3xPDL1-treated mice. Scale bar 1mm. (e) Peripheral blood was analyzed at weeks 2, 3, and 4 post-engraftment. Flow cytometry was used to assess CD25 and CD69 expression on human T cells, and to quantify CD11b+ cells. Representative flow plots and summary statistics are shown (n = 5 mice per group; mean ± SD; one-way ANOVA with Tukey’s multiple comparisons test).

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Extended Data Fig. 8 Evaluation of T cell activation and anti-tumor efficacy of HER2xCD3xPDL1 and CEAxCD3xPDL1.

(a) Flow cytometry analysis of CD25, Ki67, TNF-α, and IFN-γ in CD8+ T cells co-cultured with PBMCs and EBC-1 or LS174T cells, treated with HER2xCD3xPDL1 or CEAxCD3xPDL1, respectively. (b) Human PBMCs were reconstituted in NOG mice followed by subcutaneous implantation of EBC-1 or LS174T cells, and treatment with HER2xCD3xPDL1 or CEAxCD3xPDL1. Tumor volume kinetics were measured over time (n=6 mice, mean ± SD, one-way ANOVA with Tukey’s multiple comparisons test).

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Extended Data Fig. 9 B7H3xCD3xPDL1 enhances the engagement of T cells and myeloid cells through PDL1.

CD11b+ myeloid cells were preblocked with anti-PDL1, and subsequently co-cultured with T cells and SKOV3 cells in presence of B7H3xCD3xPDL1. The myeloid-T cell doublets were captured using imaging cytometry. (a) Schematic model. (b) Flow cytometric sortings. (c) Quantitative results (n=3 independent experiments, mean ± SD). Statistical analysis was performed using a one-way ANOVA test with Tukey’s multiple comparisons test. Panel a created with BioRender.com.

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Extended Data Fig. 10 Evaluation of myeloid cell susceptibility following Tri-TCE treatment.

(a-b) SKOV3 tumor cells were co-cultured with human T cells and CD11b+ myeloid cells (tumor: T cell: myeloid= 1: 5: 5), in the presence of control IgG or B7H3xCD3xPDL1 tri-specific antibody. CD11b+ cells were pre-labeled with a red fluorescent dye. (a) Representative images acquired by Incucyte live-cell imaging. Scale bar 200 μm. (b) Quantitative analysis of tumor cells, CD11b+ cells, and T cells was performed using ilastik-based image segmentation and cell-type classification. Data are shown as fold change in growth area (mean ± SD, n = 3 independent experiments, Two-tailed Student’s t-test).

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Nanolive imaging of tumor-PBMC co-culture after B7H3xCD3xPDL1 treatment.

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Yang, C., Guo, S., Ye, K. et al. A trispecific antibody engaging T cells with tumour and myeloid cells augments antitumour immunity. Nat. Biomed. Eng (2025). https://doi.org/10.1038/s41551-025-01569-4

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