Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

IL-2Rα-biased agonist enhances antitumor immunity by invigorating tumor-infiltrating CD25+CD8+ T cells

Nature Cancer volume 4, pages 1309–1325 (2023)Cite this article

Subjects

Abstract

To avoid regulatory T cell promotion and vascular toxicity, the interleukin-2 receptor-β/interleukin-2 receptor-γ (IL-2Rβγ)-biased approach is used by most IL-2 analogs in immuno-oncology. However, recent clinical disappointments in these IL-2 agonists have questioned this strategy. Here we show that both wild-type (IL-2wt) and IL-2Rβγ-attenuated (IL-2α-bias) agonists that preserve IL-2Rα (CD25) activities can effectively expand tumor-specific CD8+ T cells (TSTs) and exhibit better antitumor efficacy and safety than the ‘non-α’ counterpart (IL-2nα). Mechanistically, TSTs coexpress elevated CD25 and PD-1 and are more susceptible to stimulation by IL-2Rα-proficient agonists. Moreover, the antitumor efficacy of anti-PD-1 depends on activation of PD-1+CD25+ TSTs through autocrine IL-2–CD25 signaling. In individuals with cancer, a low IL-2 signature correlates with non-responsiveness to anti-PD-1 treatment. In mouse models, IL-2α-bias, but not IL-2nα, restores the IL-2 signature and synergizes with anti-PD-1 to eradicate large established tumors. These findings underscore the indispensable function of CD25 in IL-2-based immunotherapy and provide rationales for evaluating IL-2Rα-biased agonists in individuals with cancer.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: IL-2wt exhibits stronger antitumor efficacy than IL-2nα through selective expansion of TST cells.
Fig. 2: IL-2wt and IL-2nα differentially reshape the TME.
Fig. 3: IL-2α-bias elevates the CD8+ Teff cell-to-Treg cell ratio in tumors, but not in the periphery, to promote antitumor efficacy.
Fig. 4: PD-1+CD25+CD8+ TILs are enriched for TSTs in both mouse and human tumors.
Fig. 5: Antitumor efficacy of anti-PD-1 is highly dependent on the activation of PD-1+CD25+CD8+ TILs through autocrine IL-2–CD25 signaling.
Fig. 6: The IL-2 pathway signature in tumors is associated with clinical responsiveness to anti-PD-1.
Fig. 7: An IL-2Rα-biased, but not IL-2Rβγ-biased, agonist reprograms the tumor immune landscape and synergizes with anti-PD-1 therapy.

Similar content being viewed by others

Data availability

In-house-generated mouse scRNA-seq and bulk RNA-seq data that support the findings of this study have been deposited in the ArrayExpress database under accession numbers E-MTAB-12373 and E-MTAB-12372. Human scRNA-seq and bulk RNA-seq data reanalyzed here are available under accession numbers GSE176021 (LUAD and SCC data from Caushi et al.25), GSE144945 (NSCLC data from Cho et al.26), GSE178341 (CRC data from Pelka et al.27), GSE162498 (NSCLC data from Gueguen et al.28), GSE123814 (BCC and SCC data from Yost et al.29) and GSE156728 (ESCA, MM, OV, PACA, RC, THCA and UCEC data from Zheng et al.30). The melanoma bulk RNA-seq data are available from the European Nucleotide Archive under accession number PRJEB23709. Human nsqNSCLC bulk RNA-seq data were collected from the ORIENT-11 study (ClinicalTrials.gov identifier NCT03607539) published previously35.

Requests for further information, resources and reagents should be directed to the lead contact K.H. under a fully executed material and transfer agreement. Source data are provided with this paper.

Code availability

The bioinformatics analyses were performed using open-source software, including CellRanger (version 6.1.2), STAR (version 2.5.3a), StringTie (version 1.3.1), gffcompare (version 0.9.9c), GSEA (version 4.2.3), Seurat (version 4.1.3) and GSEA (version 1.42.0) and the Immunedeconv R package (version 2.1.0).

References

  1. Abbas, A. K., Trotta, E., Simeonov, D. R., Marson, A. & Bluestone, J. A. Revisiting IL-2: biology and therapeutic prospects. Sci. Immunol. 3, eaat1482 (2018).

    Article  PubMed  Google Scholar 

  2. Rosenzwajg, M. et al. Immunological and clinical effects of low-dose interleukin-2 across 11 autoimmune diseases in a single, open clinical trial. Ann. Rheum. Dis. 78, 209–217 (2019).

    Article  CAS  PubMed  Google Scholar 

  3. Hartemann, A. et al. Low-dose interleukin 2 in patients with type 1 diabetes: a phase 1/2 randomised, double-blind, placebo-controlled trial. Lancet Diabetes Endocrinol. 1, 295–305 (2013).

    Article  CAS  PubMed  Google Scholar 

  4. Rosenberg, S. A. et al. Treatment of 283 consecutive patients with metastatic melanoma or renal cell cancer using high-dose bolus interleukin 2. JAMA 271, 907–913 (1994).

    Article  CAS  PubMed  Google Scholar 

  5. Rosenberg, S. A. IL-2: the first effective immunotherapy for human cancer. J. Immunol. 192, 5451–5458 (2014).

    Article  CAS  PubMed  Google Scholar 

  6. Dutcher, J. P. et al. High dose interleukin-2 (aldesleukin)—expert consensus on best management practices—2014. J. Immunother. Cancer 2, 26 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Mullard, A. Restoring IL-2 to its cancer immunotherapy glory. Nat. Rev. Drug Discov. 20, 163–165 (2021).

    Article  CAS  PubMed  Google Scholar 

  8. Zhu, E. F. et al. Synergistic innate and adaptive immune response to combination immunotherapy with anti-tumor antigen antibodies and extended serum half-life IL-2. Cancer Cell 27, 489–501 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Arenas-Ramirez, N. et al. Improved cancer immunotherapy by a CD25-mimobody conferring selectivity to human interleukin-2. Sci. Transl. Med. 8, 367ra166 (2016).

    Article  PubMed  Google Scholar 

  10. Klein, C. et al. Cergutuzumab amunaleukin (CEA-IL2v), a CEA-targeted IL-2 variant-based immunocytokine for combination cancer immunotherapy: overcoming limitations of aldesleukin and conventional IL-2-based immunocytokines. Oncoimmunology 6, e1277306 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Charych, D. H. et al. NKTR-214, an engineered cytokine with biased IL2 receptor binding, increased tumor exposure, and marked efficacy in mouse tumor models. Clin. Cancer Res. 22, 680–690 (2016).

    Article  CAS  PubMed  Google Scholar 

  12. Cancer Discovery. Bempeg failure unlikely to affect other IL2 drugs. Cancer Discov. 12, 1604–1605 (2022).

    Article  Google Scholar 

  13. Krieg, C., Létourneau, S., Pantaleo, G. & Boyman, O. Improved IL-2 immunotherapy by selective stimulation of IL-2 receptors on lymphocytes and endothelial cells. Proc. Natl Acad. Sci. USA 107, 11906–11911 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ribba, B. et al. Prediction of the optimal dosing regimen using a mathematical model of tumor uptake for immunocytokine-based cancer immunotherapy. Clin. Cancer Res. 24, 3325–3333 (2018).

    Article  CAS  PubMed  Google Scholar 

  15. Jin, P. et al. Molecular signatures induced by interleukin-2 on peripheral blood mononuclear cells and T cell subsets. J. Transl. Med. 4, 26 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Panelli, M. C. et al. Gene-expression profiling of the response of peripheral blood mononuclear cells and melanoma metastases to systemic IL-2 administration. Genome Biol. 3, RESEARCH0035 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Zilionis, R. et al. Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. Immunity 50, 1317–1334 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Herr, F. et al. IL-2 phosphorylates STAT5 to drive IFN-γ production and activation of human dendritic cells. J. Immunol. 192, 5660–5670 (2014).

    Article  CAS  PubMed  Google Scholar 

  19. 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 

  20. Wang, X., Rickert, M. & Garcia, K. C. Structure of the quaternary complex of interleukin-2 with its α, β, and γc receptors. Science 310, 1159–1163 (2005).

    Article  CAS  PubMed  Google Scholar 

  21. Peterson, L. B. et al. A long-lived IL-2 mutein that selectively activates and expands regulatory T cells as a therapy for autoimmune disease. J. Autoimmun. 95, 1–14 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kim, H. P. & Leonard, W. J. The basis for TCR-mediated regulation of the IL-2 receptor α chain gene: role of widely separated regulatory elements. EMBO J. 21, 3051–3059 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Chikuma, S. et al. PD-1-mediated suppression of IL-2 production induces CD8+ T cell anergy in vivo. J. Immunol. 182, 6682–6689 (2009).

    Article  CAS  PubMed  Google Scholar 

  24. Li, Y. et al. Regulatory T cells control toxicity in a humanized model of IL-2 therapy. Nat. Commun. 8, 1762 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Caushi, J. X. et al. Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers. Nature 596, 126–132 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Cho, J. W. et al. Dysregulation of TFH–B–TRM lymphocyte cooperation is associated with unfavorable anti-PD-1 responses in EGFR-mutant lung cancer. Nat. Commun. 12, 6068 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Pelka, K. et al. Spatially organized multicellular immune hubs in human colorectal cancer. Cell 184, 4734–4752 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Gueguen, P. et al. Contribution of resident and circulating precursors to tumor-infiltrating CD8+ T cell populations in lung cancer. Sci. Immunol. 6, eabd5778 (2021).

    Article  CAS  PubMed  Google Scholar 

  29. 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 

  30. Zheng, L. et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374, abe6474 (2021).

    Article  PubMed  Google Scholar 

  31. Lowery, F. J. et al. Molecular signatures of antitumor neoantigen-reactive T cells from metastatic human cancers. Science 375, 877–884 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Fourcade, J. et al. CD8+ T cells specific for tumor antigens can be rendered dysfunctional by the tumor microenvironment through upregulation of the inhibitory receptors BTLA and PD-1. Cancer Res. 72, 887–896 (2012).

    Article  CAS  PubMed  Google Scholar 

  33. Spranger, S. et al. Mechanism of tumor rejection with doublets of CTLA-4, PD-1/PD-L1, or IDO blockade involves restored IL-2 production and proliferation of CD8+ T cells directly within the tumor microenvironment. J. Immunother. Cancer 2, 3 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Su, E. W. et al. IL-2Rα mediates temporal regulation of IL-2 signaling and enhances immunotherapy. Sci. Transl. Med. 7, 311ra170 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Yang, Y. et al. Updated overall survival data and predictive biomarkers of sintilimab plus pemetrexed and platinum as first-line treatment for locally advanced or metastatic nonsquamous NSCLC in the phase 3 ORIENT-11 study. J. Thorac. Oncol. 16, 2109–2120 (2021).

    Article  CAS  PubMed  Google Scholar 

  36. Taube, J. M. et al. Colocalization of inflammatory response with B7-H1 expression in human melanocytic lesions supports an adaptive resistance mechanism of immune escape. Sci. Transl. Med. 4, 127ra137 (2012).

    Article  Google Scholar 

  37. Chow, A. et al. The ectonucleotidase CD39 identifies tumor-reactive CD8+ T cells predictive of immune checkpoint blockade efficacy in human lung cancer. Immunity 56, 93–106 (2023).

    Article  CAS  PubMed  Google Scholar 

  38. Gide, T. N. et al. Distinct immune cell populations define response to anti-PD-1 monotherapy and anti-PD-1/anti-CTLA-4 combined therapy. Cancer Cell 35, 238–255 (2019).

    Article  CAS  PubMed  Google Scholar 

  39. Miller, B. C. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Philip, M. et al. Chromatin states define tumour-specific T cell dysfunction and reprogramming. Nature 545, 452–456 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kim, T. K., Vandsemb, E. N., Herbst, R. S. & Chen, L. Adaptive immune resistance at the tumour site: mechanisms and therapeutic opportunities. Nat. Rev. Drug Discov. 21, 529–540 (2022).

    Article  CAS  PubMed  Google Scholar 

  42. Gros, A. et al. PD-1 identifies the patient-specific CD8+ tumor-reactive repertoire infiltrating human tumors. J. Clin. Invest. 124, 2246–2259 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 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 

  44. Liu, Z. et al. Detecting tumor antigen-specific T cells via interaction-dependent fucosyl-biotinylation. Cell 183, 1117–1133 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Meng, X. et al. FBXO38 mediates PD-1 ubiquitination and regulates anti-tumour immunity of T cells. Nature 564, 130–135 (2018).

    Article  CAS  PubMed  Google Scholar 

  46. Carter, L. et al. PD-1:PD-L inhibitory pathway affects both CD4+ and CD8+ T cells and is overcome by IL-2. Eur. J. Immunol. 32, 634–643 (2002).

    Article  CAS  PubMed  Google Scholar 

  47. Hernandez, R., Põder, J., LaPorte, K. M. & Malek, T. R. Engineering IL-2 for immunotherapy of autoimmunity and cancer. Nat. Rev. Immunol. 22, 614–628 (2022).

    Article  CAS  PubMed  Google Scholar 

  48. Overacre-Delgoffe, A. E. et al. Interferon-γ drives Treg fragility to promote anti-tumor immunity. Cell 169, 1130–1141 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sharma, M. et al. Bempegaldesleukin selectively depletes intratumoral Tregs and potentiates T cell-mediated cancer therapy. Nat. Commun. 11, 661 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Gillies, S. D. et al. A low-toxicity IL-2-based immunocytokine retains antitumor activity despite its high degree of IL-2 receptor selectivity. Clin. Cancer Res. 17, 3673–3685 (2011).

    Article  CAS  PubMed  Google Scholar 

  51. Shanafelt, A. B. et al. A T-cell-selective interleukin 2 mutein exhibits potent antitumor activity and is well tolerated in vivo. Nat. Biotechnol. 18, 1197–1202 (2000).

    Article  CAS  PubMed  Google Scholar 

  52. Hashimoto, M. et al. PD-1 combination therapy with IL-2 modifies CD8+ T cell exhaustion program. Nature 610, 173–181 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Codarri Deak, L. et al. PD-1-cis IL-2R agonism yields better effectors from stem-like CD8+ T cells. Nature 610, 161–172 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Gillessen, S. et al. A phase I dose-escalation study of the immunocytokine EMD 521873 (Selectikine) in patients with advanced solid tumours. Eur. J. Cancer 49, 35–44 (2013).

    Article  CAS  PubMed  Google Scholar 

  55. Laurent, J. et al. T-cell activation by treatment of cancer patients with EMD 521873 (Selectikine), an IL-2/anti-DNA fusion protein. J. Transl. Med. 11, 5 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Hsu, E. J. et al. A cytokine receptor-masked IL2 prodrug selectively activates tumor-infiltrating lymphocytes for potent antitumor therapy. Nat. Commun. 12, 2768 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Zhang, Y., Huo, M., Zhou, J. & Xie, S. PKSolver: an add-in program for pharmacokinetic and pharmacodynamic data analysis in Microsoft Excel. Comput. Methods Programs Biomed. 99, 306–314 (2010).

    Article  PubMed  Google Scholar 

  58. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    Article  CAS  PubMed  Google Scholar 

  64. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).

    Article  CAS  PubMed  Google Scholar 

  66. Jiang, H., Lei, R., Ding, S. W. & Zhu, S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics 15, 182 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Sturm, G., Finotello, F. & List, M. Immunedeconv: an R package for unified access to computational methods for estimating immune cell fractions from bulk RNA-sequencing data. 20, 223–232 (2020).

Download references

Acknowledgements

We thank D. Yu, Y.-J. Liu, L. L. Lanier, L. Fong and C. Garcia-Echeverria for helpful discussions and staff at the Innovent Flow Cytometry Core and Pathology Core for assistance in experiments. K.H. was supported by the Innovent Guoqing Academy discovery research fund.

Author information

Author notes

  1. These authors contributed equally: Weiwei Wu, Tiongsun Chia.

Authors and Affiliations

  1. Department of Pharmacology and Toxicology, Innovent Guoqing Academy, Suzhou, China

    Weiwei Wu, Jia Lu, Ye Feng, Junjie Deng, Ying Yao, Ying Zhang, Jinling Xu, Min Wu & Bingliang Chen

  2. Department of Cancer Biology, Innovent Guoqing Academy, Suzhou, China

    Tiongsun Chia, Jian Guan, Yaning Li, Huisi Liu & Kaijie He

  3. Department of Immunology, Innovent Guoqing Academy, Suzhou, China

    Xue Li & Jia Zou

  4. Department of Antibody Discovery and Protein Engineering, Innovent Guoqing Academy, Suzhou, China

    Fenggen Fu, Shuaixiang Zhou, Xiaomin Ling, Zhihai Wu, Feifei Wang & Xue Liang

  5. Department of Translational Medicine, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China

    Jiya Sun

Authors
  1. Weiwei Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tiongsun Chia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jia Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xue Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jian Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yaning Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Fenggen Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Shuaixiang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ye Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Junjie Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jia Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jiya Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Ying Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Xiaomin Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Zhihai Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Ying Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Jinling Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Feifei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Xue Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Min Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Huisi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Bingliang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Kaijie He
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization and supervision: K.H. In vitro cell experiments: J.G. Animal studies: W.W., J.L., Y.L., Y.F., Y.Y., Y.Z., J.X., M.W., B.C. and H.L. Bioinformatics: T.C., X.L., J.S. and J.Z. Protein sciences: F.F., S.Z., X.L., Z.W., F.W. and X.L. Immunohistochemistry: J.D. Writing, original draft: K.H. Writing, review and editing: K.H., W.W., T.C., X.L., J.G. and J.Z.

Corresponding author

Correspondence to Kaijie He.

Ethics declarations

Competing interests

All authors are current employees of Innovent Biologics. K.H., F.F., W.W. and S.Z. are inventors of a related patent (WO2021185361A1).

Peer review

Peer review information

Nature Cancer thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 In vitro and in vivo activities of IL-2wt or IL-2nα.

(a–c) Binding kinetics of anti-human B7H3 antibody (hz20G5) fused with either IL-2wt or IL-2nα detected by bio-layer interferometry (BLI). IL-2wt (A) and IL-2nα (B) binding kinetics to human IL-2Rα, human IL-2Rβγ, mouse IL-2Rα and mouse IL-2Rβγ. Table summarizing binding affinity of each molecule (C). (d) Binding kinetics of antibody hz20G5 to human B7H3 and mouse B7H3, showing high affinity of hz20G5 to human B7H3 but no binding activity to mouse B7H3 antigen. (e) Dose response curves of pSTAT5% in CD4+CD25-T cell and NK cells treated with serial diluted IL-2wt or IL-2nα (experiment performed in duplicates). (f) Individual MC38 tumor growth curves in each treatment group (related to Fig. 1c). (g–i) Three repeated experiments to confirm in vivo efficacy and safety in MC38 tumor-bearing mice. Average tumor volume changes, body weight changes, and individual tumor growth curves were shown for each experiment. High dose (10 mg/kg) of IL-2 agonists was used in one experiment (G), and the remaining experiments used 3mg/kg. n = 7 (G) or n = 6 (H-I) mice per group. (j–k) Evaluation of wet lung weight in wild-type C57BL/6 mice (J) or Rag1-/- C57BL/6 mice (K) after a single administration of hIgG, IL-2wt or IL-2nα at indicated dosage 5 days post treatment. n = 5 (J) or n = 8 (K) mice per group. Mean + s.e.m. is presented, and p-values are calculated using two-way ANOVA for tumor growth curves and one-way ANOVA for bar graphs.

Source data

Extended Data Fig. 2 PK and PD analyses in MC38 tumor-bearing mice after IL-2wt or IL-2nα treatment.

(a) Pharmacokinetics of IL-2wt and IL-2nα in C57BL/6 mice after single 3mg/kg intravenous administration. n = 9 mice per group. (b) Frequency of CD8+T and Treg in CD45+ cells and CD8/ Treg ratio in tumor, spleen and blood from MC38 tumor-bearing mice at the end of study after repeated dosing of indicated reagents (n = 4~5 mice, 3mg/kg, ip. injection, QWx3). (c) Representative FACS plots and bar graphs showing the frequencies of MuLV p15E-tetramer+ in different CD8+TILs subsets from IgG control treated tumors. (n = 4 mice). (d) CD39+% and CD39-PD-1-% in CD8+TILs after treatment. (n = 4 mice). (e) Representative contour plots of PD-1 versus CD39 in CD8+TILs (black) and dot plots showing p15E-tetramer+CD8+TILs (red) after treatment, and bar graphs showing p15E-tetramer+% in CD8+TILs on the right. (n = 4 mice). (f) CD25 mean fluorescent intensity (MFI) on total CD8+TILs (left panel); CD25 MFI on CD39- or CD39+ CD8+TILs after treatment (right panel). (n = 4 mice). (g) CD25 MFI on p15E-tetramer+CD8+TILs after treatment. (n = 4 mice). (h) Representative FACS plots and bar graphs showing Treg cells in each treatment group. (n = 4 mice). Mean + s.e.m. is presented, and p-values are calculated using two-way ANOVA for PK analysis and one-way ANOVA for bar graphs.

Source data

Extended Data Fig. 3 In vivo efficacy, safety and TIL analysis in B16F10-OVA and CT26 tumors.

(a–n) B16F10-OVA tumor study related to Fig. 1f–l. (n = 7 mice per group. But n = 6 (hIgG), n = 7 (IL-2wt) and n = 5 (IL-2nα) mice were analyzed for (b–n), as data was not available due to tumor ulceration (n = 1 in hIgG) and premature deaths (n = 2 in IL-2nα treated group)). (a) Individual tumor growth curves. (n = 7 mice). (b, c) Tumor weight and wet lung weight measured at the end of the study. (d) Intratumoral CD8 to Treg ratio in each treatment group. (e) Representative contour plots of PD-1 versus CD39 in CD8+TILs (black) and dot plots showing OVA-tetramer+CD8+TILs (red) after treatment. (f) Frequency of CD25+% and CD25 expression levels (MFI) in CD39- or CD39+ CD8+TIL subsets. (g) Frequency (in CD45+ cells) and absolute cell numbers of OVA-tetramer+CD8+TILs in tumors. (h) Frequency (in CD45+ cells) and absolute cell numbers of Ki67+OVA-tetramer+ CD8+TILs in tumors. (i-j) Frequency (in CD45+ cells) and absolute cell numbers of CD8+ and Ki67+CD8+ TILs in tumors. (k-l) Frequency (in CD45+ cells) and absolute cell numbers of CD4+ and Ki67+CD4+ TILs in tumors. (m-n) Frequency (in CD45+ cells) and absolute cell numbers of intratumoral Treg and Ki67+Treg cells. (o–r) Efficacy and safety evaluation in CT26 tumor model. Average tumor volume changes (o), body weight changes (p), individual tumor growth curves (q) and wet lung weight (r) were shown. n = 7 mice per group. One mouse died prematurely in IL-2nα group, so only n = 6 data points were shown in IL-2nα group in (r). Mean + s.e.m. is presented, and p-values calculated using one-way ANOVA for bar graphs, two-way ANOVA for tumor growth curves and body weight changes, and two-sided paired t-test for symbols & lines plots.

Source data

Extended Data Fig. 4 scRNA-seq analysis of immune cell clusters in MC38 tumors.

Related to Fig. 2. C57BL/6 mice bearing MC38 tumors (n = 3 mice) were treated weekly with 3 mg/kg of IL-2wt, IL-2nα or IgG for 3 times. Tumors were dissected five days after the last dose and CD45+ cells were isolated for scRNA-seq. (a) UMAP, (b) cell cluster composition and (c) heatmap of CD45+ cells. (d) Heatmap and (e) feature plots of CD3+ cell clusters. (f) Heatmap comparing the CD8+ naïve T cluster and the CD8+ (IFN-stimulated genes) cluster. Genes associated with interferon stimulation are highlighted. (g) UMAP, (h) cell cluster composition, (i) heat map and (j) PD-L1 violin plots (total n = 5865, IgG=3304, IL-2wt= 1637, IL-2nα =924) of the mononuclear phagocytes cluster from (A). The center lines of boxplots within violin plots indicate median values, box limits indicate the 25th and 75th percentiles and whiskers extend 1.5x the interquartile range from 25th and 75th percentiles. (k) Feature plots showing PD-L1 expression (top) and the average expression levels of genes from “GOBP Response To Interferon Gamma’ signature (bottom).

Extended Data Fig. 5 In vitro and in vivo activities of IL-2α-bias or IL-2nα/N88D.

(a, b) Binding kinetics of IL-2α-bias (a) and IL-2nα/N88D (b) detected by bio-layer interferometry (BLI) to human IL-2Rα, human IL-2Rβγ, mouse IL-2Rα and mouse IL-2Rβγ. (c) Table summarizing binding affinity of each molecule to IL-2Rs. (d) Dose response curves of pSTAT5% in CD4+CD25-T cell and NK cells after stimulation. (experiment performed in duplicates). (e) Table summarizing in vitro activities (pSTAT5 stimulation) of four IL-2 agonists. (f) Dose response curves of pSTAT5 activation in CTLL-2 pSTAT5 reporter cells after IL-2 agonists stimulation. (experiment performed in triplicates). (g) Tumor weight dot plot and body weight change curves related to Fig. 3d. (n = 6 mice). (h) Average and individual tumor growth curves of MC38 tumors in Rag1-/- mice after transferring 5 million congenic CD8+T cells on day7 post implantation with or without 3mg/kg IL-2α-bias treatment (n = 6 mice). (i-j) In vivo efficacy of IL-2α-bias in OT-I cells transferred B16F10-OVA model. 0.2 million OT-I cells were transferred into Rag1-/- or NOG mice when B16F10-OVA tumors were palpable (4~7 days post implantation), then tumor-bearing mice were treated with IgG or IL-2α-bias @3mg/kg weekly for three times. (i) Individual tumor growth curves and survival plot of Rag1-/- mice (n = 8 mice). (j) Individual tumor growth curves and tumor volumes at the end of study in NOG mice (n = 8 mice). Mean + s.e.m. is presented, and p-values are calculated using one-way ANOVA for bar graphs and two-way ANOVA for tumor growth curves.

Source data

Extended Data Fig. 6 In vivo efficacy of IL-2α-bias with or without depletion of CD4+T, CD8 +T and NK cells.

(a) Individual tumor growth curves in each treatment group (related to Fig. 3e). (b) Tumor weight measured at the end of study. (c) Body weight change curves. (d) Representative dot plots showing the cell depletion efficiency in tumors. (e) Frequency of CD8+T, CD4+T, Treg and NK cells in CD45+ cells in tumors (top) and blood (bottom). N = 7 mice per group. Mean + s.e.m. is presented, and p-values are calculated using one-way ANOVA for bar graphs in B and E (Dunnett’s multiple comparisons test).

Source data

Extended Data Fig. 7 In vivo activity and safety profiles of IL-2α-bias in 5 different syngeneic tumors.

(a–d) Tumor growth curves and body weight changes in B16F10 (a), LLC1 (b), CT26 (c) and EMT6 (d) tumor models after indicated treatments (n = 7 mice). (e) Wet lung weight measured from treated mice at the end of study (n = 7 mice). The center lines indicate median values, box limits indicate the 25th and 75th percentiles and whiskers extend 1.5x the interquartile range from 25th and 75th percentiles. (F-H) In vivo activities of IL-2α-bias in MC38 tumors (n = 8 mice), related to Fig. 4e, f. Dot plots showing PD-1 and CD25 expressions on CD8+TILs in MC38 tumors (f), frequency and absolute number of IFN-γ+CD8+TILs (g), and enrichment fold of IFN-γ+% in PD-1-CD25-, PD-1+CD25- and PD-1+CD25+ subsets of CD8+TILs after treatment (H). (i) Tumor growth curves showing efficacy of IL-2α-bias in B16F10-OVA tumors (n = 6~7 mice). (J) Dot plots showing PD-1 and CD25 expressions on CD8+TILs in B16F10-OVA tumors. (k) OVA-tetramer+% in PD-1-CD25-, PD-1+CD25- and PD-1+CD25+ subsets of CD8+TILs after IgG or IL-2α-bias treatment (n = 6~7 mice). Mean + s.e.m. is presented, and p-values are calculated using two-way ANOVA for tumor growth curves (A-D and I, Tukey’s multiple comparisons test) and for H and K (Šídák’s multiple comparisons test), one-way ANOVA (Tukey’s multiple comparisons test) for box plots (E), and two-sided t-test for bar graphs (G).

Source data

Extended Data Fig. 8 Dynamics of immune cell subsets in tumors and spleens derived from five syngeneic tumor models.

MC38, B16F10, CT26, EMT6 and LLC were collected and digested 1 week, 2 weeks and 3 weeks post implantations. Cell suspensions of tumors and spleens were stained and analyzed by flow cytometry (n = 6~8 mice). (a) Stacked bar graphs showing the proportion and absolute number of each immune cell subpopulation in different tumor models. (n = 6~8 mice). (b) Quantitation of CD25 expressions (MFI, measured by flow cytometry) in PD-1- versus PD-1+ CD8+TILs from different timepoints. Two-side paired t-test was used to calculate p-values. (c) Representative histograms showing mouse CD25 expression levels in CD8+TILs among five different tumor types (left). Correlational analysis between CD25+CD8+ frequency in intratumor CD8 and tumor growth inhibition of IL-2α-bias in five different syngeneic tumor models (right). (d) Correlational analysis of intratumor CD8+T, Treg or CD8/Treg ratio versus tumor growth inhibition (TGI%) of IL-2α-bias in five different syngeneic tumor models. (e) Correlational analysis of other intratumor immune subpopulations versus TGI% of IL-2α-bias in five different syngeneic tumor models (n = 6~8 mice). (f) Cell number/area and the percentage of CD8+T cell in tumor and adjacent tissue from CRC tissue microarray (n = 50 tumor samples) in Fig. 4g. Mean ± s.e.m. is presented, and p-values for linear regressions (C-E) were calculated by default using Graphpad Prism8 software, and p-values for dot plots were calculated by two-sided t-test (F).

Source data

Extended Data Fig. 9 αPD-1 efficacy is dependent on the activation of PD-1+CD25+CD8+TILs through autocrine IL-2/CD25 signaling.

(a) Quantification of intracellular IL-2% in CD8+T cells after 24hrs activation by aAPC in the lower chamber. Dash line indicates data from PBMC only control. Statistical analysis by two-sided student’s t-test (Experiment done once, with 5 technical replicates). (b-c) Representative histograms (b) and bar graphs (c) showing intracellular IL-2% in different CD8+T cell subsets after 24hrs activation by aAPC (Experiment done once, with 5 technical replicates). (d-e) Ex vivo measurement of intracellular IFN-γ (D) and TNF-α (e) in CD8+TILs from MC38 tumors. Similar to Fig. 5h–j. (f) MC38 tumors treated with αPD-1 antibody (5mg/kg, QWx2 as indicated by arrows) with or without CD25 blocking antibody (n = 7 mice). Experiments were repeated three times. Tumor growth curves (left), representative FACS plots of GZMB expression (middle) and the corresponding MFI plots of the indicated cell subsets (right). Mean + s.e.m. is presented, and p-values are calculated using two-way ANOVA unless otherwise indicated.

Source data

Extended Data Fig. 10 IL-2 pathway signature correlates with αPD-1 response.

(a-g) Data from ORIENT-11 and melanoma studies. (A) GSEA of combo group responders versus non-responders, supplementary to Fig. 6. (b) GSEA of “PID_IL2_1PATHWAY’ signature in nsqNSCLC patients responding to chemotherapy versus non-responders. (c) Waterfall plot in chemo group. (d) Bar plot representing statistical difference (two-sided student t-test) between responders and non-responders for each signaling pathway. Red dashed line: p = 0.05. X-axis represents –log10(p-value); Y-axis represents signaling pathways. (e) The relative percentage of distinct cell types (including CD45+ immune cells and CD45- non-immune cells) were deconvoluted from the RNA-seq data of each patient sample. (f) Box and whisker plots showing “CD8+ T cells ratio in CD45+ (% CD8)’ and “PID_IL2_1PATHWAY’ signature score among non-responders (NR) or responders (R) to different treatments. The center lines indicate median values, box limits indicate the 25th and 75th percentiles and whiskers extend 1.5x the interquartile range from 25th and 75th percentiles. P-values were calculated using two-sided Wilcoxon test. (g) Kaplan–Meier analysis of progression-free survival and Overall survival in Combo_High_%CD8, Combo_Low_%CD8, Chemo_high_%CD8 and Chemo_low_%CD8 groups. HR, hazard ratio; p-values were computed using log-rank (Mantel–Cox) test. (h-i) Small tumors (60–80 mm3) and large tumors (200–300 mm3) with or without the indicated IL-2 treatment were extracted for RNAseq analysis. Four mice in each group. S, small tumor; L, large tumor; UNT, untreated. (h) Heatmap of GSVA immune signature scores. Each lane represents the signature score from a single mouse. (i) GSEA of small tumor versus large tumor. (j-k) Individual tumor growth curves in mice bearing MC38 (j) or EMT6 (k) tumors from different treatment groups as in Fig. 7g, h respectively. Low dose: 3mg/kg; High dose: 10mg/kg. n = 7 mice per group.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and Tables 1 and 2.

Reporting Summary

Source data

Source Data Figs. 1, 3 and 5 and Extended Data Figs. 1–3 and 5–10

Raw assay readout.

Source Data Fig. 4

Raw assay readout and raw bioinformatics analysis output.

Source Data Fig. 6

Raw bioinformatics analysis output.

Source Data Fig. 7

Raw assay readout and raw RNA-seq output.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, W., Chia, T., Lu, J. et al. IL-2Rα-biased agonist enhances antitumor immunity by invigorating tumor-infiltrating CD25+CD8+ T cells. Nat Cancer 4, 1309–1325 (2023). https://doi.org/10.1038/s43018-023-00612-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43018-023-00612-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer