Abstract
The mechanisms underlying the efficacy of anti-programmed cell death protein 1 (PD-1) and anti-cytotoxic T lymphocyte-associated protein 4 (CTLA-4) therapy are incompletely understood. Here, by immune profiling responding PD-1+CD8+ T (TResp) cell populations from patients with advanced melanoma, we identified differential programming of TResp cells in response to combination therapy, from an exhausted toward a more cytotoxic effector program. This effect does not occur with anti-PD-1 monotherapy. Single-cell transcriptome and T cell receptor repertoire analysis was used to identify altered effector programming of expanding PD-1+CD8+ T cell clones with distinct regulon usage, STAT1 and STAT3 utilization and antitumor specificity connected to interleukin (IL)-21 signaling in combination and anti-CTLA-4 monotherapy. Therapeutic efficacy of CTLA-4 blockade was lost in B16F10 melanoma models with either Il21r− deficiency or anti-IL-21 receptor blockade. Together, these results show how IL-21 signaling to TResp is critical for anti-CTLA-4-based checkpoint therapies and highlight major signaling differences to anti-PD-1 monotherapy.
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Data availability
CITE–seq data have been deposited in the Gene Expression Omnibus under accession number GSE268364. ScRNA-seq and TCR-seq data are available via Zenodo at https://doi.org/10.5281/zenodo.13971562 (ref. 43).
Code availability
RDS objects including the regulon analysis are available via Zenodo at https://doi.org/10.5281/zenodo.13971562 (ref. 43). Code availability for CheckMate 238: original code is deposited in Zenodo (https://doi.org/10.5281/zenodo.14224147). Requests for resources or further information should be directed to the lead contact at NYU Grossman School of Medicine for CheckMate 238.
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Acknowledgements
We thank the patients contributing their biomaterials to the study. We thank A. Kabdalova and P. Otto-Mora for help with cytokine titrations and flow cytometry. We thank H. Pircher for provision of the B16 cell line. We thank D. J. Tenney at Bristol Myers Squibbs (BMS) for support with the analysis of CheckMate 238 samples. BMS had no role in funding, design or interpretation of results in the present study. The study was supported by the Deutsche Forschungsgemeinschaft (DFG) research grant numbers 520992132 (Heisenberg), 441891347 (SFB1479-OncoEscape), 390939984 (CIBSS), 272983813 (TRR179), 505372148 (FOR5560) and the German Cancer Consortium (DKTK). Z.Z. and K.M. were further supported by a stipend provided by the Chinese Scholarship Council. This work was supported by the US National Institutes of Health (grant numbers CA244936 and P50CA225450 to R.S.H. and J.W. and P30CA016087 to R.S.H.) and through a Career Enhancement Program award from the NYU SPORE program (grant number P50CA225450) to R.S.H.
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Authors and Affiliations
Contributions
B.B. provided the study concept and design. A.C.V., I.G., J.S., K.M., K.Z., S.S., Sagar and Z.Z. carried out the human experiments and procedures. M.L., R.Z. and V.F. carried out the murine experiments and procedures. D.R.S., F.M., J.S.W., N.B., R.D. and S.L. recruited patients and acquired clinical data. E.S., F.W., J.S., K.M., M.B., N.R., R.S.H., Sagar and Z.Z. did the bioinformatics and statistical analysis. B.B. and Z.Z. interpreted the data and drafted the paper. C.N.H., F.M., F.W., M.H., R.E.V., R.H., R.T., R.Z. and T.B. revised the paper for important intellectual content. B.B. supervised the study.
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Competing interests
F.M. served as consultant and/or has received honoraria or travel support from Novartis, Roche, BMS, MSD, Pierre Fabre and Sunpharma. D.R.S. served as consultant and/or has received honoraria from Roche and BMS and travel support from Sunpharma and Sanofi. R.Z. has received honoraria from Novartis, INcyte, MNK and Sanofi. R.V. has received honoraria or travel support from Abbvie, AstraZeneca, Galapagos, Janssen-Cilag, Novartis, Pfizer and Roche, and research grant support from Novartis and Pfizer. J.W. consulted for and received <US$10,000 per annum from Merck, Genentech, AstraZeneca, GSK, Novartis, Nektar, Celldex, Incyte, Biond, Moderna, ImCheck, Sellas, Evaxion, Pfizer, Regeneron and EMD Serono and received US$10,000–25,000 from BMS for membership on advisory boards. J.W. also held equity in Biond, Evaxion, OncoC4 and Instil Bio, was on the scientific advisory boards for CytomX, Incyte, ImCheck, Biond, Sellas, Instil Bio, OncoC4 and NexImmune, and was remunerated with between US$10,000 and US$50,000. In addition, J.W. is named on a patent filed by Moffitt Cancer Center on an ipilimumab biomarker and on tumor-infiltrating lymphocyte preparation, and on a PD-1 patent filed by Biodesix. J.W. received <US$6,000 in royalties. The remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Study outline and clinical outcome of patients.
(A) Schematic of major samples and experimental techniques used in the study. (B) Tumor response of patients evaluated after 3 months. CR: Complete response; PR: partial response; SD: stable disease; PD: progressive disease. In adjuvant group, patients were evaluated either as CR or PD. (C) Kaplan-Meier progression-free survival analysis of patients stratified by therapy regimens (P = 0.045). Statistical significance was determined by Two-sided Log-rank test in (C) (* P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001). Illustrations in A were created using BioRender.com.
Extended Data Fig. 2
(A) Exemplary dot plot of surface PD-1 and IgG4 staining at baseline and post-therapy of one patient undergoing dual therapy (left); representative stainings of PD-1 and Ki-67 on CD8+ T cells (right) (B) CD8+ T cells from 10 patients (adju n = 2, mono n = 4, dual n = 4) analyzed in one batch were projected by UMAP embedding and clustered by FLOWSOM algorithm. As in Fig. 1d, 8 clusters were identified and frequencies of cluster 2-8 in patient samples across therapy regimens and time points are indicated. For (C–H), ICB therapy-treated patients within different treatment groups (adjuvant (adju) (n = 11), palliative (mono) (n = 15), and combination therapy with anti-CTLA-4 (n = 20)) were included. (C) Scatter plots depict frequencies of populations gated for different expression levels of perforin and granzyme B expression on PD-1+CD8+ T cells (Perforinint adju P = 0.2695, mono P = 0.0246, dual P = 0.0032; GZMBint adju P = 0.2061, mono P = 0.5995, dual P = 0.0002; Perforinhi adju P = 0.1475, mono P = 0.3028, dual P = 0.1769; GZMBhi adju P = 0.5195, mono P = 0.0189, dual P = 0.8480). (D) Frequencies of IL-2 (adju P = 0.7002, mono P = 0.8672, dual P = 0.6742) and CCL3/4 (adju P = 0.5566, mono P = 0.8672, dual P = 0.1991) produced by PD-1+CD8+ T cells after PMA/ ionomycin stimulation are indicated. (E) Scatter plots depicting frequencies of indicated markers within Ki-67+CD8+ T cells (Perforinint adju P = 0.2783, mono P = 0.0215, dual P = 0.0037; GZMBint adju P = 0.5771, mono P = 0.0706, dual P < 0.0001; Perforinhi adju P = 0.7002, mono P = 0.4131, dual P = 0.3683; GZMBhi adju P = 0.4648, mono P = 0.5245, dual P = 0.8408). (F) Cytokine production (IFN-γ adju P = 0.7002, mono P = 0.1395, dual P = 0.0010; TNF adju P = 0.0420, mono P = 0.6698, dual P = 0.0002; CCL3/4 adju P = 0.7646, mono P = 0.5830, dual P = 0.0441) and functional exhaustion score (FES) of Ki-67+CD8+ T cells across therapy regimens and time points (adju P = 0.4131, mono P = 0.2166, dual P < 0.0001). (G) Scatter plots depict frequencies of populations expressing indicated markers of PD-1-CD8+ T cells (Ki-67: adju P = 0.1445, mono P = 0.9012, dual P = 0.0005; CD38hi: adju P = 0.0830, mono P = 0.1102, dual P < 0.0001; CD39: adju P = 0.2783, mono P = 0.7615, dual P = 0.0006; LAG3: adju P = 0.4785, mono P = 0.3591, dual P = 0.0066; CD127: adju P = 0.6211, mono P = 0.5614, dual p = 0.0004) (H) Fold change of Ki-67, CD38hi, CD39, LAG-3, CD127, IFN-γ, TNF, IL-2, GZMBint, GZMBhi, Perforinint, Perforinhi PD-1+CD8+ T cells (++) vs. PD-1-CD8+ T cells (+-) after ICB therapy within different treatment groups. Where applicable, samples pre-therapy are represented in open circles and post-therapy in open squares in scatter plots. Bar and error bar represent median and 95% CI in scatter plots. Statistical significance was determined by two-sided paired Wilcoxon signed-rank test (B–G) or two-sided Kruskall-Wallis Test (H) (ns = non-significant, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001).
Extended Data Fig. 3
(A) Expression of selected genes by PD-1+CD8+ T cells similar to Fig. 2c are indicated on the UMAP (purple: high expression, gray: low expression). (B) Top 20 reactome pathway analysis of genes up-regulated in dual-ICB after cycle 1 (pseudobulk analysis). –log FDR of indicated gene-sets are indicated by the bar length.
Extended Data Fig. 4
Dose-response titration of IL-15 and IL-21 inducing STAT phosphorylation in PD-1+CD8+ T cells. Concentration used and % of cells positive for pSTAT1/ 3/ 5 are plotted for IL-15- and IL-21-exposed T cells, as indicated (ng/ml).
Extended Data Fig. 5 Effect of STAT inhibition and CTLA-4 blockade on IL-21 signaling.
(A) Workflow of cytokine (IL-21: 80 ng/ml)/ anti-CTLA-4/ and inhibitor treatment (PerV:1 mM, Gando:1 µM, Stattic: 5 µM, Tofa: 100 nM, Upada: 1 µM), flow cytometric staining and data analysis (n = 5). (B) Representative histograms of flow cytometric data showing pSTAT1, pSTAT3, and pSTAT5 expression in PD-1+CD8+ T cells with or without anti-CTLA-4 treatment. (C) Median fluorescence intensity (MFI) and frequencies (%) of pSTAT1 (MFI: unstim. versus IL-21 P = 0.0034, IL-21 versus IL-21+Tofa P = 0.0447, IL-21 versus IL-21+Upada P = 0.0374; frequencies: unstim. versus IL-21 P = 0.0028, IL-21 versus IL-21+Upada P = 0.0311), pSTAT3 (MFI: unstim versus IL-21 P = 0.0146, DMSO vs. IL-21 P = 0.0098, IL-21 versus IL-21+Upada P = 0.0011; frequencies: DMSO versus IL-21 P = 0.0065, IL-21 versus IL-21+Upada P = 0.0007), and pSTAT5 (MFI: IL-21 versus IL-21+Gando P = 0.0374; frequencies: IL-21 vs. IL-21+Gando P = 0.0447) on PD-1+CD8+ T cells with anti-CTLA-4 treatment. (D) Heatmaps of the MFI of pSTATs on PD-1+CD8+ T cells and PD-1-CD8+ T cells. pSTATs expression was scaled by Z-score. (E) Comparison of MFI of pSTAT1, pSTAT3, and pSTAT5 gated on PD-1+CD8+ T cells with or without anti-CTLA-4 treatment. (F) MFI and frequencies of pSTAT1, pSTAT3 and pSTAT5 on PD-1+CD8+ T cells with anti-CTLA-4 or iplimumab treatment. Two-sided Friedman analysis was performed in (C). Two-sided pairedWilcoxon signed-rank test was performed in (E). P values are indicated (P * < 0.05, P ** < 0.01, P *** < 0.005, P **** < 0.001). Error bars indicate mean ± SEM. Gando, Gandotinib; JAK, janus kinase; PerV, pervanadate; pSTATs, phosphorylated-STATs; Tofa, Tofacitinib citrate; Upada, Upadacitinib. Schematic in A was created using BioRender.com.
Extended Data Fig. 6
(A) t-SNE dimension reduction was performed on influenza (FLU)-specific CD8+ T cells cultured with (0 ng/ml, 30 ng/ml, or 80 ng/ml of IL-21) after 7 d of culture. Overlay dot plot (left) and normalized colored-marker expression (right) of indicated cytotoxic, activation and exhaustion-associated genes are visualized on the t-SNE map. (B) Production of IL-2 (adju P = 0.5928, mono P = 0.4921, dual P = 0.0259), TNF (adju P = 0.1299, mono P = 0.1040, dual P = 0.9452), IFN-γ (adju p = 0.4922, mono p = 0.0107, dual p = 0.1160) and CCL-3/4 (adju P = 0.7148, mono P = 0.9785, dual P = 0.6762) was determined by CD4+ T cells after PMA/ ionomycin stimulation. Scattered dot plots indicate frequency of cytokine-positive cells by patient groups (adjuvant (adju) (n = 11), palliative (mono) (n = 14), and combination therapy with anti-CTLA-4 (n = 20)) and time point. (C) IgG (adju P = 0.7145, mono P = 0.3281, dual P = 0.0026), IgA (adju P = 0.2101, mono P = 0.8796, dual P = 0.0064) and IgM (adju P = 0.3031, mono P = 0.9645, dual P = 0.6387) concentration in patient plasma was measured by nephelometry (adjuvant (adju) (n = 14), palliative (mono) (n = 15), and combination therapy with anti-CTLA-4 (n = 15)). Samples pre-therapy are represented in open circles and post-therapy in open squares in scatter plots. Bar and error bar represent median and 95% CI in scatter plots. Statistical significance was determined by two-sided paired Wilcoxon signed-rank test (* P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001).
Extended Data Fig. 7
CD8+ T-cell responses during anti-PD-1 (nivolumab) or anti-CTLA-4 (ipilimumab) monotherapy in the CheckMate 238 cohort were analyzed by scRNASeq at week 1 and week 3 (n = 4 patients per treatment). (A) Pseudobulk PCA analysis indicates differential transcriptomic programing at week 3 between treatment conditions. PC1 and PC2 are shown, samples are colored by treatment regimen. (B) UMAP visualization of CD8+ T cells per analysis time point and treatment regimen. Clusters are indicated by color. (C) Expression of selected cytotoxic genes and IL-21 are depicted by violin plot stratified by treatment regimen. (D) Selected cytotoxic genes are visualized for cluster 3 cells by violin diagram. (E) Heatmap indicating differential gene expression is shown for clusters at week 3 time point.
Extended Data Fig. 8
C57/Bl6 WT mice were irradiated and transplanted with either WT or Il21r-/- bone marrow for 30 days, followed by intravenous inoculation of B16.F10 melanoma cell line. Mice were treated with anti-PD-1- or anti-CTLA-4 monotherapy, dual anti-PD-1- and anti-CTLA-4 therapy or isotype control on day 1, 4, 8, 16 and 22. Survival was monitored for up to 80 days. Kaplan-Meier plots of mice plots grouped according to chimerism genotype are shown, WT/WT chimera (A) and WT/IL21R-/- (B).
Extended Data Fig. 9 Gating strategies.
(A) TEX panel, (B) Surface panel, (C) Cytokine panel, (D) Melan-A panel.
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Zhang, Z., Langenbach, M., Sagar, S. et al. Efficacy of CTLA-4 checkpoint therapy is dependent on IL-21 signaling to mediate cytotoxic reprogramming of PD-1+CD8+ T cells. Nat Immunol 26, 92–104 (2025). https://doi.org/10.1038/s41590-024-02027-0
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DOI: https://doi.org/10.1038/s41590-024-02027-0
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