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Rebalancing TGFβ1/BMP signals in exhausted T cells unlocks responsiveness to immune checkpoint blockade therapy

Nature Immunology volume 24, pages 280–294 (2023)Cite this article

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Abstract

T cell dysfunctionality prevents the clearance of chronic infections and cancer. Furthermore, epigenetic programming in dysfunctional CD8+ T cells limits their response to immunotherapies, including immune checkpoint blockade (ICB). However, it is unclear which upstream signals drive acquisition of dysfunctional epigenetic programs, and whether therapeutically targeting these signals can remodel terminally dysfunctional T cells to an ICB-responsive state. Here we innovate an in vitro model system of stable human T cell dysfunction and show that chronic TGFβ1 signaling in posteffector CD8+ T cells accelerates their terminal dysfunction through stable epigenetic changes. Conversely, boosting bone morphogenetic protein (BMP) signaling while blocking TGFβ1 preserved effector and memory programs in chronically stimulated human CD8+ T cells, inducing superior responses to tumors and synergizing the ICB responses during chronic viral infection. Thus, rebalancing TGFβ1/BMP signals provides an exciting new approach to unleash dysfunctional CD8+ T cells and enhance T cell immunotherapies.

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Fig. 1: Posteffector TGFβ1 drives severe dysfunction of chronically stimulated human CD8+ T cells.
Fig. 2: Chronic TGFβ1 and TCR signals establish a stable dysfunctional program in CD8+ T cells.
Fig. 3: BMP4 agonist treatment limits exhaustion features and enhances survival of human CD8+ T cells.
Fig. 4: Boosting BMP signaling while targeting TGFβ1 modulates terminal T cell dysfunction.
Fig. 5: Rebalancing TGFβ1/BMP signals remodels gene expression programs in dysfunctional human T cells.
Fig. 6: Epigenetic remodeling of dysfunctional human T cells by targeting TGFβ1 and boosting BMP.
Fig. 7: Treated CD8+ T cells exhibit superior antitumor cytotoxic functions.
Fig. 8: Combined TGFβR1i and BMP4a treatment synergizes CD8+ T cell responses to PD-L1 blockade.

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Data availability

WGBS datasets and RNA-seq data have been deposited in GEO under the accession codes GSE217087 and GSE217072, respectively. Published datasets accessed throughout this study include Supplementary Table 1 (Fig.1i; see table for dataset accession numbers), ChIP-Atlas Enrichment Analysis (https://chip-atlas.org/enrichment_analysis; Figs. 5d,6i), C7 Immune Signature Database GSE41867 and GSE9650 (Fig. 5f), WebGestalt (http://www.webgestalt.org/# for GO and ORA; Fig. 5g–i), Enrichr using NCI-Nature 2016 database for pathway enrichment analysis (http://amp.pharm.mssm.edu/Enrichr; Fig. 6e and Extended Data Fig. 6b), PRJNA546023, GSE149810 and GSE84105 (Extended Data Fig. 3a,b), Hallmark gene signature MSigDB database for GSEA from UC San Diego and Broad Institute (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp; Extended Data Fig. 6d), Yellow fever vaccination RNA-seq data (GSE100745; Extended Data Fig. 5b,d), Pan-Cancer TILs expression RNA-seq data (GSE156728; Extended Data Fig. 7) and scATAC-seq data (GSE129785; Extended Data Fig. 8d,e). Source data are provided with this paper.

Code availability

The code generated and used for the analysis of sequencing data is available from the corresponding author on reasonable request.

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Acknowledgements

We are grateful for the insightful discussions, helpful suggestions, and editing of the manuscript by E. Oltz at the College of Medicine. We would like to thank P. Collins, J. Lio and A. Zayed at the Ohio State University for their expert assistance. We thank the human cord blood donors and the Leukemia Tissue Bank at the Ohio State University Comprehensive Cancer Center, Columbus, OH, for blood sample collection. We would also like to thank A. Wetzel and the team of the IGM Genomic Services Lab of the Research Institute at Nationwide Children’s Hospital, Columbus, OH, for their help with genomic sequencing. We thank the NIH Tetramer Facility at Emory University in Atlanta, GA, for providing the monomers used in this study for the detection of LCMV-specific tetramer+ CD8+ T cells. Research reported in this publication was funded by the Ohio State University Comprehensive Cancer Center and College of Medicine.

Author information

Author notes

  1. These authors contributed equally: Abbey A. Saadey, Amir Yousif.

Authors and Affiliations

  1. Department of Microbial Infection and Immunity, College of Medicine, The Ohio State University, Columbus, OH, USA

    Abbey A. Saadey, Amir Yousif, Nicole Osborne, Roya Shahinfar, Yu-Lin Chen, Brooke Laster, Meera Rajeev, Parker Bauman & Hazem E. Ghoneim

  2. Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH, USA

    Abbey A. Saadey & Hazem E. Ghoneim

  3. Molecular, Cellular, and Developmental Biology Graduate Program, The Ohio State University, Columbus, OH, USA

    Amir Yousif & Hazem E. Ghoneim

  4. Biomedical Informatics Shared Resources, College of Medicine, The Ohio State University, Columbus, OH, USA

    Amy Webb

  5. The Pelotonia Institute for Immuno-Oncology, James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA

    Hazem E. Ghoneim

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Contributions

H.E.G. conceived and designed the study and developed methodologies. A.A.S., A.Y., R.S., B.L., M.R. and Y.-L.C. performed in vitro experiments. A.A.S., A.Y., N.O., P.B., B.L., R.S. and H.E.G. performed in vivo infection and tumor experiments. H.E.G., A.W. and A.Y. participated in bioinformatics data analysis and interpretation. B.L. and A.Y. participated in library preparation for NGS sequencing. H.E.G., A.A.S. and A.Y. drafted the manuscript. All authors contributed to data analysis, figure preparation and manuscript editing.

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Correspondence to Hazem E. Ghoneim.

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Nature Immunology thanks Maike Hofmann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: N. Bernard in collaboration with the Nature Immunology team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 TGFβ1 signaling accelerates dysfunction in chronically stimulated human CD8+ T cells.

(a) Gating strategy showing the phenotype of freshly isolated human CBMCs before ex vivo stimulation. (b) Summary bar graphs showing MFI of IFNG, (c) TNFα, (d) CD107a, (e) T-bet, and (f) TCF-1 for ‘Weak’ TCR stimulated IFNG + CD8+ T cells after in vitro PMA/Ionomycin stimulation on day 28. (g) Longitudinal tracking of CD8+ T cell numbers per 100 ul (left y-axis, solid lines) and frequency of dead CD8+ T cells (right y-axis, red dotted lines) under ‘Strong’ TCR stimulation from day 0–28. (h) Bar graphs showing frequency of dead CD8+ T cells on day 28, or MFI of (i) CD39, (j) LAG3, and (k) CD103 on CD8+ T cells under ‘Strong’ TCR and TGFβ1 after PMA/Ionomycin stimulation on day 28. (l) Schematic of human adult naïve CD8+ T cell isolation followed by acute or chronic stimulation for 28 days. (m) Bar graphs showing frequency of IFNG + CD107a+, (n) IFNG + TNFα + adult CD8+ T cells, (o) MFI of CD107a on IFNG + CD8+ T cells, and (p) MFI of Perforin, (q) CD101, (r) CD103, (s) CD39, or (t) PD-1 on adult CD8+ T cells after PMA/Ionomycin stimulation on day 28. (u) Bar graphs showing MFI of CD103 and (v) frequencies of IFNG + TNFa+ CD8+ T cells in adult polyclonal effector memory CD8+ T cells (TEM) after PMA/Ionomycin stimulation on day 28. All n = 3–4 biological replicates per group, representative of 2–3 independent experiments. * P-value < 0.05, ** p < 0.01, *** p < 0.001; comparisons were made using the Mann-Whitney U test (unpaired, two-sided)-(panels b-f), or Adjusted P-value ** p < 0.01, *** p < 0.001, **** p < 0.0001 using one-way ANOVA with Tukey’s multiple comparison (panels h-k) as indicated, or one-way ANOVA with Bonferroni’s multiple comparisons relative to Ch.TCR group (panels m-v). Error bars = mean ± SEM.

Source data

Extended Data Fig. 2 Chronic TGFβ1 and TCR signals establish a stable dysfunctional program in CD8+ T cells.

(a) Summary bar graphs showing MFI of IFNG, (b) T-bet, (c) TNFα, and (d) CD107a in IFNG + CD8+ T cells after 7 days of resting from chronic or acute stimulation (day 35) as show in Fig. 2a. (e) Bar graphs showing MFI of TNFα, and (f) CD107a in IFNG + CD8+ T cells after 14 days of rest (say 42). (g) Bar graphs showing MFI of GZMB, and (h) CD11a in CD8+ T cells after 14 days of rest (Day 42). N = 3 biological replicates per group, representative of 2–3 independent experiments. Adjusted P-value ** p < 0.01, *** p < 0.001, **** p < 0.0001 comparisons were made using one-way ANOVA with Bonferroni’s multiple comparisons relative to Dysf. group. Error bars = mean ± SEM.

Source data

Extended Data Fig. 3 Boosting BMP while blocking TGFβ1 recovers effector function in dysfunctional CD8+ T cells.

(a) IGV Snapshots showing: DNA methylation levels (top panel) at individual CpG sites within mouse naïve (black), antigen-specific exhausted WT (red) or Dnmt3a-deficient (green) CD8+ T cells on day 35 post-chronic LCMV infection. Blue-to-red ratio indicates % of unmethylated versus methylated reads (GSE99450); open chromatin peaks (middle panel) detected in naïve (black), progenitor (PD-1+ Tim-3-; green), or terminally exhausted (PD-1+ Tim-3 + ; red) CD8+ T cells from chronically infected mice (PRJNA546023); and levels of H3K27 acetylation marks (bottom panel) in the progenitor (green), cytolytic (blue; Cx3cr1+PD-1+), and terminally exhausted (Cx3cr1- PD-1+ Tim-3+) CD8 T cells during chronic LCMV infection (GSE149810) at the Smad1, Smad5, and Tgfbr3 loci. (b) Heatmap of RNA levels in exhausted T cell subsets during chronic LCMV infection (GSE84105). (c) tSNE plot visualization of human CD8+ T cells for the described conditions in Fig. 4a after PMA/ionomycin stimulation on day 28, and representative plots showing protein expression levels of IFNG, TNFα, CD107a, T-bet, and CD103 in PMA/Ionomycin-stimulated CD8+ T cells on day 28 (n = 4 wells per condition). (d) Bar graphs showing expression levels of TOX in adult blood-derived CD8+ T cells on day 21 of chronic ‘Strong’ or (e) ‘Weak’ TCR stimulation. (f) Bar graphs showing MFI of IL-7R and (g) CD103 in adult CD8+ T cells on day 21 of ‘Weak’ TCR stimulation. For panels d-g, n = 3–4 biological replicates per group per experiment, data pooled from 2 independent experiments. Adjusted P-value ** p < 0.01, *** p < 0.001, **** p < 0.0001 comparisons were made using one-way ANOVA with Tukey’s multiple comparisons as indicated (panels d,f) or one-way ANOVA with Bonferroni’s multiple comparisons relative to Dysf. group (panels e,g). Error bars = mean ± SEM.

Source data

Extended Data Fig. 4 Treated CD8+ T cells maintain a heritable functional memory-like program.

(a) Representative FACS plots showing the gating strategy for sorting dysfunctional human CD8+ T cells on day 14 after chronic TCR plus TGFβ1 stimulation, and day 28 expression of IFNG and CD103 in PMA/ionomycin-stimulated CD8+ T cells after treatment. (b) Summary bar graphs showing frequency of IFNG + TNFα + CD8+ T cells, and (c) MFI of CD101 in sorted CD8+ T cells after PMA/ionomycin stimulation on day 28. (d) Representative FACS plots showing CD8+ T cell divisions and expression levels of IL-7R in CFSE-labeled CD8+ T cells from each condition as described in Fig. 4a on day 35 (after 7 day-rest under homeostatic conditions). (e) MFI of IL-7R in divided CD8+ T cells on day 35. (f) Representative FACS plots showing CD8+ T cell divisions and expression levels of CD103 in CFSE-labeled CD8+ T cells on day 35. (g) MFI of CD103 in divided CD8+ T cells on day 35. (h) Bar graphs showing frequencies of divided CD8+ T cells (≥3 proliferation cycles), and (i) MFI of IL-7R and (j) CD103 in CD8+ T cells on day 35. (k) Bar graphs showing MFI of TNFα and (l) Perforin in IFNG + CD8+ T cells after PMA/Ionomycin stimulation on day 35. N = 3 (panels a-c) or 4 (panels d-l) biological replicates per group, representative of 2–3 independent experiments. Adjusted P-value ** p < 0.01, *** p < 0.001, **** p < 0.0001 comparisons were made using one-way ANOVA with Tukey’s multiple comparisons as indicated (panels b, c, h–l) or one-way ANOVA with Bonferroni’s multiple comparisons relative to Dysf. group (panels e, g). Error bars = mean ± SEM.

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Extended Data Fig. 5 Transcriptional re-wiring of dysfunctional human CD8+ T cells.

(a) Volcano plot showing DEGs in acute TCR stimulated versus naïve human CD8+ T cells, or (c) in chronic TCR stimulated versus Dysf. human CD8+ T cells. (b) Gene set enrichment analysis for Ac.TCR versus Naïve CD8+ T cells or (d) Ac.TCR versus Dysf. CD8+ T cells compared against common upregulated genes in human CD8+ T cell subsets following yellow fever vaccination (GSE100745) (e) Heatmap of DEGs between each condition based on relative Z-score and organized into clusters: C1 (yellow; up in Naïve and Dysf.), C2 (orange; up in Dysf. only), C3 (blue; up in Ch.TCR and Ac.TCR), C4 (green; up in Treated.I-III), C5 (purple; up in Ch.TCR, Ac.TCR, and Treated.I-III), and C6 (pink; up in Naïve, Ch.TCR, Ac.TCR, and Treated.I-III). Example genes with significant fold change in expression (fold change >2, p < 0.05) are listed per cluster. N = 2 biological replicates per condition. Panels (5a,5c,5e) were statistically analyzed by DESeq2 from Partek. Panels (5b and 5d) were statistically analyzed by GSEA using Partek.

Extended Data Fig. 6 BMP4 agonist treatment promotes transcriptional recovery of effector and memory programs.

(a) Venn diagram of overlapping genes that are downregulated in Dysf. versus Treated.I-III CD8+ T cells on day 28. (b) Pathway enrichment analysis of genes that are upregulated in Treated.II versus Dysf. CD8+ T cells or overlap with the upregulated genes in Treated.III versus Dysf. CD8+ T cells (NCI-Nature 2016 pathway database). Significance was determined by Enrichr62. (c) Venn diagram of overlapping genes upregulated in Dysf. versus Treated.I-III CD8+ T cells. (d) Gene set enrichment analysis of biological pathways linked to the upregulated genes in Dysf. versus Treated.I-III CD8+ T cells using Hallmark gene signature database. (e) Interaction plots from RNA-seq data showing average expression levels of KLF3, (f) S1PR1, (g) GZMK, (h) FCGR3A, (i) TGFBR3, (j) CX3CR1, (k) PRF1, (l) CD28, (m) CD109, (n) MYO7A, (o) ITGAE, and (p) SMAD6 transcripts among chronically (Ch.TCR, Dysf., Treated.I-III) or acutely (Ac.TCR) stimulated CD8+ T cells on day 28.

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Extended Data Fig. 7 TGFβ1 exposure drives transcriptional similarity to exhausted CD8+ T cells in human cancer.

(a) UMAP visualization of CD8+ T cell meta-clusters identified in single-cell RNA-seq analysis of human CD8+ T cell subsets including tumor-infiltrating lymphocytes (TILs) from 21 types of human cancer (GSE156728)45 (b, c) UMAP visualization showing RNA expression levels of some selected signature genes in human TILs and memory CD8+ T cell clusters that overlap with transcriptional features of in vitro-differentiated Dysfunctional and Treated human CD8+ T cells.

Extended Data Fig. 8 BMP signals maintain effector and memory programs in human CD8+ T cells.

(a) Bar graph showing numbers of methylated (top) or demethylated (bottom) DMRs in Dysf. CD8+ T cells relative to all other conditions on day 28. (b) Venn diagrams showing transcription factor (TF)-binding motifs enriched in hypermethylated or (c) hypomethylated genomic regions in in Dysf. versus Treated.I, II, or III human CD8+ T cells on day 28. (d) Snapshots from scATAC-seq database (GSE129785) showing chromatin accessibility changes at selected loci of effector and memory, or (e) dysfunctional programs in human CD8+ T cell subsets from patients with cancer49.

Extended Data Fig. 9 Treatment of dysfunctional CD8+ T cells improves cytotoxicity against tumor cell lines.

(a) Representative FACS plots showing E-cadherin and PD-L1 expression on THP-1 (AML) and MDA-MB-231 (breast adenocarcinoma) tumor cells. (b) Summary bar graphs showing frequencies of PD-1 + LAG3+, (c) total LAG3 + CD8+ T cells, or (d) MFI of PD-1 and (e) GZMB on CD8+ T cells following 18-hour co-culture with AML cells on day 28. (f) Bar graph showing MFI of CD103 on CD8+ T cells following co-culture with MDA-MB-231 cells at day 28. (g) Bar graph showing frequency of LAG3 + CD101 + CD8+ T cells under ‘Strong’ TCR stimulation following co-culture with AML cells at day 28. (h) Summary of dead AML cells per 100 viable CD8+ T cells under ‘Strong’ TCR following co-culture with AML cells at varying T cell: tumor cell ratios on day 28. (i) Bar graph of dead AML cells per 100 viable CD8+ T cells after rest from ‘Strong’ TCR under homeostatic conditions, following co-culture with AML cells on day 35. (j) MFI of LAG3 in CD8+ T cells after PMA/Ionomycin stimulation on day 35. For panels b–f, n = 3–4 biological replicates per group per experiment, with data pooled from 2 independent experiments. For panels g–j, n = 4 biological replicates per group, representative of 2–3 independent experiments. Adjusted P-value ** p < 0.01, *** p < 0.001, **** p < 0.0001; comparisons were made using one-way ANOVA with Tukey’s multiple comparison (panels b, d, j, k) as indicated, one-way ANOVA with Bonferroni’s multiple comparisons relative to Dysf. group (panels c, e–g, i), or two-way ANOVA with Tukey’s multiple comparisons (panel h). Error bars = mean ± SEM.

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Extended Data Fig. 10 Combined TGFβR1i and BMP4 agonist enhances ICB response during chronic LCMV.

(a) Summary bar graphs showing numbers of polyclonal LCMV-specific (CD44hi PD-1+), (b) proliferating (Ki67+) CD44hi PD-1+, (c) Cytolytic (CD44hi Cx3cr1+ PD-1+) and (d) Terminally Exhausted (Tim3+ PD-1 + Cx3cr1-) CD8+ T cells in spleen on day 33 following primary treatment using RepSox, BMP4a, or combined treatment as described in Fig. 8a. (e) Summary dot plot of LCMV viral titers (PFU per ml) in serum from chronically LCMV-infected mice on day 33 following primary treatment. (f) Summary bar graphs showing numbers of proliferating (Ki67 + CD44hi PD-1+) CD8+ T cells, and (g) proliferating P14 (Ki67 + Thy1.1 + GP33-tetramer+) CD8+ T cells in spleen following anti-PD-L1 treatment. All N = 4–6 mice per treatment group per experiment, with data pooled from 2–3 independent experiments. Adjusted P-value ** p < 0.01, *** p < 0.001, **** p < 0.0001; comparisons were made using one-way ANOVA with Tukey’s multiple comparison (panels a–d), or Kruskal-Wallis Test with Dunn’s multiple comparisons (panels f, g) as indicated. Error bars = mean ± SEM.

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Supplementary information

Supplementary Information

Supplementary data for CD103 MFI on P14 cells from Fig. 7n (raw flow histograms and data/statistics).

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Supplementary Tables

Supplementary Table 1: IPA datasets used in Fig. 1i. Supplementary Table 2: RNA-seq normalized counts. Supplementary Table 3: Differentially expressed genes from DESEq2 analysis of RNA-seq data. Supplementary Table 4: DMRs from WGBS data

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Saadey, A.A., Yousif, A., Osborne, N. et al. Rebalancing TGFβ1/BMP signals in exhausted T cells unlocks responsiveness to immune checkpoint blockade therapy. Nat Immunol 24, 280–294 (2023). https://doi.org/10.1038/s41590-022-01384-y

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