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Intratumoral follicular regulatory T cells curtail anti-PD-1 treatment efficacy

Nature Immunology volume 22, pages 1052–1063 (2021)Cite this article

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Abstract

Immune-checkpoint blockade (ICB) has shown remarkable clinical success in boosting antitumor immunity. However, the breadth of its cellular targets and specific mode of action remain elusive. We find that tumor-infiltrating follicular regulatory T (TFR) cells are prevalent in tumor tissues of several cancer types. They are primarily located within tertiary lymphoid structures and exhibit superior suppressive capacity and in vivo persistence as compared with regulatory T cells, with which they share a clonal and developmental relationship. In syngeneic tumor models, anti-PD-1 treatment increases the number of tumor-infiltrating TFR cells. Both TFR cell deficiency and the depletion of TFR cells with anti-CTLA-4 before anti-PD-1 treatment improve tumor control in mice. Notably, in a cohort of 271 patients with melanoma, treatment with anti-CTLA-4 followed by anti-PD-1 at progression was associated with better a survival outcome than monotherapy with anti-PD-1 or anti-CTLA-4, anti-PD-1 followed by anti-CTLA-4 at progression or concomitant combination therapy.

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Fig. 1: Tumor-infiltrating TFR cells are highly prevalent in human cancers.
Fig. 2: Tumor-infiltrating TFR cells are primarily located in the TLS.
Fig. 3: Comparison of human tumor-infiltrating TREG and TFR cells.
Fig. 4: Frequency and functional responsiveness of TFR cells in murine tumor models.
Fig. 5: Intratumoral TFR cells gradually increase over time.
Fig. 6: TFR cells are highly responsive to ICB.
Fig. 7: Clinical benefit of sequential ICB.

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Data availability

Expression data have been deposited in the Gene Expression Omnibus database under the Super Series accession number GSE132297. This Super Series includes data from human and mouse samples. Source data are provided with this paper.

Code availability

Scripts used for this study are available in our repository on GitHub (https://github.com/vijaybioinfo/TFR_2021). An explanation of each is included, as well as version changes.

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Acknowledgements

We thank L. Chudley, K. McCann, O. Wood, M. Chamberlain, K. Amer, D. Jeffrey, M. Lane, C. Fixmer, M. Lopez, N. Graham, M. Machado, T. Mellows and B. Johnson for their assistance with the recruitment of study subjects and processing of samples. We thank M. Wheater for providing access to the clinical data from the joint practice with I.K. and C.H.O. We thank A. Upadhye for contributions to the experimental work. We thank J. B. Lilley for his help with the data collection and analysis of the survival cohort. We thank C. Kim, D. Hinz and C. Dillingham for their assistance with cell sorting (FACSAria Fusion Cell Sorter; grant no. S10 RR027366); S. Liang, A. Wang and H. Simon for assistance with library preparation, next-generation sequencing using an Illumina HiSeq 2500 (NIH grant no. S10OD016262) and NovaSeq6000 (grant no. S10OD025052-01). We thank the members of the Vijayanand laboratory for their assistance with editing the figures and manuscript. We thank J. Linden and S. Fuchs for providing B16F10-OVA and MC38-OVA tumor cell lines, respectively. This work was supported by the Wessex Clinical Research Network and the National Institute of Health Research, UK (sample collection), the William K. Bowes Jr Foundation (P.V.), Whitaker Foundation (T.S.-E. and C.H.O.), a Cancer Research UK Centres Network Accelerator Award Grant (grant no. A21998; T.S.-E. and C.H.O.), the Faculty of Medicine of the University of Southampton (T.S.-E. and C.H.O.) and Cancer Research UK (J.C. and C.H.O.). The funders have no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors jointly supervised this work: Christian H. Ottensmeier, Pandurangan Vijayanand.

Authors and Affiliations

  1. La Jolla Institute for Immunology, La Jolla, CA, USA

    Simon Eschweiler, James Clarke, Ciro Ramírez-Suástegui, Bharat Panwar, Ariel Madrigal, Ferhat Ay, Christian H. Ottensmeier & Pandurangan Vijayanand

  2. NIHR and CRUK Southampton Experimental Cancer Medicine Center, Faculty of Medicine, University of Southampton, Southampton, UK

    Serena J. Chee, Ioannis Karydis, C. J. Hanley, G. J. Thomas & Christian H. Ottensmeier

  3. NIHR Southampton Biomedical Research Center, Faculty of Medicine, University of Southampton, Southampton, UK

    Serena J. Chee, C. J. Hanley, G. J. Thomas, Tilman Sanchez-Elsner, Christian H. Ottensmeier & Pandurangan Vijayanand

  4. Southampton University Hospitals NHS Foundation Trust, Southampton, UK

    Serena J. Chee, Ioannis Karydis, Edwin Woo, Aiman Alzetani & Christian H. Ottensmeier

  5. Department of Cellular Pathology, Nottingham University Hospital, Nottingham, England

    Somaia Elsheikh

  6. Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, England

    Somaia Elsheikh

  7. Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK

    Peter S. Friedmann, Tilman Sanchez-Elsner & Pandurangan Vijayanand

  8. Department of Pediatrics, University of California San Diego, La Jolla, CA, USA

    Ferhat Ay

  9. Department of Medicine, University of California San Diego, La Jolla, CA, USA

    Pandurangan Vijayanand

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  1. Simon Eschweiler
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Contributions

S.E., J.C., P.S.F., T.S.-E., F.A., C.H.O. and P.V., conceived the work. S.E. and J.C. performed experiments. S.E., J.C., B.P., C.R.-S. and A.M. analyzed data under the supervision of F.A., C.H.O. and P.V. C.J.H. performed the immunohistochemical analyses under the supervision of G.J.T. A.A., E.W., S.J.C., I.K. and S.E. assisted with patient recruitment, obtaining consent and sample collection. S.E. wrote the first draft of the manuscript that was revised and edited by P.S.F., F.A., C.H.O. and P.V.

Corresponding author

Correspondence to Pandurangan Vijayanand.

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The authors declare no competing interests.

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Peer review information Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Selection criteria for the integrated single-cell analysis and gating strategies.

a, Violin plots depicting single-cell expression levels for BCL6, CXCR5 and FOXP3 transcripts (left panel) in tumor-infiltrating CD4+ T cells of an exemplary dataset61; dotted lines indicate threshold used for defining positive cells. The scatter plot (right panel) shows expression levels of BCL6 and CXCR5 transcripts in FOXP3-expressing CD4+ T cells b, Gating strategy (surface panel) to sort tumor-infiltrating TREG (LIN–CD45+CD3+CD4+CXCR5–CD127–CD25+) and TFR (LIN–CD45+CD3+CD4+CXCR5+GITR+) cells is shown in the representative FACS plots. c, Gating strategy (intracellular panel) to identify tumor-infiltrating TREG (LIN–CD45+CD3+CD4+CXCR5–FOXP3+BCL-6-) and TFR (LIN–CD45+CD3+CD4+BCL-6+FOXP3+) cells is shown in the representative FACS plots. d, Representative immunohistochemistry staining for one of the ten NSCLC patients in (Fig. 1d–i) is shown, PanCK (white), CD4 (light blue), CXCR5 (yellow), CD20 (magenta) FOXP3 (green) and BCL-6 (red), scale bars are 25 μm.

Extended Data Fig. 2 Transcriptome analysis of murine TFR cells and characterization of TFR cells in murine tumors.

a, Schematic of immunization model in which mice were immunized intraperitoneally (i.p.) with Ovalbumin in complete Freund’s adjuvant, Ovalbumin in Monophosphoryl Lipid A or mock PBS. b, tSNE plot of TEFF (CD19–CD45+CD3+CD4+CXCR5−GITR–CD25–CD62L–CD44+), TREG (CD19–CD45+CD3+CD4+CXCR5–GITR+CD25+), TFH (CD19–CD45+CD3+CD4+CXCR5+GITR–) and TFR (CD19–CD45+CD3+CD4+CXCR5+GITR+). Each symbol represents data from an individual mouse sample (n = 9 for TEFF, n = 11 for TREG, n = 11 for TFH, n = 11 for TFR) that passed quality controls. c, Euler diagrams show the overlap of differentially expressed genes (left, upregulated in TFR, right, downregulated in TFR) in TFR cells compared to the indicated cell types. d, Heatmap comparing gene signatures of TEFF, TREG, TFH and TFR cells. Depicted are transcripts that change in expression more than 2-fold with a DEseq2 adjusted-P value of ≤ 0.05. e, Log-transformed RNA-seq expression values for each of the indicated differentially expressed genes. Each symbol represents an individual sample, data are mean + /− s.e.m. f, Representative histogram plot showing MFI of the surface expression of indicated markers in human tumor-infiltrating TFR cells (n = 4).

Source data

Extended Data Fig. 3 Transcriptome analysis of human tumor-infiltrating TFR cells.

a, Weighted gene co-expression network analysis (WGCNA) depicted as a Topological Overlap Matrix (TOM) heatmap. It included all genes used in the WGCNA analysis and each row and column correspond to a single gene. Red color indicates the degree of topological overlap. The signed network was generated with bulk RNA-seq data of sorted cells enriched for tumor-infiltrating TREG (LIN–CD45+CD3+CD4+CXCR5–CD127–CD25+) and TFR (LIN–CD45+CD3+CD4+CXCR5+GITR+) populations respectively from 10 treatment naïve NSCLC patients (as described in Fig. 2a–d). b, Spearman correlation analysis of the modules identified in (a), depicting module correlation with TFR phenotype. Genes in the pink module are visualized in Gephi, BCL6 and FOXP3 are highlighted. c, Ingenuity pathway analysis of genes in pink module (b). Shown are the top 5 canonical pathways ordered by P value. d, flow cytometric analysis of the frequency (upper panel, P = 0.002 for indicated comparison) and MFI (lower panel, P = 0.002 for indicated comparison) of Ki67-expressing cells, representative histogram plots (right panel) for tumor-infiltrating CD8+ T cells, TREG and TFR cells from n = 10 NSCLC patient samples (described in Fig. 1e,f). e, Heatmap comparing gene expression signatures of enriched population of tumor-infiltrating TREG cells (green) and TFR cells (yellow). Depicted are transcripts that change in expression more than 2-fold with an adjusted-P value of ≤ 0.05. f, Weighted gene co-expression network analysis visualized in Gephi, the nodes are colored and sized according to the number of edges (connections), and the edge thickness is proportional to the edge weight (strength of correlation). The top 10 most differentially expressed genes between TREG and TFR cells are highlighted. g, flow cytometric analysis of the frequency of tumor-infiltrating TCF-1+ TREG and TFR cells from n = 5 NSCLC patient samples, P = 0.0159). Data are mean + /− s.e.m. Significance for comparisons were computed using two-tailed Wilcoxon matched-pairs signed-rank test between TREG and TFR cells (d) or two-tailed Mann–Whitney test (g).

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Extended Data Fig. 4 Cell-trajectory analysis of human TREG and TFR cells from primary tumor tissue and metastasized tumor-infiltrated lymph nodes.

a, Single-cell pseudotime trajectory of cells in cluster 1 (TREG cells) and cluster 6 (TFR cells) (left) or cells from primary tumor tissue or metastatic tumor-infiltrated lymph nodes (right) constructed using the Monocle3 algorithm. b, Normalized gene expression of IL1R2, CCR8, TNFRSF9, TNFRSF18 and PDCD1 on pseudotime path as in (a).

Extended Data Fig. 5 TCR-seq analysis of tumor-infiltrating TREG and TFR cells.

a, the pie chart illustrates the mean percentage of TFR clonotypes that were shared with TREG cells (light blue) and non-TREG cells (gray) respectively, from 4 patients with the highest numbers of clonally expanded FOXP3-expressing cells from a published single-cell RNA-seq dataset20. The lower panel plot displays the percentage of TFR clonotypes that overlap with 4-1BB– or 4-1BB+ tumor-infiltrating TREG cells. b, Euler diagram depicting the degree of clonal overlap between TREG, TFH and TFR cells. c, Representative TraCer plot of patient 101020 depicting all clonally expanded cells, color indicates the type of tumor-infiltrating CD4+ T cells: non-TREG (gray, FOXP3–), 4-1BB– TREG (green), 4-1BB+ TREG (red) and TFR (yellow) cells. d, Single-cell pseudotime trajectory of 4-1BB–, 4-1BB+ TREG, clonally expanded, TCR-sharing TREG and TFR cells (indicated with colored circles) constructed using the Monocle3 algorithm. e, Correlation of Monocle component 1 (x-axis) with the genes commonly unregulated in 4-1BB+ TREG, clonally expanded, TCR-sharing TREG and TFR cells compared to 4-1BB– TREG cells (y-axis). The solid line represents LOESS fitting between the shared signature and Monocle component 1. f, flow cytometric analysis of the frequency (left panel, P = 0.002 for indicated comparison), MFI (middle panel, P = 0.002 for indicated comparison) for 4-1BB expression in tumor-infiltrating CD8+ T cells, TREG and TFR cells (n = 10 treatment naïve NSCLC patients as in Fig. 2a–d). Data are mean + /− s.e.m. Significance for comparisons were computed using two-tailed Wilcoxon matched-pairs signed-rank test between TREG and TFR cells.

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Extended Data Fig. 6 Characterization of murine TFR cells in an immunization and cancer setting.

a, Gating strategy to identify tumor-infiltrating TREG (CD19–CD45+CD3+CD4+BCL-6–FOXP3+) and TFR (CD19–CD45+CD3+CD4+BCL-6+FOXP3+) cells in B16F10-OVA inoculated mice at d21 (upper panel), shown are representative FACS plots. The FACS plots in the lower panel illustrate intracellular expression of BCL-6 in the indicated cell types (left panel), expression of GITR (middle upper panel), KI-67 (right upper panel), PD-1 (middle lower panel), and CTLA-4 (right lower panel) versus FOXP3 in CD4+ T cells. b, Contour plots depicting the expression levels of FOXP3 in the indicated cell populations from (Fig. 4d). c, Luminex analysis of supernatants from an in vitro proliferation assay (repeat of in vitro suppression assay experiment in Fig. 4g,h), depicted is the concentration of secreted IFN-γ, IL-2 and TNF. d, Flow-cytometric analysis of the frequency of tumor-infiltrating TREG and TFR cells (P = 0.0025 in MC38-OVA, n = 5 mice for day 14 and n = 7 mice for day 21; P = 0.0017 in B16F10-OVA, n = 10 mice for day 14 and n = 6 mice for day 21) in indicated tumor models at indicated time points. Data are mean + /− s.e.m., Significance for comparisons were computed using two-tailed Mann–Whitney test (d). Data in b-d are representative of two independent experiments.

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Extended Data Fig. 7 Human TFR cells are responsive to anti-PD-1 therapy.

a, Heatmap comparing gene signatures of human tumor-infiltrating TFR cells pre- (n = 21 patients) and post- (n = 26 patients) anti-PD-1 therapy20. TFR cells from 5 patients (P2, P3, P12, P15, P20) receiving anti-PD-1 monotherapy were combined. IPA analysis of transcripts (n = 98) more highly expressed post anti-PD-1 treatment (right upper panel) and transcripts that overlap with CD28 signaling, ICOS-ICOSL signaling and T cell receptor signaling are highlighted (right lower panel and heatmap). b-i, Mice were s.c. inoculated with B16F10-OVA cells and treated with tamoxifen (days 5-8 and days 11-14) and anti-PD-1 Abs (day 9). Tumor volume (b,f), TFR cell frequencies (c, P = n.s., g, P = 0.035), eGFP cell frequencies (d, P = 0.0025, h, P = 0.0012) and FOXP3 frequencies (e,i) for n = 6 Foxp3eGFP-cre-ERT2 mice, n = 7 Foxp3eGFP-cre-ERT2/wt x Bcl6fl/fl mice, n = 7 Foxp3eGFP-cre-ERT2 mice and n = 5 Foxp3eGFP-cre-ERT2/wt mice. Data are mean + /− s.e.m., Significance for comparisons were computed using two-tailed Mann–Whitney test (b-i). Data in b-i are representative of two independent experiments.

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Extended Data Fig. 8 Murine TFR cells are depleted by anti-CTLA-4 thereapy.

a,b, Foxp3YFPcre/YFPcre Bcl6fl/fl (TFR knockout) mice or Foxp3YFPcre/YFPcre Bcl6+/+ control mice were s.c. inoculated with B16F10-OVA cells and treated with isotype control or anti-PD-1 Abs at indicated time points, frequency and Ki-67 expression of CD8+ T cells and CD4+ T cells in tumor-draining lymph nodes of mice treated as indicated in, n = 7 mice for ctrl+isotype ctrl, n = 6 mice for ctrl+anti-PD-1, n = 9 mice for the two TFR ko groups. c, Mice were s.c. inoculated with B16F10-OVA or MC38-OVA cells and treated with anti-CTLA-4 Abs at day 10 and day 13. Flow-cytometric analysis of the frequency of tumor-infiltrating TREG and TFR cells, as well as fold depletion of both cell types following anti-CTLA-4 therapy in the B16F10-OVA model (left panel, n = 9 mice, P = 0.0435) and MC38-OVA model (right panel, n = 5 mice, P = 0.0079). d, Survival curves of an independent cohort of melanoma patients (n = 29) stratified into TFRhi (>5.075% of CD4+ cells co-expressing FOXP3 and BCL-6) and TFRlo (<5.075% of cells co-expressing FOXP3 and BCL-6) e, IHC analysis of the frequency of FOXP3+BCL6+ TFR cells with a cutoff (orange line) set to upper limit of normal of 5.075% pertaining to (Extended Data Fig. 8d), P = 0.0654. f, Survival curves of melanoma patients stratified into CXCR5hi (frequency of CXCR5 + cells >8.336%) and CXCR5lo (frequency of CXCR5 + cells <8.336%). g, IHC analysis of the frequency of CXCR5 + cells with a cutoff (orange line) set to upper limit of normal of 8.375% pertaining to (Extended Data Fig. 8f), P = 0.0002. Data are mean + /− s.e.m., Significance for comparisons were computed using two-tailed Mann–Whitney test (c,e,g) or Mantel–Cox test (d,f). Data in (a–c) are representative of two independent experiments.

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Eschweiler, S., Clarke, J., Ramírez-Suástegui, C. et al. Intratumoral follicular regulatory T cells curtail anti-PD-1 treatment efficacy. Nat Immunol 22, 1052–1063 (2021). https://doi.org/10.1038/s41590-021-00958-6

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