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Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade

An Author Correction to this article was published on 03 October 2019

This article has been updated

Abstract

T cell dysfunction is a hallmark of many cancers, but the basis for T cell dysfunction and the mechanisms by which antibody blockade of the inhibitory receptor PD-1 (anti-PD-1) reinvigorates T cells are not fully understood. Here we show that such therapy acts on a specific subpopulation of exhausted CD8+ tumor-infiltrating lymphocytes (TILs). Dysfunctional CD8+ TILs possess canonical epigenetic and transcriptional features of exhaustion that mirror those seen in chronic viral infection. Exhausted CD8+ TILs include a subpopulation of ‘progenitor exhausted’ cells that retain polyfunctionality, persist long term and differentiate into ‘terminally exhausted’ TILs. Consequently, progenitor exhausted CD8+ TILs are better able to control tumor growth than are terminally exhausted T cells. Progenitor exhausted TILs can respond to anti-PD-1 therapy, but terminally exhausted TILs cannot. Patients with melanoma who have a higher percentage of progenitor exhausted cells experience a longer duration of response to checkpoint-blockade therapy. Thus, approaches to expand the population of progenitor exhausted CD8+ T cells might be an important component of improving the response to checkpoint blockade.

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Fig. 1: Chronic viral infection and tumors elicit analogous subsets of exhausted CD8+ T cells.
Fig. 2: Progenitor and terminally exhausted CD8+ TILs have distinct epigenetic and transcriptional features.
Fig. 3: Progenitor exhausted and terminally exhausted CD8+ TILs have distinct functional properties.
Fig. 4: Progenitor exhausted CD8+ T cells differentiate into terminally exhausted CD8+ T cells.
Fig. 5: Progenitor exhausted CD8+ T cells persist in the absence of antigen and mediate long-term tumor control in vivo.
Fig. 6: Anti-PD-1 treatment increases progenitor exhausted cell numbers and differentiation into terminally exhausted cells.
Fig. 7: Increased fraction of progenitor exhausted CD8+ T cells is associated with duration of response to checkpoint blockade in patients with advanced melanoma.

Data availability

All sequencing data from this study have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) and are accessible through the GEO Series accession code GSE122713. All other relevant data are available from the corresponding author on request.

Change history

  • 03 October 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. 1.

    Thommen, D. S. & Schumacher, T. N. T cell dysfunction in cancer. Cancer Cell. 33, 547–562 (2018).

    CAS  Article  Google Scholar 

  2. 2.

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

    CAS  Article  Google Scholar 

  3. 3.

    Zajac, A. J. et al. Viral immune evasion due to persistence of activated T cells without effector function. J. Exp. Med. 188, 2205–2213 (1998).

    CAS  Article  Google Scholar 

  4. 4.

    Moskophidis, D., Lechner, F., Pircher, H. & Zinkernagel, R. M. Virus persistence in acutely infected immunocompetent mice by exhaustion of antiviral cytotoxic effector T cells. Nature 362, 758–761 (1993).

    CAS  Article  Google Scholar 

  5. 5.

    Wherry, E. J. T cell exhaustion. Nat. Immunol. 12, 492–499 (2011).

    CAS  Article  Google Scholar 

  6. 6.

    Gallimore, A. et al. Induction and exhaustion of lymphocytic choriomeningitis virus-specific cytotoxic T lymphocytes visualized using soluble tetrameric major histocompatibility complex class I–peptide complexes. J. Exp. Med. 187, 1383–1393 (1998).

    CAS  Article  Google Scholar 

  7. 7.

    Barber, D. L. et al. Restoring function in exhausted CD8 T cells during chronic viral infection. Nature 439, 682–687 (2006).

    CAS  Article  Google Scholar 

  8. 8.

    Sen, D. R. et al. The epigenetic landscape of T cell exhaustion. Science 354, 1165–1169 (2016).

    CAS  Article  Google Scholar 

  9. 9.

    Doering, T. A. et al. Network analysis reveals centrally connected genes and pathways involved in CD8+. T cell exhaustion versus memory. Immunity 37, 1130–1144 (2012).

    CAS  Article  Google Scholar 

  10. 10.

    Mognol, G. P. et al. Exhaustion-associated regulatory regions in CD8+ tumor-infiltrating T cells. Proc. Natl Acad. Sci. USA 114, E2776–E2785 (2017).

    CAS  Article  Google Scholar 

  11. 11.

    Wherry, E. J. et al. Molecular signature of CD8+ T cell exhaustion during chronic viral infection. Immunity 27, 670–684 (2007).

    CAS  Article  Google Scholar 

  12. 12.

    Paley, M. A. et al. Progenitor and terminal subsets of CD8+ T cells cooperate to contain chronic viral infection. Science 338, 1220–1225 (2012).

    CAS  Article  Google Scholar 

  13. 13.

    Utzschneider, D. T. et al. T cell factor 1-expressing memory-like CD8+ T cells sustain the immune response to chronic viral infections. Immunity 45, 415–427 (2016).

    CAS  Article  Google Scholar 

  14. 14.

    Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).

    CAS  Article  Google Scholar 

  15. 15.

    He, R. et al. Follicular CXCR5-expressing CD8+ T cells curtail chronic viral infection. Nature 537, 412–428 (2016).

    CAS  Article  Google Scholar 

  16. 16.

    Leong, Y. A. et al. CXCR5+ follicular cytotoxic T cells control viral infection in B cell follicles. Nat. Immunol. 17, 1187–1196 (2016).

    CAS  Article  Google Scholar 

  17. 17.

    Wu, T. et al. The TCF1-Bcl6 axis counteracts type I interferon to repress exhaustion and maintain T cell stemness.Sci. Immunol. 1, 1–12 (2016).

    Article  Google Scholar 

  18. 18.

    Blackburn, S. D., Shin, H., Freeman, G. J. & Wherry, E. J. Selective expansion of a subset of exhausted CD8 T cells by αPD-L1 blockade. Proc. Natl Acad Sci. USA 105, 15016–15021 (2008).

    CAS  Article  Google Scholar 

  19. 19.

    Baitsch, L. et al. Exhaustion of tumor-specific CD8+ T cells in metastases from melanoma patients. J. Clin. Invest. 121, 2350–2360 (2011).

    CAS  Article  Google Scholar 

  20. 20.

    Sakuishi, K. et al. Targeting Tim-3 and PD-1 pathways to reverse T cell exhaustion and restore anti-tumor immunity. J. Exp. Med. 207, 2187–2194 (2010).

    CAS  Article  Google Scholar 

  21. 21.

    Schietinger, A. et al. Tumor-specific T cell dysfunction is a dynamic antigen-driven differentiation program initiated early during tumorigenesis. Immunity 45, 389–401 (2016).

    CAS  Article  Google Scholar 

  22. 22.

    Singer, M. et al. A distinct gene module for dysfunction uncoupled from activation in tumor-infiltrating T cells. Cell 166, 1500–1511 e1509 (2016).

    CAS  Article  Google Scholar 

  23. 23.

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

    CAS  Article  Google Scholar 

  24. 24.

    Wei, S. C. et al. Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Cell 170, 1120–1133 e1117 (2017).

    CAS  Article  Google Scholar 

  25. 25.

    Blackburn, S. D. et al. Coregulation of CD8+ T cell exhaustion by multiple inhibitory receptors during chronic viral infection. Nat. Immunol. 10, 29–37 (2009).

    CAS  Article  Google Scholar 

  26. 26.

    Fidler, I. J. Biological behavior of malignant melanoma cells correlated to their survival in vivo. Cancer Res. 35, 218–224 (1975).

    CAS  PubMed  Google Scholar 

  27. 27.

    Jenkins, M. H. et al. Multiple murine BRaf(V600E) melanoma cell lines with sensitivity to PLX4032. Pigment. Cell. Melanoma. Res. 27, 495–501 (2014).

    CAS  Article  Google Scholar 

  28. 28.

    Wang, R. et al. T cell factor 1 regulates thymocyte survival via a RORγt-dependent pathway. J. Immunol. 187, 5964–5973 (2011).

    CAS  Article  Google Scholar 

  29. 29.

    Ferreira, V. et al. In vivo inhibition of NF-κB in T-lineage cells leads to a dramatic decrease in cell proliferation and cytokine production and to increased cell apoptosis in response to mitogenic stimuli, but not to abnormal thymopoiesis. J. Immunol. 162, 6442–6450 (1999).

    CAS  PubMed  Google Scholar 

  30. 30.

    Woolf, E. et al. Runx3 and Runx1 are required for CD8 T cell development during thymopoiesis. Proc. Natl Acad. Sci. USA 100, 7731–7736 (2003).

    CAS  Article  Google Scholar 

  31. 31.

    Martinez, G. J. et al. The transcription factor NFAT promotes exhaustion of activated CD8+ T cells. Immunity 42, 265–278 (2015).

    CAS  Article  Google Scholar 

  32. 32.

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

    CAS  Article  Google Scholar 

  33. 33.

    Pauken, K. E. et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354, 1160–1165 (2016).

    CAS  Article  Google Scholar 

  34. 34.

    Ho, P. C. et al. Phosphoenolpyruvate is a metabolic checkpoint of anti-tumor T cell responses. Cell 162, 1217–1228 (2015).

    CAS  Article  Google Scholar 

  35. 35.

    Horton, B. L., Williams, J. B., Cabanov, A., Spranger, S. & Gajewski, T. F. Intratumoral CD8+ T-cell apoptosis is a major component of T-cell dysfunction and impedes antitumor immunity. Cancer Immunol. Res. 6, 14–24 (2018).

    CAS  Article  Google Scholar 

  36. 36.

    Schmitz, J. E. et al. Control of viremia in simian immunodeficiency virus infection by CD8+ lymphocytes. Science 283, 857–860 (1999).

    CAS  Article  Google Scholar 

  37. 37.

    Brummelman, J. et al. High-dimensional single cell analysis identifies stem-like cytotoxic CD8+ T cells infiltrating human tumors. J. Exp. Med. 215, 2520–2535 (2018).

    CAS  Article  Google Scholar 

  38. 38.

    Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013 e1020 (2018).

    CAS  Article  Google Scholar 

  39. 39.

    Thommen, D. S. et al. A transcriptionally and functionally distinct PD-1+ CD8+ T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade. Nat. Med. 24, 994–1004 (2018).

    CAS  Article  Google Scholar 

  40. 40.

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

    Article  Google Scholar 

  41. 41.

    Welsh, R. M. & Seedhom, M. O. Lymphocytic choriomeningitis virus (LCMV): propagation, quantitation, and storage.Curr. Protoc. Microbiol. 8, 15A.1.1–15A.1.11 (2008).

    Article  Google Scholar 

  42. 42.

    Yates, K., Bi, K., Haining, W. N., Cantor, H. & Kim, H. J. Comparative transcriptome analysis reveals distinct genetic modules associated with Helios expression in intratumoral regulatory T cells. Proc. Natl Acad. Sci. USA 115, 2162–2167 (2018).

    CAS  Article  Google Scholar 

  43. 43.

    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).

    CAS  Article  Google Scholar 

  44. 44.

    Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    CAS  Article  Google Scholar 

  45. 45.

    Waltman, L. & van Eck, N. J. A smart local moving algorithm for large-scale modularity-based community detection. Europ. Phys. J. B 86, 471 (2013).

    Article  Google Scholar 

  46. 46.

    DeTomaso, D. & Yosef, N. FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data. BMC Bioinformatics 17, 315 (2016).

    Article  Google Scholar 

  47. 47.

    Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).

    CAS  Article  Google Scholar 

  48. 48.

    Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    CAS  Article  Google Scholar 

  49. 49.

    Carey, C. D. et al. Topological analysis reveals a PD-L1-associated microenvironmental niche for Reed–Sternberg cells in Hodgkin lymphoma. Blood 130, 2420–2430 (2017).

    CAS  Article  Google Scholar 

  50. 50.

    Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).

    Article  Google Scholar 

Download references

Acknowledgements

W.N.H. received support from NIAID R01 AI115712, NIAID U19 AI133524, Parker Institute for Cancer Immunotherapy, and The G. Harold and Leila Y. Mathers Charitable Foundation. A.H.S. was supported by NIAID P01 AI56299 and funding from the Ludwig Center at Harvard Medical School. B.C.M. was supported by the 2016 AACR-Bristol-Myers Squibb Fellowship in Translational Immuno-oncology grant number 16-40-15-MILL, Wong Family Awards in Translational Oncology, National Center for Advancing Translational Sciences/National Institutes of Health Award KL2 TR002542, NCI T32 CA009172, and the Jane C. Wright, MD, Endowed Young Investigator Award from ASCO. J.J.I. received funding from NCI T32 CA009172. Y.V.V. received funding from NIAID K23 AI130408. M.W.L. received funding from NCI T32 CA207021. J.R.K. and S.A.W. were supported by NIGMS T32 GM007753. G.K.G. received funding from NHLBI T32 HL007627. This work was supported, in part, by the Center for Immuno-Oncology at DFCI. We would like to thank the Dana-Farber Flow Cytometry Core for their assistance.

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Authors

Contributions

B.C.M., D.R.S. and W.N.H. conceived the study. B.C.M., D.R.S., A.H.S. and W.N.H. designed the experiments. B.C.M., D.R.S., R.A.A., K.B., Y.V.V., M.W.L., K.B.Y., J.R.K., J.J.I., J.L.C., A.P., S.M., D.E.C., S.A.W. and F.D.B. performed mouse experiments and/or data analysis. M.D.Z., R.T.M. and F.S.H. provided critical reagents. A.L., K.F., G.S.N., M.M., E.G., G.K.G., F.S.H. and S.J.R. collected human samples and data. B.C.M., D.R.S. and W.N.H. wrote the manuscript. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to W. Nicholas Haining.

Ethics declarations

Competing interests

W.N.H. receives funding from Roche and Novartis. A.H.S. has patents on the PD-1 pathway licensed by Roche/Genentech and Novartis and consults for Novartis. S.J.R. receives research support from Bristol-Myers Squibb, Merck, Affimed, and KITE pharmaceuticals. G.S.N. is a current employee of Leap Therapeutics. F.S.H. receives research support from Bristol-Myers Squibb, personal fees from Bristol-Myers Squibb, Merck, EMD Serono, Novartis, Celldex, Amgen, Genentech/Roche, Incyte, Apricity, Bayer, Aduro, Partners Therapeutics, Sanofi, Pfizer, Pionyr, Verastem, Compass Therapeutics, and Takeda. F.S.H. is an advisor for 7 Hills Pharma and Torque. F.S.H. has the following patents: Methods for Treating MICA-Related Disorders (no. 20100111973) with royalties paid, Tumor antigens and uses thereof (no. 7250291) issued, Angiopoiten-2 Biomarkers Predictive of Anti-immune checkpoint response (no. 20170248603) pending, Compositions and Methods for Identification, Assessment, Prevention, and Treatment of Melanoma using PD-L1 Isoforms (no. 20160340407) pending, Therapeutic peptides (no. 20160046716) pending, Therapeutic Peptides (no. 20140004112) pending, Therapeutic Peptides (no. 20170022275) pending, Therapeutic Peptides (no. 20170008962) pending, Therapeutic Peptides (no. 9402905) issued and Methods of Using Pembrolizumab and Trebananib pending.

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Integrated supplementary information

Supplementary Figure 1 Similar populations of progenitor exhausted CD8+ T cells identified in chronic viral infection and mouse tumors by single-cell RNA-seq.

a, Serum viral titers of LCMV Clone 13 infected mice pooled to generate single-cell RNA-seq data, n = 13 (pool 1) or 15 (pool 2) mice. b, Heatmap of top differentially expressed genes in each cluster, with key transcripts highlighted on the right. Groups colored according to clustering (Fig. 1a). c, Expression of indicated genes overlaid on LCMV CD8+ T cell tSNE projection of 9,194 single cells (Fig. 1a). d, tSNE projection of single-cell RNA-seq profiles from 4,313 SIINFEKL tetramer+ CD8+ T cells from day 20 B16-OVA tumors colored by cluster. Unlabeled cluster in gray represents cell doublets. e, Expression of indicated genes overlaid on tSNE projection from d. f, Enrichment of a signature of genes upregulated in exhausted vs. effector CD8+ T cells (GSE9650) or stem-like exhausted vs. terminally exhausted CD8+ T cells (GSE84105). g, Violin plots of the enrichment score of the gene signature derived from stem-like exhausted vs. terminally exhausted CD8+ T cells (GSE84105) for each cell cluster in Supplementary Fig. 1d, n > 263 single cells per cluster. Mean +/- s.d. (a), two-sided Student’s t test (a), two-sided Kolmogorov–Smirnov test (g); n.s. p > 0.05, **** p ≤ 0.0001.

Supplementary Figure 2 Progenitor exhausted and terminally exhausted CD8+ T cell populations confirmed in multiple mouse tumor models by flow cytometry.

a, Representative flow cytometry gating strategy for progenitor and terminally exhausted cells (left) with tetramer full-minus one (FMO, right). b, Frequency of progenitor exhausted (Tcf1+Tim-3) and terminally exhausted (Tcf1Tim-3+) CD8+ T cells from B16-OVA tumors (top), B16.F10 tumors (middle) or D4M.3A-OVA tumors (bottom) gated on PD-1+CD44+ cells. Representative flow plot (left) and summary (right) of three independent experiments, n = 17 mice (B16-OVA), n = 5 mice (B16.F10), or one of two independent experiments, n = 5 mice (D4M.3A-OVA). c, Frequency of Tcf1+ cells within Slamf6+Tim-3 or Slamf6Tim-3+ cells (top) or frequency of Slamf6+ cells within Tcf1+Tim-3 or Tcf1Tim-3+ cells (bottom) from B16-OVA tumors. Representative flow plots (left) and summary (right) of one of two independent experiments, n = 9 mice. d, Scatter plot of transcript abundance (log10) between replicates for all 13,012 transcripts. e, Hypergeometric overlap of gene expression profiles from progenitor exhausted or terminally exhausted gene signatures from LCMV or TILs with expression data from indicated cell states from the literature, top 150 differentially expressed transcripts used for comparison. Mean +/- s.d. (b,c), two-sided Student’s t test (b,c); two-sided hypergeometric test (e); **** p ≤ 0.0001.

Supplementary Figure 3 Progenitor and terminally exhausted CD8+ TILs have distinct epigenetic and transcriptional features.

a, Scatter plot of normalized chromatin accessibility between replicates at each peak for all 67,221 peaks. b, Representative ATAC-seq tracks at the Slamf6 (top) and Havcr2 (bottom) loci. c, Heatmap showing average mRNA expression of neighboring genes within each cluster (Fig. 2e) in each cell state. d, Enrichment of gene signatures from MSigDB (rows) from cluster of regions in LCMV Cl13 or tumor (Fig. 2e). Q-values (hypergeometric test) presented as –log10. All ATAC-seq data representative of two biologically independent pooled samples. e, Scatter plot of differential motif enrichment in progenitor exhausted or terminally exhausted cluster. X and Y axis represent -log10 of q value (hypergeometric test). f, GSEA of signatures in the ranked list of genes differentially expressed by progenitor exhausted vs. terminally exhausted CD8+ T cells from B16-OVA tumors. All RNA-seq data representative of two biologically independent pooled samples. FDR < 0.05 for each comparison by gene set permutation test.

Supplementary Figure 4 Progenitor exhausted and terminally exhausted CD8+ TILs have distinct functional properties.

a, Frequency of IFN-γ+ and IFN-γ+TNF+ progenitor exhausted (Tcf1+Tim-3) or terminally exhausted (Tcf1Tim-3+) TILs stimulated for 6 hours ex vivo with PMA and ionomycin. Summary of one of two independent experiments, n = 10 mice. b, Representative flow plots of IL-2 production from progenitor exhausted or terminally exhausted TILs stimulated for 6 hours ex vivo with PMA and ionomycin, n = 3 independent wells. c, Representative histograms of H-2Kb expression on B16-OVA and B2m-null B16-OVA cells after 24 hours in vitro IFN-γ stimulation from one experiment. Percentage of cells within indicated gate shown. d, Schema of experimental design for in vitro killing assay. e, Representative flow plots of tumor cells after 40 hour co-culture with no T cells (left) or tetramer+ CD8+ TILs (right) stained against H-2Kb. f, Frequency of surviving target cells (B16-OVA) to control cells (B2m-null B16-OVA) normalized to no T cell wells. One of three independent experiments, n = 2 independent wells per condition. Mean +/- s.d. (a); two-sided Student’s paired t test (a); ** p ≤ 0.01.

Supplementary Figure 5 Progenitor exhausted CD8+ T cells differentiate into terminally exhausted CD8+ T cells.

a, Representative flow plots of the cell surface phenotype of naive CD8+ splenocytes, progenitor exhausted TILs, or terminally exhausted TILs pre-stim (left) or after 40 hours co-culture with tumor cells (right) in the in vitro cytotoxicity assay (Supplementary Fig. 4d). b, Summary of the phenotype of tetramer+ sorted T cells after 40 hours co-culture with tumor cells. One of three independent experiments, n = 6 (progenitor exh.) or 21 (naive and terminally exh.) independent wells. c, Schema of experimental design for in vivo persistence and tumor assays. d, Summary of the phenotype of transferred progenitor exhausted cells harvested from spleen or tumors. Summary of two independent experiments, n = 9 (spleen) or 16 (tumor) mice post-transfer.

Supplementary Figure 6 Progenitor exhausted CD8+ T cells have greater persistence than that of terminally exhausted CD8+ T cells.

a, Cell numbers/mg tumor of transferred (CD45.2+) progenitor or terminally exhausted cells in recipient tumors. Summary of two of three independent experiments, n = 16 (progenitor exh.) or 17 (terminally exh.) mice. b, Frequency of transferred progenitor exhausted or terminally exhausted cells in draining lymph nodes of recipient mice. Summary of two independent experiments, n = 16 (progenitor exh.) or 17 (terminally exh.) mice. c, Frequency of transferred progenitor or terminally exhausted cells in lymph nodes from in vivo persistence assay mice without tumor implantation (Supplementary Fig. 5c). Summary of two independent experiments, n = 7 mice. d, GSEA of central vs. effector memory signature (GSE23321) in the ranked list of genes differentially expressed by progenitor exhausted vs. terminally exhausted from B16-OVA tumors (left) or ranked list of genes differentially expressed by a second, independent dataset of central memory versus effector memory cells (GSE98640), for comparison. FDR shown for each comparison calculated by gene set permutation test. Mean +/- s.d. (a-c); two-sided Student’s t test (a-c); * p ≤ 0.05,**** p ≤ 0.0001.

Supplementary Figure 7 Treatment with anti-PD-1 or combination anti-PD-1 plus anti-CTLA-4 increases the relative abundance of terminally exhausted cells in the tumor.

a, Cell number/mg tumor of total tetramer+ CD8+ T cells from isotype control or anti-PD-1 treated B16-OVA tumors. Summary of two independent experiments, n = 13 mice. b, Frequency of progenitor exhausted (Tcf1+Tim-3) and terminally exhausted (Tcf1Tim-3+) PD-1+CD44+ CD8+ T cells from isotype control or anti-PD-1 treated B16-OVA tumors. Representative flow plots (left) and summary (right) of three independent experiments, n = 17 (control tx) or 15 (anti-PD-1 tx) mice. c, Cell number/mg tumor of progenitor exhausted and terminally exhausted CD8+ T cells from isotype control or anti-PD-1 treated B16-OVA tumors, gated on tetramer+ cells. Summary of two independent experiments, n = 13 mice. d, Growth curves of B16-OVA tumors treated with 100µg anti-PD-1 +/- 100µg anti-CTLA-4 or isotype control antibodies on days 9 and 12. One of two representative experiments, n = 6 mice (anti-PD-1 + anti-CDTL-4 tx), 7 mice (anti-PD-1 tx), or 8 mice (control tx). e, Summary of the frequency of progenitor exhausted and terminally exhausted cells from one of two representative experiments, n = 6 mice (anti-PD-1 + anti-CDTL-4 tx), 7 mice (anti-PD-1 tx), or 8 mice (control tx). Mean +/- s.e.m. (d); Mean +/- s.d. (a-c, e); two-sided Student’s t test (a-e); n.s. p > 0.05, * p ≤ 0.05, ** p ≤ 0.01, **** p ≤ 0.0001.

Supplementary Figure 8 Increased fraction of progenitor exhausted CD8+ T cells is associated with duration of response to checkpoint blockade in patients with advanced melanoma.

a, Diagram of the functions of progenitor exhausted and terminally exhausted CD8+ T cells in the tumor microenvironment. b, Swimmer plots of 25 patients with advanced melanoma who received nivolumab (anti-PD-1) and ipilimumab (anti-CTLA-4) showing progression-free survival and overall survival. c, Frequency of progenitor exhausted cells in all activated/exhausted cells plotted against progression-free survival (days) in patients without durable clinical benefit (non-responders, n = 11). Linear regression line shown. d, Graph plotting the significance value from survival analysis for different cutoff ratios of TCF1+ in PD-1+CD8+ T cells (black line) or CD8+ T cells in all nucleated cells (gray line), from 0 to max ratio in responders (n = 14). Dotted line at p = 0.05. e,f, Kaplan-Meier curves of overall survival in responders (n = 14) by high vs. low percentage of total CD8+ T cells in all nucleated cells (e, cutoff at median 7.6%) or by percentage of progenitor exhausted cells (TCF1+) in all activated/exhausted CD8+ T cells (f, cutoff at median 14.9%). two-sided Likelihood ratio test (d-f); n.s. p > 0.05.

Supplementary information

Supplementary Figures 1–8

Supplementary Figures 1–8

Reporting Summary

Supplementary Table 1

: Gene expression by cluster in scRNA-seq from LCMV Clone 13. Differentially expressed mRNA transcripts from each cluster of gp33 tetramer–positive CD8+ T cells in LCMV Clone 13. Each of the four tabs corresponds to a cluster, with rows representing genes and columns containing the log2 fold change vs. all, percentage of cells with non-zero transcript in given cluster, percentage of cells with non-zero transcript in all other clusters, P value and FDR correction of observed differences (calculated by Seurat using two-sided test).

Supplementary Table 2

: Differential gene expression between CD8+ T cells from LCMV and those from B16-ova. Differential expression of 13,012 total transcripts listed in the following conditions: LCMV progenitor exhausted vs. LCMV terminally exhausted, tumor progenitor exhausted vs. tumor terminally exhausted, tumor progenitor exhausted with control treatment vs. anti-PD-1 therapy, tumor terminally exhausted with control treatment vs. anti-PD-1 therapy. Each row represents a single gene, and columns provide the log2 fold change in mRNA expression, P value and FDR correction of observed differences (calculated by DESeq2 using two-sided test).

Supplementary Table 3

: Chromatin-accessible regions (ChARs) identified in CD8+ T cells from LCMV infection and B16-ova tumors. The two tabs represent two peak universes used in this study. Both include progenitor exhausted and terminally exhausted CD8+ T cells isolated from LCMV clone 13–infected mice and B16-ova tumors; the first tab also includes memory GP33 tetramer–positive CD8+ T cells from LCMV Armstrong (universe size = 84,694), but the second tab does not (universe size = 67,221). Each row represents a ChAR, and the columns contain the chromosomal location, number of normalized ATAC-Seq cuts in each sample, P values and FDRs for differential abundance of cuts (calculated by DESeq2 using two-sided test).

Supplementary Table 4

: Enrichment of MSigDB and ImmSigDB terms in ChAR modules. All significantly enriched terms between memory cells vs. all exhausted cells (Fig. 2c) and within the four, state-specific ChAR modules (Fig. 2e) are listed. Each row represents an individual term in either MSigDB or ImmSigDB, and the column represents the –log10 FDR of the significance of the enrichment (two-sided binomial test; GREAT online tool).

Supplementary Table 5

: TCRβ sequencing of progenitor exhausted and terminally exhausted T cells. Summary of the TCRβ sequences of tetramer-negative progenitor or terminally exhausted CD8+ T cells sorted from B16-OVA tumors (two biological replicates of each population).

Supplementary Table 6

: Patient characteristics and clinical information. Summary of the clinical characteristics of all 25 subjects whose tumor biopsies were profiled in this study, including tumor stage, patient age and sex, tumor biopsy details and pre-biopsy treatment regimens. Progression-free survival and overall survival are reported in days.

Supplementary Table 7

: Survival analyses of clinical data. Summary of overall survival and progression-free survival analyses of human clinical characteristics; hazard ratio and 95% confidence intervals (CI) are shown, and P value (determined by two-sided likelihood ratio test).

Supplementary Table 8

: Antibodies. Summary of all antibodies used in this study, separated by species, listing the target protein, antibody clone, manufacturer, conjugated fluorophore, and dilution.

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Miller, B.C., Sen, D.R., Al Abosy, R. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat Immunol 20, 326–336 (2019). https://doi.org/10.1038/s41590-019-0312-6

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