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.

  • Letter
  • Published:

TOX is a critical regulator of tumour-specific T cell differentiation

Abstract

Tumour-specific CD8 T cell dysfunction is a differentiation state that is distinct from the functional effector or memory T cell states1,2,3,4,5,6. Here we identify the nuclear factor TOX as a crucial regulator of the differentiation of tumour-specific T (TST) cells. We show that TOX is highly expressed in dysfunctional TST cells from tumours and in exhausted T cells during chronic viral infection. Expression of TOX is driven by chronic T cell receptor stimulation and NFAT activation. Ectopic expression of TOX in effector T cells in vitro induced a transcriptional program associated with T cell exhaustion. Conversely, deletion of Tox in TST cells in tumours abrogated the exhaustion program: Tox-deleted TST cells did not upregulate genes for inhibitory receptors (such as Pdcd1, Entpd1, Havcr2, Cd244 and Tigit), the chromatin of which remained largely inaccessible, and retained high expression of transcription factors such as TCF-1. Despite their normal, ‘non-exhausted’ immunophenotype, Tox-deleted TST cells remained dysfunctional, which suggests that the regulation of expression of inhibitory receptors is uncoupled from the loss of effector function. Notably, although Tox-deleted CD8 T cells differentiated normally to effector and memory states in response to acute infection, Tox-deleted TST cells failed to persist in tumours. We hypothesize that the TOX-induced exhaustion program serves to prevent the overstimulation of T cells and activation-induced cell death in settings of chronic antigen stimulation such as cancer.

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

Access options

Buy this article

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

Fig. 1: TOX is highly expressed in tumour-infiltrating CD8 T cells of mouse and human tumours.
Fig. 2: Chronic TCR stimulation drives TOX expression in tumour-specific CD8 T cells.
Fig. 3: Ectopic expression of TOX is sufficient to induce a global molecular program characteristic of T cell exhaustion.
Fig. 4: Phenotypic, functional, transcriptional and epigenetic analysis of TOX-deficient T cells.

Similar content being viewed by others

Data availability

All data generated and supporting the findings of this study are available within the paper. The RNA-seq and ATAC-seq data have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE126974. Source Data are provided with the online version of the paper. Additional information and materials will be made available upon request.

References

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  3. 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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

  6. Henning, A. N., Roychoudhuri, R. & Restifo, N. P. Epigenetic control of CD8+ T cell differentiation. Nat. Rev. Immunol. 18, 340–356 (2018).

    Article  CAS  Google Scholar 

  7. Stahl, S. et al. Tumor agonist peptides break tolerance and elicit effective CTL responses in an inducible mouse model of hepatocellular carcinoma. Immunol. Lett. 123, 31–37 (2009).

    Article  CAS  Google Scholar 

  8. Kaech, S. M. & Cui, W. Transcriptional control of effector and memory CD8+ T cell differentiation. Nat. Rev. Immunol. 12, 749–761 (2012).

    Article  CAS  Google Scholar 

  9. O’Flaherty, E. & Kaye, J. TOX defines a conserved subfamily of HMG-box proteins. BMC Genomics 4, 13 (2003).

    Article  Google Scholar 

  10. Aliahmad, P. & Kaye, J. Development of all CD4 T lineages requires nuclear factor TOX. J. Exp. Med. 205, 245–256 (2008).

    Article  CAS  Google Scholar 

  11. Aliahmad, P., Seksenyan, A. & Kaye, J. The many roles of TOX in the immune system. Curr. Opin. Immunol. 24, 173–177 (2012).

    Article  CAS  Google Scholar 

  12. Seehus, C. R. et al. The development of innate lymphoid cells requires TOX-dependent generation of a common innate lymphoid cell progenitor. Nat. Immunol. 16, 599–608 (2015).

    Article  CAS  Google Scholar 

  13. Page, N. et al. Expression of the DNA-binding factor TOX promotes the encephalitogenic potential of microbe-induced autoreactive CD8+ T cells. Immunity 48, 937–950 (2018).

    Article  CAS  Google Scholar 

  14. Wherry, E. J. & Kurachi, M. Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol. 15, 486–499 (2015).

    Article  CAS  Google Scholar 

  15. Schietinger, A. & Greenberg, P. D. Tolerance and exhaustion: defining mechanisms of T cell dysfunction. Trends Immunol. 35, 51–60 (2014).

    Article  CAS  Google Scholar 

  16. Bloom, M. B. et al. Identification of tyrosinase-related protein 2 as a tumour rejection antigen for the B16 melanoma. J. Exp. Med. 185, 453–459 (1997).

    Article  CAS  Google Scholar 

  17. Overwijk, W. W. et al. Tumor regression and autoimmunity after reversal of a functionally tolerant state of self-reactive CD8+ T cells. J. Exp. Med. 198, 569–580 (2003).

    Article  CAS  Google Scholar 

  18. Li, H. et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176, 775–789 (2019).

    Article  CAS  Google Scholar 

  19. Simoni, Y. et al. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557, 575–579 (2018).

    Article  ADS  CAS  Google Scholar 

  20. West, E. E. et al. Tight regulation of memory CD8+ T cells limits their effectiveness during sustained high viral load. Immunity 35, 285–298 (2011).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  22. Macian, F. NFAT proteins: key regulators of T-cell development and function. Nat. Rev. Immunol. 5, 472–484 (2005).

    Article  CAS  Google Scholar 

  23. Man, K. et al. Transcription factor IRF4 promotes CD8+ T cell exhaustion and limits the development of memory-like T cells during chronic infection. Immunity 47, 1129–1141 (2017).

    Article  CAS  Google Scholar 

  24. Scott-Browne, J. P. et al. Dynamic changes in chromatin accessibility occur in CD8+ T cells responding to viral infection. Immunity 45, 1327–1340 (2016).

    Article  CAS  Google Scholar 

  25. Flanagan, W. M., Corthésy, B., Bram, R. J. & Crabtree, G. R. Nuclear association of a T-cell transcription factor blocked by FK-506 and cyclosporin A. Nature 352, 803–807 (1991).

    Article  ADS  CAS  Google Scholar 

  26. Jain, J. et al. The T-cell transcription factor NFATp is a substrate for calcineurin and interacts with Fos and Jun. Nature 365, 352–355 (1993).

    Article  ADS  CAS  Google Scholar 

  27. Staveley-O’Carroll, K. et al. In vivo ligation of CD40 enhances priming against the endogenous tumour antigen and promotes CD8+ T cell effector function in SV40 T antigen transgenic mice. J. Immunol. 171, 697–707 (2003).

    Article  Google Scholar 

  28. Wang, Q., Strong, J. & Killeen, N. Homeostatic competition among T cells revealed by conditional inactivation of the mouse Cd4 gene. J. Exp. Med. 194, 1721–1730 (2001).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  30. Brockstedt, D. G. et al. Listeria-based cancer vaccines that segregate immunogenicity from toxicity. Proc. Natl Acad. Sci. USA 101, 13832–13837 (2004).

    Article  ADS  CAS  Google Scholar 

  31. Sinnathamby, G. et al. Priming and activation of human ovarian and breast cancer-specific CD8+ T cells by polyvalent Listeria monocytogenes-based vaccines. J. Immunother. 32, 856–869 (2009).

    Article  CAS  Google Scholar 

  32. Espinosa-Carrasco, G. et al. Systemic LPS translocation activates cross-presenting dendritic cells but is dispensable for the breakdown of CD8+ T cell peripheral tolerance in irradiated mice. PLoS ONE 10, e0130041 (2015).

    Article  Google Scholar 

  33. Zhang, N. & Bevan, M. J. TGF-β signaling to T cells inhibits autoimmunity during lymphopenia-driven proliferation. Nat. Immunol. 13, 667–673 (2012).

    Article  CAS  Google Scholar 

  34. Hartley, S. W. & Mullikin, J. C. QoRTs: a comprehensive toolset for quality control and data processing of RNA-seq experiments. BMC Bioinformatics 16, 224 (2015).

    Article  Google Scholar 

  35. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer New York, 2009).

  36. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  Google Scholar 

  37. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  Google Scholar 

  38. Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    Article  CAS  Google Scholar 

  39. 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  ADS  CAS  Google Scholar 

  40. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  Google Scholar 

  41. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  42. Liu, T. Use model-based Analysis of ChIP–seq (MACS) to analyze short reads generated by sequencing protein–DNA interactions in embryonic stem cells. Methods Mol. Biol. 1150, 81–95 (2014).

    Article  CAS  Google Scholar 

  43. Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–165 (2016).

    Article  Google Scholar 

  44. Martin, Marcel. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Article  Google Scholar 

  45. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

Download references

Acknowledgements

We thank the members of the Schietinger laboratory, S. Reiner and M. Li for discussions and technical help; R. Nadler for technical assistance; A. Hurwitz and N. Restifo for providing TRP2 transgenic mice; MSKCC Flow Cytometry Core, especially R. Gardner; Integrated Genomics Operation Core, especially A. Viale, N. Mohibullah, A. Farina and R. Patel. We thank the J. Sun laboratory and B. Moltedo (Rudensky laboratory) for providing LCMV strains. D.Z., A. Schietinger, M.D.H. and C.A.K. are members of the Parker Institute for Cancer Immunotherapy, which supports the MSKCC Cancer Immunotherapy Program. This work was supported by NIH-NCI grants DP2 CA225212, R00 CA172371 (to A. Schietinger), U54 CA209975, V Foundation for Cancer Research (to A. Schietinger), the Anna Fuller Foundation (to A. Schietinger), the William and Ella Owens Medical Research Foundation (to A. Schietinger), the Josie Robertson Young Investigator Award (to A. Schietinger), NIH-NCI grant K08 CA158069 (to M.P.), V Foundation Scholar Award (to M.P.), Serodino Family Adventure Allee Fund (to M.P.), the Parker Institute for Cancer Immunotherapy (C.A.K. and A. Schietinger), the Weill Cornell Medicine Core Laboratories Center (P.Z., F.D., D.B.), Francois Wallace Monahan Fellowship (to O.L.), NIH-NIAID U19 AI11143 (to M.S.G.), NIH-NIAID R01 AI054977 (to J.K.), the Damon Runyon Cancer Research Foundation CI-96-18 (to C.A.K.), NCI R33 CA22539 (to C.A.K.), the Manhasset Women’s Coalition Against Breast Cancer (to C.A.K.), the MSK Cancer Center Core Grant P30 CA008748. The Integrated Genomics Operation Core was supported by Cycle for Survival and the Marie-Josée and Henry R. Kravis Center for Molecular Oncology.

Author information

Authors and Affiliations

Authors

Contributions

A.C.S., M.P. and A. Schietinger conceived and designed the study. A.C.S., M.P., D.B., F.D., P.Z. and A. Schietinger conceived the computational analyses; D.B., F.D. and P.Z. performed all of the computational analyses. A.C.S., M.P., P.T., L.M., M.S., H.A. and S.S.C. carried out experiments. A.C.S., M.P., F.D., P.Z., D.B., S.S.C., C.A.K. and A. Schietinger interpreted data. S.C. and H.A. assisted with mouse breeding; T.W., A. Snyder, D.Z., M.D.H., M.R.F., E.A.C., H.Y.W. and C.A.K. provided human samples; N.A., Y.L. and N.K.A. contributed to the analysis of human samples. O.L. and M.S.G. provided help in establishing the knockout model. O.L., M.S.G. and J.K. provided mice. P.L. provided Listeria strains. A.C.S., M.P., F.D., P.Z., D.P. and A. Schietinger wrote the manuscript, with all authors contributing to writing and providing feedback.

Corresponding authors

Correspondence to Mary Philip or Andrea Schietinger.

Ethics declarations

Competing interests

C.A.K. is a consultant and/or advisor to Aleta Biotherapeutics, Bellicum Pharmaceuticals, Bristol-Meyers Squibb, Cell Design Labs, G1 Therapeutics, Klus Pharma, Obsidian Therapeutics and Rxi Therapeutics. C.A.K. receives research funding unrelated to this work from Kite/Gilead. M.D.H. has received research funding from Bristol-Myers Squibb; is paid as a consultant to Merck, Bristol-Myers Squibb, AstraZeneca, Genentech/Roche, Janssen, Nektar, Syndax, Mirati, and Shattuck Labs; has received travel support/honoraria from AstraZeneca and BMS; a patent has been filed by MSK related to the use of tumour mutation burden to predict response to immunotherapy (PCT/US2015/062208), which has received licensing fees from PGDx. A. Snyder is a current employee and owns stock in Merck. D.Z. reports grants from Merck, and consulting fees from Merck, Synlogic Therapeutics, Trieza Therapeutics, and Tesaro. D.Z. owns a patent concerning the use of Newcastle Disease Virus for cancer therapy. M.S.G. is a paid consultant to Vedanta Biosciences and Takeda for work unrelated to this study. E.A.C. is a consultant to Pfizer, Novartis, Genentech/Roche, Cota, Heron Therapeutics, and Bristol-Myers Squibb; has received travel support/honoraria from Pfizer and Novartis; and receives research funding from Genentech/Roche unrelated to this work.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 T cell differentiation during tumorigenesis.

a, Scheme of autochthonous liver cancer model to investigate tumour-specific CD8 T cell differentiation and dysfunction. AST×Cre liver cancer model. Cre-mediated deletion of the flox-stop cassette leads to TAG expression and tumour initiation. TAG-specific CD8 T cells isolated from TCRTAG transgenic mice recognize TAG epitope I (shown in red) on major histocompatibility complex (MHC) class I H-2Db. Tamoxifen-inducible Cre-ERT2 (AST×Cre-ERT2) or constitutive Alb-Cre (AST×Alb-Cre) mouse strains are used as indicated. b, Top, scheme of Listeria infection. Bottom, phenotypic characterization of Thy1.1+ effector and memory TCRTAG cells isolated from spleens 7 and more than 35 days after transfer into B6 mice followed by Listeria infection. Gating strategy is shown. KLRG1, CD127, CD44 and CD62L expression levels are shown. c, Naive congenically marked (Thy1.1+) TCRTAG CD8 T cells were adoptively transferred into (Thy1.2+) B6 mice and immunized with TAG-expressing Listeria strain, or were transferred into tumour-bearing (Thy1.2+) AST×Alb-Cre mice. T cells were isolated 7 or more than 20 days after transfer from either spleens (for effector and memory T cells after Listeria infection) or liver tumour lesions of AST×Alb-Cre mice. TOX expression was assessed by flow cytometry. TOX isotype is shown as a control for each sample. Naive TCRTAG cells are shown in grey as a control. d, Flow cytometric analysis of TCRTAG cells isolated from liver lesions of AST×Cre-ERT2 mice more than 20 days after transfer (red). TOX expression with PD-1, LAG-3, 2B4, CD39, TIGIT, TIM-3, CD101, CD38, CTLA4 and TCF-1 expression levels are shown. Naive TCRTAG cells are shown in grey as a control. eg, Intracellular IFNγ and TNF production of TCRTAG cells isolated at days 7–10 and day 60 after transfer into AST×Cre-ERT2 mice after 4-h ex vivo peptide stimulation with antigen-presenting cells (APCs) (from B6 spleens) (e), or peptide stimulation with in vitro (f, top) or in vivo (f, bottom) LPS-activated splenocytes (f), or stimulation with PMA and ionomycin (g). LPS-mediated activation of APCs was confirmed by flow cytometric analysis assessing the upregulation of MHC-II, CD80, CD86 and CD40 on CD11c+ APCs, CD11b+ APCs and CD19+ B cells (splenocytes). Memory TCRTAG cells are shown as controls. Gates are set based on no-peptide controls. All FACS plots are gated on CD8+Thy1.1+ TCRTAG cells (experiments in f and g are repeated twice). These data are representative of more than ten independent experiments.

Extended Data Fig. 2 Antigen-specific CD8 T cell differentiation during acute and chronic viral LCMV infections, acute Listeria infection, and during tumorigenesis.

a, Top, experimental scheme for acute L. monocytogenes (expressing TAG epitope I) infection (green) and AST×Cre-ERT2 liver tumorigenesis after treatment with tamoxifen (red). Bottom, experimental scheme for acute (Armstrong; blue) and chronic (clone 13; orange) infection with LCMV. b, Expression profiles of TOX, PD-1, LAG-3 and TCF-1 at various time points after infection or tamoxifen treatment. Relative MFI values are shown normalized to naive transgenic TCRP14 T cells (specific for the LCMV epitope GP33) or naive TCRTAG T cells (dashed grey line). c, Top, flow cytometric analysis of TOX, TCF-1, PD-1, LAG-3, 2B4, TIM-3, CD39, TIGIT, CD38 and CTLA4 expression levels of TCRTAG T cells after Listeria infection (green) or tamoxifen treatment (red). Bottom, flow cytometric analysis of TOX, TCF-1, PD-1, LAG-3, 2B4, TIM-3 and CD39 expression levels of GP33-specific T cells at indicated time points after infections with LCMV Armstrong (blue) and LCMV clone 13 (orange). Naive T cells are shown in grey as a control. Data are mean ± s.d. and are representative of two independent experiments with n = 2 (Listeria) and n = 2–3 (AST×Cre-ERT2; LCMV Armstrong; LCMV clone 13) mice per time point.

Source Data

Extended Data Fig. 3 Phenotypic and functional characterization of TILs from mouse and human tumours.

ac, TCRTRP2 (TRP2) and TCRPMEL (PMEL) TILs in mouse B16 melanoma tumours. a, TOX expression and TCF-1, PD-1, LAG-3, CD39, 2B4 and TIM-3 expression levels of TRP2 (Thy1.1+) TILs (red; top) and PMEL (Thy1.1+) TILs (red; bottom) isolated more than 15 days after adoptive transfer from established B16 melanoma tumours growing subcutaneously in B6 (Thy1.2+) mice. Naive CD8 T cells are shown in grey as a control. T cells are gated on CD8+Thy1.1+ cells. b, Intracellular IFNγ and TNF production of TRP2 and PMEL TILs after 4-h peptide stimulation ex vivo. c, Relative MFI values of TOX, TCF-1 and PD-1 of the indicated tumour models and TIL specificities shown on a log10 scale. Each symbol represents an individual mouse. Data are mean ± s.e.m of n = 2 (PMEL); n = 4 (TRP2); and n = 5 (TAG) mice, and are representative of two independent experiments. dg, Phenotypic characterization and TOX expression profiles of human TILs and PBMCs isolated from patients with melanoma, lung, breast and ovarian cancer. d, Flow cytometric analysis of PBMCs and TILs of patients with breast cancer. TOX expression of TILs and matched PBMC CD8+ T cells. Gating strategy is shown. CD45RO+PD-1hiCD39hi (TILs; red), CD45RO+PD-1hi (PBMCs; blue), CD45RO+PD-1lo (PBMCs; green), and CD45RA+CD45RO (naive PBMCs; grey). TOX isotypes are shown as controls for each sample. e, Top, TOX expression in human CD45RO+PD-1loCD39lo (dark blue) and CD45RO+PD-1hiCD39hi (red) TILs isolated from human primary melanoma. Isotypes are shown and data correspond to Fig. 1f. Bottom, TOX expression of TILs and matched PBMC CD8+ T cells from patients with melanoma. CD45RO+PD-1hi (TIL; red; n = 4), CD45RO+PD-1hi (PBMCs; blue, n = 4). TOX isotypes are shown as controls for each sample/patient. Bar plot shows MFI values for TOX. Each symbol represents an individual TIL and PBMC matched pair. f, TOX expression in human PD-1hi TILs isolated from human primary ovarian tumours. Flow plots are gated on CD8+CD45RO+PD-1hi T cells (red). CD8+CD45RO+ T cells from healthy donors are shown in grey. Gating strategy is shown. Each symbol represents a patient or healthy donor sample. g, TOX, CD39, TIM-3 and LAG-3 expression of CD8+CD45RO+PD-1hi (red) and CD8+CD45RO+PD-1lo (blue) TILs from human melanoma (n = 5), breast (n = 5) and lung (n = 6) tumours. Each symbol represents an individual matched PD-1hi/PD-1lo patient sample. Data are mean ± s.e.m. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, two-sided Student’s t-test. ns, not significant.

Source Data

Extended Data Fig. 4 Phenotypic, functional, transcriptional and epigenetic characterization of TCRTAG and TCROT1 cells in liver tumours.

a, Approximately 3 × 104 TCRTAG (TAG, red; Thy1.1+) and TCROT1 (OT1, black; Ly5.1+) T cells were transferred into wild-type B6 mice or liver tumour-bearing AST×Alb-Cre mice and immunized with 5 × 106 CFU of Listeria LmTAG-I-OVA. Three to four weeks after immunization, livers from AST×Alb-Cre mice and spleens from B6 mice were analysed for the presence of donor TAG and OT1 T cells by FACS; the percentages of CD8 T cells are shown. Expression of CD62L, CD44, CD69 and Ki67 of TAG and OT1 T cells. Naive T cells are shown in grey as a control. CD107 expression after 4-h TAG or OVA peptide stimulation of TAG and OT1 TILs isolated 3–4 weeks after transfer. Flow plots are gated on CD8+Thy1.1+ and CD8+Ly5.1+ cells. Data are representative of three independent experiments. b, Heat map of RNA-seq-normalized expression values (log2(counts per million)) across all samples (colour corresponds to z-scores) for genes differentially expressed between TAG and OT1 T cells (FDR < 0.05). c, GSEA of RNA-seq data generated from TAG and OT1 T cells isolated from AST×Cre liver lesions 3 weeks after adoptive transfer and Listeria infection. Gene sets used: T cell exhaustion during chronic viral infection20 (GEO accession GSE30962) and mutant/constitutively-active form of NFAT1-overexpressing CD8 T cells21. NES, normalized enrichment score. d, Venn diagrams showing the numbers and percentage of significantly opening (left) and closing (right) peaks between TAG and OT1 T cells (FDR < 0.05, log2-transformed fold change > 2). e, Genome browser view of ATAC-seq signal intensities of TAG and OT1 T cells at Pdcd1, Entpd1, Cd38 and Cd244 loci. Red or blue boxes indicate peaks that become significantly more accessible or inaccessible in TAG versus OT1 T cells, respectively. ATAC-seq peaks from naive TAG T cells are shown in grey as a control. f, Chromatin accessibility heat map for TAG and OT1 T cells. Each row represents one peak (differentially accessible between TAG and OT1 T cells; FDR < 0.05) displayed over a 2-kb window centred on the peak summit; regions were clustered using k-means clustering. Genes associated with individual clusters are highlighted.

Extended Data Fig. 5 Chromatin accessibility of the mouse and human Tox locus.

a, Accessibility of TOX and TCF7 loci in human tumour-infiltrating PD-1hiCD8+ T cells. ATAC-seq signal profiles of TOX (left) and TCF7 (right) in naive CD8+CD45RA+ (grey), CD8+CD45RO+CD62L+ central memory T cells (green) and CD8+CD45RO+PD-1hi TILs isolated from patients with melanoma and lung cancer (red). Red or blue boxes, respectively, indicate peaks that become accessible or inaccessible in PD-1hi TILs as compared to naive or memory T cells. Naive and memory T cells were isolated from PBMCs of healthy donors. b, c, NFAT1 binds to differentially accessible regions in the Tox locus in mice and pharmacological targeting of NFAT1 reduces TOX expression. b, Genome browser view of the Tox locus and numerous ATAC-seq and ChIP–seq tracks. On top, ATAC-seq signals of naive (N; grey), effector (E5, E7; green), memory (M; green), dysfunctional liver tumour-infiltrating TCRTAG cells (blue series, with D indicating the days after transfer when T cells were isolated from liver lesions) are shown. These data are from ref. 5. These are followed by newly generated ATAC-seq data from TCRTAG (TAG; orange) and TCROT1 (OT1; green) cells from AST×Cre liver lesions (as described in Fig. 2) as well as NFAT1 ChIP–seq tracks generated previously22 representing wild-type NFAT1 (blue) and mutant/constitutive active NFAT1-overexpressing T cells (red) (with and without stimulation). The vertical bars at the bottom of the plot represent statistically significantly enriched NFAT1-binding sites (peaks) as well as regions with statistically significantly changing accessibility between ATAC-seq of OT1 and TAG T cells. Red stars and pink boxes highlight NFAT1-binding sites that overlap with regions of increased chromatin accessibility in dysfunctional TCRTAG compared to TCROT1 cells. c, Pharmacological targeting of NFAT signalling decreases TOX expression in vivo. Naive TCRTAG (Thy1.1+) cells were transferred into AST×Cre-ERT2 (Thy1.2+) mice, which were treated with tamoxifen (Tam) 1 day later. At days 2–9, mice were treated with the calcineurin inhibitor FK506 (2.5 mg per kg per mouse; blue, n= 3) or PBS (control group; black, n = 3). At day 10, TCRTAG cells were isolated from livers and assessed for expression of CD44, TOX, PD-1 and TCF-1. Linear regression analysis of MFI values are shown. Naive TCRTAG cells are shown in grey as a control (n = 1). Each symbol represents an individual mouse. R2 = 0.6886 (TOX/TCF-1); R2 = 0.947 (TOX/PD-1); data are representative of two independent experiments. Dotted lines represent 95% confidence interval.

Source Data

Extended Data Fig. 6 Ectopic expression of TOX in T cells in vitro induces a molecular signature of T cell exhaustion.

a, Gating strategy for TOX–GFP-expressing (blue) and GFP-expressing (green) TCRTAG cells, and their corresponding TOX expression levels. TOX isotypes are shown for each sample. Naive TCRTAG cells adoptively transferred into AST×Cre mice and isolated from liver tumours after transfer (red), and naive TCRTAG cells (grey) are shown as controls. Inset numbers show MFI values. b, Heat map of RNA-seq expression values (row normalized log2(counts per million)) for genes differentially expressed between TOX–GFP and GFP TCRTAG cells (FDR < 0.10). c, Relative expression of selected genes as determined by digital droplet PCR. Data show raw droplet counts normalized to the housekeeping gene, Gapdh; n = 2 (TOX–GFP, GFP). d, Flow cytometric analysis of PD-1, 2B4, CD160, CD39 and TIM-3 expression levels of TOX–GFP (n = 3) or GFP (n = 3)-expressing TCRTAG cells. e, FACS analysis of TOX expression (left) on day 6 after spinfection of TCRTAG cells transduced with TOX–GFP (n = 2) or GFP (n = 2), and cytokine production (right) after 4-h peptide stimulation. f, Percentage of Ki67+ cells (top), and GZMB+ cells (with or without 4-h peptide stimulation) (bottom) in TCRTAG cells transduced with TOX–GFP (blue, n = 3) or GFP (green, n = 3). Naive TCRTAG cells are shown in grey as a control (n = 1). Data are mean ± s.e.m and representative of two independent experiments (n = 3 per experiment, with n representing a biological replicate/individual transduced spleen). *P ≤ 0.05, **P ≤ 0.01, two-sided Student’s t-test. g, GSEA of TCRTAG cells transduced with TOX–GFP or GFP. T cell exhaustion gene sets used: tumour-specific T cell dysfunction5 (left), and T cell exhaustion during chronic viral infection20 (GEO accession GSE30962) (right). Corresponding heat maps with selected genes with significant enrichment scores are shown below.

Source Data

Extended Data Fig. 7 Phenotypic and functional characterization of TOX wild-type and knockout TCRTAG mice.

a, Mouse strains generated and used in this study. We define wild type as littermate controls TCRTAG;dLck-Cre;Tox+/+ or TCRTAG;Toxfl/fl. We define knockout as TOX-deficient T cells from TCRTAG;dLck-Cre;Toxfl/fl mice. b, Thymocytes and peripheral CD8 T cells from knockout mice develop normally. CD4 and CD8 flow staining of thymocytes isolated from knockout (red, n = 5) or littermate controls (grey, n= 3). TCR Vβ7 and CD44 expression, and enumeration of single-positive CD8+ thymocytes from knockout and wild-type mice. c, Enumeration of total splenocytes (n = 5) and CD8+ splenocytes (n = 4) of knockout and wild-type mice. d, e, TOX is not required for effector and memory CD8 T cell differentiation during acute Listeria infection. d, Approximately 1 × 105 congenically marked naive wild-type and knockout TCRTAG T cells were adoptively transferred into B6 mice, and infected with Listeria 1 day later. Flow cytometric analysis of CD44, CD62L, CD127 and KLRG1 expression directly ex vivo (inset numbers show percentage in respective quadrants) of wild-type and knockout effector TCRTAG cells isolated from spleens of LmTAG-immunized B6 mice 7 days after immunization. e, Flow cytometric analysis of CD44, CD62L, CD127 and KLRG1 expression of wild-type and knockout memory TCRTAG cells isolated from spleens of LmTAG-immunized B6 mice 3 weeks after immunization. Right, intracellular IFNγ and TNF production after 4-h ex vivo TAG peptide stimulation of wild-type (n = 4) and knockout (n = 4) memory TCRTAG T cells. Flow plots are gated on CD8+Thy1.1+ cells. Data are representative of at least three independent experiments. fi, Phenotypic and functional characterization of TOX wild-type and knockout TCRTAG cells differentiating in developing liver tumours of AST×Cre mice. f, Top, CD44, CD69, CD25 and PD-1 expression and CellTrace Violet (CTV) dilution of adoptively transferred, CTV-labelled naive wild-type (black) or knockout (red) TCRTAG cells isolated from livers of AST×Cre mice 3 days after transfer. Data are representative of three independent experiments. Middle, expression of CD44 and proliferation (CTV dilution) of wild-type (black) or knockout (red) TCRTAG cells isolated from AST×Cre liver lesions 5 days after transfer. CTV-labelled TCRTAG cells transferred into B6 control mice are shown in grey as controls transferred and isolated at the same time points. Bottom, PD-1 and LAG-3 expression together with TOX expression of wild-type and knockout TCRTAG cells isolated from the livers of AST×Cre mice 8 days after transfer. All FACS plots are gated on CD8+ and Thy1.1+. g, Flow cytometric analysis of intracellular IFNγ and TNF production (top), CD107 degranulation (middle), and GZMB expression (bottom) of day 7–10 wild-type (black) or knockout (red) TCRTAG cells after 4-h peptide stimulation. h, i, PMA and ionomycin stimulation (h) or 4-h peptide stimulation using in vivo LPS-activated APCs (i). Each sample is gated on its respective no-peptide control. All flow plots are gated on CD8+Thy1.1+ T cells. Data are representative of three independent experiment and shown as mean ± s.e.m. P values determined by two sided Student’s t-test.

Source Data

Extended Data Fig. 8 TOX wild-type and knockout TCRTAG cells reveal differences in genes and proteins associated with apoptosis.

a, Flow cytometric analysis of PD-1 (n = 3 (KO); n = 5 (WT)), LAG-3 (n = 4 (KO); n = 5 (WT)), CD38 (n = 4 (KO); n = 5 (WT)), 2B4 (n = 2 (KO); n = 3 (WT)), and TCF-1 (n = 4 (KO); n = 5 (WT)), expression levels in wild-type (black) or knockout (red) TCRTAG cells isolated from liver lesions approximately 3 weeks after adoptive transfer. Data are representative of at least three independent experiments. b, Flow cytometric analysis of TOX wild-type (black) and knockout (red) TCRTAG cells isolated 7–10 days after transfer from AST×Cre liver lesions. BIM, BCL-2 and BCL-xL expression levels were assessed directly ex vivo. Each pair of symbols represents an individual mouse (n = 4). Data are representative of two independent experiments. c, Flow cytometric analysis of active caspases 3 and 7 in TOX wild-type (black) and knockout (red) TCRTAG cells. These data are combined results of two experiments (n = 11). Each pair of symbols represents an individual mouse. d, Representative histograms and quantification of annexin V+ wild-type (black, n = 3) and knockout (red, n = 3) TCRTAG cells isolated 7–10 days after transfer from AST×Cre liver lesions. e, GSEA of DEGs between TOX wild-type and knockout T cells. ‘Hallmark_apoptosis’ and ‘wikipathways_MM_apoptosis_WP254’ gene sets show normalized enrichment score (NES) of −1.52 and −1.1, respectively, and the corresponding heat maps of genes with significant enrichment scores are shown. Data are mean ± s.e.m. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, two-sided Student’s t-test.

Source Data

Extended Data Fig. 9 TOX wild-type and knockout TCRTAG cells reveal transcriptional and chromatin accessibility changes.

a, Heat map of RNA-seq expression (row normalized log2(counts per million)) for genes differentially expressed between TOX wild-type and knockout TCRTAG cells (FDR < 0.05). b, GSEA between wild-type and knockout TCRTAG. T cell exhaustion gene sets used: tumour-specific T cell dysfunction5 (left) and T cell exhaustion during chronic viral infection20 (GEO accession GSE30962) (right). Selected genes with significant enrichment score are listed. c, Pie chart showing the proportions of reproducible ATAC-seq peaks in indicated regions for all peaks within the atlas. d, Venn diagrams showing the numbers and percentages of significantly opening (top) and closing (bottom) peaks between TOX wild-type and knockout TCRTAG cells (FDR < 0.05, log2-transformed fold change > 2). e, Gains and losses of regulatory elements for the top 100 most DEGs between TOX wild-type and knockout TCRTAG cells that were part of the gene set of tumour-specific T cell dysfunction5. The plot is divided into top and bottom 50 genes with the highest and lowest respective log2-transformed fold change of gene expression. Each gene is illustrated by a stack of diamonds, in which each diamond represents a region of high chromatin accessibility (peak) overlapping with the locus of the respective gene. Red diamonds denote peaks that are more accessible in wild-type (and less accessible in TOX KO) T cells; blue diamonds denote peaks that are more accessible in TOX knockout T cells. f, Molecular function (GO terms) enriched in genes associated with peaks that are more accessible in TOX knockout versus wild-type T cells. g, ATAC-seq signal profiles across the Pdcd1 and Entpd1 loci. Peaks less accessible in knockout TCRTAG cells are highlighted in red.

Source Data

Extended Data Fig. 10 Comparison of functional TOXlow OT1 and dysfunctional TOX knockout T cells in tumours with proposed model on the role of TOX in tumour-specific CD8 T cell exhaustion and dysfunction.

a, DEGs of the TAG versus OT1 comparison (see Fig. 2) were compared with DEGs of the wild-type versus TOX-knockout comparison (see Fig. 4). There were 389 genes identified to be significantly differentially expressed in both (WT vs KO and TAG vs OT1). b, Heat map of normalized expression values (log2(counts per million)) across all samples (colour corresponds to z-scores) for these 389 genes. Selected genes of interest are highlighted. c, Proposed model on the role of TOX in tumour-specific CD8 T cell exhaustion and dysfunction. Top, antigen-specific T cells in solid tumours are continuously triggered with tumour antigen. Chronic TCR stimulation leads to NFAT-mediated expression of TOX. TOX induces a transcriptional and epigenetic program and phenotype associated with exhaustion, including the expression of numerous inhibitory receptors (for example, PD-1, LAG-3, 2B4, CD39 and CD38) and downregulation of transcription factors (such as TCF-1). The TOX-mediated exhaustion program prevents T cells from overactivation or overstimulation and activation-induced cell death. Bottom, TOX-deficient T cells do not upregulate inhibitory receptors, become overstimulated or overactivated, and eventually undergo activation-induced cell death. Despite their non-exhausted phenotype, TOX-deficient T cells are dysfunctional.

Supplementary information

Reporting Summary

Supplementary Table 1

Summary of differentially expressed genes by RNA-seq.

Supplementary Table 2

Summary of differentially accessible peaks by ATAC-seq.

Supplementary Table 3

Summary of differentially expressed genes by RNA-seq.

Supplementary Table 4

Summary of NFAT1 ChIP-seq samples from Martinez et al. (GSE64407) used for analysis.

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Scott, A.C., Dündar, F., Zumbo, P. et al. TOX is a critical regulator of tumour-specific T cell differentiation. Nature 571, 270–274 (2019). https://doi.org/10.1038/s41586-019-1324-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-019-1324-y

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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