Chromatin states define tumour-specific T cell dysfunction and reprogramming (original) (raw)

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References

  1. Hellström, I., Hellström, K. E., Pierce, G. E. & Yang, J. P. Cellular and humoral immunity to different types of human neoplasms. Nature 220, 1352–1354 (1968)
    Article PubMed ADS Google Scholar
  2. Khalil, D. N., Smith, E. L., Brentjens, R. J. & Wolchok, J. D. The future of cancer treatment: immunomodulation, CARs and combination immunotherapy. Nat. Rev. Clin. Oncol. 13, 273–290 (2016)
    Article CAS PubMed PubMed Central Google Scholar
  3. Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014)
    Article PubMed PubMed Central CAS Google Scholar
  4. Kelderman, S., Schumacher, T. N. & Haanen, J. B. Acquired and intrinsic resistance in cancer immunotherapy. Mol. Oncol. 8, 1132–1139 (2014)
    Article CAS PubMed PubMed Central Google Scholar
  5. Rizvi, N. A. et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015)
    Article CAS PubMed PubMed Central ADS Google Scholar
  6. 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 PubMed PubMed Central Google Scholar
  7. Zhang, J. A., Mortazavi, A., Williams, B. A., Wold, B. J. & Rothenberg, E. V. Dynamic transformations of genome-wide epigenetic marking and transcriptional control establish T cell identity. Cell 149, 467–482 (2012)
    Article CAS PubMed PubMed Central Google Scholar
  8. Scharer, C. D., Barwick, B. G., Youngblood, B. A., Ahmed, R. & Boss, J. M. Global DNA methylation remodeling accompanies CD8 T cell effector function. J. Immunol. 191, 3419–3429 (2013)
    Article CAS PubMed Google Scholar
  9. Russ, B. E. et al. Distinct epigenetic signatures delineate transcriptional programs during virus-specific CD8+ T cell differentiation. Immunity 41, 853–865 (2014)
    Article CAS PubMed PubMed Central Google Scholar
  10. Shih, H. Y. et al. Developmental acquisition of regulomes underlies innate lymphoid cell functionality. Cell 165, 1120–1133 (2016)
    Article CAS PubMed PubMed Central Google Scholar
  11. 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)
    Article CAS PubMed PubMed Central Google Scholar
  12. Staveley-O’Carroll, K. et al. In vivo ligation of CD40 enhances priming against the endogenous tumor antigen and promotes CD8+ T cell effector function in SV40 T antigen transgenic mice. J. Immunol. 171, 697–707 (2003)
    Article PubMed Google Scholar
  13. Brockstedt, D. G. et al. _Listeria_-based cancer vaccines that segregate immunogenicity from toxicity. Proc. Natl Acad. Sci. USA 101, 13832–13837 (2004)
    Article CAS PubMed ADS PubMed Central Google Scholar
  14. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010)
    Article CAS PubMed PubMed Central Google Scholar
  15. Araki, Y., Fann, M., Wersto, R. & Weng, N. P. Histone acetylation facilitates rapid and robust memory CD8 T cell response through differential expression of effector molecules (eomesodermin and its targets: perforin and granzyme B). J. Immunol. 180, 8102–8108 (2008)
    Article CAS PubMed Google Scholar
  16. Denton, A. E., Russ, B. E., Doherty, P. C., Rao, S. & Turner, S. J. Differentiation-dependent functional and epigenetic landscapes for cytokine genes in virus-specific CD8+ T cells. Proc. Natl Acad. Sci. USA 108, 15306–15311 (2011)
    Article CAS PubMed ADS PubMed Central Google Scholar
  17. Best, J. A. et al. Transcriptional insights into the CD8+ T cell response to infection and memory T cell formation. Nat. Immunol. 14, 404–412 (2013)
    Article CAS PubMed PubMed Central Google Scholar
  18. Peng, M. et al. Aerobic glycolysis promotes T helper 1 cell differentiation through an epigenetic mechanism. Science 354, 481–484 (2016)
    Article CAS PubMed PubMed Central ADS Google Scholar
  19. Cuylen, S. et al. Ki-67 acts as a biological surfactant to disperse mitotic chromosomes. Nature 535, 308–312 (2016)
    Article CAS PubMed PubMed Central ADS Google Scholar
  20. Kaech, S. M. & Cui, W. Transcriptional control of effector and memory CD8+ T cell differentiation. Nat. Rev. Immunol. 12, 749–761 (2012)
    Article CAS PubMed PubMed Central Google Scholar
  21. Sen, D. R. et al. The epigenetic landscape of T cell exhaustion. Science 354, 1165–1169 (2016)
    Article CAS PubMed PubMed Central ADS Google Scholar
  22. Pauken, K. E. et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354, 1160–1165 (2016)
    Article CAS PubMed PubMed Central ADS Google Scholar
  23. 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 PubMed PubMed Central Google Scholar
  24. Macian, F. NFAT proteins: key regulators of T-cell development and function. Nat. Rev. Immunol. 5, 472–484 (2005)
    Article CAS PubMed Google Scholar
  25. Martinez, G. J. et al. The transcription factor NFAT promotes exhaustion of activated CD8+ T cells. Immunity 42, 265–278 (2015)
    Article CAS PubMed PubMed Central Google Scholar
  26. Teague, R. M. et al. Interleukin-15 rescues tolerant CD8+ T cells for use in adoptive immunotherapy of established tumors. Nat. Med. 12, 335–341 (2006)
    Article CAS PubMed Google Scholar
  27. Li, Y. et al. MART-1-specific melanoma tumor-infiltrating lymphocytes maintaining CD28 expression have improved survival and expansion capability following antigenic restimulation in vitro. J. Immunol. 184, 452–465 (2010)
    Article CAS PubMed Google Scholar
  28. 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 CAS PubMed ADS Google Scholar
  29. 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 CAS PubMed ADS Google Scholar
  30. Gattinoni, L. et al. Wnt signaling arrests effector T cell differentiation and generates CD8+ memory stem cells. Nat. Med. 15, 808–813 (2009)
    Article CAS PubMed PubMed Central Google Scholar
  31. Schietinger, A., Delrow, J. J., Basom, R. S., Blattman, J. N. & Greenberg, P. D. Rescued tolerant CD8 T cells are preprogrammed to reestablish the tolerant state. Science 335, 723–727 (2012)
    Article CAS PubMed PubMed Central ADS Google Scholar
  32. Waugh, K. A. et al. Molecular profile of tumor-specific CD8+ T cell hypofunction in a transplantable murine cancer model. J. Immunol. 197, 1477–1488 (2016)
    Article CAS PubMed Google Scholar
  33. 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 PubMed Google Scholar
  34. 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 PubMed Google Scholar
  35. Engels, B. et al. Relapse or eradication of cancer is predicted by peptide-major histocompatibility complex affinity. Cancer Cell 23, 516–526 (2013)
    Article CAS PubMed PubMed Central Google Scholar
  36. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie2. Nat. Methods 9, 357–359 (2012)
    Article CAS PubMed PubMed Central Google Scholar
  37. Adey, A. et al. Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition. Genome Biol. 11, R119 (2010)
    Article CAS PubMed PubMed Central Google Scholar
  38. Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008)
    Article PubMed PubMed Central CAS Google Scholar
  39. Zigler, C. M. & Belin, T. R. The potential for bias in principal causal effect estimation when treatment received depends on a key covariate. Ann. Appl. Stat. 5, 1876–1892 (2011)
    Article MathSciNet PubMed PubMed Central MATH Google Scholar
  40. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)
    Article PubMed PubMed Central Google Scholar
  41. Lawrence, M. et al. Software for computing and annotating genomic ranges. PLOS Comput. Biol. 9, e1003118 (2013)
    Article CAS PubMed PubMed Central Google Scholar
  42. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010)
    Article CAS PubMed PubMed Central Google Scholar
  43. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011)
    Article CAS PubMed PubMed Central Google Scholar
  44. Bailey, T. L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009)
    Article CAS PubMed PubMed Central Google Scholar
  45. Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014)
    Article CAS PubMed PubMed Central Google Scholar
  46. Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011)
    Article CAS PubMed PubMed Central Google Scholar
  47. Hinrichs, A. S. et al. The UCSC Genome Browser Database: update 2006. Nucleic Acids Res. 34, D590–D598 (2006)
    Article CAS PubMed Google Scholar
  48. Denas, O. et al. Genome-wide comparative analysis reveals human-mouse regulatory landscape and evolution. BMC Genomics 16, 87 (2015)
    Article PubMed PubMed Central Google Scholar
  49. Dobin, A . et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013)
    Article CAS PubMed Google Scholar
  50. McLean, C. Y. et al. GREAT improves functional interpretation of _cis_-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010)
    Article CAS PubMed PubMed Central Google Scholar

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Acknowledgements

We thank J. van der Veeken, V. Krisnawan, Y. Pritykin, and B. Gasmi for discussions and technical support; S. Reiner for discussions; and the MSKCC Flow Cytometry Core and Animal Facility. T.M., M.H., J.D.W., and A.S. are members of the Parker Institute for Cancer Immunotherapy. This work was supported by NIH-NCI grants R00 CA172371 (to A.S.), K08 CA158069 (to M.P.), and U54 CA209975 (to C.S.L. and A.S.), NHGRI grant U01 HG007893 (to C.S.L.), V Foundation for Cancer Research (to A.S.), the William and Ella Owens Medical Research Foundation (to A.S.), the Josie Robertson Young Investigator Award (to A.S.), and the MSKCC 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.

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Authors and Affiliations

  1. Immunology Program, Memorial Sloan Kettering Cancer Center, New York, 10065, New York, USA
    Mary Philip, Ellen L. Horste, Steven Camara, Mojdeh Shakiba, Andrew C. Scott & Andrea Schietinger
  2. Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, 10065, New York, USA
    Lauren Fairchild & Christina S. Leslie
  3. Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medical College, New York, 10065, New York, USA
    Lauren Fairchild
  4. Integrated Genomics Operation, Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, 10065, New York, USA
    Liping Sun & Agnes Viale
  5. Weill Cornell Medical College, Cornell University, New York, 10065, New York, USA
    Mojdeh Shakiba, Andrew C. Scott, Taha Merghoub, Matthew D. Hellmann, Jedd D. Wolchok & Andrea Schietinger
  6. Aduro Biotech, Inc., Berkeley, 94720, California, USA
    Peter Lauer
  7. Melanoma and Immunotherapeutics Service, Memorial Sloan Kettering Cancer Center, New York, 10065, New York, USA
    Taha Merghoub & Jedd D. Wolchok
  8. Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, 10065, New York, USA
    Matthew D. Hellmann
  9. Ludwig Center for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, 10065, New York, USA
    Jedd D. Wolchok

Authors

  1. Mary Philip
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  2. Lauren Fairchild
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  3. Liping Sun
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  4. Ellen L. Horste
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  5. Steven Camara
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  6. Mojdeh Shakiba
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  7. Andrew C. Scott
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  8. Agnes Viale
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  9. Peter Lauer
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  10. Taha Merghoub
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  11. Matthew D. Hellmann
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  12. Jedd D. Wolchok
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  13. Christina S. Leslie
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  14. Andrea Schietinger
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Contributions

M.P. and A.S. conceived and designed the study, carried out experiments, and analysed and interpreted data. L.F. designed and performed all high-throughput computational analyses; C.S.L. designed and supervised computational analyses; E.H., S.C., M.S., and A.C.S. assisted with experiments; L.S. and A.V. performed ATAC-seq; P.L. generated Listeria strains; and T.M., M.H., and J.D.W. provided human samples. M.P. and A.S. wrote the manuscript, with all authors contributing to writing and providing feedback.

Corresponding author

Correspondence toAndrea Schietinger.

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The authors declare no competing financial interests.

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Reviewer Information Nature thanks J. Wherry and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Phenotypic and functional characteristics of naive TCRTAG CD8 T cells differentiating to effector and memory T cells during acute Listeria infection.

Naive TCRTAG cells (N; Thy1.1+) were transferred into B6 (Thy1.2+) mice, which were immunized with _Lm_TAG one day later. At days 5, 7, and 60+ after _Lm_TAG, effector (E5 and E7), and memory (M) T cells were isolated from spleens and assessed for phenotype and function. Flow cytometric analysis of CD44, CD62L, IL7Rα, TBET, and GZMB expression directly ex vivo (upper panel; inset numbers show MFI), and intracellular IFNγ and TNFα production and CD107 expression after 4 h of ex vivo TAG peptide stimulation (lower panel). Flow plots are gated on CD8+Thy1.1+ cells. For cytokine production, in grey are shown no-peptide control cells. (n = 8 total, with n = 2 per cell state). Each symbol represents an individual mouse. Data show mean ± s.e.m.; P values calculated using unpaired, two-tailed Student’s _t_-test. Data are representative of more than four independent experiments.

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Extended Data Figure 2 Fragment length distribution plots of ATAC-seq samples.

a, b, Plots are shown for all mouse (a) and human (b) CD8 T cell ATAC-seq samples displaying fragment length (bp; x axis) and read counts (y axis). (S1, S2, S3 represent replicates per sample group.)

Extended Data Figure 3 Epigenetic and transcriptional regulation of normal CD8 differentiation.

a, ATAC-seq data reveals massive chromatin remodelling during normal CD8 T cell differentiation. MA plot of naive (N) and day 5 effectors (E5) showing log2 ratios of peak accessibility (E5/N) versus mean read counts for all atlas peaks. Significantly differentially accessible peaks are shown in red (FDR < 0.05). b, Epigenetic and transcriptional regulation of CD8 effector genes. ATAC-seq (left) and RNA-seq (right) signal profiles of Prf1 and Tnf in naive, effectors (E5 and E7), and memory (M) TCRTAG cells during acute _Lm_TAG infection. c, d, Epigenetic and transcriptional regulation of early CD8 response genes in TCRTAG cells during acute listeria infection. Published expression data from the Immunological Genome Project (ref. 17; GSE15907) were used; early-response genes increase in expression within the first 12–24 h and late-response genes increase expression 24–48 h after naive T cells encounter _Lm_OVA as determined in ref. 17. c, Cumulative distribution function of peak accessibility changes between N and E5. Peaks associated with early-response genes show fewer changes in accessibility as compared to peaks associated with late-response genes. The black line shows all peaks accessible in N or E5, the red line shows peaks associated with early-response genes and the blue line shows peaks associated with late-response genes. d, ATAC-seq signal profiles (left) and RNA expression (right) of the early response genes Ldha (top) and Mki67 (bottom) in N, E5/E7, and M TCRTAG cells during acute _Lm_TAG infection (blue line; GSE89309, current data set) overlaid with expression data from ref. 17/Immunological Genome Project (red line).

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Extended Data Figure 4 Chromatin peak accessibility changes during normal and dysfunctional CD8 T cell differentiation.

a, Number of DESeq-determined chromatin peak accessibility changes during each transition during normal CD8 T cell differentiation (Listeria infection) (right) and CD8 T cell differentiation to dysfunction during tumorigenesis (left) broken down by log2(FC) > 2, log2(FC) = 1–2, and log2(FC) < 1. **b**, Chromatin accessibility peaks gained or lost during normal and dysfunctional CD8 T cell differentiation were mainly found in intergenic and intronic regions. Pie charts showing the proportions of reproducible ATAC-seq peaks in exonic, intronic, intergenic, and promoter regions (left, distribution for all peaks in the atlas). Green box: normal CD8 T cell differentiation during _Lm_TAG immunization; distribution for common and variably accessible peaks in N, E5, E7, and M functional CD8 T cells. Blue box: differentiation to dysfunction in progressing tumours; distribution for common and variably accessible peaks in N, L5, L7, L14, L21, L28, L35, and L60+. Variable: significant change in at least one cell type comparison (FDR < 0.05, log2(FC) > 1). Common: no change in any cell type comparison. c, Venn diagrams show the number of significantly changed peaks during the transition from naive (N) to day 5 effectors (E5) TCRTAG cells during acute listeria _Lm_TAG infection versus N to L5 early malignant lesion-infiltrating TCRTAG cells (FDR < 0.05, log2(FC) > 2). Upper, Venn diagram shows opening peaks; lower, Venn diagram shows closing peaks. d, Selected biological process (BP) Gene Ontology (GO) terms enriched in peaks open in L5 relative to E5 as determined through GREAT analysis.

Extended Data Figure 5 NFATC1 targets become significantly more accessible during differentiation to dysfunction in early malignant lesions as compared to normal effector differentiation.

a, The 20 most significantly enriched transcription factor motifs in peaks opening (red) and closing (blue) between L5 and E5. b, Scatterplot comparing the changes in peak accessibility for all differentially accessible peaks containing the NFATC1 motif during the transition from naive (N) to day 5 effectors (E5) TCRTAG cells during acute listeria _Lm_TAG infection versus N to L5 in pre-malignant lesions (FDR < 0.05, log2(FC) > 1). Highlighted are NFATC1 target peaks associated with genes encoding negative regulatory transcription factors and inhibitory receptors. Some genes, for example, Cblb and Klf4, had multiple NFATC1 target peaks, including peaks that decreased in accessibility. c, Genes with more accessible NFATC1 target peaks during differentiation to dysfunction in malignant lesions show increased expression levels. Gene expression for genes with peaks in sector 1 and sector 2, with increased and decreased accessibility in L5 versus E5, respectively. Heat maps show RNA-seq expression data (row-normalized) for differentially expressed (P < 0.01, log2(FC) > 1) genes with NFATC1 target peaks contained in sector 1 (red box) or sector 2 (blue box) of scatterplot presented b. The majority of sector 1 genes (195 out of 223, 87%) revealed increased expression in dysfunctional TST as compared to E5, whereas the majority of sector 2 genes (21 out of 33, 63%) had decreased expression. Genes are clustered by row according to expression across the samples. Interestingly, although many genes in sector 1 had transiently increased expression in L5 and L7 (red box, upper left), many genes increased in expression at later stages of tumorigenesis at L14 and beyond (red box, upper right). This suggests that NFATC1 activation of downstream targets (negative regulators of T cell function) may not only induce early dysfunction, but may cause or contribute to the transition from plastic to fixed dysfunction.

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Extended Data Figure 6 Epigenetic and transcriptional changes during the L7 to L14 transition.

a, Transcription factor footprinting in chromatin accessible regions. ATAC cut site distributions show footprints for CTCF, LEF1, NFATC1, and TCF7 in naive CD8 T cells. Shown is the mean number of ATAC cut sites on the forward (red) or reverse (blue) strand 100 bp up and downstream of the transcription factor motif site, calculated for atlas peaks predicted by FIMO to be bound by the respective transcription factor (P < 10−4). b, TCF1 expression (MFI; mean fluorescence intensity). Each symbol represents individual mouse. Mean ± s.e.m. shown; ***P ≤ 0.0001 (Student’s _t_-test). c, Selected biological processes (BP) (gene ontology (GO) terms) enriched in genes which significantly lost chromatin accessibility during the L7 to L14 transition as determined through GREAT analysis. d, Gain and losses of regulatory elements for top 50 most differentially expressed genes associated with TCR signalling during the L7 to L14 transition. Top 25 genes associated with TCR signalling with highest and lowest logFC gene expression changes are shown. Each gene is illustrated by a stack of diamonds, where each diamond represents a chromatin peak associated with the gene. Red diamonds denote peaks gained in the transition, blue diamonds denote peaks that were lost.

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Extended Data Figure 7 Pharmacological targeting of NFAT and Wnt/β-catenin signalling prevents TST differentiation to the fixed dysfunctional state in vivo.

a, Experimental scheme. Naive TCRTAG cells (Thy1.1+) were transferred into AST-Cre-ERT2 (Thy1.2+) mice which were treated with tamoxifen (tam) one day later. At days 2–9 mice were treated with the calcineurin inhibitor FK506 (2.5 mg kg−1 per mouse) alone (FK506 treatment group; orange), or in combination with the GSK3β inhibitor TWS119 (0.75 mg per mouse; days 5–8) (FK506 + TWS119 treatment group; green), or PBS/DMSO (control group; blue) as indicated. At day 10, TCRTAG cells were isolated from livers and assessed for phenotype and function. b, Flow cytometric analysis of CD44, PD1, LAG3, TCF1, and EOMES expression of TCRTAG cells. c, Production of IFNγ and TNFα by TCRTAG cells isolated at day 10 (left panel; ex vivo), and after 3 days IL-15 in vitro culture (right panel). Each symbol represents an individual mouse. Data show mean ± s.e.m.; P values calculated using unpaired two-tailed _t_-test. d, Representative flow cytometric analysis of CD38 and CD101 expression of TCRTAG cells (numbers indicate %); CD38, CD101 and CD5 expression. Each symbol represents an individual mouse. Data show mean ± s.e.m.; P values calculated using unpaired two-tailed _t_-test. These data are representative of 2 independent experiments (with total n = 10 for experiment 1; n = 9, experiment 2).

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Extended Data Figure 8 Epigenetic and expression dynamics of membrane proteins and transcription factors associated with T cell dysfunction.

a, ATAC-seq signal profile across the Cd38 loci with ‘state 2’ uniquely accessible peaks highlighted in pink; activation-associated peaks highlighted in blue. b, Expression profiles of N, L5, L7, L14, and L60+ TCRTAG cells for CD101 versus CD38, TCF versus PD1, and TCF1 versus CD38 by flow cytometric analysis. c, Expression of transcription factors and other proteins on tumour-specific TCRTAG T cells over the course of tumorigenesis (MFI; mean fluorescence intensity). Each symbol represents an individual mouse. Data shows mean ± s.e.m. (bottom panel). Representative flow histogram overlays are shown. d–f, TCROT1 TST in established B16-OVA tumours enter plastic and fixed dysfunctional states. d, Immunophenotype of and cytokine production by TCROT1 cells re-isolated from established B16-OVA tumours 5 (D5) and 13 (D13) days after transfer. e, CD38, CD101 and CD5 expression on day 5 and day 13 TCROT1 cells. f, Cytokine production by day 5 and day 21 TCROT1 cells after 3 days of IL-15 in vitro culture. Each symbol represents individual mouse. Mean ± s.e.m. shown; *P = 0.03, **P = 0.002, ***P ≤ 0.0003 (Student’s _t_-test).

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Extended Data Figure 9 Chromatin state dynamics of memory TCRTAG cells differentiating to the dysfunctional state in solid tumours.

a, PD1 and LAG3 expression and cytokine production of memory TCRTAG cells in liver tumours. Each symbol represents individual mouse. Mean ± s.e.m. shown; *P = 0.03, **P = 0.006, ***P < 0.0001 (Student’s _t_-test); representative of four independent experiments. **b**, Numbers of ATAC-seq peaks significantly opening or closing (FDR < 0.05) during each transition as memory TCRTAG cells differentiate to the dysfunctional state 7, 14, and 35 days after transfer into hepatocellular-carcinoma-tumour bearing AST-Alb-Cre mice with ((+); left) and without ((−); right) listeria _Lm_TAG immunization; peaks opening (red), peaks closing (blue). **c**, Principal component analysis of peak accessibility during naive TCRTAG cells differentiation in acute infection (green), early tumorigenesis (blue), and memory TCRTAG cells in established hepatocellular carcinomas (red). Circles, with _Lm_TAG immunization; diamonds, no _Lm_TAg immunization. **d**, Chromatin accessibility heat map. Each row represents 1 of 11,698 selected peaks (differentially accessible between any sequential cell comparison; FDR < 0.05, log2(FC) > 2). Shown are ±1 kb from the peak summit (2 kb total per region). e, ATAC-seq signal profiles of Pdcd1, Ctla4, Cd38, Tcf7, and Ifng genes of naive (N; grey), memory (M; green), L7, L14, L35 (blue series), and ML7, ML14, and ML35 (red series) TCRTAG cells. Pink boxes highlight peaks that become accessible in dysfunctional T cells compared to naive and memory; blue boxes highlight peaks that become inaccessible in dysfunctional TCRTAG cells compared to naive and memory TCRTAG cells. f, CD38, CD101, CD30L, and CD5 expression on ML7, ML14, ML21. Inset numbers show MFI.

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Extended Data Figure 10 Chromatin states of human PD1hi tumour-infiltrating CD8+ T cells and model for CD8 TST differentiation and dysfunction in tumours.

a, Sorting scheme of peripheral blood lymphocytes for naive (N), effector memory (EM), central memory (CM) CD8 T cell populations (left), and PD1hi CD8 TIL from patients with melanoma or non-small-cell lung cancer. b, Differentially accessible ATAC-seq peaks grouped by DESeq-defined differential accessibility pattern. Each column represents one biological replicate. Samples shown include CD45RA+CD45RO− (naive; grey), CD45RA−CD45RO+CD62L− (effector memory; light green) and CD45RA−CD45RO+CD62L+ (central memory; dark green) peripheral blood CD8+ T cells from healthy donors, and CD45RA−CD45RO+PD1hiCD8+ T cells isolated and flow-sorted from human melanoma and lung tumours (PD1hi TIL; blue). Open, accessible chromatin regions are presented in red; inaccessible chromatin regions are presented in blue. c, ATAC-seq signal profiles of SELL in naive, effector memory, and central memory. Blue boxes highlight peaks that remain accessible in central memory or become inaccessible in effector member compared to naive respectively. d, ATAC-seq signal profiles of IFNG, EGR2, CD5, and CTLA4. Pink and blue boxes highlight peaks that become accessible or inaccessible in PD1hi TIL compared to naive or central memory, respectively. e, Model for tumour-specific CD8 T cell differentiation and dysfunction in tumours.

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Philip, M., Fairchild, L., Sun, L. et al. Chromatin states define tumour-specific T cell dysfunction and reprogramming.Nature 545, 452–456 (2017). https://doi.org/10.1038/nature22367

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