Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates (original) (raw)

References

  1. Rosenberg, S. A. & Restifo, N. P. Adoptive cell transfer as personalized immunotherapy for human cancer. Science 348, 62–68 (2015).
    Article ADS PubMed CAS PubMed Central Google Scholar
  2. Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).
    Article ADS PubMed CAS Google Scholar
  3. Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).
    Article ADS PubMed PubMed Central CAS Google Scholar
  4. Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 (2017).
    Article ADS PubMed CAS Google Scholar
  5. Lavin, Y. et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169, 750–765.e717 (2017).
    Article PubMed PubMed Central CAS Google Scholar
  6. Chevrier, S. et al. An immune atlas of clear cell renal cell carcinoma. Cell 169, 736–749.e718 (2017).
    Article PubMed PubMed Central CAS Google Scholar
  7. Gubin, M. M. et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515, 577–581 (2014).
    Article ADS PubMed PubMed Central CAS Google Scholar
  8. Fehlings, M. et al. Checkpoint blockade immunotherapy reshapes the high-dimensional phenotypic heterogeneity of murine intratumoural neoantigen-specific CD8+ T cells. Nat. Commun. 8, 562 (2017).
    Article ADS PubMed PubMed Central CAS Google Scholar
  9. Liu, X. S. & Mardis, E. R. Applications of immunogenomics to cancer. Cell 168, 600–612 (2017).
    Article PubMed PubMed Central CAS Google Scholar
  10. Yarchoan, M., Johnson, B. A. III, Lutz, E. R., Laheru, D. A. & Jaffee, E. M. Targeting neoantigens to augment antitumour immunity. Nat. Rev. Cancer 17, 209–222 (2017).
    Article PubMed PubMed Central CAS Google Scholar
  11. Zheng, C. et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169, 1342–1356.e1316 (2017).
    Article PubMed CAS Google Scholar
  12. Newell, E. W. et al. Combinatorial tetramer staining and mass cytometry analysis facilitate T-cell epitope mapping and characterization. Nat. Biotechnol. 31, 623–629 (2013).
    Article PubMed PubMed Central CAS Google Scholar
  13. Khodadoust, M. S. et al. Antigen presentation profiling reveals recognition of lymphoma immunoglobulin neoantigens. Nature 543, 723–727 (2017).
    Article ADS PubMed PubMed Central CAS Google Scholar
  14. Cohen, C. J. et al. Isolation of neoantigen-specific T cells from tumor and peripheral lymphocytes. J. Clin. Invest. 125, 3981–3991 (2015).
    Article PubMed PubMed Central Google Scholar
  15. McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).
    Article ADS PubMed PubMed Central CAS Google Scholar
  16. Chheda, Z. S. et al. Novel and shared neoantigen derived from histone 3 variant H3.3K27M mutation for glioma T cell therapy. J. Exp. Med. 215, 141–157 (2018).
    Article PubMed CAS PubMed Central Google Scholar
  17. Flecken, T. et al. Immunodominance and functional alterations of tumor-associated antigen-specific CD8+ T-cell responses in hepatocellular carcinoma. Hepatology 59, 1415–1426 (2014).
    Article PubMed CAS Google Scholar
  18. Koido, S. et al. Induction of antigen-specific CD4− and CD8-mediated T-cell responses by fusions of autologous dendritic cells and metastatic colorectal cancer cells. Int. J. Cancer 117, 587–595 (2005).
    Article PubMed CAS Google Scholar
  19. Liu, G. et al. HER-2, gp100, and MAGE-1 are expressed in human glioblastoma and recognized by cytotoxic T cells. Cancer Res. 64, 4980–4986 (2004).
    Article PubMed CAS Google Scholar
  20. Matsuzaki, J. et al. Tumor-infiltrating NY-ESO-1-specific CD8+ T cells are negatively regulated by LAG-3 and PD-1 in human ovarian cancer. Proc. Natl Acad. Sci. USA 107, 7875–7880 (2010).
    Article ADS PubMed PubMed Central Google Scholar
  21. 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).
    Article PubMed PubMed Central CAS Google Scholar
  22. Johnston, R. J. et al. The immunoreceptor TIGIT regulates antitumor and antiviral CD8+ T cell effector function. Cancer Cell 26, 923–937 (2014).
    Article PubMed CAS Google Scholar
  23. Gros, A. et al. PD-1 identifies the patient-specific CD8+ tumor-reactive repertoire infiltrating human tumors. J. Clin. Invest. 124, 2246–2259 (2014).
    Article PubMed PubMed Central CAS Google Scholar
  24. Erkes, D. A. et al. Virus-specific CD8+ T cells infiltrate melanoma lesions and retain function independently of PD-1 expression. J. Immunol. 198, 2979–2988 (2017).
    Article PubMed CAS Google Scholar
  25. Canale, F. P. et al. CD39 expression defines cell exhaustion in tumor-infiltrating CD8+ T cells. Cancer Res. 78, 115–128 (2018).
    Article PubMed CAS Google Scholar
  26. Gupta, P. K. et al. CD39 expression identifies terminally exhausted CD8+ T cells. PLoS Pathog. 11, e1005177 (2015).
    Article PubMed PubMed Central CAS Google Scholar
  27. Soares, A. et al. Novel application of Ki67 to quantify antigen-specific in vitro lymphoproliferation. J. Immunol. Methods 362, 43–50 (2010).
    Article PubMed PubMed Central CAS Google Scholar
  28. Wherry, E. J. et al. Molecular signature of CD8+ T cell exhaustion during chronic viral infection. Immunity 27, 670–684 (2007).
    Article PubMed CAS Google Scholar
  29. Baitsch, L. et al. Exhaustion of tumor-specific CD8+ T cells in metastases from melanoma patients. J. Clin. Invest. 121, 2350–2360 (2011).
    Article PubMed PubMed Central CAS Google Scholar
  30. Pauken, K. E. & Wherry, E. J. Overcoming T cell exhaustion in infection and cancer. Trends Immunol. 36, 265–276 (2015).
    Article PubMed PubMed Central CAS Google Scholar
  31. Nahar, R. et al. Elucidating the genomic architecture of Asian EGFR-mutant lung adenocarcinoma through multi-region exome sequencing. Nat. Commun. 9, 216 (2018).
    Article ADS PubMed PubMed Central CAS Google Scholar
  32. Gainor, J. F. et al. EGFR mutations and ALK rearrangements are associated with low response rates to PD-1 pathway blockade in non-small cell lung cancer: a retrospective analysis. Clin. Cancer Res. 22, 4585–4593 (2018).
    Article CAS Google Scholar
  33. Huang, A. C. et al. T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature 545, 60–65 (2017).
    Article ADS PubMed PubMed Central CAS Google Scholar
  34. Simoni, Y. et al. Human innate lymphoid cell subsets possess tissue-type based heterogeneity in phenotype and frequency. Immunity 46, 148–161 (2017).
    Article PubMed CAS Google Scholar
  35. Bakker, A. H. et al. Conditional MHC class I ligands and peptide exchange technology for the human MHC gene products HLA-A1, -A3, -A11, and -B7. Proc. Natl Acad. Sci. USA 105, 3825–3830 (2008).
    Article ADS PubMed PubMed Central Google Scholar
  36. Toebes, M. et al. Design and use of conditional MHC class I ligands. Nat. Med. 12, 246–251 (2006).
    Article PubMed CAS Google Scholar
  37. Coulie, P. G., Van den Eynde, B. J., van der Bruggen, P. & Boon, T. Tumour antigens recognized by T lymphocytes: at the core of cancer immunotherapy. Nat. Rev. Cancer 14, 135–146 (2014).
    Article PubMed CAS Google Scholar
  38. Lundegaard, C. et al. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11. Nucleic Acids Res. 36, W509-512 (2008).
    Article CAS Google Scholar
  39. Li, Y. & Ghosh, D. A two-step hierarchical hypothesis set testing framework, with applications to gene expression data on ordered categories. BMC Bioinformatics 15, 108 (2014).
    Article PubMed PubMed Central Google Scholar
  40. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
    Article PubMed PubMed Central CAS Google Scholar
  41. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).
    Article PubMed PubMed Central CAS Google Scholar
  42. Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28, 1811–1817 (2012).
    Article PubMed CAS Google Scholar
  43. Wang, S. et al. Label-free imaging, detection, and mass measurement of single viruses by surface plasmon resonance. Proc. Natl Acad. Sci. USA 107, 16028–16032 (2010).
    Article ADS PubMed PubMed Central Google Scholar
  44. 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 PubMed CAS Google Scholar
  45. Harrow, J., et al GENCODE: producing a reference annotation for ENCODE. Genome biology 7, 1–9 (2006).
    Article PubMed Google Scholar
  46. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
    Article PubMed PubMed Central CAS Google Scholar
  47. R Core Team. R: A Language and Environment for Statistical Computing https://www.R-project.org/ (R Foundation for Statistical Computing, Vienna, Austria, 2013).
  48. Nunes-Alves, C. et al. Human and murine clonal CD8+ T cell expansions arise during tuberculosis because of TCR selection. PLoS Pathog. 11, e1004849 (2015).
    Article PubMed PubMed Central CAS Google Scholar
  49. Becher, B. et al. High-dimensional analysis of the murine myeloid cell system. Nat. Immunol. 15, 1181–1189 (2014).
    Article PubMed CAS Google Scholar
  50. Wong, M. T. et al. Mapping the diversity of follicular helper T cells in human blood and tonsils using high-dimensional mass cytometry analysis. Cell Reports 11, 1822–1833 (2015).
    Article PubMed CAS Google Scholar
  51. Finck, R. et al. Normalization of mass cytometry data with bead standards. Cytometry A 83, 483–494 (2013).
    Article PubMed PubMed Central CAS Google Scholar
  52. Amir, A. D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013).
    Article PubMed Central CAS Google Scholar
  53. Van der Maaten, L. & Hinton, G. Visualizing data using _t_-SNE. J. Mach. Learn. Res. 9, 85 (2008).
    MATH Google Scholar
  54. Moore, W. A. & Parks, D. R. Update for the logicle data scale including operational code implementations. Cytometry A 81, 273–277 (2012).
    Article PubMed PubMed Central Google Scholar

Download references

Acknowledgements

The authors thank all members of the Newell laboratory, the SIgN community, the flow cytometry platform, all participating patients, the clinical research coordinators from NCCS, S. Quezada and J. Reading. This study was funded by A-STAR/SIgN core funding and A-STAR/SIgN immunomonitoring platform funding assigned to E.W.N. as well as NMRC/CSA-INV/0001/2014 grant (GIS) and core A-STAR/GIS funding to I.B.T. and the lung TCR grant NMRC/TCR/007-NCC/2013.

Reviewer information

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

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Authors and Affiliations

  1. Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore, Singapore
    Yannick Simoni, Etienne Becht, Michael Fehlings, Chiew Yee Loh, Karen Wei Weng Teng, Joe Poh Sheng Yeong, Hassen Kared, Kaibo Duan, Nicholas Ang, Michael Poidinger, Anis Larbi & Evan W. Newell
  2. immunoSCAPE, Singapore, Singapore
    Michael Fehlings
  3. Division of Medical Oncology, National Cancer Centre Singapore (NCCS), Singapore, Singapore
    Si-Lin Koo, Wan Teck Lim, Chee Keong Toh, Eng Huat Tan, Daniel S. W. Tan & Iain Beehuat Tan
  4. Department of Anatomical Pathology, Singapore General Hospital, Singapore, Singapore
    Joe Poh Sheng Yeong, Angela Takano & Tony Kiat Hon Lim
  5. Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore (GIS), Singapore, Singapore
    Rahul Nahar, Tong Zhang, Yin Yeng Lee, Alexis J. Khng, Axel M. Hillmer, Tony Kiat Hon Lim, Weiwei Zhai, Daniel S. W. Tan & Iain Beehuat Tan
  6. Department of Colorectal Surgery, Singapore General Hospital, Singapore, Singapore
    Emile Tan, Cherylin Fu & Ronnie Mathew
  7. Division of Surgical Oncology, National Cancer Centre Singapore (NCCS), Singapore, Singapore
    Melissa Teo & Tina Koh
  8. Department of Cardiothoracic Surgery, National Heart Centre Singapore (NHCS), Singapore, Singapore
    Boon-Hean Ong
  9. Duke–National University of Singapore Medical School, Singapore, Singapore
    Tony Kiat Hon Lim & Iain Beehuat Tan

Authors

  1. Yannick Simoni
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  2. Etienne Becht
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  3. Michael Fehlings
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  4. Chiew Yee Loh
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  5. Si-Lin Koo
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  6. Karen Wei Weng Teng
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  7. Joe Poh Sheng Yeong
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  8. Rahul Nahar
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  9. Tong Zhang
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  10. Hassen Kared
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  11. Kaibo Duan
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  12. Nicholas Ang
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  15. Anis Larbi
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  16. Alexis J. Khng
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  17. Emile Tan
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  18. Cherylin Fu
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  19. Ronnie Mathew
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  20. Melissa Teo
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  21. Wan Teck Lim
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  22. Chee Keong Toh
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  23. Boon-Hean Ong
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  24. Tina Koh
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  25. Axel M. Hillmer
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  26. Angela Takano
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  27. Tony Kiat Hon Lim
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  28. Eng Huat Tan
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  30. Daniel S. W. Tan
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  31. Iain Beehuat Tan
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  32. Evan W. Newell
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Contributions

Y.S. designed and performed research, analysed data and wrote the paper. E.B. analysed transcriptomic and clinical data and reviewed the paper. M.F. provided technical help with CyTOF, performed experiments and reviewed the paper. C.Y.L., K.W.W.T., J.P.S.Y. and N.A. helped to process samples and perform experiments. S.-L.K., E.T., C.F., R.M., M.T., W.T.L., C.K.T., B.-H.O., T.K., A.T., T.K.H.L., E.H.T., D.S.W.T., I.B.T., H.K., K.D., A.L., M.P., Y.Y.L. provided samples and discussed data. R.N., T.Z., A.J.K., A.M.H. and W.Z. performed neoantigen predictions. E.W.N. initiated and led the project, developed scripts for CyTOF analysis and wrote the paper.

Corresponding authors

Correspondence toYannick Simoni or Evan W. Newell.

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Competing interests

E.W.N. is a board director and shareholder of immunoSCAPE. M.F. is director, scientific affairs and shareholder of immunoSCAPE. All other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Tumour-infiltrating CD8+ T cells are heterogeneous.

a, Mass cytometry dot plots showing expression of markers expressed by tumour-infiltrating CD8+ TILs (gated on CD45+ cisplatin−). Data from at least 10 independent mass cytometry experiments. Representative data from one patient. b, For high-dimensional assessment of CD8+ T cell heterogeneity, we used _t_- SNE, which accounts for nonlinear relationships between markers and projects high dimensional data into a low dimensional space by making a pairwise comparison of cellular phenotypes to optimally plot similar cells near each other (see Methods). Parallel analysis of CD8+ T cells from PBMCs (blue), tumour-adjacent tissue (yellow) and tumour tissue (red) from lung or colorectal cancer patients was performed, which allows for accurate comparison of the phenotypes of cells from each of these sample types. _t_-SNE map of CD8+ T cells isolated from PBMC (blue), tumour adjacent tissue (lung or colon, yellow) and tumour (red). _t-_SNE was performed separately on each patient. Data are from at least 10 independent mass cytometry experiments. Representative data from three patients for each malignancy. c, _t_-SNE analyses focusing only on CD8+ T cells from tumour infiltrates to explore the heterogeneity of CD8+ TILs within individual patient tumours. _t_-SNE map of CD8+ TILs isolated from a colorectal tumour. Data are from at least 10 independent mass cytometry experiments. Representative data from one patient.

Extended Data Fig. 2 Multiplex tetramer staining by mass cytometry.

To investigate the antigen specificity of CD8+ TILs, we performed multiplex MHC-tetramer staining as reported previously (see Methods). By using a three-metal coding scheme, we encoded up to 120 different tetramers specific for neoantigens, tumour-associated antigens (TAA) and cancer-unrelated epitopes (peptide list is shown in Supplementary Tables 1–3). Data are from at least 10 independent mass cytometry experiments (See Fig. 2). Representative data from three patients. Each MHC tetramer+ cell population is positive for a unique code, composed of three differently-labelled streptavidin populations.

Extended Data Fig. 3 Identification and phenotypic profiles of LELC tumour-specific CD8+ TILs.

a, Immunohistochemistry of lung adenocarcinoma and lung from LELC, stained with haematoxylin (blue) and EBV-encoded small RNA in situ hybridation (EBERish, brown). Primary LELC is rare and often associated with EBV infection in lung epithelial cells. Using EBERish staining on tissue sections, we confirmed the presence of EBV virus in tumour cells from patient A311. Data are from one experiment. b, Flow dot plot representing two populations specific for EBV-derived peptides (parts of the BRFL1 and BMFL1 proteins) infiltrating a LELC tumour. Since EBV proteins are presented by tumour cells, CD8+ TILs specific for EBV epitope are also tumour specific. Frequency of MHC tetramer+ cells among CD8+ TILs. Data are from two independant experiments. Data from patient A311. c, Flow dot plot representing expression of CD69, CD103 and CD39 by LELC-specific CD8**+** TILs. As observed for neoantigen-specific CD8**+** TIL cells (Figs. 2, 3), LELC tumour-specific CD8**+** TILS express CD39. Frequency among MHC tetramer+ cells. Data are from two independent experiments. Data from patient A311.

Extended Data Fig. 4 Cancer-unrelated CD8+ TILs express tissue-resident markers.

Flow dot plot representing cancer-unrelated CD8+ TILs specific for different viral epitopes, and expression of CD69 and CD103 by these cells identified by mass cytometry screening. Data are from three independent experiments.

Extended Data Fig. 5 Phenotypes of cancer-unrelated and tumour-specifc CD8+ TILs.

Mass cytometry dot plot representing the expression of memory, co-activatory or inhibitory markers by cancer-unrelated CD8+ TILs (blue, EBV-specific MHC tetramer+, SSCSSCPLSK), neo-antigen specific CD8+ TILs (red, mutAHR tetramer+, GISQELPYK) and total CD8+ TILs cells from colorectal tumour (See Fig. 2). Data are from at least ten independent mass cytometry experiments. Representative data from one individual, patient 1053.

Extended Data Fig. 6 Differential gene expression profiles of CD39− and CD39+ CD8+ TILs.

a, In order to better characterize CD39+ CD8+ TIL cells, we sorted and performed transcriptomic profiling on CD39− and CD39+ CD8+ TILs. Using PCA on the complete transcriptomic data we observed a natural ordering of samples from naive to effector memory PBMCs, then CD39− CD8+ TILs, and finally CD39+ CD8+ TILs along the PC1 axis (See Fig. 4). We then used GSEA to biologically interpret PC1. Among all pathways that were significantly upregulated, we found CD39+ CD8+ TILs were enriched in pathways related to cell proliferation and the adaptive immune response, which suggests that these cells were subjected to higher TCR signalling (See detailed list on Supplementary Table 5). b, To obtain a more comprehensive overview of the difference between CD39− and CD39+ CD8+ TILs, we studied gene sets specific for exhaustion, a pathway characteristic of chronically stimulated T cells28,29. In line with the hypothesis that CD39 marks CD8+ TILs for chronic antigen stimulation, the gene set described for exhaustion (c) was significantly enriched in CD39+ CD8+ TILs in both colorectal and lung cancer (See Fig. 4).

Extended Data Fig. 7 Skewed TCR repertoire between CD39− and CD39+ CD8+ TILs.

To further explore the specificity of CD39+ CD8+ TILs, we performed TCRα and TCRβ sequencing of CD39− and CD39+ CD8+ TILs. We assume that a less diverse TCRα or TCRβ profile in CD39+ CD8+ TILs would suggest tumour antigen-driven clonal expansion, as suggested23. a, The clonality index, incorporating the frequency of each unique TCRα or TCRβ clone in paired samples (n = 8 patients), indicated a lower TCRα and TCRβ diversity in CD39+ CD8+ TILs. Two-tailed paired _t_-test. Data are from two independent experiments. b, c, We also compared TCRα repertoires between these populations and found that the most highly represented clones were not shared between CD39− and CD39+ CD8+ TILs. Taken together, the less clonal and skewed TCRα profile of CD39+ CD8+ TILs supports the notion that these cells underwent tumour antigen-driven clonal expansion. Data are from two independent experiments.

Extended Data Fig. 8 Exhausted profiles of CD39+ CD8+ TILs.

a, Frequencies of the expression of activation markers (left panel) and inhibitory markers (right panel) by CD39− (blue) and CD39+ (red) CD8+ TILs in lung tumours. Data are from at least ten independent mass cytometry experiments. Data are means ± s.d. Two-tailed paired _t_-test (n = 12 to n = 30 patients). b, _t_-SNE map of CD39+ CD8+ TILs cells isolated from a colorectal tumour. _t_-SNE was performed on data from one patient. Despite the phenotypic differences between CD39+ and CD39− CD8+ TILs, using t-SNE we observed that the CD39+ CD8+ TILs were heterogeneous and we could not describe any simple rules related to the hierarchical expression of various co-stimulatory receptors, inhibitory receptors or proliferation markers c, Mass cytometry dot plots representing expression of IFNγ, TNFα and IL-2 by CD8+ TILs plotted against CD39 expression (representative data from one patient with a colorectal tumour). Data from two independent experiments. d, Frequency of the expression of IFNγ, TNFα and IL-2 by CD39− (blue) and CD39+ (red) CD8+ TILs from colorectal tumour. n = 11 patients, data from two independent experiments. Two-tailed paired _t_-test.

Extended Data Fig. 9 Relationships between frequencies of CD39+ CD8+ TILs and clinical parameters of colorectal cancer patients.

a, Mass cytometry dot plots representing expression of CD39 by CD8+ TILs in lung and colorectal tumours. Representative data from two patients. Data are from at least ten independent mass cytometry experiments. b, CD39+ cells as a percentage of CD8+ TILs stratified by microsatellite-stable (MSS) (n = 3 patients) or microsatellite-instable (MSI) (n = 33 patients) status. Data are means from at least ten independent mass cytometry experiments. c, Mutation rate (in mutational events per megabase, plotted on a log scale) versus CD39+ CD8+ TIL frequencies in colorectal tumours (n = 26 patients). Data from at least ten independent mass cytometry experiments. d, Box plots representing CD39+ frequencies among CD8+ TILs stratified by the consensus molecular subtypes of each tumour. CMS1 (n = 6 patients), CMS2 (n = 34 patients), CMS3 (n = 8 patients), CMS4 (n = 3 patients). Box plots show the median, box edges represent the first and third quartiles, and the whiskers extend from minimum to maximum. Data are from at least ten independent mass cytometry experiments. e, Dot plots representing CD39+ frequencies among CD8+ TILs stratified by tumour stages. Stage I (n = 6 patients), stage II (n = 10 patients), stage III (n = 10 patients), stage IV (n = 8 patients). Data are mean ± s.d. from at least ten independent mass cytometry experiments. f, Number of driver mutations against CD39+ CD8+ TILs frequencies in colorectal tumours. Two-tailed _t_-test; Pearson’s correlation. n = 27 patients. Data are from at least ten independent mass cytometry experiments.

Extended Data Fig. 10 Gene set enrichment in tumours with high CD39+ CD8+ TIL count.

We investigated transcriptomic profiles of whole tumours using bulk RNA sequencing in conjunction with the percentage CD39 expression in CD8+ TILs as measured by mass cytometry. Among the 25% most varying genes, we identified ten gene modules by performing hierarchical clustering on the Pearson correlation matrix of the genes. Notably, a cluster whose expression correlated with the frequency of CD39+ TILs was enriched in genes related to ‘adaptive immune response’, ‘T cell receptor signalling pathway’ and ‘interferon-gamma mediated signalling pathway’ (see also Supplementary Table 4). Pathways related to peptide presentation by MHC molecules were also overrepresented in this cluster, which contained genes such as class I MHC molecules, TAP1 and TAP2 molecules and proteasome-related genes. n = 46 patients. Data are from at least five independent experiments. Two-sided hypergeometric test.

Supplementary information

Reporting Summary

Supplementary Table 1

HLA-A*24:02 Tumor associated antigens used for multiplex tetramer staining. Prediction was made using NetMHC 3.4.

Supplementary Table 2

HLA-A*11:01 Tumor associated antigens used for multiplex tetramer staining. Prediction was made using NetMHC 3.4.

Supplementary Table 3

Cancer unrelated epitopes used for multiplex tetramer staining. Prediction was made using NetMHC 3.4.

Supplementary Table 4

Patient’s characteristics for Neoantigens epitopes prediction. (*) antigens screened, na: no data available.

Supplementary Table 5

Gene set pathway analysis. 5.1 - GSEA of PC1 loading, see Fig. 4a. n=24 biologically independent individual, GSEA empirical test – two-sided. 5.2 - GSEA on t-Statistics for CD39– vs CD39+ CD8+ TILs, see Fig. 4b. n=15 biologically independent individual, GSEA empirical test – two-sided. 5.3 - Gene modules, see Extended Data Fig. 10. 5.4 - Enrichment tests on gene module 6, see Extended Data Fig. 10. n=46 biologically independent individual, Test hypergeometric - two-sided.

Supplementary Table 6

Clinical characteristics of the patients recruited in the study. n/a: not applicable.

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Simoni, Y., Becht, E., Fehlings, M. et al. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates.Nature 557, 575–579 (2018). https://doi.org/10.1038/s41586-018-0130-2

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