Various forms of immunotherapy, such as checkpoint blockade immunotherapy, are proving to be effective at restoring T cell-mediated immune responses that can lead to marked and sustained clinical responses, but only in some patients and cancer types1,2,3,4. Patients and tumours may respond unpredictably to immunotherapy partly owing to heterogeneity of the immune composition and phenotypic profiles of tumour-infiltrating lymphocytes (TILs) within individual tumours and between patients5,6. Although there is evidence that tumour-mutation-derived neoantigen-specific T cells play a role in tumour control2,4,7,8,9,10, in most cases the antigen specificities of phenotypically diverse tumour-infiltrating T cells are largely unknown. Here we show that human lung and colorectal cancer CD8+ TILs can not only be specific for tumour antigens (for example, neoantigens), but also recognize a wide range of epitopes unrelated to cancer (such as those from Epstein–Barr virus, human cytomegalovirus or influenza virus). We found that these bystander CD8+ TILs have diverse phenotypes that overlap with tumour-specific cells, but lack CD39 expression. In colorectal and lung tumours, the absence of CD39 in CD8+ TILs defines populations that lack hallmarks of chronic antigen stimulation at the tumour site, supporting their classification as bystanders. Expression of CD39 varied markedly between patients, with some patients having predominantly CD39 CD8+ TILs. Furthermore, frequencies of CD39 expression among CD8+ TILs correlated with several important clinical parameters, such as the mutation status of lung tumour epidermal growth factor receptors. Our results demonstrate that not all tumour-infiltrating T cells are specific for tumour antigens, and suggest that measuring CD39 expression could be a straightforward way to quantify or isolate bystander T cells.

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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.

Author information


  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


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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.

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.

Corresponding authors

Correspondence to Yannick Simoni or Evan W. Newell.

Extended data figures and tables

  1. 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.

  2. 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 13). 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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).

  7. 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.

  8. 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.

  9. 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.

  10. 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

  1. Reporting Summary

  2. Supplementary Table 1

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

  3. Supplementary Table 2

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

  4. Supplementary Table 3

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

  5. Supplementary Table 4

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

  6. 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.

  7. Supplementary Table 6

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

  8. Supplementary Table 7

    Antibodies list.

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