Abstract
The cytokine interferon (IFN)-γ produced by tumor-reactive T cells is a key effector molecule with pleiotropic effects during anti-tumor immune responses. Although IFN-γ production is targeted at the immunologic synapse, its spatiotemporal activity within the tumor remains elusive. In the present study, we report that, although IFN-γ secretion requires local antigen recognition, IFN-γ diffuses extensively to alter the tumor microenvironment in distant areas. Using intravital imaging and a reporter for STAT1 translocation, we provide evidence that T cells mediate sustained IFN-γ signaling in remote tumor cells. Furthermore, tumor phenotypic alterations required several hours of exposure to IFN-γ, a feature that disfavored local IFN-γ activity over diffusion and bystander activity. Finally, single-cell RNA-sequencing data from melanoma patients also suggested bystander IFN-γ activity in human tumors. Thus, tumor-reactive T cells act collectively to create large cytokine fields that profoundly modify the tumor microenvironment.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
RNA-seq data reported in this paper are deposited in the National Center for Biotechnology Information GEO database (GSE140191). Previously published human single-cell RNA-seq data that were reanalyzed in the present study are available under accession codes GSE123139 and GSE103322. All other data supporting the findings of the present study are available from the corresponding author on reasonable request.
Code availability
The R and Matlab scripts used for the transcriptomic and image analysis are available on GitHub (https://github.com/PierreBSC/IFNG_Cancer_project).
References
Kaplan, D. H. et al. Demonstration of an interferon gamma-dependent tumor surveillance system in immunocompetent mice. Proc. Natl Acad. Sci. USA 95, 7556–7561 (1998).
Street, S. E., Cretney, E. & Smyth, M. J. Perforin and interferon-gamma activities independently control tumor initiation, growth, and metastasis. Blood 97, 192–197 (2001).
Dunn, G. P., Koebel, C. M. & Schreiber, R. D. Interferons, immunity and cancer immunoediting. Nat. Rev. Immunol. 6, 836–848 (2006).
Garris, C. S. et al. Successful anti-PD-1 cancer immunotherapy requires T cell–dendritic cell crosstalk involving the cytokines IFN-gamma and IL-12. Immunity 49, 1148–1161 e1147 (2018).
Gao, J. et al. Loss of IFN-gamma pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy. Cell 167, 397–404 e399 (2016).
Chin, Y. E. et al. Cell growth arrest and induction of cyclin-dependent kinase inhibitor p21 WAF1/CIP1 mediated by STAT1. Science 272, 719–722 (1996).
Ikeda, H., Old, L. J. & Schreiber, R. D. The roles of IFN gamma in protection against tumor development and cancer immunoediting. Cytokine Growth Factor Rev. 13, 95–109 (2002).
Braumuller, H. et al. T-helper-1-cell cytokines drive cancer into senescence. Nature 494, 361–365 (2013).
Wang, W. et al. CD8+ T cells regulate tumour ferroptosis during cancer immunotherapy. Nature 569, 270–274 (2019).
Kammertoens, T. et al. Tumour ischaemia by interferon-gamma resembles physiological blood vessel regression. Nature 545, 98–102 (2017).
Meunier, M. C. et al. T cells targeted against a single minor histocompatibility antigen can cure solid tumors. Nat. Med. 11, 1222–1229 (2005).
Groom, J. R. & Luster, A. D. CXCR3 ligands: redundant, collaborative and antagonistic functions. Immunol. Cell Biol. 89, 207–215 (2011).
Takeda, K. et al. IFN-gamma is required for cytotoxic T cell-dependent cancer genome immunoediting. Nat. Commun. 8, 14607 (2017).
Garcia-Diaz, A. et al. Interferon receptor signaling pathways regulating PD-L1 and PD-L2 expression. Cell Rep. 19, 1189–1201 (2017).
Dong, H. et al. Tumor-associated B7-H1 promotes T-cell apoptosis: a potential mechanism of immune evasion. Nat. Med. 8, 793–800 (2002).
Freeman, G. J. et al. Engagement of the PD-1 immunoinhibitory receptor by a novel B7 family member leads to negative regulation of lymphocyte activation. J. Exp. Med. 192, 1027–1034 (2000).
Pai, C. S. et al. Clonal deletion of tumor-specific T cells by interferon-gamma confers therapeutic resistance to combination immune checkpoint blockade. Immunity 50, 477–492 e478 (2019).
Benci, J. L. et al. Tumor interferon signaling regulates a multigenic resistance program to immune checkpoint blockade. Cell 167, 1540–1554 e1512 (2016).
Altan-Bonnet, G. & Mukherjee, R. Cytokine-mediated communication: a quantitative appraisal of immune complexity. Nat. Rev. Immunol. 19, 205–217 (2019).
Huse, M., Lillemeier, B. F., Kuhns, M. S., Chen, D. S. & Davis, M. M. T cells use two directionally distinct pathways for cytokine secretion. Nat. Immunol. 7, 247–255 (2006).
Helmstetter, C. et al. Individual T helper cells have a quantitative cytokine memory. Immunity 42, 108–122 (2015).
Honda, T. et al. Tuning of antigen sensitivity by T cell receptor-dependent negative feedback controls T cell effector function in inflamed tissues. Immunity 40, 235–247 (2014).
Sanderson, N. S. et al. Cytotoxic immunological synapses do not restrict the action of interferon-gamma to antigenic target cells. Proc. Natl Acad. Sci. USA 109, 7835–7840 (2012).
Perona-Wright, G., Mohrs, K. & Mohrs, M. Sustained signaling by canonical helper T cell cytokines throughout the reactive lymph node. Nat. Immunol. 11, 520–526 (2010).
Müller, A. J. et al. CD4+ T cells rely on bystander effector activity to control local infection. Immunity 37, 147–157 (2012).
Harris, A. W. et al. The E mu-myc transgenic mouse. A model for high-incidence spontaneous lymphoma and leukemia of early B cells. J. Exp. Med. 167, 353–371 (1988).
Hart, I. R. The selection and characterization of an invasive variant of the B16 melanoma. Am. J. Pathol. 97, 587–600 (1979).
Sugiura, K. & Stock, C. C. Studies in a tumor spectrum. I. Comparison of the action of methylbis(2-chloroethyl)amine and 3-bis(2-chloroethyl)aminomethyl-4-methoxymethyl-5-hydroxy-6-methylpyridine on the growth of a variety of mouse and rat tumors. Cancer 5, 382–402 (1952).
Milo, I. et al. The immune system profoundly restricts intratumor genetic heterogeneity. Sci. Immunol. 3, eaat1435 (2018).
Oyler-Yaniv, J. et al. Catch and release of cytokines mediated by tumor phosphatidylserine converts transient exposure into long-lived inflammation. Mol. Cell 66, 635–647 e637 (2017).
Li, H. et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176, 775–789 e718 (2019).
Fan, J. et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods 13, 241–244 (2016).
Kupfer, A., Mosmann, T. R. & Kupfer, H. Polarized expression of cytokines in cell conjugates of helper T cells and splenic B cells. Proc. Natl Acad. Sci. USA 88, 775–779 (1991).
Egen, J. G. et al. Intravital imaging reveals limited antigen presentation and T cell effector function in mycobacterial granulomas. Immunity 34, 807–819 (2011).
Olekhnovitch, R., Ryffel, B., Muller, A. J. & Bousso, P. Collective nitric oxide production provides tissue-wide immunity during leishmania infection. J. Clin. Invest. 124, 1711–1722 (2014).
Postat, J., Olekhhnovitch, R., Lemaïtre, F. & Bousso, P. A metabolism-based quorum sensing mechanism contributes to termination of inflammatory responses. Immunity 49, 654–665 (2018).
Oyler-Yaniv, A. et al. A tunable diffusion–consumption mechanism of cytokine propagation enables plasticity in cell-to-cell communication in the immune system. Immunity 46, 609–620 (2017).
Stahl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
Shah, S., Lubeck, E., Zhou, W. & Cai, L. seqFISH accurately detects transcripts in single cells and reveals robust spatial organization in the hippocampus. Neuron 94, 752–758 e751 (2017).
Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, pii: eaat5691 (2018).
Cazaux, M. et al. Single-cell imaging of CAR T cell activity in vivo reveals extensive functional and anatomical heterogeneity. J. Exp. Med. 216, 1038–1049 (2019).
Vincent, L. Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans. Image Process. 2, 176–201 (1993).
Janky, R. et al. iRegulon: from a gene list to a gene regulatory network using large motif and track collections. PLoS Comput. Biol. 10, e1003731 (2014).
Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv https://arxiv.org/abs/1802.03426 (2018).
Acknowledgements
We thank members of P. Bousso’s laboratory for critical review of the manuscript. We thank the mouse facility and Technology Core of the Center for Translational Science (CRT) at Institut Pasteur for support in conducting the present study. The work was supported by Institut Pasteur, INSERM, a starting grant (Lymphocytecontact) and an advanced grant (ENLIGHTEN) from the European Research Council (to P. Bousso) and by the Bristol-Myers Squibb Foundation for Research in Immuno-Oncology.
Author information
Authors and Affiliations
Contributions
R.T., I.M., M.C., F.L., Z.G., B.B. and C.C. conducted the experiments. R.T., I.M., M.C. and P. Bousso designed the experiments. I.A. and B.S. contributed to the sequencing analysis. R.T., I.M., M.C., P. Bost and P. Bousso analyzed the data and wrote the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Tumor cell responses to IFN-γ in vitro.
a-b, Tumor cells respond to IFN-γ in an analogic manner in vitro. a E0771 mammary breast tumor cells were stimulated with indicated IFN-γ concentrations in vitro for 24h. H2-Kb (top), H2-Db (middle) and PD-L1 (bottom) surface expression was then analyzed by flow cytometry. Representative of two independent experiments. b, Representative histograms of H2-Db (left) and PD-L1 (right) surface expression in B16.F10 (top) or Eµ-myc (bottom) cells, after treatment with indicated IFN-γ concentrations for 24h. Representative of three independent experiments. c-f, Impact of IFN-γ and TNF-α on tumor cell death. B16.F10 (c,e) or Eµ-myc (d,f) cells were incubated with the indicated IFN-γ and/or TNF-α concentrations in vitro for 24h. Cells were then counted by flow cytometry. Representative of two independent experiments.
Extended Data Fig. 2 Gating strategy for flow cytometry analysis of the tumor microenvironment.
The figure depicts the gating strategies for identifying tumor cells, OT-I T cells, NK cells, monocytes and neutrophils present in the bone marrow of tumor bearing mice.
Extended Data Fig. 3 NK cells are dispensable for tumor phenotypic changes upon T cell transfer.
a, Experimental set-up. Rag2-/-γc-/- recipient mice were injected i.v with OVA-expressing Eµ-myc B lymphoma cells. On day 12-13, in vitro activated OT-I CD8+ T cells were injected i.v. Two days later, the recipient bone marrow was harvested and analyzed by flow cytometry. b, Representative examples of histograms showing H2-Kb (left) and PD-L1 (right) surface expression on tumor cells isolated from the bone marrow of mice that were left untreated (filled grey) or injected with OT-I T cells (line, blue). c, H2-Kb (left), H2-Db (middle) and PD-L1 (right) surface expression on tumor cells isolated from mice treated or not with OT-I T cells, as assessed by flow cytometry. Each dot represents one mouse with n=4 mice per group. Red lines indicate mean values. (* P<0.05, two-tailed Mann-Whitney U-test).
Extended Data Fig. 4 OT-I T cells arrest on antigen-positive tumor cells independently of IFN-γ.
a, Rag2-/- mice were injected with a 1:1 mixture of OVA-expressing and non-expressing Eµ-myc B lymphoma cells, labeled with CFP and YFP, respectively. On day 12-13, mice were injected with activated GFP+ OT-I T cells. Two days later, intravital imaging of the bone marrow was performed. The graph shows the relationship between mean T cell velocity and straightness in the OVA+ (left) and OVA- (right) tumor areas. Each dot represents one T cell track (OVA- areas n= 103 tracks, OVA+ areas n=75 tracks). b-d, Rag2-/- mice were injected with a 1:1 mixture of OVA-expressing and non-expressing Eµ-myc B lymphoma cells, labeled with CFP and GFP, respectively. On day 12-13, mice were injected with activated WT or IFN-γ-deficient OT-I T cells transduced to express the mCherry fluorescent protein. Two days later, intravital imaging of the bone marrow was performed. b, Representative image of OVA+ (blue) tumor patches infiltrated with IFN-γ-deficient OT-I T cells (red). Scale bar: 50 µm. Right. Time lapse images (corresponding to the dashed squares) showing IFN-γ-deficient OT-I T cells (pointed by arrows) forming stable contacts with OVA+ Eµ-myc cells. Scale bar: 15 µm. c-d, Both WT and IFN-γ-deficient OT-I T cells decelerate in OVA+ tumor areas. Graphs show mean velocities (c) and arrest coefficient (d), for individual WT or IFN-γ-deficient T cells in OVA+ (blue) and OVA- (orange) tumor areas. Each dot represents one track. Red lines indicate mean values. Data shown in b-d, are representative of two independent experiments with n=3 mice per group. (* P<0.05; ** P<0.01; *** P<0.001; two-tailed Mann-Whitney U-test). e, OT-I T cells infiltrate antigen-negative tumors in the bone marrow. Rag2-/- mice were injected with either Eµ-myc alone or a 1:1 mixture of Eµ-myc and OVA-expressing Eµ-myc B lymphoma cells (labeled with different fluorescent proteins). On day 12-13, recipients were injected with OT-I T cells or left untreated. Two days later, the bone marrow of the mice was recovered and analyzed by flow cytometry. The graph shows that OT-I T cells can efficiently infiltrate tumors that contain only antigen-negative cells. Each dot represents one mouse with n=4 mice per group. Red lines indicate mean values.
Extended Data Fig. 5 T cell-derived IFN-γ can control bystander tumor cells.
a-b, Estimating the intratumoral IFN-γ concentration upon T cell transfer. a, The ratio of OT-I T cells to OVA+Eµ-myc tumors in the bone marrow (measured at day 2) is graphed as a function of the total number of transferred T cells. Results are shown as mean+SD with n=5 mice per group. b, The fold change in H2-Db levels measured in Eµ-myc tumors in vitro is graphed as a function of IFN-γ concentration. The graph was used to infer the putative IFN-γ concentration in vivo (dashed red line) upon transfer of 20x106 T cells. c-f, Control of tumor burden by T cell-derived IFN-γ. c-d, Recipient female Rag2-/- mice were injected with H-Y+ Eµ-myc B lymphoma cells. After 3 weeks, mice were either injected i.v. with recombinant IFN-γ (1µg) twice (one day apart) or left untreated. On day 2, tumor burden c, and phenotype d, in the bone marrow were assessed by flow cytometry. Each dot represents one mouse (n=3 and n=4 mice for the untreated and the treated group, respectively). Red lines indicate mean values. (* P<0.05, two-tailed unpaired t-test). e-f, Functional activity of intratumoral IFN-γ. e, Experimental set-up. Rag2-/- mice were injected with a 1:1 mixture of Eµ-myc and OVA-expressing Eµ-myc B lymphoma cells (labeled with different fluorescent proteins). On day 12-13, recipients were injected with either WT or IFN-γ-deficient OT-I T cells or left untreated. Five days later, the bone marrow was recovered and analyzed by flow cytometry. f, Graphs show the residual numbers of antigen-positive (top) and antigen-negative (bottom) tumors following transfer of the indicated T cell populations. Each dot represents one mouse (n=6 mice per group). Red lines indicate mean values. (* P<0.05, ** P<0.01, two-tailed Mann-Whitney U-test). Representative of two independent experiments. Of note, small deviation from the initial ratio seen in the absence of T cells is most likely due to minor differences in tumor cell division time.
Extended Data Fig. 6 Automated procedure for quantifying STAT1 translocation in tumor cells imaged in vitro or in vivo.
a, The diagram recapitulates the various steps used for image processing and quantification (see methods for details). b, Representative example of cell segmentation from the STAT1-GFP signal in tumor cells. Scale bar: 50 µm.
Extended Data Fig. 7 Tumor-reactive CD8+ T cells mediate widespread and sustained STAT1 activity in the tumor microenvironment.
a, Experimental set-up. Recipient Rag2-/-γc-/- mice were injected with OVA+ Eµ-myc B lymphoma cells expressing the STAT1-GFP reporter and a nuclear mCherry protein. After 3 weeks, activated CD8+ T cells bearing the OT-I TCR were injected i.v. One day later, recipients were subjected to intravital imaging of the bone marrow. b, STAT1-GFP is largely excluded from the nucleus in tumor cells developing in the absence of T cells. Representative two-photon images (scale bar: 20µm), highlighting two specific regions (insets, scale bar: 10µm). c, Detection of nuclear STAT1-GFP in T cell-infiltrated tumors. Representative two-photon images (scale bar: 20µm), highlighting two specific regions (insets, scale bar: 10µm). d, Translocation score was computed from two-photon images obtained in mice left untreated (no T cells) or transferred with OT-I T cells (n=10 cells per group, box plot showing the median, first and third quartile and min and max values, *** P<0.001, two-tailed Mann-Whitney U-test). Data in b-d are representative of two independent experiments. e-h STAT1 signaling is detected at distance from antigen-positive tumor cells. e, Experimental set-up. Rag2-/- mice were injected with Eµ-myc cells expressing the STAT1 reporter either alone or mixed at a 1:1 ratio with OVA+ Eµ-myc (expressing mCFP). Two weeks later, all mice were adoptively transferred with activated OT-I T cells. After 2 days, mice were subjected to intravital imaging of the bone marrow. f, STAT1-GFP is largely excluded from the nucleus in tumor cells developing in the absence of antigen-positive tumors. Representative two-photon images (scale bar: 20µm), highlighting two specific regions. Scale bar, 20 μm. g, Detection of STAT1 translocation in antigen-negative tumor cells developing in the presence of antigen-positive tumor cells. Representative two-photon images (scale bar: 20µm), highlighting two specific regions. Scale bar, 20 μm. h, Translocation score was computed from two-photon images obtained in mice bearing STAT1-GFP-expressing tumor cells alone, or bearing mosaic tumors containing both STAT1-GFP and OVA+ tumor cells. (n=10 cells per group, box plot showing the median, first and third quartile and min and max values, ** P<0.01, two-tailed Mann-Whitney U-test). Data in f-h are representative of n=3 mice per group.
Extended Data Fig. 8 Prolonged STAT1 translocation in tumor cells with sustained exposure to IFN-γ.
a, Experimental set-up. Eµ-myc cells expressing STAT1-GFP and nuclear mCherry were cultured for one hour in presence of recombinant IFN-γ. Cells were washed and either incubated with anti-IFN-γ mAb (to limit cytokine exposure), or re-incubated with IFN-γ (to prolong cytokine exposure). After an additional hour, cells were imaged. b, STAT1 translocation score was computed automatically for all individual cells (unstimulated n=593 cells. IFN-γ n=583, anti-IFN-γ n=570 cells). Each dot represents one cell. Red lines indicate mean values. (**** P<0.0001, *** P<0.001, two-tailed Mann-Whitney U-test).
Extended Data Fig. 9 Determination of an IFNG signature in monocyte/macrophage cluster.
15 over-dispersed gene sets were determined for the monocyte/macrophage cluster as explained in materials and methods. Graph shows the contribution of IFNG or type I IFN-related genes for each gene sets. Arrow highlights a gene signature that is specific of IFN-γ signaling (hereafter called IFNG signature).
Extended Data Fig. 10 Distribution of IFN-γ signature in tumor cells from head and neck squamous cell carcinoma patients.
a, Gene contribution to the IFNG signature in the tumor cells. b, Distribution of IFNG signature in tumor cells from 13 different patients with head and neck squamous cell carcinoma patients. Although the mean of the distribution varies from patient to patient, a relatively uniform distribution is observed in most patients.
Supplementary information
Supplementary Video 1
Tumor antigen expression drives the selective accumulation and arrest of intratumoral T cells. Rag2−/− mice were injected with a 1:1 mixture of OVA-expressing and OVA-non-expressing Eµ-myc B lymphoma cells, labeled with CFP and YFP, respectively. On days 12–13, mice were injected with activated GFP+ OT-I T cells. After 2 d, intravital imaging of the bone marrow was performed. The video illustrates that CD8+ T cells (green) specifically accumulate and arrest in antigen-expressing cellular patches (blue) of mosaic tumors but not in antigen-negative patches (orange). It represents two independent experiments with n = 3 mice per group.
Supplementary Video 2
Tumor-reactive (H-Y-specific) CD8+ T cells mediate widespread and sustained STAT1 activity in the tumor microenvironment. Recipient Rag2−/− mice were injected with male Eµ-myc B lymphoma cells expressing the STAT1–GFP reporter and a nuclear mCherry protein. After 3 weeks, activated CD8+ T cells bearing the anti-H-Y MataHari TCR were injected i.v. After 3 d, recipients were subjected to intravital imaging of the bone marrow. The video shows widespread STAT1 activity (yellow) in the tumor in the presence, but not the absence, of T cells. T cells are shown in blue. It represents two independent experiments with n = 3 mice per group.
Supplementary Video 3
Tumor-reactive (OVA-specific) CD8+ T cells mediate widespread and sustained STAT1 activity in the tumor microenvironment. Recipient Rag2−/−γc−/− mice were injected with OVA+ Eµ-myc B lymphoma cells expressing the STAT1–GFP reporter and a nuclear mCherry protein. After 3 weeks, activated CD8+ T cells bearing the OVA-specific OT-I TCR were injected i.v. After 3 d, recipients were subjected to intravital imaging of the bone marrow. The video shows widespread STAT1 activity (yellow) in the tumor in the presence, but not the absence, of T cells. T cells appear bright yellow. It represents two independent experiments.
Source data
Source data Fig. 1
Statistical source data.
Source data Fig. 2
Statistical source data.
Source data Fig. 3
Statistical source data.
Source data Fig. 4
Statistical source data.
Source data Fig. 5
Statistical source data.
Source data Fig. 6
Statistical source data.
Extended data Fig. 1
Statistical source data.
Rights and permissions
About this article
Cite this article
Thibaut, R., Bost, P., Milo, I. et al. Bystander IFN-γ activity promotes widespread and sustained cytokine signaling altering the tumor microenvironment. Nat Cancer 1, 302–314 (2020). https://doi.org/10.1038/s43018-020-0038-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43018-020-0038-2
This article is cited by
-
A novel safer CD19CAR with shRNA interference of IFN-γ can reduce multiple cytokine levels without significantly compromising its killing efficacy
Apoptosis (2024)
-
Role of interferon-induced transmembrane protein family in cancer progression: a special focus on pancreatic cancer
Medical Oncology (2024)
-
In vivo imaging of inflammatory response in cancer research
Inflammation and Regeneration (2023)
-
ERK mediates interferon gamma-induced melanoma cell death
Molecular Cancer (2023)
-
Disrupting the phase separation of KAT8–IRF1 diminishes PD-L1 expression and promotes antitumor immunity
Nature Cancer (2023)
