Group 2 innate lymphoid cells (ILC2s) are essential to maintain tissue homeostasis. In cancer, ILC2s can harbor both pro-tumorigenic and anti-tumorigenic functions, but we know little about their underlying mechanisms or whether they could be clinically relevant or targeted to improve patient outcomes. Here, we found that high ILC2 infiltration in human melanoma was associated with a good clinical prognosis. ILC2s are critical producers of the cytokine granulocyte-macrophage colony-stimulating factor, which coordinates the recruitment and activation of eosinophils to enhance antitumor responses. Tumor-infiltrating ILC2s expressed programmed cell death protein-1, which limited their intratumoral accumulation, proliferation and antitumor effector functions. This inhibition could be overcome in vivo by combining interleukin-33-driven ILC2 activation with programmed cell death protein-1 blockade to significantly increase antitumor responses. Together, our results identified ILC2s as a critical immune cell type involved in melanoma immunity and revealed a potential synergistic approach to harness ILC2 function for antitumor immunotherapies.
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scRNA sequencing data that support the findings of this study have been deposited in the GEO under accession code GSE149615. All data needed to evaluate the conclusions in the paper are present in the main text, extended data or Supplementary Information.
Source code for statistical learning models is publicly available at https://github.com/DavisLaboratory/Jacquelot_2021_reproducibility/.
Park, S. L. et al. Tissue-resident memory CD8+ T cells promote melanoma–immune equilibrium in skin. Nature 565, 366–371 (2019).
Gebhardt, T., Palendira, U., Tscharke, D. C. & Bedoui, S. Tissue-resident memory T cells in tissue homeostasis, persistent infection and cancer surveillance. Immunol. Rev. 283, 54–76 (2018).
Bruggen, M. C. et al. In situ mapping of innate lymphoid cells in human skin: evidence for remarkable differences between normal and inflamed skin. J. Invest. Dermatol. 136, 2396–2405 (2016).
Salimi, M. et al. Activated innate lymphoid cell populations accumulate in human tumour tissues. BMC Cancer 18, 341 (2018).
Bie, Q. et al. Polarization of ILC2s in peripheral blood might contribute to immunosuppressive microenvironment in patients with gastric cancer. J. Immunol. Res. 2014, 923135 (2014).
Moral, J. A. et al. ILC2s amplify PD-1 blockade by activating tissue-specific cancer immunity. Nature 579, 130–135 (2020).
Trabanelli, S. et al. Tumour-derived PGD2 and NKp30-B7H6 engagement drives an immunosuppressive ILC2-MDSC axis. Nat. Commun. 8, 593 (2017).
Suzuki, A., Leland, P., Joshi, B. H. & Puri, R. K. Targeting of IL-4 and IL-13 receptors for cancer therapy. Cytokine 75, 79–88 (2015).
Zaiss, D. M. W., Gause, W. C., Osborne, L. C. & Artis, D. Emerging functions of amphiregulin in orchestrating immunity, inflammation, and tissue repair. Immunity 42, 216–226 (2015).
Busser, B., Sancey, L., Brambilla, E., Coll, J. L. & Hurbin, A. The multiple roles of amphiregulin in human cancer. Biochim. Biophys. Acta 1816, 119–131 (2011).
Chevalier, M. F. et al. ILC2-modulated T cell-to-MDSC balance is associated with bladder cancer recurrence. J. Clin. Invest. 127, 2916–2929 (2017).
Saranchova, I. et al. Type 2 innate lymphocytes actuate immunity against tumours and limit cancer metastasis. Sci. Rep. 8, 2924 (2018).
Huang, Q. et al. Type 2 innate lymphoid cells protect against colorectal cancer progression and predict improved patient survival. Cancers 13, 559 (2021).
Wagner, M. et al. Tumor-derived lactic acid contributes to the paucity of intratumoral ILC2s. Cell Rep. 30, 2743–2757 (2020).
Lucarini, V. et al. IL-33 restricts tumor growth and inhibits pulmonary metastasis in melanoma-bearing mice through eosinophils. Oncoimmunology 6, e1317420 (2017).
Schuijs, M. J. et al. ILC2-driven innate immune checkpoint mechanism antagonizes NK cell antimetastatic function in the lung. Nat. Immunol. 21, 998–1009 (2020).
Long, A. et al. Type 2 innate lymphoid cells impede IL-33-mediated tumor suppression. J. Immunol. 201, 3456–3464 (2018).
Grisaru-Tal, S., Itan, M., Klion, A. D. & Munitz, A. A new dawn for eosinophils in the tumour microenvironment. Nat. Rev. Cancer 20, 594–607 (2020).
Moreira, A., Leisgang, W., Schuler, G. & Heinzerling, L. Eosinophilic count as a biomarker for prognosis of melanoma patients and its importance in the response to immunotherapy. Immunotherapy 9, 115–121 (2017).
Hollande, C. et al. Inhibition of the dipeptidyl peptidase DPP4 (CD26) reveals IL-33-dependent eosinophil-mediated control of tumor growth. Nat. Immunol. 20, 257–264 (2019).
Ercolano, G., Falquet, M., Vanoni, G., Trabanelli, S. & Jandus, C. ILC2s: new actors in tumor immunity. Front Immunol. 10, 2801 (2019).
Dankort, D. et al. BrafV600E cooperates with Pten loss to induce metastatic melanoma. Nat. Genet. 41, 544–552 (2009).
Putz, E. M. et al. Targeting cytokine signaling checkpoint CIS activates NK cells to protect from tumor initiation and metastasis. Oncoimmunology 6, e1267892 (2017).
Knight, D. A. et al. Host immunity contributes to the anti-melanoma activity of BRAF inhibitors. J. Clin. Invest. 123, 1371–1381 (2013).
Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).
Sathe, P. et al. Innate immunodeficiency following genetic ablation of Mcl1 in natural killer cells. Nat. Commun. 5, 4539 (2014).
Eberl, G. et al. An essential function for the nuclear receptor RORγt in the generation of fetal lymphoid tissue inducer cells. Nat. Immunol. 5, 64–73 (2004).
Oliphant, C. J. et al. MHCII-mediated dialog between group 2 innate lymphoid cells and CD4+ T cells potentiates type 2 immunity and promotes parasitic helminth expulsion. Immunity 41, 283–295 (2014).
Seillet, C. et al. Deciphering the innate lymphoid cell transcriptional program. Cell Rep. 17, 436–447 (2016).
Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).
Louis, C. et al. NK cell-derived GM-CSF potentiates inflammatory arthritis and is negatively regulated by CIS. J. Exp. Med. 217, e20191421 (2020).
Codarri, L. et al. RORγt drives production of the cytokine GM-CSF in helper T cells, which is essential for the effector phase of autoimmune neuroinflammation. Nat. Immunol. 12, 560–567 (2011).
Taube, J. M. et al. Colocalization of inflammatory response with B7-h1 expression in human melanocytic lesions supports an adaptive resistance mechanism of immune escape. Sci. Transl. Med. 4, 127ra137 (2012).
Jacquelot, N. et al. Immunophenotyping of stage III melanoma reveals parameters associated with patient prognosis. J. Invest. Dermatol. 136, 994–1001 (2016).
Jacquelot, N. et al. Immune biomarkers for prognosis and prediction of responses to immune checkpoint blockade in cutaneous melanoma. Oncoimmunology 6, e1299303 (2017).
Mortha, A. et al. Microbiota-dependent cross-talk between macrophages and ILC3 promotes intestinal homeostasis. Science 343, 1249288 (2014).
Pearson, C. et al. ILC3 GM-CSF production and mobilisation orchestrate acute intestinal inflammation. Elife 5, e10066 (2016).
Demaria, O. et al. Harnessing innate immunity in cancer therapy. Nature 574, 45–56 (2019).
Hoeller, C., Michielin, O., Ascierto, P. A., Szabo, Z. & Blank, C. U. Systematic review of the use of granulocyte-macrophage colony-stimulating factor in patients with advanced melanoma. Cancer Immunol. Immunother. 65, 1015–1034 (2016).
Liu, B. L. et al. ICP34.5 deleted herpes simplex virus with enhanced oncolytic, immune stimulating, and anti-tumour properties. Gene Ther. 10, 292–303 (2003).
Andtbacka, R. H. I. et al. Talimogene laherparepvec improves durable response rate in patients with advanced melanoma. J. Clin. Oncol. 33, 2780–2788 (2015).
Ikutani, M. et al. Identification of innate IL-5-producing cells and their role in lung eosinophil regulation and antitumor immunity. J. Immunol. 188, 703–713 (2012).
Reichman, H. et al. Activated eosinophils exert antitumorigenic activities in colorectal cancer. Cancer Immunol. Res. 7, 388–400 (2019).
Broz, M. L. et al. Dissecting the tumor myeloid compartment reveals rare activating antigen-presenting cells critical for T cell immunity. Cancer Cell 26, 638–652 (2014).
Zhan, Y. et al. GM-CSF increases cross-presentation and CD103 expression by mouse CD8+ spleen dendritic cells. Eur. J. Immunol. 41, 2585–2595 (2011).
Yu, Y. et al. Single-cell RNA-seq identifies a PD-1hi ILC progenitor and defines its development pathway. Nature 539, 102–106 (2016).
Liu, X., Hogg, G. D. & DeNardo, D. G. Rethinking immune checkpoint blockade: ‘beyond the T cell’. J. Immunother. Cancer 9, e001460 (2021).
Helou, D. G. et al. PD-1 pathway regulates ILC2 metabolism and PD-1 agonist treatment ameliorates airway hyperreactivity. Nat. Commun. 11, 3998 (2020).
Ribas, A. et al. Oncolytic virotherapy promotes intratumoral T cell infiltration and improves anti-PD-1 immunotherapy. Cell 170, 1109–1119 (2017).
Chesney, J. et al. Randomized, open-label phase II study evaluating the efficacy and safety of talimogene laherparepvec in combination with ipilimumab versus ipilimumab alone in patients with advanced, unresectable melanoma. J. Clin. Oncol. 36, 1658–1667 (2018).
Seremet, T. et al. Successful treatment with intralesional talimogene laherparepvec in two patients with immune checkpoint inhibitor-refractory, advanced-stage melanoma. Melanoma Res. 29, 85–88 (2019).
Garcia, S., DiSanto, J. & Stockinger, B. Following the development of a CD4 T cell response in vivo: from activation to memory formation. Immunity 11, 163–171 (1999).
Narni-Mancinelli, E. et al. Fate mapping analysis of lymphoid cells expressing the NKp46 cell surface receptor. Proc. Natl Acad. Sci. USA 108, 18324–18329 (2011).
Jackson, J. T. et al. Id2 expression delineates differential checkpoints in the genetic program of CD8α+ and CD103+ dendritic cell lineages. EMBO J. 30, 2690–2704 (2011).
Kara, E. E. et al. CCR2 defines in vivo development and homing of IL-23-driven GM-CSF-producing TH17 cells. Nat. Commun. 6, 8644 (2015).
Keir, M. E., Freeman, G. J. & Sharpe, A. H. PD-1 regulates self-reactive CD8+ T cell responses to antigen in lymph nodes and tissues. J. Immunol. 179, 5064–5070 (2007).
Dranoff, G. et al. Involvement of granulocyte-macrophage colony-stimulating factor in pulmonary homeostasis. Science 264, 713–716 (1994).
Lee, N. A. et al. Expression of IL-5 in thymocytes/T cells leads to the development of a massive eosinophilia, extramedullary eosinophilopoiesis, and unique histopathologies. J. Immunol. 158, 1332–1344 (1997).
Yu, C. et al. Targeted deletion of a high-affinity GATA-binding site in the GATA-1 promoter leads to selective loss of the eosinophil lineage in vivo. J. Exp. Med. 195, 1387–1395 (2002).
Lee, J. J. et al. Defining a link with asthma in mice congenitally deficient in eosinophils. Science 305, 1773–1776 (2004).
Kato, M. et al. Transgenic mouse model for skin malignant melanoma. Oncogene 17, 1885–1888 (1998).
Bankhead, P. et al. QuPath: open-source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).
Cao, Y. et al. Potent neutralizing antibodies against SARS-CoV-2 identified by high-throughput single-cell sequencing of convalescent patients’ B cells. Cell 182, 73–84 (2020).
Adams, T. S. et al. Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis. Sci. Adv. 6, eaba1983 (2020).
Björklund, A. K. et al. The heterogeneity of human CD127+ innate lymphoid cells revealed by single-cell RNA sequencing. Nat. Immunol. 17, 451–460 (2016).
Ricardo-Gonzalez, R. R. et al. Tissue signals imprint ILC2 identity with anticipatory function. Nat. Immunol. 19, 1093–1099 (2018).
Novershtern, N. et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144, 296–309 (2011).
Choi, J. et al. Haemopedia RNA-seq: a database of gene expression during haematopoiesis in mice and humans. Nucleic Acids Res. 47, D780–D785 (2019).
We thank J. Janssen, S. Cree, S. Shaw, E. Mettes, J. Leahy, F. Almeida, E. Pan, A. Johnston, D. Tantalo, members of the Belz, Neeson and Nutt laboratories, and members of the Flow Cytometry, Histology and Bioservices facilities at the Walter and Eliza Hall Institute of Medical Research for technical assistance and for helpful discussions. We express our gratitude to J. Cockwill, our long-term consumer representative, for fruitful discussions and significant consumer input. We are grateful to S. Wilcox and the Genomics platform for their technical help with scRNA-seq. The results published here are in part based upon data generated by the TCGA Research Network (https://www.cancer.gov/tcga/). This work was supported by grants and fellowships from the National Health and Medical Research Council (NHMRC) of Australia (1165443, 1122277, 1054925 and 1135898 to G.T.B.; 1165443 and 1123000 to C.S.; 1175134 to P.M.H.; 1113577 and 1154325 to I.W.; 1158024 to D.H.D.G.; 1140406 to F.S.-F.-G.; 1155342 to S.L.N.; and 1196235 to M. Chopin), Reid Charitable Trusts (I.W.), a grant to The University of Queensland Chair of Immunology (Diamantina Institute, to G.T.B.), Cancer Council NSW (RG21-05 to G.T.B. and N.J.), Cure Cancer Australia and Cancer Australia through the Cancer Australia Priority-driven Cancer Research Scheme (1163990 to N.J., 1158085 to F.S-F.-G.), a fellowship from the Foundation ARC pour la recherche sur le cancer (to N.J.), Centenary Fellowships of the Walter and Eliza Hall Institute (sponsored by CSL, to J.G. and W.S.), a fellowship from the Victorian Government Department of Health and Human Services acting through the Victorian Cancer Agency (to A.B.), and the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation, 259332240/RTG 2099 to V.U.) and scholarships of the Australian Government Research Training Program (to Q.H.) and Melbourne Research Scholarship (to S.Z.). A.N.J.M. was supported by the UK Medical Research Council (U105178805). A.M. is supported by the US-Israel Binational Science Foundation (grant no. 2015163), the Israel Science Foundation (grant nos. 886/15 and 542/20), the Israel Cancer Research Fund, the Richard Eimert Research Fund on Solid Tumors (TAU), the Israel Cancer Association Avraham Rotstein Donation, the Cancer Biology Research Center (TAU) and the Emerson Collective. This research was carried out in part at the Translational Research Institute, Australia. The Translational Research Institute is supported by a grant from the Australian Government. This work was supported through Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIIS.
E.V. is an employee of Innate Pharma. F.S.-F.-G. is a consultant and has a funded research agreement with Biotheus. P.N. has received research funding from Bristol Myers Squibb, Roche Genentech, Merck Sharp & Dohme, CRISPR Therapeutics, Allergan and Compugen. All other authors declare no competing interests.
Peer review information Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Zoltan Fehervari was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Flow cytometric analyses of innate lymphoid cell subsets in BRAFCA;PTENloxp;Tyr::CreERT2 mice.
a, Kinetics of individual tumor growth in BRAFCA;PTENloxp;Tyr::CreERT2 mice. Tumor induction was performed at day 0. Individual data were pooled from 3 independent experiments (n = 16, 3–9 mice per experiment). Each line shows the growth curve for an individual mouse and the mean growth is shown in dark red. HT, hydroxytamoxifen. b, Representative flow cytometric contour plots showing the gating strategy used to identify tumor-infiltrating immune cell populations. c, Frequency of NK cells, ILC1, ILC2 and ILC3 within skin and tumors. The skin was collected from flank on the opposite side to tumor induction. Tumor and skin-resident immune cell populations were identified as indicated in b. Data are pooled from 4 independent experiments (n = 14 mice; 2–5 mice/experiment). c, Each dot represents one mouse and data show mean ± s.e.m. Statistical analyses were performed using a Student’s paired t test. p-values are indicated.
Extended Data Fig. 2 Flow cytometric analyses of innate lymphoid cell subsets in Ret melanoma tumor-bearing mice.
a,b, Enumeration (a and b) and frequency (a) of NK cells, ILC1, ILC2 and ILC3 in tumors (a) and control and tumor-draining lymph nodes (b) at 7 and 17 days after Ret tumor cell inoculation of C57BL/6 J mice. NK cells were CD45+CD3-TCRβ-CD19-(lin-)CD11b+/-NK1.1+NKp46+EOMES+CD49a-, ILC1 lin-CD11b+/-NK1.1+NKp46+EOMES-CD49a+; ILC2 lin-CD11b+/-NK1.1-NKp46-RORγt-GATA3+; ILC3 lin-CD11b+/-NK1.1-NKp46-RORγt+ GATA3- cells. Data pooled from 2 independent experiments with 4–6 mice/experiment/time-point (day 7, n = 8 mice; day 17, n = 12 mice). c–k, Intracellular cytokine production in in vitro stimulated ILC subsets from tumors in C57BL/6 J mice at day 7 and 17 for IFN-γ and TNF-α (c), IL-5 and IL-13 (f), and IL-17A and IL-22 (i) in NK cells and ILC1 (c), ILC2 (f) and ILC3 (i). Representative samples for each time point (n = 4–6 mice/experiment). d,g and j, Heatmaps showing the log2 fold change between day 7 and 17 of the mean intracellular IFN-γ (d), IL-5 and IL-13 (g), and IL-17A and IL-22 (j) in NK cells and ILC1 (d), ILC2 (g) and ILC3 (j) from different tissues. Data are pooled from 2 experiments (day 7, n = 8 mice, except for the lung, n = 4; day 17, n = 12 mice; 4–6 mice per experiment/time point). Insufficient ILC1 were recovered from the contralateral lymph nodes for accurate data interpretation (indicated by a cross). e,h and k, Frequency of tumor-infiltrating IFN-g+ NK cells and ILC1 (e), IL-5+ and IL-13+ ILC2 (h), and IL-17A+ and IL-22+ ILC3 (k) relative to tumor weight (g). Data shows pooled results from day 7 (n = 8) and 17 (n = 12). a,b,e,h, and k, Each dot represents one mouse. a and b, Data show the mean ± s.e.m and statistical analyses were performed using unpaired Student’s t tests (a) or ANOVA followed with Tukey’s multiple comparison tests (b). e,h, and k, Correlations were assessed using non-parametric Spearman’s correlation test. Non-linear fitting curves (one-phase decay) were overlaid. Spearman’s Rho (rs) and p-values are indicated.
a-e, Ret tumor growth and immune cell composition of Ly5.1+/+ chimeric mice reconstituted with C57BL/6 J (CD45.2+/+) or Il7rCre/+RoraΔ/Δ (CD45.2+/+)28 bone marrow. a, Schematic representation of the experimental design. b, Ret tumor growth over time (left) and day 18 tumor size (right). Statistical analyses of tumor growth were performed using TumGrowth. Data represents one experiment with 6 mice/genotype and show the mean ± s.e.m. c-d, Representative flow cytometric contour plots (left panels) and enumeration (right panels) of intestinal (c) and tumor-infiltrating (d) ILC2 at day 13 after Ret tumor cell inoculation. ILC2 were defined as live CD45.2+lin-(CD3ε-TCRβ-CD19-CD11b-NKp46-RORγt-) CD90.2+GATA3+ cells. e, Enumeration of tumor infiltrating leukocytes cells at day 13 post Ret tumor inoculation. Leukocytes were defined as live CD45+; B cells as live CD45+CD3ε-TCRβ-CD19+; CD4+ T cells as live CD45+CD3ε+TCRβ+CD4+; CD8+ T cells as live CD45+CD3ε+TCRβ+CD8+; NK cells as live CD45+CD3ε-TCRβ-CD19-(lin-)NKp46+Eomes+; ILC1 as live lin-NKp46+Eomes-; and ILC3 as live lin-CD11b-NKp46-RORγt+. Data pooled from 2 independent experiments (n = 6 mice/genotype/experiment) and show the mean ± s.e.m. b-e. Each circle represents one mouse and p-values are indicated.
Ret tumor growth and tumor immune cell composition of C57BL/6 J and ICOS-T mice (CD4Cre/+ICOSfl-DTr/+) treated with PBS or rmIL-33 and/or diphtheria toxin (DTx). a, Experimental design and treatment regime for amplification and deletion of ILC2 in vivo. b, Ret tumor growth (left panel) and tumor weight (g) (right panel) at day 17. Data show the mean ± s.e.m. Each dot represents one mouse. Statistical differences were assessed using an unpaired Student’s t test between the groups ICOS-T + IL-33 and ICOS-T DTx + IL-33 and the p value is indicated. Data show C57BL/6 J + PBS (tumor growth, n = 3; tumor weight, n = 2 mice), C57BL/6 J + IL-33 (tumor growth, n = 3; tumor weight, n = 3 mice), ICOS-T + IL-33 (tumor growth, n = 6; tumor weight, n = 4 mice), ICOS-T + IL-33 + Dtx (tumor growth, n = 6; tumor weight, n = 4 mice). c,d, Representative flow cytometric contour plots (left panels) and quantification (middle panels) of tumor-infiltrating ILC2 (CD45+lin-(CD3ε-TCRβ-CD11b-NK1.1-NKp46-) RORγt-GATA3+) and regulatory CD4+ T cells (Tregs, CD45+CD3ε+TCRβ+CD8-CD4+CD25+Foxp3+) in each treatment. Data show mean ± s.e.m. (middle panels). Dot plots show the correlation of the frequency of (c, right) tumor infiltrating ILC2 or (d, right) Tregs with tumor weight (g) (right panels). Individual data points are colored by experimental group. Correlations were assessed using non-parametric Spearman’s correlation tests and overlaid by linear regression curves. Each dot represents one mouse (n = 13 mice). Spearman’s Rho (rs) and p-values are indicated. e, Frequency of tumor-infiltrating CD8+ (live CD45+CD3ε+TCRβ+CD8+CD4-) T cells. Data show mean ± s.e.m (left panel). Dot plots showing correlation between the frequency of tumor-infiltrating CD8+ T cells and tumor weight (g) (right panel). Individual data points are colored by experimental group. The correlation was assessed using a non-parametric Spearman’s correlation test. A simple linear regression curve was been overlaid on data for individual animals (n = 13) from one experiment. Spearman’s Rho and p-values are indicated.
Extended Data Fig. 5 Internal validation and prognosis of an ILC2/type 2 immune cell signature in human melanoma.
a,b, ILC2 infiltration probability is predicted from NanoString transcriptome profiles, which measures a panel of 730 genes. For each patient, the predicted infiltration probability is correlated with IHC markers (a) and immune cell infiltration estimates (b) from Pan Immune multiplex IHC obtained from that patient. Spearman correlation values are indicated. c, Analysis of the impact of enrichment of type 2 immune cell infiltration on melanoma patient survival using the publicly available TCGA database. Tumor infiltration probability was determined as described in the Methods using machine learning. Kaplan-Meier overall survival curves of metastatic melanomas (n = 367) plotted against the likelihood of high and low infiltration of tumors by ILC2. f, Kaplan-Meier survival statistical analysis was performed using a Log-rank test. p-value is indicated.
Extended Data Fig. 6 Ret tumor-infiltrating ILC2 express high levels of GM-CSF and high Csf2 expression in human melanoma tumors is associated with increased survival.
a, Representative flow cytometric full gating strategy used to identify GM-CSF-expressing ILC2 in Ret melanoma tumors 7 days after tumor inoculation. GM-CSF expression in other immune and non-immune cell subsets is also depicted. b, Frequency of GM-CSF producing cells in Ret melanoma tumors. Each circle represents one mouse and data show mean ± s.e.m. Data are pooled from 2 independent experiments (n = 12 mice) with 6 mice/experiment. c, Representative flow-cytometric gating strategy used to identify polyfunctional ILC2 (IL-5+IL-13+GM-CSF+). a-c, Single cell suspensions of digested tumor cells were stimulated with 50 ng/ml PMA, 500 ng/ml ionomycin in the presence of GolgiStopTM for 4 h before intracellular staining for IL-5, IL-13 and GM-CSF. a and c, Data show one of two independent experiments performed with 6 mice/experiment. d,e, Analysis of the publicly available TCGA database. d, Plot shows tumor CSF2 gene expression according to type 2 immune cell tumor enrichment probabilities in individual human metastatic melanoma samples. Tumor enrichment probabilities were determined as described in the Material and Methods using machine learning. Mean difference in CSF2 expression between TCGA metastatic samples predicted with type 2 immune cell infiltration (red) or no infiltration (blue). Data show statistical significance was determined by Student’s t test. e, Kaplan-Meier overall survival curves of metastatic melanomas (n = 367) plotted against the likelihood of high and low tumor CSF2 expression. Kaplan-Meier survival statistical analysis was performed using a Log-rank test. p-values are indicated.
Myeloid immune cell composition of Ly5.1+/+ chimeric mice reconstituted with C57BL/6 J (CD45.2+/+) or Il7rCre/+RoraΔ/Δ (CD45.2+/+)28 bone marrow. a, Schematic representation of the experimental design. b-d, Representative flow cytometric contour plots (left panels) and enumeration (right panels) of splenic (b) and tumor-infiltrating (c and d) eosinophils, dendritic cells (DC), neutrophils and macrophages at day 13 after Ret tumor cell inoculation. Immune subsets were defined as eosinophils: live CD45.2+CD64-F4/80-CD3ε-CD19-CD11c+/-MHCII+/-CD11b+Siglec-F+Ly6G- cells; DC: live CD45.2+CD64-F4/80-CD3ε-CD19-CD11c+MHCII+ cells; macrophages: live CD45.2+CD64+F4/80+ cells; neutrophils: live CD45.2+CD64-F4/80-CD3ε-CD19-CD11c+/-MHC II+/-CD11b+Siglec-F-Ly6G+ cells. d, Tumor-infiltrating DC were segregated into CD11b+ DC and CD103+ DC. b-d, Data are pooled from 2 independent experiments (n = 6 mice/genotype/experiment) and show the mean ± s.e.m. Each dot represents one mouse. b-f, Statistical differences were assessed using unpaired Student’s t tests and exact p-values are indicated.
Extended Data Fig. 8 ILC2/type 2 immune cell enriched human melanoma tumors are associated with increased eosinophil infiltration and gene expression.
a and b, Analysis of the impact of tumor ILC2/type 2 immune cell and eosinophil infiltration on melanoma patient survival using the publicly available TCGA database. a, Dot plot of the probability of tumor eosinophil infiltration versus the probability of type 2 immune cell tumor enrichment in human metastatic melanoma samples (n = 367 tumors). Tumor-infiltration probabilities were determined as described in the Material and Methods using machine learning. The correlation was assessed by using Pearson’s correlation test. Each dot represents one human sample b, Kaplan-Meier overall survival curves of metastatic melanomas (n = 367) plotted against to the likelihood of the high and low infiltration of tumors by type 2 immune cells and eosinophils. Kaplan-Meier survival statistical analysis was performed using a Log-rank test. p-value is indicated. c, Pearson’s correlation analyses of the tumor-infiltrating ILC2 and eosinophil probabilities with CSF2, IL33, IL5, GATA2, RNASE3, EPX, PRG2, TNF and IFNG genes in metastatic tumor melanoma samples available from the publicly TCGA database. Populations and genes that were found to positively and negatively correlate between the indicated populations are shown in red or blue, respectively. Circle size represents the strength of the correlation between two populations.
Flow cytometric analyses of immune cells subsets in intestinal tissues and mesenteric lymph nodes collected from C57BL/6 J and Pdcd1-/- mice at steady-state. a, Representative flow cytometric histogram showing PD-1 expression on C57BL/6 J and Pdcd1-/- intestinal ILC2. Representative of 3 independent experiments (n = 3 mice/experiment) with similar PD-1 expression on C57BL/6 J ILC2. b, Enumeration of ILC2 in the mesenteric lymph node (left) and the lamina propria of the small intestine collected (right) from C57BL/6 J and Pdcd1-/- mice. Data (C57BL/6J, n = 9 mice; Pdcd1-/-, n = 8 mice) are pooled from 3 independent experiments with 2-3 mice/genotype/experiment. c, Schematic representation of the experimental design (top) and frequency of bone marrow derived ILC2 in the mesenteric lymph nodes and small intestine of mixed bone marrow chimeric mice (bottom). Data are pooled from 2 independent experiments (n = 12 mice; 6 mice/experiment). d, Flow cytometric contour plots (left panels) and quantification (right panels) of proliferation (Ki67+ cells) in ILC2 isolated from the mesenteric lymph nodes and small intestine of mixed bone marrow chimeric mice. Data are pooled from 2 independent experiments (n = 12 mice; 6 mice/experiment). b-d, Each circle represents one mouse and data show mean ± s.e.m. Statistical analyses were performed using paired (d) and unpaired (b) Student’s t tests or ANOVA followed with Tukey’s multiple comparison tests (c). p-values are indicated.
Extended Data Fig. 10 IL-33 stimulation induces expansion and proliferation of KLRG1+ ILC2 associated with increased cytokines production and PD-1 expression.
a-f, Flow cytometric analyses of the impact of IL-33 stimulation on purified intestinal ILC2. Live ILC2 were identified as follow: CD45+CD3ε-CD19-TCRβ-CD11b-NK1.1-c-kit-Sca-1+KLRG1+/-. a, Experimental design. Purified intestinal ILC2 were cultured for 2 and 5 days in complete media supplemented with rIL-7 or rIL-7+rIL-33 (all 40 ng/ml) before flow cytometric analyses. b, ILC2 enumeration. c-e, Representative flow-cytometric contour plots (c, d and e, left panels) and frequencies or geometrical mean fluorescent intensities of KLRG1 (c, middle panel), Ki-67 (c, right), IL-5 (d) and GM-CSF (e). c-e Data show one of two experiments showing individual responses (open circles) and the mean of three biological replicates. Flow cytometric contour plots depict a representative analysis performed at day 5 of culture. f, GM-CSF concentration in culture supernatant of purified human ILC2 isolated from the blood of three healthy donors and stimulated with rIL-2 (5 ng/ml) or rIL-2+rIL-33 (5 ng/ml and 10 ng/ml, respectively). ILC2 were identified as Live+CD45+lin-(TCRαβ-TCRγδ-CD14-CD3-CD19-CD34-FcεRI-CD123-CD303-CD15-CD33-CD11c-CD56-CD16-) CD127+ CRTH2+. Data show the mean GM-CSF production of 3 healthy donors pooled from two independent experiments. N.D., not detected. g, Histograms (left panel) and frequencies (right panel) of PD-1 expression on wild-type and Pdcd1-/- ILC2 (2.5 × 103 cells/well) cultured in vitro for 2 and 5 days in complete media supplemented with rIL-7 or rIL-7+rIL-33 (all 40 ng/ml). Data show the mean of three biological replicates. h, Representative flow-cytometric contour plots showing PD-1 expression on purified human ILC2 isolated from blood of healthy donors after two days of stimulation with rIL-2 (5 ng/ml) or rIL-2+rIL-33 (5 ng/ml and 10 ng/ml, respectively). ILC2 were identified as in f. One representative healthy donor out of two yielding similar results is shown. b-f, Statistical analyses were performed using paired Student’s t-test. p-values are indicated.
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Jacquelot, N., Seillet, C., Wang, M. et al. Blockade of the co-inhibitory molecule PD-1 unleashes ILC2-dependent antitumor immunity in melanoma. Nat Immunol (2021). https://doi.org/10.1038/s41590-021-00943-z
Nature Immunology (2021)