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Blockade of the co-inhibitory molecule PD-1 unleashes ILC2-dependent antitumor immunity in melanoma

Abstract

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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Fig. 1: Innate lymphoid cells infiltrate mouse and human melanoma tumors.
Fig. 2: ILC2-dependent anti-melanoma immunity.
Fig. 3: GM-CSF-expressing group 2 innate lymphoid cells mediate anti-melanoma responses.
Fig. 4: ILC2-driven eosinophil recruitment controls melanoma antitumor immunity.
Fig. 5: ILC2-derived GM-CSF control eosinophil homeostasis, survival and effector functions.
Fig. 6: Melanoma-infiltrating ILC2 express high levels of PD-1.
Fig. 7: PD-1 expression inhibits tumor ILC2 infiltration and ILC2-dependent antitumor functions.
Fig. 8: IL-33 combined with anti-PD-1 unleashes anti-melanoma immunity mediated by the ILC2–eosinophil axis.

Data availability

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.

Code availability

Source code for statistical learning models is publicly available at https://github.com/DavisLaboratory/Jacquelot_2021_reproducibility/.

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Acknowledgements

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.

Author information

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Authors

Contributions

N.J. and G.T.B. supervised the study and wrote the manuscript with the input of all the co-authors. N.J., C.S. and G.T.B. designed the experiments, analyzed and interpreted data and designed figures. N.J., C.S., A.P., M.W., S.G.-T., C.L., Q.H., J.S., F.S.-F.-G., K.T., S.M., M. Camilleri., K.L., S.Z., M. Chopin and T.M.-H. performed experiments. Y.L. and S.H.-z. performed bioinformatics analyses under the supervision of W.S. and M.J.D. M.W., A.P. and S.M. performed CyTOF analyses. M.W. and K.T. performed mIHC analyses. C.A.d.G., S.L.N., V.U., B.C., J.R.G., P.S.F., P.M.H., A.N.J.M., D.H.D.G., A.B., J.C., E.V., I.W., J.A.T., A.M., M.J.D., W.S. and P.J.N. provided tools, reagents, mouse strains and intellectual input. All authors reviewed, edited, provided input on and approved the manuscript before submission.

Corresponding authors

Correspondence to Nicolas Jacquelot or Gabrielle T. Belz.

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Competing interests

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.

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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.

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Extended data

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). ck, 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.

Extended Data Fig. 3 ILC2-dependent anti-tumor immunity in Il7rCre/+RoraΔ/Δ mice.

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.

Extended Data Fig. 4 ILC2-dependent anti-tumor immunity in ICOS-T mice.

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.

Extended Data Fig. 7 Reduced eosinophils in Ret tumor-bearing ILC2 deficient mice.

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.

Extended Data Fig. 9 PD-1 inhibits tissue ILC2 accumulation and ILC2 proliferation.

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

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