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A conserved dendritic-cell regulatory program limits antitumour immunity

An Author Correction to this article was published on 05 June 2020

This article has been updated

Abstract

Checkpoint blockade therapies have improved cancer treatment, but such immunotherapy regimens fail in a large subset of patients. Conventional type 1 dendritic cells (DC1s) control the response to checkpoint blockade in preclinical models and are associated with better overall survival in patients with cancer, reflecting the specialized ability of these cells to prime the responses of CD8+ T cells1,2,3. Paradoxically, however, DC1s can be found in tumours that resist checkpoint blockade, suggesting that the functions of these cells may be altered in some lesions. Here, using single-cell RNA sequencing in human and mouse non-small-cell lung cancers, we identify a cluster of dendritic cells (DCs) that we name ‘mature DCs enriched in immunoregulatory molecules’ (mregDCs), owing to their coexpression of immunoregulatory genes (Cd274, Pdcd1lg2 and Cd200) and maturation genes (Cd40, Ccr7 and Il12b). We find that the mregDC program is expressed by canonical DC1s and DC2s upon uptake of tumour antigens. We further find that upregulation of the programmed death ligand 1 protein—a key checkpoint molecule—in mregDCs is induced by the receptor tyrosine kinase AXL, while upregulation of interleukin (IL)-12 depends strictly on interferon-γ and is controlled negatively by IL-4 signalling. Blocking IL-4 enhances IL-12 production by tumour-antigen-bearing mregDC1s, expands the pool of tumour-infiltrating effector T cells and reduces tumour burden. We have therefore uncovered a regulatory module associated with tumour-antigen uptake that reduces DC1 functionality in human and mouse cancers.

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Fig. 1: Identification of a dendritic-cell cluster enriched in immunoregulatory and maturation molecules.
Fig. 2: The mregDC1 program is associated with uptake of tumour antigens.
Fig. 3: IL-4 blockade enhances DC1 functionality and antitumour immunity.
Fig. 4: Human NSCLC lesions are populated by mregDCs.

Data availability

All mice sequencing data are publicly available (GEO accession code GSE131957). All human sequencing data is available on NCBI with BioProject ID PRJNA609924.

Code availability

Scripts to reproduce clustering and differential expression analyses, as well as for direct reproduction of figures related to computational results, are available at https://github.com/effiken/Maier_et_al_nature_2020.

Change history

  • 05 June 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

This work was supported by National Institutes of Health (NIH) grants R01 CA154947, R01 s (to M.M.), 1R01CA212376 (to S.G. and C.V.R), F30CA243210 (to S.T.C.) and 5T32CA078207 (to A.M.L.). We thank C. Berin for helpful discussions; D. Farber and P. Dogra for critical comments on the manuscript; and the Mount Sinai flow cytometry core, Human Immune Monitoring Center and Mount Sinai Biorepository for support. Research support was provided by Regeneron and Takeda.

Author information

Affiliations

Authors

Contributions

M.M. conceived the project. B.M., B.D.B. and M.M. designed the experiments. B.M., A.M.L., B.D.B. and M.M. wrote the manuscript. A.M.L. and E.K. performed computational analysis. T.M. provided intellectual input and facilitated access to human samples. A.H.R. provided input to single-cell mapping strategies. B.M., S.T.C., N.T., C.C., A.C., S.M., J.L. and L.W. performed experiments. J.P.F. and N.B. provided B16-BFP/OVA cells. B.R. and M.E.K. provided OP4-DL1 cells. C.V.R. and S.G. provided Axl−/− and Axl−/− Mertk−/− bone marrow, and assisted with experiment design. A.W. and R.F. provided human tumour lesions. N.R.D. funded part of the study.

Corresponding author

Correspondence to Miriam Merad.

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Competing interests

Research support for these studies was provided by Regeneron and Takeda. The authors declare no other competing financial interests.

Additional information

Peer review information Nature thanks Cornels Melief and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 mregDCs are a distinct dendritic-cell cluster present in numerous tumour models.

a, Digested lungs of naive or KP-tumour-bearing mice at day 28 post tumour-cell injection were stained with antibodies conjugated either to fluorophores for FACS or to oligonucleotides for CITE-seq analysis. CD45+ Siglec F Ly6G MHCII+ CD11c+ cells were sorted and loaded onto a 10x Chromium chip for scRNA-seq and CITE-seq analysis. Dendritic-cell clusters were identified according to marker-gene expression after clustering of transcriptomes. Heat maps show UMI counts of lineage genes across all clusters after downsampling to 2,000 UMIs per cell. b, Left, gene–gene correlation of highly variable genes, with relevant gene modules outlined and annotated; right, scRNA expression divided by cluster. Genes on left and right panels are aligned. c, CD45+ Siglec F Ly6G MHCII+ CD11c+ cells from lungs of naive or B16-BFP/OVA-tumour-bearing mice at day 22 were sorted and loaded onto a 10x Chromium platform for scRNA-seq. DCs were mapped to the clusters generated for the experiment shown in Fig. 1 by maximum-likelihood classification. Heat maps show UMI counts of lineage genes across all clusters after downsampling to 2,000 UMIs per cell. d, Mouse-tumour public scRNA data for immune cells from an M38 model13 and a T3 sarcoma model28 were accessed from GEO. Top, broad cell types were sorted in silico using gene lists, resulting in pure DC populations. pDC, plasmacytoid DC. Bottom left, DC1s, DC2s and mregDCs were identified using scores generated from gene lists that defined these populations. Bottom middle, annotations in the heat map were derived from k-means clustering (k = 3) of coordinates in the dendritic-cell-score scatter plot. Bottom right, DCs of each annotation are quantified. Gene lists defining cell types for in silico sorting and stratification of dendritic-cell subtypes are in Supplementary Table 2. e, Lung DC1s and migratory DC1s (migDC1) from DLNs were sorted and analysed by RNA-seq. Genes highlighted in red identify a reference set of genes from migratory DCs7. f, Lung DC1s and migratory DC1s from DLNs in both naive and KP-tumour-bearing mice were sorted and analysed by RNA-seq. The plot compares migDC1 gene expression with lung DC1 expression by log2FC in naive (x-axis) and KP-tumour-bearing (y-axis) mice. Genes upregulated in mregDCs relative to DC1s (log2FC greater than 2; Benjamini–Hochberg-adjusted P-value less than 0.01), as assayed by scRNA-seq, are shown in gold. g, Stratification of dendritic-cell transcriptomes using dendritic-cell subtype scores in naive and KP-tumour-bearing lungs. Scores for each subtype were generated from gene lists that were differentially expressed among clusters. Single cells are coloured by cluster identification (left) or CITE-seq surface marker expression (colour-bar units are log10(1 + ADT counts)). Gene scores are the same as in d (lower left).

Extended Data Fig. 2 The mregDC program is enriched in both canonical dendritic-cell subsets upon tumour-antigen uptake.

a, b, CD45+ Siglec F Ly6G MHCII+ CD11c+ cells were sorted from lungs of Ccr7−/− mice and loaded onto the 10x Chromium followed by scRNA-seq. Transcriptomes were mapped to the clusters generated for the wild-type experiment shown in Fig. 1 by maximum-likelihood classification. a, The heat map shows UMI counts of selected genes in dendritic-cell clusters after downsampling to 2,000 UMIs per cell, comparing cells from Ccr7−/− mice to cells from WT mice. b, Comparison of differential expression analyses between mregDCs and resting DCs in WT mice (x-axis) and Ccr7−/− mice (y-axis) (b). c, Frequencies of mregDCs as a percentage of total DCs, as measured by scRNA-seq in naive and KP–GFP-tumour-bearing mice. d, Gating strategy for subsets of conventional lung DCs. e, Flow cytometry of GFP+ versus GFP DC2s (CD11b+ CD103) from KP–GFP-tumour-bearing mice. f, Flow cytometry of GFP+ versus GFP DC1s or DC2s from KP–GFP-tumour-bearing mice. g, Flow cytometry of BFP+ versus BFP DC1s from B16-BFP/OVA tumour-bearing mice. The experiment shown is representative of two independent experiments; *P < 0.05; **P < 0.01, ***P < 0.001, ****P < 0.0001 (Student’s t-test); data are means ± s.d. (eg). h, KP–GFP cells were exposed to ultraviolet radiation for 30 min, rested for 24 h, and stained with annexin V and propidium iodide in order to confirm induction of apoptosis before experiments involving coculture of DCs. i, Differential expression between mregDCs identified by transcriptome from KP-tumour-bearing and naive mice. Genes in green are significantly differentially expressed (Benjamini–Hochberg-adjusted P-value of less than 0.15); selected immune genes are shown in orange.

Extended Data Fig. 3 The mregDC program is independent of MyD88/TRIF, inflammasome signalling and lymphocytes.

ad, Flow-cytometry analysis of DC1s isolated from KP–GFP-tumour-bearing lungs in Asc−/− (a), Il1r−/− (b), Myd88−/−Trif−/− (c) and Rag1−/− (d) mice. e, Heat map showing average TAM receptor RNA expression in mregDC, DC1 and DC2 scRNA-seq clusters. f, Differential expression of TH2 response genes across dendritic-cell clusters identified by scRNA-seq, showing a log2FC between average mregDC expression and resting dendritic-cell expression. Genes in green are differentially expressed (Benjamini–Hochberg-adjusted P-value less than 0.15); TH2 response genes are in orange. g, k, Mice were injected with KP–GFP tumour cells, treated with anti-IL-4 (αIL-4) or control IgG on days 21, 24 and 26, and analysed on day 28. GFP+ DC1s carrying tumour antigens in lung and DLNs (g) and T cells in DLNs (k) were analysed by flow cytometry. h, KP–GFP-tumour-bearing mice were injected with an agonistic CD40 antibody (CD40a) on days 25 and 27; lungs were analysed on day 28. i, KP–GFP-tumour-bearing mice were injected with polyI:C on day 27, and lungs were analysed on day 28. j, GFP+ conventional DC1s were purified from KP–GFP-tumour-bearing lungs from B6D2 mice treated either with anti-IL-4 or control IgG and cocultured with naive CD8+ JEDI T cells isolated from JEDI mouse spleens. JEDI T cells were analysed on day 2. One experiment, representative of two independent experiments, is shown (ad, gk). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 (Student’s t-test (ad, g, k) or one-way ANOVA and Tukey’s test (h, i)). Data are shown as means ± s.d. (ad, gi, k).

Extended Data Fig. 4 Protein expression profile of mregDCs in human NSCLC lesions.

a, Average CITE-seq surface protein staining intensity of dendritic-cell clusters in non-involved lung (nLung) and tumour lesions isolated from human NSCLC resections (n = 7). b, scRNA-seq data from a published dataset29 of matched nLung and tumour from resection specimens of eight patients with NSCLC were mapped to the clusters generated for the NSCLC data in Fig. 4 by maximum-likelihood classification. Heat maps show downsampled UMI counts in dendritic-cell clusters after downsampling cells to 2,000 UMIs per cell and evenly sampling cells from dendritic-cell types.

Supplementary information

Reporting Summary

Supplementary Table 1

Single-cell RNA-seq sample quality metrics.

Supplementary Table 2

Gene sets used in this study, including gene sets used to filter cells prior to clustering, gene sets used to filter cells from public datasets, and gene sets used for scoring dendritic cell subpopulations.

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Maier, B., Leader, A.M., Chen, S.T. et al. A conserved dendritic-cell regulatory program limits antitumour immunity. Nature 580, 257–262 (2020). https://doi.org/10.1038/s41586-020-2134-y

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