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Anti-CTLA-4 antibodies drive myeloid activation and reprogram the tumor microenvironment through FcγR engagement and type I interferon signaling

Nature Cancer volume 3, pages 1336–1350 (2022)Cite this article

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Abstract

Despite the clinical success of checkpoint inhibitors, a substantial gap still exists in our understanding of their mechanism of action. While antibodies to cytotoxic T lymphocyte-associated protein-4 (CTLA-4) were developed to block inhibitory signals in T cells, several recent studies have demonstrated that Fcγ receptor (FcγR)-dependent depletion of regulatory T cells (Treg) is critical for antitumor activity. Here, using single-cell RNA sequencing, we dissect the impact of anti-CTLA-4-blocking, Treg cell-depleting and FcR-engaging activity on the immune response within tumors. We observed a rapid remodeling of the innate immune landscape as early as 24 h after treatment. Using genetic Treg cell ablation models, we show that immune remodeling was not driven solely by Treg cell depletion or CTLA-4 blockade but mainly through FcγR engagement, downstream activation of type I interferon signaling and reduction of suppressive macrophages. Our findings indicate that FcγR engagement and innate immune remodeling are involved in successful anti-CTLA-4 treatment, supporting the development of optimized immunotherapy agents bearing these features.

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Fig. 1: Characterization of immune dynamics in the TME following anti-CTLA-4 treatments.
Fig. 2: Anti-CTLA-4 m2a treatment drives rapid and broad rearrangement of the adaptive and innate immune TME.
Fig. 3: Molecular dissection of the immediate response to mAbs to CTLA-4.
Fig. 4: FcγR-dependent activation underlies myeloid reprogramming following anti-CTLA-4 m2a treatment.
Fig. 5: FcγR engagement drives a robust activation of type I IFN signaling.

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Data availability

Raw and processed scRNA-seq data can be downloaded from NCBI Gene Expression Omnibus with accession number GSE186484. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

Custom code has been deposited to GitHub and can be accessed at https://github.com/tomerlan/paper_aCTLA4_MOA.

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Acknowledgements

We thank N.D. Geller from the Scientific Illustration unit of the Weizmann Institute for artwork. I.Y. is a Cancer Research Institute Irvington Fellow supported by the Cancer Research Institute. I.A. is an Eden and Steven Romick Professorial Chair, supported by Merck KGaA, Darmstadt, Germany, the Chan Zuckerberg Initiative, the HHMI International Scholar award, the European Research Council Consolidator grant (ERC-COG) 724471- HemTree2.0, an SCA award of the Wolfson Foundation and Family Charitable Trust, an MRA Established Investigator Award (509044), the Helen and Martin Kimmel award for innovative investigation, the NeuroMac DFG/Transregional Collaborative Research Center grant, a Cancer Research Institute Technology Impact Award (CRI Award CRI3250), an International Progressive MS Alliance/NMSS PA-1604 08459 and an Adelis Foundation grant. S.A.Q. is funded by a Cancer Research UK (CRUK) Senior Cancer Research Fellowship (C36463/A22246) and a CRUK Biotherapeutic Program grant (C36463/A20764). K.S.P. received funding from the NIHRBTRU for Stem Cells and Immunotherapies (167097). C.C. is funded by Becas Chile. Part of this work was undertaken at UCL with support from the CRUK-UCL Centre (C416/A18088) and the Cancer Immuno-therapy Accelerator Award (CITA-CRUK) (C33499/A20265).

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  1. These authors contributed equally: Ido Yofe, Tomer Landsberger, Adam Yalin.

Authors and Affiliations

  1. Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel

    Ido Yofe, Tomer Landsberger, Adam Yalin, Eyal David, Chamutal Borenstein, Oren Barboy & Ido Amit

  2. Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK

    Isabelle Solomon, Cristobal Costoya, Dafne Franz Demane, Mansi Shah, Ignacio Matos, Karl S. Peggs & Sergio A. Quezada

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Contributions

I.Y., T.L., A.Y., S.A.Q. and I.A. conceived the project, designed the experiments, analyzed the data and wrote the manuscript. I.Y., T.L., A.Y., I.S., C.C. and D.F.D. performed the experiments, E.D. contributed to data analysis, M.S., C.B., O.B. and I.M. contributed experimentally and K.S.P., S.A.Q. and I.A. supervised the project.

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Correspondence to Sergio A. Quezada or Ido Amit.

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Extended data

Extended Data Fig. 1 Flow cytometry gating strategy and scRNAseq data pre-processing steps.

(a) Gating strategy used to sort CD45+ cells or TCRb+ (T) cells. (b) Violin plot depicting the distribution of detected genes per cell per sample. (c) Violin plot depicting the distribution of unique molecular identifiers (UMIs) per cell per sample. (d) Violin plot depicting the distribution of percentage of UMIs coming from mitochondrial genes per cell per sample. (e) Explained variance (Y) as function of principal components (PCs, X) for PCA of normalized expression values of all cells and 3,000 most variable genes (Methods). UMAP of all cells before (left) and after (right) batch correction by CCA (Methods).

Extended Data Fig. 2 Characterization of immune dynamics in the TME following anti-CTLA-4 treatments.

(a) Principle component analysis (PCA) of the relative abundance of each cell type in each sample, or the mean gene expression of genes with UMI > 2 in cells > 5 in each sample. Performed separately on lymphoid and myeloid cells. Pointes are samples color coded for treatment arm. Dashed line separates control and anti-CTLA-4 m1 from anti-CTLA-4 m2a. (b) Comparison of the fractions of the indicated lymphoid cell types in individual mice in each treatment group. n = 3, 2 and 3 biologically independent samples for Ctrl, m1 and m2a groups respectively were used for statistical analysis. (c) Comparison of the fractions of the indicated myeloid cell types in individual mice in each treatment group. n = 5, 3 and 5 biologically independent samples for Ctrl, m1 and m2a groups respectively were used for statistical analysis. Bars represent mean ± SE of mice per treatment arm. Two-tailed Student’s t test was used.

Extended Data Fig. 3 Anti-CTLA-4 m2a treatment drives rapid and broad rearrangement of the adaptive and innate immune TME.

(a) UMAP of scRNA-seq data from day 1 post-treatment tumor infiltrating immune cells. Color code for cell type assignment is indicated in the legend. (b) UMAP projections of relative cell density per treatment, or relative CTLA-4 expression. (c) Comparison of the fractions of the indicated lymphoid cell types in individual mice in each treatment group. n = 3, 6 and 7 biologically independent samples for Ctrl, m1 and m2a groups respectively were used for statistical analysis. (d) Comparison of the fractions of the indicated myeloid cell types in individual mice in each treatment group. n = 2, 6 and 7 biologically independent samples for Ctrl, m1 and m2a groups respectively were used for statistical analysis. (e) Consensus hierarchical clustering (n clusters = 2, Methods) of cellular makeup across samples with Spearman correlation as distance metric. Color coded for P co-clustering after 500 iterations. (f) Histogram of CD4+ draining lymph node-derived cells frequency per FOXP3 protein expression, as measured by flow cytometry. n = 5 per group. Average percentage to the right of the dashed line is stated. Day 4 post-treatment. (g) As in (f) for day 1 post-treatment. (h) Growth curves of MCA-205 tumors in mice untreated, or treated with 50 µg/mouse anti-CTLA-4 m2a at days 6, 9, 12, with or without 100 µg/mouse anti-Gr1 at days 5 and 13. n = 5 mice per arm. In (c) and (d), bars represent mean ± SE of mice per treatment arm. Two-way ANOVA (fraction ~ treatment * batch) was used.

Extended Data Fig. 4 Molecular dissection of the immediate response to anti-CTLA-4 mAbs.

(a) 12 leading DEGs for myeloid cell populations. Log2 fold-change is relative to all other cells. (b) Number of DEGs per metacluster in anti-CTLA-4 m1 vs. control. (c) Scatterplot comparing anti-CTLA-4 m2a (Y) vs. Control (X) gene expression changes in monocytes. Leading DEGs are annotated. (d) Scatterplot comparing anti-CTLA-4 m2a (Y) vs. Control (X) gene expression changes in TAMs. Leading DEGs are annotated. (e) Scatterplot comparing anti-CTLA-4 m2a (Y) vs. Control (X) gene expression changes in monocytes and TAMs combined. Leading DEGs are annotated. Differential gene expression is estimated using two-sided Wilcoxon rank-sum test, Benjamini-Hochberg adjusted p-value < 0.05, FC > 1.25.

Source data

Extended Data Fig. 5 FcγR dependent activation underlies myeloid reprograming following anti-CTLA-4 m2a treatment.

(a) Enrichment of lymphoid cell type frequencies in DT vs control in Foxp3DTR background, or anti-CTLA-4 m2a versus control in WT background. Values are log2 fold change of the mean frequency of each cell type across mice, dot size indicates the average frequency of the cell population between treatments, and color the p-value of two-tailed Student’s t test between each treatment and control. (b) Comparison of the fractions of the indicated lymphoid cell types in individual mice in each treatment group. n = 4 and 5 biologically independent samples for Ctrl and DT groups respectively were used for statistical analysis. (c) As in (a), but for myeloid cells. (d) As in (b), but for myeloid cells. n = 4 and 5 biologically independent samples for Ctrl and DT groups respectively were used for statistical analysis. (e) Enrichment of lymphoid cell type frequencies in anti-CTLA-4 m2a versus anti-CTLA-4 m1 in FCGR KO background, or anti-CTLA-4 m2a versus anti-CTLA-4 m1 in WT background. Values are log2 fold change of the mean frequency of each cell type across mice, dot size indicates the average frequency of the cell population between treatments, and color the p-value of two-tailed Student’s t test between each treatment and control. (f) Comparison of the fractions of the indicated lymphoid cell types in individual mice in each treatment group. n = 7 and 4 biologically independent samples for m1 and m2a groups respectively were used for statistical analysis. (g) As in (e), but for myeloid cells. (h) As in (f), but for myeloid cells. n = 6 and 6 biologically independent samples for m1 and m2a groups respectively were used for statistical analysis. In (b), (d), (f), and (h), Bars represent mean ± SE of mice per treatment arm. Two-tailed Student’s t test was used.

Source data

Extended Data Fig. 6 FcγR engagement drives a robust activation of type-I IFN signaling.

(a) Comparison of the fractions of the indicated cell populations in individual mice in each genotype and treatment group. n = 6, 4, and 3 biologically independent samples for each of Ctrl, m1 and m2a in WT, FCGR KO and IFNAR KO groups respectively, were used for statistical analysis. (b) Scatterplot comparing anti-CTLA-4 m2a WT (Y) vs. anti-CTLA-4 m2a FCGR KO (X) gene expression changes in BMDM cells. Leading DEGs are annotated. (c) Scatterplot comparing anti-CTLA-4 m2a WT (Y) vs. anti-CTLA-4 m2a IFNAR KO (X) gene expression changes in BMDM cells. Leading DEGs are annotated. (d) Scatterplot comparing anti-CTLA-4 m2a FCGR KO (Y) vs. anti-CTLA-4 m2a IFNAR KO (X) gene expression changes in BMDM cells. Leading DEGs are annotated. (e) Percent of proliferating T cells and interferon gamma secretion in idle T cells, activated T cells, and activated T cells co-cultured with Cd11b+ cells purified from MCA-205 tumors treated with anti-CTLA-4 m2a in WT or IFNAR KO mice. n = 4 biologically independent samples for each group were used for statistical analysis. In (a), bars represent mean ± SE of mice per treatment arm. Two-way ANOVA (fraction ~ treatment * batch) was used. Differential gene expression is estimated using two-sided Wilcoxon rank-sum test, Benjamini-Hochberg adjusted p-value < 0.05, FC > 1.25.

Source data

Extended Data Fig. 7 melanoma patients data provides mechanistic link between anti-tumor activity of anti-CTLA-4 treatment in mice and humans.

(a–d) Gene set enrichment analysis (GSEA) for responder vs non-responder patients. Green line shows running enrichment score for indicated gene ontology (GO) terms. All gene log2 fold-changes are ranked (bottom, grey) and genes belonging to the gene set are shown in black (top).

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Yofe, I., Landsberger, T., Yalin, A. et al. Anti-CTLA-4 antibodies drive myeloid activation and reprogram the tumor microenvironment through FcγR engagement and type I interferon signaling. Nat Cancer 3, 1336–1350 (2022). https://doi.org/10.1038/s43018-022-00447-1

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