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Circadian control of tumor immunosuppression affects efficacy of immune checkpoint blockade

Nature Immunology volume 25, pages 1257–1269 (2024)Cite this article

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Abstract

The circadian clock is a critical regulator of immunity, and this circadian control of immune modulation has an essential function in host defense and tumor immunosurveillance. Here we use a single-cell RNA sequencing approach and a genetic model of colorectal cancer to identify clock-dependent changes to the immune landscape that control the abundance of immunosuppressive cells and consequent suppression of cytotoxic CD8+ T cells. Of these immunosuppressive cell types, PD-L1-expressing myeloid-derived suppressor cells (MDSCs) peak in abundance in a rhythmic manner. Disruption of the epithelial cell clock regulates the secretion of cytokines that promote heightened inflammation, recruitment of neutrophils and the subsequent development of MDSCs. We also show that time-of-day anti-PD-L1 delivery is most effective when synchronized with the abundance of immunosuppressive MDSCs. Collectively, these data indicate that circadian gating of tumor immunosuppression informs the timing and efficacy of immune checkpoint inhibitors.

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Fig. 1: Circadian clock disruption alters the immune landscape.
Fig. 2: Genetic and environmental clock disruption alter the immune landscape.
Fig. 3: Disruption of the circadian clock promotes the accumulation of MDSCs.
Fig. 4: Circadian clock disruption and Wnt signaling promote an inflammatory response.
Fig. 5: Time-of-day anti-PD-L1 treatment controls efficacy.
Fig. 6: Model of circadian regulation of MDSCs for anti-PD-L1 treatment.

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Data availability

scRNA-seq data have been deposited at the NCBI Gene Expression Omnibus under accession code GSE262267 and in the Sequence Read Archive under accession code PRJNA1003452. Source data are provided with this paper.

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Acknowledgements

B.M.F. and S.M.P. were supported by the National Cancer Institute (NCI) T32 Interdisciplinary Cancer Research Training Program (grant no. T32CA009054) and B.M.F. was also supported by the NCI grant F31CA287992. A.L.M. was supported by the National Science Foundation Graduate Research Fellowship Program (grant no. DGE1839285). A.M. was supported by the NCI grant F31AR083279. We acknowledge the support of the Chao Family Comprehensive Cancer Center at the University of California, Irvine, which is supported by the NCI (P30 CA062203). Shared resources included use of the Genomics Research and Technology Hub. We thank the Institute for Immunology Flow Cytometry Facility and the Stem Cell Flow Core at the University of California, Irvine for technical assistance. The Pannunzio laboratory is supported by National Institutes of Health (NIH)/NCI grants R37CA266042 and R01CA276470. The Seldin laboratory is supported by the NIH/National Institute of Diabetes and Digestive and Kidney Diseases grant DP1DK130640. The Marangoni laboratory is supported by the Melanoma Research Alliance Bristol Meyers Squibb Young Investigator Award no. 929155 and the DoD Team grant ME220176P1. Financial support for the Masri laboratory is provided through the NIH/NCI (R01CA244519 and R01CA259370), the V Foundation for Cancer Research and Johnson & Johnson.

Author information

Authors and Affiliations

  1. Department of Biological Chemistry, University of California Irvine, Irvine, CA, USA

    Bridget M. Fortin, Shannon M. Pfeiffer, Jacob Insua-Rodríguez, Wei A. Song, Alisa L. Mahieu, Sung Kook Chun, Amber N. Lewis, Alex Hsu, Nicholas R. Pannunzio, Marcus M. Seldin, Ivan Marazzi, Kai Kessenbrock & Selma Masri

  2. Department of Physiology and Biophysics, University of California Irvine, Irvine, CA, USA

    Jacob Insua-Rodríguez, Alexander Moshensky, Isam Adam, Francesco Marangoni & Devon A. Lawson

  3. Department of Pathology, University of Hail, Hail, Saudi Arabia

    Hamad Alshetaiwi

  4. Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, CA, USA

    Hamad Alshetaiwi, Oliver S. Eng, Nicholas R. Pannunzio, Marcus M. Seldin, Ivan Marazzi, Francesco Marangoni, Devon A. Lawson, Kai Kessenbrock & Selma Masri

  5. Department of Surgery, Division of Surgical Oncology, University of California Irvine, Orange, CA, USA

    Oliver S. Eng

  6. Department of Medicine, Division of Hematology/Oncology, University of California Irvine, Irvine, CA, USA

    Nicholas R. Pannunzio

  7. Center for Epigenetics and Metabolism, University of California Irvine, Irvine, CA, USA

    Nicholas R. Pannunzio, Marcus M. Seldin, Ivan Marazzi, Devon A. Lawson, Kai Kessenbrock & Selma Masri

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Contributions

Conceptualization was the responsibility of B.M.F. and S.M. Methodology was the responsibility of B.M.F., J.I.R., H.A., O.S.E., N.R.P., M.M.S., I.M., F.M., D.A.L., and K.K. Investigation was carried out by B.M.F., S.M.P., J.I.R., H.A., A.N.L., A.M., W.A.S., A.L.M., S.K.C., A.H., I.A. and M.M.S. Visualization was carried out by B.M.F. and S.M.P. Supervision was carried out by S.M. B.M.F., S.M.P. and S.M. were responsible for writing.

Corresponding author

Correspondence to Selma Masri.

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The authors declare no competing interests.

Peer review

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Nature Immunology thanks Dmitry Gabrilovich and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: N. Bernard, in collaboration with the Nature Immunology team.

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

Extended data

Extended Data Fig. 1 Circadian clock disruption promotes intestinal tumorigenesis.

(a) Graphic of mouse genotypes. (b) Gene targeting strategy for generation of intestine-specific knockout of Bmal1 and Apc. (c) Small intestinal polyp count from WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice (n = 15 mice/genotype). (d) Spleen weight from WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice (n = 15 mice/genotype). (e) Violin plots of RNA features, count, and percent of mitochondrial RNA for each immune cell cluster prior to filtering mitochondrial RNA < 10% and nFeature < 3000. (f) Heatmap of top three genes expressed by each immune cell cluster. Data represent the mean ± SEM and statistical significance was determined by one-way ANOVA with Tukey’s multiple comparison test for C and D. Asterisks represent p-values from multiple comparisons, with **** indicating a p-value of < 0.0001.

Extended Data Fig. 2 scRNA-seq demonstrates that clock disruption alters the immune landscape.

a) Dot-plot of gene expression by each immune cell cluster. (b) Pie chart of CD25+ T cells and CD3+ T cells from WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice as determined by scRNA-seq. (c) Bar graph of immune cell count for neutrophils, monocytes/macrophages, and dendritic cells (DCs) from WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice as determined by scRNA-seq. (d) Bar graph of immune cell count for naïve B cells, proliferating B cells, mature B cells, CD8+ T cells, CD4+ T cells, and CD25+ T cells from WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice as determined by scRNA-seq. (e) Dot-plot of gene expression for B cell clusters.

Extended Data Fig. 3 Gating strategies for flow cytometric analysis of the immune landscape.

(a) Gating strategy for flow cytometric analysis of immune cells. Markers include Sytox Blue, APC CD45, PerCP-Cy7 CD11b, PE F4/80, FITC Ly6G, BV605 Ly6C, PE CD3, FITC CD8, APC-Cy7 CD4, and BV605 CD25. (b) Gating strategy for sorting live, CD45+CD11b+Gr1+ cells. Markers include Sytox Blue, APC CD45, PE Gr1, and FITC CD11b. (c) Gating strategy for T cells post co-culture with CD11b+Gr1+ cells. Markers include FITC Zombie, PE-Cy7 CD8, and DAPI CD4.

Extended Data Fig. 4 Flow cytometry demonstrates that clock disruption alters immune proportions.

(a) Bar graph of immune cells as percent of live cells from the intestine of WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice sacrificed at ZT 4 and analyzed by flow cytometry (n = 7 mice/genotype). (b) Bar graph of immune cell counts for neutrophils, monocytes, and macrophages from WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice sacrificed at ZT 4 and analyzed by flow cytometry (n = 7 mice/genotype). (c) Bar graph of immune cell counts for CD8+ T cells and CD4+ T cells from WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice sacrificed at ZT 4 and analyzed by flow cytometry (n = 7 mice/genotype). (d) Bar graph of immune cells as percent of live for neutrophils, monocytes, and macrophages from WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− sacrificed at ZT 4 and analyzed by flow cytometry (n = 7 mice/genotype). (e) Bar graph of immune cells as percent of live for CD4+ T cells and CD8+ T cells from WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice sacrificed at ZT 4 and analyzed by flow cytometry (n = 7 mice/genotype).

Extended Data Fig. 5 Genetic and environmental clock disruption alter the immune landscape.

(a) CD3+ T cells, CD4+ T cells, and CD25+ T cells shown as percent of CD45+ cells from the small intestine of WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice sacrificed at ZT 4 and analyzed by flow cytometry (n = 7 mice/genotype). (b) Neutrophils, monocytes, macrophages, CD8+ T cells, and CD25+ T cells shown as percent of CD45+ cells from the spleen of WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice sacrificed at ZT 4 and analyzed by flow cytometry (n = 7 mice/genotype). (c) Spleen weight of WT mice subjected to 12:12 LD paradigm versus SD (n = 10 mice/genotype). (d) CD4+ T cells shown as percent of CD45+ cells from the small intestine of WT mice subjected to 12:12 LD versus SD. Mice were sacrificed at ZT 4 and analyzed by flow cytometry (n = 8 mice/genotype). (e) Spleen weight of WT mice subjected to 12:12 LD paradigm, 1 week SD, 3 weeks SD, and 5 weeks SD (n = 6 mice/genotype). (f) CD4+ T cells shown as percent of CD45+ cells from the small intestine of WT mice subjected to 12:12 LD paradigm, 1 week SD, 3 weeks SD, and 5 weeks SD. Mice were sacrificed at ZT 4 and analyzed by flow cytometry (n = 6 mice/genotype). Data represent the mean ± SEM and statistical significance was determined by two-tailed Mann-Whitney T-test for A–D, and one-way ANOVA with Tukey’s multiple comparison test for E and F. Asterisks represent p-values from multiple comparisons, with * indicating a p-value of < 0.05, ** indicating a p-value of < 0.01, **** indicating a p-value of < 0.0001, and ns = not significant.

Extended Data Fig. 6 scRNA-seq demonstrates that clock disruption promotes MDSC accumulation.

(a) UMAP of the expression of Ifitm1, Wfdc17, s100a8, s100a9, Irg1, and Arg2 by monocytes/macrophages, neutrophils, and DCs. (b) UMAP of monocytes/macrophages, neutrophils, and DCs in WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice as determined by scRNA-seq. c) Histogram of CD4+ T cell and CD8+ T cell counts after co-culture with Gr1+ cells sorted from WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice spleen. (d) Counts of generation 1, 2, and 3 CD4+ T cells and CD8+ T cells after co-culture with Gr1+ cells sorted from WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice spleen (n = 3 mice/genotype). Data represent the mean ± SEM and statistical significance was determined by one-way ANOVA with Tukey’s multiple comparison test for D. Asterisks represent p-values from multiple comparisons, with ** indicating a p-value of < 0.01, *** indicating a p-value of < 0.001, **** indicating a p-value of < 0.0001, and ns = not significant.

Extended Data Fig. 7 Circadian clock disruption promotes an inflammatory response.

(a) Western blot of MYC and p84 in WT and Bmal1−/− intestinal organoids untreated or treated with 100 ng/mL recombinant Wnt3a for 72 hours. (b) WT and Bmal1−/− intestinal organoids untreated or treated with 25, 50, or 100 ng/mL recombinant Wnt3a for 72 hours. Gene expression of c-Myc, Survivin, and Cxcl5 was determined by qPCR (n = 3 independent organoid lines/genotype). (c) WT and Bmal1−/− intestinal organoids untreated or treated with 100 ng/mL recombinant Wnt3a for 24, 48, or 72 hours. Gene expression of c-Myc, Survivin and Cxcl5 was determined by qPCR (n = 3 independent organoid lines/genotype). (d) Concentration of CCL5, IL-17, and CXCL9 in WT and Bmal1−/− intestinal monolayer lysate as determined by ELISA (n = 3 independent monolayer lines/genotype). (e) WT intestinal monolayers were left untreated or treated with 100 ng/mL recombinant Wnt3a for 96 hours (n = 3 untreated and 5 Wnt3a-treated independent monolayer lines/genotype). Concentration of CXCL1, G-CSF, M-CSF, GM-CSF, IL-2, IFNγ, CXCL2, and TNF in WT monolayer cell lysate was determined by ELISA. Data represent the mean ± SEM and statistical significance was determined by two-tailed Mann-Whitney T-test for D, and one-way ANOVA with Tukey’s multiple comparison test for B-C, and E. Asterisks represent p-values from multiple comparisons, with * indicating a p-value of < 0.05, ** indicating a p-value of < 0.01, *** indicating a p-value of < 0.001, **** indicating a p-value of < 0.0001, and ns = not significant.

Source data

Extended Data Fig. 8 Wnt signaling mediates the inflammatory response in the intestine.

(a) WT and Bmal1−/− intestinal monolayers were left untreated or treated with 100 ng/mL recombinant Wnt3a for 96 hours (n = 3 untreated and 4 Wnt3a-treated independent monolayer lines/genotype). Concentration of CXCL5, CXCL6, and CXCL2 in intestinal monolayer lysate as determined by ELISA. (b) Expression of c-Myc, Survivin, Axin2, Cxcl5, Cxcl1, M-csf, and Gm-csf as determined by qPCR in mouse embryonic fibroblasts untreated or treated with 50, 100, or 200 ng/mL recombinant Wnt3a for 4 hours (n = 3 independent biological replicates/condition). (c) Expression of c-Myc, Survivin, Cxcl5, and Cxcl1 as determined by qPCR using WT intestinal monolayers infected with shEV or shMyc (n = 3 independent monolayer lines). Data represent the mean ± SEM and statistical significance was determined by two-tailed Mann-Whitney T-test for C, and one-way ANOVA with Tukey’s multiple comparison test for A-B. Asterisks represent p-values from multiple comparisons, with * indicating a p-value of < 0.05, ** indicating a p-value of < 0.01, *** indicating a p-value of < 0.001, **** indicating a p-value of < 0.0001, and ns = not significant.

Extended Data Fig. 9 scRNA-seq analysis of Gr1 and PD-L1 abundance in human CRC.

Dataset from Pelka et al. (a) Dot-plot of gene expression by each immune cell cluster in matched normal colon and tumor samples. (b) Heatmap of cell types clustered by single-cell transcriptional analysis (n = 55,535 cells, n = 36 patients). (c) Stacked bar chart of human immune cell cluster composition determined using scRNA-seq dataset from Pelka et al. Data was based on 36 patients with matched normal colon and CRC tumor samples. (d) UMAP of human immune cell types clustered by single-cell transcriptional analysis broken down by normal and tumor (n = 36 patient samples/group). (e) Bar graph of cell counts expressing PD-L1 by human immune cell clusters in matched normal colon and CRC samples. (f) Dot-plot of PD-L1 expression by each human immune cell cluster in matched normal colon and CRC samples. Statistical significance was determined by one-way ANOVA with Tukey’s multiple comparison test for E. Asterisks represent p-values from multiple comparisons, with *** indicating a p-value of < 0.001, **** indicating a p-value of < 0.0001, and ns = not significant.

Extended Data Fig. 10 The circadian clock regulates immunosuppression and anti-PD-L1 efficacy.

(a, b) Box plot or dot-plot of PD-L1 expression by each immune cell cluster in mouse scRNA-seq data (n = 3 mice/genotype). Box plot center line represents the median, the boundaries represent IQR, and the whisker length represents 1.5 x IQR. (c) Bar graph of PD-L1 expression by monocyte/macrophage and neutrophil clusters in the intestine of WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice determined by scRNA-seq (n = 3 mice/genotype). (d-e) UMAP of myeloid cell clusters and PD-L1 expression in the intestine of WT, Bmal1−/−, Apc+/−, and Apc+/−;Bmal1−/− mice (n = 3 mice/genotype). (f-g) Small intestinal polyps and spleen weight in Apc+/−;Bmal1−/− mice (n = 7 mice/group). (h-i) Mesenteric lymph node or blood Gr1+ cells as percent of CD45+ cells from WT and Apc+/−;Bmal1−/− mice sacrificed at ZT 4 and ZT 16 and analyzed by flow cytometry (n = 3 mice/genotype for H, n = 4 mice/genotype for I). (j) Combined total Gr1+ cells from the small intestine and spleen as percent of CD45+ cells from Apc+/−;Bmal1−/− mice sacrificed at ZT 4 and ZT 16 and analyzed by flow cytometry (n = 7 mice/group). (k) Combined total Gr1+PD-L1+ cells from the small intestine and spleen as percent of CD45+ cells from Apc+/−;Bmal1−/− mice. Animals were sacrificed at ZT 4 and ZT 16 and analyzed by flow cytometry (n = 5 mice/group). (l) Small intestinal polyp count from Apc+/−;Bmal1−/− mice untreated or treated with anti-PD-L1 (n = 6 mice/group). (m) Tumor volume over time for WT mice after subcutaneous injection of CMT167 cells and treatment with IgG or anti-PD-L1 at ZT 4 or ZT 16 (n = 5 mice/group, 2 tumors/mouse). (n) Tumor volume over time for WT mice after subcutaneous injection of D4M-S cells and treatment with IgG or anti-PD-L1 at ZT 4 or ZT 16 (n = 5 mice/group, 2 tumors/mouse). Data represent the mean ± SEM and statistical significance was determined by two-tailed Mann-Whitney T-test for F-K, and by one-way ANOVA with Tukey’s multiple comparison test for A, C, and L. Asterisks represent p-values from multiple comparisons, with * indicating a p-value of < 0.05, ** indicating a p-value of < 0.01, **** indicating a p-value of < 0.0001, and ns = not significant.

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Source Data Fig. 4

Unprocessed immunoblots.

Source Data Extended Data Fig. 7

Unprocessed immunoblots.

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Fortin, B.M., Pfeiffer, S.M., Insua-Rodríguez, J. et al. Circadian control of tumor immunosuppression affects efficacy of immune checkpoint blockade. Nat Immunol 25, 1257–1269 (2024). https://doi.org/10.1038/s41590-024-01859-0

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