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Tumor immunoevasion via acidosis-dependent induction of regulatory tumor-associated macrophages

Nature Immunology volume 19, pages 1319–1329 (2018)Cite this article

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Abstract

Many tumors evolve sophisticated strategies to evade the immune system, and these represent major obstacles for efficient antitumor immune responses. Here we explored a molecular mechanism of metabolic communication deployed by highly glycolytic tumors for immunoevasion. In contrast to colon adenocarcinomas, melanomas showed comparatively high glycolytic activity, which resulted in high acidification of the tumor microenvironment. This tumor acidosis induced Gprotein–coupled receptor–dependent expression of the transcriptional repressor ICER in tumor-associated macrophages that led to their functional polarization toward a non-inflammatory phenotype and promoted tumor growth. Collectively, our findings identify a molecular mechanism of metabolic communication between non-lymphoid tissue and the immune system that was exploited by high-glycolytic-rate tumors for evasion of the immune system.

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Fig. 1: Melanoma but not colon adenocarcinomas are genetically equipped for strong tumor acidosis.
Fig. 2: ICER-deficient mice specifically mount effective antimelanoma immune responses.
Fig. 3: Melanoma acidosis induces expression of ICER in TAMs.
Fig. 4: Acidic pHe induces ICER expression in macrophages.
Fig. 5: Adenylyl cyclase and cAMP-dependent macrophage polarization.
Fig. 6: ICER-deficient macrophages orchestrate the rejection of melanomas.
Fig. 7: ICER-deficient TAMs are functionally polarized toward a pro-inflammatory macrophage phenotype.
Fig. 8: Therapeutic intervention to prevent tumor acidosis inhibits growth of melanomas.

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Data availability

Human transcriptome datasets for patients with melanoma and colonadenocarcinoma are available from the TCGA Research Network at http://cancergenome.nih.gov/. Mouse BioSample metadata are available in the BioSample database under accessions SRP157911 and SRP157897. The analyzed data are depicted in Figs. 1a,b,g, 2b,d, 4b,e and 7a and in Supplementary Figs. 1a and 2a,b.

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Acknowledgements

We are grateful to C.J.M. Melief for critical reading of our manuscript. We thank C. Braun, S. Fischer and S. Hesse-Kerolli for expert technical help. We thank M. Diken (Translationale Onkologie, University Medical Center, Johannes Gutenberg University Mainz) for OVA-expressing B16F10 (B16) melanoma cells; and K. Rajalingam (Institute for Immunology, University Medical Center, Johannes Gutenberg University Mainz) for LLC1 cells. This work was supported by Deutsche Forschungsgemeinschaft (DFG) SFB 1292 TP01 (T.Bopp and E.S.), TP13 (H.S. and H.-C.P.) and TP15 (E.v.S.); by DFG grants BO 3306/1-1, SCHM 1014/7-1, SCHM 1014/5-1, SFB 1066 projects B13 (E.S.) and B8 (T.Bopp and C.B.) and TR SFB 156 (E.v.S., H.-C.P. and H.S.); and by the Universitäres Centrum für Tumorerkrankungen and the Forschungszentrum Immuntherapie, core facility Histology, of the University Medical Center, Johannes Gutenberg University Mainz (E.v.S., E.S. and T.Bopp).

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Author notes

  1. These authors contributed equally: Toszka Bohn, Steffen Rapp.

Authors and Affiliations

  1. Institute for Immunology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany

    Toszka Bohn, Matthias Klein, Till-Julius Bruehl, Jennifer Hahlbrock, Sabine Muth, Christina Lueckel, Danielle Arnold-Schild, Hans-Christian Probst, Hansjoerg Schild, Edgar Schmitt & Tobias Bopp

  2. Molecular Genetics, Johannes Gutenberg University Mainz, Mainz, Germany

    Steffen Rapp

  3. Dermatology, University Medical Center, Johannes Gutenberg-University Mainz, Mainz, Germany

    Natascha Luther & Christian Becker

  4. Faculty of Life Sciences, Toyo University, Gunma, Japan

    Nobuhiko Kojima

  5. Third Medical Clinic, University Medical Center, Johannes Gutenberg-University Mainz, Mainz, Germany

    Pamela Aranda Lopez

  6. Aging Neuroscience Research Team, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan

    Shogo Endo

  7. Department of Nuclear Medicine, University Medical Center, Johannes Gutenberg-University Mainz, Mainz, Germany

    Stefanie Pektor

  8. Internal Medicine III, University of Regensburg, Regensburg, Germany

    Almut Brand, Kathrin Renner & Marina Kreutz

  9. Regensburg Center for Immunology (RCI), Regensburg, Germany

    Kathrin Renner & Marina Kreutz

  10. Department of Medicine 1, University of Erlangen-Nürnberg, Erlangen, Germany

    Vanessa Popp, Katharina Gerlach & Benno Weigmann

  11. Institute for Medical Microbiology and Hospital Hygiene, University of Marburg, Marburg, Germany

    Dennis Vogel, Christina Lueckel & Magdalena Huber

  12. Institute of Research on Cancer and Aging, University of Nice-Sophia Antipolis, Nice, France

    Jacques Pouyssegur

  13. Medical Biology Department, Centre Scientifique de Monaco (CSM), Monaco, Monaco

    Jacques Pouyssegur

  14. Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg-University Mainz, Mainz, Germany

    Jochem Koenig

  15. Research Center for Immunotherapy (FZI), University Medical Center, Johannes Gutenberg-University Mainz, Mainz, Germany

    Hans-Christian Probst, Christian Becker, Hansjoerg Schild, Edgar Schmitt & Tobias Bopp

  16. Dermatology and Venereology, University Medical Center Cologne, Cologne, Germany

    Esther von Stebut

  17. University Cancer Center, University Medical Center, Johannes Gutenberg-University Mainz, Mainz, Germany

    Hansjoerg Schild & Tobias Bopp

  18. German Cancer Consortium (DKTK), Heidelberg, Germany

    Tobias Bopp

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  1. Toszka Bohn
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Contributions

T.Bohn performed and analyzed most experiments. S.R. performed all bioinformatical analyses and together with M.Klein was responsible for next-generation sequencing-based transcriptome analyses. N.L., T.-J.B., P.A.L., J.H. and D.A.-S. helped design and perform some experiments. E.v.S. performed experiments involving human materials and planned and assessed experiments involving immunohistochemistry. N.K. and S.E. generated Crem–/– and Cremfl/fl mice. S.P. performed experiments involving positron-emission tomography. A.B., K.R., J.P. and M.Kreutz generated and provided tumor cell lines. C.L., D.V. and M.H. designed and performed glycolytic Seahorse measurements. V.P., K.G. and B.W. designed and performed the orthotopic MC38 tumor model. J.K. supervised statistical analyses of all experiments. H.-C.P. and S.M. provided anti-CTLA-4 and helped to perform experiments involving immunological-checkpoint blockade. E.S., C.B. and H.S. helped design, analyze and interpret some of the experiments. T.Bopp supervised the project, designed experiments and wrote the manuscript. All authors reviewed and approved the manuscript.

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Integrated supplementary information

Supplementary Figure 1 Relative gene expression of cAMP-controlled genes in SKCM and COAD biopsies.

(a) Relative expression of genes that are regulated by cAMP-mediated signaling in primary melanoma (SKCM, n = 104) and colon carcinoma (COAD, n = 285) biopsies. Expression of mRNAs encoding ICER is shown (CREM). (b) Overview of the Crem gene. The green boxes denote exons of Crem and Icer. The red box (Icer-specific exon) highlights the exon that is exclusively part of Icer but not of Crem. ****P ≤ 0.0001, two-tailed, unpaired t test with Welch’s correction.

Supplementary Figure 2 Expression of LDH and ICER positively correlates in human melanoma.

Correlation of LDH and mRNAs encoding ICER in 104 primary human melanoma samples (SCKM – http://cancergenome.nih.gov/). (a) The LDHA and LDHB expression of primary melanomas (n = 104) is divided from the 10th–90th percentile, and expression of mRNAs encoding ICER in LDHA or LDHB high-expressing (blue), LDHA or LDHB low-expressing (orange), and LDHA or LDHB intermediate-expressing (black) primary melanomas is shown. Data are shown as box plots. Center lines represent the median, whiskers indicate minimum and maximum values, boxes indicate the interquartile range. Outliers were identified and removed from the dataset by the ROUT method using GraphPad Prism 7 software. (b) Graph showing expression of the ICER specific exon relative to LDHB expression. Statistical analysis was done using Pearson correlation (r = 0.3485, P = 0.0003). ****P ≤ 0.0001, two-tailed, unpaired t test with Welch’s correction.

Supplementary Figure 3 GPCR132 inhibition leads to reduction of the acid-induced expression of ICER-encoding mRNAs.

Relative expression of mRNAs encoding ICER in BMDMs stimulated under acidic pHe and in the presence of the GPCR132 antagonist l-α-lysophosphatidylcholine (LPC). 1 × 106 BMDMs (n = 4 each condition) were plated in 24-well plate for 8 h (Ctrl.). BMDMs were stimulated under acidic pHe (pH 6.1) by adding the inorganic acid HCl. Additionally, 160 µM or 40 µM of the GPCR132 antagonist LPC was added to the culture. Icer expression was analyzed by qPCR. Individual values are plotted within the bar graph.

Supplementary Figure 4 Cremfl/flLyz2-Cre mouse macrophages show strongly reduced expression of ICER-encoding mRNAs.

Upon RNA isolation of 1 × 106 in vitro–cultured BMDMs (a) or ex vivo–purified TAMs (b) of Cremfl/fl (WT) and Cremfl/flLyz2-Cre mice relative expression of mRNAs encoding ICER was analyzed by qPCR.

Supplementary Figure 5 Low-glycolytic B16 tumors are not rejected by Cremfl/flLyz2-Cre mice.

(a,b), Extracellular acidification rate (ECAR) of (a) indicated tumor cell lines and (b) B16 melanoma sub-lines with high glycolytic activity (B16), low glycolytic activity (Low glycolytic B16) and MC38 colon adenocarcinoma cells (n = 8 each group). The Seahorse assay was repeated four times. Each measurement time point ± s.d. is plotted. (c) Growth of ‘‘Low-glycolytic B16’’ in mice with an ICER-deficiency in myeloid cells (Cremfl/flLyz2-Cre, n = 10) and respective litter-mate control mice (Cremfl/fl, n = 10). 2 × 105 low-glycolytic B16 melanoma cells were inoculated s.c. into the left flank. Tumor development was observed for 21 d. **P ≤ 0.01, ***P ≤ 0.001 using a two-tailed, unpaired t test with Welch’s correction.

Supplementary Figure 6 Ldha/Ldhb double deficiency of B16-Ldhnull melanoma cells.

(a) Western blot analysis of LDHA and LDHB in B16-Ldhnull and WT melanoma cells. 20 µg of total protein extracts of in vitro cultured B16-Ldhnull (Ldhnull) and WT melanoma cells were used for Western Blot analysis. Samples were resolved on a 12% gel and western blotting was performed using anti-LDHA and anti-LDHB antibodies. (b) Lactate concentration in the supernatants of in vitro cultured B16-Ldhnull and WT melanoma cells. ***P ≤ 0.001 using a two-tailed, unpaired t test with Welch’s correction. (c) C57BL/6 WT mice were inoculated with B16-Control or B16-Ldhnull tumors s.c. On day 21 TAM (n = 5 each) were FACS sorted from WT mice bearing tumors and qPCR was conducted to analyze expression of the indicated genes associated with a non-inflammatory macrophage phenotype.

Supplementary Figure 7 Adenylyl cyclase inhibition does not affect B16 proliferation.

Proliferation capacity of MDL-12-treated B16 melanoma cells. 2 × 105 B16 cells were treated with 100 µM MDL-12 for 3 days. As control the solvent was added to the cell culture. After 3 days, 0.5 µCi/well 3H-thymidine was supplemented for 18 h. Uptake of 3H-thymidine was detected using a Β-counter. 6 replicates per group were measured. Data are represented as mean +/− s.d.; data points are plotted as dots.

Supplementary Figure 8 CTLA-4 and MDL-12 administration in B16 bearing B6 mice.

Tumor growth of CTLA-4 antibody– and adenylate cyclase inhibitor MDL-12-treated and untreated (n = 8 per group) B16 bearing C57BL/6 J mice. 2 × 105 B16 melanoma cells were inoculated s.c. into C57BL/6 J mice. On day 6 after tumor cell inoculation, either 0.5 mg CTLA-4 antibody was injected i.v. or additionally 20 µM MDL-12 was injected s.c. peritumoral. The MDL-12 administration was repeated every 3 d. As a control, PBS was injected i.v. and s.c. peritumoral. Tumor size was measured over a time period of 19 d using a caliper. Tumor volume was calculated using the formula width2 × length × 0.5. Individual tumor growth curves are shown.

Supplementary Figure 9 Examples of gating strategy.

Exemplifying gating strategies for FACS analysis and cell sorting are shown. (a) Gating strategy for tumor-associated macrophages. (b) Gating strategy for tumor-infiltrating T cells.

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Bohn, T., Rapp, S., Luther, N. et al. Tumor immunoevasion via acidosis-dependent induction of regulatory tumor-associated macrophages. Nat Immunol 19, 1319–1329 (2018). https://doi.org/10.1038/s41590-018-0226-8

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