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Epigenetic remodelling shapes inflammatory renal cancer and neutrophil-dependent metastasis

Nature Cell Biology volume 22, pages 465–475 (2020)Cite this article

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Abstract

Advanced clear cell renal cell carcinoma (ccRCC) frequently causes systemic inflammation. Recent studies have shown that cancer cells reshape the immune landscape by secreting cytokines or chemokines. This phenotype, called cancer-cell-intrinsic inflammation, triggers a metastatic cascade. Here, we identified the functional role and regulatory mechanism of inflammation driven by advanced ccRCC cells. The inflammatory nature of advanced ccRCC was recapitulated in a preclinical model of ccRCC. Amplification of cancer-cell-intrinsic inflammation during ccRCC progression triggered neutrophil-dependent lung metastasis. Massive expression of inflammation-related genes was transcriptionally activated by epigenetic remodelling through mechanisms such as DNA demethylation and super-enhancer formation. A bromodomain and extra-terminal motif inhibitor synchronously suppressed C-X-C-type chemokines in ccRCC cells and decreased neutrophil-dependent lung metastasis. Overall, our findings provide insight into the nature of inflammatory ccRCC, which triggers metastatic cascades, and suggest a potential therapeutic strategy.

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Fig. 1: Establishment of inflammatory ccRCC cell derivatives.
Fig. 2: Priming of neutrophils by inflammatory ccRCC cells to facilitate lung metastasis.
Fig. 3: Regulatory mode of cancer-cell-intrinsic inflammation in inflammatory ccRCC cells.
Fig. 4: DNA demethylation of the SAA promoter as a key feature of advanced ccRCC.
Fig. 5: Regulation of CXCL production by SE formation in inflammatory ccRCC cells.
Fig. 6: Synchronized suppression of CXC chemokines and regulation of neutrophil dynamics by BETi.
Fig. 7: Suppression of neutrophil-dependent lung metastasis of inflammatory ccRCC by BETi.

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Data availability

The ChIP-sequencing and RNA-sequencing data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under accession codes GSE131137, GSE131138 and GSE131139. The human ccRCC data were derived from the TCGA Research Network at http://cancergenome.nih.gov/. The dataset derived from this resource that supports the findings of this study is available in cBioPortal at https://www.cbioportal.org/. Source data for Figs. 1–7 and Extended Data Figs. 1–9 are presented with the paper. All other data supporting the findings of this study are available from the corresponding authors upon reasonable request.

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Acknowledgements

We thank Y. Morishita for technical assistance, H. Miyoshi for providing lentiviral vectors and Y. Hayakawa for a fruitful discussion. This work was supported by a grant for Practical Research for Innovative Cancer Control (18ck0106193h0003) from the Japan Agency for Medical Research and Development (AMED; to K. Miyazono and S.E.); a KAKENHI Grant-in-Aid for Scientific Research on Innovative Area on Integrated Analysis and Regulation of Cellular Diversity (17H06326) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan (to K. Miyazono); a Grant-in-Aid for the Japan Society for the Promotion of Science (JSPS) Research Fellow (16J05993; to J.N.); a KAKENHI Grant-in-Aid for Scientific Research (C) (19K07684; to S.E.) from JSPS; and the Princess Takamatsu Cancer Research Fund (to S.E.). This work was also in part supported by a grant for Endowed Department (Department of Medical Genomics) from Eisai Co., Ltd.

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Authors and Affiliations

  1. Department of Molecular Pathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Jun Nishida, Yusaku Momoi, Kosuke Miyakuni, Yusuke Tamura, Kei Takahashi, Daizo Koinuma, Kohei Miyazono & Shogo Ehata

  2. Department of Medical Genomics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Shogo Ehata

  3. Environmental Science Center, The University of Tokyo, Tokyo, Japan

    Shogo Ehata

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Contributions

J.N. and S.E. conceived the study and wrote the manuscript. J.N. performed most of the experiments. Y.M. assisted in the cell culture experiments. K. Miyakuni, Y.M. and Y.T. assisted in the sequencing experiments. Y.M. and K.T. assisted in the vivo experiments. J.N. analysed the sequencing data with the supervision of D.K., and K. Miyazono supervised the project and wrote the manuscript. All authors provided comments on the manuscript.

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Correspondence to Kohei Miyazono or Shogo Ehata.

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Extended data

Extended Data Fig. 1 Malignant phenotypes of ccRCC derivatives.

a, In vivo bioluminescence imaging of mice inoculated with OS-RC-2 cells. b, Survival of OS-RC-2 derivative-bearing mice. The indicated cells were inoculated orthotopically. n = 7 (OSPa and OS5K-1) and n = 8 (OS5K-2 and -3) mice. Log-rank test. c, d, Ex vivo bioluminescence imaging of primary and metastatic lung tumours of 786-O derivatives. Mice bearing 786-O derivatives were analysed 35 d after orthotopic inoculation. In each figure, the left panels show representative images of primary tumours (c) or metastatic lung tumours (d). n = 6 (786-Pa) and n = 5 (786-3K) mice. The right panels show quantification of the overall bioluminescence for the indicated groups. e, f, Colony-formation assays for OS-RC-2 and 786-O derivatives. In each figure, the left panels show representative images of colonies from OS-RC-2 (e) and 786-O (f) derivatives. The right panels indicate the number of colonies for the indicated groups. n = 15 sights from n = 3 independent samples. g, h, Transwell assays performed with OS-RC-2 and 786-O derivatives. In each figure, the left panels show representative images of the migrated OS-RC-2 (g) and 786-O (h) derivatives. The right panels show the percentages of the areas covered by the migrated cells for the indicated groups. n = 15 sights from n = 3 independent samples. In all panels in this figure, the bars represent means ± S.D.

Source data

Extended Data Fig. 2 Quantification of neutrophils in mice bearing inflammatory ccRCC cells.

a, Histological analysis of primary tumours from the 786-O derivatives shown in Extended Data Fig. 1c,d. In each figure, the upper panels show HE staining. An enlarged view of the indicated polymorphonuclear cell-infiltrated area is shown. The lower panels show immunostaining for Ly-6G. Scale bar = 50 μm. b–g, Flow cytometric analysis of cells in primary tumours and lungs of mice bearing OS-RC-2 and 786-O derivatives from the experiments shown in Fig. 1b,c, and Extended Data Fig. 1c,d. b, c, Representative gating strategy for primary tumours (b) and lungs (c) of mice bearing OS-RC-2 derivatives. The percentages of each fraction are indicated. SSC: side scatter. d, e, Quantification of relative neutrophil populations (CD11b+Ly-6G+ cells) compared to all living cells in primary tumours (d) and lung (e) of mice bearing 786-O derivatives. Two-sided Student’s t test. f, g, Quantification of the relative CD11b+F4/80+ cell population compared to all living cells in primary tumours of mice bearing OS-RC-2 (f) or 786-O derivatives (g). One-way ANOVA and Sidak’s test (f) or Two-sided Welch’s t test (g). h, i, Representative gating strategy for blood (h) and BM (i) of mice bearing OS-RC-2 derivatives. The percentages of each fraction are indicated. SSC: side scatter. j, k, Flow cytometric analysis of cells in blood and BM of mice bearing 786-O derivatives. Quantification of relative neutrophil populations (CD11b+Ly-6G+ cells) compared to CD45+ cells in blood (j) and BM (k). n = 6 (786-Pa) and n = 5 (786-3K) mice. Two-sided Welch’s t test. l, Giemsa staining of Ly-6G+ cells sorted from blood of mice bearing OS-RC-2 and 786-O derivatives. Scale bar = 50 μm. In all panels in this figure, the bars represent means ± S.D.

Source data

Extended Data Fig. 3 Role of neutrophils in inflammatory ccRCC progression.

a, b, Flow cytometric analysis of cells in the primary tumours and lungs of OS5K-3-bearing mice in Fig. 2a–c. a, Representative gating strategy used for primary tumours. The percentages of each fraction are indicated. b, Quantification of the relative neutrophil populations in primary tumours (left) and lungs (right). c–e Neutrophil reduction in 786-3K-bearing mice. Mice were inoculated with 786-3K cells and treated with an anti-Ly-6G antibody for 24 d. n = 8 (IgG) mice and n = 7 (anti-Ly-6G). c, Quantification of the relative neutrophil populations in primary tumours (left) and lungs (right). Two-sided Welch’s t test (left). d, e, Ex vivo bioluminescence imaging. In each figure, the left panels show representative images of primary (d) and metastatic lung (e) tumours. The right panels show quantification of the overall bioluminescence for the indicated groups. f–h, Neutrophil reduction in OSPa-bearing mice. Mice were inoculated with OSPa cells and treated with an anti-Ly-6G antibody for 14 d. n = 6 mice/group. f, Quantification of the relative neutrophil populations in primary tumours (left) and lungs (right). g, h, Ex vivo bioluminescence imaging. In each figure, the left panels show representative images of primary (g) and metastatic lung (h) tumours. The right panels show quantification of the overall bioluminescence for the indicated groups. i, Flow cytometric analysis of cells in the lungs of mice inoculated OS5K-3 cells from the experiments shown in Fig. 2d,e. Quantification of the relative neutrophil populations in the lungs. In quantification of the relative neutrophil populations, the number of CD11b+, Ly-6Cmed, and SSChigh cells was compared to all living cells. In all panels in this figure, two-sided Student’s t test was performed unless otherwise described. The bars represent means ± S.D.

Source data

Extended Data Fig. 4 Cancer cell-intrinsic effect of inflammatory ccRCC cells on neutrophil phenotypes.

a, Transwell assay of BM-derived neutrophils from intact mice. Neutrophils were allowed to migrate for 2 h toward culture supernatants from the indicated cells. n = 3 mice/group. One-way ANOVA and Tukey’s test. b, Apoptosis-detection assay for BM-derived neutrophils from intact mice. Neutrophils were co-cultured with culture supernatants for 1 d. (left) Representative panel for detecting apoptotic cells. The percent of each fraction is indicated. (right) Percentage of Annexin V+ PI– cell population. n = 3 mice/group. One-way ANOVA and Tukey’s test. c, RNA-sequencing analysis of peripheral neutrophils. Heat map showing expression of the top 10 genes increased in neutrophils from OS-RC-2 derivative- compared to vehicle- and OSPa cell-inoculated mice. n = 2 mice/group. Genes were screened following the criteria; RPKM ≥1 in any of neutrophils. d, e, qRT-PCR analysis for the indicated mRNAs in BM-derived neutrophils from OS5K-3-bearing (d) or 786-3K-bearing (e) mice, co-cultured with vehicle or culture supernatants of OS5K-3 (d) or 786-3K (e) cells, respectively. Log2-transformed fold-changes compared to vehicle were indicated. n = 3 (d; Ms4a4a), n = 7 (d; Ms4a6c and Ms4a6d), and n = 4 (e) mice. Two-sided Welch’s t test. f, qRT-PCR analysis for the Ms4a6d mRNAs in neutrophils derived from bone, blood, and tumour of mice bearing OSPa or OS5K-3 cells. n = 2 mice/group. In all panels in this figure, the bars represent means ± S.D.

Source data

Extended Data Fig. 5 Activated signalling in inflammatory ccRCC cells.

a, Scatter plot of individual genes expressed in OSPa and OS5K-3 cells. The dots represent 258 upregulated (red) and 220 downregulated (blue) genes in OS5K-3 cells (fold-change > 2). The data presented were from the RNA-sequencing analysis shown in Fig. 3. b, c, Promoter and enhancer analysis of cultured OS-RC-2 derivatives. The data presented were from the ChIP-sequencing analysis (H3K27ac) shown in Fig. 3. b, Volcano plot showing the fold-changes of individual H3K27ac peak intensity between OSPa and OS5K-3 cells. The dots represent 944 increased (red) and 437 decreased (blue) peaks in OS5K-3 cells (fold-change > 10, adjusted p-value < 0.05). Statistical analysis was performed in HOMER software. c, Heat map showing correlations between the H3K27ac peak intensities among the average value of each indicated OS-RC-2 derivative. Pearson’s r. d, GSEA (hallmark gene sets) showing genes related to ‘hallmark_hypoxia’. The data presented were from the RNA-sequencing analysis shown in Fig. 3. Genes were screened following the criteria; RPKM ≥ 3 in either OSPa or OS5K-3 cells. Statistical analysis was performed by the GSEA algorithm. e, Immunoblotting for p65, α-tubulin, and HDAC1 in 786-O derivatives. f, Immunoblotting for C/EBPβ, C/EBPδ, and α-tubulin protein expression in 786-O derivatives. g, Re-analysis of TCGA KIRC cohort data showing expression of the indicated genes. n = 267 (stage I), n = 57 (stage II), n = 123 (stage III), and n = 83 (stage IV) patients. The bars represent means ± S.D. One-way ANOVA and Tukey’s test. h, Re-analysis of TCGA KIRC cohort data showing relationships between the expression of each indicated gene and overall patient survival. All patient samples were divided, based on expression of the indicated genes. n =266 patients/group. Log-rank test. Hazard ratio with 95% confidence interval was also indicated.

Source data

Extended Data Fig. 6 Clinical significance of SAA expression in ccRCC.

a, Re-analysis of TCGA KIRC cohort data showing expression of the indicated genes. n = 267 (stage I), n = 57 (stage II), n = 123 (stage III), and n = 83 (stage IV) patients. One-way ANOVA and Tukey’s test. b, Re-analysis of TCGA KIRC cohort data showing the relationships between the expression of each indicated gene and overall patient survival. All patient samples were divided, based on expression of the indicated genes. n =266 patients/group. Log-rank test. Hazard ratio with 95% confidence interval was also indicated. c, Immunoblotting for FLAG-tagged IκBαM and α-tubulin in IκBαM-expressing OS5K-3 cells. d, Immunoblotting for p65, α-tubulin, and HDAC1 protein expression in IκBαM-expressing OS5K-3 cells. e, Immunoblotting for LIP form of C/EBPβ and α-tubulin in LIP-expressing OS5K-3 cells. f, Reporter luciferase analysis with an SAA1 promoter–luciferase construct. The luciferase reporter with truncated SAA1 promoters introduced in OS5K-3 cells. Numbers represent the location from the TSS of SAA1. n = 4 biologically independent samples. g, Bisulphite-sequencing of the SAA1 promoter region. The black and white circles represented methylated and unmethylated CpG sites, respectively. Numbers represent the location from the TSS of SAA1. n = 17 (OSPa), n = 15 (OS5K-1 and -3), and n = 22 (OS5K-2) independent clones. h, Re-analysis of TCGA KIRC cohort data showing the methylation status near the indicated gene loci. n = 155 (stage I), n = 31 (stage II), n = 73 (stage III), and n = 58 (stage IV) patients. In all panels in this figure, the bars represent means ± S.D. One-way ANOVA and Tukey’s test.

Source data

Extended Data Fig. 7 Clinical significance of CXCL expression in ccRCC.

a, qRT-PCR analysis for the indicated mRNAs in 786-3K cells. Log2-transformed fold-changes compared to 786-Pa cells were indicated. n = 3 biologically independent samples. Two-sided Welch’s t test. b, Quantitative detection of the indicated proteins in culture supernatants of OS-RC-2 derivatives. n = 3 biologically independent samples. One-way ANOVA and Sidak’s test. c, Re-analysis of TCGA KIRC cohort data showing expression of the indicated genes. n = 267 (stage I), n = 57 (stage II), n = 123 (stage III), and n = 83 (stage IV) patients. One-way ANOVA and Tukey’s test. d, Re-analysis of TCGA KIRC cohort data showing relationships between the expression level of each indicated gene and overall patient survival. All patient samples were divided, based on expression of the indicated genes. n =266 patients/group. Log-rank test. Hazard ratio with 95% confidence interval was also indicated. e, Hierarchical clustering analysis of TCGA cohort of overall patient survival. All patient samples were divided based on synchronized, heterogenous, and low expression of CXC chemokines. n = 39 (CXCLsync), n = 427 (CXCLhetero), and n = 58 (CXCLlow) patients. In all panels in this figure, the bars represent means ± S.D.

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Extended Data Fig. 8 Effect of the BETi on inflammatory ccRCC cells.

a, qRT-PCR analysis of the indicated mRNAs in BM-derived neutrophils. Neutrophils were co-cultured with culture supernatants from JQ1-pretreated OS5K-3 cells for 1 d. Log2-transformed fold-changes compared to those from DMSO-pretreated OS5K-3 cells were indicated. n = 3 mice. Two-sided Welch’s t test. b, Transwell assay of BM-derived neutrophils from intact mice. Neutrophils were incubated for 2 h and allowed to migrate toward the culture supernatants of JQ1-pretreated 786-3K cells. n = 3 mice. Two-sided Student’s t test. c, Apoptosis-detection assay of BM-derived neutrophils from intact mice. Neutrophils were co-cultured with culture supernatants of JQ1-pretreated 786-3K cells for 1 d. (left) Representative panel showing the detection of apoptotic cells. The percent of each fraction is indicated. (right) Percentages of Annexin V+ PI– cell populations. n = 3 mice. Two-sided Student’s t test. d, Apoptosis assay of BM-derived neutrophils. Neutrophils were co-cultured with culture supernatants of OS5K-3 cells containing SB265610 for 1 d. (left) Representative panel showing the approach used for detecting apoptotic cells. The percentages of each fraction are indicated. (right) The percentages of Annexin V+ PI– cell populations. n = 3 mice. Two-sided Student’s t test. e, Heat map showing expression of the indicated genes. RNA-expression data were from the RNA-sequencing analysis shown in Figs. 3–5, and Extended Data Fig. 5. f, Apoptosis-detection assay of BM-derived neutrophils from intact mice. Neutrophils were co-cultured with culture supernatants of OS5K-3 cells containing the indicated neutralizing antibodies for 1 d. (left) Representative panel showing the detection of apoptotic cells. The percent of each fraction is indicated. (right) Percentages of Annexin V+ PI– cell populations. n = 3 mice. One-way ANOVA and Tukey’s test. g, qRT-PCR analysis of the indicated mRNAs in OS5K-3 cells. The cells were treated with JQ1 for 1 d. Log2-transformed fold-changes compared to DMSO-treated OS5K-3 cells were indicated. n = 3 biologically independent samples.

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Extended Data Fig. 9 Effect of the BETi on inflammatory ccRCC tumour.

a, b, Flow cytometric analysis of cells in primary tumours from the experiments shown in Fig. 7a–d. n = 5 mice/group. a, Representative gating strategy used for analysing primary tumours. The percentages of each fraction are indicated. b, Relative quantification of CD11b+ and F4/80+ cell populations compared to all living cells in primary tumours. Two-sided Student’s t test. c–e Flow cytometric analysis of cells in primary tumours of mice bearing JQ1-pretreated OS5K-3 cells. n = 6 mice/group. c, Experimental overview. d, e, Quantification of relative neutrophil (CD11b+ and Ly-6G+ cell) (d), and CD11b+ and F4/80+ cell populations (e) compared to all living cells in primary tumours. Two-sided Student’s t test. In all panels in this figure, the bars represent means ± S.D.

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Extended Data Fig. 10 Cancer cell-intrinsic inflammation amplified in tumour microenvironment triggers neutrophil-dependent lung metastasis of ccRCC.

Schematic overview of the study findings. BET inhibitor could counteract these metastatic steps by suppressing expression of genes related to neutrophil phenotypes.

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Nishida, J., Momoi, Y., Miyakuni, K. et al. Epigenetic remodelling shapes inflammatory renal cancer and neutrophil-dependent metastasis. Nat Cell Biol 22, 465–475 (2020). https://doi.org/10.1038/s41556-020-0491-2

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