Epigenetic remodelling shapes inflammatory renal cancer and neutrophil-dependent metastasis

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

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CONTENT BEING VIEWED BY OTHERS A TRANSCRIPTIONAL METASTATIC SIGNATURE PREDICTS SURVIVAL IN CLEAR CELL RENAL CELL CARCINOMA Article Open access 30 September 2022 MDK PROMOTES M2 MACROPHAGE

POLARIZATION TO REMODEL THE TUMOUR MICROENVIRONMENT IN CLEAR CELL RENAL CELL CARCINOMA Article Open access 06 August 2024 SINGLE-CELL MULTIOMICS ANALYSIS REVEALS REGULATORY PROGRAMS IN CLEAR

CELL RENAL CELL CARCINOMA Article Open access 19 July 2022 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. REFERENCES * Atkins, M. B. &

Tannir, N. M. Current and emerging therapies for first-line treatment of metastatic clear cell renal cell carcinoma. _Cancer Treat. Rev._ 70, 127–137 (2018). Article  CAS  PubMed  Google

Scholar  * Rini, B. I., Campbell, S. C. & Escudier, B. Renal cell carcinoma. _Lancet_ 373, 1119–1132 (2009). Article  CAS  PubMed  Google Scholar  * Chang, Y. et al. Systemic

inflammation score predicts postoperative prognosis of patients with clear-cell renal cell carcinoma. _Br. J. Cancer_ 113, 626–633 (2015). Article  CAS  PubMed  PubMed Central  Google

Scholar  * Donskov, F. & von der Maase, H. Impact of immune parameters on long-term survival in metastatic renal cell carcinoma. _J. Clin. Oncol._ 24, 1997–2005 (2006). Article  PubMed 

Google Scholar  * Jensen, H. K. et al. Presence of intratumoral neutrophils is an independent prognostic factor in localized renal cell carcinoma. _J. Clin. Oncol._ 27, 4709–4717 (2009).

Article  PubMed  Google Scholar  * Wang, T. et al. An empirical approach leveraging tumorgrafts to dissect the tumor microenvironment in renal cell carcinoma identifies missing link to

prognostic inflammatory factors. _Cancer Discov._ 8, 1142–1155 (2018). Article  CAS  PubMed  PubMed Central  Google Scholar  * Grivennikov, S. I., Greten, F. R. & Karin, M. Immunity,

inflammation, and cancer. _Cell_ 140, 883–899 (2010). Article  CAS  PubMed  PubMed Central  Google Scholar  * Wellenstein, M. D. & de Visser, K. E. Cancer-cell-intrinsic mechanisms

shaping the tumor immune landscape. _Immunity_ 48, 399–416 (2018). Article  CAS  PubMed  Google Scholar  * Li, J. et al. Tumor cell-intrinsic factors underlie heterogeneity of immune cell

infiltration and response to immunotherapy. _Immunity_ 49, 178–193.e7 (2018). Article  CAS  PubMed  PubMed Central  Google Scholar  * Ancrile, B., Lim, K. H. & Counter, C. M. Oncogenic

Ras-induced secretion of IL6 is required for tumorigenesis. _Genes Dev._ 21, 1714–1719 (2007). Article  CAS  PubMed  PubMed Central  Google Scholar  * Pylayeva-Gupta, Y., Lee, K. E., Hajdu,

C. H., Miller, G. & Bar-Sagi, D. Oncogenic Kras-induced GM-CSF production promotes the development of pancreatic neoplasia. _Cancer Cell_ 21, 836–847 (2012). Article  CAS  PubMed  PubMed

Central  Google Scholar  * Schwitalla, S. et al. Loss of p53 in enterocytes generates an inflammatory microenvironment enabling invasion and lymph node metastasis of carcinogen-induced

colorectal tumors. _Cancer Cell_ 23, 93–106 (2013). Article  CAS  PubMed  Google Scholar  * Meylan, E. et al. Requirement for NF-κB signalling in a mouse model of lung adenocarcinoma.

_Nature_ 462, 104–107 (2009). Article  CAS  PubMed  PubMed Central  Google Scholar  * Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of

tumors associated with local immune cytolytic activity. _Cell_ 160, 48–61 (2015). Article  CAS  PubMed  PubMed Central  Google Scholar  * Turajlic, S. et al. Deterministic evolutionary

trajectories influence primary tumor growth: TRACERx Renal. _Cell_ 173, 595–610.e11 (2018). Article  CAS  PubMed  PubMed Central  Google Scholar  * Yao, X. et al. VHL deficiency drives

enhancer activation of oncogenes in clear cell renal cell carcinoma. _Cancer Discov._ 7, 1284–1305 (2017). Article  CAS  PubMed  Google Scholar  * Rodrigues, P. et al. NF-κB–dependent

lymphoid enhancer co-option promotes renal carcinoma metastasis. _Cancer Discov._ 8, 850–865 (2018). Article  CAS  PubMed  PubMed Central  Google Scholar  * Kubota, S. I. et al. Whole-body

profiling of cancer metastasis with single-cell resolution. _Cell Rep._ 20, 236–250 (2017). Article  CAS  PubMed  Google Scholar  * Nishida, J., Miyazono, K. & Ehata, S. Decreased

TGFBR3/betaglycan expression enhances the metastatic abilities of renal cell carcinoma cells through TGF-β-dependent and -independent mechanisms. _Oncogene_ 37, 2197–2212 (2018). Article 

CAS  PubMed  PubMed Central  Google Scholar  * Takahashi, K. et al. Pancreatic tumor microenvironment confers highly malignant properties on pancreatic cancer cells. _Oncogene_ 37, 2757–2772

(2018). Article  CAS  PubMed  PubMed Central  Google Scholar  * Engblom, C. et al. Osteoblasts remotely supply lung tumors with cancer-promoting SiglecFhigh neutrophils. _Science_ 358,

eaal5081 (2017). Article  CAS  PubMed  PubMed Central  Google Scholar  * Roe, J.-S. et al. Enhancer reprogramming promotes pancreatic cancer metastasis. _Cell_ 170, 875–888.e20 (2017).

Article  CAS  PubMed  PubMed Central  Google Scholar  * An, J. & Rettig, M. B. Mechanism of von Hippel–Lindau protein-mediated suppression of nuclear factor kappa B activity. _Mol. Cell.

Biol._ 25, 7546–7556 (2005). Article  CAS  PubMed  PubMed Central  Google Scholar  * Vanharanta, S. et al. Epigenetic expansion of VHL-HIF signal output drives multiorgan metastasis in

renal cancer. _Nat. Med._ 19, 50–56 (2013). Article  CAS  PubMed  Google Scholar  * Fischer, K., Theil, G., Hoda, R. & Fornara, P. Serum amyloid A: a biomarker for renal cancer.

_Anticancer Res._ 32, 1801–1804 (2012). CAS  PubMed  Google Scholar  * Choi, H. et al. Augmented serum amyloid A1/2 mediated by TNF-induced NF-κB in human serous ovarian epithelial tumors.

_Immune Netw._ 17, 121–127 (2017). Article  PubMed  PubMed Central  Google Scholar  * Wculek, S. K. & Malanchi, I. Neutrophils support lung colonization of metastasis-initiating breast

cancer cells. _Nature_ 528, 413–417 (2015). Article  CAS  PubMed  PubMed Central  Google Scholar  * Sparmann, A. & Bar-Sagi, D. Ras-induced interleukin-8 expression plays a critical role

in tumor growth and angiogenesis. _Cancer Cell_ 6, 447–458 (2004). Article  CAS  PubMed  Google Scholar  * Acharyya, S. et al. A CXCL1 paracrine network links cancer chemoresistance and

metastasis. _Cell_ 150, 165–178 (2012). Article  CAS  PubMed  PubMed Central  Google Scholar  * Kamioka, Y., Takakura, K., Sumiyama, K. & Matsuda, M. Intravital Förster resonance energy

transfer imaging reveals osteopontin-mediated polymorphonuclear leukocyte activation by tumor cell emboli. _Cancer Sci._ 108, 226–235 (2017). Article  CAS  PubMed  PubMed Central  Google

Scholar  * Zlotnik, A., Yoshie, O. & Nomiyama, H. The chemokine and chemokine receptor superfamilies and their molecular evolution. _Genome Biol._ 7, 243 (2006). Article  CAS  PubMed 

PubMed Central  Google Scholar  * Huang, B., Yang, X.-D., Zhou, M.-M., Ozato, K. & Chen, L.-F. Brd4 coactivates transcriptional activation of NF-κB via specific binding to acetylated

RelA. _Mol. Cell. Biol._ 29, 1375–1387 (2009). Article  CAS  PubMed  Google Scholar  * Lovén, J. et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. _Cell_ 153,

320–334 (2013). Article  CAS  PubMed  PubMed Central  Google Scholar  * Ruffell, B., Affara, N. I. & Coussens, L. M. Differential macrophage programming in the tumor microenvironment.

_Trends Immunol._ 33, 119–126 (2012). Article  CAS  PubMed  PubMed Central  Google Scholar  * Coffelt, S. B., Wellenstein, M. D. & De Visser, K. E. Neutrophils in cancer: neutral no

more. _Nat. Rev. Cancer_ 16, 431–446 (2016). Article  CAS  PubMed  Google Scholar  * Kumar, V., Patel, S., Tcyganov, E. & Gabrilovich, D. I. The nature of myeloid-derived suppressor

cells in the tumor microenvironment. _Trends Immunol._ 37, 208–220 (2016). Article  CAS  PubMed  PubMed Central  Google Scholar  * Ng, L. G., Ostuni, R. & Hidalgo, A. Heterogeneity of

neutrophils. _Nat. Rev. Immunol._ 19, 255–265 (2019). Article  CAS  PubMed  Google Scholar  * Granot, Z. et al. Tumor entrained neutrophils inhibit seeding in the premetastatic lung. _Cancer

Cell_ 20, 300–314 (2011). Article  CAS  PubMed  PubMed Central  Google Scholar  * Casbon, A.-J. et al. Invasive breast cancer reprograms early myeloid differentiation in the bone marrow to

generate immunosuppressive neutrophils. _Proc. Natl Acad. Sci. USA_ 112, E566–E575 (2015). Article  CAS  PubMed  PubMed Central  Google Scholar  * Spiegel, A. et al. Neutrophils suppress

intraluminal NK cell-mediated tumor cell clearance and enhance extravasation of disseminated carcinoma cells. _Cancer Discov._ 6, 630–649 (2016). Article  CAS  PubMed  PubMed Central  Google

Scholar  * Kim, I. S. et al. Immuno-subtyping of breast cancer reveals distinct myeloid cell profiles and immunotherapy resistance mechanisms. _Nat. Cell Biol._ 21, 1113–1126 (2019).

Article  CAS  PubMed  PubMed Central  Google Scholar  * The Cancer Genome Atlas Research Network. Comprehensivemolecular characterization of clear cell renal cell carcinoma. _Nature_ 499,

43–49 (2013). Article  CAS  PubMed Central  Google Scholar  * Álvarez-Errico, D., Vento-Tormo, R., Sieweke, M. & Ballestar, E. Epigenetic control of myeloid cell differentiation,

identity and function. _Nat. Rev. Immunol._ 15, 7–17 (2015). Article  CAS  PubMed  Google Scholar  * Taniguchi, K. & Karin, M. NF-κB, inflammation, immunity and cancer: coming of age.

_Nat. Rev. Immunol._ 18, 309–324 (2018). Article  CAS  PubMed  Google Scholar  * Sack, G. H. Serum amyloid A—a review. _Mol. Med._ 24, 46 (2018). Article  CAS  PubMed  PubMed Central  Google

Scholar  * Witte, S., O’Shea, J. J. & Vahedi, G. Super-enhancers: asset management in immune cell genomes. _Trends Immunol._ 36, 519–526 (2015). Article  CAS  PubMed  PubMed Central 

Google Scholar  * Brown, J. D. et al. NF-κB directs dynamic super enhancer formation in inflammation and atherogenesis. _Mol. Cell_ 56, 219–231 (2014). Article  CAS  PubMed  PubMed Central 

Google Scholar  * Adachi, K. et al. Thienotriazolodiazepine compound and medicinal use thereof. International Patent PCT/JP2006/310709 (WO/2006/129623) (2006). * Filippakopoulos, P. et al.

Selective inhibition of BET bromodomains. _Nature_ 468, 1067–1073 (2010). Article  CAS  PubMed  PubMed Central  Google Scholar  * Delmore, J. E. et al. BET bromodomain inhibition as a

therapeutic strategy to target c-Myc. _Cell_ 146, 904–917 (2011). Article  CAS  PubMed  PubMed Central  Google Scholar  * Shu, S. et al. Response and resistance to BET bromodomain inhibitors

in triple-negative breast cancer. _Nature_ 529, 413–417 (2016). Article  CAS  PubMed  PubMed Central  Google Scholar  * Xu, F. et al. The oncogenic role of COL23A1 in clear cell renal cell

carcinoma. _Sci. Rep._ 7, 1–9 (2017). Article  CAS  Google Scholar  * Joyce, J. A. & Fearon, D. T. T cell exclusion, immune privilege, and the tumor microenvironment. _Science_ 348,

74–80 (2015). Article  CAS  PubMed  Google Scholar  * Ehata, S. et al. Transforming growth factor-β decreases the cancer-initiating cell population within diffuse-type gastric carcinoma

cells. _Oncogene_ 30, 1693–1705 (2010). Article  CAS  PubMed  Google Scholar  * Katsura, A. et al. ZEB1-regulated inflammatory phenotype in breast cancer cells. _Mol. Oncol._ 11, 1241–1262

(2017). Article  CAS  PubMed  PubMed Central  Google Scholar  * Lung, H. L. et al. SAA1 polymorphisms are associated with variation in antiangiogenic and tumor-suppressive activities in

nasopharyngeal carcinoma. _Oncogene_ 34, 878–889 (2015). Article  CAS  PubMed  Google Scholar  * Yokoyama, Y. et al. Autocrine BMP-4 signaling is a therapeutic target in colorectal cancer.

_Cancer Res._ 77, 4026–4038 (2017). Article  CAS  PubMed  Google Scholar  * Koinuma, D. et al. Chromatin immunoprecipitation on microarray analysis of Smad2/3 binding sites reveals roles of

ETS1 and TFAP2A in transforming growth factor beta signaling. _Mol. Cell. Biol._ 29, 172–186 (2009). Article  CAS  PubMed  Google Scholar  Download references 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. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * 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 * Department of Medical Genomics, Graduate School of Medicine, The University of Tokyo, Tokyo,

Japan Shogo Ehata * Environmental Science Center, The University of Tokyo, Tokyo, Japan Shogo Ehata Authors * Jun Nishida View author publications You can also search for this author

inPubMed Google Scholar * Yusaku Momoi View author publications You can also search for this author inPubMed Google Scholar * Kosuke Miyakuni View author publications You can also search for

this author inPubMed Google Scholar * Yusuke Tamura View author publications You can also search for this author inPubMed Google Scholar * Kei Takahashi View author publications You can

also search for this author inPubMed Google Scholar * Daizo Koinuma View author publications You can also search for this author inPubMed Google Scholar * Kohei Miyazono View author

publications You can also search for this author inPubMed Google Scholar * Shogo Ehata View author publications You can also search for this author inPubMed Google Scholar 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. CORRESPONDING AUTHORS Correspondence to Kohei Miyazono or Shogo Ehata. ETHICS DECLARATIONS COMPETING INTERESTS The authors

declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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. Source data

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. Source data 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. Source data 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.

SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary Fig. 1. REPORTING SUMMARY SUPPLEMENTARY TABLES Supplementary Tables 1–5. SOURCE DATA SOURCE DATA FIG. 1 Statistical source

data SOURCE DATA FIG. 2 Statistical source data SOURCE DATA FIG. 3 Statistical source data SOURCE DATA FIG. 3 Unprocessed western blots SOURCE DATA FIG. 4 Statistical source data SOURCE DATA

FIG. 4 Unprocessed western blots SOURCE DATA FIG. 5 Statistical source data SOURCE DATA FIG. 6 Statistical source data SOURCE DATA FIG. 7 Statistical source data SOURCE DATA EXTENDED DATA

FIG. 1 Statistical source data SOURCE DATA EXTENDED DATA FIG. 2 Statistical source data SOURCE DATA EXTENDED DATA FIG. 3 Statistical source data SOURCE DATA EXTENDED DATA FIG. 4 Statistical

source data SOURCE DATA EXTENDED DATA FIG. 5 Statistical source data SOURCE DATA EXTENDED DATA FIG. 5 Unprocessed western blots SOURCE DATA EXTENDED DATA FIG. 6 Statistical source data

SOURCE DATA EXTENDED DATA FIG. 6 Unprocessed western blots SOURCE DATA EXTENDED DATA FIG. 7 Statistical source data SOURCE DATA EXTENDED DATA FIG. 8 Statistical source data SOURCE DATA

EXTENDED DATA FIG. 9 Statistical source data RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE 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 Download citation * Received:

02 May 2019 * Accepted: 23 February 2020 * Published: 23 March 2020 * Issue Date: April 2020 * DOI: https://doi.org/10.1038/s41556-020-0491-2 SHARE THIS ARTICLE Anyone you share the

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