Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

IL-1β inflammatory response driven by primary breast cancer prevents metastasis-initiating cell colonization

Nature Cell Biology volume 20, pages 1084–1097 (2018)Cite this article

Subjects

Abstract

Lack of insight into mechanisms governing breast cancer metastasis has precluded the development of curative therapies. Metastasis-initiating cancer cells (MICs) are uniquely equipped to establish metastases, causing recurrence and therapeutic resistance. Using various metastasis models, we discovered that certain primary tumours elicit a systemic inflammatory response involving interleukin-1β (IL-1β)-expressing innate immune cells that infiltrate distant MIC microenvironments. At the metastatic site, IL-1β maintains MICs in a ZEB1-positive differentiation state, preventing MICs from generating highly proliferative E-cadherin-positive progeny. Thus, when the inherent plasticity of MICs is impeded, overt metastases cannot be established. Ablation of the pro-inflammatory response or inhibition of the IL-1 receptor relieves the differentiation block and results in metastatic colonization. Among patients with lymph node-positive breast cancer, high primary tumour IL-1β expression is associated with better overall survival and distant metastasis-free survival. Our data reveal complex interactions that occur between primary tumours and disseminated MICs that could be exploited to improve patient survival.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Primary tumours inhibit the outgrowth of distant MICs independently of the adaptive immune system.
Fig. 2: Primary tumours prevent the differentiation and proliferation of distant MICs.
Fig. 3: Myeloid cells are necessary for preventing MIC differentiation and outgrowth.
Fig. 4: Myeloid cell-derived IL-1β is sufficient to prevent MIC differentiation.
Fig. 5: IL-1R1 signalling is necessary for preventing MIC differentiation.
Fig. 6: Primary tumours initiate a sustained systemic inflammatory response.
Fig. 7: Inhibiting inflammation at the primary tumour site results in differentiation of disseminated MICs.
Fig. 8: Low primary tumour IL-1β correlates with reduced overall survival in breast cancer.

Similar content being viewed by others

References

  1. Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2016. CA Cancer J. Clin. 66, 7–30 (2016).

    Article  PubMed  Google Scholar 

  2. Klein, C. A. Parallel progression of primary tumours and metastases. Nat. Rev. Cancer 9, 302–312 (2009).

    Article  PubMed  CAS  Google Scholar 

  3. Pantel, K., Brakenhoff, R. H. & Brandt, B. Detection, clinical relevance and specific biological properties of disseminating tumour cells. Nat. Rev. Cancer 8, 329–340 (2008).

    Article  PubMed  CAS  Google Scholar 

  4. Luzzi, K. J. et al. Multistep nature of metastatic inefficiency: dormancy of solitary cells after successful extravasation and limited survival of early micrometastases. Am. J. Pathol. 153, 865–873 (1998).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Cameron, M. D. et al. Temporal progression of metastasis in lung: cell survival, dormancy, and location dependence of metastatic inefficiency. Cancer Res. 60, 2541–2546 (2000).

    PubMed  CAS  Google Scholar 

  6. Chambers, A. F., Groom, A. C. & MacDonald, I. C. Dissemination and growth of cancer cells in metastatic sites. Nat. Rev. Cancer 2, 563–572 (2002).

    Article  PubMed  CAS  Google Scholar 

  7. Celia-Terrassa, T. & Kang, Y. Distinctive properties of metastasis-initiating cells. Genes Dev. 30, 892–908 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Ghajar, C. M. Metastasis prevention by targeting the dormant niche. Nat. Rev. Cancer 15, 238–247 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Redig, A. J. & McAllister, S. S. Breast cancer as a systemic disease: a view of metastasis. J. Intern. Med. 274, 113–126 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Finisguerra, V. et al. MET is required for the recruitment of anti-tumoural neutrophils. Nature 522, 349–353 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Wculek, S. K. & Malanchi, I. Neutrophils support lung colonization of metastasis-initiating breast cancer cells. Nature 528, 413–417 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Granot, Z. et al. Tumor entrained neutrophils inhibit seeding in the premetastatic lung. Cancer Cell 20, 300–314 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Coffelt, S. B. et al. IL-17-producing gammadelta T cells and neutrophils conspire to promote breast cancer metastasis. Nature 522, 345–348 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Murgai, M., Giles, A. & Kaplan, R. Physiological, tumor, and metastatic niches: opportunities and challenges for targeting the tumor microenvironment. Crit. Rev. Oncog. 20, 301–314 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  15. McAllister, S. S. & Weinberg, R. A. The tumour-induced systemic environment as a critical regulator of cancer progression and metastasis. Nat. Cell Biol. 16, 717–727 (2014).

    Article  PubMed  CAS  Google Scholar 

  16. de Visser, K. E. & Coussens, L. M. The inflammatory tumor microenvironment and its impact on cancer development. Contrib. Mirobiol. 13, 118–137 (2006).

    Article  Google Scholar 

  17. Chaffer, C. L., San Juan, B. P., Lim, E., & Weinberg, R. A. EMT, cell plasticity and metastasis. Cancer Metastasis Rev. 35, 645–654 (2016).

    Article  PubMed  Google Scholar 

  18. Borowsky, A. D. et al. Syngeneic mouse mammary carcinoma cell lines: two closely related cell lines with divergent metastatic behavior. Clin. Exp. Metastasis 22, 47–59 (2005).

    Article  PubMed  CAS  Google Scholar 

  19. Werbeck, J. L. et al. Tumor microenvironment regulates metastasis and metastasis genes of mouse MMTV-PymT mammary cancer cells in vivo. Vet. Pathol. 51, 868–881 (2014).

    Article  PubMed  CAS  Google Scholar 

  20. Olive, J. F. et al. Accounting for tumor heterogeneity when using CRISPR-Cas9 for cancer progression and drug sensitivity studies. PLoS ONE 13, e0198790 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Chaffer, C. L. et al. Normal and neoplastic nonstem cells can spontaneously convert to a stem-like state. Proc. Natl Acad. Sci. USA 108, 7950–7955 (2011).

    Article  PubMed  Google Scholar 

  22. Bierie, B. et al. Integrin-beta4 identifies cancer stem cell-enriched populations of partially mesenchymal carcinoma cells. Proc. Natl Acad. Sci. USA 114, E2337–E2346 (2017).

    Article  PubMed  CAS  Google Scholar 

  23. Naumov, G. N., Folkman, J. & Straume, O. Tumor dormancy due to failure of angiogenesis: role of the microenvironment. Clin. Exp. Metastasis 26, 51–60 (2009).

    Article  PubMed  Google Scholar 

  24. Ye, X. & Weinberg, R. A. Epithelial–mesenchymal plasticity: a central regulator of cancer progression. Trends Cell Biol. 25, 675–686 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Pattabiraman, D. R. et al. Activation of PKA leads to mesenchymal-to-epithelial transition and loss of tumor-initiating ability. Science 351, aad3680 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Wellner, U. et al. The EMT-activator ZEB1 promotes tumorigenicity by repressing stemness-inhibiting microRNAs. Nat. Cell Biol. 11, 1487–1495 (2009).

    Article  PubMed  CAS  Google Scholar 

  27. Palucka, A. K. & Coussens, L. M. The basis of oncoimmunology. Cell 164, 1233–1247 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Hagerling, C. & Werb, Z. Neutrophils: critical components in experimental animal models of cancer. Semin. Immunol. 28, 197–204 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. DeNardo, D. G. et al. Leukocyte complexity predicts breast cancer survival and functionally regulates response to chemotherapy. Cancer Discov. 1, 54–67 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Mantovani, A., Cassatella, M. A., Costantini, C. & Jaillon, S. Neutrophils in the activation and regulation of innate and adaptive immunity. Nat. Rev. Immunol. 11, 519–531 (2011).

    Article  PubMed  CAS  Google Scholar 

  31. Gordon, S. Alternative activation of macrophages. Nat. Rev. Immunol. 3, 23–35 (2003).

    Article  PubMed  CAS  Google Scholar 

  32. Muir, A. B. et al. Esophageal epithelial and mesenchymal cross-talk leads to features of epithelial to mesenchymal transition in vitro. Exp. Cell Res. 319, 850–859 (2013).

    Article  PubMed  CAS  Google Scholar 

  33. Apte, R. N. et al. The involvement of IL-1 in tumorigenesis, tumor invasiveness, metastasis and tumor–host interactions. Cancer Metastasis Rev. 25, 387–408 (2006).

    Article  PubMed  CAS  Google Scholar 

  34. Hou, Z., Falcone, D. J., Subbaramaiah, K. & Dannenberg, A. J. Macrophages induce COX-2 expression in breast cancer cells: role of IL-1β autoamplification. Carcinogenesis 32, 695–702 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Bellehumeur, C., Blanchet, J., Fontaine, J. Y., Bourcier, N. & Akoum, A. Interleukin 1 regulates its own receptors in human endometrial cells via distinct mechanisms. Hum. Reprod. 24, 2193–2204 (2009).

    Article  PubMed  CAS  Google Scholar 

  36. Windheim, M. & Hansen, B. Interleukin-1-induced activation of the small GTPase Rac1 depends on receptor internalization and regulates gene expression. Cell Signal. 26, 49–55 (2014).

    Article  PubMed  CAS  Google Scholar 

  37. Shultz, L. D. et al. Multiple defects in innate and adaptive immunologic function in NOD/LtSz-scid mice. J. Immunol. 154, 180–191 (1995).

    PubMed  CAS  Google Scholar 

  38. Garlanda, C., Dinarello, C. A. & Mantovani, A. The interleukin-1 family: back to the future. Immunity 39, 1003–1018 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Hu, Y. et al. Lipocalin 2 upregulation protects hepatocytes from IL1-β-induced stress. Cell. Physiol. Biochem. 36, 753–762 (2015).

    Article  PubMed  CAS  Google Scholar 

  40. Deshmane, S. L., Kremlev, S., Amini, S. & Sawaya, B. E. Monocyte chemoattractant protein-1 (MCP-1): an overview. J. Interferon Cytokine Res. 29, 313–326 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Pei, X. et al. The PSMP-CCR2 interactions trigger monocyte/macrophage-dependent colitis. Sci. Rep. 7, 5107 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Dinarello, C. A. Immunological and inflammatory functions of the interleukin-1 family. Annu. Rev. Immunol. 27, 519–550 (2009).

    Article  PubMed  CAS  Google Scholar 

  43. Ringner, M., Fredlund, E., Hakkinen, J., Borg, A. & Staaf, J. GOBO: gene expression-based outcome for breast cancer online. PLoS ONE 6, e17911 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article  PubMed  Google Scholar 

  45. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Ciriello, G. et al. Comprehensive molecular portraits of invasive lobular breast cancer. Cell 163, 506–519 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Cejalvo, J. M. et al. Intrinsic subtypes and gene expression profiles in primary and metastatic breast cancer. Cancer Res. 77, 2213–2221 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Dongre, A. et al. Epithelial-to-mesenchymal transition contributes to immunosuppression in breast carcinomas. Cancer Res. 77, 3982–3989 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Guerriero, J. L. et al. Class IIa HDAC inhibition reduces breast tumours and metastases through anti-tumour macrophages. Nature 543, 428–432 (2017).

    Article  PubMed  CAS  Google Scholar 

  50. Ruffell, B. & Coussens, L. M. Macrophages and therapeutic resistance in cancer. Cancer Cell 27, 462–472 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Lu, H. et al. A breast cancer stem cell niche supported by juxtacrine signalling from monocytes and macrophages. Nat. Cell Biol. 16, 1105–1117 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Schmid, M. C. et al. Combined blockade of integrin-alpha4beta1 plus cytokines SDF-1alpha or IL-1beta potently inhibits tumor inflammation and growth. Cancer Res. 71, 6965–6975 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. 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  PubMed  PubMed Central  CAS  Google Scholar 

  54. Voloshin, T. et al. Blocking IL1beta pathway following paclitaxel chemotherapy slightly inhibits primary tumor growth but promotes spontaneous metastasis. Mol. Cancer Ther. 14, 1385–1394 (2015).

    Article  PubMed  CAS  Google Scholar 

  55. Kersten, K. et al. Mammary tumor-derived CCL2 enhances pro-metastatic systemic inflammation through upregulation of IL1beta in tumor-associated macrophages. Oncoimmunology 6, e1334744 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Dinarello, C. A. Why not treat human cancer with interleukin-1 blockade? Cancer Metastasis Rev. 29, 317–329 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Castano, Z. et al. Stromal EGF and igf-I together modulate plasticity of disseminated triple-negative breast tumors. Cancer Discov. 3, 922–935 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Elkabets, M. et al. Human tumors instigate granulin-expressing hematopoietic cells that promote malignancy by activating stromal fibroblasts in mice. J. Clin. Invest. 121, 784–799 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Kuznetsov, H. S. et al. Identification of luminal breast cancers that establish a tumor-supportive macroenvironment defined by proangiogenic platelets and bone marrow-derived cells. Cancer Discov. 2, 1150–1165 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. McAllister, S. S. et al. Systemic endocrine instigation of indolent tumor growth requires osteopontin. Cell 133, 994–1005 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. De Cock, J. M. et al. Inflammation triggers Zeb1-dependent escape from tumor latency. Cancer Res. 76, 6778–6784 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Yu, M. et al. Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science 339, 580–584 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Steinbaugh, M. J. bcbioRNASeq: R package for bcbio RNA-seq analysis. F1000Res 6, 28 (2017).

    Article  Google Scholar 

  64. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  PubMed  CAS  Google Scholar 

  65. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  PubMed  CAS  Google Scholar 

  66. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Garcia-Alcalde, F. et al. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics 28, 2678–2679 (2012).

    Article  PubMed  CAS  Google Scholar 

  68. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res 4, 1521 (2015).

    Article  PubMed  CAS  Google Scholar 

  70. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Sergushichev, A. An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. Preprint at https://doi.org/10.1101/060012 (2016).

  72. Sendobry, S. M. et al. Attenuation of diet-induced atherosclerosis in rabbits with a highly selective 15-lipoxygenase inhibitor lacking significant antioxidant properties. Br. J. Pharmacol. 120, 1199–1206 (1997).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by funding from the following: International Mentoring Foundation for the Advancement of Higher Education (IMFAHE) (A.G.R.); Center for Stem Cell Bioinformatics at the Harvard Stem Cell Institute (J.N.H.); Samuel Waxman Cancer Research Foundation, Breast Cancer Research Foundation, and Ludwig Center for Molecular Oncology (R.A.W.); Advanced Medical Research Foundation and Nelune Foundation (C.L.C.); National Institutes of Health (NCI) RO1 CA166284, Presidential Early Career Award for Scientists and Engineers, American Cancer Society Research Scholar award, and Department of Defense BCMRP Era of Hope Scholar Award W81XWH-14-1-0191 (S.S.M.). We thank the following: J. Joyce for the Met1 cell line (with permission from A. Borowsky); J. Sceneay, B. Bierie, Y. Qin and K. Tracy for technical expertise; J. Olive for editorial assistance; E. Frank and the Dana-Farber/Harvard Cancer Center Breast Cancer Advocate group for critical discussion and input; Z. Herbert of the Molecular Biology Core Facilities, Dana-Farber Cancer Institute; G. Bell and B. Rosner for statistical advice; The Specialized Histopathology Core at Dana-Farber/Harvard Cancer Center (supported in part by a NCI Cancer Center Support Grant no. NIH 5 P30 CA06516); the Histology Core Facility at the KI Swanson Biotechnology Center, MIT; and the Flow Cytometry Core Facility at the Whitehead Institute for Biomedical Research. Finally, we thank GG Bernath, A. Bernath, J. Bernath, Z. Corsino-Gupta, P. Nguyen, B. Nguyen and K. Nguyen for their support during the preparation of this manuscript.

Author information

Author notes

  1. These authors contributed equally to this work: Christine L. Chaffer, Sandra S. McAllister.

Authors and Affiliations

  1. Division of Hematology, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Zafira Castaño, Molly J. DeCristo, Tyler Laszewski, Jessalyn M. Ubellacker, Ayana Henderson, Ana Garcia del Rio & Sandra S. McAllister

  2. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Zafira Castaño, Molly J. DeCristo, Jessalyn M. Ubellacker, Ayana Henderson & Sandra S. McAllister

  3. The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia

    Beatriz P. San Juan & Christine L. Chaffer

  4. Whitehead Institute for Biomedical Research, Cambridge, MA, USA

    Asaf Spiegel, Ayush Pant, Susanne R. Janssen, Anushka Dongre, Ferenc Reinhardt, Ann M. Gifford, Robert A. Weinberg & Christine L. Chaffer

  5. Molecular Biology Core Facilities, Dana-Farber Cancer Institute, Boston, MA, USA

    Zachary T. Herbert

  6. Department of Biostatistics, Harvard T.H. Chan, School of Public Health, Boston, MA, USA

    John N. Hutchinson

  7. MIT Department of Biology and Ludwig/MIT Center for Molecular Oncology, Cambridge, MA, USA

    Robert A. Weinberg

  8. Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Sandra S. McAllister

  9. Harvard Stem Cell Institute, Cambridge, MA, USA

    Sandra S. McAllister

Authors
  1. Zafira Castaño
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Beatriz P. San Juan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Asaf Spiegel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ayush Pant
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Molly J. DeCristo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tyler Laszewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jessalyn M. Ubellacker
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Susanne R. Janssen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Anushka Dongre
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ferenc Reinhardt
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ayana Henderson
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Ana Garcia del Rio
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Ann M. Gifford
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Zachary T. Herbert
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. John N. Hutchinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Robert A. Weinberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Christine L. Chaffer
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Sandra S. McAllister
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Z.C., C.L.C. and S.S.M designed and performed experiments, analysed data and wrote the manuscript. R.A.W., C.L.C and S.S.M. supervised the study and edited the manuscript. B.P.S.J., A.S., A.P., M.J.D., T.L., J.M.U., S.R.J., A.D., F.R., A.H., A.G.R. and A.M.G. performed experiments. J.N.H. and Z.T.H. performed the computational and statistical analyses. R.A.W., C.L.C., and S.S.M. acquired funding support.

Corresponding authors

Correspondence to Christine L. Chaffer or Sandra S. McAllister.

Ethics declarations

Competing interests

The authors declare no competing interest.

Additional information

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

Integrated supplementary information

Supplementary Figure 1 Primary tumours inhibit outgrowth of distant metastasis-initiating cells.

a, Met1 orthotopic primary tumour growth kinetics, FVB mice, represented in Fig. 1b (n = 10 animals). b, Schematic of sequential subcutaneous experimental model (left) and growth kinetics (right) of Met1 secondary tumours (2.5 × 105/mouse; implanted at day 14) in mice with PBS control or Met1 primary tumour (2.5 × 105/mouse; n = 5 animals/cohort). c, Numbers of pulmonary macrometastases following tail vein injection of MT2 or MT3 cells (7.5 × 105cells; n = 3 animals). Macrometastases > 100 microns were quantified on microscope tissue sections from 4 lung lobes per mouse. d, Met1 orthotopic tumour growth kinetics, Nude mice, represented in Fig. 1f (n = 5 animals). e, Schematic of experimental model (left), and number of pulmonary macrometastases following tail vein injection of 2.5 × 105 hMICs into Nude mice with either Matrigel (n = 10 animals) or HMLER primary tumours (n = 9 animals; original injection of 5.0 × 105 cells/mouse) (right). Macrometastases ( > 100 microns) or micrometastases (>5 cells or < 5 cells) were quantified from microscopic whole lung tissue sections. f, Schematic of experimental model (applies to g and h). g, Growth kinetics of HMLER primary tumours, Nude mice, described in Fig. 1h (n = 10 animals). h, 231-MIC tumour growth kinetics, Nude mice, opposite Matrigel control (n = 12 animals) or HMLER primary tumours (n = 5 animals). Representative of 2 experiments. i, Images: representative immunofluorescent images of 231-MIC tumours grown opposite Matrigel control or an HMLER primary tumour (represented in Supplementary Fig. 1h) stained with Ki67 (red), hMIT to identify human mitochondria (green), DAPI (nuclei, blue); Scale bars = 100 µm. Graph: Quantification of Ki67 + hMit + cells as a percentage of the total number of hMit + tumour cells/microscopic field (n = 9 independent images representing 3 tumours/cohort). Source data for a, b, c, d, e, g, h, i in Supplementary Table 1. 2-way ANOVA, followed by Sidak’s multiple comparison test (b, g, h); 1-sided Welch’s t test (e); 2-sided Welch’s t test (c, i)

Supplementary Figure 2 MIC Differentiation is Perturbed by the Presence of a Primary Tumour.

a, In vitro immunocytochemical flourescence showing E-cadherin (ECAD, red) and DAPI (nuclei, blue) in Met1 parental cell line (mMIC) and Met1-derived clones, MT2 and MT3 (mMIC-MT3). b, Images: Immunofluorescence showing ZEB1 and ECAD expression in cultured hMICs prior to xenotransplantation. Western blot: mesenchymal marker Vimentin (VIM) and epithelial marker ECAD protein in polyclonal HMLER cells and derivative hMIC and HMLER2 cells. GADPH shown as internal control. Positive controls: Ctrl E (epithelial-MCF7Ras); Ctrl M (mesenchymal CD44hi HMLER cells). c, Merged immunofluorescent images of mMIC-MT3 tumours (described in Fig. 1d) stained for basal cytokeratin 14 (CK14, red), luminal CK8 (green) or PyMT antigen (expressed by tumour cells only-green). Arrows - CK14 + tumour cells. d, Images: hMIC tumours (from Fig. 1i) stained with CK14 (red), VIM (green) and DAPI (blue); Graph: quantification of indicated stains on hMIC tumours grown opposite Matrigel (n = 4 tumours) or primary tumour (n = 5 tumours). e, Schematic: modelling early stages of hMIC colonization. Graph: hMIC tumour growth kinetics opposite Matrigel control or HMLER primary tumour (n = 4 tumours/group); differences not statistically significant. f, g, Immunofluorescent images (f) and quantification (g) of hMIC tumours stained for ki67 (red), LgT antigen (tumour cells, green), and DAPI (nuclei, blue) as a percentage of total LgT + cells. Control, n = 10 independent images representing 4 tumours; HMLER cohort, n = 9 independent images representing 4 tumours. h, i, Immunofluorescent images (h) and quantification (i) of staining hMIC tumours for cleaved caspase3 (CASP3, red), human-specific mitochondria (hMIT, green), and DAPI (nuclei, blue) grown in mice with Matrigel control (n = 6 independent images representing 4 tumours) or HMLER primary tumours (n = 5 independent images representing 4 tumours). j, Expression of ZEB1 (ZEB1-GFP construct) or HRAS (HRAS-tomato construct) analyzed by FACS (1.0 × 105 cells) in Control hMIC or ZEB1hi hMIC (from Fig. 2n-p). All scale bars = 100 µm. Source data for d, e, g, i in Supplementary Table 1 and d on Supplementary Figure 9. 2-way ANOVA (e); 2-sided Welch’s t test (d, i); 2-sided Mann-Whitney test (g). Unprocessed blots are available in Supplementary figure 9

Supplementary Figure 3 Innate Inflammatory Cells are Necessary for MIC Colonization.

a, Experimental schematic for RNA-seq tissue analysis (Fig. 3a-c and Supplementary Fig. 3b-e). b, Met1 primary tumour mass in FVB mice (n = 5 animals). c, d, RNA-seq analysis on lungs from mice with PBS control (n = 4 animals) or a Met1 primary tumour (n = 4 animals). Heatmap (c): top 50 differentially expressed genes (adjusted p-value, DESeq2). Blue = low, green = mean, and yellow = high relative expression levels. PBS control lungs (yellow), Met1 primary tumour-bearing lungs (purple). Volcano plot (d): DESeq2 comparison Single gene with Padj < 0.05 and absolute log2(FoldChange) > 1 (green). e, Experimental schematic and flow cytometric quantification of immune cell populations in lungs of indicated FVB mice at 28-day end point (see Fig. 1a). f, Ratio of genes expressed by pro-metastatic immunosuppressive neutrophils from K14cre;Cdh1F/F;Trp53F/F (KEP) mice to control neutrophils from wild type littermates (KEP:Normal)13 extrapolated onto our signatures from control (blue) primary tumour-bearing lungs (red). Higher ratios indicate higher pro-metastatic KEP signature. Box plot: median, 25th and 75th percentiles, whiskers extend to minimum and maximum values. g, Experimental design to identify optimal anti-Ly6G dose for neutrophil depletion. h, Primary tumour mass in Control anti-IgG2a (n = 3 mice/cohort) and anti-Ly6G (n = 4 mice/cohort). i, Flow cytometric gating strategy for neutrophils (CD45 + CD11b + Gr1 + Ly6Clo) and monocytes (CD45 + CD11b + Gr1 + Ly6Chi). j-l, Flow cytometric analysis of blood at 14d (j), 20d (k), and lungs 20d (l) in experiment described in (g). Control anti-IgG2a n = 3 mice/cohort; anti-Ly6G n = 4 mice/cohort. m, Schematic modelling neutrophil depletion experiment (n-r). n, Primary tumour mass from Fig. 3i (control IgG2a n = 4 mice/cohort; anti-Ly6G n = 8 mice/cohort). o, Immunofluorescent images of Met1 lung metastasis. Scale bars = 200 µm. p, Metastases at higher magnification (indicated in circles in (o) analyzed by ImageJ to measure shortest-length diameter). q, Single channel ECAD or PyMT staining measured by ImageJ analysis (Fig. 3k, l). r, End point flow cytometric analysis of neutrophils and monocytes in lungs of mice treated with control IgG2a (n = 4 mice/cohort) or anti-Ly6G (n = 8 mice/cohort). Source data for b, e, h, j, k, l, n, r in Supplementary Table 1. DESeq2’s Wald test two-sided (c), 2-sided Welch’s t-test (f); 2-sided Welch’s t test (n); 2-way ANOVA followed by Sidak’s multiple comparison test (f, h, j, k, l, o)

Supplementary Figure 4 Innate Inflammatory Response is sufficient for MIC colonization.

a, Some of the predominant cytokines expressed by monocytes, neutrophils or by both. b, ELISA to detect cumulative IL-1β in the conditioned media of Met1 cell line and its derived clones after 3 days in vitro (n = 5 biological replicates/group). c, Met1 parental cells and the MT2 and MT3 subclones stained as indicated (upper panel). Scale bars = 100 µm. Western blot detecting phospho-p65 (p-p65; activated form of NF-kB p65) or total NF-kB p65 (p65) in Met1, MT2 and MT3 treated in vitro with indicated doses of IL-1β at indicated time points (lower panel). d, ELISA to detect cumulative IL-1β in the conditioned media of HMLER and hMIC cell lines over 3 consecutive days of in vitro culture (n = 4 biological replicates/group). e, Experimental schematic and flow cytometric quantification of immune cell populations in digested hMIC tumours from indicated cohorts in Nude mice at the 28-day experimental end point (see Fig. 2i) (n = 3 hMIC tumours in Matrigel control and 4 hMIC tumours in HMLER cohort). Source data for b, d, e in Supplementary Table 1. 2-sided Welch’s t test (b, e); 2-way ANOVA, Sidak’s multiple comparisons test (d). Unprocessed blots are available in Supplementary figure 9

Supplementary Figure 5 IL-1R1 Pathway is sufficient to block MICs colonization.

a, qRT-PCR to determine IL-1R1 mRNA levels in hMIC expressing shRNA against IL-1R1 (shA-IL-1R1-MIC and shB-IL-1R1-MIC) compared to hMIC expressing a scrambled shRNA (sh-Ctl-MIC). B-actin (ACTB) served as the internal control (n = 3 PCR technical triplicates). b, Proliferation analysis (CyQuant assay) comparing growth of MIC-control cells to those expressing shRNA against IL-1R1 (shA-IL-1R1-MIC and shB-IL-1R1-MIC; n = 3 in triplicate. Representative of 5 independent experiments. c, hMIC tumour mass (mg) at the 4-week experimental end point (n = 8 animals/cohort, except sh-Ctl-MIC n = 7 animals). d, Met1 tumourspheres generated in 3 independent experiments, treated with IL-1β (10 ng/mL), Anakinra (500 ng/ml) or IL-1β + Anakinra combination (n = 3 replicates, 28 technical replicates) every 48 h for 7d. e, Western blot detecting phospho-p65, total p65, or ECAD in Met1 cells treated with 10 ng/ml of IL-1β and increasing doses of ANAKINRA (IL-1Ra) for 48 h. B-actin (ACTB) was used as a loading control. f, Flow cytometric analysis of monocytes (CD11b+Ly6C+Ly6Glo) presented as % CD11b + cells in blood at 10 days, prior to the injection of contralateral tumours (Matrigel n = 11 animals (combined from Groups 1 and 3); HMLER n = 7 animals). g, Final mass (mg) of hMIC tumours from indicated cohorts (differences not statistically significant, Control n = 7 animals, HMLER n = 6 animals). One experiment performed. Source data from a, b, c, d, f, g in Supplementary Table 1. 2-way ANOVA and Sidak’s multiple comparisons test (a, b); 1-way ANOVA (c, g); 2-sided Student’s t tests (d); 2-sided Mann-Whitney test (f). Unprocessed blots are available in Supplementary figure 9

Supplementary Figure 6 Primary tumours induce an inflammatory response.

a, Representative images of F4/80 staining and ImageJ analysis used to quantify area covered by positive F4/80 staining in Fig. 6b. Scale bar = 100 µm. Representative of n = 5 HMLER tumours and n = 6 HMLER2 tumours

Supplementary Figure 7 Inhibition of IL-1R1 at Primary Tumour Site Enables MIC Colonization.

a, KEGG IL-17 signalling pathway (mmu04657). The log2 fold change values comparing primary tumour to control lungs. Red, higher expression in primary tumours; green, higher expression in normal lungs (n = 3 lungs from 3 animals for Met1 primary tumours, and n = 4 control lungs from 4 animals). b, Inflammatory cytokine array analysis of conditioned media from HMLER and hMIC cells after 3 days in culture (n = 2 biological replicates, 3 technical replicates). Results are fold change (Log2) HMLER/MIC. c, Mouse inflammatory cytokine array results showing differences between plasma from mice bearing Matrigel control, HMLER, or HMLER + IL-1Ra tumours after 2 weeks in vivo (n = 2, plasma pooled from 3 individual animals per sample). Graph: cytokines statistically different between cohorts. d, hMIC tumour mass (4 weeks) grown opposite Matrigel control (n = 8 tumours), HMLER primary tumour (n = 7 tumours), or HMLER + IL-1Ra antagonist (IL-1Ra) (n = 8 tumours). One experiment was performed. e, Graph: HMLER primary tumour mass embedded in control Matrigel (n = 5 animals) or Matrigel with 100 ng/ml IL-1Ra (n = 6 animals). Incidence (%) marked on graphs. f, Hematoxylin and eosin (H&E) stains from HMLER and HMLER + IL-1Ra tumours (Fig. 7c-g), low and high magnification. g, F4/80 staining and ImageJ analysis to quantify area covered by F4/80 + staining in Fig. 7e. Boxes indicate area shown in Fig. 7e. h, Immunohistochemistry showing macrophage infiltration (F4/80 stain) in hMIC tumours grown opposite Matrigel control, HMLER, or HMLER + IL-1Ra primary tumours. i, j, Photomicrographs of hMIC tumours grown opposite Matrigel control, HMLER primary tumour, or HMLER + IL-1Ra primary tumours. ECAD (red), LgT (green – to detect human tumour cells), DAPI (blue). (h) ZEB1 (red), LgT (green), DAPI (blue). Boxes indicate area shown in Fig. 7f-g. Scale bars = 100 µm. Source data for b, c, d, e in Supplementary Table 1. DESeq2’s Wald test two-sided (a); 2-sided Mann-Whitney test (d); 2-sided Welch’s t test (e)

Supplementary Figure 8 Analysis of IL1 pathway components as predictors of disease outcome in clinical breast cancer specimens.

a, Kaplan-Meier analysis using distant metastasis-free survival (DMFS) as end point with 10-year censoring based on stratification of IL1B gene expression (log2; low, grey line, n = 740; high, red line, n = 639) in tumour tissue from 1,379 breast cancer patients. Logrank p value is shown. Analysis performed using the GOBO database (http://co.bmc.lu.se/gobo/gsa.pl)

Supplementary Figure 9 Western blot scanned films.

Boxes highlight lanes used in figures

Supplementary information

Supplementary Information

Supplementary Figures 1–9 and Supplementary Table legends.

Reporting Summary

Supplementary Table 1

Source data.

Supplementary Table 2

Significantly differentially expressed genes (DEGs) in lungs from mice bearing primary Met1 tumours versus control lungs.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Castaño, Z., San Juan, B.P., Spiegel, A. et al. IL-1β inflammatory response driven by primary breast cancer prevents metastasis-initiating cell colonization. Nat Cell Biol 20, 1084–1097 (2018). https://doi.org/10.1038/s41556-018-0173-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41556-018-0173-5

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer