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:

Bone vascular niche E-selectin induces mesenchymal–epithelial transition and Wnt activation in cancer cells to promote bone metastasis

Abstract

How disseminated tumour cells engage specific stromal components in distant organs for survival and outgrowth is a critical but poorly understood step of the metastatic cascade. Previous studies have demonstrated the importance of the epithelial–mesenchymal transition in promoting the cancer stem cell properties needed for metastasis initiation, whereas the reverse process of mesenchymal–epithelial transition is required for metastatic outgrowth. Here we report that this paradoxical requirement for the simultaneous induction of both mesenchymal–epithelial transition and cancer stem cell traits in disseminated tumour cells is provided by bone vascular niche E-selectin, whose direct binding to cancer cells promotes bone metastasis by inducing mesenchymal–epithelial transition and activating Wnt signalling. E-selectin binding activity mediated by the α1-3 fucosyltransferases Fut3/Fut6 and Glg1 are instrumental to the formation of bone metastasis. These findings provide unique insights into the functional role of E-selectin as a component of the vascular niche critical for metastatic colonization in bone.

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

Access options

Buy this article

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

Fig. 1: E-selectin is critical for bone but not lung metastasis.
Fig. 2: Specific α1–3 fucosyltransferases (Fut3 and Fut6) promote bone metastasis.
Fig. 3: In vitro and in vivo characterization of Fut3 catalytic mutants.
Fig. 4: Cell surface N-glycan analysis reveals candidate E-selectin ligands in metastatic breast cancer cells.
Fig. 5: E-selectin ligands co-localize with Glg1 at the cell surface.
Fig. 6: Glg1 is required to support bone-metastasis progression.
Fig. 7: E-selectin binding to tumour cells induces a non-canonical MET.
Fig. 8: E-selectin-induced MET activates Wnt signalling.

Similar content being viewed by others

Data availability

The microarray data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under the accession code GSE96754 and the mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD012942; the mass spectrometry data is further available in Supplementary Table 2. The human clinical breast cancer data are not available in GEO; these were derived from the TCGA Research Network and the dataset derived from this resource that supports the findings of this study is available in https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Breast%20Cancer%20(BRCA). The human breast cancer data were also derived from the NKI-295 dataset47, which is available at https://xenabrowser.net/datapages/?cohort=Breast%20Cancer%20. Finally, the human breast cancer data from the EMC-MSK dataset is available in the publication by Bos and colleagues69. The source data relevant for the clinical data analyses performed in this study are available in Supplementary Table 9. Unprocessed western blot images are provided as Supplementary Fig. 9. The source data supporting the findings of this study are provided in Supplementary Table 9. All protocols, cell lines and reagents are available from the corresponding author on request.

References

  1. Valastyan, S. & Weinberg, R. A. Tumor metastasis: molecular insights and evolving paradigms. Cell 147, 275–292 (2011).

    Article  CAS  Google Scholar 

  2. Massague, J. & Obenauf, A. C. Metastatic colonization by circulating tumour cells. Nature 529, 298–306 (2016).

    Article  CAS  Google Scholar 

  3. Yang, J. & Weinberg, R. A. Epithelial-mesenchymal transition: at the crossroads of development and tumor metastasis. Dev. Cell 14, 818–829 (2008).

    Article  CAS  Google Scholar 

  4. Nieto, M. A., Huang, R. Y., Jackson, R. A. & Thiery, J. P. EMT: 2016. Cell 166, 21–45 (2016).

    Article  CAS  Google Scholar 

  5. Esposito, M., Guise, T. & Kang, Y. The biology of bone metastasis. Cold Spring Harb. Perspect. Med. 8, a031252 (2018).

    Article  Google Scholar 

  6. Oskarsson, T., Batlle, E. & Massague, J. Metastatic stem cells: sources, niches, and vital pathways. Cell Stem Cell 14, 306–321 (2014).

    Article  CAS  Google Scholar 

  7. Yang, J. et al. Twist, a master regulator of morphogenesis, plays an essential role in tumor metastasis. Cell 117, 927–939 (2004).

    Article  CAS  Google Scholar 

  8. Mani, S. A. et al. The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell 133, 704–715 (2008).

    Article  CAS  Google Scholar 

  9. Labelle, M., Begum, S. & Hynes, R. O. Direct signaling between platelets and cancer cells induces an epithelial-mesenchymal-like transition and promotes metastasis. Cancer Cell 20, 576–590 (2011).

    Article  CAS  Google Scholar 

  10. Brabletz, T. et al. Variable β-catenin expression in colorectal cancers indicates tumor progression driven by the tumor environment. Proc. Natl Acad. Sci. USA 98, 10356–10361 (2001).

    Article  CAS  Google Scholar 

  11. Ocana, O. H. et al. Metastatic colonization requires the repression of the epithelial-mesenchymal transition inducer Prrx1. Cancer Cell 22, 709–724 (2012).

    Article  CAS  Google Scholar 

  12. Tsai, J. H., Donaher, J. L., Murphy, D. A., Chau, S. & Yang, J. Spatiotemporal regulation of epithelial-mesenchymal transition is essential for squamous cell carcinoma metastasis. Cancer Cell 22, 725–736 (2012).

    Article  CAS  Google Scholar 

  13. Korpal, M. et al. Direct targeting of Sec23a by miR-200s influences cancer cell secretome and promotes metastatic colonization. Nat. Med. 17, 1101–1108 (2011).

    Article  CAS  Google Scholar 

  14. Celia-Terrassa, T. et al. Epithelial-mesenchymal transition can suppress major attributes of human epithelial tumor-initiating cells. J. Clin. Invest. 122, 1849–1868 (2012).

    Article  CAS  Google Scholar 

  15. Liu, Y. et al. Mouse fibroblasts lacking RB1 function form spheres and undergo reprogramming to a cancer stem cell phenotype. Cell Stem Cell 4, 336–347 (2009).

    Article  CAS  Google Scholar 

  16. Chiou, S. H. et al. Coexpression of Oct4 and Nanog enhances malignancy in lung adenocarcinoma by inducing cancer stem cell-like properties and epithelial-mesenchymal transdifferentiation. Cancer Res. 70, 10433–10444 (2010).

    Article  CAS  Google Scholar 

  17. Shiozawa, Y. et al. Human prostate cancer metastases target the hematopoietic stem cell niche to establish footholds in mouse bone marrow. J. Clin. Invest. 121, 1298–1312 (2011).

    Article  CAS  Google Scholar 

  18. Ghajar, C. M. et al. The perivascular niche regulates breast tumour dormancy. Nat. Cell Biol. 15, 807–817 (2013).

    Article  CAS  Google Scholar 

  19. Walz, G., Aruffo, A., Kolanus, W., Bevilacqua, M. & Seed, B. Recognition by ELAM-1 of the sialyl-Lex determinant on myeloid and tumor cells. Science 250, 1132–1135 (1990).

    Article  CAS  Google Scholar 

  20. Frenette, P. S., Mayadas, T. N., Rayburn, H., Hynes, R. O. & Wagner, D. D. Double knockout highlights value of endothelial selectins. Immunol. Today 17, 205 (1996).

    CAS  PubMed  Google Scholar 

  21. Dimitroff, C. J., Lee, J. Y., Rafii, S., Fuhlbrigge, R. C. & Sackstein, R. CD44 is a major E-selectin ligand on human hematopoietic progenitor cells. J. Cell Biol. 153, 1277–1286 (2001).

    Article  CAS  Google Scholar 

  22. Winkler, I. G. et al. Vascular niche E-selectin regulates hematopoietic stem cell dormancy, self renewal and chemoresistance. Nat. Med. 18, 1651–1657 (2012).

    Article  CAS  Google Scholar 

  23. Dimitroff, C. J. et al. Identification of leukocyte E-selectin ligands, P-selectin glycoprotein ligand-1 and E-selectin ligand-1, on human metastatic prostate tumor cells. Cancer Res. 65, 5750–5760 (2005).

    Article  CAS  Google Scholar 

  24. Hanley, W. D., Burdick, M. M., Konstantopoulos, K. & Sackstein, R. CD44 on LS174T colon carcinoma cells possesses E-selectin ligand activity. Cancer Res. 65, 5812–5817 (2005).

    Article  CAS  Google Scholar 

  25. Kang, Y. et al. A multigenic program mediating breast cancer metastasis to bone. Cancer Cell 3, 537–549 (2003).

    Article  CAS  Google Scholar 

  26. Minn, A. J. et al. Genes that mediate breast cancer metastasis to lung. Nature 436, 518–524 (2005).

    Article  CAS  Google Scholar 

  27. Laubli, H. & Borsig, L. Selectins as mediators of lung metastasis. Cancer Microenviron. 3, 97–105 (2010).

    Article  CAS  Google Scholar 

  28. Taverna, D. et al. Increased primary tumor growth in mice null for β3- or β3/β5-integrins or selectins. Proc. Natl Acad. Sci. USA 101, 763–768 (2004).

    Article  CAS  Google Scholar 

  29. Labow, M. A. et al. Characterization of E-selectin-deficient mice: demonstration of overlapping function of the endothelial selectins. Immunity 1, 709–720 (1994).

    Article  CAS  Google Scholar 

  30. Khatib, A. M. et al. Rapid induction of cytokine and E-selectin expression in the liver in response to metastatic tumor cells. Cancer Res. 59, 1356–1361 (1999).

    CAS  PubMed  Google Scholar 

  31. Lowe, J. B. Glycan-dependent leukocyte adhesion and recruitment in inflammation. Curr. Opin. Cell Biol. 15, 531–538 (2003).

    Article  CAS  Google Scholar 

  32. Mondal, N. et al. Distinct human α(1,3)-fucosyltransferases drive Lewis-X/sialyl Lewis-X assembly in human cells. J. Biol. Chem. 293, 7300–7314 (2018).

    Article  CAS  Google Scholar 

  33. Mollicone, R. et al. Molecular basis for Lewis α(1,3/1,4)-fucosyltransferase gene deficiency (FUT3) found in Lewis-negative Indonesian pedigrees. J. Biol. Chem. 269, 20987–20994 (1994).

    CAS  PubMed  Google Scholar 

  34. Oulmouden, A. et al. Molecular cloning and expression of a bovine α(1,3)-fucosyltransferase gene homologous to a putative ancestor gene of the human FUT3-FUT5-FUT6 cluster. J. Biol. Chem. 272, 8764–8773 (1997).

    Article  CAS  Google Scholar 

  35. Goelz, S. E. et al. ELFT: a gene that directs the expression of an ELAM-1 ligand. Cell 63, 1349–1356 (1990).

    Article  CAS  Google Scholar 

  36. Buffone, A. et al. Silencing α1,3-fucosyltransferases in human leukocytes reveals a role for FUT9 enzyme during E-selectin-mediated cell adhesion. J. Biol. Chem. 288, 1620–1633 (2013).

    Article  CAS  Google Scholar 

  37. Costache, M. et al. Evolution of fucosyltransferase genes in vertebrates. J. Biol. Chem. 272, 29721–29728 (1997).

    Article  CAS  Google Scholar 

  38. Li, J. et al. Human fucosyltransferase 6 enables prostate cancer metastasis to bone. Br. J. Cancer 109, 3014–3022 (2013).

    Article  CAS  Google Scholar 

  39. Sackstein, R. et al. Ex vivo glycan engineering of CD44 programs human multipotent mesenchymal stromal cell trafficking to bone. Nat. Med. 14, 181–187 (2008).

    Article  CAS  Google Scholar 

  40. Ossola, R. et al. Biomarker validation in blood specimens by selected reaction monitoring mass spectrometry of N-glycosites. Methods Mol. Biol. 728, 179–194 (2011).

    Article  CAS  Google Scholar 

  41. Weekes, M. P. et al. Proteomic plasma membrane profiling reveals an essential role for gp96 in the cell surface expression of LDLR family members, including the LDL receptor and LRP6. J. Proteome Res. 11, 1475–1484 (2012).

    Article  CAS  Google Scholar 

  42. Price, T. T. et al. Dormant breast cancer micrometastases reside in specific bone marrow niches that regulate their transit to and from bone. Sci. Transl. Med. 8, 340ra373 (2016).

    Article  Google Scholar 

  43. Morita, Y. et al. E-selectin targeting PEGylated-thioaptamer prevents breast cancer metastases. Mol. Ther. Nucleic Acids 5, e399 (2016).

    Article  CAS  Google Scholar 

  44. Sreeramkumar, V. et al. Coordinated and unique functions of the E-selectin ligand ESL-1 during inflammatory and hematopoietic recruitment in mice. Blood 122, 3993–4001 (2013).

    Article  CAS  Google Scholar 

  45. Yasmin-Karim, S., King, M. R., Messing, E. M. & Lee, Y. F. E-selectin ligand-1 controls circulating prostate cancer cell rolling/adhesion and metastasis. Oncotarget 5, 12097–12110 (2014).

    Article  Google Scholar 

  46. Barthel, S. R. et al. Definition of molecular determinants of prostate cancer cell bone extravasation. Cancer Res. 73, 942–952 (2013).

    Article  CAS  Google Scholar 

  47. van de Vijver, M. J. et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347, 1999–2009 (2002).

    Article  Google Scholar 

  48. Wang, Y. et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365, 671–679 (2005).

    Article  CAS  Google Scholar 

  49. Winkler, I. G. et al. Positioning of bone marrow hematopoietic and stromal cells relative to blood flow in vivo: serially reconstituting hematopoietic stem cells reside in distinct nonperfused niches. Blood 116, 375–385 (2010).

    Article  CAS  Google Scholar 

  50. Pries, A. R. & Kuebler, W. M. in Handbook of Experimental Pharmacology, Vol. 176/I (eds Moncada, S. & Higgs, A.) 1–40 (Springer, 2006).

  51. Bixel, M. G. et al. Flow dynamics and HSPC homing in bone marrow microvessels. Cell Rep. 18, 1804–1816 (2017).

    Article  CAS  Google Scholar 

  52. Macdonald, B. T., Semenov, M. V. & He, X. SnapShot: Wnt/β-catenin signaling. Cell 131, 1204 (2007).

    Article  CAS  Google Scholar 

  53. Tang, B. et al. A flexible reporter system for direct observation and isolation of cancer stem cells. Stem Cell Rep. 4, 155–169 (2015).

    Article  Google Scholar 

  54. Fuerer, C. & Nusse, R. Lentiviral vectors to probe and manipulate the Wnt signaling pathway. PLoS One 5, e9370 (2010).

    Article  Google Scholar 

  55. Holst, S. et al. N-glycosylation profiling of colorectal cancer cell lines reveals association of fucosylation with differentiation and caudal type homebox 1 (CDX1)/Villin mRNA expression. Mol. Cell. Proteomics 15, 124–140 (2016).

    Article  CAS  Google Scholar 

  56. Stone, K. R., Mickey, D. D., Wunderli, H., Mickey, G. H. & Paulson, D. F. Isolation of a human prostate carcinoma cell line (DU 145). Int J. Cancer 21, 274–281 (1978).

    Article  CAS  Google Scholar 

  57. Korpal, M., Lee, E. S., Hu, G. & Kang, Y. The miR-200 family inhibits epithelial-mesenchymal transition and cancer cell migration by direct targeting of E-cadherin transcriptional repressors ZEB1 and ZEB2. J. Biol. Chem. 283, 14910–14914 (2008).

    Article  CAS  Google Scholar 

  58. Anastas, J. N. & Moon, R. T. WNT signalling pathways as therapeutic targets in cancer. Nat. Rev. Cancer 13, 11–26 (2013).

    Article  CAS  Google Scholar 

  59. Sipkins, D. A. et al. In vivo imaging of specialized bone marrow endothelial microdomains for tumour engraftment. Nature 435, 969–973 (2005).

    Article  CAS  Google Scholar 

  60. Biancone, L., Araki, M., Araki, K., Vassalli, P. & Stamenkovic, I. Redirection of tumor metastasis by expression of E-selectin in vivo. J. Exp. Med. 183, 581–587 (1996).

    Article  CAS  Google Scholar 

  61. Herschkowitz, J. I. et al. Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors. Genome Biol. 8, R76 (2007).

    Article  Google Scholar 

  62. Forozan, F. et al. Molecular cytogenetic analysis of 11 new breast cancer cell lines. Br. J. Cancer 81, 1328–1334 (1999).

    Article  CAS  Google Scholar 

  63. Ell, B. et al. Tumor-induced osteoclast miRNA changes as regulators and biomarkers of osteolytic bone metastasis. Cancer Cell 24, 542–556 (2013).

    Article  CAS  Google Scholar 

  64. Koda, Y., Kimura, H. & Mekada, E. Analysis of Lewis fucosyltransferase genes from the human gastric mucosa of Lewis-positive and -negative individuals. Blood 82, 2915–2919 (1993).

    CAS  PubMed  Google Scholar 

  65. Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).

    Article  CAS  Google Scholar 

  66. Diner, B. A. et al. The functional interactome of PYHIN immune regulators reveals IFIX is a sensor of viral DNA. Mol. Syst. Biol. 11, 787 (2015).

    Article  Google Scholar 

  67. Giguere, S. S. et al. The proteomic profile of deleted in breast cancer 1 (DBC1) interactions points to a multifaceted regulation of gene expression. Mol. Cell. Proteomics 15, 791–809 (2016).

    Article  CAS  Google Scholar 

  68. Blick, T. et al. Epithelial mesenchymal transition traits in human breast cancer cell lines parallel the CD44hi/CD24lo/- stem cell phenotype in human breast cancer. J. Mammary Gland Biol. Neoplasia 15, 235–252 (2010).

    Article  Google Scholar 

  69. Bos, P. D. et al. Genes that mediate breast cancer metastasis to the brain. Nature 459, 1005–1009 (2009).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank L. M. Wakefield for providing the SORE6–mCherry stemness reporter, R. Nusse for providing the 7×TCF–GFP Wnt reporter, G. Laevsky for assistance with microscopy and C. DeCoste for assistance with flow cytometry. This work was supported by fellowships from the NIH (grant no. F31CA192461) and NJCCR to M.E., the National Institutes of Health NHLBI (grant no. PO1 HL107146), the Program of Excellence in Glycosciences and the Team Jobie Fund to R.S., and grants from the Susan G. Komen Foundation (grant no. SAC160067), Glycomimetics Inc., Brewster Foundation, Department of Defense (grant no. BC123187) and the National Institutes of Health (grant no. R01CA141062) to Y.K. This research was also supported by the Preclinical Imaging, Genomic Editing and Flow Cytometry Shared Resources of the Rutgers Cancer Institute of New Jersey (grant no. P30CA072720).

Author information

Authors and Affiliations

Authors

Contributions

M.E. and Y.K. conceived the project and co-wrote the manuscript. M.E. designed and conducted all of the flow cytometry, xenograft, genetic, qRT–PCR, confocal and bioinformatic experiments, and analysed the data. M.E. derived the SUM159-M1a and DU145-ob1 cell lines. T.M.G., M.E. and I.M.C. performed the mass spectrometry and analysed the data. N.M. and R.S. performed the E-selectin immunoprecipitation experiments, provided advice and assisted in writing the manuscript. Y.W., C.S., H.Z. and C.C. assisted with the mouse experiments and stable cell generation. Y.W. and M.E. performed and analysed the microarray experiments. S.-C.L. and S.-H.L. stained the prostate-cancer bone biopsies. J.L.M. provided GMI-1271 and research input.

Corresponding author

Correspondence to Yibin Kang.

Ethics declarations

Competing interests

J.L.M. is the Vice President and Chief Scientific Officer of Glycomimetics, Inc., which owns the patent to GMI-1271. Y.K. received research support from Glycomimetics Inc. for experiments using GMI-1271. No other authors declare any conflicts of 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 E-selectin promotes bone but not lung metastasis.

(a) Schematic representation of the comparative E-selectin binding assay. GFP-labelled experimental cells cocultured with RFP-labelled control cells are probed with E-selectin-IgG or isotype IgG (as the baseline control for each cell line) and quantified by flow cytometry using AL647 conjugated anti-IgG. This yields a ratio of AL647 signal between GFP+ and RFP+ cells with each normalized to their isotype IgG control as a quantitative measurement of relative E-selectin binding ability. (b) Flow cytometry histogram showing binding of MDA-MB-231-Fut cells to E-selectin-IgG chimera or isotype IgG, or to E-selectin-IgG at the presence of EDTA or GMI-1271 as negative controls. Data representative of 5 independent experiments. (c) E-selectin binding to isogenic derivatives (GFP+) compared to parental MDA-MB-231 cells (RFP+). Median intensity ratios were calculated using (SELE-GFP/IgG-GFP)/(SELE-RFP/IgG-RFP). n = 6 (Parent, SCP6, LM2, SCP32, SCP2, BM2), n = 3 (SCP38) or n = 7 (SCP25) independent biological replicates. Student’s t-test, two-sided, compares respective cell derivative to the parental cell line. (d) Representative figure of a flow cytometry histogram obtained from E-selectin binding to the isogenic MDA-MB-231 series using MDA-MB-231–RFP as an internal control. Data representative of independent biological replicates from c. (e) Total RNA from bone marrow and lungs in naïve and 6 h LPS-treated NOD/SCID mice was analysed for Sele (normalized by CD31) mRNA expression levels by qPCR. n = 3 technical replicates representative of 3 independent experiments. (f) Total pooled RNA from human bone marrow and lung tissues was analysed for Sele (normalized by CD31) mRNA expression levels by qPCR. n = 3 technical replicates. Experiment performed once from pooled RNA samples. (g) BLI quantification of bone metastasis burden following intracardiac injection of the M1a cell line into WT or Sele/ SCID mice. n = 7 (KO), n= 9 (WT). Mann–Whitney U test, two-sided. (h) Representative BLI and X-ray images of bone metastasis in WT and Sele/ NOD/SCID mice. White arrows indicate osteolytic lesions. Data representative of median signal from (h). (i, j) Quantification of osteloytic area (i) and the number of osteolytic lesions (j) in the hindlimbs of animals. n = 14 (KO) or 18 (WT) hindlimbs/group. Mann–Whitney U test, two-sided. Data representative of two independent experiments (gj). (k) Primary tumour growth of M1a cells implanted orthotopically in WT or Sele/ NOD/SCID mice. n = 7 mice/group. Student’s t-test, two-sided. (l) BLI quantification of lung metastasis burden following intravenous injection of LM2 cells into WT and Sele/ NOD/SCID mice. n = 10 (KO), n = 7 (WT). Student’s t-test, two-sided. (m) Representative BLI and gross morphological images of lungs from LM2-injected mice. Data representative of median signal from l. (n) Macrometastatic nodes counted on the surface of the lungs of the LM2-injected WT or Sele/ NOD/SCID mice. n = 10 (KO), n = 6 (WT). Student’s t-test, two-sided. (o) BLI quantification of lung metastasis burden following intravenous injection of M1a cells into WT and Sele/ NOD/SCID mice. n = 10 (KO) or 9 (WT) mice/ group, Student’s t-test, two-sided. (p) Macrometastatic nodes counted on the lungs of the M1a-injected WT or Sele/ NOD/SCID mice. n = 8 (KO), n = 7 (WT). Student’s t-test, two-sided. Experiment performed once (kp). Bar graph data represent mean ± s.e.m. Boxplot data represent median, interquartile range, and spikes to upper and lower adjacent values.

Supplementary Figure 2 Quantification of α1–3 Fut expression and activity in MDA-MB-231 and M1a cells.

(a) Total RNA from MDA-MB-231 cells stably expressing each α1-3Fut was analysed for Fut3-7,9 expression by qPCR. n = 3 technical replicates. (b) Proliferation rates of MDA-MB-231-Fut3-7,9 were compared to vector control cells by the EZQuant assay. n = 3 biological replicates. Student’s t-test, two-sided. No statistically significant difference (p>0.05) between Fut groups vs Vector group. (c) Total RNA from M1a cells expressing each α1-3Fut was analysed for Fut3-7,9 expression by qPCR. n = 3 technical replicates. (d) Comparative flow cytometry analysis of E-selectin binding to stable α1-3 Fut-expressing M1a cells using SUM159-RFP as an internal control. Data representative of 4 independent experiments. (e, f) Quantification of osteloytic area (e) and the number of osteolytic lesions in the hind limbs of Nu/Nu mice receiving an intracardiac injection of M1a cells expressing each Fut enzyme (f). n = 12 (Fut4, Fut6), 10 (Fut3, Fut5, Fut9), 6 (Fut7), or 20 (Vector) hindlimbs. Mann–Whitney U test, two-sided. (g) RNA-Seq counts of each Fut enzyme’s first exon extracted from the TCGA RNA-seq clinical dataset in tumour (T) compared to adjacent normal (N) tissue. n = 955 patients. Data represent median, interquartile range and spikes to upper and lower adjacent values. (h) Isolation of subpopulations of MDA-MB-231 cells with high or low E-selectin binding ability by FACS. (i) E-selectin binding levels in sorted cell lines were quantified by comparative flow cytometry analysis after five passages. n = 3 biological replicates, Student’s t-test, two-sided. (j) Total RNA of poorly metastatic MDA-MB-231 and SCP6 cell lines compared to highly bone metastatic SCP25 and lung metastatic LM2 sublines was analysed for endogenous levels of each α1-3 Fut by qPCR. Fut5/7/9 were undetectable in each cell line. n = 3 technical replicates, Data represent mean ± s.e.m.

Supplementary Figure 3 Pharmacological and enzymatic modification of E-selectin binding.

(a) Schematic of the rationale and protocol used for the extraction and identification of N-glycosites. Both the MDA-MB-231 and M1a cell lines contain potential substrates for Fut-mediated fucosylation to create sLeX, a tetrasaccharide glycosylation necessary for E-selectin binding. To identify these E-selectin ligands, the extracellular surface of MDA-MB-231 or M1a cells was oxidized with sodium metaperiodate to form hydrazide-reactive carbonyl groups from the cis-diols contained in the carbohydrates groups of membrane glycoproteins. Biotin-hydrazide was covalently linked to these oxidized glycoproteins, which were then purified by streptavidin affinity chromatography under denaturing conditions. Specific n-glycosylated residues were identified by sequential digestion first with trypsin to remove non-glycosylated peptides, followed by specific release of glycosylated peptides with PNGase F, and subsequently by MS/MS identification. (b) Proliferation assays were performed over 48 h on M1a or BM2 cells incubated with varying concentrations of D,L-Threo PDMP, Tunicamycin, or 1-Deoxymannojirimycin and cell viability was quantified with EZQuant. n=4 (Tunicamycin, PDMP) or n=3 (1-DMJ) biological replicates per concentration. Data represent mean ± s.e.m. (c) The comparative flow cytometry assay was performed on BM2 and M1a cells incubated with non-toxic doses of each indicated metabolic inhibitor for 48 h. Cells were harvested and mixed with untreated MDA-MB-231–RFP as the internal control followed by E-selectin-IgG probing. n=3 biological replicates. Student’s t-test, two-sided. (d) M1a cells expressing each Fut enzyme were harvested, split into three samples and probed with E-selectin-IgG with PBS, GMI-1271 (80 μg/mL) or EDTA (20 mM). These were combined with SUM159-RFP cells probed with E-selectin-IgG in the absence of inhibitory agents as internal control and the comparative flow cytometry assay was conducted. The bottom panel represents the same experiment conducted with control IgG as negative controls for both test M1a-Fut and control SUM159-RFP cell lines. Dashed lines represent median binding values in the positive control condition. Data representative of 4 independent experiments (b-d). Data represent mean ± s.e.m.

Supplementary Figure 4 Glg1 supports E-selectin binding in bone metastatic cells.

(a) Cellular membrane integrity measured by DAPI uptake following oxidation with 10 mM Sodium (Meta) periodate was analysed by flow cytometry. (b, c) Biotin-hydrazide-conjugated metaperiodate-oxidized cells probed with Streptavidin-AL647 were then permeabilized to label with DAPI and were imaged by immunofluorescence (b) and quantified by flow cytometry (c). Scale bars represent 10 μm. Data representative of 3 independent experiments (a-c). (d) BLI quantification of bone metastasis burden after intracardiac injection of CD44-KO or control BM2 cell lines into Nu/Nu mice. n = 6 (CD44), n = 8 (control). Mann–Whitney U test, two-sided. (e) Survival data was tabulated when mice became moribund and used for Kaplan–Meier survival curve analysis. n = 6 (CD44) n = 8 (control). Cox’s proportional hazards model, two-sided. Experiment performed once (d-e). (f) Representative precursor ion extracted ion chromatograms (XICs) for the doubly-charged deamidated Glg1 tryptic peptide, LNLTTDPK. XICs and respective peak areas were calculated in Skyline from the data-dependent MS analysis of N-glycopeptide enrichment experiments in M1a and MDA-MB-231 cells. XICs correspond to the summed intensities of the first three precursor isotope ions (resolving power of 100,000 @ 400 m/z) as function of time. Insets, mass spectrum showing the isotope envelope of deamidated LNLTTDPK extracted at the apex of the XIC traces. The monoisotopic ion is labelled with the measured m/z. Data representative of 4 independent biological replicates. (g) mRNA levels of three main Glg1 splice variants were measured by qPCR in the sorted MDA-MB-231 cells with different E-selectin binding ability. n = 3 technical replicates. (h) mRNA expression of the same variants was also tested in the MDA-MB-231, SCP6, SCP25, and LM2 cells. n = 4 technical replicates. (i) Total RNA from MDA-MB-231 or BM2 cells expressing Glg1 variant 1 or variant 3 was analysed for Glg1 expression. n = 4 technical replicates. (j) Western blot of MDA-MB-231 and BM2 cells with stable ectopic expression of Glg1 variant 3. (k) Western blot of BM2 cells with or without CRISPR–Cas9-mediated knockout of Glg1. Data representative of 3 independent experiments (j, k). (l) Comparative flow cytometry analysis of E-selectin binding was conducted on the indicated cell lines using MDA-MB-231–RFP as an internal control. Binding levels were normalized and compared to respective controls. n = 5 independent biological replicates. Student’s t-test, two-sided. Data represent mean ± s.e.m. Unprocessed original scans of the blots in j and k are shown Supplementary Fig. 9.

Supplementary Figure 5 Glg1 localizes to the cytoplasmic membrane surface.

(a) Total lysate and membrane fractions from M1a cells were probed by western blot for Glg1 expression. Experiment performed once. (b) Immunoblot of M1a cell membrane lysates using E-selectin-IgG as a probe. (c) Immunoblot using anti-Glg1 as a probe of total cell lysates, immunoprecipitation supernatants, and E-selectin-immunoprecipitated proteins from each M1a cell line. Data representative of 2 independent experiments (b, c). Unprocessed original scans of the blots are shown Supplementary Fig. 9.

Supplementary Figure 6 Glg1 ablation reduces bone but not lung metastasis.

(a) qPCR quantification of Glg1 mRNA following stable shRNA-mediated knockdown of Glg1 in BM2 cells. n = 3 technical replicates. (b, c) BLI quantification of bone metastasis burden after intracardiac injection of control or Glg1-KO BM2 cells (b, n= 9 mice/group) or Glg1-KO M1a cells (c, n= 9 (Control) or 7 (Glg1-KO1) mice/group) into Nu/Nu mice. Mann–Whitney U test, two-sided. (d) Representative image of BLI and X-ray images from M1a Glg1-knockout-injected mice. Data representative of median signal from c. White arrows indicate osteolytic lesions. (e, f) The number of lesions/ hindlimb (e) and average osteolytic area (f) were quantified from X-rays of M1a Glg1 knockout-injected mice. n = 18 (Control) or 14 (Glg1-KO1) hindlimbs per group. Mann–Whitney U test, two-sided. Data representative of two independent experiments (b-f). (g) qPCR analysis of Glg1 expression in control or Glg1-knockdown LM2 cells. n = 4 technical replicates. (h) BLI quantification of lung metastasis burden following intravenous injection of control and Glg1-knockdown LM2 cells into Nu/Nu mice. n = 8 (shGlg1-1) or 9 (shGlg1-2, Control) mice/group. Student’s t-test, two-sided, demonstrated non-significance at all time points (p>0.05). (i) Survival data was tabulated when mice became moribund and was used for Kaplan–Meier survival curve analysis which demonstrated non-significance for all curves, Cox’s proportional hazards model, two-sided. n = 8 (shGlg1-1) or 9 (shGlg1-2, Control) mice/group. Data represent mean ± s.e.m. (j) Kaplan–Meier survival curve of breast cancer patients in the NKI-295 dataset stratified by median expression level of Glg1 and Fut3/Fut6 RNA. n = 295. Cox’s proportional hazards model, two-sided. (k) Kaplan–Meier organ-specific metastasis-free survival curves of ER- breast cancer patients in the EMC-MSK dataset stratified by median expression level of Glg1 and Fut3/Fut6 mRNA. n = 244. Cox’s proportional hazards model, two-sided.

Supplementary Figure 7 E-selectin binding to tumour cells induces MET.

(a) Confocal imaging of bone metastasis of Nu/Nu mice after intra-iliac artery injection of GFP-labelled SCP28 cells. Mice were labelled with a retro-orbital injection of 10 μg anti-CD31 prior to euthanasia. Scale bars represent 50 μm. (b) Confocal imaging of BM2-GFP bone lesions in Nu/Nu mice stained for E-selectin and Glg1. Scale bars represent 100 μm (c) Confocal imaging of E-selectin-induced intensity changes in Keratin-14 expression in BM2 cells after culturing on E-selectin compared to IgG-coated plates (10 μg/mL) for 24h. Scale bars represent 20 μm. Data representative of 3 independent experiments (a-c). (d, e) GSEA of multiple EMT-related signatures, including Blick EMT1 (d), Claudin-low (GSE18229), Sarrio-EMT (GSE8430), and Luminal (GSE22446) (e), in the ranked gene list of BM2 cells cultured in E-selectin vs. IgG-coated plates. Single mRNA isolation used for each condition in each cell line. p and q statistics by GSEA software, n = 108 gene sets queried. (f) Western blot of N-cadherin and Slug from M1a cells bound to IgG or E-selectin-coated plates (10 μg/mL) for 24h. Data representative of 2 independent experiments. (g, h)Confocal imaging of bone lesions from BM2-GFP injected mice stained for E-cadherin (g) and EpCam (h). Scale bars represent 100 μm (g) and 50 μm (h). Data representative of >5 independent biological replicates. Unprocessed original scans of the blots in f are shown Supplementary Fig. 9.

Supplementary Figure 8 E-selectin binding-induced Wnt signalling and E-selectin is critical to prostate cancer bone metastasis.

(a) Confocal imaging of ex vivo bone sections from Nu/Nu mice that developed bone metastasis following intracardiac injection of BM2-TGC cells. Live animal labelling with retro-orbital anti-CD31 was conducted to visualize vasculature. Scale bars represent 50 μm. Data representative of 5 biological replicates. (b) Western blot of analysis of hDkk1-Flag expression in BM2-TGC cells expressing Dkk1–FLAG or the control vector. Data representative of 3 independent experiments. (c) Flow cytometry measurement of Wnt activation using the BM2-TGC reporter cell line expressing either Dkk1–FLAG or empty vector seeded over IgG or E-selectin-treated plates (10 μg/mL), or treated with L Cell Wnt3a-conditioned media vs control-conditioned media. Wnt inhibitors ICG-001 (25 μM), LF3 (50 μM), and Dkk1 were tested against each condition during 48h of culture. Data representative of 3 independent experiments. (d) BLI quantification of bone metastasis burden after intracardiac injection of BM2 cells into Nu/Nu mice followed by 7 days of treatment with either LF3 (40 mg/kg body mass, n =15 mice) or vehicle (n = 12 mice). Mann–Whitney U test, two-sided. Data representative of 2 independent experiments. (e, f) Total number of lesions per hindlimb (e) and total osteolytic area (f) were quantified between treatments. n = 24 (Vehicle) or 30 (LF3) hindlimbs/ group. Mann–Whitney U test, two-sided. Data represent mean ± s.e.m. (g) Flow cytometry measurement of E-selectin-IgG binding to ob1, a bone metastatic derivative of DU145 prostate cancer cell line. Data representative of 3 independent experiments. (h) Ex vivo bioluminescence of hindlimbs from WT and Sele/ NOD/SCID mice injected with ob1 cells. n = 20 (KO) or 28 (WT) hindlimbs/group. Mann–Whitney U test, two-sided. Data represent mean. (i) Kaplan–meier survival plot of WT and Sele/ mice injected with ob1 cells. n = 11 (KO) or 8 (WT) mice/group. Cox’s proportional hazards model, two-sided. Data in h and i representative of 2 independent experiments. (j) Confocal imaging of ob1 cells plated 24h on E-selectin- or IgG-coated (10 μg/mL) ibidi plates stained for EpCAM. Scale bar represents 10μm. Data representative of 3 independent experiments. (k) Immunofluorescence staining of E-selectin, Ki67, and Glg1 in FFPE-bone sections from a bone metastasis sample from a prostate cancer patient. Scale bar represents 50 μm. Data representative of 13 sections from 2 biopsy specimens. Unprocessed original scans of the blots in b are shown Supplementary Fig. 9.

Supplementary Figure 9 Unprocessed blots.

Unprocessed scans of the non-quantitative western blots provided for the indicated figure panels.

Supplementary information

Supplementary Information

Supplementary Figures 1–9, Supplementary Table titles/legends, Supplementary Video 1 title/legend

Reporting Summary

Supplementary Table 1

Spontaneous bone metastasis rates from M1a–Fut primary tumour.

Supplementary Table 2

Mass spectrometry identification of candidate Fut substrates in MDA-MB-231 and M1a cells.

Supplementary Table 3

E-selectin ligand candidate list from N-glycan profiling.

Supplementary Table 4

Kaplan–Meier analysis of candidate E-selectin ligands.

Supplementary Table 5

Genes contained in the non-canonical MET signature.

Supplementary Table 6

Key characteristics of the cell lines used in this study.

Supplementary Table 7

List of all oligonucleotide sequences used in this study.

Supplementary Table 8

List of all antibodies used in this study.

Supplementary Table 9

Statistics source data.

Supplementary Video 1

Spinning-disc microscopy of BM2-TGC cells seeded over control IgG, E-selectin-IgG or a poly-HEMA non-adherent condition.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Esposito, M., Mondal, N., Greco, T.M. et al. Bone vascular niche E-selectin induces mesenchymal–epithelial transition and Wnt activation in cancer cells to promote bone metastasis. Nat Cell Biol 21, 627–639 (2019). https://doi.org/10.1038/s41556-019-0309-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41556-019-0309-2

This article is cited by

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