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Bone-matrix mineralization dampens integrin-mediated mechanosignalling and metastatic progression in breast cancer

Nature Biomedical Engineering volume 7, pages 1455–1472 (2023)Cite this article

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Abstract

In patients with breast cancer, lower bone mineral density increases the risk of bone metastasis. Although the relationship between bone-matrix mineralization and tumour-cell phenotype in breast cancer is not well understood, mineralization-induced rigidity is thought to drive metastatic progression via increased cell-adhesion forces. Here, by using collagen-based matrices with adjustable intrafibrillar mineralization, we show that, unexpectedly, matrix mineralization dampens integrin-mediated mechanosignalling and induces a less proliferative stem-cell-like phenotype in breast cancer cells. In mice with xenografted decellularized physiological bone matrices seeded with human breast tumour cells, the presence of bone mineral reduced tumour growth and upregulated a gene-expression signature that is associated with longer metastasis-free survival in patients with breast cancer. Our findings suggest that bone-matrix changes in osteogenic niches regulate metastatic progression in breast cancer and that in vitro models of bone metastasis should integrate organic and inorganic matrix components to mimic physiological and pathologic mineralization.

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Fig. 1: Engineered bone-matrix models to study tumour cell interactions with mineralized collagen.
Fig. 2: Mineralization of collagen alters breast cancer cell gene expression and growth.
Fig. 3: Collagen mineralization induces a stem-like phenotype in breast cancer cells.
Fig. 4: Collagen mineralization alters breast cancer cell mechanotransduction.
Fig. 5: Physiological bone scaffolds to test the effect of bone mineral on the stem-like phenotype of MDA-MB231.
Fig. 6: Mineralized bone matrix reduces tumour growth in vivo and mineral-induced gene expression signature correlates with improved patient prognosis.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. All RNA-sequencing data generated in this study are available in the NIH Gene Expression Omnibus via the accession number GSE229094. Survival analysis was conducted using publicly available datasets (Study ID: GEO2603, GSE2034). Source data for the figures are provided with this paper.

Code availability

The QuPath script used for immunochemistry analysis is available as Supplementary Information.

References

  1. Hess, K. R. et al. Metastatic patterns in adenocarcinoma. Cancer 106, 1624–1633 (2006).

    Article  PubMed  Google Scholar 

  2. Kuchuk, I. et al. Incidence, consequences and treatment of bone metastases in breast cancer patients—experience from a single cancer centre. J. Bone Oncol. 2, 137–144 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Braun, S. et al. A pooled analysis of bone marrow micrometastasis in breast cancer. N. Engl. J. Med. 353, 793–802 (2005).

    Article  CAS  PubMed  Google Scholar 

  4. Guise, T. A. The vicious cycle of bone metastases. J. Musculoskelet. Neuronal Interact. 2, 570–572 (2002).

    CAS  PubMed  Google Scholar 

  5. Lawson, M. A. et al. Osteoclasts control reactivation of dormant myeloma cells by remodelling the endosteal niche. Nat. Commun. 6, 8983 (2015).

    Article  CAS  PubMed  Google Scholar 

  6. Hüsemann, Y. et al. Systemic spread is an early step in breast cancer. Cancer Cell 13, 58–68 (2008).

    Article  PubMed  Google Scholar 

  7. Risson, E., Nobre, A. R., Maguer-Satta, V. & Aguirre-Ghiso, J. A. The current paradigm and challenges ahead for the dormancy of disseminated tumor cells. Nat. Cancer 1, 672–680 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Phan, T. G. & Croucher, P. I. The dormant cancer cell life cycle. Nat. Rev. Cancer 20, 398–411 (2020).

    Article  CAS  PubMed  Google Scholar 

  9. Croucher, P. I., McDonald, M. M. & Martin, T. J. Bone metastasis: the importance of the neighbourhood. Nat. Rev. Cancer 16, 373–386 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. Wang, H. et al. The osteogenic niche promotes early-stage bone colonization of disseminated breast cancer cells. Cancer Cell 27, 193–210 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Luo, X. et al. Stromal-initiated changes in the bone promote metastatic niche development. Cell Rep. 14, 82–92 (2016).

    Article  CAS  PubMed  Google Scholar 

  12. Yao, Z. et al. Therapy-induced senescence drives bone loss. Cancer Res. 80, 1171–1182 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Nobre, A. R. et al. Bone marrow NG2+/Nestin+ mesenchymal stem cells drive DTC dormancy via TGF-β2. Nat. Cancer 2, 327–339 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Andersen, T. L. et al. A physical mechanism for coupling bone resorption and formation in adult human bone. Am. J. Pathol. 174, 239–247 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kraemer, B. et al. Impaired bone microenvironment: correlation between bone density and presence of disseminated tumor cells. Anticancer Res. 31, 4423–4428 (2011).

    CAS  PubMed  Google Scholar 

  16. Chen, H. M., Chen, F. P., Yang, K. C. & Yuan, S. S. Association of bone metastasis with early-stage breast cancer in women with and without precancer osteoporosis according to osteoporosis therapy status. JAMA Netw. Open 2, e190429 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Blair, H. C. et al. Osteoblast differentiation and bone matrix formation in vivo and in vitro. Tissue Eng. B Rev. 23, 268–280 (2017).

    Article  CAS  Google Scholar 

  18. Reznikov, N., Bilton, M., Lari, L., Stevens, M. M. & Kröger, R. Fractal-like hierarchical organization of bone begins at the nanoscale. Science 360, eaao2189 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Ping, H. et al. Mineralization generates megapascal contractile stresses in collagen fibrils. Science 376, 188–192 (2022).

    Article  CAS  PubMed  Google Scholar 

  20. Choi, S. et al. Intrafibrillar, bone-mimetic collagen mineralization regulates breast cancer cell adhesion and migration. Biomaterials 198, 95–106 (2019).

    Article  CAS  PubMed  Google Scholar 

  21. Chaudhuri, O., Cooper-White, J., Janmey, P. A., Mooney, D. J. & Shenoy, V. B. Effects of extracellular matrix viscoelasticity on cellular behaviour. Nature 584, 535–546 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Page, J. M. et al. Matrix rigidity regulates the transition of tumor cells to a bone-destructive phenotype through integrin β3 and TGF-β receptor type II. Biomaterials 64, 33–44 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Turunen, M. J., Prantner, V., Jurvelin, J. S., Kröger, H. & Isaksson, H. Composition and microarchitecture of human trabecular bone change with age and differ between anatomical locations. Bone 54, 118–125 (2013).

    Article  PubMed  Google Scholar 

  24. Donnelly, E., Boskey, A. L., Baker, S. P. & van der Meulen, M. C. H. Effects of tissue age on bone tissue material composition and nanomechanical properties in the rat cortex. J. Biomed. Mater. Res. A 92, 1048–1056 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Burke, M. V., Atkins, A., Akens, M., Willett, T. L. & Whyne, C. M. Osteolytic and mixed cancer metastasis modulates collagen and mineral parameters within rat vertebral bone matrix. J. Orthop. Res. 34, 2126–2136 (2016).

    Article  CAS  PubMed  Google Scholar 

  26. Paschalis, E. P., Betts, F., DiCarlo, E., Mendelsohn, R. & Boskey, A. L. FTIR microspectroscopic analysis of normal human cortical and trabecular bone. Calcif. Tissue Int. 61, 480–486 (1997).

    Article  CAS  PubMed  Google Scholar 

  27. Mathis, K. M. et al. Bone resorption and bone metastasis risk. Med. Hypotheses 118, 36–41 (2018).

    Article  PubMed  Google Scholar 

  28. Lynch, M. E. et al. In vivo tibial compression decreases osteolysis and tumor formation in a human metastatic breast cancer model. J. Bone Miner. Res. 28, 2357–2367 (2013).

    Article  PubMed  Google Scholar 

  29. Swami, S. et al. Prevention of breast cancer skeletal metastases with parathyroid hormone. JCI Insight 2, e90874 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Farr, J. N. et al. Targeting cellular senescence prevents age-related bone loss in mice. Nat. Med. 23, 1072–1079 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ooi, L. L. et al. Vitamin D deficiency promotes human breast cancer growth in a murine model of bone metastasis. Cancer Res. 70, 1835–1844 (2010).

    Article  CAS  PubMed  Google Scholar 

  32. Wang, H. et al. Bone-in-culture array as a platform to model early-stage bone metastases and discover anti-metastasis therapies. Nat. Commun. 8, 15045 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Fratzl, P., Gupta, H. S., Paschalis, E. P. & Roschger, P. Structure and mechanical quality of the collagen–mineral nano-composite in bone. J. Mater. Chem. 14, 2115–2123 (2004).

    Article  CAS  Google Scholar 

  34. Gower, L. & Elias, J. Colloid assembly and transformation (CAT): the relationship of PILP to biomineralization. J. Struct. Biol. X 6, 100059 (2022).

    CAS  PubMed  Google Scholar 

  35. Chiou, A. E. et al. Fluorescent silica nanoparticles to label metastatic tumor cells in mineralized bone microenvironments. Small 17, e2001432 (2021).

    Article  PubMed  Google Scholar 

  36. Carlson, P. et al. Targeting the perivascular niche sensitizes disseminated tumour cells to chemotherapy. Nat. Cell Biol. 21, 238–250 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Olszta, M. J. et al. Bone structure and formation: a new perspective. Mater. Sci. Eng. R 58, 77–116 (2007).

    Article  Google Scholar 

  38. Reznikov, N., Chase, H., Brumfeld, V., Shahar, R. & Weiner, S. The 3D structure of the collagen fibril network in human trabecular bone: relation to trabecular organization. Bone 71, 189–195 (2015).

    Article  PubMed  Google Scholar 

  39. Londoño-Restrepo, S. M., Jeronimo-Cruz, R., Millán-Malo, B. M., Rivera-Muñoz, E. M. & Rodriguez-García, M. E. Effect of the nano crystal size on the X-ray diffraction patterns of biogenic hydroxyapatite from human, bovine, and porcine bones. Sci. Rep. 9, 5915 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Nam, S., Lee, J., Brownfield, D. G. & Chaudhuri, O. Viscoplasticity enables mechanical remodeling of matrix by cells. Biophys. J. 111, 2296–2308 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Hall, M. S. et al. Fibrous nonlinear elasticity enables positive mechanical feedback between cells and ECMs. Proc. Natl Acad. Sci. USA 113, 14043–14048 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Masuda, T. et al. ANGPTL2 increases bone metastasis of breast cancer cells through enhancing CXCR4 signaling. Sci. Rep. 5, 9789 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Bergamaschi, A. et al. CAMK1D amplification implicated in epithelial-mesenchymal transition in basal-like breast cancer. Mol. Oncol. 2, 327–339 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Dong, X., Yang, Y., Yuan, Q., Hou, J. & Wu, G. High expression of CEMIP correlates poor prognosis and the tumor microenvironment in breast cancer as a promisingly prognostic biomarker. Front. Genet. 12, 768140 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Che, M. I. et al. β1, 4-N-acetylgalactosaminyltransferase III modulates cancer stemness through EGFR signaling pathway in colon cancer cells. Oncotarget 5, 3673–3684 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Yu, J. M. et al. TRIB3 supports breast cancer stemness by suppressing FOXO1 degradation and enhancing SOX2 transcription. Nat. Commun. 10, 5720 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Choate, J. J. & Mosher, D. F. Fibronectin concentration in plasma of patients with breast cancer, colon cancer, and acute leukemia. Cancer 51, 1142–1147 (1983).

    Article  CAS  PubMed  Google Scholar 

  48. Pathi, S. P., Lin, D. D. W., Dorvee, J. R., Estroff, L. A. & Fischbach, C. Hydroxyapatite nanoparticle-containing scaffolds for the study of breast cancer bone metastasis. Biomaterials 32, 5112–5122 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Bruning, P. F. et al. Bone mineral density after adjuvant chemotherapy for premenopausal breast cancer. Br. J. Cancer 61, 308–310 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Balestrini, J. L., Chaudhry, S., Sarrazy, V., Koehler, A. & Hinz, B. The mechanical memory of lung myofibroblasts. Integr. Biol. 4, 410–421 (2012).

    Article  CAS  Google Scholar 

  51. Li, C. X. et al. MicroRNA-21 preserves the fibrotic mechanical memory of mesenchymal stem cells. Nat. Mater. 16, 379–389 (2016).

    Article  PubMed  Google Scholar 

  52. He, F. et al. Hydroxyapatite mineral enhances malignant potential in a tissue-engineered model of ductal carcinoma in situ (DCIS). Biomaterials 224, 119489 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Chiou, A. E. et al. Breast cancer-secreted factors perturb murine bone growth in regions prone to metastasis. Sci. Adv. 7, eabf2283 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Zhang, W. et al. The bone microenvironment invigorates metastatic seeds for further dissemination. Cell 184, 2471–2486.e20 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Thiagarajan, P. S. et al. Development of a fluorescent reporter system to delineate cancer stem cells in triple-negative breast cancer. Stem Cells 33, 2114–2125 (2015).

    Article  CAS  PubMed  Google Scholar 

  56. Ginestier, C. et al. ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome. Cell Stem Cell 1, 555–567 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Raha, D. et al. The cancer stem cell marker aldehyde dehydrogenase is required to maintain a drug-tolerant tumor cell subpopulation. Cancer Res. 74, 3579–3590 (2014).

    Article  CAS  PubMed  Google Scholar 

  58. Pan, G. et al. A negative feedback loop of transcription factors that controls stem cell pluripotency and self-renewal. FASEB J. 20, 1730–1732 (2006).

    Article  CAS  PubMed  Google Scholar 

  59. Krishnakumar, R. et al. FOXD3 regulates pluripotent stem cell potential by simultaneously initiating and repressing enhancer activity. Cell Stem Cell 18, 104–117 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Chu, T. L. et al. FoxD3 deficiency promotes breast cancer progression by induction of epithelial–mesenchymal transition. Biochem. Biophys. Res. Commun. 446, 580–584 (2014).

    Article  CAS  PubMed  Google Scholar 

  61. Li, W. et al. Unraveling the roles of CD44/CD24 and ALDH1 as cancer stem cell markers in tumorigenesis and metastasis. Sci. Rep. 7, 13856 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Paszek, M. J. et al. Tensional homeostasis and the malignant phenotype. Cancer Cell 8, 241–254 (2005).

    Article  CAS  PubMed  Google Scholar 

  63. Seo, B. R. et al. Collagen microarchitecture mechanically controls myofibroblast differentiation. Proc. Natl Acad. Sci. USA 117, 11387–11398 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Barney, L. E. et al. Tumor cell-organized fibronectin maintenance of a dormant breast cancer population. Sci. Adv. 6, eaaz4157 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Reznikov, N. et al. Biological stenciling of mineralization in the skeleton: local enzymatic removal of inhibitors in the extracellular matrix. Bone 138, 115447 (2020).

    Article  CAS  PubMed  Google Scholar 

  66. Boys, A. J. et al. Top-down fabrication of spatially controlled mineral-gradient scaffolds for interfacial tissue engineering. ACS Biomater. Sci. Eng. https://doi.org/10.1021/acsbiomaterials.9b00176 (2019).

  67. Zhou, H., Boys, A. J., Harrod, J. B., Bonassar, L. J. & Estroff, L. A. Mineral distribution spatially patterns bone marrow stromal cell behavior on monolithic bone scaffolds. Acta Biomater. 112, 274–285 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Fornetti, J., Welm, A. L. & Stewart, S. A. Understanding the bone in cancer metastasis. J. Bone Miner. Res. 33, 2099–2113 (2018).

    Article  CAS  PubMed  Google Scholar 

  69. Crapo, P. M., Gilbert, T. W. & Badylak, S. F. An overview of tissue and whole organ decellularization processes. Biomaterials 32, 3233–3243 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Gao, Y. et al. Metastasis organotropism: redefining the congenial soil. Dev. Cell 49, 375–391 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Cox, T. R. The matrix in cancer. Nat. Rev. Cancer 21, 217–238 (2021).

    Article  CAS  PubMed  Google Scholar 

  72. Reuten, R. et al. Basement membrane stiffness determines metastases formation. Nat. Mater. 20, 892–903 (2021).

    Article  CAS  PubMed  Google Scholar 

  73. Nam, S., Hu, K. H., Butte, M. J. & Chaudhuri, O. Strain-enhanced stress relaxation impacts nonlinear elasticity in collagen gels. Proc. Natl Acad. Sci. USA 113, 5492–5497 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Aguirre-Ghiso, J. A., Liu, D., Mignatti, A., Kovalski, K. & Ossowski, L. Urokinase receptor and fibronectin regulate the ERK(MAPK) to p38(MAPK) activity ratios that determine carcinoma cell proliferation or dormancy in vivo. Mol. Biol. Cell 12, 863–879 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Margadant, C. & Sonnenberg, A. Integrin-TGF-Β crosstalk in fibrosis, cancer and wound healing. EMBO Rep. 11, 97–105 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Ruppender, N. et al. Cellular adhesion promotes prostate cancer cells escape from dormancy. PLoS ONE 10, e0130565 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Bragado, P. et al. TGF-β2 dictates disseminated tumour cell fate in target organs through TGF-β-RIII and p38α/β signalling. Nat. Cell Biol. 15, 1351–1361 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Sosa, M. S. et al. NR2F1 controls tumour cell dormancy via SOX9- and RARβ-driven quiescence programmes. Nat. Commun. 6, 6170 (2015).

    Article  CAS  PubMed  Google Scholar 

  79. Marturano-Kruik, A. et al. Human bone perivascular niche-on-a-chip for studying metastatic colonization. Proc. Natl Acad. Sci. USA 115, 1256–1261 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Thrivikraman, G. et al. Rapid fabrication of vascularized and innervated cell-laden bone models with biomimetic intrafibrillar collagen mineralization. Nat. Commun. 10, 3520 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Singh, D. K., Patel, V. G., Oh, W. K. & Aguirre-Ghiso, J. A. Prostate cancer dormancy and reactivation in bone marrow. J. Clin. Med. 10, 2648 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Boskey, A. L. & Coleman, R. Critical reviews in oral biology and medicine: aging and bone. J. Dent. Res. 89, 1333–1348 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Azarin, S. M. et al. In vivo capture and label-free detection of early metastatic cells. Nat. Commun. 6, 8094 (2015).

    Article  PubMed  Google Scholar 

  84. Sadtler, K. et al. Divergent immune responses to synthetic and biological scaffolds. Biomaterials 192, 405–415 (2019).

    Article  CAS  PubMed  Google Scholar 

  85. Coleman, R. et al. Adjuvant bisphosphonate treatment in early breast cancer: meta-analyses of individual patient data from randomised trials. Lancet 386, 1353–1361 (2015).

    Article  Google Scholar 

  86. Coleman, R. E. et al. Breast-cancer adjuvant therapy with zoledronic acid. N. Engl. J. Med. 365, 1396–1405 (2011).

    Article  CAS  PubMed  Google Scholar 

  87. Pelham, R. J. & Wang, Y. L. Cell locomotion and focal adhesions are regulated by substrate flexibility. Proc. Natl Acad. Sci. USA 94, 13661 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Przybyla, L., Lakins, J. N., Sunyer, R., Trepat, X. & Weaver, V. M. Monitoring developmental force distributions in reconstituted embryonic epithelia. Methods 94, 101–113 (2016).

    Article  CAS  PubMed  Google Scholar 

  89. Han, S. J., Oak, Y., Groisman, A. & Danuser, G. Traction microscopy to identify force modulation in subresolution adhesions. Nat. Methods 12, 653–656 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Dobin, A. & Gingeras, T. R. Mapping RNA-seq reads with STAR. Curr. Protoc. Bioinformatics 51, 11.14.1–11.14.19 (2015).

    Article  PubMed  Google Scholar 

  91. 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  CAS  PubMed  Google Scholar 

  92. 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  Google Scholar 

  93. Grüneboom, A. et al. A network of trans-cortical capillaries as mainstay for blood circulation in long bones. Nat .Metab. 1, 236–250 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank all members of the Fischbach lab for valuable discussions of this research; the Wiesner group (Cornell University) for providing fluorescent silica nanoparticles; J. Kuo (Cornell University) for help with illustrations; L. O’Keeffe for help with preparation of bone scaffolds; the Cornell Animal Health Diagnostic Core for paraffin embedding and sectioning; J. Massague (Memorial Sloan Kettering Cancer Center) for providing bone tropic BoM1-2287 and BoM-1833; and the Cornell Center for Animal Resources and Education (CARE) staff for animal care. Financial support was provided by the Human Frontier Science Program (RGP0016/2017); the National Cancer Institute through the Center on the Physics of Cancer Metabolism (1U54CA210184); NIH F31 (F31CA228448) to A.E.C.; fellowship support by the Stem Cell Program of Cornell University to N.D.S.; and NSF GRFP (DGE-1650441) to M.A.W. and A.A.S. This work used the Cornell Center for Materials Research (CCMR), which is supported through the NSF MRSEC program (DMR-1719875); the Cornell NanoScale Science & Technology Facility (CNF), a member of the NSF-supported National Nanotechnology Coordinated Infrastructure (NNCI-2025233); and the Cornell University Biotechnology Resource Center (BRC) facilities, including a Zeiss LSM 710 confocal microscope (NIH 1S10RR025502), Zeiss LSM880 confocal/multiphoton microscope (NYSTEM (C029155) and NIH (S10OD018516)), light-sheet microscope (NIH S10OD023466), ZEISS/Xradia Versa 520 X-ray microscope (NIH S10OD012287), IVIS Spectrum (NIH S10OD025049) and Genomics Facility (RRID:SCR_021727).

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Author notes

  1. These authors contributed equally: Siyoung Choi, Matthew A. Whitman.

Authors and Affiliations

  1. Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA

    Siyoung Choi, Matthew A. Whitman, Adrian A. Shimpi, Nicole D. Sempertegui, Aaron E. Chiou, Joseph E. Druso, Stephanie C. Lux & Claudia Fischbach

  2. Caryl and Israel Englander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA

    Akanksha Verma & Olivier Elemento

  3. Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, USA

    Zhu Cheng & Matthew Paszek

  4. Department of Materials Science and Engineering, Cornell University, Ithaca, NY, USA

    Lara A. Estroff

  5. Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, NY, USA

    Lara A. Estroff & Claudia Fischbach

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Contributions

S.C., M.A.W., L.A.E. and C.F. designed the project. S.C. and M.A.W. conducted most of the experiments. A.A.S. performed and analysed FACS and RNA-seq data. N.D.S performed and analysed the hMSC experiments. A.E.C. performed the animal study for light-sheet microscopy. J.E.D. performed FACS. A.V. and O.E. analysed RNA-seq data with the SSGSEA method. S.C.L. analysed IHC images. Z.C, A.A.S. and M.P. conducted and analysed TFM. S.C., M.A.W., L.A.E. and C.F. analysed the data and wrote the paper. All authors discussed the results and commented on the paper.

Corresponding authors

Correspondence to Lara A. Estroff or Claudia Fischbach.

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Competing interests

O.E. was supported by Janssen, J&J, Astra-Zeneca, Volastra and Eli Lilly research grants. He is scientific advisor and equity holder in Freenome, Owkin, Volastra Therapeutics and One Three Biotech, and a paid scientific advisor to Champions Oncology and Pionyr Therapeutics. The other authors declare no competing interests.

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Nature Biomedical Engineering thanks Julie A. Rhoades, Hai Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Mineral-to-matrix ratio of collagen and mineralized collagen.

Mineral to matrix ratio of mineralized collagen as calculated by dividing the phosphate peak area (907–1183 cm−1) of FT-IR spectra by the amide I peak area (1580–1727 cm−1) (n = 3; ****p < 0.0001). Data are mean ± SD. Data were calculated using two-tailed unpaired t-test.

Source data

Extended Data Fig. 2 Storage and loss moduli of collagen and mineralized collagen.

a, b, Representative time sweeps (a) and quantification of storage (Gˊ) and loss (G˝) moduli (n = 3; **p = 0.0083; ##p = 0.0035) (b). Data are mean ± SD. Data in b were calculated using two-tailed unpaired t-test.

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Extended Data Fig. 3 Heatmap of differentially expressed genes (DEGs).

DEGs were defined as the genes with FDR-adjusted p-values < 0.05 and log2FC > 1. Relative expression levels of all DEGs (left) or top 50 genes (right) is split into collagen and mineralized collagen conditions.

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Extended Data Fig. 4 Top-10 enrichment plot from gene-set enrichment analysis (GSEA).

a, GSEA indicates that biological processes associated with transcription and cell migration were overrepresented by collagen mineralization. Red labeled gene sets are associated with actin remodeling and migration curated from the Molecular Signatures Data Base (MSigDB), while blue labeled gene sets have been directly linked to stemness. The top 3 significant transcription factors (FOXJ2, MED25, ZNF746) do not have clearly defined functions beyond complexing with RNA polymerase II but FOXD3 was also enriched albeit to a less significant extent (FDR q-value = 0.062). Dashed red line designates FDR q-value cutoff of 0.05. b, Enrichment plot for FOXD3 target genes as determined by GSEA. Normalized Enrichment Score (NES) = 1.68, nominal p-value = 0.009, FDR q-value = 0.062. c, Enrichment plot for the Wu Cell Migration gene signature from the MSigDB as determined by GSEA. Normalized Enrichment Score (NES) = 1.60, nominal p-value < 0.001, FDR q-value = 0.157. While cell migration was not a focus of our study, this gene set includes several genes directly related to cytoskeletal remodeling (for example NEDD9, KRT19, S100A4, and FN1).

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Extended Data Fig. 5 Pre-adsorbed fibronectin does not affect the growth of MDA-MB231.

MDA-MB231 cell growth on the different substrates for 7 d. Substrates were incubated with various concentrations of fibronectin (FN) before cell seeding (n = 4). Data are mean ± SD. Data were calculated using two-way ANOVA with Tukey’s multiple comparisons.

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Extended Data Fig. 6 Matrix mineralization correlates with decreased tumour-cell growth in the presence of bone-resident cells.

a, Representative confocal micrographs and corresponding quantification of co-cultured osteogenically differentiated hMSCs and labeled MDA-MB231 cells (n = 3; **p = 0.0061). Scale bar = 200 µm. b, Representative bright field micrographs of Alizarin Red S stained hMSCs cultured with control or osteogenic media and in the presence or absence of tumor-conditioned media (TCM). Scale bar, 500 μm and 100 μm (inset). c, Representative confocal micrographs of hMSCs stained for fibronectin. Scale bar = 200 µm. Data are mean ± SD. Data in a were calculated using two-tailed Mann Whitney U test.

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Extended Data Fig. 7 Characterization of mineralized collagen at different time points of dissolution.

a, FT-IR spectra of mineralized collagen following hydroxyapatite dissolution for up to 6 days. Non-mineralized collagen served as control. b,c, FT-IR analysis of mineral to matrix ratio (n = 3; ****p < 0.0001) (b) and crystallinity index (CI) of matrices after different periods of mineral dissolution (n = 3; ****p < 0.0001) (c). CI was calculated by drawing a baseline between 450 cm−1 and 750 cm−1 (red dashed line) and measuring the heights of phosphate peaks at 567 cm−1 and 603 cm−1 and the height of the lowest point between them relative to the baseline. d, Representative SEM and BSE images visualizing differences in mineral content and collagen fibril diameter after different periods of mineral dissolution. Pseudocolor was generated from BSE images by converting grey-scale pixel intensity to a linear 256-bit color scale. Fibril diameter was measured from SEM images. Scale bar = 2 µm. Data are mean ± SD. Data in b and c were calculated using one-way ANOVA with Dunnett’s multiple comparisons.

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Extended Data Fig. 8 E-cadherin expression of MCF7 is reduced by mineralized collagen.

Representative confocal micrographs and corresponding quantification of E-cadherin after 7 d of culture of MCF7 cells on the different substrates (n = 3; **p = 0.0012; ****p < 0.0001). Scale bar = 20 µm. Data are mean ± SD. Data were calculated using one-way ANOVA with Tukey’s multiple comparisons.

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Extended Data Fig. 9 Total traction forces of different breast-cancer cell lines.

Cells were precultured on tissue culture polystyrene (PS), collagen, and mineralized collagen. Cells were precultured on the different substrates for 7 days prior to reseeding on PA gels for traction force microscopy measurements (MDA-MB231 (PS: 44 cells examined from 3 gels; Col and MN-col: at least 61 cells examined from 6 gels); MCF7 (at least 31cells examined from 5 gels); BoM-1833 (PS: 21 cells examined from 2 gels; Col and MN-col: at least 33 cells examined from 3 gels)). *p = 0.0459; **p = 0.0076; ***p = 0.0001; #p = 0.0193. Data are mean ± SD. Data were calculated using Kruskal Wallis test with Dunn’s multiple comparisons.

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Extended Data Fig. 10 Survival analysis of patients with breast cancer.

a, Overall survival of patients with breast cancer by stratification based on mineral-induced gene expression using different breast cancer subtypes in METABRIC database. b, Overall bone metastasis-free survival of patients with breast cancer scoring high or low for expression of the mineral-induced gene signature (Supplementary Table 2).

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Choi, S., Whitman, M.A., Shimpi, A.A. et al. Bone-matrix mineralization dampens integrin-mediated mechanosignalling and metastatic progression in breast cancer. Nat. Biomed. Eng 7, 1455–1472 (2023). https://doi.org/10.1038/s41551-023-01077-3

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