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

Nature Biomedical Engineering (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.

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.

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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 (2023). https://doi.org/10.1038/s41551-023-01077-3

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