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Basement membrane stiffness determines metastases formation

Nature Materials volume 20, pages 892–903 (2021)Cite this article

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Abstract

The basement membrane (BM) is a special type of extracellular matrix and presents the major barrier cancer cells have to overcome multiple times to form metastases. Here we show that BM stiffness is a major determinant of metastases formation in several tissues and identify netrin-4 (Net4) as a key regulator of BM stiffness. Mechanistically, our biophysical and functional analyses in combination with mathematical simulations show that Net4 softens the mechanical properties of native BMs by opening laminin node complexes, decreasing cancer cell potential to transmigrate this barrier despite creating bigger pores. Our results therefore reveal that BM stiffness is dominant over pore size, and that the mechanical properties of ‘normal’ BMs determine metastases formation and patient survival independent of cancer-mediated alterations. Thus, identifying individual Net4 protein levels within native BMs in major metastatic organs may have the potential to define patient survival even before tumour formation. The ratio of Net4 to laminin molecules determines BM stiffness, such that the more Net4, the softer the BM, thereby decreasing cancer cell invasion activity.

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Fig. 1: Association of the expression of genes encoding for BM proteins with breast cancer patient survival.
Fig. 2: Net4-deficient alveolar BM favours metastases formation.
Fig. 3: Net4-deficient alveolar and mesenteric vessel BM favours metastases formation.
Fig. 4: Influence of Net4 on the mechanical properties of the BM.
Fig. 5: Decrease of BM stiffness via disrupting ternary laminin node complexes.
Fig. 6: Impact of BM mechanics on metastasis.

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Data availability

All data used to generate graphs are available in the respective source data. The generated liquid chromatography–mass spectrometry data are available via the PRIDE database of ProteomeXchange (PXD022145). Data used in this study were taken from previously published datasets including the NKI breast cancer dataset (https://ccb.nki.nl/data/), normal breast data (https://github.com/monkgroupie/Disease-Specific-Genomic-Analysis), kidney dataset (GSE3538 and GSE3931), CANBUILD dataset (GEO: GSE71340), human melanoma scRNA-seq dataset (GEO: GSE72056), bulk melanoma RNA-seq data via TCGA (Human Skin Cutaneous Melanoma (SKCM)). Source data are provided with this paper.

Code availability

Custom codes of DSGA analysis of breast and kidney cancer datasets are available at GitHub (https://github.com/monkgroupie/publication_code). The custom code of the mathematical simulation of the impact of node dilution on the laminin network and overall BM stiffness is available via https://doi.org/10.17169/refubium-28413.

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Acknowledgements

We thank N.M. Christensen at the Center for Advanced Bioimaging (CAB), University of Copenhagen for providing microscope access, M. Ghilav and C. Hoffman for valuable experimental help on mouse perfusion and electron microscopy preparation, M.K.G. Høg and Britt Kongstofte for preparation of tissue samples, and L. Amandus and B.L. Kristensen for their support with all aspects of mouse handling. The melanoma patient RNA-seq dataset analysed here is in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. This work was supported by the European Research Council (ERC-2015-CoG-682881-MATRICAN (R.R., M.N., A.E.M.-G. and J.T.E.)); the Danish Cancer Society (R204-A12454 (R.R.), R146-A9250 (A.L.) and R167-A10618 (S.R.N.)); German Cancer Aid (R.R.); a Hallas Møller Stipend from the Novo Nordisk Foundation (J.T.E.); the German Research Foundation (DFG) (NI 1895/1-1 (D.N.), CRC1114/C02 (S.Z. and R.R.N.), FOR2722/B2 (M.K.) and CRC829 (W.B.)); a PhD fellowship from the Lundbeck Foundation (R286-2018-621 (M.R.)); a postdoctoral fellowship from the Lundbeck Foundation (R250-2017-389 (E.M.S.)); a grant from the Novo Nordisk Foundation (Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, NNF17CC0027852 (E.M.S., B.T.P., H.K. and K.J.W.) and NNF19SA0035442 (B.F.)); the Danish Research Council (DFF-4002-00099 (L.W. and L.B.O.)); grants from the UK Medical Research Council (MR/R005567-1 (T.V.A.) and BHF (PG/15/92/31813 (T.V.A.)); a BHF studentship (FS/15/64/32035 (S.M.)); CANTER Research Focus of the Bavarian State Ministry for Science and Education (L.F., S.K., C.P., S.S. and H.C.S.); a CRUK Centre of Excellence Award to Barts Cancer Centre (C16420/A18066 (E.M.)); a CRUK fellowship (A27947 (O.M.T.P.)); the Ragnar Söderberg Foundation Sweden (N91/15 (C.D.M.)); the Swedish Research Council (2017-03389 (C.D.M.)); Cancerfonden Sweden (CAN 2016/783, 19 0632 Pj and 190007 (C.D.M.)); the Åke Wiberg Foundation (M17-0235 (C.D.M.)); and the Crafoord Foundation (20171049 (C.D.M.)).

Author information

Author notes

  1. Carina Prein

    Present address: Department of Physiology and Pharmacology, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Ontario, Canada

  2. These authors contributed equally: S. Zendehroud, M. Nicolau.

Authors and Affiliations

  1. Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen, Denmark

    Raphael Reuten, Monica Nicolau, Anu Laitala, Denise Nikodemus, Lena Wullkopf, Sebastian Rune Nielsen, Maria Rafaeva, Erwin M. Schoof, Benjamin Furtwängler, Bo T. Porse, Hyobin Kim, Kyoung Jae Won, Kamilla Westarp Zornhagen, Alejandro E. Mayorca-Guiliani & Janine T. Erler

  2. Department of Physics, Freie Universität Berlin, Berlin, Germany

    Sina Zendehroud & Roland R. Netz

  3. Center for Applied Tissue Engineering and Regenerative Medicine-CANTER, Munich University of Applied Sciences, Munich, Germany

    Lutz Fleischhauer, Stefanie Kiderlen, Carina Prein & Hauke Clausen-Schaumann

  4. Center for Nanoscience-CeNS, Munich, Germany

    Lutz Fleischhauer, Stefanie Kiderlen, Carina Prein, Stefanie Sudhop & Hauke Clausen-Schaumann

  5. Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK

    Sarah McNeilly & Tom Van Agtmael

  6. Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark

    Erwin M. Schoof

  7. The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark

    Erwin M. Schoof, Benjamin Furtwängler & Bo T. Porse

  8. Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark

    Erwin M. Schoof, Benjamin Furtwängler, Bo T. Porse, Hyobin Kim & Kyoung Jae Won

  9. Exercise Physiology Research Group, Department of Movement Sciences, Biomedical Sciences Group, KU Leuven, Leuven, Belgium

    Frank Suhr

  10. Centre for Tumour Microenvironment, Barts Cancer Institute, Queen Mary University of London, London, UK

    Eleni Maniati & Oliver M. T. Pearce

  11. Center for Biochemistry, Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

    Manuel Koch

  12. Institute for Dental Research and Oral Musculoskeletal Biology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

    Manuel Koch

  13. Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark

    Lene Broeng Oddershede

  14. Department of Laboratory Medicine, Division of Translational Cancer Research, Lund University, Lund, Sweden

    Chris D. Madsen

  15. Institute of Cardiovascular Research and Sport Medicine, Department of Molecular and Cellular Sport Medicine, German Sport University Cologne, Cologne, Germany

    Wilhelm Bloch

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  1. Raphael Reuten
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R.R. hypothesized the mechanism and developed the project idea. R.R. and J.T.E. conceived and designed the study. R.R. defined the BM gene list. R.R., A.L., K.W.Z. and S.R.N. performed animal experiments. M.N. performed the genomic association analysis of BM genes with assistance from R.R. S.R.N. performed staining, imaging and analysis of stromal cells within E0771 primary tumours. R.R. performed proliferation experiments. A.L. performed contraction assays. S.Z., R.R.N. and R.R. developed the laminin network stiffness model. S.Z. and R.R.N. performed elastic module simulation of the laminin network. L.F. and H.S.C. performed AFM analysis of the alveolar BM. S.K., C.P. and S.S. performed the AFM analysis of Matrigel. D.N. established the cancer cell invasion assay. D.N. and R.R. produced the recombinant proteins and performed invasion assays. M.R., E.M.S., B.F., B.T.P. and R.R. prepared and performed mass spectrometry experiments. H.K. and K.J.W. performed the scRNA-seq analysis. L.W. and L.B.O. performed optical tweezer experiments. S.M. and T.V.A. performed pressure myography measurements. R.R. and F.S. performed the immunofluorescence staining. E.M. and O.M.T.P. performed the analysis of BM gene association with ovarian patient tissue stiffness. M.K. provided the Net4 knockout mouse model and Net4-specific antibody. A.E.M.-G. performed the decellularization of lung and mesentery. C.D.M. and R.R. performed the imaging of the immunofluorescence staining. W.B. performed transmission electron microscopy analysis. R.R., J.T.E., M.N. and S.Z. wrote the paper. A.E.M.-G. drew the illustrations in Figs. 2a,g, 3b,i, 4a,c and 6b,c. R.R. generated the graphics in Figs. 5a,g and 6a and Extended Data Fig. 10a. R.R. and J.T.E. supervised the project. All authors discussed the results and commented on the manuscript text.

Corresponding authors

Correspondence to Raphael Reuten or Janine T. Erler.

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Peer review information Nature Materials thanks P. Mehlen, P. Yurchenco 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 Distribution of Normal versus Disease components in breast cancer patients and association of Net4 with kidney cancer patient survival.

a, Plots of consensus vs. overall associations in the Disease components, the Normal components, and the nonDSGA data. Most significant association with survival is captured in the Normal component of data. b, Kaplan Meier survival (death) analysis using Cox proportional hazard models for association with survival of NTN4 expression levels in the Normal component of renal cell carcinoma. Survival analysis comparison of (tumor group 1) low levels of NTN4 (33 percentile) vs. (tumor group 2) high levels of NTN4 (67 percentile) in the Normal component of data. High levels of NTN4 show significant association with good survival with P value 0.0157. c, Kaplan Meier survival (recurrence) analysis for association with survival of NTN4 levels in the Normal component of renal cell carcinoma. Survival analysis comparison of (tumor group 1) low levels of NTN4 (33 percentile) vs. (tumor group 2) high levels of NTN4 (67 percentile) in Normal component of data. High levels of NTN4 showed significant association with good survival with P value 0.0252.

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Extended Data Fig. 2 Validation of E0771 primary tumor from Net4 knockout and wildtype mice.

a, Immunofluorescence and immunohistochemistry staining of primary E0771 tumors grown in Net4 wildtype (WT) and Net4 knockout (KO) animals. Different stromal cell types were detected using αSMA (fibroblasts), F4/80 (macrophages), and CD8a (T-cells). Scale bar, 100 µm. Box and whiskers blot of cell type amount within primary tumor tissues (two-tailed Unpaired t test, Min to Max, median is shown as line, mean is displayed as cross, data points appear as grey dots; n = 6; t = 0.2948 (αSMA), t = 0.3001 (F4/80), t = 0.1246 (CD8a), df = 10; ns, not significant). b, Proliferation of cancer cell lines (E0771, 4T1, MDA-MB-231, and HCmel12) treated without (ctrl) and with different concentrations of Net4 (0.1, 3.6, 5.2 µg/well) for 96 h. Absorbance was normalized to ctrl. Scatter dot blot together with bars of proliferation assay (E0771: Kruskal-Wallis test, mean is the top of the bar; STDEV; n = 12 (ctrl and 0.1 µg/well), n = 9 (3.6 and 5.2 µg/well); ns, not significant; **P < 0.01, ***P < 0.001), (4T1, MDA-MB-231, and HCmel12: Ordinary one-way ANOVA test, mean is the top of the bar; n = 11 (4T1 and MDA-MB-231: ctrl), n = 12 (HCmel12: ctrl), n = 9 (3.6 and 5.2 µg/well); F = 5.938, DF = 37 (4T1); F = 1.254, DF = 37 (MDA-MB-231); F = 1.430, DF = 38 (HCmel12); ns, not significant; *P < 0.05, **P < 0.01). c, Contraction assay of cancer-associated fibroblast cell lines (mCAF1 and mCAF2) within collagen c1 and 7.2 µg/well) of recombinant Net4 (Net4) and its respective laminin binding mutant Net4 E195A,R199A (Mut) for 72 h. Representative image of the contraction assay showing the contracted area at the well rim. Scatter dot blot together with bars of contraction assay, in which all values are normalized to the control (Ordinary one-way ANOVA test, mean is the top of the bar; STDEV; n = 9; ns, not significant).

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Extended Data Fig. 3 Breast cancer cell sphere invasion into Matrigel supplemented with Net4.

a, Representative images of sphere invasion assay using MDA-MB-231 cells in Matrigel (ctrl) and Matrigel containing 25% Net4 molecules (Net4) relative to laminin 111 within the Matrigel matrix. Scale bar, 100 µm. Statistical analysis of sphere invasion (two-tailed Unpaired t test, Scatter dot blot, mean is displayed as black line; n = 27 of each sphere normalized to the mean of all ctrl spheres; t = 5.386, df = 52; ****P < 0.0001). b, Representative images of sphere invasion assay using MDA-MB-231 cells in 1.5 mg/ml collagen I (ctrl) and in 1.5 mg/ml collagen I containing 5.2 μg/sphere Net4 (Net4) or 5.2 μg/sphere Net4 laminin binding mutant (Mut) (Net4 molecule number equal to number in 50% Net4 Matrigel sphere). Scale bar, 100 μm. Statistical analysis of sphere invasion (Kruskal-Wallis test, Scatter dot blot, median is shown as line; n = 23 (ctrl and Mut) and n = 20 (Net4); ***P < 0.001, ****P < 0.0001).

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Extended Data Fig. 4 Validation of Net4 knockout mouse.

a, Immunofluorescence staining of decellularized lungs from Net4 WT and Net4 KO animals for Net4 (green), laminin γ1 (red), and collagen IV (cyan). Representative images of lung tissues from 3 mice of each genotype. Scale bar, 50 μm. b, Volcano plot of all identified proteins within five lung tissues from each Net4 WT and KO mice from the whole proteome LC-MS analysis. The plot visualizes the comparison of the protein abundance in lung tissues from WT vs. KO mice (x-axis: log2 ratio KO vs. WT; y-axis: -log10 P value). The red box highlights proteins with a log2 ratio KO vs. WT below -0.5 and the green box above 0.5 together with a P value above 0.05 (-log10 p-value 1.3). Both boxes indicate proteins with significant differential expression and relevant effect sizes between Net4 WT and KO lung tissue. Proteins, which belong to the total proteome, are shown as grey squares. Matrisome proteins are marked as blue squares and identified BM proteins (see Supplementary Table 1) as red squares. The volcano plot emphasizes that there are no BM proteins significantly changed between Net4 WT and KO mouse lung tissues.

Extended Data Fig. 5 Net4 deficient pulmonary BM favors metastases formation independent of endogenous Ntn4 expression.

a, Representative images of sphere invasion assay using HCmel12 cells in Matrigel (ctrl) and Matrigel containing 25% Net4 molecules (Net4) relative to laminin 111 within the Matrigel matrix. Scale bar, 100 µm. Statistical analysis of sphere invasion (two-tailed Unpaired t test, Scatter dot blot, mean is displayed as black line; n = 16 (ctrl), n = 23 (Net4); t = 4.728, df = 37; ****P < 0.0001). b, Quantitative real time PCR determining the relative concentration of mouse and human Net4 (Ntn4 and NTN4, respectively) expression to the housekeeping gene RPLP0 in different cell lines used in this study (HEK293, MDA-MB-231, E0771, 4T1, HCMel12, and KPC-mT4). Statistical analysis of expression levels (Kruskal-Wallis test, Scatter dot blot, median is shown as line; n = 12; **P < 0.01). c, Representative H&E images (from mice with the mean metastasis number and area) from Net4 WT and Net4 KO mice 28 days after intravenous injection of E0771 Ntn4 CRISPR/Cas9 knockout cells. Scale bar, 3 mm. d, Statistical analysis of macroscopic lung metastases (two-tailed Unpaired t test, Box and whiskers blot, Min to Max, median is shown as line, mean is displayed as cross, all data points appear as grey dots; n = 13 (WT), n = 12 (KO); t = 2.126, df = 23; *P < 0.05) e, Statistical analysis of microscopic lung metastasis, area/metastasis, and total metastasis area of intravenous injection of Ntn4 deficient E0771 cells into Net4 WT and Net4 KO mice after 28 days (two-tailed Mann-Whitney test, Box and whiskers blot, Min to Max, median is shown as line, mean is displayed as cross, all data points appear as grey dots; n = 13 (WT), n = 12 (KO); **P < 0.01).

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Extended Data Fig. 6 scRNA-seq analysis in melanoma patients.

a, UMAP plot highlights cell clusters (B-cells, cancer-associated fibroblasts (CAFs), endothelial cells, macrophages, melanoma cells, NK-cells, and T-cells) of 4,645 cells from 19 melanoma tumor patients. b, UMAP plot indicating cell type-specific NTN4 expression. c, UMAP plot of cell type-specific expression over the 49 BM genes.

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Extended Data Fig. 7 scRNA-seq analysis of BM protein encoding genes in melanoma patients.

Violin plots of 49 BM genes using scRNA-seq from 19 melanoma tumor patients.

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Extended Data Fig. 8 Impact of Net4 deficiency on mesenteric vessels.

a, Immunofluorescence staining of decellularized mesentery showing mesenteric vessels from Net4 WT and Net4 KO animals stained for Net4 (green), laminin γ1 (red), and collagen IV (cyan). Representative images of mesenteric vessels from 2 mice of each genotype. Scale bar, 100 μm. b, Inner diameter (µm) of isolated mesenteric arteries from 5 months old Net4 WT and KO mice as measured by pressure myography (Linear regression test, mean ± STDEV; n = 5 individual mice per group; ns, not significant). c, Outer diameter (µm) of isolated mesenteric arteries from 5 months old Net4 WT and KO mice as measured by pressure myography (Linear regression test, mean ± STDEV; n = 5 individual mice per group; ns, not significant).

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Extended Data Fig. 9 Correlation of NTN4 expression with malignant and BM markers.

a, Graph depicts the correlation between the Net4 (NTN4) gene expression level and the expression level of the malignant marker PAX8 in 31 ovarian cancer patients (Pearson’s p-value = 4.758 e−5, r = −0.6633402). b, Graph depicts the correlation between the Net4 (NTN4) gene expression level and the expression level of the malignant marker EPCAM in 31 ovarian cancer patients (Pearson’s p-value = 2.893 e−4, r = −0.6075367). c, Graph depicts the correlation between the Net4 (NTN4) gene expression level and the expression level of BM encoding genes (BM ssgsea score, calculated using R package GSVA) in 31 ovarian cancer patients (Pearson’s p-value = 2.734 e−4, r = 0.6094658). These graphs highlight a negative correlation of NTN4 expression with malignant cell markers and a positive correlation with BM encoding genes in ovarian cancer patients. d, Graph highlights the correlation of BM encoding genes with NTN4 expression levels within ovarian cancer patients (x-axis) to the Normal component of breast cancer patients (y-axis) (Pearson’s p-value = 0.031, r = 0.37). BM genes significantly correlating with NTN4 expression in both cancer types are indicated with black dots (Laminin α2 (LAMA2), fibulin-5 (FBLN5), and fibulin-3 (EFEMP1)).

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Extended Data Fig. 10 Laminin network dilutions impact stiffness.

a, Scheme of the AFM experiment to determine the Young’s modulus of Matrigel titrated with increasing Net4 amounts (red dots). b, Histograms reveal the Young’s modulus of measurements of pure Matrigel (black) and of increasing amounts (%) of Net4 molecules to laminin 111 molecules inside the Matrigel matrix (red). The amount of Net4 molecule addition is based on the ratio between Net4 and laminin 111 molecules in %. c, Graph displays data points for the stiffness change (%) of Matrigel plotted against laminin node dilution (%) obtained through the AFM measurement of different amounts of Net4 inside the Matrigel (Mixing) and the titration of Matrigel with increasing Net4 amounts (Titration). d, Scatter dot blot of the scaling exponent α of endogenous tracer movement measured by optical tweezers quantifying cytoplasmic visco-elastic properties of 4T1 cells inside Matrigel (ctrl), Matrigel supplemented with 50% Net4 (Net4) or with 50% Mut (Mut) *****(Kruskal-Wallis test, mean is displayed as grey line; n = 96 (ctrl), n = 82 (Net4), n = 104 (Mut); ns, not significant; *P < 0.05, ****P < 0.0001). e, Scatter dot blot of the scaling exponent α of endogenous tracer movement measured by optical tweezers quantifying cytoplasmic visco-elastic properties of measurements of KPC cells inside Matrigel (ctrl), Matrigel supplemented with 50% Net4 (Net4) or with 50% Mut (Mut) (Kruskal-Wallis test, mean is displayed as grey line; n = 94 (ctrl), n = 86 (Net4), n = 97 (Mut); ns, not significant; **P < 0.01, ***P < 0.001). f, Phase diagram showing the transition of the diluted honeycomb elastic networks from the floppy to the elastic state under increasing extensional strain for different dilution fractions. g, Rescaled elastic modulus Elam(ϕ)/Elam(0) extracted from simulations of the 2D diluted honeycomb network.

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Supplementary information

Reporting Summary

Supplementary Table 1

List of BM-encoding genes. Table includes the protein names of BM proteins, their human and mouse Uniprot IDs, and their presence in the NKI breast cancer, scRNA-seq and ovarian cancer datasets, and their detection in mouse lung tissue via TMT-labelled liquid chromatography–mass spectrometry analysis.

Supplementary Table 2

TMT liquid chromatography–mass spectrometry analysis table. List of all identified proteins in Net4 wildtype and knockout mice. Additionally, sheet 2 shows all identified matrisome proteins and sheet 3 is sorted for BM proteins.

Supplementary Table 3

List of correlation values for BM-encoding genes with tissue stiffness. List of Pearson’s and Spearman’s P and r values of all BM-encoding genes in 31 ovarian cancer patients (CANBUILD dataset).

Supplementary Table 4

Correlation values of BM-encoding gene expression levels with NTN4 expression. Table depicts Pearson’s P and r values of the correlation of BM-encoding genes with NTN4 in ovarian cancer patients and the Normal component of breast cancer patients. Genes that significantly correlate in both cancer types with NTN4 expression are highlighted in green.

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Reuten, R., Zendehroud, S., Nicolau, M. et al. Basement membrane stiffness determines metastases formation. Nat. Mater. 20, 892–903 (2021). https://doi.org/10.1038/s41563-020-00894-0

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