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:

Cell–cell adhesion and 3D matrix confinement determine jamming transitions in breast cancer invasion

Nature Cell Biology volume 22, pages 1103–1115 (2020)Cite this article

Subjects

Abstract

Plasticity of cancer invasion and metastasis depends on the ability of cancer cells to switch between collective and single-cell dissemination, controlled by cadherin-mediated cell–cell junctions. In clinical samples, E-cadherin-expressing and -deficient tumours both invade collectively and metastasize equally, implicating additional mechanisms controlling cell–cell cooperation and individualization. Here, using spatially defined organotypic culture, intravital microscopy of mammary tumours in mice and in silico modelling, we identify cell density regulation by three-dimensional tissue boundaries to physically control collective movement irrespective of the composition and stability of cell–cell junctions. Deregulation of adherens junctions by downregulation of E-cadherin and p120-catenin resulted in a transition from coordinated to uncoordinated collective movement along extracellular boundaries, whereas single-cell escape depended on locally free tissue space. These results indicate that cadherins and extracellular matrix confinement cooperate to determine unjamming transitions and stepwise epithelial fluidization towards, ultimately, cell individualization.

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

Access options

Buy this article

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

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

Fig. 1: Collective invasion occurs within interstitial tissue tracks.
Fig. 2: Collective invasion of breast carcinoma cells depends on availability of tracks within 3D matrix in vitro.
Fig. 3: Single-cell detachment after downregulation of E-cadherin requires non-confining matrix conditions in vitro.
Fig. 4: Variability of cell–cell junction composition and stability in breast cancer cells migrating under low- or high-confinement ECM.
Fig. 5: In silico modelling of collective and single-cell dynamics in dependence of cell–cell adhesion and extracellular confinement.
Fig. 6: Types of collective dynamics in dependence of E-cadherin status and ECM confinement.
Fig. 7: Invasion mode depends on ECM confinement in vivo.

Similar content being viewed by others

Data availability

The authors declare that data supporting the findings of this study are available within the paper (and its supplementary information files), and are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

The detailed mathematical procedures, the simulation code and videos of example realizations for in silico modelling were deposited at GitHub (https://github.com/sisyga/jamminglgca). Custom MATLAB scripts for cell-nuclei tracking and cell segmentation are available at https://github.com/steffengrosser/smart-3D-registration-2019 and https://github.com/JuergenLippoldt/On-Motility-and-Form. Codes for the PIV analysis are available at https://github.com/ComplexityBiosystems/PIV-spheroids.

References

  1. Mayor, R. & Etienne-Manneville, S. The front and rear of collective cell migration. Nat. Rev. Mol. Cell Biol. 17, 97–109 (2016).

    CAS  PubMed  Google Scholar 

  2. Friedl, P., Locker, J., Sahai, E. & Segall, J. E. Classifying collective cancer cell invasion. Nat. Cell Biol. 14, 777–783 (2012).

    PubMed  Google Scholar 

  3. Gomez, G. A., McLachlan, R. W. & Yap, A. S. Productive tension: force-sensing and homeostasis of cell–cell junctions. Trends Cell Biol. 21, 499–505 (2011).

    CAS  PubMed  Google Scholar 

  4. Reffay, M. et al. Interplay of RhoA and mechanical forces in collective cell migration driven by leader cells. Nat. Cell Biol. 16, 217–223 (2014).

    CAS  PubMed  Google Scholar 

  5. Ewald, A. J., Brenot, A., Duong, M., Chan, B. S. & Werb, Z. Collective epithelial migration and cell rearrangements drive mammary branching morphogenesis. Dev. Cell 14, 570–581 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Sunyer, R. et al. Collective cell durotaxis emerges from long-range intercellular force transmission. Science 353, 1157–1161 (2016).

    CAS  PubMed  Google Scholar 

  7. Lehembre, F. et al. NCAM-induced focal adhesion assembly: a functional switch upon loss of E-cadherin. EMBO J. 27, 2603–2615 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Wendt, M. K., Taylor, M. A., Schiemann, B. J. & Schiemann, W. P. Down-regulation of epithelial cadherin is required to initiate metastatic outgrowth of breast cancer. Mol. Biol. Cell 22, 2423–2435 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Perl, A. K., Wilgenbus, P., Dahl, U., Semb, H. & Christofori, G. A causal role for E-cadherin in the transition from adenoma to carcinoma. Nature 392, 190–193 (1998).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Derksen, P. W. B. et al. Somatic inactivation of E-cadherin and p53 in mice leads to metastatic lobular mammary carcinoma through induction of anoikis resistance and angiogenesis. Cancer Cell 10, 437–449 (2006).

    CAS  PubMed  Google Scholar 

  12. Khalil, A. A. et al. Collective invasion in ductal and lobular breast cancer associates with distant metastasis. Clin. Exp. Metastasis 34, 421–429 (2017).

    PubMed  PubMed Central  Google Scholar 

  13. Kowalski, P. J., Rubin, M. A. & Kleer, C. G. E-cadherin expression in primary carcinomas of the breast and its distant metastases. Breast Cancer Res. 5, R217–R222 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Narendra, S., Jenkins, S. M., Khoor, A. & Nassar, A. Clinical outcome in pleomorphic lobular carcinoma: a case-control study with comparison to classic invasive lobular carcinoma. Ann. Diagn. Pathol. 19, 64–69 (2015).

    PubMed  PubMed Central  Google Scholar 

  15. Padmanaban, V. et al. E-cadherin is required for metastasis in multiple models of breast cancer. Nature 573, 439–444 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Beerling, E. et al. Plasticity between epithelial and mesenchymal states unlinks EMT from metastasis-enhancing stem cell capacity. Cell Rep. 14, 2281–2288 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Fischer, K. R. et al. Epithelial-to-mesenchymal transition is not required for lung metastasis but contributes to chemoresistance. Nature 527, 472–476 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Aceto, N. et al. Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis. Cell 158, 1110–1122 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Bi, D., Yang, X., Marchetti, M. C. & Manning, M. Motility-driven glass and jamming transitions in biological tissues. Phys. Rev. X 6, 021011 (2016).

    PubMed  PubMed Central  Google Scholar 

  20. Haeger, A., Krause, M., Wolf, K. & Friedl, P. Cell jamming: collective invasion of mesenchymal tumor cells imposed by tissue confinement. Biochim. Biophys. Acta 1840, 2386–2395 (2014).

    CAS  PubMed  Google Scholar 

  21. Doxzen, K. et al. Guidance of collective cell migration by substrate geometry. Integr. Biol. 5, 1026–1035 (2013).

    CAS  Google Scholar 

  22. Gaggioli, C. et al. Fibroblast-led collective invasion of carcinoma cells with differing roles for RhoGTPases in leading and following cells. Nat. Cell Biol. 9, 1392–1400 (2007).

    CAS  PubMed  Google Scholar 

  23. Weigelin, B., Bakker, G. & Friedl, P. Intravital third harmonic generation microscopy of collective melanoma cell invasion. IntraVital 1, 32–43 (2012).

    PubMed  Google Scholar 

  24. van Helvert, S., Storm, C. & Friedl, P. Mechanoreciprocity in cell migration. Nat. Cell Biol. 20, 8–20 (2018).

    PubMed  Google Scholar 

  25. Friedl, P. & Mayor, R. Tuning collective cell migration by cell–cell junction regulation. Cold Spring Harb. Perspect. Biol. 9, a029199 (2017).

    PubMed  PubMed Central  Google Scholar 

  26. Duclos, G. et al. Spontaneous shear flow in confined cellular nematics. Nat. Phys. 14, 728–732 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Wolf, K. et al. Multi-step pericellular proteolysis controls the transition from individual to collective cancer cell invasion. Nat. Cell Biol. 9, 893–904 (2007).

    CAS  PubMed  Google Scholar 

  28. Ilina, O. et al. Intravital microscopy of collective invasion plasticity in breast cancer. Dis. Model Mech. 11, dmm034330 (2018).

    PubMed  PubMed Central  Google Scholar 

  29. Provenzano, P. P. et al. Collagen density promotes mammary tumor initiation and progression. BMC Med. 6, 11 (2008).

    PubMed  PubMed Central  Google Scholar 

  30. Wolf, K. et al. Physical limits of cell migration: control by ECM space and nuclear deformation and tuning by proteolysis and traction force. J. Cell Biol. 201, 1069–1084 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Szabo, A. et al. In vivo confinement promotes collective migration of neural crest cells. J. Cell Biol. 213, 543–555 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Balzer, E. M. et al. Physical confinement alters tumor cell adhesion and migration phenotypes. FASEB J. 26, 4045–4056 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Haeger, A. et al. Collective cancer invasion forms an integrin-dependent radioresistant niche. J. Exp. Med. 217, e20181184 (2020).

    PubMed  Google Scholar 

  34. Bi, D., Lopez, J. H., Schwarz, J. M. & Manning, M. L. A density-independent rigidity transition in biological tissues. Nat. Phys. 11, 1074 (2015).

    CAS  Google Scholar 

  35. Park, J.-A. et al. Unjamming and cell shape in the asthmatic airway epithelium. Nat. Mater. 14, 1040–1048 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Palamidessi, A. et al. Unjamming overcomes kinetic and proliferation arrest in terminally differentiated cells and promotes collective motility of carcinoma. Nat. Mater. 18, 1252–1263 (2019).

    CAS  PubMed  Google Scholar 

  37. Ilina, O., Bakker, G.-J., Vasaturo, A., Hofmann, R. M. & Friedl, P. Two-photon laser-generated microtracks in 3D collagen lattices: principles of MMP-dependent and -independent collective cancer cell invasion. Phys. Biol. 8, 15010 (2011).

    Google Scholar 

  38. Singhai, R. et al. E-Cadherin as a diagnostic biomarker in breast cancer. N. Am. J. Med. Sci. 3, 227–233 (2011).

    PubMed  PubMed Central  Google Scholar 

  39. Vos, C. B. et al. E-cadherin inactivation in lobular carcinoma in situ of the breast: an early event in tumorigenesis. Br. J. Cancer 76, 1131–1133 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Conklin, M. W. et al. Aligned collagen is a prognostic signature for survival in human breast carcinoma. Am. J. Pathol. 178, 1221–1232 (2011).

    PubMed  PubMed Central  Google Scholar 

  41. Provenzano, P. P. et al. Collagen reorganization at the tumor-stromal interface facilitates local invasion. BMC Med. 4, 38 (2006).

    PubMed  PubMed Central  Google Scholar 

  42. Elisha, Y., Kalchenko, V., Kuznetsov, Y. & Geiger, B. Dual role of E-cadherin in the regulation of invasive collective migration of mammary carcinoma cells. Sci. Rep. 8, 4986 (2018).

    PubMed  PubMed Central  Google Scholar 

  43. Cadwell, C. M., Su, W. & Kowalczyk, A. P. Cadherin tales: Regulation of cadherin function by endocytic membrane trafficking. Traffic 17, 1262–1271 (2016).

    CAS  PubMed  Google Scholar 

  44. Frisch, U., Hasslacher, B. & Pomeau, Y. Lattice-gas automata for the Navier–Stokes equation. Phys. Rev. Lett. 56, 1505–1508 (1986).

    CAS  PubMed  Google Scholar 

  45. Deutsch, A. & Dormann, S. Cellular Automaton Modeling of Biological Pattern Formation (Birkhäuser, 2018).

  46. Bussemaker, H. J., Deutsch, A. & Geigant, E. Mean-field analysis of a dynamical phase transition in a cellular automaton model for collective motion. Phys. Rev. Lett. 78, 5018–5021 (1997).

    CAS  Google Scholar 

  47. Deutsch, A. A new mechanism of aggregation in a lattice-gas cellular automaton model. Math. Comput. Modell. 31, 35–40 (2000).

    Google Scholar 

  48. Duclos, G., Garcia, S., Yevick, H. G. & Silberzan, P. Perfect nematic order in confined monolayers of spindle-shaped cells. Soft Matter 10, 2346–2353 (2014).

    CAS  PubMed  Google Scholar 

  49. Schotz, E.-M., Lanio, M., Talbot, J. A. & Manning, M. L. Glassy dynamics in three-dimensional embryonic tissues. J. R. Soc. Interface 10, 20130726 (2013).

    PubMed  PubMed Central  Google Scholar 

  50. Notbohm, J. et al. Cellular contraction and polarization drive collective cellular motion. Biophys. J. 110, 2729–2738 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Atia, L. et al. Geometric constraints during epithelial jamming. Nat. Phys. 14, 613–620 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Hayer, A. et al. Engulfed cadherin fingers are polarized junctional structures between collectively migrating endothelial cells. Nat. Cell Biol. 18, 1311–1323 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Bazellieres, E. et al. Control of cell–cell forces and collective cell dynamics by the intercellular adhesome. Nat. Cell Biol. 17, 409–420 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Angelini, T. E. et al. Glass-like dynamics of collective cell migration. Proc. Natl Acad. Sci. USA 108, 4714–4719 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Garcia, S. et al. Physics of active jamming during collective cellular motion in a monolayer. Proc. Natl. Acad. Sci. USA 112, 15314–15319 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Douezan, S. et al. Spreading dynamics and wetting transition of cellular aggregates. Proc. Natl Acad. Sci. USA 108, 7315–7320 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Truong, H. H. et al. β1 integrin inhibition elicits a prometastatic switch through the TGFβ-miR-200-ZEB network in E-cadherin-positive triple-negative breast cancer. Sci. Signal. 7, ra15 (2014).

    PubMed  Google Scholar 

  58. Shamir, E. R. et al. Twist1-induced dissemination preserves epithelial identity and requires E-cadherin. J. Cell Biol. 204, 839–856 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Aiello, N. M. et al. EMT subtype influences epithelial plasticity and mode of cell migration. Dev. Cell 45, 681–695 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Kuriyama, S. et al. In vivo collective cell migration requires an LPAR2-dependent increase in tissue fluidity. J. Cell Biol. 206, 113–127 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Wiesner, C. et al. KIF5B and KIF3A/KIF3B kinesins drive MT1–MMP surface exposure, CD44 shedding, and extracellular matrix degradation in primary macrophages. Blood 116, 1559–1569 (2010).

    CAS  PubMed  Google Scholar 

  62. Nagelkerke, A. et al. Constitutive expression of γ-H2AX has prognostic relevance in triple negative breast cancer. Radiother. Oncol. 101, 39–45 (2011).

    CAS  PubMed  Google Scholar 

  63. Alieva, M., Ritsma, L., Giedt, R. J., Weissleder, R. & van Rheenen, J. Imaging windows for long-term intravital imaging: general overview and technical insights. Intravital 3, e29917 (2014).

    PubMed  PubMed Central  Google Scholar 

  64. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS  PubMed  Google Scholar 

  65. Rueden, C. T. et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinf. 18, 529 (2017).

    Google Scholar 

  66. Tinevez, J.-Y. et al. TrackMate: An open and extensible platform for single-particle tracking. Methods 115, 80–90 (2017).

    CAS  PubMed  Google Scholar 

  67. Thielicke, W. & Stamhuis, E. J. PIVlab—towards user-friendly, affordable and accurate digital particle image velocimetry in MATLAB. J. Open Res. Softw. 2, e30 (2014).

    Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge the Microscopical Imaging Centre core support by H. Croes and J. Fransen; M. K. te Lindert for scanning electron microscopy; B. Lemmers-Van de Weem for the excellent technical assistance in animal experiments; and L. Wolf for 3D rendering of carcinoma invasion in patient samples. We thank the Centre for Information Services and High Performance Computing at TU Dresden for providing computational infrastructure. This work was supported by Deutsche Krebshilfe (106950 and 70112014), the European Union FP7 Tissue Transmigration Training Network (T3Net 237946), the Netherlands Science Organization (NWO-VICI 918.11.626), the European Research Council (617430-DEEPINSIGHT), NIH-U54 CA210184-01, and the Cancer Genomics Center (CGC.nl) to P.F.; the German Research Foundation (Deutsche Forschungsgemeinschaft, SFB-TR 79) to A.D.; the European Social Fund (ESF), co-financed by tax funds on the basis of the budget adopted by the members of the Saxon State Parliament, to S.S.; the European Research Council (ERC-741350) to J.A.K.; AIRC under IG2018-ID 21558 (principal investigator, M. Pusch) to C.A.M.L.P.; and the Lise–Meitner fellowship M 2450-NBL by the Austrian Science Fund (FWF) to O.C.

Author information

Authors and Affiliations

  1. Department of Cell Biology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, the Netherlands

    Olga Ilina, Pavlo G. Gritsenko, Manon Vullings, Gert-Jan Bakker & Peter Friedl

  2. Department of Innovative Computing, Centre for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany

    Simon Syga, Jörn Starruß & Andreas Deutsch

  3. Peter Debye Institute for Soft Matter Physics, Leipzig University, Leipzig, Germany

    Jürgen Lippoldt, Steffen Grosser & Josef A. Käs

  4. Center for Complexity and Biosystems, University of Milan, Milan, Italy

    Caterina A. M. La Porta & Stefano Zapperi

  5. Department of Environmental Science and Policy, University of Milan, Milan, Italy

    Caterina A. M. La Porta

  6. Consiglio Nazionale delle Ricerche (CNR), Istituto di Biofisica, Milan, Italy

    Caterina A. M. La Porta

  7. Institut für Theoretische Physik, Leopold-Franzens-Universität Innsbruck, Innsbruck, Austria

    Oleksandr Chepizhko

  8. Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands

    Peter Bult

  9. Department of Physics, University of Milan, Milan, Italy

    Stefano Zapperi

  10. Consiglio Nazionale delle Ricerche (CNR), Istituto di Chimica della Materia Condensata e di Tecnologie per l’Energia, Milan, Italy

    Stefano Zapperi

  11. David H. Koch Center for Applied Genitourinary Cancers, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Peter Friedl

  12. Cancer Genomics Centre, Utrecht, the Netherlands

    Peter Friedl

Authors
  1. Olga Ilina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pavlo G. Gritsenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simon Syga
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jürgen Lippoldt
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Caterina A. M. La Porta
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Oleksandr Chepizhko
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Steffen Grosser
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Manon Vullings
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Gert-Jan Bakker
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jörn Starruß
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Peter Bult
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Stefano Zapperi
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Josef A. Käs
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Andreas Deutsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Peter Friedl
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

O.I.: study design, acquisition of data, analysis and interpretation of results and writing the manuscript; P.G.G.: acquisition of data, analysis and interpretation of results and writing the manuscript; S.S.: acquisition of in silico data, and analysis and interpretation of results on mathematical modelling; M.V.: acquisition of data on metastasis, and analysis and interpretation of results on metastasis; G.-J.B.: assistance with two-photon microscopy; J.S.: generation of mathematical model; P.B.: contribution of clinical tissue samples, interpretation of data and revising the manuscript; A.D.: design of mathematical model, acquisition of in silico data, analysis and interpretation of results on mathematical modelling; J.A.K., J.L. and S.G.: cell tracking and cell segmentation analysis; O.C., C.A.M.L.P. and S.Z.: particle imaging, velocimetry and data analysis; P.F.: study design, interpretation of results and writing the manuscript.

Corresponding author

Correspondence to Peter Friedl.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Extended data

Extended Data Fig. 1 3D organization of normal and mammary carcinoma tissue and the geometry of interstitial tissue tracks.

a, Immunofluorescence staining of E-cadherin or pan-cytokeratin (pan-CK), vimentin and collagen IV in normal ducts, IDC and ILC lesions. Arrowheads indicate single carcinoma cells. Example images represent 3 patient samples per condition. b, 3D reconstruction of z-projection (200 μm thickness) from IDC and ILC samples with carcinoma cells oriented in the niches formed by aligned collagen fibers. Example images represent 7 (IDC) and 3 (ILC) patient samples. c, 3D rendering of collective carcinoma strands and tissue tracks in IDC (left panel), and a different region with tissue tracks formed by collagen bundles (right panel). d,Tissue structure surrounding E-cadherin positive duct in normal breast tissue. Example image represents 3 normal breast tissue patient samples. e, Inter-fiber distance of tissue tracks formed by collagen bundles with stroma cells present or absent within the tracks. Data represent n = 49 and 24, 139 and 78, and 107 and 93 cell-free or cell-filled tissue microtracks pooled from 3 (normal breast tissue), 7 (tumor-free IDC tissue) and 3 (tumor-free ILC tissue) patient samples, respectively. Median (red line) and dots representing individual tissue microtracks. P values, two-tailed Mann-Whitney test. Scale bars, 50 µm. For statistical source data, see Source data for ED Fig. 1.

Source data

Extended Data Fig. 2 Collective invasion of breast cancer cells in 3D collagen matrix enriched with fibroblasts or fibroblast-free matrix of different density.

a, Time-dependent collective invasion of MCF-7 spheroid into LD collagen matrix. Micrographs represent 3 independent experiments. b, Collective strand length. Data represent average length of strands from n = 9 (day 1), 13 (day 3) and 13 (day 5) spheroids (3 independent experiments). c, MCF-7 cell invasion into HD collagen matrix in dependence of cocultured MRC-5 fibroblasts. Micrographs represent 3 independent experiments. d, Length of invasion strands with and without cocultured MRC-5 fibroblasts. Data without MRC-5 represent n = 15 spheroids (day 2, 4, 6) and with MRC-5, n = 13 (day 2), 31 (day 4), 39 (day 6) spheroids/condition (3 independent experiments). e, Area of collective MCF-7 sheet moving along the collagen interface of different density. Data represent n = 47 (LD), 38 (ID), 11 (HD) spheroids/condition (3 independent experiments). f, Rat-tail (RT) collagen network at 2 (LD), 4 (ID) and 6 mg/mL (HD) density, used for 3D invasion of 4T1 cells. RT, rat tail; PD, pore diameter. g, Pore diameters at different collagen densities. Data represent n = 56 (LD), 41 (ID), 26 (HD) pores/condition (3 independent experiments). h, E-cadherin localization in 4T1 cells during collective invasion in collagen with different density (48 h). Micrographs represent 3 independent experiments. i, Cell boundary/cytoplasm ratio of E-cadherin and β-catenin intensity (conditions as in h). n.d., no detached cells detectable. Data for E-cadherin represent n = 16, 19 and 18 (strands) and 21 and 9 (detached) cells for LD, ID and HD conditions, respectively, and for β-catenin 16, 19 and 18 (strands) and 16 and 6 (detached) cells for LD, ID and HD conditions, respectively (3 independent experiments). Medians (red line); dots, means from individual spheroids (b, d, e), collagen pores (g) or individual cells (i). P values, two-tailed Mann-Whitney test. Collagen matrices described in Supplementary Table 1. Scale bars, 200 µm (a, c); 50 µm (f, h). For statistical source data, see Source data for ED Fig. 2.

Source data

Extended Data Fig. 3 Collective invasion of MCF-7 cells in dense collagen matrix depends on MMP activity.

a, E-cadherin localization in collectively invading MCF-7 cells in coculture with MRC-5 fibroblasts. Micrograph representing 3 independent experiments. b, Fibroblast-generated tracks of least resistance in HD collagen containing MCF-7 strand revealed by Col 3/4 staining. Micrograph from 3 independent experiments. c, MRC-5 and HMF fibroblasts-supported invasion of MCF-7 cells into HD collagen. Micrograph from 3 independent experiments. d, MCF-7 strand length in coculture with MRC-5 and HMF fibroblasts. Median (red line); dots represent n = 34 (MRC-5) and 27 (HMF) spheroids/condition (3 independent experiments). e, MT1-MMP/MMP14 expression by MRC-5, HMF and MCF-7 cells. Western blot from 2 independent experiments. Unmodified scan of the Western blot is provided in Source data. f, Collective invasion of MCF-7 cells into HD collagen matrix after overexpression of MT1-MMP/MMP14. Insert, parental MCF-7 cell spheroid. g, Number of collective MCF-7 strands in HD collagen and MCF-7 cells after MT1-MMP overexpression. Data represent n = 17 (parental) and 30 (MMP14-OE) spheroids/condition (2 independent experiments). h-j, MCF-7 collective invasion into HD collagen with fibroblasts (h), LD collagen (i), and LD or ID interface assay (j) after interference with MMPs (GM6001). Micrographs in (h-j) from 3 independent experiments. Length of MCF-7 strands (h, i) or sheet area (j). Data in (h-i) represent n = 71/71 (h), 44/39 (i) spheroids for DMSO/GM6001, respectively, or in (j) n = 21/32 (LD) and 16/10 (ID) spheroids for DMSO/GM6001, respectively (3 independent experiments). Median (red line), dots representing the means/spheroid (d, g, h, i, j). P values, two-tailed Mann-Whitney test. Low (LD), intermediate (ID) and high (HD) density collagen matrices are described in Supplementary Table 1. Scale bars, 100 µm (a, b); 200 µm (c, f, h-j). For statistical source data, see Source data for ED Fig. 3.

Source data

Extended Data Fig. 4 E-cadherin downregulation in MCF-7 and 4T1 cells – impact on collective invasion in collagen matrix.

a-f, Expression of E-cadherin in MCF-7-shCDH1 (a-c) and 4T1-shCDH1 cells (d-f). Example forward and side scatter plot (FSC/SSC; a, d) for gating of morphologically intact cells (3 independent experiments). Relative fluorescence intensity in MCF-7-shCDH1 cells (b) and 4T1 cells after E-cadherin downregulation with 5 different shRNA sequences (e). Western blots in (c, f) represent 3 independent experiments. Unmodified scan of the Western blots is provided in Source data. g, Expression of adhesion molecules and EMT markers after shRNA-mediated downregulation of E-cadherin in MCF-7 and 4T1 cells. Western blots represent 2 independent experiments. Unmodified scan of the Western blots is provided in Source data. h, Number of collective strands in 4T1 spheroids in matrix with different collagen densities. Dots represent means from n = 12/10 (LD), 14/11 (ID), 15/7 (HD) spheroids for shNT/shCDH1, respectively (3 independent experiments). i, Length of 4T1 collective strands in 4T1 spheroids in different collagen density. Dots represent n = 130/10 (LD), 161/26 (ID), 107/19 (HD) strands for shNT/shCDH1, respectively (3 independent experiments). j, Collective invasion of MCF-7 cells after E-cadherin downregulation during coculture with HMF fibroblasts in HD collagen. Micrographs represent 3 independent experiments. k, MCF-7 strand length in the absence or presence of HMF fibroblasts. Dots represent the average strand length per spheroid from n = 30/29 (shNT) or 20/44 (shCDH1) spheroids for either HMF condition, respectively (3 independent experiments). l, E-cadherin surface localization in MCF-7-shNT or MCF-7-CDH1 cells migrating in LD or HD interface assay. Micrographs represent 3 independent experiments. Medians (red line) in (h, i, k). P-values, two-tailed Mann-Whitney test. Low (LD), intermediate (ID) and high (HD) density collagen matrices are described in Supplementary Table 1. Scale bars, 100 µm (j); 50 µm (l). For statistical source data, see Source data for ED Fig. 4.

Source data

Extended Data Fig. 5 Collagen matrix confinement determines invasion mode of breast cancer cells in the presence of adhesion-perturbing anti-E-cadherin antibody.

a, Single-cell dissemination of MCF-7 cells in collagen matrix of different density in the absence or presence of blocking anti-E-cadherin mAb SHE78-7. Micrographs represent 3 independent experiments. b, Number of individualized cells under conditions in (a). Dots for 3D represent n = 11/16 (LD), 11/10 (ID), 34/34 (HD) and, for interface, n = 39/36 (LD), 22/16 (ID), 50/39 (HD) spheroids for IgG/SHE78-7, respectively, from 3 independent experiments. c, Dependence of the number of individually invading cells out of MCF-7 spheroids on the pore diameter within 3D collagen matrices with different density, with culture conditions as in (b). d, Single-cell detachment of MCF-7 cells by SHE78-7 mAb in LD collagen. Micrograph represents 3 independent experiments. e, Single-cell detachment of MCF-7 cells along collagen-ablated tracks in HD collagen. Micrograph represents 3 independent experiments. f, Number of individualized MCF-7 cells in collagen-ablated tracks. After 48 h tracks were completely filled with cells, resulting in multicellular strands irrespective of antibody condition. Data represent n = 7/18 (24 h), 7/18 (48 h) spheroids for IgG/SHE78-7, respectively pooled from 3 independent experiments. g, Combined interference using antibody SHE78-7 in MCF-7-shCDH1 cells migrating in interface assay with collagen of different density. Micrographs represent 3 independent experiments. h, Number of individualized MCF-7 cells after E-cadherin downregulation in interface assay. Data represent the means from n=20 spheroids per condition pooled from 3 independent experiments. Red line in (b, f, h), median. P values, two-tailed Mann-Whitney test. Low (LD), intermediate (ID) and high (HD) density collagen matrices are described in Supplementary Table 1. Scale bars, 100 µm (a, g); 50 µm (d, e). For statistical source data, see Source data for ED Fig. 5.

Source data

Extended Data Fig. 6 Variability of cell-cell junction composition and kinetics of carcinoma cell invasion in dependence of E-cadherin status and ECM confinement.

a, b, c, F-actin (a, b), E-cadherin and p120 catenin (c) localization in 4T1-shNT or 4T1-shCDH1 cells invading from multicellular spheroids along the interface of LD (a) or HD (b, c) collagen after 48 h. Micrographs in (a-c) represent cells from 2 independent experiments. Scale bars, 50 µm. d-f, Automated tracking of H2B-mCherry positive cell nuclei of 4T1 cells moving along the interface of LD or HD collagen (time period: 27-54 h). Spatial correlation of velocity (d), temporal correlation of velocity (e) and mean squared displacement (f) of 4T1-shNT or 4T1-shCDH1 cells at the outward edge. Lines in (d-f) represent the average of n=10 spheroids/condition pooled from 2 independent experiments. g, Particle image velocimetry (PIV) analysis of H2B-mCherry positive cell nuclei of 4T1 cells in LD or HD interface assay (time period: 43-48 h). Vorticities and velocities obtained from 4T1 cell nuclei at the invasion edge, mid-invasion zone or in the spheroid core. Data represent n=10 spheroids/condition pooled from 2 independent experiments. Box plots in (g) represent the median (mid-line), the second quartile (colored band) and the 1.5 interquartile range (whiskers). Two-sided Kolmogorov-Smirnov tests indicate that all possible comparisons are statistically significant (p < 10-6). For statistical source data, see Source data for ED Fig. 6.

Source data

Extended Data Fig. 7 Effects of E-cadherin downregulation on protein expression, tumor growth and spontaneous metastasis of MCF-7 and 4T1 cells after implantation into the mammary fat pad.

a, Collective invasion of 4T1 cells into the fibroblast-rich stroma 4 days after spheroid implantation into the mammary fat pad. Example images from 2 independent tumors. b, Pan-cytokeratin (upper panels) or cytokeratin-8 (lower panels) expression in 4T1-shNT or 4T1-shCDH1 tumors in the mammary fat pad (4 days post-implantation) or in the lungs (38 days after tumor implantation in the mammary fat pad). Micrographs represent 3 independent tumor samples (upper panel) or lungs samples from 5 mice/condition (lower panels). c, Pan-cytokeratin (upper panels) and E-cadherin or vimentin (lower panels) expression in MCF-7-shNT or MCF-7-shCDH1 tumors in the mammary fat pad (6 days post-implantation). Micrographs represent 2 (upper panel) or 1 (lower panels) tumors/condition. d, Time-dependent growth in MCF-7-shNT or MCF-7-shCDH1 primary tumors. Data display the mean ± SEM from n=5 mice per group. P values, two-tailed Mann-Whitney test. e, Number of metastatic foci within the lungs of MCF-7-shNT or MCF-7-shCDH1 tumor-bearing mice up to 38 days post-implantation in the mammary fat pad. Data represent the number of metastatic cell clusters per tissue slice (from 50-100 slices per lung) from n=5 (4T1-shNT, 4T1-shCDH1, MCF-7-shCDH1) or n=6 (MCF-7-shNT) mice/group. Red line, median. P values, two-tailed Mann-Whitney test. Scale bars, 50 µm (a, b, upper panel, c) and 25 µm (b, lower panel). f, Hypothetical jamming phase diagram for cell invasion into 3D tissue. For statistical source data, see Source data for ED Fig. 7.

Source data

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Table 1. Degree of confinement of collagen matrix conditions of 3D invasion assays shown in Fig. 2, 3 and 4 Supplementary Table 2. SiRNA and shRNA sequences used to target E-cadherin, N-cadherin, P-cadherin, α-catenin, β-catenin and p120 catenin in 4T1 and MCF-7 cells. Supplementary Table 3. The regions adapted for 2 mg/mL and 6 mg/mL collagen conditions (for particle image analysis).

Supplementary Video 1

3D reconstruction of invasive ductal carcinoma lesion in a patient sample (30 µm z-stack, 5 µm step). Collective strands of carcinoma cells were detected by positive E-cadherin staining; collagen bundles (SHG); nuclei (DAPI). The video is representative for 7 independent samples. Related to Fig. 1.

Supplementary Video 2

3D reconstruction of invasive lobular carcinoma lesion in a patient sample (60 µm z-stack, 5 µm step). Collective strands of carcinoma cells were detected by positive staining for pan-cytokeratin; collagen bundles (SHG); nuclei (DAPI). The video is representative for 3 independent samples. Related to Fig. 1.

Supplementary Video 3

In silico analysis of collective and single-cell migration modes in dependence of adhesion and extracellular matrix confinement. Time evolution of representative examples of four parameter combinations corresponding to low/high cell-cell adhesion and low/high ECM confinement. Strong cell-cell adhesion leads to correlated movement without single cell release (lower row, corresponding to Fig. 5e region 2); decreasing cell-cell adhesion leads to uncorrelated movement with single cell release if ECM confinement is weak (top left, corresponding to Fig. 5e region 1) and without single cell release if confinement is high (top right, corresponding to Fig. 5e region 3). Vectors correspond to local flux of cells. The color code indicates the local velocity correlation between nearest neighbours, with red/green corresponding to anti-correlated/correlated movement. Blue circles, single cells.

Supplementary Video 4

Time-lapse movies of the invasion of control (shNT) or shCDH1 4T1 cells from multicellular spheroids along the interface underneath the 3D collagen matrix of 2 mg/ml concentration. Bright-field and H2B-mCherry (nuclei) imaging. Related to Fig. 6. The video is representative for 10 spheroids per condition from 2 independent experiments.

Supplementary Video 5

Time-lapse movies of the invasion of control (shNT) or shCDH1 4T1 cells from multicellular spheroids along the interface underneath the 3D collagen matrix of 6 mg/ml concentration. Bright-field and H2B-mCherry (nuclei) imaging. Related to Fig. 6. The video is representative for 10 spheroids per condition from 2 independent experiments.

Supplementary Video 6

Automatic tracking of H2B-mCherry positive nuclei (magenta circles) of control (shNT) or shCDH1 4T1 cells invading from multicellular spheroids along the interface underneath the 3D collagen matrix of 2 mg/ml or 6 mg/ml concentration. Migration tracks (yellow lines). Related to Fig. 6. The video is representative for 10 spheroids per condition from 2 independent experiments.

Supplementary Video 7

PIV analysis of time lapse movies. The invasion of control (shNT) or shCDH1 4T1 cells from multicellular spheroids along the interface underneath the 3D collagen matrix of 2 mg/ml or 6 mg/ml concentration. The color denotes the vorticity and the arrows indicate the direction and amplitude of the local velocity. Related to Fig. 6. The video is representative for 10 spheroids per condition from 2 independent experiments.

Supplementary Video 8

Automatic 3D tracking of H2B-mCherry/LifeAct-eGFP shNT or shCDH1 4T1 cells in vivo 4 days after multicellular spheroid implantation in the mouse mammary fat pad. Time-lapse movie representing the maximum projection of 85 μm Z-stack taken every 10 min over 140 min. Related to Fig. 7. The video is representative for 3 independent experiments per condition.

Source data

Source Data Fig. 1

Statistical source data

Source Data Fig. 2

Statistical source data

Source Data Fig. 3

Statistical source data

Source Data Fig. 4

Statistical source data

Source Data Fig. 5

Statistical source data

Source Data Fig. 6

Statistical source data

Source Data Fig. 7

Statistical source data

Source Data Extended Data Fig. 1

Statistical source data

Source Data Extended Data Fig. 2

Statistical source data

Source Data Extended Data Fig. 3

Statistical source data

Source Data Extended Data Fig. 3

Unprocessed Western blots

Source Data Extended Data Fig. 4

Statistical source data

Source Data Extended Data Fig. 4

Unprocessed Western blots

Source Data Extended Data Fig. 5

Statistical source data

Source Data Extended Data Fig. 6

Statistical source data

Source Data Extended Data Fig. 7

Statistical source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ilina, O., Gritsenko, P.G., Syga, S. et al. Cell–cell adhesion and 3D matrix confinement determine jamming transitions in breast cancer invasion. Nat Cell Biol 22, 1103–1115 (2020). https://doi.org/10.1038/s41556-020-0552-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41556-020-0552-6

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

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

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