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Rigid tumours contain soft cancer cells

Nature Physics volume 18, pages 1510–1519 (2022)Cite this article

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Abstract

Palpation utilizes the fact that solid breast tumours are stiffer than the surrounding tissue. However, cancer cells tend to soften, which may enhance their ability to squeeze through dense tissue. This apparent paradox proposes two contradicting hypotheses: either softness emerges from adaptation to the tumour’s microenvironment or soft cancer cells are already present inside a rigid primary tumour mass giving rise to cancer cell motility. We investigate primary tumour explants from patients with breast and cervix carcinomas on multiple length scales. We find that primary tumours are highly heterogeneous in their mechanical properties on all scales from the tissue level down to individual cells. This results in a broad rigidity distribution—from very stiff cells to cells softer than those found in healthy tissue—that is shifted towards a higher fraction of softer cells. Atomic-force-microscopy-based tissue rheology reveals that islands of rigid cells are surrounded by soft cells. The tracking of vital cells confirms the coexistence of jammed and unjammed areas in tumour explants. Despite the absence of a percolated backbone of stiff cells and a large fraction of unjammed, motile cells, cancer cell clusters show a heterogeneous solid behaviour with a finite elastic modulus providing mechanical stability.

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Fig. 1: Viscoelasticity of breast cancer tissue.
Fig. 2: Viscoelasticity of cervix cancer tissue.
Fig. 3: Single-cell mechanics of carcinomas.
Fig. 4: Mechanics and fusion of multicellular spheroids.
Fig. 5: Two-dimensional vertex model simulations of mixtures of soft and rigid cancer cells.
Fig. 6: Emergent cellular stream caused by an invading cell inside a heterogeneous tumour.

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Data availability

All data are available upon request from the corresponding author.

Code availability

Simulation data and code are available from D.B. (d.bi@northeastern.edu) on reasonable request.

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Acknowledgements

We would like to thank A. Bruckert, J. Schnedemann and S. Klingkowski from Pathologie Hamburg-West, as well as F. Juliano from Montefiore Hospital, for the preparation of tissue samples. This work was funded by the German Science Foundation (DFG; KA1116/9-1 and KA1116/17-1), as well as project ‘FORCE’ within the EU’s Horizon 2020 research and innovation programme. Graduate students A.W.F., T.R.K., S.P. and F.W. were funded by fellowships of the DFG Excellence graduate school BuildMoNa. The research with A.N. was part of the joint project EXPRIMAGE funded by the German Federal Ministry for Education and Research (BMBF 13N9366). T.F. was funded by ERC Advanced Grant ‘HoldCancerBack’ (741350). D.B. and X.L. would like to acknowledge support from the Northeastern University TIER 1 Grant, National Science Foundation DMR-2046683; the Alfred P. Sloan Foundation; MathWorks Microgrants; and the Northeastern University Discovery Cluster. M.H.O. and J.C. were funded by CA255153. We would also like to acknowledge the Syracuse University HTC Campus Grid, NSF award ACI-1341006.

Author information

Author notes

  1. These authors contributed equally: Thomas Fuhs, Franziska Wetzel, Anatol W. Fritsch, Xinzhi Li.

Authors and Affiliations

  1. Soft Matter Physics Division, Leipzig University, Leipzig, Germany

    Thomas Fuhs, Franziska Wetzel, Anatol W. Fritsch, Roland Stange, Steve Pawlizak, Tobias R. Kießling, Erik Morawetz, Steffen Grosser, Frank Sauer, Jürgen Lippoldt, Frederic Renner, Sabrina Friebe, Mareike Zink & Josef A. Käs

  2. Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), Dresden, Germany

    Anatol W. Fritsch

  3. Department of Physics, Northeastern University, Boston, MA, USA

    Xinzhi Li & Dapeng Bi

  4. Leibniz Institute of Surface Engineering and Surface Physics, University of Leipzig, Leipzig, Germany

    Sabrina Friebe

  5. Pathologie Hamburg-West, Hamburg, Germany

    Klaus Bendrat & Axel Niendorf

  6. Institute for Biochemistry and Molecular Cell Biology, University Hospital Hamburg-Eppendorf, Hamburg, Germany

    Klaus Bendrat

  7. Institute of Medical Informatics, Charité—Universitätsmedizin Berlin, Berlin, Germany

    Jürgen Braun

  8. Department of Pathology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA

    Maja H. Oktay

  9. Gruss Lipper Biophotonics Center and Integrated Imaging Program, Albert Einstein College of Medicine, Bronx, NY, USA

    John Condeelis

  10. Department of Gynecology, Women’s and Children’s Center, University Hospital Leipzig, Leipzig, Germany

    Susanne Briest, Benjamin Wolf, Michael Höckel & Bahriye Aktas

  11. Division of Breast, Urogenital and Perinatal Pathology, University Hospital Leipzig, Leipzig, Germany

    Lars-Christian Horn

  12. Department of Physics, University of California, Santa Barbara, CA, USA

    M. Cristina Marchetti

  13. Department of Physics, Syracuse University, Syracuse, NY, USA

    M. Lisa Manning

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Contributions

J.A.K. planned and directed this study. T.F., D.B., X.L. and J.A.K. wrote the manuscript. All the authors discussed the results and commented on the manuscript. F.W. carried out experiments on the breast tumour cells and controls. A.W.F. and S.P. were responsible for the measurements of cervical samples. R.S., T.R.K., S.P., A.W.F., E.M. and F.W. developed a sample preparation and measurement routine. T.F., M.Z. and S.F. carried out the AFM measurements. F.S. carried out the MRE measurements. J.B. provided technical support for the MRE measurements. S.G. and J.L. performed the experiments on MTS. F.R. performed the experiments on tumour explants. B.A., L.-C.H., M.H., K.B., S.B., B.W., M.H.O., J.C. and A.N. provided the patient samples and scientific/medical advice for data interpretation. D.B., X.L., M.C.M. and M.L.M designed the theoretical and simulation models. D.B., X.L. and M.L.M. developed, executed and analysed the simulation results with oversight from J.A.K.

Corresponding author

Correspondence to Josef A. Käs.

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Nature Physics thanks Dennis Discher, Sanjay Kumar 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 Effect of passaging on the deformability of primary breast tumour and normal epithelial cells.

Left) Distribution of the maximum relative cell deformations (observed at t = 3 s) for a breast cancer sample stretched at the end of passage number 1-3 (sample size: P1 = 164 cells, P 2 = 496 cells, P3 = 309 cells). Right) Relative deformation distribution epithelial cells from breast reduction (HMEpC) stretched at the end of passage number 2 and 10 (sample size: P2 = 352 cells, P10 = 702 cells). With increasing passage number more and more deformable cells are found. Time between passaging was 7 to 10 days.

Extended Data Fig. 2 Individual Histograms of the relative deformation of breast tumour samples.

Cells isolated from 13 breast tumours were measured in the optical stretcher, 2 fibroadenomas (FA) and HMEpC cells serve as control. Log-Normal distributions were fitted to the histograms; details of the fits can be found in Extended Data Table 1.

Extended Data Fig. 3 Individual Histograms the relative deformation of Cervix Tumours.

Cells were isolated from 4 cervix tumours and normal cervix tissue from the same patient as control. Log-Normal distributions were fitted; details of the fits can be found in Extended Data Table 1.

Extended Data Fig. 4 The stream anisotropy as a function of fr for the invading cell.

The stream anisotropy as a function of fr for the invading cell.

Extended Data Table 1 Statistic quantities of the tumour samples
Full size table

Supplementary information

Supplementary Information

Supplementary Fig. 1.

Reporting Summary

Supplementary Video 1

Vital imaging of the tumour fragment. A small fragment of a cervix tumour was vital stained with SiR-DNA to record the movement of cell nuclei. The video is ×600 the real-time speed.

Supplementary Video 2

Cell nuclei tracking in the tumour fragment. We tracked the cell nuclei shown in Supplementary Video 1. The colour encodes the cell speed with blue colour representing slower cells and red colours representing faster cells.

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Fuhs, T., Wetzel, F., Fritsch, A.W. et al. Rigid tumours contain soft cancer cells. Nat. Phys. 18, 1510–1519 (2022). https://doi.org/10.1038/s41567-022-01755-0

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