Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.

Journal: PLoS computational biology
PMID: 34297718

Abstract

Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (MET), are believed to play key roles in facilitating the metastatic cascade. Metastatic lesions often exhibit a similar epithelial-like state to that of the primary tumour, in particular, by forming carcinoma cell clusters via E-cadherin-mediated junctional complexes. However, the factors enabling mesenchymal-like micrometastatic cells to resume growth and reacquire an epithelial phenotype in the target organ microenvironment remain elusive. In this study, we developed a workflow using image-based cell profiling and machine learning to examine morphological, contextual and molecular states of individual breast carcinoma cells (MDA-MB-231). MDA-MB-231 heterogeneous response to the host organ microenvironment was modelled by substrates with controllable stiffness varying from 0.2kPa (soft tissues) to 64kPa (bone tissues). We identified 3 distinct morphological cell types (morphs) varying from compact round-shaped to flattened irregular-shaped cells with lamellipodia, predominantly populating 2-kPa and >16kPa substrates, respectively. These observations were accompanied by significant changes in E-cadherin and vimentin expression. Furthermore, we demonstrate that the bone-mimicking substrate (64kPa) induced multicellular cluster formation accompanied by E-cadherin cell surface localisation. MDA-MB-231 cells responded to different substrate stiffness by morphological adaptation, changes in proliferation rate and cytoskeleton markers, and cluster formation on bone-mimicking substrate. Our results suggest that the stiffest microenvironment can induce MET.

Authors

  • Vlada S Rozova
    ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, Australia.
  • Ayad G Anwer
    ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, Australia.
  • Anna E Guller
    ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, Australia.
  • Hamidreza Aboulkheyr Es
    School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia.
  • Zahra Khabir
    ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, Australia.
  • Anastasiya I Sokolova
    Centre of Biomedical Engineering, Sechenov University, Moscow, Russia.
  • Maxim U Gavrilov
    Centre of Biomedical Engineering, Sechenov University, Moscow, Russia.
  • Ewa M Goldys
    ARC Centre of Excellence in Nanoscale Biophotonics (CNBP), Macquarie University , North Ryde 2109, Australia.
  • Majid Ebrahimi Warkiani
    School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia.
  • Jean Paul Thiery
    Centre of Biomedical Engineering, Sechenov University, Moscow, Russia.
  • Andrei V Zvyagin
    ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, Australia.

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