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

PLoS Comput Biol. 2021 Jul 23;17(7):e1009193. doi: 10.1371/journal.pcbi.1009193. eCollection 2021 Jul.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adaptation, Physiological
  • Antigens, CD / metabolism
  • Biomarkers, Tumor / metabolism
  • Biophysical Phenomena
  • Cadherins / metabolism
  • Cell Adhesion / physiology
  • Cell Count
  • Cell Line, Tumor
  • Cell Proliferation / physiology
  • Cell Shape / physiology
  • Computational Biology
  • Epithelial-Mesenchymal Transition / physiology*
  • Extracellular Matrix / pathology
  • Extracellular Matrix / physiology
  • Female
  • Humans
  • Machine Learning*
  • Models, Biological*
  • Neoplasm Metastasis / pathology
  • Neoplasm Metastasis / physiopathology
  • Triple Negative Breast Neoplasms / pathology*
  • Triple Negative Breast Neoplasms / physiopathology*
  • Tumor Microenvironment / physiology
  • Vimentin / metabolism

Substances

  • Antigens, CD
  • Biomarkers, Tumor
  • CDH1 protein, human
  • Cadherins
  • VIM protein, human
  • Vimentin

Grants and funding

This study was supported by the Ministry of Education and Science of the Russian Federation (minobrnauki.gov.ru), grant No. 075-15-2019-192 received by A.V.Z. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.