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

Vlada S. Rozova, Ayad G. Anwer, Anna E. Guller, Hamidreza Aboulkheyr Es, Zahra Khabir, Anastasiya I. Sokolova, Maxim U. Gavrilov, Ewa M. Goldys, Majid Ebrahimi Warkiani, Jean Paul Thiery, Andrei V. Zvyagin

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5 Citations (Scopus)
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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.
Original languageEnglish
Article numbere1009193
Pages (from-to)1-25
Number of pages25
JournalPLoS Computational Biology
Issue number7
Early online date23 Jul 2021
Publication statusPublished - Jul 2021

Bibliographical note

Copyright © 2021 Rozova et al. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


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