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

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ID: 268816
2021
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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.
Reference Key
rozova2021machineplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Rozova, Vlada S;Anwer, Ayad G;Guller, Anna E;Es, Hamidreza Aboulkheyr;Khabir, Zahra;Sokolova, Anastasiya I;Gavrilov, Maxim U;Goldys, Ewa M;Warkiani, Majid Ebrahimi;Thiery, Jean Paul;Zvyagin, Andrei V;
Journal PLoS computational biology
Year 2021
DOI
10.1371/journal.pcbi.1009193
URL
Keywords
photodynamic therapy near-infrared spectroscopy photosensitizer luminescence singlet oxygen dosimeter

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