Spatial Heterogeneity in Cytoskeletal Mechanics Response to TGF-β1 and Hypoxia Mediates Partial Epithelial-to-Meshenchymal Transition in Epithelial Ovarian Cancer Cells.
Deepraj GhoshJeffrey HsuKylen SorianoCarolina Mejia PeñaAmy H LeeDon S DizonMichelle R DawsonPublished in: Cancers (2023)
Metastatic progression of epithelial ovarian cancer (EOC) involves the partial epithelial-to-mesenchymal transition (EMT) of cancer cells in the primary tumor and dissemination into peritoneal fluid. In part to the high degree of heterogeneity in EOC cells, the identification of EMT in highly epithelial cells in response to differences in matrix mechanics, growth factor signaling, and tissue hypoxia is very difficult. We analyzed different degrees of EMT by tracking changes in cell and nuclear morphology, along with the organization of cytoskeletal proteins. In our analysis, we see a small percentage of individual cells that show dramatic response to TGF-β1 and hypoxia treatment. We demonstrate that EOC cells are spatially aware of their surroundings, with a subpopulation of EOC cells at the periphery of a cell cluster in 2D environments exhibited a greater degree of EMT. These peripheral cancer cells underwent partial EMT, displaying a hybrid of mesenchymal and epithelial characteristics, which often included less cortical actin and more perinuclear cytokeratin expression. Collectively, these data show that tumor-promoting microenvironment conditions can mediate invasive cell behavior in a spatially regulated context in a small subpopulation of highly epithelial clustered cancer cells that maintain epithelial characteristics while also acquiring some mesenchymal traits through partial EMT.
Keyphrases
- induced apoptosis
- epithelial mesenchymal transition
- single cell
- cell cycle arrest
- growth factor
- stem cells
- transforming growth factor
- squamous cell carcinoma
- endoplasmic reticulum stress
- bone marrow
- endothelial cells
- cell death
- small cell lung cancer
- mesenchymal stem cells
- big data
- smoking cessation
- deep learning
- replacement therapy