Stress-dependent growth in breast cancer arises from a mechano-osmotic coupling and cell-sizing checkpoint
Abstract
Mechanoresponsive cell proliferation is a feature of growing tumors, despite the suppression of many other regulatory checkpoints in cancer, but the underlying cell-scale mechanisms driving this behavior have not yet been established. In this study, we propose a biophysical model for cell growth as governed by actively controlled osmolarity, which we integrate with a discrete particle framework to simulate growth and remodeling of breast cancer spheroids. Confinement and biomechanical feedback from the extracellular environment are analyzed through a neural-network-accelerated finite element solver. Combining the framework with experiments, our model reveals that stress-dependent spheroid growth can arise from a sizing checkpoint for mitosis. Under sufficient extracellular loading, cell growth is restricted by high hydrostatic forces in competition with osmotic pressure from biomolecule synthesis, which prevents cells from surpassing a critical volume. Our model provides insight into mechanosensitive growth arrest in breast cancer, potentially serving as a computational tool for analyzing growth in a wider range of normal and malignant biological tissues.
Article Details
Journal Info
Proceedings of the National Academy of Sciences
National Academy of Sciences
Authors (7)
Irish Senthilkumar
Biomedical Engineering and Institute for Health Discovery and Innovation, College of Science and Engineering, University of Galway
Jef Vangheel
Mechatronics, Biostatistics, and Sensors (MeBIOS) division, Department of Biosystems, KU Leuven
Vatsal Kumar
Biomedical Engineering and Institute for Health Discovery and Innovation, College of Science and Engineering, University of Galway
Laoise McNamara
Biomedical Engineering and Institute for Health Discovery and Innovation, College of Science and Engineering, University of Galway
Bart Smeets
Mechatronics, Biostatistics, and Sensors (MeBIOS) division, Department of Biosystems, KU Leuven
Enda Howley
School of Computer Science and Data Science Institute, University of Galway
Eoin McEvoy
Biomedical Engineering and Institute for Health Discovery and Innovation, College of Science and Engineering, University of Galway