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Cluster size distribution of cells disseminating from a primary tumor.

Authors :
Mukherjee M
Levine H
Source :
PLoS computational biology [PLoS Comput Biol] 2021 Nov 10; Vol. 17 (11), pp. e1009011. Date of Electronic Publication: 2021 Nov 10 (Print Publication: 2021).
Publication Year :
2021

Abstract

The first stage of the metastatic cascade often involves motile cells emerging from a primary tumor either as single cells or as clusters. These cells enter the circulation, transit to other parts of the body and finally are responsible for growth of secondary tumors in distant organs. The mode of dissemination is believed to depend on the EMT nature (epithelial, hybrid or mesenchymal) of the cells. Here, we calculate the cluster size distribution of these migrating cells, using a mechanistic computational model, in presence of different degree of EMT-ness of the cells; EMT is treated as given rise to changes in their active motile forces (μ) and cell-medium surface tension (Γ). We find that, for (μ > μmin, Γ > 1), when the cells are hybrid in nature, the mean cluster size, [Formula: see text], where μmin increases with increase in Γ. For Γ ≤ 0, [Formula: see text], the cells behave as completely mesenchymal. In presence of spectrum of hybrid states with different degree of EMT-ness (motility) in primary tumor, the cells which are relatively more mesenchymal (higher μ) in nature, form larger clusters, whereas the smaller clusters are relatively more epithelial (lower μ). Moreover, the heterogeneity in μ is comparatively higher for smaller clusters with respect to that for larger clusters. We also observe that more extended cell shapes promote the formation of smaller clusters. Overall, this study establishes a framework which connects the nature and size of migrating clusters disseminating from a primary tumor with the phenotypic composition of the tumor, and can lead to the better understanding of metastasis.<br />Competing Interests: The authors have declared that no competing interests exist.

Details

Language :
English
ISSN :
1553-7358
Volume :
17
Issue :
11
Database :
MEDLINE
Journal :
PLoS computational biology
Publication Type :
Academic Journal
Accession number :
34758019
Full Text :
https://doi.org/10.1371/journal.pcbi.1009011