Name
Title | ||
|---|---|---|
A mechanism for epithelial-mesenchymal heterogeneity in a population of cancer cells. |
Abstract | ||
|---|---|---|
Epithelial-mesenchymal heterogeneity implies that cells within the same tumor can exhibit different phenotypes-epithelial, mesenchymal, or one or more hybrid epithelial-mesenchymal phenotypes. This behavior has been reported across cancer types, both in vitro and in vivo, and implicated in multiple processes associated with metastatic aggressiveness including immune evasion, collective dissemination of tumor cells, and emergence of cancer cell subpopulations with stem cell-like properties. However, the ability of a population of cancer cells to generate, maintain, and propagate this heterogeneity has remained a mystifying feature. Here, we used a computational modeling approach to show that epithelial-mesenchymal heterogeneity can emerge from the noise in the partitioning of biomolecules (such as RNAs and proteins) among daughter cells during the division of a cancer cell. Our model captures the experimentally observed temporal changes in the fractions of different phenotypes in a population of murine prostate cancer cells, and describes the hysteresis in the population-level dynamics of epithelial-mesenchymal plasticity. The model is further able to predict how factors known to promote a hybrid epithelial-mesenchymal phenotype can alter the phenotypic composition of a population. Finally, we used the model to probe the implications of phenotypic heterogeneity and plasticity for different therapeutic regimens and found that co-targeting of epithelial and mesenchymal cells is likely to be the most effective strategy for restricting tumor growth. By connecting the dynamics of an intracellular circuit to the phenotypic composition of a population, our study serves as a first step towards understanding the generation and maintenance of non-genetic heterogeneity in a population of cancer cells, and towards the therapeutic targeting of phenotypic heterogeneity and plasticity in cancer cell populations. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1371/journal.pcbi.1007619 | PLOS COMPUTATIONAL BIOLOGY |
DocType | Volume | Issue |
Journal | 16 | 2 |
ISSN | Citations | PageRank |
1553-734X | 1 | 0.41 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shubham Tripathi | 1 | 1 | 0.41 |
| Priyanka Chakraborty | 2 | 1 | 0.41 |
| Herbert Levine | 3 | 82 | 12.58 |
| Mohit Kumar Jolly | 4 | 1 | 0.75 |