Understanding and leveraging phenotypic plasticity during metastasis formation

• Published in: NPJ systems biology and applications Vol. 9; no. 1; pp. 48 - 11
• Main Authors: Shah, Saumil, Philipp, Lisa-Marie, Giaimo, Stefano, Sebens, Susanne, Traulsen, Arne, Raatz, Michael
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06.10.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/158/1745; 631/553/2393; 631/67; Bioinformatics; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Immunotherapy; Life Sciences; Mathematical models; Metastases; Metastasis; Phenotypes; Phenotypic plasticity; Systems Biology; Tumor cells; Tumors
• ISSN: 2056-7189; 2056-7189
• DOI: 10.1038/s41540-023-00309-1

Cancer metastasis is the process of detrimental systemic spread and the primary cause of cancer-related fatalities. Successful metastasis formation requires tumor cells to be proliferative and invasive; however, cells cannot be effective at both tasks simultaneously. Tumor cells compensate for this trade-off by changing their phenotype during metastasis formation through phenotypic plasticity. Given the changing selection pressures and competitive interactions that tumor cells face, it is poorly understood how plasticity shapes the process of metastasis formation. Here, we develop an ecology-inspired mathematical model with phenotypic plasticity and resource competition between phenotypes to address this knowledge gap. We find that phenotypically plastic tumor cell populations attain a stable phenotype equilibrium that maintains tumor cell heterogeneity. Considering treatment types inspired by chemo- and immunotherapy, we highlight that plasticity can protect tumors against interventions. Turning this strength into a weakness, we corroborate current clinical practices to use plasticity as a target for adjuvant therapy. We present a parsimonious view of tumor plasticity-driven metastasis that is quantitative and experimentally testable, and thus potentially improving the mechanistic understanding of metastasis at the cell population level, and its treatment consequences.