Cell-Cell Interactions in Solid Tumors — the Role of Cancer Stem Cells
It is increasingly argued that solid tumors follow a cellular hierarchy comparable to normal tissues, with so-called cancer stem cells on top of the hierarchy. In this model, cancer stem cells have the unique capability to initiate and propagate solid tumors. Non-stem cancer cells will form the bulk of the tumor population, but are by themselves incapable of giving rise to a continuously growing tumor. The two distinct phenotypes interact with one another and compete for common resources such as oxygen, nutrients, or available space. Single cell kinetics are parameterized with in vitro data and the interplay between cancer stem cells and their non-stem cancer cell counterpart is studied using two different modeling approaches: a cellular automaton model and a cellular Potts model. Simulations of tumor growth with both techniques reveal that cancer stem cell-driven solid tumors grow as conglomerates of self-metastases, suggesting a robust biological phenomenon. The growth rate of the tumor is dependent on the complex interplay of the underlying model parameters.
KeywordsCancer Stem Cell Cellular Automaton Monte Carlo Step Cellular Automaton Model Tumor Population
The authors wish to thank Alberto d’Onofrio, Paola Cerrai, and Alberto Gandolfi for inviting us to contribute to this book, and Philip Hahnfeldt and James Glazier for their contribution to the original development of the different models that are summarized in this chapter. This project was supported by the National Cancer Institute under Award Number U54CA149233 to L.H. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.
- 12.Enderling, H., Alexander, N.R., Clark, E.S., Branch, K.M., Estrada, L., Crooke, C., Jourquin, J., Lobdell, N., Zaman, M.H., Guelcher, S.A., Anderson, A.R., Weaver, A.M.: Dependence of invadopodia function on collagen fiber spacing and cross-linking: computational modeling and experimental evidence. Biophys. J. 95, 2203–2218 (2008)CrossRefGoogle Scholar
- 22.Glazier, J., Balter, A.:Magnetization to morphogenesis: A brief history of the Glazier-Graner-Hogeweg model. In: Anderson, A.R.A., Chaplain, M.A.J., Rejniak, K.A. (eds) Single-Cell-Based Models in Biology and Medicine. Birkhauser, Basel (2007)Google Scholar
- 24.Hahnfeldt, P., Panigrahy D, Folkman J, Hlatky L.: Tumor development under angiogenic signaling: a dynamical theory of tumor growth, treatment response, and postvascular dormancy. Cancer Res. 59, 4770–4775 (1999)Google Scholar
- 32.Marian, C.O., Wright, W.E., Shay, J.W.: The effects of telomerase inhibition on prostate tumor-initiating cells. Int. J. Cancer 127, 321–331 (2010)Google Scholar
- 33.Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1092 (1953) 204 Google Scholar
- 36.Nakada, M., Anderson, E.M., Demuth, T., Nakada, S., Reavie, L.B., Drake, K.L., Hoelzinger, D.B., Berens, M.E.: The phosphorylation of ephrin-B2 ligand promotes glioma cell migration and invasion. Int. J. Cancer 126 1155–1165 (2010)Google Scholar
- 40.Prehn, R.T.: Immunomodulation of tumor growth. Am. J. Pathol. 77, 119–122 (1974) Google Scholar
- 41.Prehn, R.T.: The inhibition of tumor growth by tumor mass. Cancer Res. 51, 2–4 (1991)Google Scholar
- 48.Tang, J., Enderling, H., Becker-Weimann, S., Pham, C., Polyzos, A., Chen, C.Y., Costes, S.V.: Phenotypic transition maps of 3D breast acini obtained by imaging-guided agent-based modeling. Integr. Biol. (Camb) 3, 408–421 (2011)Google Scholar
- 49.Vermeulen, P.B., van Laere, S.J., Dirix, L.Y.: Inflammatory breast carcinoma as a model of accelerated self-metastatic expansion by intravascular growth. Br. J. Cancer 101, 1028–1029, author reply 1030 (2009)Google Scholar