Game Theory: Cancer as an Evolutionary Game

By definition, game theory is “the study of mathematical models of conflict and cooperation between intelligent, rational decision-makers,” and it is a widely used theory in many fields including economics, computer science, and biology [1]. In biology, it is known as evolutionary game theory and has traditionally been used to explain altruistic behaviors in terms of Darwinian competition [2]. Seminally, could game theory be employed to model complex interactions between cells to understand disease?

To address the possibility, it is imperative to start with the most fundamental building block in biology: the cell. Cells form tissues, which then go on to form organs, which in turn form an organism. It necessarily must be borne in mind that, as a complex system, an organism requires the proper coordination of its constituent parts for full functionality. The proper operation of an organism is based upon the principle of cooperation. Resultantly, a cell by itself does not contribute significantly towards an organism’s function, while many cells working together do [3].

Researchers Basanta and Anderson at the Moffitt Cancer Center in Florida have used evolutionary game theory to examine cancer cell behavior in terms of an ecosystem and its components [4]. The findings placed the environment of the cancer cell at the centre of the study. This is a novel approach to the study of cancer since most studies examine cancer cells in isolation. In an ecosystem, individuals live together in a community and interact with the non-living components of their environment [5]. Likewise, in a very similar, systematic environment, a cell competes for resources and cooperates with other cells to produce new ones, in an attempt to survive and proliferate. A dynamic equilibrium is established, known as homeostasis, which in this case is the balance between cell proliferation and death. During tumorigenesis the equilibrium is disrupted as a result of a genetic mutation. This could also occur because of another change in the tissue environment. Normal cells divide, grow and die. Conversely, cancer cells divide uncontrollably and “escape death” and produce malignant tumors [6].

As a parallel, the tumor that manifests is itself a tissue microenvironment, and it can therefore be viewed as an ecosystem comprised of individual cells. These tumor cells compete for available resources, and the ones that survive will have the opportunity to metastasise, for a better chance of survival, to a different tissue. Therefore, Basada and Anderson view cancer as an evolutionary game, characterized by heterogeneity, or a combination of healthy and cancerous cells. This affects the way tumor cells interact with healthy and other tumor cells.

These observations, made possible by applying evolutionary game theory (EGT), have important implications for treatment. In particular, according to EGT, it would be wiser to focus on the interactions between the “player” cells in a tumor rather than focusing on achieving their eradication. This could be of temporary utility until something more substantial can be done. An understanding of the strategy implemented by the “players” could lead to the generation of a treatment which would change the way the “players” interact with each other and their environment. This could be employed to affect their fitness by making them less aggressive. More precisely, a “double blind” approach was used by the researchers. The pronged approach used two treatments. If the cells became resistant to one, they could still be susceptible to the other. [4]. It follows that further investigation of this model is essential. The capability to test multiple treatment combinations rapidly has the potential to be used to model disease at an individual level.

Orlando et al. (2012) have also described cancer treatment as a game, where the oncologist selects the therapy and the cells choose the strategy they will follow. From their study, it was concluded that drug interactions and evolutionary trade-offs are essential tools for understanding the particular cancer type in order to design the appropriate therapy [7]. Moreover, Csikász-Nagy et al. used both game and graph theory to model tumor cell game-like interactions and examined how these can influence tissue topology [8]. This combinatorial approach is also very promising and needs to be further investigated.

To conclude, the pathology of cancer has been a topic of major research interest and investigation for the past few years. Appreciating the molecular mechanisms behind cancer development and progression is key to preventing its occurrence. This fact is also principle when achieving a higher frequency of early diagnosis. Cancer cells are “intelligent” and have found ways to become resistant to chemotherapy treatments. Recent research suggests that cancer is nothing but an evolutionary game, which involves strategic decision-making. At the moment, researchers are trying to mathematically model tumor cells’ behavior by taking into account all of their interactions. This is done in order to produce therapies to weaken and eventually eradicate them. Notwithstanding, this is a major challenge which needs delicate handling. And as Einstein has pointed out, “everything should be made as simple as possible, but not simpler”.

References:
1. Myerson, Roger B. 1991. Game Theory: Analysis of Conflict. Cambridge, MA: Harvard University Press.
2. Trivers, R. L. 1971. The Evolution of Reciprocal Altruism. The Quarterly Review of Biology 46: 35-57.
3. Alberts, B., Bray, D. and Hopkin, K., eds. 2009. Essential Cell Biology. New York: Garland Science.
4. Basanta, D., Gatenby, R. A. and Anderson, A. R. A. 2012. Exploiting evolution to treat drug resistance: Combination therapy and the double bind. Mol Pharm 9: 914-921.
5. Ander, P. 2002. The Context of Ecosystem Theory. Ecosystems 5: 611-613.
6. Collins, K., Jacks, T. and Pavletich, N. P. 1997. The cell cycle and cancer. PNAS 94: 2776-2778.
7. Orlando, P. A., Gatenby, R. A. and Brown, J. S. 2012. Cancer treatment as a game: integrating evolutionary game theory into the optimal control of chemotherapy. Phys. Biol. 9:1-10.
8. Csikasz-Nagy, A., Cavaliere, M. and Sedwards, S. 2012. New Challenges for Cancer Systems Biomedicine. New York: Springer.

Image Credit: http://commons.wikimedia.org/wiki/File:Cancer_cells_(1).jpg?uselang=el

Melina Gyparaki is a second year student at the University of Edinburgh majoring in Molecular Genetics. Follow The Triple Helix Online on Twitter and join us on Facebook.

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  • Roy Niles

    The assumption that these cells react with intelligent strategies is somewhat of a breakthrough from the general view that somehow these biological elements react mechanically in ways that require no more choice than any engine would. But the independent use of strategies requires intelligent selection of optional choices. Which it’s about time we recognized as common to all biological functions, not just to our brains.

  • Roy Niles

    The assumption that these cells react with intelligent strategies is somewhat of a breakthrough from the general view that somehow these biological elements react mechanically in ways that require no more choice than any engine would. But the independent use of strategies requires intelligent selection of optional choices. Which it’s about time we recognized as common to all biological functions, not just to our brains.