Applying Evolutionary Game Theory to Medicine
As stated in Chapter 7 of Professor Easely’s book “Networks, Crowds, and Markets,” evolutionary game theory shows that the basic ideas of game theory can even be applied to situations in which no individual is overtly reasoning. While the book mainly describes instances of when evolutionary game theory has been used in the context of biology, evolutionary game theory is continuously increasing its role in medicine as well.
As shown in the article “Optimizing Chemotherapy Schedules Using Evolutionary Game Theory” published on University of South California’s Viterbi website, researchers are finding ways to optimize chemotherapy schedules based on the characteristics of cancer cells through computer simulation based on mathematical models. The article describes that Professor Paul Newton and post-doctoral student Jeffrey West developed a tool that is able to predict how cancer cells respond to different chemotherapy schedules as well as predict the best treatment for the specific cancer. The expenses and dangers of the experimental treatments are minimized since the scientists run computer simulations based on evolutionary game theory, rather than clinical trials.
One of the main focuses of their research is “chemotherapeutic resistance by competitive release.” In the beginning of the chemotherapy treatment, patients are given a drug that target all cancer cells which initially shrinks the tumor. However, this shrinkage is unexpectedly followed by tumor regrowth due to tumors being composed of various cancer cells that all compete at different growth rates. The chemotherapy drug targets and eliminates the most prolific cancer cells, leaving the other (drug resilient) cells with less competition and more nutrients giving them the chance to grow and divide and thus increase in size and number. Through traditional cancer treating methods, such as a biopsy or tumor imaging, doctors and researchers are not able to predict which of the cancer cells are resilient to the chemotherapy drug. The mathematical model on the other hand is able to predict the growth of the resilient cancer cell populations.
The model is based on existing data on cancers treated by two popular cancer cell therapies (maximum-tolerated drug doses and low dose metronomic cyclophosphamide) and modeled competition between cells (which was calculated and modeled through evolutionary game theory). The model randomly picks two cells for a competition where the winner is determined based on the cell type growth rate. The participants of the competition are the healthy cells, the targeted cancer cells and the resistant cancer cells. The model hosts millions of competitions in which different populations of cells compete. This results in the model being able to predict how the tumor will grow over time. The scientists plan on using the model to create schedules that continuously change the patient’s treatment as the various cancer cell populations change.
The future of utilizing evolutionary game theory in medicine, specifically in cancer cell treatment, is to have patient specific models available in a clinic and for a medical employee to easily type in the patient’s information and run the mathematical model to predict the patient’s optimal drug schedule. Thus, USC’s article efficiently displays an example of evolutionary game theory being used in the advances of science today. Furthemore, it is a prime example of using theories from mathematics, economy, and biology, and applying them to create a practical innovation.
Sources:
Optimizing chemotherapy schedules using evolutionary game theory
http://www.cs.cornell.edu/home/kleinber/networks-book/