Image Credit: IPAM UCLA Mathematics of Cancer Workshop, with speaker Professor Paul Newton
When it comes to matters of life and death, could human survival depend on our mastery of game strategy?
For biologists, evolutionary game theory is a way of studying how different traits or behaviors evolve in a population over time, impacting the probability of a population’s survival in relation to the strategies present in other competing populations. This creates a constantly shifting balance – comparable to a dynamic and ever-changing version of rock-paper-scissors – in which strategy is a matter of timing and knowing your opponent.
A research team led by Paul Newton, professor of aerospace and mechanical engineering, mathematics, and quantitative and computational biology at USC Viterbi School of Engineering, has published a new paper in PNAS demonstrating how principles of game theory can be applied to advance cancer therapy.
The authors of the paper have developed a mathematical model that taps into the dynamics of the cancer-immunity cycle, predicting the competition of cancer cells, healthy cells, and immune system cells (T-cells). The insights developed from the model have the potential to allow medical practitioners to effectively “game” the cycle – synchronizing treatment schedules based on the battles taking place in the human body.
“You can think of a tumor as an ecosystem consisting of cancer cells competing with healthy cells,” said Newton. “Chemotherapy and immunotherapy are essentially attempts to steer the evolution of the tumor in a beneficial way. But that’s not how oncologists have typically framed approaches to treatment.”
The three players in the cancer-immunity cycle evolutionary game: Cancer cells, Healthy cells, and T cells
The new paradigm proposed by the paper will seem shocking to some – after all, we’re used to thinking in terms of eliminating cancer cells, not trying to get the tumor on our side. The trouble is, the elimination method rarely works; the cells that are most sensitive to the chemotherapy will be killed off, while those that have developed resistance via mutations will survive. Those resistant cells regrow, leading to cancer recurrence.
“The motivation of our model is to develop an evolutionary game theory model which incorporates this selection dynamics of these competing cell populations,” said Newton. “Of course, we want to reduce the size of the tumor by killing some of the sensitive cells – but if you kill all of them, then the resistant cells are going to take over.”
As war games go, this is among the more complex. The immune system is an ally that requires careful management, as T cell populations start to attack the cancer cells and shape the rise and decline of different subpopulations of cells. In Newton’s framework, cancer cells act as defectors in a population dynamics game, while healthy cells act as cooperators, and the immune system serves as a dynamic regulator that modulates the rules of the game through feedback.
“Our thinking about the cyclical process of how cancer cells interact with T cells was influenced by Daniel S. Chen and Ira Mellman’s influential paper ‘Oncology meets immunology: the cancer-immunity cycle,’” Newton explained. “We set out to ask a series of important questions that build upon this foundation. What are the benefits of synchronizing chemotherapy and immunotherapy schedules with the cycle, as predicted by mathematical modelling? And could this strategic timing of treatment enable lower doses with the same – or greater – positive impact as standard doses?”
The work conducted by Newton’s team represents one of the first comprehensive mathematical models to treat the cancer-immunity cycle as a dynamic, game-theoretic system. If validated in patient populations, the findings could reshape how oncologists schedule combination therapies – not just based on standard cycles or tolerance, but on personalized biological rhythms.
Measuring the exact period of a patient’s cancer-immunity cycle remains a challenge. But advances in real-time immune-monitoring – via circulating tumor DNA, immune profiling, or imaging – may soon make it possible. Newton’s team envisions future clinical protocols that use sparse data collection and statistical inference to approximate the cycle and adjust therapy in real time. “We’re not just fighting cancer we’re negotiating with it,” said Newton. “And timing is everything.”
Published on August 12th, 2025
Last updated on August 12th, 2025