Mathematical Model Applies Game Theory to Advance Cancer Therapy

Strategic Insights -

A new mathematical model developed by a research team led by Paul Newton has the potential to revolutionize cancer treatment by applying principles of game theory to the complex interactions between cancer cells, healthy cells, and the immune system. The model predicts the outcome of these interactions, known as the cancer-immunity cycle, and suggests that synchronizing treatment schedules with the cycle could lead to more effective and less toxic therapies.

Key Takeaways:

  • The model predicts the competition of cancer cells, healthy cells, and immune system cells (T-cells) in the cancer-immunity cycle.
  • The insights from the model have the potential to allow medical practitioners to synchronize treatment schedules based on the battles taking place in the human body.
  • The model is one of the first comprehensive mathematical models to treat the cancer-immunity cycle as a dynamic, game-theoretic system.
  • The research team envisions future clinical protocols that use sparse data collection and statistical inference to approximate the cancer-immunity cycle and adjust therapy in real-time.
  • The model suggests that synchronizing chemotherapy and immunotherapy schedules with the cycle could lead to lower doses with the same or greater positive impact as standard doses.

Statistics:

  • The model predicts the outcome of the cancer-immunity cycle.
  • The research team has developed a mathematical model that incorporates the selection dynamics of competing cell populations.
  • The model has the potential to revolutionize cancer treatment by applying principles of game theory.

Sources:

  • Paul Newton, professor of aerospace and mechanical engineering, mathematics, and quantitative and computational biology at USC Viterbi School of Engineering (as quoted in the original press release).
  • Daniel S. Chen and Ira Mellman's influential paper 'Oncology meets immunology: the cancer-immunity cycle' (cited in the original press release).
  • PNAS (publisher of the paper in which the mathematical model was presented)