Mathematics

New study suggests cancer can be treated as a game between the treating physician and cancer cells

Researchers argue that cancer treatment protocols have the potential to benefit from game-theoretic models. Their work could help oncologists improve outcomes in patients with metastatic cancer.

Metastasis, the spread of cancer cells to different organs from a primary tumour, is a major cause of death from cancer. When this happens, malignant cells are usually resistant to the original chemotherapy used to keep the tumour in check. Partially supported by the EU-funded FourCmodelling project, a team of researchers challenged the standard of treatment for metastatic cancers in which drugs are typically administered continuously at the maximum tolerated dose until the tumour progresses.

The researchers published their study recently in the ‘JAMA Oncology’ journal. They said: “We investigate cancer treatment as a game theoretic contest between the physician’s therapy and the cancer cells’ resistance strategies.” The team demonstrated that the former has two big advantages over its tumour opponents. First, the physician can play rationally, whereas tumour cells cannot. “Cancer cells, like all evolving organisms, can only adapt to current conditions; they can neither anticipate nor evolve adaptations for treatments that the physician has not yet applied.” In addition, the physician has the advantage of always playing first, so the game involved has “distinctive leader-follower (or “Stackelberg”) dynamics; the “leader” oncologist plays first and the “follower” cancer cells then respond and adapt to therapy.”

Current treatment protocols for metastatic cancer typically exploit neither of these asymmetries, according to the researchers. “By repeatedly administering the same drug(s) until disease progression, the physician “plays” a fixed strategy even as the opposing cancer cells continuously evolve successful adaptive responses. Furthermore, by changing treatment only when the tumor progresses, the physician cedes leadership to the cancer cells and treatment failure becomes nearly inevitable.” To tackle this, oncologists should develop flexible strategic treatment plans, the researchers argued in the journal article.

They concluded: “Physicians can exploit the advantages inherent in the asymmetries of the cancer treatment game, and likely improve outcomes, by adopting more dynamic treatment protocols that integrate eco-evolutionary dynamics and modulate therapy accordingly. Implementing this approach will require new metrics of tumor response that incorporate both ecological (ie, size) and evolutionary (ie, molecular mechanisms of resistance and relative size of resistant population) changes.”

New game-theoretic strategies

As quoted in a press release by the Moffitt Cancer Center, journal article co-author Dr Joel Brown said: “We can and must anticipate, steer and exploit the cancer cells’ evolutionary responses to our therapies.”

Katerina Stankova , another author of the paper and assistant professor in game theory at Maastricht University, explained that the full dynamics of Stackelberg games have not yet been rigorously explored. “As we develop the mathematics in conjunction with cancer therapies, we expect that our analyses will uncover novel game-theoretic, evolutionary strategies that may increase the probability of curing even aggressive and heterogeneous cancers.”

The ongoing FourCmodelling (Conflict, Competition, Cooperation and Complexity: Using Evolutionary Game Theory to model realistic populations) project was set up to develop game-theoretical models that are both general and focused on specific real population scenarios. These “incorporate population structure and within population interactions which are both complex in character,” as stated on the project website. One of the four complementary subprojects within FourCmodelling “will model cancer as a complex adaptive system, where a population of tumour, normal and immune cells evolve within a human ecosystem.”

For more information, please see:
FourCmodelling project website

last modification: 2018-09-28 17:15:01



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