Viewing Cancer Treatment as a Game to Find Strategies That Improve Patient Outcomes

Game theory-wise treatment plans to advance the standard of care include  men with advanced prostate cancer

August 9. 2018. Game theory can be used to identify potential flaws in current cancer treatment approaches and to select new strategies to improve outcomes in patients with metastatic cancer, according to a review study published online today, August 9, in JAMA Oncology.

Authored by a mathematician, an evolutionary biologist, and clinical physicians from the Moffitt Cancer Center and Maastricht University in the Netherlands, the study challenges decades-old methods. The usual method of treating metastatic cancer involves repeatedly administering the same drug(s) until disease progression. The drugs are given according to Maximum Tolerated Dose (MTD), i.e. the highest dose of a drug or treatment that does not cause intolerable side effects. The maximum tolerated dose is decided via clinical trials by testing increasing doses on different groups of people until the highest dose with acceptable side effects is found.

“Current treatments for metastatic cancers, by giving the same drug repeatedly at the maximum tolerated dose, can inadvertently increase the speed with which cancer cells can evolve effective counter measures and then regrow,” said Robert A. Gatenby, M.D., co-director of Moffitt’s Center of Excellence in Evolutionary Therapy, one of the leaders of this new line of research.

“Today, therapy is usually changed only when the tumor progresses.  By using this strategy the physician cedes control to the cancer,” Gatenby said. “Although standard practice for decades, administering drugs at maximum-tolerated dose until progression is rarely the optimal game theoretic strategy for metastatic cancers.”

“The current maximum-tolerated dose approach will only be successful if the cancer cell population is made up of similar cells that are unable to adapt and evolve quickly,” said Joel Brown, Ph.D., an evolutionary biologist at Moffitt. “That is rarely what we see for cancers that have widely metastasized.  We can and must anticipate, steer and exploit the cancer cells’ evolutionary responses to our therapies.”

 “Implicit in conventional treatment strategies,” the authors write, ” . . . is that maximum benefit to the patient requires maximum tumor cell killing. In metastatic, incurable clinical settings, this strategy is intuitively appealing. Yet, it may be evolutionarily unwise.”

The JAMA review article includes examples from prostate cancer. “As shown in [these figures] maximum cell killing is an optimal strategy only if no cancer cells are capable of evolving a successful resistance to the applied therapy. However, if 1 or more cancer subpopulations are resistant a priori or capable of evolving adaptations quickly (i.e,. before the treatment kills them), this strategy will fail.

Figure 3 in JAMA article.

Adaptive Strategies for Metastatic Castration-Sensitive Prostate Cancer
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Two different methods of treating advanced prostate cancer in 2 different patients, A and B. For each patient, top row shows computer prediction with continuous androgen deprivation therapy (ADT) versus (lower row) intermittent ADT. B shows continuous ADT versus ADT alternating with abiraterone and drug holidays. See JAMA article for more details and more  prostate cancer examples.

So these specialists suggest that by viewing cancer therapy as a game played against the cancer cells and by exploiting knowledge of the specific cancer’s evolutionary dynamics, an oncologist can continuously adjust drugs and doses to delay or prevent cancer progression caused by the evolution of resistance.
Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. In a small trial, 10 of 11 patients maintained stable oscillations of tumor burdens; median Time to Progression at least 27 months with reduced cumulative drug use of 47% of standard dosing. The outcomes show significant improvement over published studies and [clinic patients treated with standard therapy]. See Illustrations.
With each adjustment, the oncologist updates information on the cancer’s response. On this view, the physician has two big advantages over tumor opponents. First, the physician is rational, while cancer cells are not. By understanding principles of evolution, the physician can plan ahead and anticipate the tumor cells’ response.  Cancers, like all evolving organisms, can never anticipate the future and because of this are particularly vulnerable to changes in treatment.

Further, the physician has the advantage of always “playing” first – the cancer cells cannot begin to evolve resistance until a therapy has been administered.  As the article puts it, they cannot “evolve adaptations for treatments that the physician has not yet applied.”

Ideally, an experienced medical oncologist with a full range of prescribing resources can play hard on behalf of each individual patient, continuously adjusting treatment and forcing the cancer cells to constantly change their response to unpredictable attacks from new drugs or combinations of drugs.

A first step is to set the precise goal of treatment. Is it to cure the patient or is it to prolong life? This allows the physician to better balance a therapy’s benefits against its potential toxicity and effects on the patient’s quality of life. What will be done if some of the cancer cells are resistant to therapy? If the goal is to cure, then resistant cells must be killed or prevented. If the goal is control, then physicians can use evolutionary principles to minimize the proliferation of resistant cells while limiting the toxicity of treatment.

Up against the relative stupidity of the disease, the physician can continuously analyze dynamics inside the tumor cell population based on the tumor response during each treatment cycle. With the aid of a mathematical model, this can provide information to improve the outcomes in subsequent cycles – a well-recognized approach in recursive games, termed Bellman’s Principle.

All this points toward an upgrading of “precision medicine” for metastatic cancers. In addition to using molecular techniques to identify treatment targets, precision medicine should integrate strategies to deal with the evolution of resistance which almost invariably leads to failure of even highly successful targeted therapies. The health team should design an explicit Resistance Management Plan (RMP) for each patient. And outcomes of every patient should be analyzed too, in the fashion of After Action Reports compiled by emergency response teams. By learning from each individual patient, personalized oncology can itself evolve and improve over time.

While this approach is designed for incurable metastatic cancers, Katerina Stankova, Ph.D., a mathematician at Maastricht University and an expert on game theory, notes 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,” Stankova added.

Sources & Links

Optimizing Cancer Treatment Using Game Theory:  A Review
JAMA Oncology, August 9, 2018 (full article, free).

Kateřina Staňková, PhD; Joel S. Brown, PhD; William S. Dalton, MD, PhD; et al

The study was supported by the European Union’s Horizon 2020 research and innovation program, the James S. McDonnell Foundation grant, a V Foundation grant and the National Institutes of Health/National Cancer Institute.

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