Researchers use mathematical modeling to establish components that decide adaptive remedy success

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Researchers use mathematical modeling to identify factors that determine adaptive therapy success

Microscopic image of prostate cancer adenocarcinoma (the most common form of prostate cancer) Photo credit: Wikipedia

One of the most difficult problems in cancer therapy is drug resistance development and the subsequent progression of the disease. In a new article published on the cover of Cancer Research this month, researchers at the Moffitt Cancer Center, in collaboration with Oxford University, report results from their study, which uses mathematical models to show that cell turnover affects drug resistance and is a major factor for the success of is adaptive therapy.

Cancer treatment options have increased significantly over the past few decades. However, many patients eventually develop drug resistance. Doctors strive to overcome resistance by either trying to fight cancer cells through an alternative approach or the resistance mechanism itself. However, the success with these approaches is often limited as additional mechanisms of resistance can arise.

Researchers at Moffitt's Department of Integrated Mathematical Oncology and Center of Excellence in Evolutionary Therapy believe that partial resistance can develop due to the high doses of drugs commonly used during treatment. Patients are typically given a maximum tolerated dose of therapy that kills as many cancer cells as possible with the fewest side effects. However, according to evolutionary theories, this maximally tolerated dose approach could result in drug resistance because drug-resistant cells are present before treatment even begins. Once sensitive cells are killed by cancer therapies, these drug-resistant cells can divide and multiply. Moffitt researchers believe that an alternative treatment strategy called adaptive therapy could be a better approach to killing cancer cells and minimizing drug resistance development.

"Adaptive therapy does not aim to eradicate the tumor, but to control it. The therapy is used to reduce the tumor burden to a tolerable level, but is then modulated or withdrawn to maintain a pool of drug-sensitive cancer cells," said Alexander Anderson, Ph. D., chairman of the Department of Integrated Mathematical Oncology and founding director of the Center of Excellence for Evolutionary Therapy.

Previous laboratory studies have shown that adaptive therapy can increase the time to cancer progression for several tumor types, including ovary, breast, and melanoma. Additionally, a clinical study in prostate cancer patients in Moffitt showed that adaptive therapy increased the time to cancer progression by approximately 10 months and reduced cumulative drug use by 53% compared to standard treatment.

Despite these encouraging results, it is unclear which tumor types will respond best to adaptive therapy in the clinic. Recent studies have shown that the success of adaptive therapy depends on several factors, including the degree of spatial restriction, the fitness of the resistant cell population, the initial number of resistant cells, and the mechanisms of resistance. However, it is unclear how the cost of resistance affects a tumor's response to adaptive therapy.

The cost of resistance relates to the idea that cells that become resistant have a fitness advantage over non-resistant cells when a drug is present, but this can come at a cost, such as a slower growth rate. However, drug resistance does not always come at a cost, and it is unclear whether resistance costs are required for adaptive therapy to be successful.

Moffitt's research team used mathematical models to determine how the cost of resistance was related to adaptive therapy. They modeled the growth of drug-sensitive and resistant cell populations under both continuous and adaptive therapy conditions and compared their time with disease progression in the presence and absence of resistance costs.

The researchers showed that tumors with higher cell density and those with lower pre-existing resistance performed better under adaptive therapy conditions. They also showed that cell turnover is a key factor affecting the cost of resistance and the outcomes of adaptive therapy by increasing competition between sensitive and resistance cells. To do this, they used phase level techniques, which provide a visual way to analyze the dynamics of mathematical models.

“I'm a very visual person, and I find that phase levels make it easy to get an intuition on a model. You don't have to manipulate equations, which makes them great for communicating with experimental and clinical staff. We are honored to have Cancer Research has selected our collage of phased planes for their coverage and it hopes this will help bring mathematical oncology to more people, "said Maximilian Strobl, lead study author and PhD student at Oxford University.

To validate their models, the researchers analyzed data from 67 prostate cancer patients undergoing intermittent therapy, a predecessor to adaptive therapy.

"We find that our model, while designed as a conceptual tool, can recapitulate individual patient dynamics for the majority of patients and describe patients who are continuously responding as well as patients who eventually relapse," said Anderson.

While more studies are needed to understand how adaptive therapies can benefit patients, researchers are confident that their data will lead to better indicators of which tumors are responding to adaptive therapy.

"With a better understanding of tumor growth, costs of resistance, and turnover rates, adaptive therapy can be more carefully tailored to patients who can benefit most, and most importantly, which patients can benefit from multi-drug approaches," he said .

The mathematical model predicts patient outcomes for adaptive therapy

More information:
Maximilian A.R. Strobl et al., Revenue Modulates the Need for Resistance Costs in Adaptive Therapy, Cancer Research (2020). DOI: 10.1158 / 0008-5472.CAN-20-0806

Provided by
H. Lee Moffitt Cancer Center & Research Institute

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