Wednesday , August 4 2021

The mathematical model reveals how cell turnover determines the success of adaptive therapy

One of the most challenging issues in cancer therapy is the development of drug resistance and the subsequent progression of the disease.

In the new article featured on the cover of this month on Cancer researchResearchers at the Moffit Cancer Center, in collaboration with Oxford University, report the results of their study using mathematical modeling to show that cell turnover affects drug resistance and is an important factor in the success of adaptive therapy.

Cancer treatment options have increased significantly in the last few decades; however, many patients eventually develop drug resistance. Physicians tend to overcome resistance either by trying to target cancer cells through an alternative approach or by targeting the resistance mechanism itself, but success with these approaches is often limited, as additional mechanisms of resistance may emerge.

Researchers at the Moffit Department of Integrated Mathematical Oncology and the Center for Excellence in Evolutionary Therapy believe that resistance may develop in part because of the high doses of drugs commonly used during treatment.

Patients are usually given the maximum tolerated dose of therapy that kills as many cancer cells as possible with the fewest side effects.

However, according to evolutionary theories, this approach to the maximum tolerated dose may lead to drug resistance, due to the presence of drug-resistant cells before treatment begins.

Once sensitive cells are killed by anticancer therapies, these drug-resistant cells are given the freedom to divide and multiply. Moffit researchers believe that an alternative treatment strategy called adaptive therapy may be a better approach to killing cancer cells and minimizing the development of drug resistance.

“Adaptive therapy aims not to eradicate the tumor but to control it. The therapy is used to reduce the burden on the tumor to a tolerable level but is subsequently modulated or withdrawn to maintain a pool of drug-sensitive cancer cells.” said Alexander Anderson, Ph.D. D., President of the Integrated Department of Mathematical Oncology and Founding Director of the Center for Excellence in Evolutionary Therapy.

Previous laboratory studies have shown that adaptive therapy can prolong the progression of cancer for several different types of tumor, including ovarian, breast, and melanoma. Additionally, clinical trials in patients with prostate cancer at Moffit showed that compared with standard treatment, adaptive therapy increased cancer progression time by approximately 10 months and reduced cumulative drug use by 53%.

Despite these encouraging results, it is unclear which types of tumors will respond best to adaptive therapy in the clinic. Recent studies have shown that the success of adaptive therapy depends on a variety of factors, including levels of spatial limitation, the appropriateness of the population of resistant cells, the initial number of resistant cells, and the mechanisms of resistance. However, it is not clear how the cost of resistance factors in the tumor response to adaptive therapy.

The cost of resistance refers to the idea that cells that become resistant have an advantage over fitness over non-resistant cells when the drug is present, but this can have a cost, such as a slower growth rate. However, drug resistance is not always price related and it is not clear whether resistance cost is necessary for the success of adaptive therapy.

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

The researchers found that tumors with higher cell densities and those with lower levels of pre-existing resistance improved better under adaptive therapy.

They also showed that cell turnover is a key factor influencing the cost of resistance and the results of adaptive therapy by increasing competition between sensitive and resistant cells. To do this, they used phased techniques that provide a visual way to dissect the dynamics of mathematical models.

“I’m a very visual person and I think the phase planes make it easier to gain a model intuition. You do not have to manipulate equations, which makes them great for communicating with experimental and clinical collaborators. We are honored that Cancer research “chose our collage of phase planes to cover them, and I hope this will help make mathematical oncology accessible to more people,” said Maximilian Stroble, lead author of the study and PhD candidate at Oxford University.

To confirm their models, the researchers analyzed data from 67 prostate cancer patients who were treated with intermittent therapy, a precursor to adaptive therapy.

We find that although our model is constructed as a conceptual tool, it can recapitulate the individual patient dynamics of most patients and can describe patients who are continuously responsive as well as those who eventually relapse.

Dr. Alexander Anderson, President of the Department of Integrated Mathematical Oncology and Founding Director of the Center for Excellence in Evolutionary Therapy, Cancer Center of H. Lee Lee Moffitt and Research Institute

Although more studies are needed to understand how adaptive therapies can benefit patients, the researchers hope that their data will lead to better indicators of which tumors respond to adaptive therapy.

“With a better understanding of tumor growth, resistance costs and turnover rates, adaptive therapy can be more carefully tailored to the patients who benefit most from it and, more importantly, to highlight which patients may benefit from the approaches to more “drugs,” Anderson said.


H. Cancer Center Lee Lee Moffitt and Research Institute

Newspaper reference:

Strobe, Mars, etc. (2021) Turnover modulates the need for cost resistance in adaptive therapy. Cancer research.

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