Cancer patient in oncology unit
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A team of researchers led by Kim Eunjung, PhD, at the Natural Products Informatics Research Center of the Korea Institute of Science and Technology (KIST) has developed a mathematical model that provides a theoretical foundation to predict tumor evolution that could provide a method of providing more effective adaptive cancer treatments.

One of the challenges of treating cancer is that standard of care therapies typically focus on providing the maximum dose that a patient will tolerate to eliminate as many drug sensitive cancer cells as possible. The problem that arises from this approach, however, is it allows the remaining drug resistant cancer cells to grow more rapidly in the absence of the drug-sensitive cells that have been killed.

But a newer approach—one based on the evolution of a patient’s cancer, also referred to as adaptive therapy—personalizes the treatments with the goal of maintaining a sufficient population of treatment-sensitive cancer cells that serve to control the growth of those that are resistant to therapy. This personalization is usually accomplished by altering dose levels or even taking breaks from the chosen therapy.

While this approach has shown promise, it can be difficult to determine appropriate dosing or treatment breaks due to the fact that cancer is both complex and an ever-evolving disease. Prior mathematical models used to inform adaptive therapy have provided mixed results, largely due to the fact that they overlook acquired resistance and cell plasticity—the ability of cancer cells to alter their phenotype in response to changes in their microenvironment, such as dosage fluctuations or stopping treatments.

But the new work by Eunjung and team has provided a theoretical basis for predicting tumor evolution that takes into account the development of treatment resistance by cancer cells and their ability to alter their phenotypic behavior—or plasticity—during treatment.

According to their research, published in the journal Chaos, Solitons & Fractals, their model has identified the conditions for an effective dose window, a range of doses that will help maintain tumor volume that remain unchanged and stable. The team noted that for some tumors, taking a break from treatment can allow the cells to become sensitive again and join other sensitive cells to inhibit the growth of treatment-resistant cells.

From their findings, the team has proposed an evolutionary therapy dosing regimen which involves giving treatments in cycles that include treatment holidays, minimum effective doses, along with maximum tolerated treatment doses. The model shows that:

  • Pausing treatment allows plastic cancer cells to regain sensitivity;
  • the application of a minimum effective dose controls tumor volume; and
  • a subsequent, maximum tolerated dose administration further reduces tumor size.

“In the current study, we emphasized the role of cancer cells’ phenotypic plasticity in enhancing the controllability of tumor burden with evolutionary treatment cycling doses,” Eunjung noted.

Simulations of the proposed new model, applied to a melanoma patient illustrated it could be an effective tool for adaptive therapy strategy and showed that it can redirect tumor dynamics and maintain the size of the tumor below the tolerable burden level. As such, Eunjung said that it can determine the anticancer effects of treatments based on each individual’s tumor dynamics, pointing to precision cancer treatments tailored to each patient.

The model also has the potential to aid in the design animal experiments and clinical trials for potential natural product-derived anticancer drug candidates, with the goal of establishing dosage regimens that effectively control tumor burden across a range of cancer types.

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