A more precise determination of when to switch therapies can improve the effectiveness of cancer treatment by stopping tumor growth before resistant cells gain an advantage.

Optimizing the Timing of Therapy Changes

Research shows that switching to a different type of therapy before the tumor recovers after initial treatment can improve cure rates. Instead of waiting for a relapse after the first therapy, it is suggested to change treatments while the tumor is still shrinking. This approach targets one of the main challenges in oncology— the development of drug resistance.

Causes of Cancer Recurrence

Recurrences often occur because a small number of cancer cells acquire mutations that make them resistant to therapy. These mutations arise randomly during cell division. If a mutation allows a cell to survive a drug that kills other tumor cells, that cell can continue to multiply, eventually leading to tumor regrowth.

Standard and Alternative Approaches

In traditional clinical practice, treatment continues until tests show the tumor is growing again, at which point a different therapy is prescribed. However, waiting for a visible relapse gives resistant cells more time to develop. By the time therapy is switched, some cells may already be resistant to the new treatment as well.

An alternative approach, based on evolutionary theory, suggests switching therapies before the first one stops working— that is, while the tumor is still responding to treatment. This method is known as the "hit while the tumor is weak" strategy. It may be especially useful for cancer types where even effective initial treatment often does not lead to a complete cure due to resistance.

Applying Evolutionary Principles

Evolutionary approaches have been successfully used to combat antibiotic resistance and to predict virus strains for seasonal vaccines. Similar methods could be beneficial in oncology. Bacteria that survive antibiotic exposure pass resistance on to future generations. Scientists also track the evolution of flu viruses to determine vaccine composition.

Mathematical Modeling

To study this idea, mathematical models commonly used to analyze the evolution of plants and animals under environmental pressures were adapted. In this context, each cancer treatment is viewed as an environmental factor that destroys vulnerable cells and leaves resistant ones. These models help predict how different therapy regimens affect the survival and reproduction of cancer cells.

Modeling results indicate that switching therapies before tumor regrowth can be more effective than the standard approach. However, these conclusions are based on theoretical calculations and require confirmation in laboratory and clinical studies.

Prospects and Limitations

Clinical trials have already begun for soft tissue sarcomas, prostate cancer, and breast cancer. Additional studies are underway. Models also show that two types of therapy may not be enough for large tumors. Using three or more treatments in sequence may make it harder for resistant cell populations to form.

Nevertheless, this approach is not universal and may not be suitable for all patients or cancer types. The choice of therapy depends on the type and size of the tumor, available treatment options, and the patient’s overall health. It is also important to determine the safest and most effective timing for each therapy switch.

Conclusion

This research offers a new perspective on cancer treatment: instead of reacting to therapy failure, doctors in the future may be able to anticipate the emergence of resistance and act before the tumor recovers. The full scientific article is published in the journal Genetics.