A new study suggests that doctors could improve cure rates by changing therapies before a tumor has a chance to recover. Rather than waiting for cancer to return after an initial treatment, the researchers propose switching to another therapy while the tumor is still shrinking.
The strategy is designed to address one of the biggest obstacles in cancer care: drug resistance.
Why Cancer Often Returns
Dr. Robert Noble, Senior Lecturer at the Department of Mathematics, City, St George’s, University of London, led the study.
“Although tumors may at first shrink under therapy,” he explains, “in many cases they eventually regrow. These relapses stem from a small number of cancer cells that have gained mutations making the cells resistant to the treatment.”
Mutations are changes in a cell’s genetic instructions. Some occur by chance as cancer cells divide. If one of those changes allows a cell to survive a drug that kills other tumor cells, that resistant cell can continue multiplying. Over time, its descendants may rebuild the tumor.
Under the standard clinical approach, doctors often continue using a treatment until tests show that the cancer has begun growing again. They may then move to another drug or therapy.
The problem is that waiting for a visible relapse gives surviving cancer cells more time to evolve. By the time doctors introduce a second treatment, some cells may already carry mutations that protect them from that therapy as well.
Switching Treatments Before They Fail
Evolutionary theory points to a different approach.
Instead of waiting until the first treatment stops working, doctors could switch to a second therapy while the tumor is still responding. The researchers describe this as a “kick it while it’s down” strategy.
The idea may be especially useful for cancers in which doctors already know that even the most effective initial treatment frequently fails because of resistance.
Changing treatments early could make it harder for any one resistant group of cancer cells to take over. Each new therapy would create a different challenge for the tumor, potentially limiting its ability to adapt.
As Dr. Noble explains in a podcast about the study, “Evolutionary approaches have been very successful in other contexts, such as combating antibiotic resistance, or predicting what vaccines we should use in a particular flu season. There is every reason to suppose that similar approaches should work in tumors.”
Antibiotic resistance develops through a similar evolutionary process. Bacteria that survive a drug can reproduce and pass their resistance to future generations. Scientists also track the evolution of influenza viruses to help determine which strains should be targeted by seasonal vaccines.
The researchers believe that cancer treatment could benefit from the same kind of evolutionary thinking.
Mathematical Models Track Tumor Evolution
To investigate the idea, Dr. Noble and his colleagues adapted mathematical tools normally used to study how plants and animals evolve under environmental pressures, such as climate change.
In this case, each cancer treatment acts as an environmental pressure. It kills vulnerable cells while allowing cells with useful resistance mutations to survive. Mathematical models can help researchers predict how different treatment schedules may influence which cancer cells remain and how quickly they multiply.
The team’s results suggest that switching treatments before the tumor begins growing again could generally perform better than the current standard of care.
The findings are still based on mathematical modeling, however, so the strategy will require further testing in laboratory experiments and clinical trials involving patients.
Three small clinical trials are already underway in soft-tissue cancer, prostate cancer, and breast cancer. Additional trials are in development.
Multiple Therapies Could Target Larger Tumors
The models also indicate that two treatments may not be enough in many cases.
“Our models predict that this new approach will generally outperform the standard of care,” explains Dr. Noble. “A sequence of two treatments, even if optimally timed, is likely to succeed only in relatively small tumors. But we have reason to hope that switching between three or more treatments, following the same principle, could eliminate larger tumors.”
Using three or more therapies could place cancer cells under a series of changing pressures, making it more difficult for the tumor to produce a population capable of resisting every treatment.
That does not mean the approach will work for every patient or every cancer. Treatment choices would still depend on the tumor type, its size, available therapies, and a patient’s overall health. Researchers would also need to determine the safest and most effective timing for each switch.
Still, the study offers a possible way to rethink cancer therapy. Instead of responding only after a treatment fails, doctors may eventually be able to anticipate resistance and act before the tumor regains strength.
The full research article is published in the journal Genetics.
Dr. Noble conducted the research with an international team of mathematical biologists. The project grew from the final-year work of Srishti Patil, a master’s student at the Indian Institute of Science Education and Research, Pune, who spent several months at City St George’s, University of London under Dr. Noble’s supervision.
The team also included Johns Hopkins University undergraduate Armaan Ahmed and Dr. Noble’s long-term collaborator Dr. Yannick Viossat of Université Paris Dauphine-PSL.