New research suggests doctors may be able to improve cure rates by changing treatments before tumors recover. Rather than waiting for the cancer to come back after the first treatment, researchers suggest switching to another treatment while the tumor is still shrinking.
This strategy is designed to address one of the biggest obstacles in cancer treatment: drug resistance.
Reasons why cancer is likely to recur
Dr Robert Noble, a senior lecturer in the School of Mathematics at St George’s, University of London, led the research.
“Tumors may initially shrink with treatment, but they often regrow eventually. These recurrences are because a small number of cancer cells acquire mutations that make them resistant to treatment,” he explains.
A mutation is a change in a cell’s genetic instructions. Some occur by chance when cancer cells divide. If one of these changes allows the cell to survive a drug that kills other tumor cells, the resistant cell can continue to proliferate. Over time, its descendants can rebuild the tumor.
In the standard clinical approach, doctors often continue treatment until tests show that the cancer has started to grow again. You may then move on to another drug or treatment.
The problem is that waiting for visible recurrence increases the time it takes for surviving cancer cells to evolve. By the time doctors introduce a second treatment, some cells may already have mutations that protect them from that treatment.
Switch treatments before they fail
Evolutionary theory presents a different approach.
Instead of waiting until the first treatment stops working, doctors can switch to the second treatment while the tumor is still responding. Researchers describe this as a “kick while you’re falling” strategy.
This idea could be particularly useful in cancers where doctors already know that even the most effective initial treatments often fail due to resistance.
Changing treatments early can make it harder for certain groups of resistant cancer cells to take over. Each new treatment poses different challenges to tumors and may limit their ability to adapt.
As Dr. Noble explains this research in a podcast, “Evolutionary approaches have been very successful in other situations, such as combating antibiotic resistance and predicting which vaccine to use in a given flu season. There’s good reason to think a similar approach should work in tumors.”
Antibiotic resistance also develops through a similar evolutionary process. Bacteria that survive the drug can reproduce and pass on their resistance to future generations. Scientists are also tracking the evolution of influenza viruses to determine which strains should be targeted for seasonal vaccines.
Researchers believe cancer treatment could benefit from similar evolutionary thinking.
Tracking tumor evolution with mathematical models
To explore this idea, Dr. Noble and his colleagues adapted mathematical tools typically 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 will affect which cancer cells will remain, how long they will survive, and how quickly they will grow.
The researchers’ results suggest that switching treatments before tumors start growing again may generally be better than the current standard of care.
However, the findings are still based on mathematical modeling, and the strategy requires further validation in laboratory experiments and clinical trials on patients.
Three small clinical trials are already underway in soft tissue cancer, prostate cancer, and breast cancer. Additional tests are also in development.
Multiple treatments may target larger tumors
This model also shows that two treatments may not be sufficient in many cases.
“Our model predicts that this new approach will generally outperform standard treatment,” explains Dr. Noble. “Two consecutive treatments, even if timed optimally, are likely to be successful only in relatively small tumors. However, there is reason to be hopeful that switching between three or more treatments following the same principles may remove larger tumors.”
Using more than two treatments can expose cancer cells to a series of changing pressures, making it more difficult for tumors to generate populations that can resist all treatments.
That doesn’t mean this approach will work for all patients or all cancers. The choice of treatment depends on the type of tumor, its size, available treatments, and the patient’s overall health. Researchers also need to determine the safest and most effective timing for each switch.
Still, this study offers a potential way to rethink cancer treatment. Rather than reacting only after treatments fail, doctors may eventually be able to predict resistance and act before tumors recover.
The full text of the research paper will be published in the journal genetics.
Dr. Noble conducted the research in collaboration with an international team of mathematical biologists. The project evolved from the final year of research by Srishti Patil, a master’s student at the Indian Institute of Science Education and Research in Pune, who spent several months at City St George’s, University of London, under the supervision of Dr Noble.
The team also included Johns Hopkins undergraduate student Armaan Ahmed and Dr. Noble’s longtime collaborator, Dr. Yannick Viosatte of Paris-Dauphine PSL.
