Study finds that switching between drug-resistant and drug-sensitive states can alter treatment outcomes in unexpected ways
Cancer remains a major health issue in India, with the Indian Council of Medical Research (ICMR) reporting over 1.46 million new cases in 2022, expected to rise to 1.57 million this year. With 100 out of every 100,000 people diagnosed and over 60% of cases found at advanced stages, treatment options are limited, and survival rates are low. The disease also brings heavy emotional and financial burdens to families. To tackle this growing problem, researchers at the Indian Institute of Science (IISc), Bengaluru, have developed a new mathematical model to study how cancer cells behave during treatment, offering fresh hope for better therapies, particularly through an approach called adaptive therapy.
The IISc team from the Department of Bioengineering, working with experts from the United States, created a mathematical model to understand how cancer cells grow, compete, and respond to treatments. Published in Mathematical Biosciences, the model focuses on a new treatment strategy called adaptive therapy, which differs from traditional methods like high-dose chemotherapy that aim to kill all cancer cells. Those methods often fail because wiping out drug-sensitive cells leaves resistant ones to grow unchecked, a problem called competitive release—leading to cancer returning. Adaptive therapy, instead, adjusts drug doses over time to keep some drug-sensitive cells alive, letting them compete with resistant cells to slow tumour growth. The IISc model studies how this competition works, offering new insights into making treatments last longer.
How Cancer Cells Change
Cancer cells can switch between being sensitive to drugs and resistant, a trait called phenotypic plasticity, which makes treatments harder. Many models assume cells stay fixed—sensitive cells always sensitive, resistant cells always resistant—but real tumours change constantly. The IISc team built their model to include these changes, helping them understand how they affect treatments like adaptive therapy and traditional constant-dose therapy.
What the IISc Model Found
The IISc model, tested with two drug types—cytotoxic drugs that kill cells and cytostatic drugs that stop cell growth—revealed key findings. Cell changes often make cytotoxic drugs less effective, as resistant cells return quickly when treatment pauses. But cytostatic drugs worked better, especially when given steadily. In tumours with roughly equal sensitive and resistant cells, common in late-stage cancers, adaptive therapy outperformed constant dosing. Delaying treatment or letting tumours grow too much reduced success for both methods, showing early detection is critical. The model also showed that tumours with frequent cell changes grow more slowly during adaptive therapy, which could help doctors monitor and adjust treatments.
The IISc model’s findings are especially important for India, where late diagnoses and limited access to tailored treatments are common challenges. “Our model helps us understand how cancer cell changes affect treatment, so we can choose better drugs and dosing plans,” said an IISc researcher. For tumours that change a lot, steady cytostatic dosing may work best, while adaptive therapy could help more for late-stage or mixed-cell tumours. By connecting science to real-world care, the model offers practical ideas for improving outcomes for Indian patients.
With India’s cancer cases expected to double by 2040, new tools like the IISc model are vital. Unlike simpler models that ignore cell changes, this model includes cell competition and switching, giving a clearer picture of how treatments work. While based on simulations, the next step is to test these findings in lab and hospital studies. If confirmed, the model could guide doctors to plan and adjust treatments for each patient, making care more personalized. The IISc team’s work, blending math and biology, marks a key advance in the fight to outsmart cancer’s constant changes.
