Innovative Approach Predicts Resistance to Bowel Cancer Treatments, Aiding Development of Smarter Drugs

Scientists have pioneered a novel tool capable of forecasting how bowel cancer adapts to various treatments. This advancement offers a promising path toward designing personalized therapies that effectively combat resistance, thus enabling patients to maintain better health outcomes for extended periods.

A collaborative effort between the Institute of Cancer Research in London and Queen Mary University of London resulted in the creation of this cutting-edge technology. By harnessing principles from evolutionary biology, researchers measure and predict how cancer cells evolve when exposed to chemotherapy. Bowel cancer, the fourth most common type in the UK—with approximately 44,100 new cases annually, equating to around 120 diagnoses daily—typically receives treatment through chemotherapy, a method that has remained largely unchanged for nearly five decades.

A significant challenge in treating late-stage bowel cancer is drug resistance, where cancer cells change molecularly to evade the effects of chemotherapy. Understanding the precise mechanisms behind this resistance could revolutionize treatment strategies, allowing for the development of new targeted drugs and optimizing existing treatment protocols through dose adjustments.

Until now, it has been difficult to distinguish between two main routes by which cancer cells develop resistance. A recent study published in Nature Communications detailed how researchers tracked the evolution of resistant bowel cancer cells using mathematical modeling to determine the timing and nature of resistance development. They identified whether resistance was due to rare genetic mutations or non-genetic changes.

Building on these insights, the team developed EIRAs (Evolutionary Informed Resistance Assays), a tool that integrates into the drug development process. This technology aims to tailor treatments to the specific resistance pathways of individual tumors, potentially leading to more effective, personalized therapies. The team is actively seeking commercial partners and Collaborating to advance this technology, which has already shown promise in research involving ovarian and breast cancers.

Dr. Trevor Graham, leader of the research team, emphasized the importance of this work: "Much like bacteria develop antibiotic resistance, cancer cells also evolve resistance to chemotherapy. Our machine learning models can reveal how and when these changes happen, opening opportunities to create drugs that target these adaptations and prolong the effectiveness of treatments."

Supporting this vision, Professor Kristian Helin highlighted the potential for this technology to uncover new targets for combating resistance, integrating ideas from machine learning, cancer evolution, and drug discovery to enhance treatment durability. Similarly, Professor Richard Nichols noted that framing this problem through an evolutionary lens demonstrates the innovative cross-disciplinary approach the research embodies.

This breakthrough signifies a vital step toward more effective management of bowel cancer, by preempting resistance and improving patient survival and quality of life.