AI Breakthrough: Predicting Bowel Cancer Patients' Response to NHS Drug

Scientists at London's Institute of Cancer Research and the RCSI University of Medicine and Health Sciences in Dublin have announced a new AI-driven approach to identify how patients with advanced bowel cancer will respond to bevacizumab, a drug recently introduced by the NHS.

The method uses PhenMap, an AI tool that integrates complex data on the genetic makeup of tumors, allowing researchers to track patterns of how different patients react to the drug. This development aims to spare potentially thousands of patients from being given drugs that would be ineffective in fighting their cancers.

In the UK alone, nearly 10,000 cases of advanced bowel cancer are identified every year, with young adults seeing a particular rise in diagnoses. Bowel cancer has the second-highest mortality rate of any cancer, behind only lung cancer. While survival rates can be as high as 98% when caught early, the five-year survival rate for advanced bowel cancer can be as low as 10%.

The study tracked 117 European bowel cancer patients who had been treated with chemotherapy and bevacizumab. Researchers identified a group of patients who all had the same gene mutation and were at a high risk of having negative reactions. The scientists behind the tests now hope to expand the number of patient samples and see if the results can be used in treatments for other types of cancer.

Anguraj Sadanandam, a professor in stratification and precision medicine at the ICR, said: “Once bowel cancer spreads to other parts of the body, there are very few treatment options available for patients. It is therefore positive that patients can now access the targeted drug bevacizumab on the NHS. However, we know that the majority of patients won’t benefit from the drug, meaning thousands of people in England could be facing unpleasant side effects unnecessarily.”

Sadanandam added that while the findings were encouraging, the tool would need to be tested on a larger cohort to be validated. “In future, I hope this approach will lead to a test that can be used by clinicians, to ensure patients receive personalised care that has the highest chance of working against their cancer.”