Researchers have discovered new differences in neuroblastoma, a type of cancer that mainly affects children. This could help doctors give more accurate predictions about the disease and develop better treatments.
A study done by scientists from the University of Southampton and China looked at over 1,500 samples from neuroblastoma patients. They found three different types of neuroblastoma, each with unique genetic features and different chances of recovery. This discovery could lead to more personalised treatments for each patient.
Identifying an overactive gene
The study, published in the British Journal of Cancer, focuses on a form of neuroblastoma that doesn’t involve a certain gene (called MYCN) being overactive. MYCN is important because in some cases, when it’s too active, it makes the cancer more dangerous. The research team looked for patterns in these cases to learn more about why some patients do better than others.
Dr. Yihua Wang, one of the researchers, explained that the study found three main groups of non-MYCN neuroblastomas, each with different chances of survival and responses to treatment.
Developing tailored drugs
The first group, which makes up half of these cases, has the best chance of survival—more than 85% of patients live longer than five years after being diagnosed. The second group, which represents about a quarter of cases, has a much lower survival rate of around 50%. This group has similar genetic features to the more aggressive form of the disease that involves MYCN. The researchers found that a protein called Aurora Kinase A (AURKA) was more active in this group, and this could be used to guide treatment with specific drugs.
The third group also makes up a quarter of cases and has higher activity in immune cells. This suggests that these patients might respond better to treatments that boost the immune system, like immunotherapy.
Dr. Wang said, “This research could help us treat neuroblastoma more effectively by tailoring treatments based on the type of cancer. We now have a better idea of how to predict which treatments might work best for each patient.”