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Medulloblastoma tumors are the most common type of childhood brain cancer. Each year, about 600 children are diagnosed with the disease. Today, all children with medulloblastoma over age three receive standardized high doses of radiation and chemotherapy. The good news is that this approach produces a cure in about half of the cases. That number reaches up to 80% for those tumor types identified as having a good prognosis. The bad news is that the intellectual costs of irradiating the brain of a child are high. Many of these children do not live independently as adults because of the cognitive side effects from treatment.

Unfortunately, clinicians do not have a reliable way of predicting which patients are more likely to relapse. If they did, they may someday be able to tailor treatment on a more individualized basis.

With this week’s that researchers from the Ó³»­´«Ã½, Children’s Hospital Boston and Dana-Farber Cancer Institute have developed a model to predict whether a patient with a medulloblastoma brain tumor is likely to relapse, the field of oncology is one large conceptual step closer to being able to offer tumor-specific treatment.

The Ó³»­´«Ã½ team and colleagues found that each of the six major subtypes of medulloblastoma identified by their molecular profiles have widely different prognoses. Further, they have developed a to predict whether a patient is likely to relapse based on a tumor’s molecular signature. Today, oncologists rely on imprecise clinical and pathological findings to make a prognosis. Thought not yet 100% accurate, the researchers’ new mathematical model outperforms this approach.

This may mean better identification of children with high-risk tumors that might benefit from the aggressive treatment. In addition, for those tumor types with just a 20% chance of cure, perhaps it may lead clinicians to investigate and apply different, more targeted approaches.

These findings are now being applied to the design of medulloblastoma clinical studies. The Children’s Oncology Group, the world’s largest pediatric cancer clinical trial organization, is incorporating subtype information into upcoming trials.

In the long-term, the team hopes this research can be implemented to create an increasingly practical, real-time testing approach that oncologists can readily use to make therapeutic decisions.