Researchers suggest new glioma classification
Volume 4, Issue 4, 2015
Molecular makeup of brain tumors can be used to sort patients into five categories.
Daniel H. Lachance, M.D.
Robert B. Jenkins, M.D., Ph.D.
Jeanette E. Eckel Passow, Ph.D.
The molecular makeup of brain tumors can be used to sort patients with gliomas into five categories, each with different clinical features and outcomes, say researchers in the Mayo Clinic Cancer Center and the University of California San Francisco.
These findings could change the methods that physicians rely on to determine prognosis and treatment options for gliomas.
Gliomas are tumors that arise from the glial cells of the brain and spine, and are among the most difficult forms of cancer to treat.
For a significant number of glioma cases, the standard classification uses histology to classify tumors according to their visible characteristics. This approach doesn't accurately predict the tumor's subsequent behavior, potential for response to therapy and long-term prognosis.
A study highlighting this research was published in June 2015 in the New England Journal of Medicine.
"Our findings are going to weigh heavily on the future classification of brain tumors. The time of classifying these tumors solely according to histology as astrocytoma, oligodendroglioma or mixed oligoastrocytoma could be a thing of the past," said co-author Daniel H. Lachance, M.D., a neuro-oncologist in the Mayo Clinic Cancer Center. "This molecular data helps us better classify glioma patients, so we can begin to understand who needs to be treated more aggressively and who might be able to avoid unnecessary therapies."
The new approach categorizes gliomas according to the presence of three genetic alterations: 1p/19q codeletion, IDH mutation and TERT mutation. The first two are already checked routinely in clinical practice, so a test that incorporates all three tumor markers could soon be available.
In this study, researchers explored whether these three tumor markers could be used to define molecular groups that better inform glioma treatment.
"Using molecular data enables us to develop a better picture of what is going in a patient," said lead author Jeanette E. Eckel Passow, Ph.D., an associate professor of biostatistics at Mayo Clinic. "When we analyzed patient outcomes adjusting for molecular group, histological type was no longer associated with outcome — instead, it was dictated by the molecular group. Having more meaningful classifications can have a huge impact on patients; it opens up all kinds of treatment options."