When patients are diagnosed with glioblastoma, the most common form of brain cancer, they often have only months to live. Even though researchers’ knowledge about this tumor’s biology and genomics has expanded in recent years, no significant treatment strategies have been developed during the past 25 years. Now there is preliminary but strong evidence that the appearance of these tumors in magnetic resonance images (MRIs) can be used to predict their genomic profiles. Researchers hope that MRI will soon be used for dividing glioblastomas into genomic subtypes, in turn allowing doctors to put patients on the best drug before a biopsy is even taken.
At the genetic level, two patients with glioblastoma (or any particular cancer type) may have very different tumors. For example, one patient might respond well to a therapy targeting tumor blood-vessel growth, while the other patient’s tumor might have activated a genetic program that allows it to resist such a therapy. Right now, it’s difficult for doctors to tell these patients apart and to predict responses to many other targeted therapies, so all glioblastoma patients are given the same treatments. Pathologists can perform gene-expression studies on biopsies, but these tests are expensive and not in wide use; MRI scans are standard.
“By tying imaging features to specific biology, we hope to give insights into treatment targets and patient prognosis,” says Michael Kuo, a radiologist at the University of California, San Diego, who led the study connecting MRI scans with glioblastoma genetics. In work described in today’s issue of the Proceedings of the National Academy of Sciences, Kuo’s group identified five visually discernable kinds of tumors strongly associated with particular gene-expression profiles that are tied to targeted therapies. The paper also describes for the first time a particularly aggressive subtype of the disease.
According to the paper, Kuo and his collaborators defined a set of traits present in MRI scans of 22 glioblastoma tumors. “Normally, when a radiologist looks at a tumor, he’s focused on diagnosis: is this a primary tumor, a metastasis, or an infection?” explains Kuo. The San Diego researchers defined a longer list of characteristics, including morphology and the interaction of the tumors with the surrounding tissues. “The premise here is, there is a lot more information in the images than is currently accounted for,” says Kuo.
Then the researchers looked for connections between the ten types of tumors shown in the MRIs and the activity of seven genetic programs by studying the patients’ biopsies using microarrays. These genetic programs included groups of genes associated with blood-vessel growth, cell proliferation, and other characteristic aspects of cancer biology, all of which are targeted by existing drugs. The team also looked for associations between tumor appearance and overexpression of one gene in particular, coding for epidermal growth factor receptor, a cell receptor that’s overactive in many glioblastomas.