Two US studies have identified specific genetic mutations in gliomas which correlate with how the tumours will behave and respond to treatment.
'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,' said Dr Daniel Lachance from the Mayo Clinic, Minnesota, who was involved in one of the studies, both of which were published in the New England Journal of Medicine.
Gliomas are tumours which develop from the glial cells of the brain and spine, and make up 80 percent of malignant brain tumours. Patients who develop gliomas are usually treated with a combination of radiotherapy, surgery and chemotherapy; however it is currently difficult to work out how useful these treatments will be.
The studies, one led by the Mayo Clinic and University of California, San Francisco, and the other coordinated by the National Institutes of Health, analysed 1,380 tumours in total. Using previous studies into tumour biology, three mutations were identified in patients with gliomas. Tumours taken from glioma patients were scored as positive or negative for these mutations, which led to the creation of five categories of mutation combinations.
The genetic profiles of the tumours were then associated with patient age, prognosis and the response of the tumour type to different treatments. For example, tumours with one genetic profile were shown to grow slowly, and respond well to drug treatment, therefore patients with this tumour type are good candidates for treatment by chemotherapy only.
A second tumour type was shown to respond poorly to chemotherapy only, so this profile was identified as being best treated with a combination of therapies, involving both radiotherapy and chemotherapy.
This profiling would allow doctors to choose the most appropriate treatment for an individual glioma patient based on their genetic classification. Categorisation could also improve the accuracy of patient prognoses, as survival statistics would be specific to the glioma type, as opposed to the general class of glioma.
Currently histology is used to classify gliomas by their visual characteristics; however this method is not sufficiently effective to predict how the glioma will respond to therapy. Doctors are also often unable to predict how aggressive a tumour will behave over a long period of time.
'These markers will potentially allow us to predict the course of gliomas more accurately, treat them more effectively and identify more clearly what causes them in the first place', said Professor Margaret Wrensch from the University of California, San Francisco and co-author of the study.
Writing in an editorial, Dr David Ellison of St Jude Children's Research Hospital, said: 'Both studies can justifiably claim that molecular classification
captures the biologic features of glioma variants better than does
histopathological evaluation, even though grade remains an independent