Diffuse gliomas represent 80% of all malignant brain tumours. Adult diffuse gliomas are classified and graded according to histological criteria. However, although histopathology classification is well established it suffers from unstandardized, and sometimes confusing, intra- and inter-observer variability particularly among grade II-III tumours. Now, a study from researchers led by The University of Texas MD Anderson has revealed detailed new information about diffuse glioma raising hopes that better understanding of these disease groups may aid improved clinical outcomes. The team state that their study is the largest multi-platform analysis to date of graded adult diffuse glioma and lays an important foundation to further investigate the mechanisms of epigenetics associated with glioma tumour biology. The opensource study is published in the journal Cell.
Previous studies show that glioma is classified into four groups, namely oligodendroglioma, olioastrocytoma, astrocytoma and glioblastoma; and graded from grade II to IV. However, it has been shown that the treatment-informing diagnoses varies from physician to physician. Currently, pathologists determine if a glioma is low-grade or high-grade based on the tumour tissue’s appearance under the microscope. While this approach is generally good at distinguishing between gliomas that are clearly very aggressive and those that are relatively slow-growing, it misses the mark in a significant percentage of cases leading to inappropriate treatment. The current study mapped the molecular makeup of these tumours, to provide a much more precise way of predicting which tumours are more likely to grow rapidly.
The current study comprehensively analyzed molecular profiling data from The Cancer Genome Atlas, specifically, 1,122 samples of diffuse glioma from lower to higher grades. Results show that profiling DNA methylation levels for each of the glioma patient tumour samples, determined with accuracy which patient will present the best and worst clinical outcome. Data findings show that looking at the molecular makeup of these tumours provides a more precise way of predicting which tumours are more likely to grow rapidly and can prescribe treatments accordingly.
The group explain that therapy development has been hindered by an incomplete knowledge about glioma classification. They go on to add that to counteract this they have defined a complete set of genes associated with the patient samples and used molecular profiling to improve disease classification. The lab conclude that they were able to identify molecular correlations and provide insight into disease progression from low to high grades.
The team surmise that their data has expanded knowledge of the glioma alteration landscape, emphasizing the relevance of DNA methylation profiles as a method for clinical classification. For the future, the researchers state that these findings are an important step forward in understanding of glioma as discrete disease subsets, and the mechanism driving glioma formation and progression.