An international collaborative study has revealed detailed new information about diffuse glioma, the most common type of tumor found in some 80 percent of adult brain cancer patients, raising hopes that better understanding of these disease groups may aid improved clinical outcomes.
The study, led by researchers at The University of Texas MD Anderson Cancer Center, Columbia University Medical Center, New York, and the University of Sao Paulo's Ribeirao Preto Medical School, Brazil, analyzed data from 1,122 samples of diffuse glioma from lower to higher grades.
The team's findings were published in the Jan. 28 online issue of Cell. The study is the largest multi-platform analysis to date of adult diffuse glioma, grades II to IV.
Glioma is classified into four groups (oligodendroglioma, olioastrocytoma, astrocytoma and glioblastoma) and graded from grade II to IV. However, the treatment-informing diagnoses vary from physician to physician. The investigators addressed this by comprehensively analyzing molecular profiling data from The Cancer Genome Atlas (TCGA).
"TCGA data allowed us to identify diffuse glioma subgroups with distinct molecular and clinical features and shed light on mechanisms driving disease progression," said Roel Verhaak, Ph.D., associate professor, Bioinformatics and Computational Biology at MD Anderson.
Currently, pathologists determine if a glioma is low-grade or high-grade based on the tumor 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," said Antonio Iavarone, M.D., professor of Neurology and Pathology and Cell biology at Columbia University Medical Center. "By looking at the molecular makeup of these tumors, we now have a much more precise way of predicting which tumors are more likely to grow rapidly and can prescribe treatments accordingly."
"The epigenomic data defined by profiling DNA methylation levels for each of our glioma patient tumor samples, allowed us to determine with accuracy which patient will present the best clinical outcome and the worst," said Houtan Noushmehr, Ph.D., assistant professor of Epigenomics and Bioinformatics at University of Sao Paolo. "By looking at the molecular makeup of these tumors, we now have a much more precise way of predicting which tumors are more likely to grow rapidly and can prescribe treatments accordingly. Our work lays an important foundation to further investigate the mechanisms of epigenetics associated with glioma tumor biology."
Therapy development has been hindered by an incomplete knowledge about glioma classification. The team defined a complete set of genes associated with the patient samples and used molecular profiling to improve disease classification. They were able to identify molecular correlations and provide insight into disease progression from low to high grades.
"This study has expanded our knowledge of the glioma somatic alteration landscape, emphasized the relevance of DNA methylation profiles as a method for clinical classification, and has linked TERT (telomerase reverse transcriptase) pathway alterations to telomere maintenance," said Verhaak. "Combined, these findings are an important step forward in our understanding of glioma as discrete disease subsets, and the mechanism driving glioma formation and progression."
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