New Brain Tumor Classification Approach Could Redefine Treatment, Diagnosis

A new method for classifying brain tumors more accurately predicts how serious one is, and could lead to improved treatment and diagnosis.

The study, part of The Cancer Genome Atlas, found key molecular differences between numerous forms of gliomas or brain tumors.

Co-author and W.K. Alfred Yung, M.D., of The University of Texas MD Anderson Center, said:

“We found molecular signatures that better define clinical behavior based on our analysis. We hope this will impact how physicians both diagnose and plan therapies for brain cancer.”

Yung, along with Roeland Verhaak, Ph.D., associate professor of Bioinformatics and Computational Biology at MD Anderson, led the investigation that involved more than 300 scientists from 44 different institutions.

“We looked at the six most common forms of glioma and were able to deduce that these can be effectively grouped into three distinct molecular super clusters of lower-grade gliomas,” said Verhaak. “It is exciting that our findings are likely to provide a basis for the upcoming update to the WHO classification of tumors of the central nervous system.”

Each year approximately 23,000 Americans develop a brain tumor, of which 10,000 are gliomas. Out of the 23,000, about 14,000 die from the condition.

Typically, gliomas are treated using radiation therapy, surgery, and chemotherapy. Doctors are not usually able to predict with any confidence how aggressive a glioma will turn out to be.

Utilizing these biomarkers in the diagnosis of such forms of brain tumors, the scientists believe, will lead to a much more consistent manner of diagnosis and patient management.

Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas
The Cancer Genome Atlas Research Network
NEJM June 10, 2015DOI: 10.1056/NEJMoa1402121

Abstract:

“BACKGROUND

Diffuse low-grade and intermediate-grade gliomas (which together make up the lower-grade gliomas, World Health Organization grades II and III) have highly variable clinical behavior that is not adequately predicted on the basis of histologic class. Some are indolent; others quickly progress to glioblastoma. The uncertainty is compounded by interobserver variability in histologic diagnosis. Mutations in IDH, TP53, and ATRX and codeletion of chromosome arms 1p and 19q (1p/19q codeletion) have been implicated as clinically relevant markers of lower-grade gliomas.

METHODS

We performed genomewide analyses of 293 lower-grade gliomas from adults, incorporating exome sequence, DNA copy number, DNA methylation, messenger RNA expression, microRNA expression, and targeted protein expression. These data were integrated and tested for correlation with clinical outcomes.

RESULTS

Unsupervised clustering of mutations and data from RNA, DNA-copy-number, and DNA-methylation platforms uncovered concordant classification of three robust, nonoverlapping, prognostically significant subtypes of lower-grade glioma that were captured more accurately by IDH, 1p/19q, and TP53 status than by histologic class. Patients who had lower-grade gliomas with an IDH mutation and 1p/19q codeletion had the most favorable clinical outcomes. Their gliomas harbored mutations in CIC, FUBP1, NOTCH1, and the TERT promoter. Nearly all lower-grade gliomas with IDH mutations and no 1p/19q codeletion had mutations in TP53 (94%) and ATRX inactivation (86%). The large majority of lower-grade gliomas without an IDH mutation had genomic aberrations and clinical behavior strikingly similar to those found in primary glioblastoma.

CONCLUSIONS

The integration of genomewide data from multiple platforms delineated three molecular classes of lower-grade gliomas that were more concordant with IDH, 1p/19q, and TP53 status than with histologic class. Lower-grade gliomas with an IDH mutation either had 1p/19q codeletion or carried a TP53 mutation. Most lower-grade gliomas without an IDH mutation were molecularly and clinically similar to glioblastoma.”