This new classifier is more efficient and replicable in a laboratory setting than existing diagnostic tools, and can provide more accurate predictions for survival and how glioblastoma patients may respond to different treatments.
“Current tests can help classify tumor types to a lesser degree. This new classifying system improves both the diagnostic accuracy and the efficiency of the testing process,” said Donald O’Rourke, MD, associate professor of Neurosurgery with Penn’s Abramson Cancer Center and director of the Penn Brain Tumor Tissue Bank. “The more detailed information we have about the tumor, at a molecular level, the better we can target new immunotherapies and other treatments for our patients with glioblastoma.”
Penn Medicine’s Center for Personalized Diagnostics (CPD) currently analyzes all brain tumors to determine the best treatment approach for a given tumor type. This new approach would be complementary to the work of the CPD on brain tumor specimens and enhance the overall effort of molecular sub typing of GBM tumors.
This new isoform-based classifier, which looks at variations within cellular RNA, improves prediction accuracy and requires half the variables for the analysis than the genetic-based analysis. The isoform classifier glioblastoma tumor noted the correct subtype with 92 percent accuracy, according to the study, published in Nucleic Acids Research.
The study was completed in collaboration with Ramana Davuluri, PhD, formerly at The Wistar Institute and now at Northwestern University and colleagues. For more details on the study, please see the Wistar Institute press release.
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