Abstract
Abstract Abstract: Translating the molecular signatures from classification strategies into robust diagnostic assays is essential for cancer therapy guidance and prognosis determination. To develop a clinically applicable molecular stratification assay for Glioblastoma multiforme (GBM) with significant prognostic value, we designed a novel classification system based on isoform-level gene expression profiles; an avenue unexplored for diagnostic and prognostic use. Using isoform-level expression clustering of The Cancer Genome Atlas (TCGA) samples, we identified four GBM subgroups with significant (p=0.0103) survival differences. A four-class classifier, built with 121 transcript-variants, assigns GBM patients' molecular subtype with 92% accuracy. The classifier was translated to a high-throughput RT-qPCR assay and validated on an independent cohort of 206 glioblastoma samples. We found that proneural patients have the worst prognosis except for younger patients (<40 years), while a better prognosis is observed for neural group among older patients (≥40 years). The isoform-level classifier, transformed from a high-dimensional platform to a low-dimensional RT-qPCR platform, provides a quantitative and reproducible stratification of GBM patients with prognostic significance, a requirement towards personalized medicine. The resulting diagnostic assay from the classifier has immediate clinical implications towards prioritizing patients for standard care versus aggressive therapy regimen. Citation Format: Yingtao Bi, Sharmistha Pal, Luke Macyszyn, Donald O'Rourke, Ramana Davuluri. Translation of isoform-level gene signature into robust diagnostic assay for glioblastoma subtyping. [abstract]. In: Proceedings of the Third AACR International Conference on Frontiers in Basic Cancer Research; Sep 18-22, 2013; National Harbor, MD. Philadelphia (PA): AACR; Cancer Res 2013;73(19 Suppl):Abstract nr C06.
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