Dataset Information


Molecular-Based Recursive Partitioning Analysis Model for Glioblastoma in the Temozolomide Era: A Correlative Analysis Based on NRG Oncology RTOG 0525.

ABSTRACT: Importance:There is a need for a more refined, molecularly based classification model for glioblastoma (GBM) in the temozolomide era. Objective:To refine the existing clinically based recursive partitioning analysis (RPA) model by incorporating molecular variables. Design, Setting, and Participants:NRG Oncology RTOG 0525 specimens (n?=?452) were analyzed for protein biomarkers representing key pathways in GBM by a quantitative molecular microscopy-based approach with semiquantitative immunohistochemical validation. Prognostic significance of each protein was examined by single-marker and multimarker Cox regression analyses. To reclassify the prognostic risk groups, significant protein biomarkers on single-marker analysis were incorporated into an RPA model consisting of the same clinical variables (age, Karnofsky Performance Status, extent of resection, and neurologic function) as the existing RTOG RPA. The new RPA model (NRG-GBM-RPA) was confirmed using traditional immunohistochemistry in an independent data set (n?=?176). Main Outcomes and Measures:Overall survival (OS). Results:In 452 specimens, MGMT (hazard ratio [HR], 1.81; 95% CI, 1.37-2.39; P?


PROVIDER: S-EPMC5464982 | BioStudies | 2017-01-01

REPOSITORIES: biostudies

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