Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized By Abnormalities in PDGFRA, IDH1, EGFR, and NF1

Citation

Verhaak, R. G., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., … & Cancer Genome Atlas Research Network. (2010). Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer cell, 17(1), 98-110.

Introduction

Summary

The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM). We describe a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes and integrate multidimensional genomic data to establish patterns of somatic mutations and DNA copy number. Aberrations and gene expression of EGFRNF1, and PDGFRA/IDH1 each define the Classical, Mesenchymal, and Proneural subtypes, respectively. Gene signatures of normal brain cell types show a strong relationship between subtypes and different neural lineages. Additionally, response to aggressive therapy differs by subtype, with the greatest benefit in the Classical subtype and no benefit in the Proneural subtype. We provide a framework that unifies transcriptomic and genomic dimensions for GBM molecular stratification with important implications for future studies.


Read more from SRI

  • An arid, rural Nevada landscape

    Can AI help us find valuable minerals?

    SRI’s machine learning-based geospatial analytics platform, already adopted by the USGS, is poised to make waves in the mining industry.

  • Two students in a computer lab

    Building a lab-to-market pipeline for education

    The SRI-led LEARN Network demonstrates how we can get the best evidence-based educational programs to classrooms and students.

  • Code reflected in a man's eyeglasses

    LLM risks from A to Z

    A new paper from SRI and Brazil’s Instituto Eldorado delivers a comprehensive update on the security risks to large language models.