Integrative Subtype Discovery in Glioblastoma Using iCluster

Ronglai Shen,Adam B Olshen,Chris Sander,Qianxing Mo,Marc Ladanyi,Jason Huse,Venkatraman E Seshan,Nikolaus Schultz,Vladimir Brusic

doi:10.1371/journal.pone.0035236

Abstract

Large-scale cancer genome projects, such as the Cancer Genome Atlas (TCGA) project, are comprehensive molecular characterization efforts to accelerate our understanding of cancer biology and the discovery of new therapeutic targets. The accumulating wealth of multidimensional data provides a new paradigm for important research problems including cancer subtype discovery. The current standard approach relies on separate clustering analyses followed by manual integration. Results can be highly data type dependent, restricting the ability to discover new insights from multidimensional data. In this study, we present an integrative subtype analysis of the TCGA glioblastoma (GBM) data set. Our analysis revealed new insights through integrated subtype characterization. We found three distinct integrated tumor subtypes. Subtype 1 lacks the classical GBM events of chr 7 gain and chr 10 loss. This subclass is enriched for the G-CIMP phenotype and shows hypermethylation of genes involved in brain development and neuronal differentiation. The tumors in this subclass display a Proneural expression profile. Subtype 2 is characterized by a near complete association with EGFR amplification, overrepresentation of promoter methylation of homeobox and G-protein signaling genes, and a Classical expression profile. Subtype 3 is characterized by NF1 and PTEN alterations and exhibits a Mesenchymal-like expression profile. The data analysis workflow we propose provides a unified and computationally scalable framework to harness the full potential of large-scale integrated cancer genomic data for integrative subtype discovery.

Highlights

Cancer genomes harbor a plethora of somatically acquired aberrations
We present an integrative analysis workflow using iCluster and demonstrate its utility in defining molecular subtypes of glioblastoma multiforme (GBM) by simultaneously clustering genome-wide DNA copy number, methylation, and gene expression data derived from the the Cancer Genome Atlas (TCGA) GBM samples
The most notable feature of the Glioblastoma subtype 1 identified by iCluster is the lack of chr7 gain and chr10 loss, and shows a ‘‘sporadic’’ profile of copy number alterations. This subclass is enriched for the G-CIMP phenotype and shows hypermethylation of genes involved in brain development and neuronal differentiation including DLC1, JAG2, and ALDH1A3 (Supplementary Table S1)

Summary

Introduction

Cancer genomes harbor a plethora of somatically acquired aberrations. DNA copy number aberrations are key characteristics of cancer, contributing to genomic instability and gene deregulation [1,2] such as oncogene activation by gene amplification or tumor suppressor loss as a result of gene deletion. To that end, integrating multiple data types (e.g., copy number and gene expression) can provide key information to pinpoint the genomic alterations that characterize disease subtypes of biological and clinical importance (e.g., HER2 oncogene activation through concordant DNA amplification and mRNA overexpression).

Results

Conclusion