Article

Integrative Subtype Discovery in Glioblastoma Using iCluster

Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
PLoS ONE (Impact Factor: 3.23). 04/2012; 7(4):e35236. DOI: 10.1371/journal.pone.0035236
Source: PubMed

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.

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    • "Therefore, new diagnostic and therapeutic strategies for tumor recurrence might be required to improve clinical outcomes of patients. Previously, numerous genomic profiling studies have addressed the marked heterogeneity of glioblastomas6789. Particularly, The Cancer Genome Atlas (TCGA) project recognized four distinct molecular subtypes of proneural, neural, classical, and mesenchymal, which are different in response to aggressive therapies[10,11]. "
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    • "Recent work on genome wide profiling with help of the cancer genome atlas (TCGA) [18] database, using various parameters like copy number analysis, miRNA and mRNA analysis, mutational and methylation analysis, have all led to generation of GBM tumor subtype specific network profiles [19] [20] [21]. These sub-types are classical , mesenchymal, neural, and pro-neural. "
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