A Genome-Wide Screen Reveals Functional Gene Clusters in the Cancer Genome and Identifies EphA2 as a Mitogen in Glioblastoma

Fenghua Liu,Xiuli Jiang,Arnab Chakravarti,Meredith Thomas,Lynda Chin,Rona S Carroll,Peter M Black,Elizabeth Maher,Ronald A Depinho,Isaac Kohane,Cameron Brennan,Laura Durso,Mark D Johnson,Peter J Park,Amanda Brosius,Yi Yu,Weil Lai

doi:10.1158/0008-5472.can-06-1408

Abstract

A novel genome-wide screen that combines patient outcome analysis with array comparative genomic hybridization and mRNA expression profiling was developed to identify genes with copy number alterations, aberrant mRNA expression, and relevance to survival in glioblastoma. The method led to the discovery of physical gene clusters within the cancer genome with boundaries defined by physical proximity, correlated mRNA expression patterns, and survival relatedness. These boundaries delineate a novel genomic interval called the functional common region (FCR). Many FCRs contained genes of high biological relevance to cancer and were used to pinpoint functionally significant DNA alterations that were too small or infrequent to be reliably identified using standard algorithms. One such FCR contained the EphA2 receptor tyrosine kinase. Validation experiments showed that EphA2 mRNA overexpression correlated inversely with patient survival in a panel of 21 glioblastomas, and ligand-mediated EphA2 receptor activation increased glioblastoma proliferation and tumor growth via a mitogen-activated protein kinase-dependent pathway. This novel genome-wide approach greatly expanded the list of target genes in glioblastoma and represents a powerful new strategy to identify the upstream determinants of tumor phenotype in a range of human cancers.

Highlights

Glioblastoma is one of the most malignant of all brain tumors, with a median patient survival of 12 to 14 months [1]
Unsupervised hierarchical clustering illustrated the marked distinction between the mRNA expression profiles of the glioblastoma and non-tumor brain samples (Supplementary Fig. S1)
Based upon previous studies showing correlations between DNA copy number and regional expression changes, we sought to assess in our data set whether the genes within survival clusters had an even greater correlation among their mRNA expression patterns than grouped genes that were not within these clusters

Summary

Introduction

Glioblastoma is one of the most malignant of all brain tumors, with a median patient survival of 12 to 14 months [1]. Expression profiling combined with clinical survival data have been widely used to identify changes in mRNA expression in glioblastoma and other human cancers [2,3,4]. Hundreds of genes with altered expression may be identified in such analyses, and it is often difficult to determine from these analyses alone which of the changes in mRNA expression are upstream, initiating determinants of tumor phenotype and which ones represent secondary changes or even ‘‘bystander effects’’ with no essential role in determining the neoplastic behavior of tumor cells. In an attempt to identify the upstream genetic determinants of tumor phenotype, several investigators have combined microarraybased comparative genomic hybridization (aCGH) studies of tumor DNA with mRNA expression profiling in human cancers [4,5,6,7,8,9,10,11]. The likely presence of random DNA alterations with little functional significance within the cancer genome and the adoption of somewhat arbitrary criteria for determining which DNA abnormalities are ‘‘significant’’ often result in an imprecise prediction of functionally relevant DNA alterations

Methods

Results

Conclusion