Imaging, Diagnosis, Prognosis A 4-Gene Signature Associated with Clinical Outcome in High-Grade Gliomas Marie de Tayrac 1,2 , Marc Aubry 1,2,3 , Stephan Saïkali 4 , Amandine Etcheverry 1,2,3 , Cyrille Surbled 1 , Fr ed erique Gu enot 3 , Marie-Dominique Galibert 1,2 , Abderrahmane Hamlat 5 , Thierry Lesimple 6 , V eronique Quillien 7 , Philippe Menei 8 , and Jean Mosser 1,2,3 Abstract Purpose: Gene expression studies provide molecular insights improving the classification of patients with high-grade gliomas. We have developed a risk estimation strategy based on a combined analysis of gene expression data to search for robust biomarkers associated with outcome in these tumors. Experimental Design: We performed a meta-analysis using 3 publicly available malignant gliomas microarray data sets (267 patients) to define the genes related to both glioma malignancy and patient outcome. These biomarkers were used to construct a risk-score equation based on a Cox proportional hazards model on a subset of 144 patients. External validations were performed on microarray data (59 patients) and on RT-qPCR data (194 patients). The risk-score model performances (discrimination and calibration) were evaluated and compared with that of clinical risk factors, MGMT promoter methylation status, and IDH1 mutational status. Results: This interstudy cross-validation approach allowed the identification of a 4-gene signature highly correlated to survival (CHAF1B, PDLIM4, EDNRB, and HJURP), from which an optimal survival model was built (P < 0.001 in training and validation sets). Multivariate analysis showed that the 4-gene risk score was strongly and independently associated with survival (hazard ratio ¼ 0.46; 95% CI, 0.26– 0.81; P ¼ 0.007). Performance estimations indicated that this score added beyond standard clinical parameters and beyond both the MGMT methylation status and the IDH1 mutational status in terms of discrimination (C statistics, 0.827 versus 0.835; P < 0.001). Conclusion: The 4-gene signature provides an independent risk score strongly associated with outcome of patients with high-grade gliomas. Clin Cancer Res; 17(2); 317–27. Ó2011 AACR. Introduction High-grade gliomas (HGG) are brain tumors associated with high morbidity and mortality. They are classified as either grade III or grade IV on the basis of histopathologic features established by the World Health Organization (WHO) (1). In combination with other clinical parameters, the grade has long provided important prognostic informa- tion (2). Recently, molecular biomarkers have been shown to be strongly associated with the prognostic of these tumors. O(6)-methylguanine-DNA-methyltransferase (MGMT) pro- moter hypo-methylation is involved in glioblastoma resis- tance to temozolomide chemotherapy (3) and mutations of the isocitrate dehydrogenase 1 (IDH1) gene are associated with better outcome of patients (4). Recent studies have demonstrated that molecular and genetic analysis of gliomas could help in their classification and in the design of treatment protocols (5, 6). Microarray expression profiling has characterized molecular subtypes of brain tumors associated with tumor grade, progression, and prognosis (6–11), though only a few genes have been consistently identified (12). To overcome such a lack of reproducibility, the best approach is to analyze multiple data set simultaneously to combine the results from rele- vant studies. Such analysis applied to microarray data has been shown to be a powerful tool to identify candidate biomarkers and biological pathways (13). The 2 most comprehensive glioma microarray classifica- tions schemes published to date (6, 9) are based on unsupervised analysis, and they clearly show a strong association between the tumor grading and the defined Authors' Affiliations: 1 CNRS UMR 6061 Genetic and Development, University of Rennes 1, Rennes, France; 2 Medical Genomics Unit, Mole- cular Genetics and Genomics, University Hospital Rennes, France; 3 Bio- genouest Transcriptome Platform, University of Rennes 1, Rennes, France; 4 Departments of Pathology and 5 Neurosurgery, University Hos- pital Rennes, France; 6 Clinical Research Unit, Department of Medical Oncology and 7 Department of Clinical Biology, Eug ene Marquis Cancer Institute, Rennes, France; and 8 Department of Neurosurgery, University Hospital Angers, France Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/). Marie de Tayrac and Marc Aubry contributed equally to the work. Corresponding Author: Jean Mosser, CNRS UMR 6061 Genetic and Development, University of Rennes 1, 2, av du Pr Léon Bernard, Rennes 35000, France. Phone: 33 2 23 23 44 91, Fax: þ33 2 23 23 44 78; E-mail: jean.mosser@univ-rennes1.fr. doi: 10.1158/1078-0432.CCR-10-1126 Ó2011 American Association for Cancer Research. Clinical Cancer Research www.aacrjournals.org 317 Research. on June 12, 2020. © 2011 American Association for Cancer clincancerres.aacrjournals.org Downloaded from Published OnlineFirst January 11, 2011; DOI: 10.1158/1078-0432.CCR-10-1126