Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project

• Type of Material: Article
• Authors: Rivka R Colen, M. D. Anderson Cancer Center; Mark Vangel, Massachussets General Hospital; Jixin Wang, M. D. Anderson Cancer Center; David Andrew Gutman, Emory University; Scott N Hwang, Emory University; Max Wintermark, University of Virginia; Rajan Jain, New York University Medical Center; Manal Jilwan-Nicolas, University of Virginia; James Y Chen, University of California San Diego Health System; Prashant Raghavan, University of Virginia; Chad A Holder, Emory University; Daniel Rubin, Stanford University; Eric Huang, Frederick National Laboratory for Cancer Research; Justin Kirby, Frederick National Laboratory for Cancer Research; John Freymann, Frederick National Laboratory for Cancer Research; Carl C Jaffe, NCI/NIH; Adam Flanders, Thomas Jefferson University Hospital; Pascal O Zinn, Baylor College of Medicine
• Language: English
• Date: 2014
• Publisher: BioMed Central
• Publication Version: Final Published Version
• Copyright Statement: © 2014 Colen et al.; licensee BioMed Central Ltd.
• License: Creative Commons Attribution 2.0 Generic
• Final Published Version (URL): http://www.biomedcentral.com/1755-8794/7/30
• Title of Journal or Parent Work: BMC Medical Genomics
• ISSN: 1755-8794
• Volume: 7
• Issue: 30
• Grant/Funding Information: This work was supported in part by MDACC startup funding (RRC).; This work was supported in part by the John S. Dunn Research Foundation Center for Radiological Sciences (RRC).; The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government (JK and JF).; This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E.
• Keywords: MRI segmentation; Invasion; Imaging genomics; Biomarker; Radiogenomics; Glioblastoma
• Research Categories: Health Sciences, General; Health Sciences, Radiology

Abstract

Background Invasion of tumor cells into adjacent brain parenchyma is a major cause of treatment failure in glioblastoma. Furthermore, invasive tumors are shown to have a different genomic composition and metabolic abnormalities that allow for a more aggressive GBM phenotype and resistance to therapy. We thus seek to identify those genomic abnormalities associated with a highly aggressive and invasive GBM imaging-phenotype. Methods We retrospectively identified 104 treatment-naïve glioblastoma patients from The Cancer Genome Atlas (TCGA) whom had gene expression profiles and corresponding MR imaging available in The Cancer Imaging Archive (TCIA). The standardized VASARI feature-set criteria were used for the qualitative visual assessments of invasion. Patients were assigned to classes based on the presence (Class A) or absence (Class B) of statistically significant invasion parameters to create an invasive imaging signature; imaging genomic analysis was subsequently performed using GenePattern Comparative Marker Selection module (Broad Institute). Results Our results show that patients with a combination of deep white matter tracts and ependymal invasion (Class A) on imaging had a significant decrease in overall survival as compared to patients with absence of such invasive imaging features (Class B) (8.7 versus 18.6 months, p < 0.001). Mitochondrial dysfunction was the top canonical pathway associated with Class A gene expression signature. The MYC oncogene was predicted to be the top activation regulator in Class A. Conclusion We demonstrate that MRI biomarker signatures can identify distinct GBM phenotypes associated with highly significant survival differences and specific molecular pathways. This study identifies mitochondrial dysfunction as the top canonical pathway in a very aggressive GBM phenotype. Thus, imaging-genomic analyses may prove invaluable in detecting novel targetable genomic pathways.