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

Rivka R Colen1*, Mark Vangel2, Jixin Wang1, David A Gutman3, Scott N Hwang3, Max Wintermark4, Rajan Jain5, Manal Jilwan-Nicolas4, James Y Chen67, Prashant Raghavan4, Chad A Holder3, Daniel Rubin8, Eric Huang9, Justin Kirby9, John Freymann9, Carl C Jaffe10, Adam Flanders11, TCGA Glioma Phenotype Research Group and Pascal O Zinn12*

Author Affiliations

1 Department of Diagnostic Radiology, M. D. Anderson Cancer Center, 1400 Pressler St; Unit 1482, Rm # FCT 16.5037, Houston, TX 77030, USA

2 Massachussets General Hospital, Boston, MA, USA

3 Department of Radiology, Emory University, Atlanta, GA, USA

4 University of Virginia, Charlottesville, VA, USA

5 Department of Radiology, New York University Medical Center, New York, NY, USA

6 University of California San Diego Health System, San Diego, CA, USA

7 Department of Radiology, San Diego Medical Center, San Diego, CA, USA

8 Stanford University, Stanford, CA, USA

9 Clinical Monitoring Research Program, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA

10 NCI/NIH, Rockville, MD, USA

11 Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA, USA

12 Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA

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BMC Medical Genomics 2014, 7:30  doi:10.1186/1755-8794-7-30

Published: 2 June 2014



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.


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).


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.


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.

Radiogenomics; MRI segmentation; Glioblastoma; Imaging genomics; Invasion; Biomarker