Genomic mapping and survival prediction in glioblastoma: Molecular subclassification strengthened by hemodynamic imaging biomarkers

Rajan Jain*, Laila Poisson, Jayant Narang, David Gutman, Lisa Scarpace, Scott N. Hwang, Chad Holder, Max Wintermark, Rivka R. Colen, Justin Kirby, John Freymann, Daniel J. Brat, Carl Jaffe, Tom Mikkelsen

*Corresponding author for this work

Research output: Contribution to journalArticle

95 Scopus citations

Abstract

Purpose: To correlate tumor blood volume, measured by using dynamic susceptibility contrast material-enhanced T2*-weighted magnetic resonance (MR) perfusion studies, with patient survival and determine its association with molecular subclasses of glioblastoma (GBM). Materials and Methods: This HIPAA-compliant retrospective study was approved by institutional review board. Fifty patients underwent dynamic susceptibility contrast-enhanced T2*-weighted MR perfusion studies and had gene expression data available from the Cancer Genome Atlas. Relative cerebral blood volume (rCBV) (maximum rCBV [rCBVmax] and mean rCBV [rCBVmean]) of the contrast-enhanced lesion as well as rCBV of the nonenhanced lesion (rCBV NEL) were measured. Patients were subclassified according to the Verhaak and Phillips classification schemas, which are based on similarity to defined genomic expression signature. We correlated rCBV measures with the molecular subclasses as well as with patient overall survival by using Cox regression analysis. Results: No statistically significant differences were noted for rCBVmax, rCBVmean of contrast-enhanced lesion or rCBVNEL between the four Verhaak classes or the three Phillips classes. However, increased rCBV measures are associated with poor overall survival in GBM. The rCBVmax (P =.0131) is the strongest predictor of overall survival regardless of potential confounders or molecular classification. Interestingly, including the Verhaak molecular GBM classification in the survival model clarifies the association of rCBV mean with patient overall survival (hazard ratio: 1.46, P = .0212) compared with rCBVmean alone (hazard ratio: 1.25, P = .1918). Phillips subclasses are not predictive of overall survival nor do they affect the predictive ability of rCBV measures on overall survival. Conclusion: The rCBVmax measurements could be used to predict patient overall survival independent of the molecular subclasses of GBM; however, Verhaak classifiers provided additional information, suggesting that molecular markers could be used in combination with hemodynamic imaging biomarkers in the future.

Original languageEnglish (US)
Pages (from-to)212-220
Number of pages9
JournalRadiology
Volume267
Issue number1
DOIs
StatePublished - Apr 2013

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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    Jain, R., Poisson, L., Narang, J., Gutman, D., Scarpace, L., Hwang, S. N., Holder, C., Wintermark, M., Colen, R. R., Kirby, J., Freymann, J., Brat, D. J., Jaffe, C., & Mikkelsen, T. (2013). Genomic mapping and survival prediction in glioblastoma: Molecular subclassification strengthened by hemodynamic imaging biomarkers. Radiology, 267(1), 212-220. https://doi.org/10.1148/radiol.12120846