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    Original Research

    Genomic Mapping and Survival Prediction in Glioblastoma: Molecular Subclassification Strengthened by Hemodynamic Imaging Biomarkers

    Published Online:Apr 1 2013https://doi.org/10.1148/radiol.12120846

    Abstract

    Hemodynamic imaging biomarkers (relative cerebral blood volume [rCBV] measures) did not show any significant correlation with the various molecular subclasses by using the two most commonly accepted subclassification schema of glioblastoma but did provide important prognostic information independent of the molecular subclasses, and patients with higher rCBV showed worse prognosis and poor survival.

    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 (rCBVNEL) 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 rCBVmean 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.

    © RSNA, 2012

    References

    • 1 Ohgaki H, Kleihues P. Population-based studies on incidence, survival rates, and genetic alterations in astrocytic and oligodendroglial gliomas. J Neuropathol Exp Neurol 2005;64(6):479–489. Crossref, Medline, Google Scholar
    • 2 Liang Y, Diehn M, Watson N, et al.. Gene expression profiling reveals molecularly and clinically distinct subtypes of glioblastoma multiforme. Proc Natl Acad Sci U S A 2005;102(16):5814–5819. Crossref, Medline, Google Scholar
    • 3 Phillips HS, Kharbanda S, Chen R, et al.. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 2006;9(3):157–173. Crossref, Medline, Google Scholar
    • 4 Verhaak RG, Hoadley KA, Purdom E, et al.. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 2010;17(1):98–110. Crossref, Medline, Google Scholar
    • 5 Aronen HJ, Gazit IE, Louis DN, et al.. Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology 1994;191(1):41–51. Link, Google Scholar
    • 6 Lev MH, Ozsunar Y, Henson JW, et al.. Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas [corrected]. AJNR Am J Neuroradiol 2004;25(2):214–221. Medline, Google Scholar
    • 7 Law M, Oh S, Babb JS, et al.. Low-grade gliomas: dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging—prediction of patient clinical response. Radiology 2006;238(2):658–667. Link, Google Scholar
    • 8 Law M, Young RJ, Babb JS, et al.. Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 2008;247(2):490–498. Link, Google Scholar
    • 9 Bisdas S, Kirkpatrick M, Giglio P, Welsh C, Spampinato MV, Rumboldt Z. Cerebral blood volume measurements by perfusion-weighted MR imaging in gliomas: ready for prime time in predicting short-term outcome and recurrent disease? AJNR Am J Neuroradiol 2009;30(4):681–688. Crossref, Medline, Google Scholar
    • 10 Mills SJ, Patankar TA, Haroon HA, Balériaux D, Swindell R, Jackson A. Do cerebral blood volume and contrast transfer coefficient predict prognosis in human glioma? AJNR Am J Neuroradiol 2006;27(4):853–858. Medline, Google Scholar
    • 11 Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 2008;455(7216):1061–1068. Crossref, Medline, Google Scholar
    • 12 Preprocessed level 2 data, obtained from the TCGA Data Portal. http://tcga-data.nci.nih.gov/tcga/. Accessed August 19, 2010. Google Scholar
    • 13 Cooper LA, Kong J, Gutman DA, et al.. An integrative approach for in silico glioma research. IEEE Trans Biomed Eng 2010;57(10):2617–2621. Crossref, Medline, Google Scholar
    • 14 Cancer Imaging Program, via The Cancer Imaging Archive (TCIA). http://cancerimagingarchive.net. Accessed March 3, 2011. Google Scholar
    • 15 Diehn M, Nardini C, Wang DS, et al.. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A 2008;105(13):5213–5218. Crossref, Medline, Google Scholar
    • 16 Van Meter T, Dumur C, Hafez N, Garrett C, Fillmore H, Broaddus WC. Microarray analysis of MRI-defined tissue samples in glioblastoma reveals differences in regional expression of therapeutic targets. Diagn Mol Pathol 2006;15(4):195–205. Crossref, Medline, Google Scholar
    • 17 Pope WB, Chen JH, Dong J, et al.. Relationship between gene expression and enhancement in glioblastoma multiforme: exploratory DNA microarray analysis. Radiology 2008;249(1):268–277. Link, Google Scholar
    • 18 Barajas RF, Hodgson JG, Chang JS, et al.. Glioblastoma multiforme regional genetic and cellular expression patterns: influence on anatomic and physiologic MR imaging. Radiology 2010;254(2):564–576. Link, Google Scholar
    • 19 Jain R, Poisson L, Narang J, et al.. Correlation of perfusion parameters with genes related to angiogenesis regulation in glioblastoma: a feasibility study. AJNR Am J Neuroradiol 2012;33(7):1343–1348. Crossref, Medline, Google Scholar
    • 20 TCGA-GBM collection from The Cancer Imaging Archive (TCIA). https://wiki.cancerimagingarchive.net/display/Public/TCGA-GBM. Accessed March 3, 2011. Google Scholar
    • 21 Boxerman JL, Schmainda KM, Weisskoff RM. Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 2006;27(4):859–867. Medline, Google Scholar
    • 22 Huse JT, Phillips HS, Brennan CW. Molecular subclassification of diffuse gliomas: seeing order in the chaos. Glia 2011;59(8):1190–1199. Crossref, Medline, Google Scholar
    • 23 Stupp R, Mason WP, van den Bent MJ, et al.. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005;352(10):987–996. Crossref, Medline, Google Scholar
    • 24 Krex D, Klink B, Hartmann C, et al.German Glioma Network. Long-term survival with glioblastoma multiforme. Brain 2007;130(Pt 10):2596–2606. Crossref, Medline, Google Scholar
    • 25 Hirai T, Murakami R, Nakamura H, et al.. Prognostic value of perfusion MR imaging of high-grade astrocytomas: long-term follow-up study. AJNR Am J Neuroradiol 2008;29(8):1505–1510. Crossref, Medline, Google Scholar

    Article History

    Received April 15, 2012; revision requested June 19; revision received July 18; accepted August 2; final version accepted September 7.
    Published online: Apr 2013
    Published in print: Apr 2013