Identifying the mesenchymal molecular subtype of glioblastoma using quantitative volumetric analysis of anatomic magnetic resonance images.

BACKGROUND Subtypes of glioblastoma multiforme (GBM) based on genetic and molecular alterations are thought to cause alterations in anatomic MRI owing to downstream biological changes, such as edema production, blood-brain barrier breakdown, and necrosis. The purpose of the current study was to identify a potential relationship between imaging features and the mesenchymal (MES) GBM subtype, which has the worst patient prognosis. METHODS MRIs from 46 patients with histologically confirmed GBM were retrospectively analyzed. The volume of contrast enhancement, regions of central necrosis, and hyperintensity of T2/fluid attenuated inversion recovery (FLAIR) were measured. Additionally, the ratio of T2/FLAIR hyperintense volume to the volume of contrast enhancement and necrosis was calculated. RESULTS The volume of contrast enhancement, volume of central necrosis, combined volume of contrast enhancement and central necrosis, and the ratio of T2/FLAIR to contrast enhancement and necrosis were significantly different in MES compared with non-MES GBM (Mann-Whitney, P < .05). Receiver-operator characteristics indicated that these 4 metrics were all significant predictors of the MES phenotype. The volume ratio of T2 hyperintensity to contrast enhancement and central necrosis was significantly lower in MES vs non-MES GBM (P < .0001), was a significant predictor of the MES phenotype (area under the curve = 0.93, P < .001), and could be used to stratify short- and long-term overall survival (log-rank, P = .0064 using cutoff of 3.0). These trends were also present when excluding isocitrate dehydrogenase 1 mutant tumors and incorporating covariates such as age and KPS score. CONCLUSIONS Results suggest that volume ratio may be a simple, cost-effective, and noninvasive biomarker for quickly identifying MES GBM.

[1]  Ferenc A. Jolesz,et al.  Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme , 2011, PloS one.

[2]  K. Aldape,et al.  Identification of noninvasive imaging surrogates for brain tumor gene-expression modules , 2008, Proceedings of the National Academy of Sciences.

[3]  M. Kuo,et al.  Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. , 2007, Journal of vascular and interventional radiology : JVIR.

[4]  Thomas D. Wu,et al.  Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. , 2006, Cancer cell.

[5]  S. Gabriel,et al.  Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. , 2010, Cancer cell.

[6]  J. Minna,et al.  Radiogenomics predicting tumor responses to radiotherapy in lung cancer. , 2010, Seminars in radiation oncology.

[7]  H. Saya,et al.  Germline p53 gene mutations in subsets of glioma patients. , 1994, Journal of the National Cancer Institute.

[8]  Pascal O. Zinn,et al.  A Novel Volume-Age-KPS (VAK) Glioblastoma Classification Identifies a Prognostic Cognate microRNA-Gene Signature , 2012, PloS one.

[9]  Ganesh Rao,et al.  The transcriptional coactivator TAZ regulates mesenchymal differentiation in malignant glioma. , 2011, Genes & development.

[10]  宁北芳,et al.  疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A , 2005 .

[11]  Paul S Mischel,et al.  Relationship between gene expression and enhancement in glioblastoma multiforme: exploratory DNA microarray analysis. , 2008, Radiology.

[12]  References , 1971 .

[13]  T. Cloughesy,et al.  Relationship between Tumor Enhancement, Edema, IDH1 Mutational Status, MGMT Promoter Methylation, and Survival in Glioblastoma , 2012, American Journal of Neuroradiology.

[14]  D. Botstein,et al.  Gene expression profiling reveals molecularly and clinically distinct subtypes of glioblastoma multiforme. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Howard Y. Chang,et al.  Decoding global gene expression programs in liver cancer by noninvasive imaging , 2007, Nature Biotechnology.

[16]  Paul S Mischel,et al.  Quantitative volumetric analysis of conventional MRI response in recurrent glioblastoma treated with bevacizumab. , 2011, Neuro-oncology.

[17]  S. Horvath,et al.  Relationship between Survival and Edema in Malignant Gliomas: Role of Vascular Endothelial Growth Factor and Neuronal Pentraxin 2 , 2007, Clinical Cancer Research.

[18]  Paul S Mischel,et al.  Phase II study of bevacizumab plus temozolomide during and after radiation therapy for patients with newly diagnosed glioblastoma multiforme. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[19]  Maria Grazia Bruzzone,et al.  Genetic signature of adult gliomas and correlation with MRI features , 2009, Expert review of molecular diagnostics.

[20]  Zhaoshi Jiang,et al.  Evidence for sequenced molecular evolution of IDH1 mutant glioblastoma from a distinct cell of origin. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[21]  S. Horvath,et al.  Gene Expression Profiling of Gliomas Strongly Predicts Survival , 2004, Cancer Research.

[22]  S. Horvath,et al.  Gene expression analysis of glioblastomas identifies the major molecular basis for the prognostic benefit of younger age , 2008, BMC Medical Genomics.

[23]  Stanley F. Nelson,et al.  Stem cell associated gene expression in glioblastoma multiforme: relationship to survival and the subventricular zone , 2009, Journal of Neuro-Oncology.