Preoperative dynamic contrast-enhanced MRI correlates with molecular markers of hypoxia and vascularity in specific areas of intratumoral microenvironment and is predictive of patient outcome.

BACKGROUND Measures of tumor vascularity and hypoxia have been correlated with glioma grade and outcome. Dynamic contrast-enhanced (DCE) MRI can noninvasively map tumor blood flow, vascularity, and permeability. In this prospective observational cohort pilot study, preoperative imaging was correlated with molecular markers of hypoxia, vascularity, proliferation, and progression-free and overall patient survival. METHODS Pharmacokinetic modeling methods were used to generate maps of tumor blood flow, extraction fraction, permeability-surface area product, transfer constant, washout rate, interstitial volume, blood volume, capillary transit time, and capillary heterogeneity from preoperative DCE-MRI data in human glioma patients. Tissue was obtained from areas of peritumoral edema, active tumor, hypoxic penumbra, and necrotic core and evaluated for vascularity, proliferation, and expression of hypoxia-regulated molecules. DCE-MRI parameter values were correlated with hypoxia-regulated protein expression at tissue sample sites. RESULTS Patient survival correlated with DCE parameters in 2 cases: capillary heterogeneity in active tumor and interstitial volume in areas of peritumoral edema. Statistically significant correlations were observed between several DCE parameters and tissue markers. In addition, MIB-1 index was predictive of overall survival (P = .044) and correlated with vascular endothelial growth factor expression in hypoxic penumbra (r = 0.7933, P = .0071) and peritumoral edema (r = 0.4546). Increased microvessel density correlated with worse patient outcome (P = .026). CONCLUSIONS Our findings suggest that DCE-MRI may facilitate noninvasive preoperative predictions of areas of tumor with increased hypoxia and proliferation. Both imaging and hypoxia biomarkers are predictive of patient outcome. This has the potential to allow unprecedented prognostic decisions and to guide therapies to specific tumor areas.

[1]  H. Aronen,et al.  Dynamic susceptibility contrast MRI of gliomas. , 2002, Neuroimaging clinics of North America.

[2]  Michael H Lev,et al.  Dynamic magnetic resonance perfusion imaging of brain tumors. , 2004, The oncologist.

[3]  Glyn Johnson,et al.  Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. , 2004, AJNR. American journal of neuroradiology.

[4]  G. Jayson,et al.  Do Imaging Biomarkers Relate to Outcome in Patients Treated with VEGF Inhibitors? , 2012, Clinical Cancer Research.

[5]  D. Gillespie,et al.  Inhibition of hypoxia inducible factor-1alpha (HIF-1alpha) decreases vascular endothelial growth factor (VEGF) secretion and tumor growth in malignant gliomas. , 2006, Journal of neuro-oncology.

[6]  P. Wesseling,et al.  Hypoxia in a human intracerebral glioma model. , 2000, Journal of neurosurgery.

[7]  F Zerrin Yetkin,et al.  Hypoxia imaging in brain tumors. , 2002, Neuroimaging clinics of North America.

[8]  D. Kong,et al.  Diagnostic Dilemma of Pseudoprogression in the Treatment of Newly Diagnosed Glioblastomas: The Role of Assessing Relative Cerebral Blood Flow Volume and Oxygen-6-Methylguanine-DNA Methyltransferase Promoter Methylation Status , 2011, American Journal of Neuroradiology.

[9]  R. Jensen.  Hypoxia in the tumorigenesis of gliomas and as a potential target for therapeutic measures. , 2006, Neurosurgical focus.

[10]  R K Jain,et al.  Determinants of tumor blood flow: a review. , 1988, Cancer research.

[11]  Sanjay Behari,et al.  Discriminant analysis to classify glioma grading using dynamic contrast-enhanced MRI and immunohistochemical markers , 2012, Neuroradiology.

[12]  G. Semenza,et al.  Expression of hypoxia‐inducible factor 1α in brain tumors , 2000 .

[13]  Rakesh K. Gupta,et al.  Differentiation of infective from neoplastic brain lesions by dynamic contrast-enhanced MRI , 2008, Neuroradiology.

[14]  J. Petrella,et al.  MR perfusion imaging of the brain: techniques and applications. , 2000, AJR. American journal of roentgenology.

[15]  L. Astrakas,et al.  Diffusion tensor and dynamic susceptibility contrast MRI in glioblastoma , 2012, Clinical Neurology and Neurosurgery.

[16]  D. Brat,et al.  Malignant Glioma Physiology: Cellular Response to Hypoxia and Its Role in Tumor Progression , 2003, Annals of Internal Medicine.

[17]  D. Gillespie,et al.  Inhibition of hypoxia inducible factor-1α (HIF-1α) decreases vascular endothelial growth factor (VEGF) secretion and tumor growth in malignant gliomas , 2006, Journal of Neuro-Oncology.

[18]  D. Gillespie,et al.  Hypoxia‐regulated protein expression, patient characteristics, and preoperative imaging as predictors of survival in adults with glioblastoma multiforme , 2008, Cancer.

[19]  J. Knisely,et al.  Importance of hypoxia in the biology and treatment of brain tumors. , 2002, Neuroimaging clinics of North America.

[20]  P. T. H. V. Rajshekhar.  Post-traumatic malignant glioma: case report and review of the literature , 2000, British journal of neurosurgery.

[21]  R. Pandey,et al.  Comparative Evaluation of Dynamic Contrast-Enhanced Perfusion With Diffusion Tensor Imaging Metrics in Assessment of Corticospinal Tract Infiltration in Malignant Glioma , 2010, Journal of computer assisted tomography.

[22]  Glyn Johnson,et al.  Intracranial mass lesions: dynamic contrast-enhanced susceptibility-weighted echo-planar perfusion MR imaging. , 2002, Radiology.

[23]  Peter Vajkoczy,et al.  Vascular microenvironment in gliomas. , 2004, Cancer treatment and research.

[24]  Edward V R DiBella,et al.  A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: II. In vivo results , 2010, Physics in medicine and biology.

[25]  C. Davatzikos,et al.  Survival Analysis of Patients with High-Grade Gliomas Based on Data Mining of Imaging Variables , 2012, American Journal of Neuroradiology.

[26]  M. Ollerenshaw,et al.  Expression of hypoxia‐inducible factor 1α in tumours of patients with glioblastoma , 2002, Neuropathology and applied neurobiology.

[27]  P. Narayana,et al.  Serial diffusion tensor imaging to characterize radiation-induced changes in normal-appearing white matter following radiotherapy in patients with adult low-grade gliomas , 2008, Radiation Medicine.

[28]  T. Mikkelsen,et al.  Predicting survival in glioblastomas using diffusion tensor imaging metrics , 2010, Journal of magnetic resonance imaging : JMRI.

[29]  Glen R Morrell,et al.  Uncertainty in T1 mapping using the variable flip angle method with two flip angles , 2009, Physics in medicine and biology.

[30]  S. Rockwell.  Oxygen delivery: implications for the biology and therapy of solid tumors. , 1997, Oncology research.

[31]  L. Astrakas,et al.  Spectroscopic and perfusion magnetic resonance imaging predictors of progression in pediatric brain tumors , 2004, Cancer.

[32]  Mitchel S Berger,et al.  Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging. , 2012, Neuro-oncology.

[33]  Vipin Saxena,et al.  Improved T(1)-weighted dynamic contrast-enhanced MRI to probe microvascularity and heterogeneity of human glioma. , 2007, Magnetic resonance imaging.

[34]  Toshihiro Kumabe,et al.  Malignant astrocytic tumors: clinical importance of apparent diffusion coefficient in prediction of grade and prognosis. , 2006, Radiology.

[35]  P. Choyke,et al.  Imaging of angiogenesis: from microscope to clinic , 2003, Nature Medicine.

[36]  K. Tichauer,et al.  Kinetic model optimization for characterizing tumour physiology by dynamic contrast-enhanced near-infrared spectroscopy , 2013, Physics in medicine and biology.

[37]  D. Parker,et al.  Uncertainty and bias in contrast concentration measurements using spoiled gradient echo pulse sequences , 2008, Physics in medicine and biology.

[38]  T. Mikkelsen,et al.  Correlation between Magnetic Resonance Spectroscopy Imaging and Image-guided Biopsies: Semiquantitative and Qualitative Histopathological Analyses of Patients with Untreated Glioma , 2001, Neurosurgery.

[39]  G. Reifenberger,et al.  The WHO Classification of Tumors of the Nervous System , 2002, Journal of neuropathology and experimental neurology.

[40]  A. Sorensen,et al.  Comparison of permeability in high-grade and low-grade brain tumors using dynamic susceptibility contrast MR imaging. , 2002, AJR. American journal of roentgenology.

[41]  J. Mintorovitch,et al.  Comparison of Magnetic Properties of MRI Contrast Media Solutions at Different Magnetic Field Strengths , 2005, Investigative radiology.

[42]  Mark W. Dewhirst,et al.  Hypoxia and radiotherapy: opportunities for improved outcomes in cancer treatment , 2007, Cancer and Metastasis Reviews.

[43]  R. Strausberg,et al.  Transcriptional response to hypoxia in human tumors. , 2001, Journal of the National Cancer Institute.

[44]  O. S. Nielsen,et al.  Hypoxia in human soft tissue sarcomas: Adverse impact on survival and no association with p53 mutations , 2001, British Journal of Cancer.

[45]  C. Scopa,et al.  Expression of cell cycle inhibitors p21, p27, p14 and p16 in gliomas. Correlation with classic prognostic factors and patients' outcome , 2008, Neuropathology : official journal of the Japanese Society of Neuropathology.

[46]  P. Adamson,et al.  Anti-angiogenic agents for the treatment of brain tumors. , 2002, Neuroimaging clinics of North America.

[47]  T. Mikkelsen,et al.  Prediction of Glioblastoma Multiform Response to Bevacizumab Treatment Using Multi-Parametric MRI , 2012, PloS one.

[48]  P. LaViolette,et al.  Vascular change measured with independent component analysis of dynamic susceptibility contrast MRI predicts bevacizumab response in high-grade glioma. , 2013, Neuro-oncology.

[49]  E F Halpern,et al.  Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. , 1994, Radiology.

[50]  K. Jungermann,et al.  Signaling cross-talk between hypoxia and glucose via hypoxia-inducible factor 1 and glucose response elements. , 2002, Biochemical pharmacology.

[51]  T. Yoshimine,et al.  Prognostic value of WT1 protein expression level and MIB-1 staining index as predictor of response to WT1 immunotherapy in glioblastoma patients , 2010, Brain Tumor Pathology.

[52]  Matthias C. Schabel.  A unified impulse response model for DCE‐MRI , 2012, Magnetic resonance in medicine.

[53]  K. Camphausen,et al.  Imaging biomarker dynamics in an intracranial murine glioma study of radiation and antiangiogenic therapy. , 2013, International journal of radiation oncology, biology, physics.

[54]  P Vaupel,et al.  Association between tumor hypoxia and malignant progression in advanced cancer of the uterine cervix. , 1996, Cancer research.

[55]  G. Semenza,et al.  Expression of hypoxia-inducible factor 1alpha in brain tumors: association with angiogenesis, invasion, and progression. , 2000, Cancer.

[56]  A. Harris,et al.  Hypoxia-inducible factors HIF-1alpha and HIF-2alpha in head and neck cancer: relationship to tumor biology and treatment outcome in surgically resected patients. , 2002, Cancer research.

[57]  Mohammad Haris,et al.  Quantification of physiological and hemodynamic indices using T1 dynamic contrast‐enhanced MRI in intracranial mass lesions , 2007, Journal of magnetic resonance imaging : JMRI.

[58]  Paul S Mischel,et al.  MR imaging correlates of survival in patients with high-grade gliomas. , 2005, AJNR. American journal of neuroradiology.

[59]  P. Black,et al.  Microvessel density is a prognostic indicator for patients with astroglial brain tumors , 1996, Cancer.

[60]  H. Takeuchi,et al.  Prognostic significance of microvessel density determined by an anti‐CD105/endoglin monoclonal antibody in astrocytic tumors: Comparison with an anti‐CD31 monoclonal antibody , 2005, Neuropathology : official journal of the Japanese Society of Neuropathology.

[61]  L. Astrakas,et al.  Voxel-Based Morphometry and Diffusion Tensor Imaging of the Optic Pathway in Primary Open-Angle Glaucoma: A Preliminary Study , 2012, American Journal of Neuroradiology.

[62]  J. Simons,et al.  Direct comparison of GAPDH, beta-actin, cyclophilin, and 28S rRNA as internal standards for quantifying RNA levels under hypoxia. , 1999, Biochemical and biophysical research communications.