Standard clinical assessment of brain tumor response to treatment consists of examining contrast enhancement and T2-weighted signal abnormalities on standard magnetic resonance imaging (MRI) scans. While these techniques provide important information regarding tumor pathophysiology, they do not enable direct visualization of tumor growth and invasion. Numerous studies over the past 20 years have shown that tumor cell invasion extends well beyond the margins of abnormalities detected on traditional MRI scans, and this invasion is the primary reason for poor prognosis and 100% fatality rate in glioblastoma multiforme (GBM), the most common and malignant type of brain tumor. Therefore, the overall goal of this project is to establish a valuable clinical imaging biomarker fr visualization and quantification of brain tumor growth and invasion using diffusion MRI techniques. We have demonstrated in our preliminary data that diffusion MRI is sensitive to tumor cell density, and voxel-wise changes in diffusion MRI over time can be used to predict the response to both chemotherapy and anti-angiogenic therapies. In a recent manuscript, we have developed a novel spatiotemporal model of ADC change aimed at quantifying voxel-wise microscopic proliferation and cell invasion rates termed Cell Invasion, Motility, and Proliferation Level Estimate (CIMPLE) maps. Our preliminary data suggests CIMPLE maps correlate with MR spectroscopy measurements of malignant potential, correlate with tumor grade, may predict regions of future contrast enhancement, predict survival in patients with recurrent glioblastoma treated with bevacizumab, and spatially correlates well with abnormal positron emission tomography measurements of amino acid uptake. Despite promising preliminary results from our laboratory, more testing and improvements are necessary as outlined in the specific experiments in the current proposal.
Specific Aim #1 focuses on improving the diffusion-weighted image acquisition for advanced CIMPLE map applications by exploring the use of high angular resolution diffusion imaging (HARDI). Success of this specific aim will allow CIMPLE maps to be calculated with high accuracy through higher signal-to-noise diffusion images as well as create a tensor-based solution to CIMPLE maps that may provide directionally-specific maps of tumor invasion.
Specific Aim #2 will focus on testing whether CIMPLE maps calculated during radiotherapy are early predictive biomarkers of tumor response to standard therapy. Specifically, we aim to determine whether CIMPLE maps accurately predict spatial regions of future tumor progression as well as predict six- and twelve-month progression-free and overall survival. Lastly, Specific Aim #3 will focus on validating CIMPLE maps through the use of histological information at tumor recurrence and 18F-fluoro-thymidine positron emission tomography measurements of tumor proliferation. Successful completion of this aim will provide additional evidence validating non-invasive CIMPLE map measurements of proliferation and invasion rate.

Public Health Relevance

There is a general consensus in the neuro-oncology community that current methods of monitoring malignant glioma growth and response to treatment are inadequate, particularly when trying to detect brain tumor invasion. This project aims to further establish, validate, and clinically translate CIMPLE maps as a non-invasive imaging surrogate for quantification of tumor cell invasion and proliferation in gliomas. Successful completion of this project will help establish CIMPLE maps as a personalized clinical monitoring tool that will help tailor drug selection and detect drug failure in individual patients much sooner than conventional techniques.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Exploratory/Developmental Grants (R21)
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Cancer Biomarkers Study Section (CBSS)
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Zhang, Yantian
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University of California Los Angeles
Schools of Medicine
Los Angeles
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Leu, K; Boxerman, J L; Cloughesy, T F et al. (2016) Improved Leakage Correction for Single-Echo Dynamic Susceptibility Contrast Perfusion MRI Estimates of Relative Cerebral Blood Volume in High-Grade Gliomas by Accounting for Bidirectional Contrast Agent Exchange. AJNR Am J Neuroradiol 37:1440-6
Ellingson, Benjamin M; Kim, Eunhee; Woodworth, Davis C et al. (2015) Diffusion MRI quality control and functional diffusion map results in ACRIN 6677/RTOG 0625: a multicenter, randomized, phase II trial of bevacizumab and chemotherapy in recurrent glioblastoma. Int J Oncol 46:1883-92
Chang, Warren; Pope, Whitney B; Harris, Robert J et al. (2015) Diffusion MR Characteristics Following Concurrent Radiochemotherapy Predicts Progression-Free and Overall Survival in Newly Diagnosed Glioblastoma. Tomography 1:37-43
Nechifor, Ruben E; Harris, Robert J; Ellingson, Benjamin M (2015) Novel Magnetic Resonance Imaging Techniques in Brain Tumors. Top Magn Reson Imaging 24:137-46
Ellingson, Benjamin M; Lai, Albert; Nguyen, Huytram N et al. (2015) Quantification of Nonenhancing Tumor Burden in Gliomas Using Effective T2 Maps Derived from Dual-Echo Turbo Spin-Echo MRI. Clin Cancer Res 21:4373-83
Karavaeva, Elena; Harris, Robert J; Leu, Kevin et al. (2015) Relationship Between [18F]FDOPA PET Uptake, Apparent Diffusion Coefficient (ADC), and Proliferation Rate in Recurrent Malignant Gliomas. Mol Imaging Biol 17:434-42
Banerjee, Pia; Leu, Kevin; Harris, Robert J et al. (2015) Association between lesion location and language function in adult glioma using voxel-based lesion-symptom mapping. Neuroimage Clin 9:617-24
Hathout, Leith; Pope, Whitney B; Lai, Albert et al. (2015) Radial expansion rates and tumor growth kinetics predict malignant transformation in contrast-enhancing low-grade diffuse astrocytoma. CNS Oncol 4:247-56
Harris, Robert J; Cloughesy, Timothy F; Hardy, Anthony J et al. (2015) MRI perfusion measurements calculated using advanced deconvolution techniques predict survival in recurrent glioblastoma treated with bevacizumab. J Neurooncol 122:497-505
Leu, Kevin; Enzmann, Dieter R; Woodworth, Davis C et al. (2014) Hypervascular tumor volume estimated by comparison to a large-scale cerebral blood volume radiographic atlas predicts survival in recurrent glioblastoma treated with bevacizumab. Cancer Imaging 14:31

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