Cell invasion, motility, and proliferation level estimate maps in gliomas

描述（由申请人提供）：脑肿瘤治疗反应的标准临床评估包括检查对比增强和标准磁共振成像（MRI）扫描的t2加权信号异常。虽然这些技术提供了关于肿瘤病理生理学的重要信息，但它们不能直接可视化肿瘤的生长和侵袭。过去20年的大量研究表明，肿瘤细胞的侵袭远远超出了传统MRI扫描所检测到的异常范围，这种侵袭是多形性胶质母细胞瘤（GBM）预后差和死亡率100%的主要原因，GBM是最常见的恶性脑肿瘤类型。因此，本项目的总体目标是建立一种有价值的临床成像生物标志物，用于利用弥散MRI技术可视化和量化脑肿瘤的生长和侵袭。我们已经在初步数据中证明，弥散性MRI对肿瘤细胞密度敏感，弥散性MRI随时间的体素变化可用于预测对化疗和抗血管生成治疗的反应。在最近的一篇手稿中，我们开发了一种新的ADC变化时空模型，旨在量化体素方向的微观增殖和细胞侵袭率，称为细胞侵袭、运动和增殖

DESCRIPTION (provided by applicant): 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.