Validation of imaging brain tumor metabolism using deuterated glucose

异常代谢越来越被认为是癌症的标志。瓦尔堡效应是众所周知的 这种异常癌症代谢的一个例子，它需要从氧化葡萄糖转变为糖酵解葡萄糖 代谢（尽管存在氧气），通常也增加葡萄糖摄取。检测到这一 通过放射性类似物（2 - 18 F-氟-2-脱氧-D-葡萄糖，FDG）与正电子 发射断层扫描（PET）通常用于肿瘤的诊断、分期和评估疾病进展 在大脑之外。然而，在患有脑肿瘤的患者中，FDG-PET经常是不确定的，因为正常的脑组织中的FDG-PET是不确定的。 健康脑中的高葡萄糖摄取与肿瘤中的相当，从而模糊了肿瘤-脑图像 对比度因此，FDG-PET不常用于这些患者。这使得脑肿瘤患者没有 代谢成像的好处，这对他们的疾病管理有显着的负面影响。 最近开发的基于MRI的方法，氘代谢成像（DIM）可以是一种替代方法 检测异常葡萄糖代谢策略。磁共振成像基于3D氘（2H）磁共振 光谱成像（MRSI）。给予非放射性氘代葡萄糖后， 葡萄糖及其下游代谢物乳酸盐和谷氨酸盐。在显示出瓦尔堡效应的癌细胞中 乳酸盐和谷氨酸盐中的2H标记反映了从氧化代谢到糖酵解代谢的典型转变。马达加斯加 检测这种2H标记，并通过高肿瘤-脑图像揭示癌症特异性葡萄糖代谢 对比度由于这些特征和该方法的易用性，ESTA可以成为一个强大的代谢 脑肿瘤的成像技术，迄今为止一直缺失。 本提案的目的是验证葡萄糖代谢的快速成像作为一种潜在的成像工具， 神经肿瘤学，特别是胶质母细胞瘤，最常见和致命的原发性脑肿瘤。我们设想， 对于脑肿瘤患者，MRI可以提供与FDG-PET对许多肿瘤患者相似的益处 在大脑之外。因此，在目标1中，我们试图验证乳酸和谷氨酸中的2H标记模式 通过将它们与绝对测量值进行比较， GBM啮齿动物模型中的瓦尔堡效应。目的2是关注于潜在的肿瘤标志物，以提供早期的生物标志物 对标准化疗的反应。为了确认与当前相比， 比较了临床上可用的方法，在Aim 3中，由1H MRSI、FDG-PET和SPECT生成的代谢图， 用于GBM患者的肿瘤-脑图像对比。拟议的目标将使人们更好地了解 基于DMI的图像对比度的基本过程，提供了其价值的第一个洞察力， 监测治疗和疾病进展，并将其性能作为新的代谢成像 法这些成就将为进一步发展作为临床上可行的 代谢成像技术。

Aberrant metabolism is increasingly recognized as a hallmark of cancer. The Warburg effect is a well-known example of such abnormal cancer metabolism, which entails a shift away from oxidative to glycolytic glucose metabolism (despite the presence of oxygen) and usually also increased glucose uptake. The detection of this increased glucose uptake, via a radioactive analogue (2-18F-fluoro-2-deoxy-D-glucose, FDG) with positron emission tomography (PET), is often used for diagnosis, staging, and evaluating disease progression of tumors outside the brain. However, in patients with brain tumors FDG-PET is frequently inconclusive because the normal high glucose uptake in healthy brain is comparable to that in tumors, thereby obscuring the tumor-to-brain image contrast. As a result, FDG-PET is not frequently used in these patients. That leaves brain tumor patients without the benefits of metabolic imaging, which has a significant negative impact on the management of their disease. The recently developed MRI-based method, deuterium metabolic imaging (DMI) can be an alternative strategy to detect abnormal glucose metabolism. DMI is based on 3D deuterium (2H) magnetic resonance spectroscopic imaging (MRSI). After administration of the nonradioactive deuterated glucose, DMI can detect both glucose and its downstream metabolites lactate and glutamate. In cancer cells that show the Warburg effect the 2H-labeling in lactate and glutamate reflects the typical shift from oxidative to glycolytic metabolism. DMI can detect this 2H-labeling and reveal the cancer-specific glucose metabolism with high tumor-to-brain image contrast. Because of these features and the ease of use of the method, DMI can become a robust metabolic imaging technique for brain tumors that so far has been missing. The goal of this proposal is to validate DMI of glucose metabolism as a potential imaging tool for neurooncology, particularly for glioblastoma, the most common and lethal primary brain tumor. We envision that, for patients with brain tumors, DMI can provide a similar benefit as FDG-PET has for many patients with tumors outside of the brain. In Aim 1 we therefore seek to validate the 2H-labeling pattern in lactate and glutamate detected with DMI as surrogates of the Warburg effect, by comparing them with absolute measurements of the Warburg effect in rodent models of GBM. Aim 2 is focused on the potential of DMI to provide an early biomarker of response to standard of care chemotherapy. To confirm the improved performance of DMI relative to current clinically available methods, in Aim 3 metabolic maps generated by 1H MRSI, FDG-PET and DMI, are compared for tumor-to-brain image contrast in patients with GBM. The proposed aims will provide better understanding of the fundamental processes underlying the DMI-based image contrast, provide the first insight in its value for monitoring therapy and disease progression, and benchmark its performance as a new metabolic imaging method. These achievements will strengthen the foundation for further development of DMI as a clinically viable technology for metabolic imaging.