(PQC5) Detecting small clusters of tumor cells with a PTPmu probe

描述（由申请人提供）：早期肿瘤检测是肿瘤学成像中至关重要的目标，因为它可以在疾病的早期阶段进行治疗（或重新定向治疗）。然而，挑战在于对小的肿瘤细胞集合进行成像，因为常规的体内成像方法受到有限的分辨率和肿瘤对比度的影响。磁共振成像，它提供了精致的解剖细节和许多对比机制，仍然经常被证明无法检测到小簇的肿瘤细胞。我们的战略将是开发一种具有巨大放大潜力的磁共振成像（MRI）试剂，并使用磁共振（MR）采集和图像处理技术提供足够的对比度，以检测甚至非常小的肿瘤。我们的提案建立在大量的初步数据基础上，汇集了探针化学、肿瘤模型、肿瘤生物学、独特的3D显微冷冻成像、先进的MR技术和定量图像分析方面的重要多学科专业知识。到目前为止，我们已经发现了一种新的分子成像策略，通过靶向肿瘤微环境中大量发现的PTP的细胞外片段;创建了胶质瘤的小鼠原位模型;开发了冷冻成像方法来可视化和量化肿瘤大小，细胞分散，白色物质束和3D脑重建中的血管密度;并发现荧光PTP？探针快速标记主肿瘤以及分散的细胞，甚至单个迁移的细胞远离主肿瘤块达3.5mm。最近使用钆共轭PTP <$探针的初步研究表明，我们可以使用MRI看到小肿瘤。我们寻求资金，以证明分散的肿瘤边界的划定和微小的癌细胞簇的检测使用PTP <$分子成像探针通过MRI。由于MR信号的非特异性、非定量性质，即使典型的空间分辨率约为1x 1x 5 mm 3，即使熟练的放射科医生也没有信心特异性地诊断肿瘤，直到肿瘤大于5x 5x 5 mm 3。我们的方法是通过PTP <$MRI探头（PTP <$-Gd）的放大特性、硬件、采集和软件改进，大大增加MR信号相对于背景解剖结构变化的对比度。我们假设，我们可以通过使用PTP分子靶向剂结合高分辨率和定量MR来特异性地识别和表征比目前可能的小2-3个数量级的肿瘤。优化和测试分子PTP <$-Gd探针检测原位异种移植物中脑肿瘤的能力，并与“常规”脑肿瘤MRI进行比较。2.确定PTP的能力？-Gd，与来自显微镜冷冻成像的金标准GFP标记的肿瘤相比，精确地对分散的脑肿瘤成像。3.使用临床上可行的（3 T）双试剂方法优化定量MRI，以测试在一个临床上可行的（3 T）双试剂方法中检测小的孤立脑肿瘤的限度。 高度分散的肿瘤模型。

DESCRIPTION (provided by applicant): Early tumor detection is a critically important goal in oncologic imaging because it would enable treatment (or redirection of treatment) at earlier stages of disease. However, the challenge is imaging small collections of tumor cells because conventional in vivo imaging methods suffer from limited resolution and tumor contrast. MRI, which presents exquisite anatomical detail and many contrast mechanisms, still often proves incapable of detecting small clusters of tumor cells. Our strategy will be to develop a magnetic resonance imaging (MRI) agent with great amplification potential and to use magnetic resonance (MR) acquisition and image processing techniques to provide contrast sufficient to detect even very small tumors. Our proposal builds upon substantial preliminary data and brings together significant multi-disciplinary expertise in probe chemistry, tumor models, tumor biology, unique 3D microscopic cryo-imaging, advanced MR techniques, and quantitative image analysis. To date, we have discovered a novel molecular imaging strategy by targeting extracellular fragments of PTP¿ abundantly found in the tumor microenvironment; created mouse orthotopic models of gliomas; developed cryo-imaging methods to visualize and quantify tumor size, cell dispersal, white matter tracts and blood vessel density in 3D brain reconstructions; and discovered that a fluorescent PTP¿ probe quickly labeled main tumor as well as dispersed cells and even single migrating cells up to 3.5 mm away from the main tumor mass. Recently preliminary studies using a gadolinium conjugated PTP¿ probe indicate that we can see small tumors using MRI. We seek funding to demonstrate delineation of the dispersing tumor boundary and detection of tiny clusters of cancer cells using the PTP¿ molecular imaging probe by MRI. Even a skilled radiologist will not have the confidence to specifically diagnose a tumor until it is bigger than 5x5x5 mm3 even though typical spatial resolution is about 1x1x5 mm3 because of the non-specific, non-quantitative nature of the MR signal. Our approach will be to greatly increase contrast of the MR signal relative to background anatomical variations through amplification characteristics of PTP¿ MRI probe (PTP¿-Gd), hardware, acquisition, and software improvements. We hypothesize that we can specifically identify and characterize tumors 2-3 orders of magnitude smaller than currently possible through the use of the PTP¿ molecular targeting agent combined with high resolution and quantitative MR. The Specific Aims are: 1. Optimize and test the ability of the molecular PTP¿-Gd probe to detect brain tumors in orthotopic xenografts and compare to "conventional" brain tumor MRI. 2. Determine the ability of PTP¿-Gd to accurately image dispersing brain tumors as compared to gold-standard GFP-labeled tumor from microscopic cryo-imaging. 3. Optimize quantitative MRI using a clinically feasible (3T) dual-agent approach to test the limits for detecting small isolated brain tumors in a very highly dispersing tumor model.