Interpreting limits to nanoparticle delivery in high-stroma low-perfusion tumors

解释高基质低灌注肿瘤中纳米颗粒递送的限制

• 批准号: 9623468
• 负责人: Robert Richard. Alfano
• 金额: $ 7.85万
• 依托单位: CITY COLLEGE OF NEW YORK
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2019-06-20
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9623468
• 关键词: Interpreting; limits; nanoparticle; delivery; stroma

4.4.7 Project Summary/Abstract The central theme in this work is that critical spatial patterns exist in highly resistant cancer stroma and vascular density that inherently inhibit larger nanoparticle penetration into cancer, and that these phenotypes can be imaged in vivo. We will use in vivo diagnostic imaging, combined with ex vivo analysis to test this in pancreatic cancer, which has as well known drug penetration limitation. Specifically, we will quantify nanoparticle penetration in pancreas cancer, which has high stroma content and low vascular density. The analysis and prediction of efficacy will be quantitatively developed by methodological correlation of in-vivo and ex vivo images using Fourier spatial frequency analysis. We will determine the characteristic spatial patterns of these tumor microstructures that present as barriers to nanoparticle transport, as assayed through in vivo/ex vivo studies. We have seen that these characteristic spectral features appear in high-field magnetic resonance imaging (HF-MRI) scans and micro-Computed Tomography (uCT) scans of tumors imaged within the ongoing nanoparticle project at DHMC. The scope of this project is to conduct a secondary analysis on the images that are being produced within these projects, with two specific aims. 1) We will directly correlate nanoparticle penetration and distribution to the Fourier spatial frequencies found in in vivo images by Fourier spatial frequency analysis in which we have demonstrated expertise. The in vivo images will be analyzed by correlating them with histological sections of nanoparticle distribution post-treatment. Tumors will be classified on two levels as either a high or low permeability to a specific nanoparticle formulation (to quantify the amount of agent delivered), and as having high or low isotropy (to quantify the dispersion of the agent). 2) We will then apply this characteristic morphology analysis to pre-treatment, pre-operative HF-MRI, uCT images, and analyze their value as a potential diagnostic classifier. We will use a Support Vector Machine Analysis to predict the permeability and isotropy of unknown tumors, and validate our results against experimental outcomes. An iterative strategy will optimize the predictive power of the method, and be used to distinguish between characteristic spectra that are good and bad classifiers. The research will be produced using the unique software systems that we have designed during preliminary studies, and will be deployed on an analysis platform that can be integrated with the hospital- based DICOM and virtual pathology environment to allow clinical investigators to plan adjuvant therapies to promote nanoparticle efficacy. Several hundred high-quality scans are now available for analysis, which will be processed and reported on within the first year of funding. By year two, the established system is projected to be able to analyze images within a few minutes post-scan. These analysis methods will give us the key background needed to advance our fundamental understanding of nanoparticle in-vivo delivery, and test ways to interrupt transport barriers in interventional future work.

4.4.7项目总结/摘要 这项工作的中心主题是，关键的空间模式存在于高度耐药的癌症间质中， 血管密度，固有地抑制较大的纳米颗粒渗透到癌症，这些 表型可以在体内成像。我们将使用体内诊断成像，结合离体 分析以在胰腺癌中测试这一点，胰腺癌具有众所周知的药物渗透限制。 具体来说，我们将量化纳米颗粒在胰腺癌中的渗透，胰腺癌具有高基质含量 和低血管密度有效性的分析和预测将通过以下方式进行定量开发： 使用傅立叶空间频率分析的体内和体外图像的方法学相关性。 我们将确定这些肿瘤微结构的特征性空间模式， 通过体内/离体研究测定的纳米颗粒转运屏障。我们已经看到， 在高场磁共振成像（HF-MRI）扫描中出现特征性光谱特征， 正在进行的纳米粒子项目中成像的肿瘤的微型计算机断层扫描（uCT）， DHMC。该项目的范围是对正在进行的图像进行二次分析， 在这些项目中，有两个具体目标。1)我们将直接关联纳米粒子 通过傅立叶空间分析， 频率分析是我们的专长。将通过以下方法分析体内图像： 将它们与治疗后纳米颗粒分布的组织学切片相关联。肿瘤将是 在两个水平上分类为对特定纳米颗粒制剂的高或低渗透性（以 量化递送的药剂的量），以及具有高或低的各向同性（以量化 代理）。2)然后，我们将这种特征形态学分析应用于治疗前、手术前 HF-MRI、uCT图像，并分析其作为潜在诊断分类器的价值。我们将使用支持 向量机分析预测未知肿瘤的渗透性和各向同性，并验证我们的 实验结果对比。迭代策略将优化 方法，并用于区分特征光谱，是好的和坏的分类器。 这项研究将使用我们在研究期间设计的独特软件系统进行 初步研究，并将部署在一个分析平台，可以与医院集成- 基于DICOM和虚拟病理学环境，允许临床研究者计划辅助 治疗，以促进纳米颗粒的功效。数百个高质量的扫描现在可用于 分析，将在供资的第一年内处理和报告。第二年， 已建立的系统预计能够在扫描后几分钟内分析图像。这些 分析方法将为我们提供必要的关键背景，以促进我们对 纳米颗粒体内递送，以及在干预性未来工作中中断运输屏障的测试方法。