Multiscale Image-based Modeling of Antiangiogenic Resistance in Breast Cancer

基于图像的乳腺癌抗血管生成耐药性的多尺度建模

• 批准号: 8941820
• 负责人: Arvind P Pathak
• 金额: $ 36.77万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8941820
• 关键词: Angiogenesis Inhibitors; Angiogenic Proteins; Animals; Biological Markers; Biomimetics; Blood Vessels; Blood Volume; Blood flow; Breast Cancer Model; Breast Cancer Patient; Clinical; Computational Biology; Computer Simulation; Confocal Microscopy; Data; Data Set; Development; Diffusion; Disease; Drug Targeting; Drug resistance; Endothelial Cells; Environment; Failure; Goals; Growth Factor Inhibition; Health; Human; Hypoxia; Image; Integrins; Investigation; Knowledge; Laser Scanning Confocal Microscopy; Lung; MDA MB 231; Magnetic Resonance; Magnetic Resonance Imaging; Malignant Neoplasms; Maps; Measures; Medicine; Metastatic breast cancer; Microscopy; Mission; Modeling; Molecular; Neoplasm Metastasis; Pathway interactions; Patients; Peptides; Perfusion; Pharmaceutical Preparations; Positioning Attribute; Public Health; Research; Research Personnel; Resistance; Resistance development; Staging; Stroke; Systems Biology; Testing; Time; Vascular Endothelial Growth Factors; Vascular remodeling; Xenograft procedure; angiogenesis; base; data modeling; density; efficacy testing; imaging biomarker; in vivo; in vivo imaging; innovation; lymph nodes; malignant breast neoplasm; microCT; mortality; novel; novel therapeutics; oxygen transport; protein distribution; protein expression; tumor; tumor microenvironment; tumorigenic

DESCRIPTION (provided by applicant): The failure of antiangiogenic therapy in the treatment of metastatic breast cancer is due to the development of resistance to inhibition of vascular endothelial growth factor (VEGF), and our inability to identify patients most likely to respond to such therapies. To elucidate the factors underlying 'antiangiogenic resistance' and develop translational biomarkers for identifying resistance in patients, it is necessary to relate molecular/structural/functional changes in the tumor microenvironment to changes measured with in vivo imaging, and develop well-­‐validated computational biology models of antiangiogenic resistance in breast cancer. Since such multiscale angiogenesis data and models do not currently exist, we have proposed to develop them in this application. Therefore, our goal is to elucidate the mechanisms of antiangiogenic resistance in breast cancer and develop imaging biomarkers to identify patients that could benefit most from antiangiogenic agents. Guided by compelling preliminary data, we will pursue three Specific Aims: (1) To characterize angiogenesis and metastatic burden in a human breast cancer model with multiscale imaging; (2) To develop a multiscale image-­based computational model of antiangiogenic resistance in breast cancer; and (3) To determine if treatment with a non-­VEGF targeted antiangiogenic/anti-­tumorigenic peptide can circumvent resistance. Under Aim1, we will create co-­registered, quantitative angiogenesis data at complementary spatial scales, over time, by combining in vivo MRI, ex vivo magnetic resonance microscopy (μMRI) ex vivo micro-­CT (μCT) and laser scanning confocal microscopy (LSCM). Multiscale imaging will relate in vivo angiogenic changes to alterations in tumor microvasculature and angiogenic protein expression. Under Aim2, we will use multiscale imaging data from Aim1 to develop a computational model of antiangiogenic resistance that is comprehensively validated in an experimental breast cancer xenograft. We will vary model parameters implicated in antiangiogenic resistance to identify changes in the tumor microenvironment that can be exploited as clinical biomarkers of antiangiogenic resistance in breast cancer. Under Aim3, we will treat a human breast cancer model with a non-­VEGF targeted multimodal antiangiogenic/anti-­tumorigenic peptide to determine if targeting multiple pathways can overcome antiangiogenic resistance in breast cancer. The approach is innovative because it blends cutting-­edge advances in multiscale imaging, multiscale computational modeling and biomimetic peptides. This approach has the potential to impact 'systems biology' investigations of other cancers and diseases involving the pathological vasculature. The proposed research is significant because we expect to: (i) elucidate the mechanisms via which antiangiogenic resistance develops in breast cancer patients; (ii) identify potential imaging-­based biomarker of antiangiogenic resistance, and (iii) test therapies that circumvent antiangiogenic resistance in breast cancer. Ultimately, such knowledge has the potential to identify new therapeutic strategies and reduce mortality from metastatic breast cancer.

描述（由适用提供）：抗血管生成疗法在转移性乳腺癌治疗中的失败是由于对抑制血管内皮生长因子（VEGF）的抵抗力的发展，以及我们无法识别最有可能对这种疗法反应的患者。为了阐明“抗血管生成抗性”的基本因素，并开发了转化生物标志物来鉴定患者的耐药性，有必要将肿瘤微环境中的分子/结构/结构/功能变化与体内成像，开发良好的抗抗病性生物学模型的乳腺癌耐药性耐药性抗性的计算生物学模型相关联。由于目前尚不存在这样的多尺度血管生成数据和模型，因此我们建议在此应用程序中开发它们。因此，我们的目标是阐明乳腺癌中抗血管生成抗性的机制，并开发成像生物标志物，以鉴定可以从抗血管生成剂中受益的患者。在引人入胜的初步数据的指导下，我们将追求三个特定的目的：（1）在具有多尺度成像的人类乳腺癌模型中表征血管生成和转移性燃烧； （2）开发乳腺癌中抗血管生成抗性的基于多尺度图像的计算模型； （3）确定用非VEGF靶向抗血管生成/抗肿瘤肽的治疗是否可以规避耐药性。在AIM1下，我们将在互补空间尺度上创建共同注册的，定量的血管生成数据，随着时间的流逝，通过在体内MRI中组合，Ex Vivo磁共振显微镜（μMRI）EX VIVO Micro-CT（μCT）和激光扫描扫描的共镜显微镜（LSCM）。多尺度成像将使体内血管生成的变化与肿瘤微举行和血管生成蛋白表达的改变有关。在AIM2下，我们将使用来自AIM1的多尺度成像数据来开发一种抗血管生成抗性的计算模型，该模型在实验性的乳腺癌元素中得到了全面验证。我们将改变在抗血管生成抗性中实施的模型参数，以鉴定肿瘤微环境的变化，这些变化可以作为乳腺癌中抗血管生成抗性的临床生物标志物探索。在AIM3下，我们将使用 非VEGF靶向多模式抗血管生成/抗肿瘤肽以确定靶向多种途径是否可以克服乳腺癌中的抗血管生成性。该方法具有创新性，因为它融合了多尺度成像，多尺度计算建模和仿生辣椒的尖端进步。这种方法有可能影响涉及病理脉管系统的其他癌症和疾病的“系统生物学”研究。拟议的研究很重要，因为我们期望：（i）阐明乳腺癌患者抗血管生成抗性的机制； （ii）确定抗血管生成抗性的潜在基于成像的生物标志物，以及（iii）测试疗法，这些疗法规避了乳腺癌中抗血管生成的耐药性。最终，这种知识有潜力确定新的治疗策略并降低转移性乳腺癌的死亡率。