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（由申请人提供）：抗血管生成疗法在治疗转移性乳腺癌中的失败是由于对血管内皮生长因子（VEGF）抑制产生耐药性，并且我们无法识别最有可能对此类疗法产生反应的患者为了阐明“抗血管生成耐药性”的潜在因素并开发用于识别患者耐药性的转化生物标志物，有必要将肿瘤微环境中的分子/结构/功能变化与体内成像测量的变化联系起来，并且开发经过充分验证的乳腺癌抗血管生成抵抗的计算生物学模型，由于目前尚不存在此类多尺度血管生成数据和模型，因此我们建议在此应用中开发它们。因此，我们的目标是阐明乳腺癌中的抗血管生成抵抗的机制。乳腺癌并开发成像生物标记物，以确定最能从抗血管生成药物中受益的患者。在令人信服的初步数据指导下，我们将追求三个具体目标：（1）描述人类乳腺癌模型中的血管生成和转移负担。多尺度成像；（2）开发基于多尺度图像的乳腺癌抗血管生成耐药性计算模型；（3）为了确定非 VEGF 靶向抗血管生成/抗肿瘤生成肽治疗是否可以规避耐药性。随着时间的推移，将通过结合体内 MRI、离体定量磁共振显微镜 (μMRI)、离体微型 CT (μCT) 和激光扫描，在互补的空间尺度上创建共同注册的血管生成数据共聚焦显微镜 (LSCM) 将体内血管生成变化与肿瘤微血管和血管生成蛋白表达的变化联系起来，我们将使用 Aim1 的多尺度成像数据来开发抗血管生成耐药性的计算模型，并在实验中得到全面验证。我们将改变与抗血管生成抗性相关的模型参数，以确定肿瘤微环境的变化，这些变化可用作乳腺抗血管生成抗性的临床生物标志物。在 Aim3 项目下，我们将用一种药物治疗人类乳腺癌模型。 非 VEGF 靶向多模式抗血管生成/抗肿瘤生成肽，以确定靶向多种途径是否可以克服乳腺癌的抗血管生成耐药性，因为它融合了多尺度成像、多尺度计算模型和仿生肽的前沿进展。潜在影响涉及病理性脉管系统的其他癌症和疾病的“系统生物学”研究。拟议的研究意义重大，因为我们期望：（i）阐明抗血管生成的机制。乳腺癌患者中出现耐药性；（ii）确定潜在的基于成像的抗血管生成耐药性生物标志物，以及（iii）测试规避乳腺癌抗血管生成耐药性的疗法，最终，这些知识有可能确定新的治疗策略并降低死亡率。转移性乳腺癌。