Multiscale Image-based Modeling of Antiangiogenic Resistance in Breast Cancer

描述（由申请方提供）：抗血管生成治疗在转移性乳腺癌治疗中的失败是由于对血管内皮生长因子（VEGF）抑制的耐药性的发展，以及我们无法确定最可能对此类治疗有反应的患者。为了阐明“抗血管生成耐药”的潜在因素并开发用于识别患者耐药的翻译生物标志物，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，并开发经过充分验证的乳腺癌抗血管生成耐药计算生物学模型。由于这种多尺度血管生成数据和模型目前还不存在，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抗血管生成耐药的机制，并开发成像生物标志物，以确定最能从抗血管生成药物中获益的患者。在令人信服的初步数据的指导下，我们将追求三个具体目标：（1）用多尺度成像表征人乳腺癌模型中的血管生成和转移负荷;（2）开发乳腺癌中抗血管生成耐药性的基于多尺度成像的计算模型;和（3）确定用非靶向VEGF的抗血管生成/抗肿瘤生成肽治疗是否可以规避耐药性。在目标1下，我们将通过结合体内MRI、离体磁共振显微镜（μMRI）、离体显微CT（μCT）和激光扫描共聚焦显微镜（LSCM），在互补的空间尺度上随时间推移创建共聚焦配准的定量血管生成数据。多尺度成像将在体内血管生成的变化，肿瘤微血管和血管生成蛋白表达的改变。在Aim 2下，我们将使用Aim 1的多尺度成像数据来开发抗血管生成抗性的计算模型，该模型在实验性乳腺癌异种移植物中得到全面验证。我们将改变与抗血管生成耐药有关的模型参数，以确定肿瘤微环境的变化，这些变化可用作乳腺癌抗血管生成耐药的临床生物标志物。在Aim 3下，我们将用 非VEGF靶向的多模式抗血管生成/抗肿瘤生成肽，以确定靶向多个途径是否可以克服乳腺癌中的抗血管生成耐药性。该方法是创新的，因为它融合了多尺度成像，多尺度计算建模和仿生肽的前沿进展。这种方法有可能影响其他癌症和涉及病理血管系统的疾病的“系统生物学”研究。这项研究意义重大，因为我们希望：（i）阐明乳腺癌患者中抗血管生成耐药性形成的机制;（ii）确定潜在的基于成像的抗血管生成耐药性生物标志物;（iii）测试规避乳腺癌抗血管生成耐药性的治疗方法。最终，这些知识有可能确定新的治疗策略并降低转移性乳腺癌的死亡率。

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.