权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Predictive experiment-based multiscale models of angiogenesis in breast cancer

基于预测实验的乳腺癌血管生成多尺度模型

基本信息

批准号：
9321588
负责人：
ALEKSANDER S. POPEL
金额：
$ 52.61万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-02-13 至 2019-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9321588
关键词：
Alpha Cell American Cancer Society Angiogenic Factor Antimetastatic Agent Binding Blood Blood Vessels Blood Volume Brain Breast Cancer Model Breast Cancer cell line Cancer Biology Cancer Etiology Cancer cell line Cell Communication Cell Line Cell Surface Receptors Cells Cessation of life Characteristics Clinical Data Computer Simulation Computers Coupled Data Development Diagnosis Differential Equation Diffuse Dimensions Drug Kinetics ERBB2 gene Epithelial Cells Estrogens Extracellular Matrix Family Fatty acid glycerol esters Female Goals Growth Human Hybrids Hypoxia IL6 gene Image Imaging Techniques Immune Immunohistochemistry Intercellular Fluid Interleukin-6 Lead Ligands Luciferases Lung Lymphatic Lymphatic Endothelial Cells Lymphatic vessel MCF7 cell MDA MB 231 Malignant Neoplasms Malignant neoplasm of lung Mammary Neoplasms Mammary gland Measurement Mesenchymal Metastatic Neoplasm to the Lung Microscopy Modeling Molecular Molecular Computations Mus NRP1 gene Neoadjuvant Therapy Neoplasm Metastasis Operative Surgical Procedures Organ PGF gene Patients Pharmaceutical Preparations Pharmacodynamics Play Primary Neoplasm Process Progesterone RANTES Regimen Regulation Research Role Signal Transduction Site Source Testing Therapeutic Therapeutic Intervention Tissues Transport Process Tumor Angiogenesis Tumor stage United States VEGFA gene Validation Vascular Endothelial Growth Factor Receptor-1 Vascular Endothelial Growth Factors Vascular Permeabilities Vascular blood supply Woman Xenograft procedure angiogenesis base bone cancer cell cancer subtypes cell type chemokine computer studies cytokine effective therapy experimental study in vivo Model insight killings malignant breast neoplasm molecular modeling multi-scale modeling neoplastic cell outcome forecast pharmacodynamic model pharmacokinetic model public health relevance receptor reconstruction screening targeted agent therapeutic biomarker therapeutic target three-dimensional modeling triple-negative invasive breast carcinoma tumor tumor growth tumor microenvironment tumor progression tumor xenograft

项目摘要

DESCRIPTION (provided by applicant): This research will focus on triple-negative breast cancer (TNBC), which is highly metastatic, has the worst prognosis among breast cancer subtypes, and is lacking effective therapies. Interactions between different cell types in the tumor microenvironment and metastatic niches are determinants of metastatic progression. In particular, tumor angiogenesis plays an important role since tumors require blood supply to grow and metastasize. A quantitative understanding of the complexity of these interactions is presently lacking. To achieve a better understanding of these processes, the development of predictive experiment-based molecular-detailed computational models of tumor growth and metastasis is necessary. The long-term goal of this project is to develop experiment-based mechanistic models of breast cancer and apply them to modeling therapeutic interventions. Specifically, we will use experimental and computational approaches to: (1) investigate key angiogenic factors, cytokines and chemokines in the progression of breast tumors to metastases; (2) investigate the characteristics of lung metastasis, the most common site for TNBC metastases; (3) test anti-metastatic agents by targeting selective cytokines, angiogenic factors and chemokines. The computational developments will be tightly coupled to the cutting-edge imaging techniques at the molecular, cellular, microvascular, and tissue levels. Invasive human breast cancer cell lines will be used to generate orthotopic xenografts in the mammary fat pad of female mice. The measurements will include the characterization and localization of receptor and ligand expression for a wide range of molecules of the VEGF family together with selected cytokines and chemokines at different stages of tumor growth and metastasis, such as interleukin-6 and CCL5; temporal and spatial development of hypoxia and microvasculature in growing tumors, and functional characteristics of the tumor vasculature and interstitium, e.g. blood volume, vascular permeability, diffusive transport in the tumor extracellular matrix (ECM). Immunohistochemistry and 3D microscopy will be used to characterize lung metastases. Part of these data will serve as the input to computational models and part used for model validation. Several therapeutic, anti-metastatic agents targeting selective cytokines, chemokines and angiogenic factors will be used, and their molecular interactions and transport will be modeled using ordinary differential equation-based compartmental models, three-dimensional partial differential equation-based models, agent-based models, and hybrid models. The research will contribute to a fundamental understanding of breast cancer biology, to the identification of therapeutic targets and biomarkers, and to a quantitative interpretation of clinical data. The synergistic combination of computational and experimental studies will provide significant insights into metastatic TNBC.

描述（申请人提供）：本研究将聚焦于三阴性乳腺癌（triple negative breast cancer， TNBC），这是乳腺癌亚型中转移性高、预后最差的一种，缺乏有效的治疗方法。肿瘤微环境和转移壁龛中不同细胞类型之间的相互作用是转移进展的决定因素。特别是，肿瘤血管生成起着重要的作用，因为肿瘤的生长和转移需要血液供应。目前还缺乏对这些相互作用复杂性的定量理解。为了更好地了解这些过程，有必要开发基于预测实验的肿瘤生长和转移的分子详细计算模型。该项目的长期目标是开发基于实验的乳腺癌机制模型，并将其应用于治疗干预的建模。具体来说，我们将使用实验和计算方法：(1)研究乳腺癌肿瘤转移过程中的关键血管生成因子、细胞因子和趋化因子；(2)探讨TNBC最常见的转移部位——肺转移的特点；(3)通过靶向选择性细胞因子、血管生成因子和趋化因子来检测抗转移药物。计算的发展将与分子、细胞、微血管和组织水平的尖端成像技术紧密结合。侵袭性人乳腺癌细胞系将用于在雌性小鼠乳腺脂肪垫中产生原位异种移植物。测量将包括表征和定位广泛的VEGF家族分子的受体和配体表达，以及肿瘤生长和转移不同阶段的选定细胞因子和趋化因子，如白细胞介素-6和CCL5；肿瘤生长过程中缺氧和微血管的时空发育，肿瘤血管和间质的功能特征，如血容量、血管通透性、肿瘤细胞外基质（ECM）的弥漫性运输。免疫组织化学和三维显微镜将用于表征肺转移。这些数据的一部分将作为计算模型的输入，一部分将用于模型验证。将使用几种针对选择性细胞因子、趋化因子和血管生成因子的治疗性、抗转移性药物，它们的分子相互作用和运输将使用基于常微分方程的室室模型、基于三维偏微分方程的模型、基于agent的模型和混合模型进行建模。这项研究将有助于对乳腺癌生物学的基本理解，对治疗靶点和生物标志物的确定，以及对临床数据的定量解释。计算和实验研究的协同结合将为转移性TNBC提供重要的见解。