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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：
8756923
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/8756923
关键词：
Accounting American Cancer Society Angiogenic Factor Binding Biological Markers Blood Blood Vessels Blood Volume Brain Breast Cancer Cell Breast Cancer Model 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 Diagnostic Neoplasm Staging Differential Equation 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 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 Staging 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 cell type chemokine computer studies cytokine effective therapy in vivo Model insight killings malignant breast neoplasm multi-scale modeling neoplastic cell outcome forecast pharmacodynamic model public health relevance receptor reconstruction research study screening 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)：这项研究将集中在三阴性乳腺癌(TNBC)，它是高度转移性的，在乳腺癌亚型中预后最差，缺乏有效的治疗方法。肿瘤微环境中不同细胞类型和转移壁龛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作用，因为肿瘤需要血液供应才能生长和转移。目前还缺乏对这些相互作用的复杂性的定量了解。为了更好地了解这些过程，开发基于实验的预测性分子详细的肿瘤生长和转移计算模型是必要的。该项目的长期目标是开发基于实验的乳腺癌机制模型，并将其应用于模拟治疗干预措施。具体地说，我们将使用实验和计算方法来：(1)研究乳腺癌向转移发展过程中的关键血管生成因子、细胞因子和趋化因子；(2)研究肺转移的特征，这是TNBC转移最常见的部位；(3)通过靶向选择性细胞因子、血管生成因子和趋化因子来测试抗转移药物。计算技术的发展将与分子、细胞、微血管和组织层面的尖端成像技术紧密结合。侵袭性的人类乳腺癌细胞株将被用来在雌性小鼠的乳房脂肪垫中产生原位异种移植。这些测量将包括肿瘤生长和转移不同阶段的多种受体和配体以及选定的细胞因子和趋化因子(如白介素6和CCL5)的特征和定位；生长肿瘤中缺氧和微血管的时空发展；以及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)中的扩散运输。免疫组织化学和3D显微镜将用于确定肺转移的特征。这些数据的一部分将作为计算模型的输入，另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、抗转移药物，它们的分子相互作用和运输将使用基于常微分方程式的隔室模型、基于三维偏微分方程的模型、基于试剂的模型和混合模型来模拟。这项研究将有助于对乳腺癌生物学的基本了解，有助于确定治疗靶点和生物标记物，并有助于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。