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

Multiscale Mathematical Modeling of Cancer Progression

癌症进展的多尺度数学模型

基本信息

批准号：
8628754
负责人：
Vito Quaranta
金额：
$ 117.89万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2004
资助国家：
美国
起止时间：
2004-09-30 至 2017-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8628754
关键词：
Affect Algorithms Antineoplastic Agents Behavior Biological Breast Breast Cancer Cell Cancer Detection Cancer Model Cancer Prevention Intervention Cancer cell line Cell Line Cell Proliferation Cell Survival Cells Clinic Clinical Clinical Trials Computer Simulation Continuing Education Coupling Data Data Set Detection Disease Doxorubicin Drug Interactions Drug resistance ERBB2 gene Education and Outreach Educational workshop Epidemiology Evolution Funding Game Theory Gene Expression Regulation Generations Genetic Health Health behavior outcomes Heterogeneity Human Image Immune Immunology Immunotherapy In Vitro Individual Intervention Laboratory Finding Lead Life Life Style Malignant - descriptor Malignant Neoplasms Malignant neoplasm of lung Measures Metabolism Methods Microscopy Molecular Molecular Genetics Molecular Profiling Molecular Target Mus Nanotechnology Natural Immunity Outcome Patients Pharmaceutical Preparations Pharmacotherapy Phase Phenotype Predisposition Quality of Care Radiation Radio Regulation Resistance Scientist Shapes Signal Pathway Signal Transduction Pathway Statistical Models Systems Biology Theoretical model Therapeutic Time Tissues Translating Tyrosine Kinase Inhibitor Variant Vertebral column Viral adaptive immunity cancer cell cancer prevention cancer risk cell motility clinical practice cost cytotoxic data modeling design drug discovery fitness hormone therapy image processing improved malignant breast neoplasm mathematical model molecular marker molecular oncology neoplastic cell non-genetic novel novel strategies predictive modeling programs radiation resistance response small molecule theories trait tumor tumor growth tumor microenvironment tumor progression

项目摘要

DESCRIPTION (provided by applicant): The overarching theme of our application is to quantify the impact of cancer cell heterogeneity in tumor growth and treatment resistance. It logically extends results from the previous funding period, pointing to phenotypic heterogeneity as key determinant of progression and invasion. We will consider heterogeneity with respect to phenotypic traits (Proliferation, Motility and Metabolism), in the ICBP-43 breast cancer cell line panel and in drug resistant breast, or radiation responsive lung, cancer cell lines. Trait heterogeneity will be quantified primarily by high-content automated microscopy and image processing. Between cell lines, trait variability will be compared as averages and distribution shapes. Within a cell line, ceil-to-cell variability (presumably non-genetic) will be represented as subpopulations by statistical modeling, e.g., bayesian information criteria and clustering algorithms. To estimate adaptability, we will measure trait variation in response to perturbations mimicking tumor microenvironment conditions. This large dataset (3 traits in >50 lines under >10 perturbations) will be input to mathematical and computational predictive models, tracking the fate of individual cancer cells and the microenvironment in space-time during tumor growth. With the experimental component, this suite of theoretical models forms a Center "Backbone" deployed towards three Projects. Project 1 will quantify adaptive advantage in cancer progression by incorporating cell trait heterogeneity data into mathematical and computational models that exploit evolution dynamics and game theory concepts. Project 2 will measure impact of trait heterogeneity and fitness cost in the rise of breast cancer resistance to first- and second-line drugs (doxorubicin, hormone therapy and HER2 tyrosine kinase inhibitors). Project 3 will attempt to improve and/or predict outcomes of radiation treatment in lung cancer cell lines by coupling experimentally defined radio-phenotype heterogeneity to predictive models. Hypotheses/predictions from Projects 1-3 will be validated in vitro and in mouse tumors, by iteration loops of experimentation and theory. Finally, we will continue education/outreach efforts, e.g., hands-on cancer modeling workshops, to attract physical and biological scientists, especially the brightest of the new generations.

描述（由申请人提供）：我们申请的首要主题是量化癌细胞异质性对肿瘤生长和治疗抗性的影响。它在逻辑上扩展了前一个资助期的结果，指出表型异质性是疾病进展和侵袭的关键决定因素。我们将考虑ICBP-43乳腺癌细胞系组和耐药乳腺癌或放射反应性肺癌细胞系中表型性状（增殖、运动和代谢）的异质性。性状异质性将主要通过高内容自动显微镜和图像处理进行量化。在细胞系之间，将性状变异性作为平均值和分布形状进行比较。在细胞系内，细胞间变异性（推测为非遗传性）将通过统计建模表示为亚群，例如，最佳信息准则和聚类算法。为了估计适应性，我们将测量响应于模拟肿瘤微环境条件的扰动的性状变化。这个大型数据集（在>10次扰动下，>50个品系中的3个性状）将被输入到数学和计算预测模型中，跟踪肿瘤生长期间单个癌细胞的命运和时空中的微环境。通过实验部分，这套理论模型形成了一个中心“骨干”，部署在三个项目中。项目1将通过将细胞性状异质性数据纳入利用进化动力学和博弈论概念的数学和计算模型来量化癌症进展中的适应性优势。项目2将测量乳腺癌对一线和二线药物（多柔比星，激素治疗和HER 2酪氨酸激酶抑制剂）耐药性上升的性状异质性和健身成本的影响。项目3将尝试通过将实验定义的放射表型异质性与预测模型相结合来改善和/或预测肺癌细胞系放射治疗的结果。项目1-3的假设/预测将通过实验和理论的迭代循环在体外和小鼠肿瘤中得到验证。最后，我们将继续开展教育/外展工作，例如，动手癌症建模研讨会，以吸引物理和生物科学家，特别是最聪明的新一代。