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A Systems Approach To Iron Metabolism In Cancer Cells

癌细胞铁代谢的系统方法

基本信息

批准号：
8191693
负责人：
REINHARD LAUBENBACHER
金额：
$ 18.2万
依托单位：
VIRGINIA POLYTECHNIC INST AND ST UNIV
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-08-01 至 2013-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8191693
关键词：
Appointment Binding Biochemical Pathway Bioinformatics Breast Breast Cancer Treatment Cancer Biology Cells Collaborations Complex Data Empiricism Epithelial Epithelial Cells Epithelium Exhibits Exposure to Faculty Feedback Future Generations Goals Growth Health Human Institutes Iron Joints Lead Learning Malignant - descriptor Malignant Neoplasms Mammary gland Messenger RNA Metabolic Metabolism Modeling Neoplasm Metastasis Nodal Normal Cell Pathogenesis Phenotype Proteins Publications Reactive Oxygen Species Recording of previous events Regulation Regulator Genes Research Personnel Research Project Grants Role Scientist Signal Transduction System Systems Biology Telephone Testing Therapeutic Time Universities Virginia Visit Work cancer cell career driving force experience forest human tissue insight interdisciplinary collaboration iron metabolism malignant breast neoplasm malignant phenotype mathematical model medical schools new therapeutic target novel strategies post-doctoral training predictive modeling professor protein metabolite symposium therapeutic target tool

项目摘要

DESCRIPTION (provided by applicant): Computational systems biology has brought many new insights to cancer biology through the quantitative analysis of metabolic, gene regulatory and signaling networks. The goal of this project is to apply a systems biology approach to the understanding of iron metabolism in normal breast epithelium as it transitions to breast cancer. In recent years, much has been learned about iron metabolism, revealing a complex regulatory network with intertwined feedback loops. Proteins of iron metabolism exhibit substantial changes as epithelial cells undergo malignant transformation; further, intracellular labile iron contributes to the generation of reactive oxygen species, which have been implicated in the pathogenesis of breast cancer. However, intracellular iron metabolism has not yet benefited from a systems biology approach. The proposed project will test the hypothesis that iron is regulated differently in normal and malignant cells. This will be done by constructing predictive mathematical models of a core iron metabolism network in normal breast cells. These models will then be used to formulate hypotheses about changes in the network that occur as cells transition to a cancer phenotype. These hypotheses will then be tested experimentally. Carefully gathered time course data will be used to construct, validate, refine, and to test the predictive power of the models. Although normal and cancer cells exhibit substantial differences in proteins of iron metabolism, previous attempts to exploit these differences to therapeutic advantage have been largely empirical and disappointing. The project goal is to move beyond empiricism to a rational predictive model that will enable an understanding of basic forces that drive changes in iron metabolism during malignant progression. The ultimate aim is to use this approach not only to understand the role of iron in cancer formation, growth and metastasis, but to identify key nodal points that may represent new therapeutic targets in the future, a potentially very high impact on human health. PUBLIC HEALTH RELEVANCE: This project impacts our understanding of the role of iron metabolism in the transformation of epithelial breast cells to malignancy. It is relevant to the search for potential new therapeutic targets for the treatment of breast cancer.

描述(申请人提供)：计算系统生物学通过对代谢、基因调节和信号网络的定量分析，为癌症生物学带来了许多新的见解。这个项目的目标是应用系统生物学的方法来了解正常乳腺上皮向乳腺癌转化过程中的铁代谢。近年来，人们对铁代谢有了很多了解，揭示了一个复杂的调控网络，其中有相互交织的反馈回路。随着上皮细胞的恶性转化，铁代谢的蛋白质发生了实质性的变化；此外，细胞内活性铁参与了活性氧的产生，这与乳腺癌的发病机制有关。然而，细胞内的铁代谢尚未从系统生物学方法中受益。这项拟议的项目将检验铁在正常细胞和恶性肿瘤细胞中受到不同调控的假设。这将通过构建正常乳腺细胞中核心铁代谢网络的预测数学模型来实现。然后，这些模型将被用来阐明当细胞转变为癌症表型时发生的网络变化的假设。然后，这些假设将得到实验验证。仔细收集的时间进程数据将用于构建、验证、改进和测试模型的预测能力。尽管正常细胞和癌细胞在铁代谢的蛋白质上表现出很大的差异，但以前利用这些差异来治疗优势的尝试主要是经验上的，令人失望。该项目的目标是超越经验主义，进入一个合理的预测模型，使人们能够理解在恶性进展过程中推动铁代谢变化的基本力量。最终目的是使用这一方法不仅了解铁在癌症形成、生长和转移中的作用，而且确定可能代表未来新的治疗靶点的关键结点，这可能对人类健康产生非常高的影响。公共卫生相关性：这个项目影响了我们对铁代谢在乳腺上皮细胞向恶性转化过程中的作用的理解。这对于寻找治疗乳腺癌的潜在新靶点具有重要意义。