BUILDING CELL TYPE-SPECIFIC SIGNALING MODELS IN BREAST CANCER CELL LINES

在乳腺癌细胞系中构建细胞类型特异性信号传导模型

• 批准号: 8365480
• 负责人: PETER Karl SORGER
• 金额: $ 2.87万
• 依托单位: BATTELLE PACIFIC NORTHWEST LABORATORIES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8365480
• 关键词: Behavior; Biology; Breast Cancer Cell; Cancer cell line; Cell Line; Cells; Data; Development; Drug effect disorder; Exposure to; Funding; Genetic Transcription; Goals; Grant; Growth Factor Receptors; Kinetics; Mass Spectrum Analysis; Measures; Messenger RNA; Modeling; National Center for Research Resources; Pharmaceutical Preparations; Pilot Projects; Principal Investigator; Proteins; Proteome; Proteomics; Protocols documentation; Recombinant Proteins; Relative (related person); Research; Research Infrastructure; Resources; Signal Transduction; Signal Transduction Pathway; Signaling Protein; Source; Techniques; Therapeutic; Tissues; Transcript; United States National Institutes of Health; Western Blotting; base; cell type; cost; improved; mathematical model; novel; predictive modeling; research study; tool

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Mathematical modeling of signal transduction pathways is used to understand the actions of growth factor receptors, to predict the consequences of exposure to therapeutic drugs, and to devise optimal treatments. Biochemically realistic models constructed to date suffer from the fact that few if any of the concentrations of key signaling proteins have been determined experimentally. Thus, concentrations must be estimated or fitted computationally, greatly limiting the ability to create accurate, predictive models. To determine the relative concentrations of signaling proteins in human breast cancer cell lines we will apply a novel mass spectrometry technique. Protein data will be compared to data from transcription profiles, enabling us to better understand the connection between mRNA and protein levels. Selected proteins and transcripts will also be measured using Western blotting and qPCR, while recombinant proteins will serve as absolute standards. Measured protein and mRNA levels will then be used to inform kinetic models with the specific goal of explaining cell-type specific signaling dynamics. The proposed pilot study will contribute to two related fields. For proteomics, the ability to measure the concentrations of multiple proteins and to understand the correlation between mRNA and protein levels will be a powerful tool. It will enable more general proteomic characterization of cell lines, tissues and primary cells, and reveal whether mRNA arrays and qPCR experiments are useful for understanding protein-based mechanisms. For modeling, quantifying the proteome will enable us decrease the number of free parameters and improve model identifiability. A full proteomic characterization might make it possible to predict behaviors in different cells on the basis of variability in protein levels alone. Taken together, these developments should accelerate our ability to predict the efficacy of drug action and ultimately to personalize therapeutic protocols.

该子项目是利用资源的众多研究子项目之一 由 NIH/NCRR 资助的中心拨款提供。子项目的主要支持 并且子项目的主要研究者可能是由其他来源提供的， 包括其他 NIH 来源。 子项目可能列出的总成本 代表子项目使用的中心基础设施的估计数量， NCRR 赠款不直接向子项目或子项目工作人员提供资金。 信号转导途径的数学模型用于了解生长因子受体的作用，预测接触治疗药物的后果，并设计最佳治疗方法。迄今为止构建的生化真实模型的缺点是，关键信号蛋白的浓度很少（如果有的话）是通过实验确定的。因此，必须通过计算来估计或拟合浓度，这极大地限制了创建准确的预测模型的能力。 为了确定人类乳腺癌细胞系中信号蛋白的相对浓度，我们将应用一种新颖的质谱技术。蛋白质数据将与转录谱数据进行比较，使我们能够更好地理解 mRNA 和蛋白质水平之间的联系。选定的蛋白质和转录本也将使用蛋白质印迹和 qPCR 进行测量，而重组蛋白质将作为绝对标准。然后，测量的蛋白质和 mRNA 水平将用于为动力学模型提供信息，其具体目标是解释细胞类型特定的信号动力学。 拟议的试点研究将有助于两个相关领域。对于蛋白质组学来说，测量多种蛋白质浓度并了解 mRNA 和蛋白质水平之间的相关性将是一个强大的工具。它将能够对细胞系、组织和原代细胞进行更一般的蛋白质组学表征，并揭示 mRNA 阵列和 qPCR 实验是否有助于理解基于蛋白质的机制。对于建模而言，量化蛋白质组将使我们能够减少自由参数的数量并提高模型的可识别性。完整的蛋白质组表征可能使得仅根据蛋白质水平的变异性来预测不同细胞的行为成为可能。总而言之，这些进展应该会加速我们预测药物作用功效并最终制定个性化治疗方案的能力。