Dynamic State Space Modeling of Cancer Cell Response to Therapy

癌细胞对治疗反应的动态空间建模

基本信息

批准号：
8538308
负责人：
GARRY P NOLAN
金额：
$ 114.95万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至 2016-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8538308
关键词：
11q23 Acute Myelocytic Leukemia Affect Animal Model Anthracyclines Behavior Binding Biological Assay Burkitt Lymphoma Cell Line Cell physiology Cell surface Cells Clinical Complex Computing Methodologies Cyclophosphamide Cytarabine Cytogenetics Cytolysis Data Data Set Development Disease Environment Equation Event Evolution Experimental Designs Future Gene Expression Profile Gene Proteins Gene Transfer Genes Genetic Genetic Transcription Genome Genomics Genotype Goals Heterogeneity Human In Vitro Individual Intervention Kinetics Knowledge Lead Learning MLL gene MLLT3 gene Malignant Neoplasms Mammalian Cell Measurement Measures Messenger RNA Methods Modeling Molecular Molecular Models Multiple Partners Mutation Non-Hodgkin&apos s Lymphoma Outcome Pathway interactions Patient Education Patients Phenotype Phosphorylation Physiological Post-Translational Protein Processing Printing Protein Array Protein C Proteins Proteome Proteomics RNA Interference RUNX1 gene Regulation Research Project Grants Resistance Sampling Series Shotguns Signal Transduction Signaling Protein Simulate Space Models Staging System TP53 gene Technology Therapeutic Time base cancer cell chemotherapy clinically relevant design expression vector falls genome-wide improved instrument leukemia/lymphoma mRNA Expression mathematical model model development molecular modeling molecular phenotype mouse model network models novel outcome forecast protein B protein protein interaction research study response single cell proteins t(8 21)(q22 q22)tumor tumor growth tumor microenvironment tumor progression

项目摘要

Patients with the cancers acute myelogenous leukemia (AML) and non-Hodgkins lymphoma (NHL) fall into several subtypes. Mechanistic models of how therapies, environment and genotype affect clinical outcome in AML and NHL patients are needed. Development of these models requires measuring data on transcription, signaling and protein interactions from patients in various stages of disease. This is a daunting task due to the complexity of mammalian cells. In collaborative research project 1 (RP1, Dynamic State Space Modeling of Cancer Cell Response to Therapy), we will carry out synchronized experiments to measure protein abundance, protein-interactions and protein modification in genetically defined animal models of AML and NHL in years 1-4 and in human samples in year 5. These measurements will be integrated using new computational methods to analyze the topology of regulatory and signaling networks and dynamic parameters of these networks. To facilitate this analysis, our experiments will be collected as time series and different measurement technologies (i.e., measurements of protein modification, protein levels and protein interactions) will be carried out on cells drawn from synchronized samples. This strict coordination of samples across genomic measurement technologies will enable the integration of our combined dataset into predictive molecular networks that can be used by RP2 and RPS (projects that model the evolution and spatial growth of the tumor explicitly) to predict cell state given tumor microenvironment, genotype and treatment. Our team includes primary developers of genomics technologies and our novel capabilities include scalable methods for making biochemically detailed measurements of real-time protein interaction, technologies for measuring the abundance and types of protein modifications, novel methods for shotgun proteomics and new methods for reconstructing regulatory networks. The project will be defined by our strict focus on proteins/genes critical for tumor development and proteins active in AML and NH which will insure the clinical relevance of our models.

患有癌症急性骨髓性白血病（AML）和非霍奇金斯淋巴瘤（NHL）的患者落入几种亚型。需要疗法，环境和基因型如何影响AML和NHL患者的临床结果的机理模型。这些模型的开发需要测量疾病各个阶段患者的转录，信号传导和蛋白质相互作用的数据。由于哺乳动物细胞的复杂性，这是一项艰巨的任务。在协作研究项目1（RP1，癌细胞对治疗反应的动态状态空间建模）中，我们将进行同步实验，以测量遗传定义的AML和NHL动物模型的蛋白质丰度，蛋白质交织和蛋白质修饰的AML和NHL动物模型，在1-4年内，在5年内，在5年内，这些测量型的型号将分析的方法分析。这些网络的参数。为了促进这种分析，我们的实验将作为时间序列收集，并且将在从同步样品中得出的细胞上进行不同的测量技术（即，蛋白质修饰，蛋白质水平和蛋白质相互作用的测量）。在基因组测量技术中的样品的严格协调将使我们的合并数据集整合到预测性分子网络中，RP2和RPS可以使用，RP2和RPS（这些项目明确地对肿瘤的进化和空间生长进行了模拟），以预测细胞态给定肿瘤微环境，基因型和治疗给定细胞状态。我们的团队包括基因组技术的主要开发人员，我们的新功能包括可扩展的方法，用于对实时蛋白质相互作用进行生化详细测量，用于测量蛋白质修饰的丰度和类型的技术，用于shot弹枪蛋白质组的新颖方法以及用于重建调节网络的新方法。该项目将由我们严格关注对肿瘤发育至关重要的蛋白质/基因和活性在AML和NH中的蛋白质，这将确保模型的临床相关性。