Reverse Sensitivity Analysis for Identifying Predictive Proteomics Signatures of Cancer

用于识别癌症预测蛋白质组学特征的反向敏感性分析

基本信息

批准号：
9923630
负责人：
Wei-Jun Qian
金额：
$ 71.57万
依托单位：
BATTELLE PACIFIC NORTHWEST LABORATORIES
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-05-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9923630
关键词：
Address Affect Automobile Driving Behavior Biological Models Breast Cancer cell line Breast Epithelial Cells CRISPR library Cancer Cell Growth Cells Clustered Regularly Interspaced Short Palindromic Repeats Complex Computer Models DNA Sequence Alteration Data Development Disease Drug resistance Feedback Flow Cytometry Gene Dosage Gene Expression Gene Mutation Gene Proteins Generations Genes Genetic Lead Libraries Link Machine Learning Malignant Neoplasms Maps Measurement Measures Methodology Methods Modeling Modernization Molecular Mutate Mutation Normal Cell Pathway interactions Pattern Pharmaceutical Preparations Phenotype Phosphorylation Play Predictive Cancer Model Proteins Proteomics Proto-Oncogene Proteins c-akt Reagent Regulation Research Resistance Role Signal Pathway Signal Transduction Systems Biology Techniques Technology Testing Therapeutic Intervention Translating Work base cancer cell cancer type cell behavior design experimental study mathematical model melanoma novel strategies personalized medicine phosphoproteomics predictive modeling proteomic signature response screening targeted treatment tool

项目摘要

Title: Reverse Sensitivity Analysis for Identifying Proteomics Signatures of Cancer Abstract Cancer is a complex disease in which genetic disruptions in cell signaling networks are known to play a significant role. A major aim of cancer systems biology is to build models that can predict the impact of these genetic disruptions to guide therapeutic interventions (i.e. personalized medicine). A prominent driver of cancer cell growth is signaling pathway deregulation from mutations in key regulatory nodes and loss/gain in gene copy number (CNV). However, current mathematical modeling approaches do not adequately capture the impact of these genetic changes. Reasons for this include the poorly understood layers of regulation between gene expression and protein activity, and limitations in most modeling and protein measurement technologies. In addition, there is a paucity of overarching hypotheses that can link specific gene expression or mutation patterns to the cancer phenotype. Recent work by our group has resolved some of the technical challenges that have hindered the application of proteomics technologies to cancer systems biology research. It has also suggested a new approach for using quantitative proteomics data to understand mechanisms driving cancer cell behavior. Using an ultrasensitive, targeted proteomics platform that can measure both abundance and phosphorylation of proteins present at only hundreds of copies per cell, we found that signaling pathways appeared to be controlled by only a limited number of key nodes whose activity is tightly regulated through low abundance and feedback phosphorylation. We propose to build on these findings by critically testing the hypothesis that CNV and genetic mutations dysregulate signaling pathways in cancer by shifting control from tightly regulated nodes to poorly regulated ones. This will be done by systematically identifying key regulatory nodes of normal and cancer cells using CRISPRa/i screens, determine the relationship between protein abundance and signaling pathway activities using ultrasensitive targeted proteomics and phosphoproteomics and then use these data to semi-automatically generate mathematical models of the functional topology of the signaling pathways. Specifically, we propose to: 1) Use targeted CRISPR gene perturbation libraries to identify the regulatory topologies of signaling pathways important in cancer and how they are disrupted by common cancer mutations, 2) Use the CRISPR perturbation and proteomics data to semi-automatically build predictive models of cancer cell signaling pathways, and 3) Combine modeling and perturbation screens to understand how feedback regulation in cancer contributes to drug resistance. This work will result in simplified, computationally tractable yet mechanistic models of signaling pathways and provide network maps of feedback and crosstalk circuits that can be used to rapidly map the regulatory state of cells. Most important, it will provide a generic platform for translating protein abundance and phosphorylation patterns into a “state” snapshot of cancers that can lead to predicting their response to specific drugs.

标题：识别癌症蛋白质组学特征的反向敏感性分析抽象的癌症是一种复杂的疾病，已知细胞信号网络中的遗传破坏会产生影响癌症系统生物学的一个主要目标是建立可以预测这些影响的模型。指导治疗干预的遗传破坏（即个性化医疗）的一个重要驱动因素。癌细胞的生长是信号通路因关键调控节点突变和缺失/增加而失调的过程。然而，当前的数学建模方法并不能充分捕获基因拷贝数（CNV）。这些基因变化的影响的原因包括人们对它们之间的调控层次知之甚少。基因表达和蛋白质活性，以及大多数建模和蛋白质测量技术的局限性。此外，缺乏可以将特定基因表达或突变联系起来的总体假设我们小组最近的工作解决了一些技术挑战。也阻碍了蛋白质组学技术在癌症系统生物学研究中的应用。提出了一种使用定量蛋白质组学数据来了解癌症驱动机制的新方法使用超灵敏的靶向蛋白质组学平台，可以测量丰度和细胞行为。每个细胞中蛋白质的磷酸化仅存在数百个拷贝，我们发现信号通路似乎仅由有限数量的关键节点控制，这些节点的活动通过低我们建议通过严格测试这些发现来构建丰度和反馈磷酸化。假设 CNV 和基因突变通过转移控制来失调癌症中的信号通路从严格监管的节点到监管不力的节点，这将通过系统地识别关键节点来完成。使用 CRISPRa/i 筛选正常细胞和癌细胞的调节节点，确定之间的关系使用超灵敏靶向蛋白质组学和磷酸蛋白质组学，然后使用这些数据半自动生成数学模型具体来说，我们建议：1）使用靶向CRISPR基因。扰动库来识别癌症中重要的信号通路的调控拓扑以及如何它们受到常见癌症突变的干扰，2) 使用 CRISPR 扰动和蛋白质组学数据半自动构建癌细胞信号通路的预测模型，以及 3) 将建模与扰动筛选以了解癌症中的反馈调节如何导致耐药性。这项工作将产生简化的、计算上易于处理的信号通路机械模型提供反馈和串扰电路的网络图，可用于快速映射调节状态最重要的是，它将提供一个用于翻译蛋白质丰度和磷酸化的通用平台。将模式转化为癌症的“状态”快照，从而可以预测癌症对特定药物的反应。