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

• 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
• 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

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：用于识别癌症蛋白质组学特征的反向灵敏度分析 抽象的 癌症是一种复杂的疾病，其中已知细胞信号网络中的遗传破坏可以发挥作用 重要作用。癌症系统生物学的主要目的是建立可以预测这些影响的模型 引导热干预措施（即个性化医学）的遗传破坏。一个杰出的司机 癌细胞的生长是关键调节节点突变的信号通路通路，并在 基因拷贝数（CNV）。但是，当前的数学建模方法不能充分捕获 这些遗传变化的影响。原因包括在 大多数建模和蛋白质测量技术中的基因表达和蛋白质活性以及局限性。 另外，总体假设很少可以连接特定的基因表达或突变 癌症表型的模式。我们小组最近的工作解决了一些技术挑战 阻碍了蛋白质组学技术在癌症系统生物学研究中的应用。它也有 提出了一种使用定量蛋白质组学数据来了解驱动癌症的机制的新方法 细胞行为。使用超敏感的靶向蛋白质组学平台，可以衡量丰度和 蛋白质的磷酸化仅以每个细胞的数百份，我们发现信号通路 似乎仅由有限数量的关键节点控制，其活动受到低调的调节 抽象和反馈磷酸化。我们建议通过批判性测试来建立这些发现 假设CNV和基因突变通过转移对照来使癌症中的信号传导途径失调 从严格调节的节点到受调节良好的节点。这将通过系统地识别密钥来完成 使用CRISPRA/I屏幕对正常和癌细胞的调节淋巴结，确定 蛋白质丰度和信号通路活动使用超敏感的靶向蛋白质组学和 磷蛋白质组学，然后使用这些数据半自动地生成数学模型 信号通路的功能拓扑。具体来说，我们建议：1）使用靶向CRISPR基因 扰动文库以确定信号通路的调节拓扑中重要 它们被普通癌症突变所破坏，2）使用CRISPR扰动和蛋白质组学数据 半自动上建立癌细胞信号通路的预测模型，3）结合建模和 扰动筛选以了解癌症的反馈调节如何促进耐药性。这 工作将导致信号通路的简化，计算上可拖动但机械的机械模型 提供反馈和串扰电路的网络图，可用于快速映射监管状态 细胞。最重要的是，它将提供一个通用平台，用于翻译蛋白质丰度和磷酸化 模式进入癌症的“状态”快照，可以预测其对特定药物的反应。