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: 10395957
• 负责人: Wei-Jun Qian
• 金额: $ 57.38万
• 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/10395957
• 关键词: Address; Affect; Automobile Driving; Behavior; Biological Models; Breast Cancer cell line; Breast Epithelial Cells; CRISPR library; CRISPR-mediated transcriptional activation; 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.

反向敏感性分析用于识别癌症蛋白质组学特征 摘要 癌症是一种复杂的疾病，在这种疾病中，细胞信号网络中的遗传中断被认为是一种 重要的角色。癌症系统生物学的一个主要目标是建立能够预测这些影响的模型 指导治疗干预措施的基因中断(即个性化药物)。一位杰出的推动者 癌细胞生长是信号通路解除对关键调控节点突变的调控，并在 基因拷贝数(CNV)。然而，当前的数学建模方法不能充分地捕捉到 这些基因变化的影响。造成这种情况的原因包括缺乏了解的监管层级 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，缺乏能将特定基因表达或突变联系起来的总体假说。 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。它还 提出了一种利用定量蛋白质组学数据来理解癌症致病机制的新方法 细胞行为。使用超灵敏的靶向蛋白质组学平台，可以测量丰度和 蛋白质的磷酸化只存在于每个细胞数百个拷贝，我们发现信号通路 似乎只由有限数量的关键节点控制，这些节点的活动通过Low受到严格控制 丰度和反馈磷酸化。我们建议在这些发现的基础上，通过严格测试 CNV和基因突变通过转移控制来调节癌症信号通路的假说 从严格监管的节点到监管不力的节点。这将通过系统地确定关键字来实现 利用CRISPRA/I筛选正常细胞和癌细胞的调节节点，确定两者之间的关系 利用超灵敏靶向蛋白质组学研究蛋白质丰度和信号通路活性 然后使用这些数据半自动地生成 信号通路的功能拓扑。具体地说，我们建议：1)使用靶向CRISPR基因 用于识别在癌症中重要的信号通路的调控拓扑的扰动文库以及如何 它们被常见的癌症突变所干扰，2)使用CRISPR扰动和蛋白质组学数据 半自动建立癌细胞信号通路的预测模型，以及3)将建模和 微扰筛选以了解癌症中的反馈调节如何有助于耐药。这 这项工作将产生简化的、易于计算的但机械化的信号通路模型和 提供反馈和串扰电路的网络图，可用于快速绘制 细胞。最重要的是，它将为翻译蛋白质丰度和磷酸化提供一个通用平台 模式转换成癌症的“状态”快照，这可以导致预测他们对特定药物的反应。