Reverse Sensitivity Analysis for Identifying Predictive Proteomics Signatures of Cancer

标题：用于识别癌症蛋白质组学特征的反向灵敏度分析 摘要 癌症是一种复杂的疾病，已知细胞信号网络中的遗传破坏在其中发挥了重要作用。 重要的角色。癌症系统生物学的一个主要目标是建立模型，可以预测这些影响。 遗传破坏，以指导治疗干预（即个性化医疗）。一个杰出的司机， 癌细胞生长是信号通路的失调，来自关键调控节点的突变和 基因拷贝数（CNV）。然而，当前的数学建模方法不能充分地捕捉 这些基因变化的影响。其原因包括人们对监管层的理解不足， 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设 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 提出了一种新的方法，利用定量蛋白质组学数据来了解癌症的驱动机制， 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 磷酸化的蛋白质目前在每个细胞只有数百个拷贝，我们发现， 似乎只由有限数量的关键节点控制，这些节点的活动通过低水平的 丰度和反馈磷酸化。我们建议在这些发现的基础上， CNV和基因突变通过改变控制来失调癌症信号通路假说 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的 使用CRISPRa/i筛选正常和癌细胞的调节节点，确定 使用超灵敏靶向蛋白质组学的蛋白质丰度和信号通路活性， 磷酸化蛋白质组学，然后使用这些数据半自动生成数学模型的磷酸化蛋白质组学， 信号通路的功能拓扑结构。具体而言，我们建议：1）使用靶向CRISPR基因 干扰库，以确定在癌症中重要的信号传导途径的调控拓扑结构，以及如何 它们被常见的癌症突变破坏，2）使用CRISPR扰动和蛋白质组学数据， 半自动地建立癌细胞信号传导途径的预测模型，以及3）联合收割机建模和 干扰筛选，以了解癌症中的反馈调节如何有助于耐药性。这 工作将导致简化，计算上易于处理，但机制模型的信号通路， 提供反馈和串扰电路的网络图，可用于快速映射 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 模式转化为癌症的“状态”快照，可以预测它们对特定药物的反应。

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.