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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