Predicting actionable cancer vulnerabilities enabled by mutant-directed protein-protein interactions

通过突变导向的蛋白质-蛋白质相互作用预测可操作的癌症脆弱性

• 批准号: 10528836
• 负责人: ANDREY IVANOV
• 金额: $ 18.29万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10528836
• 关键词: Address; Algorithms; Automobile Driving; Bioinformatics; Biological; Biological Models; Cancer Biology; Cancer Model; Cancer Patient; Cancer cell line; Cause of Death; Cell Survival; Cell physiology; Cells; Cessation of life; Clinical; Clinical Pharmacology; Communities; Computational Science; Computing Methodologies; DNA Sequence Alteration; Data; Data Set; Development; Environment; Genes; Genetic study; Genomics; Goals; Informatics; Lead; Malignant Neoplasms; Methods; Missense Mutation; Molecular; Mutation; National Cancer Institute; Oncogenic; Outcome; Pathway interactions; Performance; Pharmaceutical Preparations; Process; Program Development; Protein Analysis; Proteins; Proteome; Publications; Sampling; Suppressor Mutations; Systems Biology; Testing; Therapeutic; Time; Translating; Tumor Suppressor Proteins; analytical tool; base; cancer genome; cancer genomics; cancer type; clinically actionable; computerized tools; design; experience; genetic signature; genomic data; informatics tool; innovation; insight; mutant; novel; novel therapeutic intervention; personalized approach; personalized therapeutic; precision medicine; programs; protein protein interaction; protein structure; response; screening; tool; tumor; tumorigenesis

Cancer is the second leading cause of death worldwide, causing more than 10 million deaths every year. In response, tremendous efforts have been made over the past decades to understand the molecular mechanisms of tumorigenesis and inform new therapeutic strategies in cancer. Unraveling the cancer genome and proteome landscapes revealed that genomic alterations, such as missense mutations, promote tumorigenesis by rewiring networks of protein-protein interactions (PPI). However, the understanding of how mutant-directed neomorph PPIs (neoPPI) lead to the acquisition of cancer hallmarks and the discovery of neoPPI-enabled cancer vulnerabilities remain major challenges. We propose to address this challenge by developing novel computational methods termed Averon Notebook to discover actionable vulnerabilities enabled by rewired oncogenic networks. To achieve this goal, we will leverage our expertise in both cancer bioinformatics and experimental cancer biology demonstrated in numerous publications and long-time participation in the Cancer Target Discovery and Development (CTD^2) Network of the National Cancer Institute. Over the past decade, we have established comprehensive bioinformatics workflows and novel analytical tools to collect, process, integrate, and analyze different types of cancer-related data. To integrate cancer genomics data with protein- protein interaction networks and clinical compounds, we have developed the OncoPPi Portal, which has already enabled the discovery of multiple new molecular mechanisms of tumorigenesis. In this project, we will capitalize on our expertise in computational science and cancer biology to develop i) a new algorithm to determine the neoPPI-regulated biological programs, and ii) methods to determine actionable vulnerabilities in neoPPI- regulated pathways. Ultimately, this project will provide the first computational environment specially designed to rapidly identify actionable targets and pathways enabled by mutant-directed protein-protein interactions to inform target discovery in cancer.

癌症是全球第二大死亡原因，每年造成超过1000万人死亡。在 反应，在过去的几十年里，人们做出了巨大的努力来了解分子机制， 并为癌症的新治疗策略提供信息。解开癌症基因组和蛋白质组 景观显示，基因组的改变，如错义突变，促进肿瘤发生的重新布线， 蛋白质相互作用网络（PPI）。然而，对基因导向的新形态 PPI（neoPPI）导致获得癌症标志和发现neoPPI激活的癌症 脆弱性仍然是重大挑战。我们建议通过开发新的 称为Averon笔记本的计算方法，以发现通过重新布线实现的可操作漏洞 致癌网络为了实现这一目标，我们将利用我们在癌症生物信息学和 实验癌症生物学在许多出版物和长期参与癌症 国家癌症研究所的靶点发现和开发（CTD ^2）网络。在过去的十年里，我们 建立了全面的生物信息学工作流程和新颖的分析工具， 整合和分析不同类型的癌症相关数据。把癌症基因组数据和蛋白质- 蛋白质相互作用网络和临床化合物，我们已经开发了OncoPPi门户网站，它已经 使发现多种新的肿瘤发生的分子机制成为可能。在这个项目中，我们将利用 利用我们在计算科学和癌症生物学方面的专业知识，开发i）一种新的算法来确定 neoPPI调节的生物程序，和ii）确定neoPPI中可操作漏洞的方法- 调控途径。最终，该项目将提供第一个专门设计的计算环境， 快速鉴定由多核苷酸导向的蛋白质-蛋白质相互作用实现的可操作的靶标和途径， 为癌症中靶点发现提供信息。