Unraveling the Complexities of Risk and Mechanism in Cancer

揭示癌症风险和机制的复杂性

• 批准号: 10462799
• 负责人: John Quackenbush
• 金额: $ 91.99万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10462799
• 关键词: Biological; Biological Process; Cancer Biology; Characteristics; Complex; Computing Methodologies; Data; Development; Disease; Disease Management; Epigenetic Process; Genetic; Genetic Transcription; Genomics; Goals; Heart; Light; Malignant Neoplasms; Mathematics; Mediating; Medicine; Methodology; Methods; Modeling; Nature; Phenotype; Property; Research Personnel; Risk; Seeds; Structure; Systems Biology; Woman; Work; analytical method; design; disorder risk; insight; men; multiple omics; network models; success; treatment response

Project Summary/Abstract: Cancer is a complex disease in which multiple genetic, genomic, epigenetic, environmental, and other factors combine to influence one’s risk—and ultimately to mediate cancer development and progression. While cancer researchers recognize this complexity, most analytical methods in use have been built around relatively simple approximations that fail to capture the multifactorial nature of cancer. During the past few years, my colleagues and I have worked to develop new methodological approaches to analyze the nature of complex diseases such as cancer. At the heart of these methods is the postulate that what defines each phenotype is a characteristic network, that differences in networks between phenotypes can provide insight into biological mechanisms, and that the structure and properties of these networks can shed light on the factors that drive disease risk, development, and progression. While many methods have been developed to model networks, we believe that what distinguishes our methods is that they use our understanding of biological processes, such as transcription, to seed the model in a principled way and that, by design, their goal is translational, bridging the gap between mathematics and medicine to support our understanding of cancer. This R35 project would allow me to build on my successes, expanding my work in systems biology approaches by integrating multi-omic and multi-factorial cancer data into our models with an emphasis on providing insight into both the underlying biology of cancer and new ways to treat and manage the disease.

项目概要/摘要： 癌症是一种复杂的疾病，其中多个遗传、基因组、表观遗传、环境和其他因素影响了癌症。 各种因素联合收割机影响一个人的风险，并最终介导癌症的发展和进展。而 癌症研究人员认识到这种复杂性，大多数使用的分析方法都是围绕相对 简单的近似法无法捕捉癌症的多因素性质。在过去的几年里，我 我和我的同事们一直致力于开发新的方法论途径来分析复杂性的本质， 疾病，如癌症。这些方法的核心是假设定义每个表型的是一个 特征网络，表型之间的网络差异可以提供对生物学特性的洞察。 机制，这些网络的结构和性质可以揭示驱动这些网络的因素。 疾病风险、发展和进展。虽然已经开发了许多方法来对网络进行建模， 我们相信，我们的方法的区别在于，它们使用了我们对生物过程的理解， 例如转录，以原则性的方式为模型播种，并且通过设计，他们的目标是翻译， 弥合数学和医学之间的差距，以支持我们对癌症的理解。R35项目 这将使我能够在我的成功基础上，通过整合系统生物学方法来扩展我的工作， 多组学和多因素癌症数据到我们的模型中，重点是提供对 癌症的潜在生物学以及治疗和管理疾病的新方法。