Unraveling the Complexities of Risk and Mechanism in Cancer

揭示癌症风险和机制的复杂性

基本信息

批准号：
10665644
负责人：
John Quackenbush
金额：
$ 65.03万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
依托单位国家：
美国
项目类别：
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

项目摘要

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.

项目摘要/摘要：癌症是一种复杂的疾病，涉及多种遗传、基因组、表观遗传、环境和其他因素。多种因素结合起来会影响一个人的风险，并最终介导癌症的发生和进展。癌症研究人员认识到这种复杂性，使用的大多数分析方法都是围绕相对而言建立的简单的近似无法捕捉到癌症的多因素性质。我和同事们致力于开发新的方法论来分析复杂性的本质这些方法的核心是定义每种表型的假设。特征网络，表型之间网络的差异可以提供对生物学的洞察机制，并且这些网络的结构和属性可以揭示驱动因素虽然已经开发了许多方法来模拟网络，我们相信，我们的方法的独特之处在于它们利用了我们对生物过程的理解，例如转录，以有原则的方式播种模型，并且通过设计，他们的目标是翻译， R35 项目弥合了数学和医学之间的差距，以支持我们对癌症的理解。将使我能够在我的成功的基础上再接再厉，通过整合来扩展我在生物系统方法方面的工作将多组学和多因素癌症数据纳入我们的模型，重点是提供对癌症的基础生物学以及治疗和控制该疾病的新方法。