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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，在这种疾病中，多种基因、基因组、表观遗传、环境和其他因素各种因素结合在一起影响一个人的风险--并最终调节癌症的发展和进展。而当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建立在相对未能捕捉到癌症的多因素本质的简单近似。在过去几年里，我的我和同事们一直在努力开发新的方法论方法来分析复杂的本质癌症等疾病。这些方法的核心是这样一个假设，即定义每个表型的是一个特征网络，表型之间网络的差异可以提供对生物学的洞察机制，以及这些网络的结构和性质可以揭示驱动因素疾病风险、发展和进展。虽然已经开发了许多方法来对网络建模，我们认为，我们的方法的不同之处在于，它们使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，通过设计，他们的目标是翻译，弥合数学和医学之间的鸿沟，以支持我们对癌症的理解。这个R35项目将使我能够在我的成功的基础上，通过将将多组和多因素癌症数据整合到我们的模型中，重点是提供对癌症的基本生物学以及治疗和管理这种疾病的新方法。