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：
9762881
负责人：
John Quackenbush
金额：
$ 91.61万
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，其中多种遗传，基因组，表观遗传学，环境和其他因素结合了影响一个人的风险，并最终介导癌症的发展和进展。尽管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构建的简单的近似值无法捕获癌症的多因素性质。在过去的几年中，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性质癌症等疾病。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的见解机制，这些网络的结构和特性可以阐明驱动因素疾病风险，发育和进展。尽管已经开发了许多方法来建模网络，但我们认为，我们方法的区别在于，他们利用我们对生物过程的理解，例如转录，以主要的方式查看该模型，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。这个R35项目可以让我在成功的基础上建立自己的成功，从而扩展我在系统生物学方法中的工作多运动和多因素的癌症数据融入了我们的模型，重点是提供对两者的见解癌症的潜在生物学以及治疗和管理疾病的新方法。