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.

项目总结/文摘: