Personalization of graphical models using multi-omics data for subtype discovery and prognosis

使用多组学数据个性化图形模型以进行亚型发现和预后

基本信息

批准号：
10743786
负责人：
Hung N Luu
金额：
$ 39.27万
依托单位：
AUBURN UNIVERSITY AT AUBURN
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-15 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10743786
关键词：
Address Apoptosis Automobile Driving Breast Cancer Patient Cancer Center Case/Control Studies Characteristics Clinical Clinical Research Communities Copy Number Polymorphism Data Data Set Defense Mechanisms Development Disease ERBB2 gene ESR1 gene Epidermal Growth Factor Receptor Estrogen Receptor alpha Event Gene Expression Genes Genetic Goals Hospitals Individual Inhibition of Apoptosis Knowledge Malignant Neoplasms Malignant neoplasm of pancreas Measurement Medicine Methodology Methods Methylation MicroRNAs Modeling Molecular Mucinous Neoplasm Multiomic Data Mutation Oncogenes Pancreas Papillary Pathway Analysis Pathway interactions Patients Privatization Process Prognosis Proliferating Public Health Resistance Resistance development Rest Sample Size Sampling Signal Transduction Solid Neoplasm Subgroup System Testing Therapeutic Intervention Time Visualization Work analysis pipeline anticancer research cancer cell cancer health disparity cancer subtypes cancer therapy clinically relevant cohort epigenome genetic signature improved individual patient innovation mRNA Expression molecular subtypes molecular targeted therapies multiple omics novel strategies overexpression pancreatic cancer patients patient oriented patient prognosis patient stratification patient subsets personalized medicine prototype public database repository response risk prediction risk stratification targeted treatment tool transcriptome transfer learning tumor web app

项目摘要

SUMMARY Recent clinical advances in cancer treatments have been attributed to targeting specific genes such as ER-𝛼, HER2, etc. However, a significant percentage of patients do not respond to targeted therapies or develop resistance over time. This implies that current methods for tumor characterization and therapeutic interventions are not sufficiently accurate. In particular, current disease/patient subtyping approaches all look for differences at the level of individual genes, ignoring pathway-level interactions that can hold key characteristics of cancer disparities. The main goal of this project is to pioneer a new approach to disease/patient subtyping that departs from the traditional paradigm: subtyping and characterization at the pathway level, using personalized pathway profiles, rather than at the gene level. The hypothesis driving this work is that an emerging condition for an individual patient can be triggered through different genes and molecular levels (e.g., transcriptome, epigenome, etc.) but might involve the same mechanism(s). This is because, while alterations of impacted genes could be very diverse between patients the pathways involved could be the same. The innovation of this work is the development of a novel approach able to compute pathway profiles of individual patients by effectively taking into account gene topology and pathway crosstalk. Fundamental to this approach is effective integration of multi-omics and multi-cohort data to take advantage of complimentary information among different data types and address the small size problem associated with many cohorts. The goal of this project will be achieved through four specific aims: 1a) identify impacted pathways in individual patients, 1b) integrate mutation, copy number variation, methylation, microRNA, and gene expression, 2) integrate multi-cohort data, 3) identify pathway signatures for each subtype, and 4) validate the proposed pathway-level subtyping methodology and associated risk prediction by leveraging public data as well as data from two clinical studies at UPMC Hillman Cancer Center. The significance of the proposed work lies on its potential to provide new methods and tools for better cancer management and prognosis. In the longer term, personalized pathway analysis will improve our understanding of disease mechanisms and resistance to treatments, enabling the development of new treatments for personalized medicine. The methods and tools will be made available through an open-access web application and a CRAN R package.

概括癌症治疗的最新临床进展归因于针对特定基因，例如 ER-𝛼、 HER2 等。然而，很大一部分患者对靶向治疗没有反应或出现这意味着当前的肿瘤表征和治疗干预方法。特别是，当前的疾病/患者亚型分析方法都在寻找差异。在个体基因水平上，忽略了可以保持癌症关键特征的途径水平相互作用该项目的主要目标是开创一种疾病/患者亚型分型的新方法，背离传统范式：在路径层面上进行子类型化和表征，使用个性化推动这项工作的假设是一种新兴的条件。对于个体患者来说，可以通过不同的基因和分子水平（例如转录组、表观基因组等）但可能涉及相同的机制，这是因为，虽然受影响的改变。患者之间的基因可能非常不同，所涉及的途径可能是相同的，这是一项创新。工作是开发一种新方法，能够通过以下方式计算个体患者的路径概况：有效考虑基因拓扑和通路串扰是该方法的基础。整合多组学和多队列数据，以利用相互之间的互补信息不同的数据类型并解决与许多队列相关的小规模问题。将通过四个具体目标来实现：1a）确定个体患者受影响的途径，1b）整合突变、拷贝数变异、甲基化、microRNA 和基因表达，2) 整合多队列数据， 3) 识别每个亚型的途径特征，4) 验证所提出的途径水平亚型利用公共数据以及两项临床研究的数据进行方法论和相关风险预测 UPMC Hillman 癌症中心的这项工作的意义在于它提供新的潜力。从长远来看，个性化路径是更好的癌症管理和预后的方法和工具。分析将提高我们对疾病机制和治疗耐药性的理解，使将提供个性化医疗新疗法的开发方法和工具。通过开放访问的 Web 应用程序和 CRAN R 包。