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

• 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
• 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

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， 但是，HER2等。但是，很大一部分患者对靶向疗法没有反应或发展 随着时间的推移阻力。这意味着当前的肿瘤表征和治疗干预方法 不够准确。特别是，当前的疾病/患者亚型方法都在寻找差异 在单个基因的水平上，忽略可以保持癌症关键特征的途径级相互作用 差异。该项目的主要目的是先驱一种新的疾病/患者亚型方法 使用个性化的 途径剖面，而不是在基因水平上。推动这项工作的假设是新兴条件 对于单个患者，可以通过不同的基因和分子水平触发（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多样化，所涉及的途径可能相同。这个创新 工作是一种新型方法的发展，能够通过 有效考虑基因拓扑和途径串扰。这种方法的基础是有效的 集成多媒体和多核心数据，以利用免费信息 不同的数据类型并解决了与许多队列相关的小尺寸问题。这个项目的目标 将通过四个特定目标来实现：1a）确定个别患者的影响途径，1B）整合 突变，拷贝数变化，甲基化，microRNA和基因表达，2）综合多核病数据， 3）确定每个亚型的途径签名，4）验证所提出的途径级亚型 方法和相关风险预测通过利用公共数据以及两项临床研究的数据 在UPMC Hillman癌症中心。拟议工作的重要性在于其提供新的潜力 改善癌症管理和预后的方法和工具。从长远来看，个性化途径 分析将提高我们对疾病机制和对治疗的抵抗力的理解，从而实现 开发个性化医学的新疗法。将提供方法和工具 通过开放访问Web应用程序和Cran R软件包。