Disease subtyping guided by clinical phenotype for precision medicine

以临床表型为指导的疾病亚型精准医学

• 批准号: 10558889
• 负责人: George C. Tseng
• 金额: $ 30.5万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-03 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10558889
• 关键词: Biological; Biological Markers; Childhood Asthma; Clinical; Cluster Analysis; Collaborations; Complex; Copy Number Polymorphism; Data; Data Analyses; Data Set; Databases; Disease; Disease Progression; Exclusion; Foundations; Future; Generations; Genes; Graph; Knowledge; Letters; Literature; Lobular Carcinoma; Lung diseases; Messenger RNA; Methods; Methylation; MicroRNAs; Modeling; Molecular; Molecular Disease; Molecular Profiling; Multiomic Data; Outcome; Pathway interactions; Patients; Process; Prognosis; Proteome; Randomized; Sampling; Structure; Technology; Translating; Validation; clinical phenotype; clinical practice; clinical subtypes; clinically actionable; cohort; data integration; design verification; disease heterogeneity; disease prognosis; disorder subtype; experimental study; feature selection; high dimensionality; improved; individualized medicine; interest; machine learning method; malignant breast neoplasm; methylome; new therapeutic target; novel; patient subsets; precision medicine; survival outcome; transcriptome; treatment effect; treatment response; unsupervised learning

Abstract Unsupervised cluster analysis has been widely applied to omics data analysis for identifying molecular disease subtypes, which may present distinct disease prognosis and/or unique underlying disease mechanism. The findings can ultimately establish foundations for precision medicine. Existing disease subtyping methods in the literature are, however, purely unsupervised. The identified disease subtypes are often irrelevant to clinical outcome and cannot be translated into clinical practice. We hypothesize that an outcome-guided molecular disease subtyping framework with systematic integration of multi-omics data, clinical information and biological pathway knowledge will generate disease relevant and clinically actionable subtypes towards precision medicine. The developed methods are expected to be applicable for a wide range of complex diseases, where disease subtyping may be instrumental for finding novel therapeutic targets or improving treatment decisions. The specific aims are: (1) Develop outcome-guided clustering (OG-Clust) framework for disease subtyping using a single omics dataset. (2) Develop outcome-guided clustering (OG-Clust) framework for integrating multiple omics datasets. (3) Application and validation in breast cancer and pediatric asthma.

抽象的 无监督聚类分析已广泛应用于组学数据分析以识别分子疾病 亚型，可能呈现不同的疾病预后和/或独特的潜在疾病机制。这 研究结果最终可以为精准医学奠定基础。现有的疾病分型方法 然而，文学是完全不受监督的。已确定的疾病亚型通常与临床无关 结果无法转化为临床实践。我们假设结果导向分子 系统整合多组学数据、临床信息和生物学信息的疾病亚型框架 途径知识将产生与疾病相关且临床上可操作的亚型，以实现精准医学。 所开发的方法预计将适用于广泛的复杂疾病，其中疾病 亚型分析可能有助于寻找新的治疗靶点或改善治疗决策。这 具体目标是： (1) 使用结果引导聚类（OG-Clust）框架进行疾病亚型分析 单个组学数据集。 (2) 开发整合多组学的结果引导聚类（OG-Clust）框架 数据集。 (3)在乳腺癌和小儿哮喘中的应用和验证。