Enabling new translational discoveries using a genomic data-driven nosology

使用基因组数据驱动的疾病分类学实现新的转化发现

• 批准号: 8246320
• 负责人: ATUL J BUTTE
• 金额: $ 62.54万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-30 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8246320
• 关键词: Address; Age; American; Automobile Driving; Biological; Biological Markers; Cancer Immunology Science; Classification; Clinic; Clinical; Clinical Medicine; Clinical Trials; Collaborations; Complex; Daphne plant; Data; Databases; Development; Diagnostic tests; Disease; Disease model; Drug Delivery Systems; Epidemiology; Funding; Gene Expression; Genetic; Genomics; Government; Imagery; Immune System Diseases; Informatics; Insurance; International Classification of Diseases; Journals; Knowledge; Life; Link; Malignant neoplasm of lung; Measurement; Medicine; Mercury; Messenger RNA; Methods; Modeling; Molecular; New York; Nomenclature; Ontology; Pathologist; Pathology; Pathway interactions; Pharmaceutical Preparations; Pharmacogenomics; Physicians; Proteins; Proteomics; Publications; Quantitative Trait Loci; Rare Diseases; Research; Research Personnel; Science; Serum; Signal Transduction; Structure; System; Taxonomy; Techniques; Testing; Therapeutic; Time; Tissues; Vocabulary; Work; World Health Organization; base; disease classification; health organization; human disease; knowledge base; lung small cell carcinoma; mouse model; news; novel; novel diagnostics; novel therapeutics; repository; research study; symposium; therapeutic target; tool

DESCRIPTION (provided by applicant): Many forms of biomolecular (e.g., gene expression, genetics, proteomics) and clinical (e.g., clinical biomarkers, drug targets and indications) data pertaining to many different diseases are now readily available from publicly- available data repositories and knowledge-bases. There is now an opportunity to integrate these data into a unified, globally coherent representation of human disease, or nosology. Such a nosology would express how diseases are related to one another across multiple molecular and clinical axes. In this competitive renewal, we are planning a major expansion for this project. We plan to capture data from newer public repositories with more types of molecular measurements. Inclusion of genetic and protein measurements will enable a richer modeling of diseases and disease similarity, beyond mRNA measurements. To help link the molecular changes seen in disease to genetic differences, we plan to incorporate Expression Quantitative Trait Loci (eQTLs) into our disease models, built from simultaneous genetic and expression measurements. To expand the utility of our nosology in personalized medicine, we plan to incorporate more quantitative epidemiological measurements on disease, and to model transitions between disease states using probabilistic relational modeling. We will compare our nosology with the well-known ICD-10 as well as ICD-11, under development. We will develop novel visualization methods for the complex of edges and nodes seen in nosologies. We also plan to test our nosology in two Driving Biological Projects, in small cell lung cancer and immunology and disease, specifically yielding novel diagnostics and therapeutics ready for clinical trials. PUBLIC HEALTH RELEVANCE: In this competitive renewal, building from 36 publications in the first funding period, we plan to create a new disease classification based on clinical, molecular, and epidemiological data and knowledge, and to use this classification to identify novel diagnostics and drugs for small cell lung cancer and immunological disease.

描述（由申请人提供）：与许多不同疾病相关的多种形式的生物分子（例如基因表达、遗传学、蛋白质组学）和临床（例如临床生物标志物、药物靶标和适应症）数据现在可以从公开的数据存储库和知识库中轻松获得。现在有机会将这些数据整合到统一的、全球一致的人类疾病或疾病分类学表示中。这样一种疾病分类学将表达疾病如何在多个分子和临床轴上相互关联。在这次竞争性更新中，我们计划对该项目进行重大扩建。我们计划通过更多类型的分子测量从较新的公共存储库中捕获数据。纳入遗传和蛋白质测量将能够对疾病和疾病相似性进行更丰富的建模，而不仅仅是 mRNA 测量。为了帮助将疾病中观察到的分子变化与遗传差异联系起来，我们计划将表达定量性状基因座 (eQTL) 纳入我们的疾病模型中，该模型是根据同步遗传和表达测量构建的。为了扩大我们的疾病分类学在个性化医疗中的效用，我们计划纳入更多关于疾病的定量流行病学测量，并使用概率关系模型来模拟疾病状态之间的转变。我们将把我们的疾病分类学与著名的 ICD-10 以及正在开发的 ICD-11 进行比较。我们将为分类学中看到的边缘和节点的复杂性开发新颖的可视化方法。我们还计划在小细胞肺癌和免疫学与疾病的两个驱动生物学项目中测试我们的疾病分类学，特别是为临床试验准备新的诊断和治疗方法。 公共健康相关性：在这次竞争性更新中，我们计划根据第一个资助期的 36 篇出版物创建一个新的疾病分类，该分类基于临床、分子和流行病学数据和知识，并使用该分类来确定小细胞肺癌和免疫性疾病的新诊断方法和药物。