Center for Undiagnosed Diseases at Stanford

斯坦福大学未确诊疾病中心

• 批准号: 10600493
• 负责人: Euan A Ashley
• 金额: $ 61.7万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10600493
• 关键词: Address; Administrative Supplement; Algorithms; Animal Model; Archives; Area; Award; Caring; Cell Line; Cell model; Cryopreservation; Data; Data Analyses; Data Coordinating Center; Detection; Diagnosis; Diagnostic; Disease; Enrollment; Evaluation; Evaluation Research; Family member; Fibroblasts; Funding; Future; Generations; Genetic; Genetic Counseling; Genomics; Immune; Investigation; Letters; Literature; Medical; Medical Records; Methods; Mission; Modeling; Participant; Patient Care; Patients; Phase; Phenotype; Process; Research; Resources; Sampling; Scientist; Site; Standardization; Technology; Therapeutic; Time; Translational Research; Variant; Visit; Work; accurate diagnosis; archive data; archived data; base; biobank; clinical practice; clinical research site; cohort; cost; data archive; data integration; drug discovery; experience; gene function; genome sequencing; genomic data; improved; induced pluripotent stem cell; insertion/deletion mutation; metabolomics; multiple omics; non-genetic; novel; novel strategies; phenotypic data; preservation; proband; programs; research clinical testing; sample collection; stem cell biology; technology development; transcriptomics

ABSTRACT The Undiagnosed Diseases Network (UDN) has increased access for patients with undiagnosed diseases to the nation’s leading clinicians and scientists. The extension of Phase II of the Center for Undiagnosed Diseases at Stanford will facilitate the continuation of UDN efforts toward sustainability, refinement of methods, and integration with regular clinical practice. Here, we propose a program of study that will (1) facilitate accurate diagnosis of patients with undiagnosed diseases; (2) improve diagnostic rates through novel approaches to data analysis and integration; and (3) explore underlying mechanisms of disease to accelerate therapeutic drug discovery. In Aim 1, we propose to complete clinical evaluations and associated work for participants enrolled in the UDN at Stanford. This will include pre-visit chart review and genetic counseling followed by an individualized visit during which standardized phenotypic and environmental data are collected. Biosamples facilitate genomic, multi-omic, and cellular evaluation of disease. Expansion of fibroblasts and, in selected cases, generation of induced Pluripotent Stem Cell (iPSC) lines facilitates scientific investigation of the underlying diseases. We propose to continue implementation of novel methods in areas of high potential to increase diagnostic yield. This includes algorithms for the detection of small genomic insertions and deletions, structural variation and differentially methylated regions. We will continue evaluation of long-read sequencing technologies. We will apply novel computational approaches for systematic integration of genomic, transcriptomic, metabolomic, and phenotypic data with the entire medical literature to improve diagnostic yield. We also propose to facilitate diagnosis through enhanced cellular and model organisms phenotyping. We will implement immunomic approaches for undiagnosed cases with suspected immune mechanisms. In Aim 2, we will prepare for the transition to the Phase III model of the UDN. This will include preparing both samples and data for archiving, together with continuing network activities to enhance the value of the UDN beyond the funding period. We will continue our efforts to seek diagnosis using available resources for all participants.

抽象的 未诊断的疾病网络（UDN）的患者的访问量增加了 美国的主要临床医生和科学家。未诊断中心II期的扩展 斯坦福大学的疾病将促进UDN为可持续性的努力，方法的完善， 并与定期的临床实践相结合。在这里，我们提出了一个研究计划，该计划将（1）促进 精确诊断未诊断的疾病患者； （2）通过小说提高诊断率 数据分析和集成的方法； （3）探索疾病的潜在机制以加速 治疗药物发现。在AIM 1中，我们建议完成临床评估和相关工作 参与者参加了斯坦福大学的UDN。这将包括访问前图表审查和遗传咨询 然后进行个性化访问，在此期间收集了标准化的表型和环境数据。 生物样本促进了疾病的基因组，多词和细胞评估。膨胀成纤维细胞和 选定的病例，诱导多能干细胞（IPSC）系的生成促进了科学研究 潜在疾病。我们建议继续在高潜力领域的新方法实施 增加诊断产量。这包括用于检测小基因组插入和缺失的算法， 结构变异和不同的甲基化区域。我们将继续评估长阅读测序 技术。我们将应用新颖的计算方法进行基因组的系统整合， 具有整个医学文献的转录组，代谢组和表型数据，以提高诊断产量。 我们还建议通过增强的细胞和模型生物表型来促进诊断。我们将 针对具有可疑免疫机制的未诊断病例的实施免疫学方法。在AIM 2中，我们 将为向UDN的III阶段模型的过渡做准备。这将包括准备两个样品和 归档的数据以及继续进行网络活动，以增强UDN的价值 资金期。我们将继续努力为所有参与者使用可用资源寻求诊断。