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 未确诊疾病网络 (UDN) 为患有未确诊疾病的患者提供了更多机会 国家领先的临床医生和科学家。未确诊中心二期扩建 斯坦福大学的疾病将促进 UDN 继续努力实现可持续性、改进方法、 并与常规临床实践相结合。在此，我们提出一项研究计划，该计划将 (1) 促进 对未确诊疾病患者进行准确诊断； (2)通过新方法提高诊断率 数据分析和整合的方法； (3)探索疾病的潜在机制以加速 治疗药物的发现。在目标 1 中，我们建议完成临床评估和相关工作 参与者在斯坦福大学 UDN 注册。这将包括访问前图表审查和遗传咨询 随后进行个性化访问，收集标准化表型和环境数据。 生物样本有助于对疾病进行基因组、多组学和细胞评估。成纤维细胞的扩增，并且 选定的案例中，诱导多能干细胞 (iPSC) 系的产生有利于科学研究 基础疾病。我们建议继续在高潜力领域实施新方法 提高诊断率。这包括检测小基因组插入和缺失的算法， 结构变异和差异甲基化区域。我们将继续评估长读长测序 技术。我们将应用新颖的计算方法来系统整合基因组、 将转录组学、代谢组学和表型数据与整个医学文献结合起来，以提高诊断率。 我们还建议通过增强细胞和模型生物表型来促进诊断。我们将 对疑似免疫机制的未确诊病例实施免疫学方法。在目标 2 中，我们 将准备过渡到 UDN 第三阶段模型。这将包括准备样品和 用于归档的数据，以及持续的网络活动，以增强 UDN 的价值 资助期限。我们将继续努力利用所有参与者的可用资源寻求诊断。