Center for Undiagnosed Diseases at Stanford

摘要 未诊断疾病网络(UDN)增加了未诊断疾病患者获得 全国顶尖的临床医生和科学家。未确诊中心二期扩建工程 斯坦福大学的疾病将有助于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。在这里，我们提出了一个学习计划，它将(1)促进 对未确诊疾病的患者进行准确诊断；(2)通过创新提高诊断率 数据分析和集成的方法；以及(3)探索疾病的潜在机制，以加速 治疗性药物发现。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注册了统一数字域名。这将包括就诊前图表回顾和遗传咨询。 然后进行个体化访问，在此期间收集标准化的表型和环境数据。 生物样本有助于对疾病的基因组、多基因组和细胞评估。成纤维细胞的扩增和 精选病例，诱导多能干细胞(IPSC)系的产生促进了对 潜在的疾病。我们建议继续在具有以下潜力的领域实施新方法 提高诊断率。这包括用于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。我们将继续对长读测序进行评估 技术。我们将应用新的计算方法来系统地整合基因组， 转录学、代谢学和表型数据与整个医学文献，以提高诊断产量。 我们还建议通过增强的细胞和模式生物表型来促进诊断。我们会 对疑似免疫机制的未确诊病例实施免疫组学方法。在目标2中，我们 将为过渡到统一数字网络的第三阶段模式做准备。这将包括准备样品和 用于存档的数据，以及持续的网络活动，以增强UDN的价值 资助期。我们将继续努力，利用现有资源为所有参与者寻求诊断。

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.