Center for Undiagnosed Diseases at Stanford

摘要 未确诊疾病网络（UDN）增加了未确诊疾病患者获得 全国顶尖的临床医生和科学家网络第二阶段将促进UDN工作的过渡 走向可持续发展，通过扩大临床站点，完善方法，并与 定期临床实践。在这里，我们提出了一个研究计划，将（1）促进及时，准确的诊断 未确诊疾病的患者;（2）通过新的数据分析方法提高诊断率 和整合;（3）探索疾病的潜在机制，以加速治疗药物的发现。在 目的1，我们建议通过包括访视前图表的方案来评估转诊至UDN的患者 审查和遗传咨询，然后进行个性化访问，在此期间，标准化的表型和 收集环境数据。生物样品有助于疾病的基因组、多组学和细胞评估。 成纤维细胞的扩增，以及在选定的情况下，诱导多能干细胞（iPSC）系的产生 有助于对潜在疾病进行科学研究。我们将扩大我们的病人外展计划， 特别是对服务不足的人群。我们将扩展我们基于UDN的基因组医学教育 在范围和扩大其资格的方案。在目标2中，我们建议开发和实施新的 在高潜力领域的方法，以提高诊断率。这包括用于检测 小的基因组插入和缺失以及大规模的结构变异。我们将发展联盟 使用图参考基因组的算法，并促进长读序测序技术的使用。我们将 将机器学习应用于RNA测序、代谢组学和表型数据的系统整合 与电子病历和整个医学文献一起使用，以提高诊断率。在目标3中， 建议通过增强的细胞和模式生物表型来促进诊断。我们将 实施免疫组学和宏基因组学方法，例如T细胞、B细胞和未知生物体 未确诊病例的测序。我们将利用中通量和高通量表型分析方法， iPS衍生细胞，并通过高通量药物筛选促进新药发现， 批准的药物和大规模小分子文库。除了第二阶段，斯坦福大学医学已经取得了 坚定地致力于通过多方面的合作，继续斯坦福大学的未确诊疾病中心， 100万美元的机构承诺。总括而言，我们的目标是在《城市发展网络》第一阶段成功的基础上， 简化流程，最大限度地提高合作和推广，优化计算算法， 将科学研究扩展到治疗发现，并促进医院的参与 领导人、临床医生、科学家、政策制定者和慈善家，以确保这一国家资源的持续 在这个奖项的持续时间之外。

Abstract The Undiagnosed Diseases Network (UDN) has increased access for patients with undiagnosed diseases to the nation’s leading clinicians and scientists. Phase II of the Network will facilitate the transition of UDN efforts toward sustainability, through the expansion of clinical sites, refinement of methods, and integration with regular clinical practice. Here, we propose a program of study that will (1) facilitate timely, 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 evaluate patients referred to the UDN through a protocol that includes 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 will expand our program of patient outreach, particularly to under-served populations. We will extend our UDN-based genomic medicine educational program both in scope and by broadening its eligibility. In Aim 2, we propose to develop and implement novel methods in areas of high potential to increase diagnostic yield. This includes algorithms for the detection of small genomic insertions and deletions as well as large scale structural variation. We will develop alignment algorithms using graph reference genomes and promote the use of long-read sequencing technologies. We will apply machine learning to the systematic integration of RNA sequencing, metabolomic, and phenotypic data with the electronic medical record and the entire medical literature to improve diagnostic yield. In Aim 3, we propose to facilitate diagnosis through enhanced cellular and model organisms phenotyping. We will implement immunomic and metagenomic approaches such as T cell, B cell and unknown organism sequencing for undiagnosed cases. We will utilize methods for moderate- and high-throughput phenotyping of iPS-derived cells and promote novel drug discovery via high throughput drug screening both with FDA- approved drugs and large scale small molecule libraries. Beyond Phase II, Stanford Medicine has made a strong commitment to the continuation of the Center for Undiagnosed Diseases at Stanford through a multi- million dollar institutional commitment. In summary, we aim to build on the success of Phase I of the UDN by streamlining processes, maximizing collaboration and outreach, optimizing computational algorithms, extending scientific investigation towards therapeutic discovery, and promoting engagement of hospital leaders, clinicians, scientists, policy-makers, and philanthropists to ensure this national resource is sustained long beyond the duration of this award.