What comes next? Engaging stakeholders in governance of participant data and relationships during the sunset of large genomic medicine research initiatives

接下来是什么？

• 批准号: 10162151
• 负责人: Euan A Ashley
• 金额: $ 10万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-21 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10162151
• 关键词: Algorithms; Animal Model; Area; Award; B-Lymphocytes; Biological Assay; Caring; Cell Line; Cell model; Cells; Child Health; Collaborations; Committee Membership; Computational algorithm; Computerized Medical Record; Consent; Country; Data; Data Analyses; Detection; Development; Diagnosis; Diagnostic; Disease; Education; Eligibility Determination; Ensure; Evaluation; FDA approved; Family; Fibroblasts; Gene Silencing; Generations; Genetic Counseling; Genomic medicine; Genomics; Goals; Graph; Healthcare; Hospitals; Human; International; Investigation; Investments; Leadership; Libraries; Literature; Machine Learning; Medical; Medicine; Metagenomics; Methods; Mission; Modeling; Multiomic Data; Network-based; Ontology; Organism; Organoids; Participant; Patient Care; Patients; Pharmaceutical Preparations; Phase; Phenotype; Physicians; Play; Policy Maker; Principal Investigator; Procedures; Process; Protocols documentation; Publications; Reagent; Recording of previous events; Research; Resources; Robotics; Role; Scientist; Site; Standardization; Structure; System; T-Lymphocyte; Technology; Testing; Therapeutic; Time; Tissues; Training; Translational Research; Underserved Population; United States National Institutes of Health; Universities; Variant; Visit; accurate diagnosis; base; clinical practice; clinical research site; cohort; data integration; deep learning; drug discovery; experience; follow-up; genome-wide; genomic data; high-throughput drug screening; improved; induced pluripotent stem cell; innovation; insertion/deletion mutation; meetings; metabolomics; multiple omics; next generation; novel; novel strategies; novel therapeutics; operation; outreach; patient outreach; phenotypic data; preservation; programs; reference genome; relating to nervous system; research clinical testing; sample collection; screening; small molecule libraries; socioeconomics; stem cell biology; success; support network; technology development; tool; transcriptome sequencing; variant detection; virtual screening

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.

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。该网络的第二阶段将促进统一数字网络努力的过渡 通过扩大临床站点、改进方法和与 定期的临床实践。在这里，我们提出了一个研究方案，它将(1)促进及时、准确的诊断 (2)通过新的数据分析方法提高诊断率 以及(3)探索疾病的潜在机制，以加速治疗药物的发现。在……里面 目的1，我们建议通过包括访问前图表的方案来评估转诊到UDN的患者 复习和遗传咨询，然后进行个体化访问，在此期间标准化表型和 收集环境数据。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，在某些情况下，诱导多能干细胞(IPSC)的产生 促进对潜在疾病的科学调查。我们将扩大我们的病人外展计划， 尤其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 计划的范围和扩大其资格。在目标2中，我们建议开发和实施小说 方法在高潜力地区，以提高诊断产量。这包括用于检测 基因组的小插入和小缺失以及大规模的结构变异。我们将制定调整方案 算法使用图形参考基因组，并推广使用长读测序技术。我们会 将机器学习应用于RNA测序、代谢组和表型数据的系统集成 配合电子病历和整个医疗文献，提高诊断率。在目标3中，我们 建议通过增强的细胞和模式生物表型来促进诊断。我们会 实施免疫组学和元基因组学方法，如T细胞、B细胞和未知生物 对未确诊病例进行测序。我们将利用中等和高通量的表型鉴定方法 通过与FDA的高通量药物筛选，促进新药发现 批准的药物和大型小分子文库。在第二阶段之后，斯坦福医学已经取得了 坚定地致力于继续在斯坦福大学通过多个 百万美元的机构承诺。总而言之，我们的目标是在统一数字域名第一阶段取得成功的基础上，通过 简化流程，最大限度地扩大协作和外联，优化计算算法， 将科学研究延伸到治疗发现，促进医院参与 领导、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 远远超过了这个奖项的期限。