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.

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 该网络的第二阶段将促进 UDN 工作的过渡。 通过扩大临床地点、改进方法以及与 在此，我们提出了一项研究计划，该计划将 (1) 促进及时、准确的诊断。 患有未确诊疾病的患者；（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。 目标 1，我们建议通过包含预访图表的方案来评估转介至 UDN 的患者 审查和遗传咨询，然后进行个性化访问，在此期间标准化表型和 收集环境数据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，以及在特定情况下生成诱导多能干细胞 (iPSC) 系 促进对潜在疾病的科学调查。我们将扩大患者外展计划， 特别是针对服务不足的人群，我们将扩展基于 UDN 的基因组医学教育。 在目标 2 中，我们建议制定和实施新颖的计划。 提高诊断率的高潜力领域的方法，其中包括检测算法。 我们将开发小基因组插入和缺失以及大规模结构变异。 我们将使用图参考基因组的算法并推广长读长测序技术的使用。 将机器学习应用于 RNA 测序、代谢组学和表型数据的系统集成 在目标 3 中，我们利用电子病历和整个医学文献来提高诊断率。 我们将建议通过增强细胞和模型生物表型来促进诊断。 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 我们将利用中通量和高通量表型分析方法对未确诊病例进行测序。 iPS 衍生细胞并通过 FDA 的高通量药物筛选促进新药发现 除了第二阶段之外，斯坦福大学医学中心还建立了一个经过批准的大型小型药物库。 坚定致力于通过多方面的努力继续维持斯坦福大学未确诊疾病中心的地位 总之，我们的目标是通过以下方式在 UDN 第一阶段的成功基础上再接再厉。 简化流程，最大限度地协作和推广，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、群众、科学家、政策制定者和慈善家，以确保这一国家资源的可持续发展 远远超出了该奖项的有效期。