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