Advancing the implementation of variant-level functional data into clinical databases and clinical practice

推进变异级功能数据在临床数据库和临床实践中的实施

基本信息

批准号：
10674373
负责人：
Lea Starita
金额：
$ 82.62万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-01 至 2028-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10674373
关键词：
Acute Address BRCA1 gene Biological Assay Case/Control Studies Classification ClinVar Clinical Clinical Data Code Communities Data Data Set Databases Deposition Development Disease Educational Curriculum Educational workshop Equity Exclusion Frequencies Genes Genetic Genomic medicine Genomics Guidelines Human Genetics Human Genome Individual Infrastructure International Knowledge Laboratories Leadership Link Measurement Medical Medical Genetics Modeling Notification Pathogenicity Patients Population Population Heterogeneity Process Provider Quality Control Recommendation Research Research Personnel Resources Single Nucleotide Polymorphism Standardization System TP53 gene Testing Training Untranslated RNA Update Variant Visualization clinical care clinical database clinical decision-making clinical encounter clinical practice clinically relevant dashboard data modeling data standards empowerment genetic variant genomic data health care disparity human disease improved insight interest knowledgebase metadata standards multiplex assay repository research clinical testing segregation statistics technology development uptake variant of unknown significance

项目摘要

SUMMARY One of the major challenges limiting the potential of genomic medicine to revolutionize clinical care is the current lack of knowledge about the function of most human genetic variants. Consequently, the majority of clinically encountered genetic variants are classiﬁed as variants of uncertain signiﬁcance (VUS), which cannot be used for clinical decision making given their unknown relationship to disease. The VUS problem is particularly acute for individuals from populations historically excluded from research, compounding existing healthcare inequities when implementing genomic medicine. Variant-level functional data has the potential to overcome many of these challenges by providing pathogenicity information for variants from diverse populations. For example, we and others have demonstrated that multiplexed assays of variant effect (MAVEs) can resolve a large fraction of VUS in clinically important genes (e.g., 49% of BRCA1 VUS, 69% of TP53 VUS, and 93% of DDX3X VUS), and several U.S. and international consortia are currently using MAVEs to produce functional data for all possible coding and non-coding variants associated with all genes that have been linked to human disease. Given the potential of functional information to augment the implementation of genomic medicine, clinical guidelines have recently been updated to recommend the use of variant-level functional data when interpreting variant pathogenicity. However, despite the potential clinical utility of large-scale functional datasets, how best to implement them such that clinicians can appropriately incorporate variant functional data into clinical practice remains unknown. In this proposal we aim to address this unmet need in genomic medicine using the following approach: ● First, we will generate a framework for standardizing and disseminating curated large-scale functional data into clinician-facing resources, and implement this framework into ClinVar (Aim 1) ● Second, we will perform a proof-of-concept integration of variant level functional data in ClinVar into two large clinical practices to evaluate the clinical uptake and impact of variant-level functional data (Aim 2). ● Finally, we will build and disseminate resources for training clinicians on best practices for integrating functional data into clinical practice (Aim 3). Overall, this proposal has the potential to significantly advance the implementation of genomic data into clinical practice by enabling clinicians to appropriately leverage emerging variant-level functional data to resolve VUS, thereby making genomic medicine more equitable and impactful.

概括限制基因组医学彻底改变临床护理潜力的主要挑战之一是目前对大多数人类基因变异的功能缺乏了解。临床遗传变异被归类为意义不确定变异（VUS），不能鉴于其与疾病的关系未知，可用于临床决策。对于历史上被排除在研究之外的人群中的个人来说尤其严重，从而加剧了现有的实施基因组医学时的医疗保健不平等。变异水平的功能数据有可能通过提供致病性来克服许多这些挑战例如，我们和其他人已经证明，来自不同人群的变异的信息。变异效应多重检测 (MAVE) 可以解析临床重要基因中的大部分 VUS （例如，BRCA1 VUS 的 49%、TP53 VUS 的 69% 和 DDX3X VUS 的 93%）以及一些美国和国际联盟目前正在使用 MAVE 为所有可能的编码和非编码变体生成功能数据考虑到功能信息的潜力，与所有与人类疾病相关的基因相关。为了加强基因组医学的实施，临床指南最近已更新为尽管建议在解释变异致病性时使用变异水平的功能数据。大规模功能数据集的潜在临床效用，如何最好地实施它们能否将变异功能数据适当地纳入临床实践仍然未知。在本提案中，我们的目标是使用以下方法解决基因组医学中这一未满足的需求： ● 首先，我们将建立一个框架，用于标准化和传播策划的大规模功能性数据转化为面向临床医生的资源，并将该框架实施到 ClinVar 中（目标 1） ● 其次，我们将进行概念验证，将 ClinVar 中的变体级功能数据集成为两个大型临床实践来评估变异级功能数据的临床采用和影响（目标 2）。 ● 最后，我们将建立并传播资源，为上级提供整合最佳实践方面的培训。将功能数据应用于临床实践（目标 3）。总体而言，该提案有可能显着推进基因组数据在临床中的应用通过使助推器能够适当地利用新兴的变体级功能数据来解决 VUS 问题，从而使基因组医学更加公平和有影响力。