A Clinical Trial of GenomeDiver for Improved Diagnosis of Pediatric Rare Diseases

GenomeDiver 改善儿科罕见病诊断的临床试验

• 批准号: 10433004
• 负责人: John Greally
• 金额: $ 28.31万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-23 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10433004
• 关键词: Algorithms; Area; Caring; Categories; Child; Childhood; Clinical; Clinical Trials; Communication; Communities; Complex; Computer software; Confidence Intervals; Control Groups; DNA Sequence; Data; Development; Diagnosis; Diagnostic; Disease; Drops; Electronic Mail; Environment; Exons; Feedback; Foundations; Genes; Genetic Diseases; Genome; Genomics; Genotype; Goals; Health system; Human; Immune System Diseases; Individual; Intervention; Laboratories; Left; Maintenance; Motivation; Multicenter Trials; Names; National Human Genome Research Institute; Neurologic; Ontology; Parents; Pathogenicity; Patient Care; Patients; Performance; Phase; Phenotype; Physicians; Pilot Projects; Population Heterogeneity; Process; Production; Provider; Randomized; Rare Diseases; Running; Source; Testing; Time; Update; Variant; base; clinical care; design; digital medicine; exome; experience; file format; follow-up; genome sequencing; improved; interest; open source; primary outcome; secondary outcome; software development; standard of care; tool; whole genome

ABSTRACT We propose to study the performance of GenomeDiver in helping to make diagnoses from whole genome sequencing (WGS) in children with neurological, cardiological and immunological diseases. This project will focus on ~400 children left without a diagnosis following the NYCKidSeq study, part of the NHGRI CSER consortium focused on implementing diagnostic WGS in diverse populations. GenomeDiver was developed as part of the NYCKidSeq study, with the goal of improving the ability of the clinical geneticist to provide phenotypic information as part of the diagnostic process. GenomeDiver is a digital medicine application that uses as its input the patient’s genomic sequence information (as a variant call format (VCF) file) and the phenotypic information made available to the diagnostic laboratory. The software embeds Exomiser to prioritize variants, allowing the extraction of Human Phenotype Ontology (HPO) terms that characterize and distinguish the highest-ranked variants. These HPO terms are presented within the GenomeDiver interface to the clinical geneticist who categorizes them as Present, Absent or Uncertain in the patient. The enhanced phenotypic information is then used to re-run Exomiser, which then presents the gene names and associated diseases to the clinician, who can flag any of interest before returning all the information to the diagnostic laboratory to augment the information they can use diagnostically. In this project, we will use GenomeDiver on diverse Bronx patients from the Montefiore Health System who have participated in our NYCKidSeq project. Approximately 74% have been left without a diagnosis, a common problem in diagnostic sequencing even when all exons or the entire non-repetitive genome is sequenced. We note that both the patient’s phenotype and the discovery of new pathogenic variants evolves over time, and that re-analysis should be expected to permit new diagnoses to be made in some patients. We will therefore divide ~400 patients into two groups, one of which will have a GenomeDiver intervention added to the standard of care. This pilot study is designed primarily to get feedback from clinician users about the design and utility of the software, allowing its further refinement. We will also compare diagnostic yield in the two groups, generating an estimate of the 95% confidence interval that will allow us to design a follow up, expanded multicenter trial of GenomeDiver. Our overall goal is to understand how we can implement a provider-facing software app in clinical care of patients with genetic disorders to improve diagnostic yield of WGS. Our process of prompting the clinician to look for specific phenotypic features based on genomic information is unusual, and something we describe as ‘reverse phenotyping’. Part of our motivation is to demonstrate to clinicians that reverse phenotyping is a practical and valuable component of the diagnostic process, and that a tool such as GenomeDiver can be part of the decision support in the care of complex genetic disorders.

摘要 我们建议研究GenomeDiver在帮助进行全基因组诊断方面的性能 神经、心脏和免疫疾病儿童的测序(WGS)。 该项目将关注约400名在NYCKidSeq研究后没有得到诊断的儿童，这是NHGRI的一部分 CSER联盟专注于在不同人群中实施诊断WGS。GenomeDiver是 作为NYCKidSeq研究的一部分开发，目的是提高临床遗传学家的能力 提供表型信息作为诊断过程的一部分。 GenomeDiver是一个数字医学应用程序，它使用患者的基因组序列信息作为其输入 (作为可变呼叫格式(VCF)文件)和向诊断实验室提供的表型信息。 该软件嵌入Exomiser来对变体进行优先排序，允许提取人类表型本体 (HPO)描述和区分排名最高的变体的术语。这些HPO术语如下 在GenomeDiver与临床遗传学家的接口内，临床遗传学家将它们归类为存在、不存在或不确定 在病人身上。然后使用增强的表型信息重新运行Exomiser，Exomiser然后呈现 将基因名称和相关疾病发送给临床医生，临床医生可以在返回所有 向诊断实验室提供信息，以增加他们可以用于诊断的信息。 在这个项目中，我们将使用GenomeDiver对来自Montefiore Health System的不同Bronx患者进行治疗，这些患者 参与了我们的NYCKidSeq项目。大约74%的人没有得到诊断，这是一种常见的 诊断测序中的问题，即使所有外显子或整个非重复基因组都已测序。我们 请注意，患者的表型和新的致病变异的发现都会随着时间的推移而演变，而且 重新分析应该可以允许在一些患者中做出新的诊断。因此，我们将分裂 大约400名患者分成两组，其中一组将在护理标准中增加GenomeDiver干预。 这项试验性研究的主要目的是从临床医生用户那里获得关于 软件，允许其进一步完善。我们还将比较两组的诊断效率，生成 估计95%的可信区间，这将使我们能够设计一项后续的、扩大的多中心试验 基因潜水员。 我们的总体目标是了解如何在患者临床护理中实施面向提供商的软件应用程序 提高WGS的诊断率。我们促使临床医生寻找 基于基因组信息的特定表型特征是不寻常的，我们将其描述为相反的东西 表型鉴定‘。我们的部分动机是向临床医生证明反向表型是一种实用和 诊断过程的重要组成部分，以及诸如GenomeDiver之类的工具可以作为决策的一部分 支持复杂遗传疾病的护理。