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: 10689316
• 负责人: John Greally
• 金额: $ 22.41万
• 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/10689316
• 关键词: Algorithms; Area; Cardiology; 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; Heterozygote; Human; Immune System Diseases; Individual; Intervention; Laboratories; Left; Maintenance; Motivation; Multicenter Trials; Names; National Human Genome Research Institute; Neurologic; Newly Diagnosed; 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; 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.

抽象的 我们建议研究 GenomeDiver 在帮助全基因组诊断方面的性能 患有神经系统、心脏病和免疫系统疾病的儿童的测序（WGS）。 该项目将重点关注 NYCKidSeq 研究（NHGRI 的一部分）后约 400 名未得到诊断的儿童 CSER 联盟专注于在不同人群中实施诊断性 WGS。 作为 NYCKidSeq 研究的一部分开发，旨在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 是一款数字医学应用程序，使用患者的基因组序列信息作为输入 （作为变体调用格式 (VCF) 文件）和可供诊断实验室使用的表型信息。 该软件嵌入 Exomiser 来优先考虑变体，从而提取人类表型本体 描述和区分排名最高的变体的 (HPO) 术语 这些 HPO 术语被呈现。 在 GenomeDiver 界面中，临床遗传学家将其分类为“存在”、“不存在”或“不确定” 然后使用增强的表型信息重新运行 Exomiser，然后显示 基因名称和相关疾病提供给临床医生，临床医生可以在返回所有信息之前标记任何感兴趣的内容 向诊断实验室提供信息，以增加他们可用于诊断的信息。 在这个项目中，我们将使用 GenomeDiver 对来自 Montefiore 卫生系统的不同布朗克斯患者进行治疗，这些患者患有 参与我们的 NYCKidSeq 项目的人大约有 74% 没有得到诊断，这是一种常见的情况。 即使对所有外显子或整个非重复基因组进行测序，诊断测序也会出现问题。 请注意，患者的表型和新致病变异的发现都会随着时间的推移而演变，并且 重新分析预计可以对某些患者做出新的诊断，因此我们将进行划分。 约 400 名患者分为两组，其中一组将在标准护理中添加 GenomeDiver 干预。 这项试点研究的主要目的是从临床医生用户那里获取有关该设备设计和实用性的反馈。 我们还将比较两组的诊断率，从而生成一个结果。 95% 置信区间的估计将使我们能够设计后续的、扩展的多中心试验 基因组潜水员。 我们的总体目标是了解如何在患者的临床护理中实施面向提供商的软件应用程序 我们提示临床医生寻找遗传性疾病的过程，以提高 WGS 的诊断率。 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“反向” 我们的部分动机是向辩护者证明反向表型是一种实用且可行的方法。 诊断过程的重要组成部分，并且 GenomeDiver 等工具可以成为决策的一部分 支持治疗复杂的遗传性疾病。