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.

摘要 我们建议研究GenomeDiver在帮助从全基因组进行诊断方面的性能 测序（WGS）在儿童神经，心脏和免疫疾病。 该项目将重点关注NHGRI的NYCKidSeq研究后未经诊断的约400名儿童 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输入 (as变异识别格式（VCF）文件）和诊断实验室可用的表型信息。 该软件嵌入了Exomiser来区分变体的优先级，从而可以提取人类表型本体 (HPO)描述和区分最高等级变体的术语。这些HPO术语 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 在病人身上。然后，增强的表型信息用于重新运行Exomiser，然后呈现 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信息 信息提供给诊断实验室，以增加他们可以用于诊断的信息。 在本项目中，我们将使用GenomeDiver治疗来自蒙蒂菲奥里卫生系统的不同布朗克斯患者，这些患者患有 参与了我们的NYCKidSeq项目。大约74%的人没有得到诊断，这是一个常见的问题。 即使当所有外显子或整个非重复基因组被测序时，诊断测序也存在问题。我们 请注意，患者的表型和新的致病性变体的发现都随着时间的推移而演变， 应预期再分析允许在一些患者中作出新的诊断。因此，我们将 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。 这项初步研究主要是为了从临床医生用户那里获得关于设计和实用性的反馈， 软件，使其进一步完善。我们还将比较两组的诊断率， 95%置信区间的估计，这将使我们能够设计一个随访，扩大多中心试验， GenomeDiver。 我们的总体目标是了解如何在患者的临床护理中实现面向提供者的软件应用程序 以提高WGS的诊断率。我们鼓励临床医生寻找 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“反向”， 表型分析。我们的部分动机是向临床医生证明反向表型分析是一种实用的， 诊断过程中有价值的组成部分，并且GenomeDiver等工具可以成为决策的一部分 支持复杂遗传疾病的护理。