跳至主要内容
临床试验/NCT07839754
NCT07839754尚未招募不适用

EpiSign International: Health System Impact Assessment and Expanding Clinical Utilization of Epi/Genomic Testing in Rare Diseases and Beyond

University of Manchester1 个研究点 分布在 1 个国家目标入组 192 人开始时间: 2026年10月1日最近更新:
适应症
干预措施

试验速览

阶段
不适用
状态
尚未招募
入组人数
192
试验地点
1
主要终点
Additional diagnostic yield of EpiSign analysis

研究概览

简要总结

The purpose of this study is to assess the utility of EpiSign software and an integrated DNA methylation and copy number variant (CNV) microarray technology in helping to diagnose individuals with rare diseases. EpiSign is a proprietary technology developed by EpiSign Inc. that uses DNA methylation patterns as biomarkers for rare diseases, including genetic disorders and conditions associated with environmental exposures. DNA methylation microarrays measure DNA methylation levels at specific locations across the genome. CNV microarray technology uses a similar approach to identify gains or losses of DNA. CNV microarray analysis is an established methodology used in the diagnosis of rare diseases.

The first phase of the study will assess the utility of EpiSign analysis using standard EPIC DNA methylation microarrays as part of the diagnostic assessment of individuals with suspected rare diseases. The second phase will assess the utility of EpiSign analysis using newly developed integrated EPIC/CNV microarrays and evaluate the technical performance of these microarrays in detecting CNVs, as part of the diagnostic assessment of individuals with suspected rare diseases.

Patients with suspected rare diseases will be recruited from Manchester University NHS Foundation Trust. Participants will provide a blood sample, which will be used to analyse their DNA methylation profile and detect CNVs. The study is expected to last approximately 24 months from study initiation.

详细描述

Rare diseases are conditions that affect fewer than 1 in 2,000 individuals and can be serious, chronic or life-threatening. Collectively, rare diseases affect an estimated 3.5-5.9% of the global population, corresponding to approximately 236-446 million people worldwide, with new rare disorders continuing to be identified. Prenatal environmental exposures and maternal conditions may also be considered in the differential diagnosis of rare genetic disorders, as these can affect foetal development. Such factors include exposure to drugs and medications, lifestyle factors such as alcohol consumption, infectious agents, and maternal conditions including diabetes mellitus and epilepsy.

Diagnostic assessment of patients with suspected rare disorders often includes chromosomal microarray analysis (CMA) to identify large structural copy number variations (CNVs), with an average diagnostic yield of approximately 10-15%. Where initial genomic testing is negative or inconclusive, further testing may include targeted gene panels, whole-exome sequencing (WES) or whole-genome sequencing (WGS). These approaches have substantially improved the diagnosis of rare diseases, but a significant proportion of patients remain unresolved following genomic testing. Some unresolved patients may have genetic variants of uncertain significance (VUS), while others may have disease-causing variants or disease mechanisms that are not readily identifiable using current genomic approaches.

Patients with rare diseases may experience prolonged diagnostic journeys, with delays of several years and, in some cases, decades. Recent studies suggest that approximately 25% of patients in the UK may wait between five and thirty years for a final diagnosis, while global estimates of diagnostic delay range from approximately five to twelve years, with some patients remaining undiagnosed for up to thirty years. This diagnostic odyssey can have significant consequences for patients and their families, as well as for healthcare systems. Establishing a molecular diagnosis can inform clinical management, particularly for patients with genetically heterogeneous disorders or atypical presentations. Earlier diagnosis may facilitate timely access to appropriate treatment, surveillance and support, including during critical periods of development, with the potential to improve patient outcomes.

There is therefore a need for additional cost-effective, sensitive and specific approaches that can support the diagnosis of patients who remain unresolved or have ambiguous findings following conventional genomic testing. Such approaches may be particularly valuable where genetic and environmental factors contribute to disease presentation, or where clinical features are atypical or overlap between multiple disorders.

Epigenomic approaches, and specifically DNA methylation analysis, provide one potential avenue for addressing this diagnostic gap. DNA methylation is a reversible epigenetic modification involving the addition of a methyl group to cytosine residues within CpG dinucleotides across the genome. DNA methylation has important roles in genomic imprinting, silencing of retroviral elements, regulation of tissue-specific gene expression, X-chromosome inactivation and chromatin structure. DNA methylation is also important during development, with dynamic changes in DNA methylation and demethylation contributing to the spatiotemporal regulation of gene expression, particularly during neuronal development.

研究设计

研究类型
Observational
观察模型
Cohort
时间视角
Prospective

入排标准

性别
All
接受健康志愿者
否

入选标准

  • •Eligible for, or have undergone, standard-of-care first-tier genetic testing at the participating site with a negative or inconclusive result.
  • •No established molecular or clinical diagnosis explaining their presentation.
  • •No diagnostic result from subsequent/reflex genetic testing available at enrolment.
  • •For Phase 2 CNV-positive participants: have undergone standard-of-care CNV testing at the participating site and have a CNV reported by standard-of-care testing.
  • •For Phase 2 first-tier participants: meet the Phase 1 inclusion criteria.
  • •For Phase 2 CNV-positive participants, priority will be given to those with VUS, pathogenic/likely pathogenic, or other CNVs where EpiSign analysis may provide clinically relevant information.

排除标准

  • •Do not meet the applicable inclusion criteria.
  • •Isolated, non-syndromic autism or isolated learning disability without additional features suggestive of a syndromic rare disorder.

研究组 & 干预措施

Phase 1: EpiSign with EPIC DNA Methylation Array

Participants undergoing standard-of-care first-tier genetic testing who remain without an established diagnosis will undergo DNA methylation analysis using the standard EPIC microarray. DNA methylation data will be analysed using EpiSign to assess for disease-associated episignatures. EpiSign results will be evaluated alongside clinical and genetic information to assess diagnostic and clinical utility.

干预措施: EpiSign analysis with EPIC DNA methylation microarray (Diagnostic Test)

Phase 2: Integrated EPIC/CNV Array and EpiSign

Participants will undergo analysis using a newly developed integrated EPIC/CNV microarray, which enables assessment of both genome-wide DNA methylation and copy number variation from a single array. DNA methylation data will be analysed using EpiSign to assess for disease-associated episignatures, while CNV data will be assessed to evaluate the technical performance and analytical yield of the integrated array for CNV detection.

干预措施: Integrated EPIC/CNV microarray with EpiSign analysis (Diagnostic Test)

结局指标

主要结局

Additional diagnostic yield of EpiSign analysis

时间窗: Following completion of relevant genetic and EpiSign testing and collection of clinical data, expected approximately 12-18 months after study initiation.

Proportion (%) of participants for whom EpiSign identifies a relevant episignature that contributes to a molecular or clinical diagnosis.

次要结局

  • Change in clinical management following EpiSign analysis(Following completion of relevant genetic and EpiSign testing and collection of clinical data, expected approximately 12-18 months after study initiation.)
  • Change in diagnostic pathway following EpiSign analysis(Following completion of relevant genetic and EpiSign testing and collection of clinical data, expected approximately 12-18 months after study initiation.)
  • Time to diagnosis following EpiSign analysis(Following completion of relevant genetic and EpiSign testing and collection of clinical data, expected approximately 12-18 months after study initiation.)
  • Additional diagnostic investigations prompted by EpiSign analysis(Following completion of relevant genetic and EpiSign testing and collection of clinical data, expected approximately 12-18 months after study initiation.)
  • Genetic findings that would otherwise have remained unidentified(Following completion of relevant genetic and EpiSign testing and collection of clinical data, expected approximately 12-18 months after study initiation.)

研究者

申办方类型
Other
责任方
Principal Investigator
主要研究者

Siddharth Banka

Professor of Genomic Medicine and Rare Diseases

University of Manchester

研究点 (1)

Loading locations...

相似试验