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数据共享以提高实验室间变异解释的一致性:来自加拿大开放遗传资源库的结果。

Data sharing to improve concordance in variant interpretation across laboratories: results from the Canadian Open Genetics Repository.

机构信息

Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.

Pathology and Laboratory Medicine, Mount Sinai Hospital, Sinai Health, Toronto, Ontario, Canada.

出版信息

J Med Genet. 2022 Jun;59(6):571-578. doi: 10.1136/jmedgenet-2021-107738. Epub 2021 Apr 19.

DOI:
10.1136/jmedgenet-2021-107738
PMID:33875564
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8523590/
Abstract

BACKGROUND

This study aimed to identify and resolve discordant variant interpretations across clinical molecular genetic laboratories through the Canadian Open Genetics Repository (COGR), an online collaborative effort for variant sharing and interpretation.

METHODS

Laboratories uploaded variant data to the Franklin Genoox platform. Reports were issued to each laboratory, summarising variants where conflicting classifications with another laboratory were noted. Laboratories could then reassess variants to resolve discordances. Discordance was calculated using a five-tier model (pathogenic (P), likely pathogenic (LP), variant of uncertain significance (VUS), likely benign (LB), benign (B)), a three-tier model (LP/P are positive, VUS are inconclusive, LB/B are negative) and a two-tier model (LP/P are clinically actionable, VUS/LB/B are not). We compared the COGR classifications to automated classifications generated by Franklin.

RESULTS

Twelve laboratories submitted classifications for 44 510 unique variants. 2419 variants (5.4%) were classified by two or more laboratories. From baseline to after reassessment, the number of discordant variants decreased from 833 (34.4% of variants reported by two or more laboratories) to 723 (29.9%) based on the five-tier model, 403 (16.7%) to 279 (11.5%) based on the three-tier model and 77 (3.2%) to 37 (1.5%) based on the two-tier model. Compared with the COGR classification, the automated Franklin classifications had 94.5% sensitivity and 96.6% specificity for identifying actionable (P or LP) variants.

CONCLUSIONS

The COGR provides a standardised mechanism for laboratories to identify discordant variant interpretations and reduce discordance in genetic test result delivery. Such quality assurance programmes are important as genetic testing is implemented more widely in clinical care.

摘要

背景

本研究旨在通过加拿大开放遗传资源库(COGR)识别和解决临床分子遗传学实验室之间不一致的变异解释,这是一个用于变异共享和解释的在线协作努力。

方法

实验室将变异数据上传到 Franklin Genoox 平台。报告将发送给每个实验室,总结出与其他实验室的分类不一致的变异。然后,实验室可以重新评估变异以解决不一致的问题。使用五级模型(致病性(P)、可能致病性(LP)、意义不明的变异(VUS)、可能良性(LB)、良性(B))、三级模型(LP/P 为阳性,VUS 为不确定,LB/B 为阴性)和二级模型(LP/P 为临床可操作,VUS/LB/B 为不可操作)计算不一致性。我们将 COGR 分类与 Franklin 自动分类进行了比较。

结果

12 个实验室对 44510 个独特变异进行了分类。有 2419 个变异(5.4%)被两个或更多实验室分类。从基线到重新评估后,根据五级模型,不一致的变异数量从报告的两个或更多实验室的 833 个(占变异的 34.4%)减少到 723 个(29.9%),根据三级模型,从 403 个(16.7%)减少到 279 个(11.5%),根据二级模型,从 77 个(3.2%)减少到 37 个(1.5%)。与 COGR 分类相比,自动化的 Franklin 分类对于识别可操作(P 或 LP)变异,具有 94.5%的敏感性和 96.6%的特异性。

结论

COGR 为实验室提供了一种标准化的机制,用于识别不一致的变异解释,并减少遗传测试结果交付中的不一致性。随着基因检测在临床护理中得到更广泛的应用,此类质量保证计划非常重要。

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