功在当代,利在千秋:用信息含量评分来衡量变异效应多重检测的临床价值。

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS BMC Bioinformatics Pub Date : 2024-09-06 DOI:10.1186/s12859-024-05920-5
John Michael O Ranola, Carolyn Horton, Tina Pesaran, Shawn Fayer, Lea M Starita, Brian H Shirts
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引用次数: 0

摘要

背景:与人类疾病相关的变异体可以是致病的,也可以是良性的。目前从良性到致病性的分类类别反映了对当前认识的概率总结。变异效应多重检测(MAVE)临床实用性的一个主要衡量标准是可从不确定性(VUS)中重新分类的变异数量。然而,这一效用衡量标准的不足之处在于它未能充分反映从多重变异效应检测中获得的信息。本研究的目的是为 MAVE 实用性开发一种改进的量化指标。我们建议采用信息含量法,其中包括不对变体进行重新分类的数据,这样可以更好地反映真实的信息增益。我们采用信息含量法来评估 BRCA1、PTEN 和 TP53 的 MAVE 的信息增益(以比特为单位)。在这里,一个比特表示从无信息开始对单个变体进行完全分类所需的信息量:BRCA1 MAVEs 总共产生了 831.2 比特的信息,占 BRCA1 错义信息总量的 6.58%,比只有助于 VUS 重新分类的信息增加了 22 倍。PTEN MAVEs产生了2059.6比特的信息,占PTEN中错义信息总量的32.8%,比有助于VUS重新分类的信息增加了85倍。TP53 MAVEs产生了277.8比特的信息,占TP53总错义信息的6.22%,比VUS重新分类的信息增加了3.5倍:结论:与计算重新分类的变异体数量相比,信息内容法能更准确地描述通过 MAVE 图谱工作获得的信息。这种信息内容方法还有助于确定指南变更的影响,因为指南变更会修改用于变异组分类的信息定义。
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Assigning credit where it is due: an information content score to capture the clinical value of multiplexed assays of variant effect.

Background: A variant can be pathogenic or benign with relation to a human disease. Current classification categories from benign to pathogenic reflect a probabilistic summary of the current understanding. A primary metric of clinical utility for multiplexed assays of variant effect (MAVE) is the number of variants that can be reclassified from uncertain significance (VUS). However, a gap in this measure of utility is that it underrepresents the information gained from MAVEs. The aim of this study was to develop an improved quantification metric for MAVE utility. We propose adopting an information content approach that includes data that does not reclassify variants will better reflect true information gain. We adopted an information content approach to evaluate the information gain, in bits, for MAVEs of BRCA1, PTEN, and TP53. Here, one bit represents the amount of information required to completely classify a single variant starting from no information.

Results: BRCA1 MAVEs produced a total of 831.2 bits of information, 6.58% of the total missense information in BRCA1 and a 22-fold increase over the information that only contributed to VUS reclassification. PTEN MAVEs produced 2059.6 bits of information which represents 32.8% of the total missense information in PTEN and an 85-fold increase over the information that contributed to VUS reclassification. TP53 MAVEs produced 277.8 bits of information which represents 6.22% of the total missense information in TP53 and a 3.5-fold increase over the information that contributed to VUS reclassification.

Conclusions: An information content approach will more accurately portray information gained through MAVE mapping efforts than by counting the number of variants reclassified. This information content approach may also help define the impact of guideline changes that modify the information definitions used to classify groups of variants.

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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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