Exceptions to the rule: Case studies in the prediction of pathogenicity for genetic variants in hereditary cancer genes
Exceptions to the rule: Case studies in the prediction of pathogenicity for genetic variants in hereditary cancer genes
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DOI：
10.1111/cge.12560
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发表时间：
2015-12-01
影响因子：
3.5
通讯作者：
Wenstrup, R. J.
中科院分区：
文献类型：
--
作者：
Rosenthal, E. T.;Bowles, K. R.;Wenstrup, R. J.
根据目前的共识指南和标准实践，在临床检测中检测到的许多遗传变异在缺乏额外数据的情况下，根据其对基因正常表达或功能的预测影响被归类为致病。然而，我们的实验室已经确定了遗传性癌症基因中的此类变体的子集，在首次观察到变体后进行初步评估后，出现了令人信服的矛盾证据。预测BRCA 1、BRCA 2和MSH 2中的三个代表性变体会破坏剪接、过早截短蛋白质或去除起始密码子，通过使用多种分类算法分析临床数据来评估致病性。所有三种变体的现有临床数据与预期的致病分类相矛盾。这些变异说明了与变异分类标准方法相关的潜在陷阱，以及与监测数据、更新分类和向负责将检测结果转化为适当临床行动的临床医生报告潜在矛盾解释相关的挑战。现在重要的是要解决这些挑战，因为临床测试模型转向使用大型多基因组和全外显子组/基因组分析，这将大大增加识别的遗传变异的数量。
Based on current consensus guidelines and standard practice, many genetic variants detected in clinical testing are classified as disease causing based on their predicted impact on the normal expression or function of the gene in the absence of additional data. However, our laboratory has identified a subset of such variants in hereditary cancer genes for which compelling contradictory evidence emerged after the initial evaluation following the first observation of the variant. Three representative examples of variants in BRCA1, BRCA2 and MSH2 that are predicted to disrupt splicing, prematurely truncate the protein, or remove the start codon were evaluated for pathogenicity by analyzing clinical data with multiple classification algorithms. Available clinical data for all three variants contradicts the expected pathogenic classification. These variants illustrate potential pitfalls associated with standard approaches to variant classification as well as the challenges associated with monitoring data, updating classifications, and reporting potentially contradictory interpretations to the clinicians responsible for translating test outcomes to appropriate clinical action. It is important to address these challenges now as the model for clinical testing moves toward the use of large multi-gene panels and whole exome/genome analysis, which will dramatically increase the number of genetic variants identified.