Classification of BRCA1 missense variants of unknown clinical significance
Classification of BRCA1 missense variants of unknown clinical significance
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DOI：
10.1136/jmg.2004.024711
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发表时间：
2005-02-01
影响因子：
4
通讯作者：
Monteiro, ANA
中科院分区：
文献类型：
--
作者：
Phelan, CM;Dapic, V;Monteiro, ANA
背景：BRCA1是一种具有多效性的肿瘤抑制因子。BRCA1的种系突变是导致大部分乳腺癌-卵巢癌家族的原因。在整个基因中已经发现了一些错义变异，但由于缺乏关于它们对BRCA1功能影响的信息，预测性测试并不总是有用的。将错义变异分为有害/高风险或中性/低临床意义是识别高危个体的必要条件。目的：研究一组误义变异。方法和结果：在一个全面的框架下对该小组进行了调查，包括：(1)基于转录激活的功能分析；(2)分离分析和利用不完整系谱数据计算因果关系几率的方法；(3)基于种间序列变异的方法。结果表明，转录激活试验可用于表征BRCA1羧基端包含残基1396 - 1863的突变。具体研究了13种错义变体（H1402Y、L1407P、H1421Y、S1512I、M1628T、M1628V、T1685I、G1706A、T1720A、A1752P、G1788V、V1809F和W1837R）。结论：虽然BRCA1等位基因的个体分类方案仍然存在局限性，但几种方法的结合提供了一种更有效的方法来识别与乳腺癌和卵巢癌高风险相关的变异。这里提出的框架使这些变体更接近临床适用性。
Background: BRCA1 is a tumour suppressor with pleiotropic actions. Germline mutations in BRCA1 are responsible for a large proportion of breast - ovarian cancer families. Several missense variants have been identified throughout the gene but because of lack of information about their impact on the function of BRCA1, predictive testing is not always informative. Classification of missense variants into deleterious/ high risk or neutral/low clinical significance is essential to identify individuals at risk.Objective: To investigate a panel of missense variants.Methods and results: The panel was investigated in a comprehensive framework that included ( 1) a functional assay based on transcription activation; ( 2) segregation analysis and a method of using incomplete pedigree data to calculate the odds of causality; ( 3) a method based on interspecific sequence variation. It was shown that the transcriptional activation assay could be used as a test to characterise mutations in the carboxy-terminus region of BRCA1 encompassing residues 1396 - 1863. Thirteen missense variants (H1402Y, L1407P, H1421Y, S1512I, M1628T, M1628V, T1685I, G1706A, T1720A, A1752P, G1788V, V1809F, and W1837R) were specifically investigated.Conclusions: While individual classification schemes for BRCA1 alleles still present limitations, a combination of several methods provides a more powerful way of identifying variants that are causally linked to a high risk of breast and ovarian cancer. The framework presented here brings these variants nearer to clinical applicability.