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MLH1 意义未明变异体的评估。

Evaluation of MLH1 variants of unclear significance.

机构信息

Biomedizinisches Forschungslabor, Medizinische Klinik 1, Universitätsklinik Frankfurt, Frankfurt, Germany.

Laboratorio de Oncología y Genética Molecular, Clínica Los Condes, Santiago, Chile.

出版信息

Genes Chromosomes Cancer. 2018 Jul;57(7):350-358. doi: 10.1002/gcc.22536. Epub 2018 Apr 30.

Abstract

Inactivating mutations in the MLH1 gene cause the cancer predisposition Lynch syndrome, but for small coding genetic variants it is mostly unclear if they are inactivating or not. Nine such MLH1 variants have been identified in South American colorectal cancer (CRC) patients (p.Tyr97Asp, p.His112Gln, p.Pro141Ala, p.Arg265Pro, p.Asn338Ser, p.Ile501del, p.Arg575Lys, p.Lys618del, p.Leu676Pro), and evidence of pathogenicity or neutrality was not available for the majority of these variants. We therefore performed biochemical laboratory testing of the variant proteins and compared the results to protein in silico predictions on structure and conservation. Additionally, we collected all available clinical information of the families to come to a conclusion concerning their pathogenic potential and facilitate clinical diagnosis in the affected families. We provide evidence that four of the alterations are causative for Lynch syndrome, four are likely neutral and one shows compromised activity which can currently not be classified with respect to its pathogenic potential. The work demonstrates that biochemical testing, corroborated by congruent evolutionary and structural information, can serve to reliably classify uncertain variants when other data are insufficient.

摘要

MLH1 基因中的失活突变导致癌症易感性林奇综合征,但对于小的编码遗传变异,其是否失活大多不清楚。在南美结直肠癌 (CRC) 患者中已经鉴定出 9 种这样的 MLH1 变异体 (p.Tyr97Asp、p.His112Gln、p.Pro141Ala、p.Arg265Pro、p.Asn338Ser、p.Ile501del、p.Arg575Lys、p.Lys618del、p.Leu676Pro),并且大多数这些变异体的致病性或中性证据尚不可用。因此,我们对变异蛋白进行了生化实验室测试,并将结果与结构和保守性的蛋白质计算机预测进行了比较。此外,我们收集了所有相关家族的可用临床信息,以确定它们的致病潜力,并为受影响的家族提供临床诊断。我们提供的证据表明,其中四种改变是林奇综合征的致病原因,四种可能是中性的,一种表现出功能受损,目前不能根据其致病潜力对其进行分类。这项工作表明,当其他数据不足时,生化测试与一致的进化和结构信息相结合,可以可靠地对不确定的变异进行分类。

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