通过蛋白质多态性-错配修复多变量分析（MAPP-MMR）对MLH1/MSH2错义变异进行准确分类。

Accurate classification of MLH1/MSH2 missense variants with multivariate analysis of protein polymorphisms-mismatch repair (MAPP-MMR).

作者信息

Chao Elizabeth C, Velasquez Jonathan L, Witherspoon Mavee S L, Rozek Laura S, Peel David, Ng Pauline, Gruber Stephen B, Watson Patrice, Rennert Gad, Anton-Culver Hoda, Lynch Henry, Lipkin Steven M

机构信息

Genetic Epidemiology Research Institute, University of California, Irvine, Irvine, California, USA.

出版信息

Hum Mutat. 2008 Jun;29(6):852-60. doi: 10.1002/humu.20735.

DOI:10.1002/humu.20735

PMID:18383312

Abstract

Lynch syndrome, also known as hereditary nonpolyposis colon cancer (HNPCC), is the most common known genetic syndrome for colorectal cancer (CRC). MLH1/MSH2 mutations underlie approximately 90% of Lynch syndrome families. A total of 24% of these mutations are missense. Interpreting missense variation is extremely challenging. We have therefore developed multivariate analysis of protein polymorphisms-mismatch repair (MAPP-MMR), a bioinformatic algorithm that effectively classifies MLH1/MSH2 deleterious and neutral missense variants. We compiled a large database (n>300) of MLH1/MSH2 missense variants with associated clinical and molecular characteristics. We divided this database into nonoverlapping training and validation sets and tested MAPP-MMR. MAPP-MMR significantly outperformed other missense variant classification algorithms (sensitivity, 94%; specificity, 96%; positive predictive value [PPV] 98%; negative predictive value [NPV], 89%), such as SIFT and PolyPhen. MAPP-MMR is an effective bioinformatic tool for missense variant interpretation that accurately distinguishes MLH1/MSH2 deleterious variants from neutral variants.

摘要

林奇综合征，也称为遗传性非息肉病性结直肠癌（HNPCC），是已知的结直肠癌（CRC）最常见的遗传综合征。MLH1/MSH2突变是约90%林奇综合征家族的病因。这些突变中共有24%是错义突变。解释错义变异极具挑战性。因此，我们开发了蛋白质多态性-错配修复多变量分析（MAPP-MMR），这是一种生物信息学算法，可有效分类MLH1/MSH2有害和中性错义变异。我们汇编了一个包含大量（n>300）具有相关临床和分子特征的MLH1/MSH2错义变异的数据库。我们将该数据库分为不重叠的训练集和验证集，并对MAPP-MMR进行了测试。MAPP-MMR的表现显著优于其他错义变异分类算法（敏感性为94%；特异性为96%；阳性预测值[PPV]为98%；阴性预测值[NPV]为89%），如SIFT和PolyPhen。MAPP-MMR是一种有效的生物信息学工具，用于解释错义变异，可准确区分MLH1/MSH2有害变异和中性变异。