Classifying DNA Mismatch Repair Gene Variants of Unknown Significance

描述(申请人提供)：在临床癌症遗传学中，分子诊断测试现在通常用于寻找癌症易感基因的致病突变。该领域的一个关键挑战是解释基因变异是否会导致疾病。Lynch综合征(LS)是最常见的遗传性结直肠癌综合征，由四个DNA错配修复(MMR)基因之一的胚系突变引起-MLH1、MSH2、MSH6和PMS2。在MMR和其他癌症易感基因中发现的大约20%-30%的变异是错义或非编码变化，这些变化可能是致病的，也可能不是致病的，但其对功能和疾病的影响不容易解释。它们被指定为“非分类变种”或“意义未知的变种”(VU)。通过确定哪些个体携带有害的基因变异，从而从筛查和治疗措施中受益，将变异归类为致病和中性变异，显著改善了LS和其他遗传性癌症综合征的管理。科学上的问题是将LS基因检测发现的所有MMR基因变异归类为“致病”或“非致病”。对变异的正确分类需要结合临床病理、流行病学、生物信息学和体外数据。使用这些方法的最佳方式尚不清楚。我们的假设是，临床、计算机和实验室数据可以定性和定量地结合起来，以对MMR基因的所有变异进行分类。这项研究将使用大量的MMR变种，并改进整合这些数据的方法。目的1.建立MMR基因变异的参考集，根据临床和流行病学数据将其分类为可能致病、可能中性和未知。这些集合将用于校准和改进集成多种数据类型的分类模型。目的2.对个体数据类型进行分析以对变异进行分类：分配和校准多种数据类型的致病预测值和优势比，包括：1)临床和家系 病史，2)肿瘤组织学，3)肿瘤MMR蛋白的免疫组织化学，4)肿瘤微卫星不稳定性，5)肿瘤MLH1甲基化，BRAF V600E突变，6)通过功能分析对错义变体进行体外评估，7)通过基于序列和结构的算法对错义变体进行电子评估，8)通过剪接实验对外显子变体进行体外评估，以及9)外显子序列变体对剪接效应的电子预测。目标3.开发一个整合数据的模型。这些模型将经历三个阶段：(I)定性模型，(Ii)独立考虑每种数据类型的定量贝叶斯模型，以及(Iii)同时考虑所有已验证数据类型的两组分混合模型。相关性：解释哪些基因变异会增加遗传性癌症的风险，哪些不会增加遗传性癌症的风险可能很困难。这项研究使用临床病理、流行病学、体外和电子计算机研究MMR基因来解释哪些基因改变导致LS，哪些是无害的。改进对遗传变异的解释将改善对遗传性癌症和其他遗传病的管理。

DESCRIPTION (provided by applicant): In clinical cancer genetics, molecular diagnostic testing is now commonly performed looking for pathogenic mutations in cancer susceptibility genes. A critical challenge in the field is interpreting whether a genetic variant causes disease o not. Lynch syndrome (LS), the most common hereditary colorectal cancer syndrome, is caused by germline mutations in one of four DNA mismatch repair (MMR) genes- MLH1, MSH2, MSH6, and PMS2. About 20-30% of the variants identified in MMR and other cancer susceptibility genes are missense or non-coding changes that may or may not be pathogenic, but whose effects on function and disease cannot be interpreted easily. They are designated "Unclassified Variants or "Variants of Unknown Significance" (VUS). Classifying variants as pathogenic and neutral significantly improves the management of LS and other hereditary cancer syndromes by identifying which individuals carry a harmful genetic variant and thus benefit from screening and therapeutic measures. The scientific problem is to classify as either "pathogenic" or "not pathogenic" all MMR gene variants found by genetic testing for LS. Correct classification of variants requires integrating clinico-pathologic, epidemiologic, bioinformatic, and in vitro data. The optimal way to use these methods is unknown. Our hypothesis is that clinical, in silico, and laboratory data can be integrated qualitatively and quantitatively to classify all variants in MMR genes. This study will use a large set of MMR variants and refine a method that integrates these data. Aim 1. Development of reference sets of gene variants in MMR genes that are classified by clinical and epidemiological data as Likely Pathogenic, Likely Neutral, and Unknown. These sets will be used to calibrate and refine a classification model integrating multiple data types. Aim 2. Analysis of individual data types to classify variants: To assign and calibrate predictive values and odds ratios for pathogenicity for multiple data types, including: 1) clinical and family history, 2) tumor histology 3) tumor immunohistochemistry for MMR proteins, 4) tumor Microsatellite Instability, 5) tumor MLH1 methylation, BRAF V600E mutation, 6) in vitro assessment of missense variants by functional assays, 7) in silico assessment of missense variants by sequence and structure-based algorithms, 8) in vitro assessment of exonic variants by splicing assays, and 9) in silico predictions of splice effects from exonic sequence variants. Aim 3. Development of a model for integrating data. These models will pass through three stages: (i) a qualitative model, (ii) a quantitative Bayesian model that considers each data type independently, and (iii) a two component mixture model that considers all validated data types simultaneously. Relevance: Interpreting which genetic variants increase risk for hereditary cancer and which do not can be difficult. This research uses clinicopathologic, epidemiologic, in vitro, and in silico studies of MMR genes to interpret which genetic changes cause LS and which are harmless. Improving the interpretation of genetic variation will improve the management of hereditary cancers and other genetic diseases.