Classifying DNA Mismatch Repair Gene Variants of Unknown Significance

描述（由申请人提供）：在临床癌症遗传学中，分子诊断检测现在通常用于寻找癌症易感基因的致病性突变。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，是由四种DNA错配修复（MMR）基因MLH1、MSH2、MSH6和PMS2中的一种种系突变引起的。在MMR和其他癌症易感基因中发现的约20-30%的变异是错义或非编码变化，可能致病，也可能不致病，但其对功能和疾病的影响不易解释。它们被称为“未分类变体”或“未知意义变体”（VUS）。将变异分类为致病性和中性，通过确定哪些个体携带有害的遗传变异，从而从筛查和治疗措施中受益，显著改善了LS和其他遗传性癌症综合征的管理。科学问题是将通过LS基因检测发现的所有MMR基因变异分类为“致病”或“非致病”。变异的正确分类需要整合临床病理、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，对MMR基因的所有变异进行分类。本研究将使用大量的MMR变体，并改进整合这些数据的方法。目的1。根据临床和流行病学数据将MMR基因分类为可能致病、可能中性和未知的参考基因变异集。这些集将用于校准和改进集成多种数据类型的分类模型。目标2。分析个体数据类型以对变异进行分类：分配和校准多种数据类型致病性的预测值和优势比，包括：1)临床和家庭

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.