Classifying DNA Mismatch Repair Gene Variants of Unknown Significance

对意义不明的 DNA 错配修复基因变异进行分类

• 批准号: 8439776
• 负责人: MARC S GREENBLATT
• 金额: $ 59.73万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2017-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8439776
• 关键词: Algorithms; BRAF gene; Bioinformatics; Biological Assay; Calibration; Cancer-Predisposing Gene; Classification; Clinical; Colon Carcinoma; Colorectal Cancer; Communities; Computer Simulation; DNA; Data; Development; Diagnosis; Disease; Epidemiology; Family; Family member; Functional RNA; Gene Mutation; Genes; Genetic Variation; Genetic screening method; Germ-Line Mutation; Goals; Hereditary Disease; Hereditary Malignant Neoplasm; Hereditary Neoplastic Syndromes; Hereditary Nonpolyposis Colorectal Neoplasms; Histology; Immunohistochemistry; In Vitro; Individual; Inherited; Laboratories; MLH1 gene; MSH2 gene; MSH6 gene; Malignant Neoplasms; Measures; Methods; Methylation; Microsatellite Instability; Mismatch Repair; Modeling; Molecular Diagnostic Testing; Mutation; Mutation Analysis; Odds Ratio; Oncogenes; Other Genetics; Outcome; Output; PMS2 gene; Pathogenicity; Pathologic; Patients; Penetrance; Predictive Value; Predisposition; Probability; Proteins; Quality of life; RNA Splicing; ROC Curve; Recording of previous events; Reporting; Research; Risk; Site; Staging; Statistical Models; Stress; Structure; Syndrome; System; Test Result; Testing; Therapeutic; Training; Translating; Validation; Variant; Work; base; cancer genetics; colorectal cancer screening; cost; genetic variant; improved; model development; mortality; mutation carrier; public health relevance; screening; tumor

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.

描述（由申请人提供）：在临床癌症遗传学中，目前通常进行分子诊断检测，以寻找癌症易感基因中的致病性突变。该领域的一个关键挑战是解释遗传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）基因之一的种系突变引起的-MLH 1，MSH 2，MSH 6和PMS 2。在MMR和其他癌症易感基因中鉴定的约20-30%的变异是错义或非编码变化，其可能是或可能不是致病性的，但其对功能和疾病的影响不能容易地解释。它们被称为“未分类的变异体”或“意义不明的变异体”（VUS）。将变异分类为致病性和中性，通过识别哪些个体携带有害的遗传变异，从而受益于筛查和治疗措施，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LS基因检测发现的所有MMR基因变异归类为“致病性”或“非致病性”。正确分类的变异需要整合临床病理学，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是未知的。我们的假设是，临床，在硅片，和实验室数据可以整合定性和定量分类MMR基因的所有变体。这项研究将使用大量的MMR变体，并改进整合这些数据的方法。目标1。开发MMR基因中基因变异的参考集，这些基因变异根据临床和流行病学数据分类为可能致病性、可能中性和未知。这些数据集将用于校准和完善整合多种数据类型的分类模型。目标2.分析个体数据类型以分类变异：为多种数据类型的致病性分配和校准预测值和比值比，包括：1）临床和家族 历史，2）肿瘤组织学3）MMR蛋白的肿瘤免疫组织化学，4）肿瘤微卫星不稳定性，5）肿瘤MLH 1甲基化，BRAF V600 E突变，6）通过功能测定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计算机模拟评估，8）通过剪接测定对外显子变体进行体外评估，和9）来自外显子序列变体的剪接效应的计算机预测。目标3.建立数据整合模型。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）同时考虑所有验证数据类型的双组分混合模型。相关性：解释哪些遗传变异会增加遗传性癌症的风险，哪些不会是困难的。本研究使用临床病理学、流行病学、体外和计算机模拟研究MMR基因来解释哪些遗传变化导致LS，哪些是无害的。改善对遗传变异的解释将改善对遗传性癌症和其他遗传性疾病的管理。