Upgrading rigor and efficiency of germline cancer gene variant classification for the 2020s

提高 2020 年代种系癌症基因变异分类的严谨性和效率

基本信息

批准号：
10392170
负责人：
Sean Vahram Tavtigian
金额：
$ 59.07万
依托单位：
UNIVERSITY OF UTAH
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-01 至 2027-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10392170
关键词：
Adopted Alleles American Area Attention BRCA1 gene BRCA2 gene Back Bayesian Modeling Benchmarking Benign Biological Assay Breast Calibration Cancer-Predisposing Gene Categories Classification Clinical Clinical Management Colorectal Cancer Communities Computer Analysis Counseling Country DNA Sequence Data Decision Trees Dependence Disease susceptibility Elements Evaluation Expert Opinion Family Cancer History Gene Frequency Genes Genetic Genetic Predisposition Testing Group Meetings Guidelines Hereditary Nonpolyposis Colorectal Neoplasms Human Individual Intercept International Agency for Research on Cancer Legal patent Li-Fraumeni Syndrome Malignant Neoplasms Malignant neoplasm of ovary Massive Parallel Sequencing Measurement Measures Medical Medical Genetics Mendelian disorder Messenger RNA Methods Oncogenes Operative Surgical Procedures Output PALB2 gene Paper Parents Pathogenicity Patient observation Patients Predisposition Probability Proteins Publishing RNA Splicing Reporting Role Route Sequence Analysis Susceptibility Gene System Testing Variant Veins Weight base brca gene cancer predisposition cancer risk clinically actionable computerized tools family management genetic variant improved insertion/deletion mutation insight malignant breast neoplasm medical schools meetings melanoma parallel computer prevent protein function segregation variant of unknown significance working group

项目摘要

ABSTRACT Since approximately 2010, the scale of clinical cancer predisposition genetic testing has increased dramatically. While a large fraction of sequence variants observed during testing are easily classified as benign or pathogenic, many others – principally missense substitutions, in-frame indels, and splice junction variants – are not easily placed on a spectrum from benign to clearly pathogenic. These are termed Variants of Uncertain Significance (VUS), and the clinical management of families in which they segregate would be improved if they could actually be classified. Consortium efforts to develop methods for evaluation and classification of VUS in BRCA1 and BRCA2 date back to a Breast Cancer Information Core satellite meeting held at the ASHG annual meeting in 2000; and methods that had been developing separately within the breast cancer, colorectal cancer, melanoma, and Li Fraumeni-syndrome genetics communities were cross-pollinated at a 2008 International Agency for Research on Cancer (IARC) working group meeting on VUS in cancer susceptibility genes. However, neither the qualitative nor the quantitative methods that sprouted from that meeting produced a generalizable overall approach. In 2015, the American College of Medical Genetics (ACMG) published guidelines for evaluating VUS across all Mendelian disease susceptibility genes. These guidelines produced a practical VUS evaluation framework that has been adopted by testing labs and organizations around the country. However, the ACMG system is entirely qualitative, with evidence weighted by expert opinion rather than by empirical evidence. Subsequently, we fitted the ACMG system into a quantitative Bayesian framework, providing a route to replacing qualitative evidence criteria from the ACMG system with empirically measured counterparts. Indeed, we hypothesize that there will be clear instances where strength accorded to current ACMG evidence criteria is contradicted by empirical measurement; correcting these will self-evidently improve the rigor of VUS evaluation. Aim 1 will place related ACMG data types into larger, logically consistent sets and then reduce or eliminate hidden dependencies between those sets. Noting that the ACMG variant classification guidelines were almost entirely qualitative, Aim 2 will improve the rigor of calibration for key data types through empirical measurement. Recently, we derived a quantitative Bayesian point-system for VUS evaluation and classification, which is back compatible with its parent quantitative Bayesian framework and the qualitative ACMG variant classification guidelines. Thus Aim 3 will refine this Bayesian point-system, taking advantage of the improved outputs from Aims 1 and 2. Finally, Aim 4 will benchmark elements of VUS evaluation and classification. Successful completion of these Aims will improve rigor in the system used for evaluation of VUS in cancer susceptibility genes, enabling higher throughput VUS evaluation and improving confidence in the resulting classifications.

抽象的自2010年以来，临床癌症易感基因检测的规模增加了虽然在测试过程中观察到的很大一部分序列变体很容易归类为良性或致病性，许多其他人 - 主要是错义替代，框架内和剪接连接变体 - 不容易放置在良性到明显致病性的光谱上。这些被称为变体不确定的意义（VUS），以及他们分离的家庭的临床管理将是如果实际上可以对其进行分类，则可以进行改进。财团努力开发评估方法在BRCA1和BRCA2中的VUS分类可以追溯到乳腺癌信息核心卫星会议在2000年的ASHG年会上举行；和在乳房内分别发育的方法癌症，大肠癌，黑色素瘤和Li Fraumeni-Syndrome遗传学群落被交叉授粉在2008年国际癌症研究机构（IARC）癌症VUS的工作组会议敏感基因。但是，既不是从中萌芽的定性和定量方法会议产生了可推广的整体方法。 2015年，美国医学遗传学院（ACMG）发布了所有孟德尔病敏感基因评估VU的指南。这些指南产生了一个实用的VUS评估框架，该框架已通过测试实验室和全国各地的组织。但是，ACMG系统完全是定性的，有证据加权通过专家意见而不是经验证据。随后，我们将ACMG系统拟合到定量贝叶斯框架，提供了从ACMG替换定性证据标准的途径迫切测量的系统。确实，我们假设会有明确的实例根据当前ACMG证据标准的强度与经验测量相矛盾；纠正这些将自我意识地改善VUS评估的严格性。 AIM 1将放置相关的ACMG数据类型为较大的，逻辑上一致的集合，然后减少或消除那些隐藏的依赖关系套。指出ACMG变体分类指南几乎完全是定性的，AIM 2将通过经验测量来改善关键数据类型的校准严格。最近，我们得出了定量贝叶斯点系统用于VUS评估和分类，这与其兼容家长定量贝叶斯框架和定性ACMG变体分类指南。这个目标 3将利用AIMS 1和2的提高输出来完善这个贝叶斯点系统。最后 AIM 4将基准基准VUS评估和分类的要素。这些目标的成功完成将改善用于评估癌症易感性基因VUS的系统的严格性，使得能够更高吞吐量VUS评估并提高对所得分类的信心。