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

摘要 自大约2010年以来，临床癌症易感性基因检测的规模有所增加 戏剧性的。虽然在测试期间观察到的很大一部分序列变体很容易被归类为 良性的或致病的，许多其他的--主要是错义替换、框架内插入和剪接连接 变种--不容易从良性到明显致病。这些被称为变种 不确定的意义(VU)，以及他们分离的家庭的临床管理将是 如果它们真的能被分类，那就更好了。财团努力制定评估和评估方法 BRCA1和BRCA2中VU的分类可以追溯到乳腺癌信息核心卫星会议 在2000年ASHG年会上举行的；以及在乳房内单独开发的方法 癌症、结直肠癌、黑色素瘤和Li Fraumeni综合征遗传学社区进行了交叉授粉 在2008年国际癌症研究机构(IARC)关于VUS治疗癌症的工作组会议上 易感基因。然而，无论是定性方法还是定量方法，都是从这一点上萌芽的 会议产生了一种概括性的总体方法。2015年，美国医学遗传学学院 美国医学会(ACMG)公布了评估所有孟德尔疾病易感基因的VUS指南。这些 指南制定了一个实用的VUS评估框架，该框架已被测试实验室和 全国各地的组织。然而，ACMG系统完全是定性的，证据是加权的。 依据的是专家意见，而非经验证据。随后，我们将ACMG系统安装到一个 定量贝叶斯框架，为取代ACMG的定性证据标准提供了一条途径 具有经验测量的对应物的系统。事实上，我们假设将会有明确的例子 符合ACMG现行证据标准的强度与经验测量相矛盾； 纠正这些问题将不言而喻地提高VUS评估的严谨性。AIM 1将放置相关ACMG数据 类型到更大的、逻辑上一致的集合，然后减少或消除它们之间的隐藏依赖 布景。注意到ACMG变体分类指南几乎完全是定性的，目标2将 通过经验测量提高关键数据类型校准的严谨性。最近，我们推导出了一个 用于VUS评估和分类的量化贝叶斯积分系统，该系统与ITS重新兼容 父母定量贝叶斯框架和定性ACMG变体分类指南。因此瞄准 3将利用目标1和目标2的改进的输出来改进这个贝叶斯点数系统。 目标4将对VUS评估和分类的要素进行基准测试。这些目标的成功实现将 改进用于评估癌症易感基因中的VUS的系统的严密性，从而实现更高的 吞吐量VUS评估和提高对结果分类的置信度。

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.