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

摘要 大约从2010年开始，临床癌症易感基因检测的规模不断增加 戏剧性地。虽然在测试期间观察到的大部分序列变体很容易被分类为 良性或致病性，许多其他-主要是错义取代，框内插入缺失和剪接连接 变异-不容易被置于从良性到明显致病的范围内。这些被称为变体 不确定的意义（VUS），以及他们隔离的家庭的临床管理将是 如果它们真的能被分类的话。联合会努力制定评价方法， BRCA 1和BRCA 2中VUS的分类可以追溯到乳腺癌信息核心卫星会议 在ASHG 2000年年会上举行;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-syndrome遗传学社区进行了异花授粉 在2008年国际癌症研究机构（IARC）关于癌症VUS的工作组会议上， 易感基因然而，无论是定性还是定量方法， 会议产生了一个可推广的总体办法。2015年，美国医学遗传学学院（American College of Medical Genetics） （ACMG）发表了在所有孟德尔疾病易感基因中评估VUS的指南。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采用， 全国各地的组织。然而，ACMG系统完全是定性的，证据加权 专家的意见，而不是经验证据。随后，我们将ACMG系统安装到一个 定量贝叶斯框架，提供了替代ACMG定性证据标准的途径 系统与经验测量对应物。事实上，我们假设会有明显的例子 符合现行ACMG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。目标1将放置相关ACMG数据 类型转换成更大的、逻辑上一致的集合，然后减少或消除这些集合之间隐藏的依赖关系。 集.注意到ACMG变体分类指南几乎完全是定性的，目标2将 通过实证测量提高关键数据类型校准的严谨性。最近，我们推导出一个 定量贝叶斯点系统VUS评估和分类，这是回兼容其 父定量贝叶斯框架和定性ACMG变异分类指南。因此，目标 第三项目标将利用目标1和2的改进成果，完善这一贝叶斯点系统。最后， 目标4将为VUS评价和分类的要素设定基准。成功实现这些目标， 提高用于评估癌症易感基因中VUS的系统的严谨性， 吞吐量VUS评估和提高所得分类的置信度。

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.