Golubeva Volha A, Nepomuceno Thales C, Monteiro Alvaro N A
Cancer Epidemiology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.
Cancers (Basel). 2019 Apr 12;11(4):522. doi: 10.3390/cancers11040522.
Genetic testing allows for the identification of germline DNA variations, which are associated with a significant increase in the risk of developing breast cancer (BC) and ovarian cancer (OC). Detection of a or pathogenic variant triggers several clinical management actions, which may include increased surveillance and prophylactic surgery for healthy carriers or treatment with the PARP inhibitor therapy for carriers diagnosed with cancer. Thus, standardized validated criteria for the annotation of and variants according to their pathogenicity are necessary to support clinical decision-making and ensure improved outcomes. Upon detection, variants whose pathogenicity can be inferred by the genetic code are typically classified as pathogenic, likely pathogenic, likely benign, or benign. Variants whose impact on function cannot be directly inferred by the genetic code are labeled as variants of uncertain clinical significance (VUS) and are evaluated by multifactorial likelihood models that use personal and family history of cancer, segregation data, prediction tools, and co-occurrence with a pathogenic variant. Missense variants, coding alterations that replace a single amino acid residue with another, are a class of variants for which determination of clinical relevance is particularly challenging. Here, we discuss current issues in the missense variant classification by following a typical life cycle of a missense variant through detection, annotation and information dissemination. Advances in massively parallel sequencing have led to a substantial increase in VUS findings. Although the comprehensive assessment and classification of missense variants according to their pathogenicity remains the bottleneck, new developments in functional analysis, high throughput assays, data sharing, and statistical models are rapidly changing this scenario.
基因检测能够识别种系DNA变异,这些变异与患乳腺癌(BC)和卵巢癌(OC)风险的显著增加相关。检测到一个或多个致病变异会引发多项临床管理行动,这可能包括对健康携带者加强监测和进行预防性手术,或者对被诊断患有癌症的携带者采用聚(ADP-核糖)聚合酶(PARP)抑制剂疗法进行治疗。因此,根据其致病性对变异进行注释的标准化验证标准对于支持临床决策和确保改善治疗结果是必要的。检测到的变异,如果其致病性可通过遗传密码推断,则通常分类为致病、可能致病、可能良性或良性。那些对功能的影响不能通过遗传密码直接推断的变异被标记为临床意义未明的变异(VUS),并通过多因素似然模型进行评估,该模型使用个人和家族癌症病史、分离数据、预测工具以及与致病变异的共现情况。错义变异,即编码改变,用另一个氨基酸残基取代单个氨基酸残基,是一类确定临床相关性特别具有挑战性的变异。在此,我们通过追踪错义变异从检测、注释到信息传播的典型生命周期,来讨论错义变异分类中的当前问题。大规模平行测序技术的进步导致VUS发现大量增加。尽管根据致病性对错义变异进行全面评估和分类仍然是瓶颈,但功能分析、高通量检测、数据共享和统计模型方面的新进展正在迅速改变这种局面。