Simcere Diagnostics Co., Ltd., Nanjing, 210042, Jiangsu, China.
State Key Laboratory of Translational Medicine and Innovative Drug Development, Nanjing, 210042, Jiangsu, China.
Genome Med. 2019 Aug 23;11(1):53. doi: 10.1186/s13073-019-0664-4.
Clinical laboratories implement a variety of measures to classify somatic sequence variants and identify clinically significant variants to facilitate the implementation of precision medicine. To standardize the interpretation process, the Association for Molecular Pathology (AMP), American Society of Clinical Oncology (ASCO), and College of American Pathologists (CAP) published guidelines for the interpretation and reporting of sequence variants in cancer in 2017. These guidelines classify somatic variants using a four-tiered system with ten criteria. Even with the standardized guidelines, assessing clinical impacts of somatic variants remains to be tedious. Additionally, manual implementation of the guidelines may vary among professionals and may lack reproducibility when the supporting evidence is not documented in a consistent manner.
We developed a semi-automated tool called "Variant Interpretation for Cancer" (VIC) to accelerate the interpretation process and minimize individual biases. VIC takes pre-annotated files and automatically classifies sequence variants based on several criteria, with the ability for users to integrate additional evidence to optimize the interpretation on clinical impacts. We evaluated VIC using several publicly available databases and compared with several predictive software programs. We found that VIC is time-efficient and conservative in classifying somatic variants under default settings, especially for variants with strong and/or potential clinical significance. Additionally, we also tested VIC on two cancer-panel sequencing datasets to show its effectiveness in facilitating manual interpretation of somatic variants.
Although VIC cannot replace human reviewers, it will accelerate the interpretation process on somatic variants. VIC can also be customized by clinical laboratories to fit into their analytical pipelines to facilitate the laborious process of somatic variant interpretation. VIC is freely available at https://github.com/HGLab/VIC/ .
临床实验室实施了各种措施来对体细胞序列变异进行分类，并识别具有临床意义的变异，以促进精准医学的实施。为了标准化解释过程，分子病理学协会（AMP）、美国临床肿瘤学会（ASCO）和美国病理学家学院（CAP）于 2017 年发布了癌症序列变异解读和报告指南。这些指南使用四级分类系统和十个标准来对体细胞变异进行分类。即使有了标准化的指南，评估体细胞变异的临床影响仍然很繁琐。此外，由于没有以一致的方式记录支持证据，指南的手动实施可能因专业人员而异，并且可能缺乏可重复性。
我们开发了一种名为“癌症变异解读（VIC）”的半自动工具，以加速解读过程并最大程度地减少个体偏见。VIC 采用预注释文件，并根据几个标准自动对序列变异进行分类，用户还可以整合其他证据，以优化对临床影响的解读。我们使用了几个公开可用的数据库来评估 VIC，并与几个预测软件程序进行了比较。我们发现，在默认设置下，VIC 对体细胞变异的分类既高效又保守，尤其是对于具有强和/或潜在临床意义的变异。此外，我们还在两个癌症panel 测序数据集上测试了 VIC，以展示其在促进体细胞变异手动解读方面的有效性。
虽然 VIC 不能替代人工审阅者，但它可以加速体细胞变异的解读过程。临床实验室也可以根据自己的分析流程对 VIC 进行定制，以简化体细胞变异解读这一繁琐的过程。VIC 可在 https://github.com/HGLab/VIC/ 免费获取。
