BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, WuHan, China.
BGI Genomics, BGI-Shenzhen, ShenZhen, China.
Hum Mutat. 2021 Apr;42(4):359-372. doi: 10.1002/humu.24177. Epub 2021 Mar 6.
Cancer is one of the most important health issues globally and the accuracy of interpretation of cancer-related variants is critical for the clinical management of hereditary cancer. ClinGen Sequence Variant Interpretation Working Groups have developed many adaptations of American College of Medical Genetics and Genomics and the Association of Molecular Pathologists guidelines to improve the consistency of interpretation. We combined the most recent adaptations to expand the number of the criteria from 28 to 48 and developed a tool called Cancer SIGVAR to help genetic counselors interpret the clinical significance of cancer germline variants. Our tool can accept VCF files as input and realize fully automated interpretation based on 21 criteria and semiautomated interpretation based on 48 criteria. We validated the performance of our tool with the ClinVar and CLINVITAE benchmark databases, achieving an average consistency for pathogenic and benign assessment up to 93.71% and 79.38%, respectively. We compared Cancer SIGVAR with two similar tools, InterVar and PathoMAN, and analyzed the main differences in criteria and implementation. Furthermore, we selected 911 variants from another two in-house benchmark databases, and semiautomated interpretation reached an average classification consistency of 98.35%. Our findings highlight the need to optimize automated interpretation tools based on constantly updated guidelines. Cancer SIGVAR is publicly available at http://cancersigvar.bgi.com/.
癌症是全球最重要的健康问题之一,准确解读与癌症相关的变异对于遗传性癌症的临床管理至关重要。ClinGen 序列变异解释工作组已经对美国医学遗传学与基因组学学院和分子病理学家协会的指南进行了许多改编,以提高解释的一致性。我们结合了最新的改编版本,将标准数量从 28 个扩展到 48 个,并开发了一个名为 Cancer SIGVAR 的工具,帮助遗传咨询师解读癌症种系变异的临床意义。我们的工具可以接受 VCF 文件作为输入,并根据 21 个标准实现完全自动化解释,根据 48 个标准实现半自动解释。我们使用 ClinVar 和 CLINVITAE 基准数据库验证了我们工具的性能,致病性和良性评估的平均一致性分别达到 93.71%和 79.38%。我们将 Cancer SIGVAR 与两个类似的工具 InterVar 和 PathoMAN 进行了比较,并分析了标准和实现方面的主要差异。此外,我们从另外两个内部基准数据库中选择了 911 个变体,半自动解释达到了 98.35%的平均分类一致性。我们的研究结果强调了需要根据不断更新的指南优化自动化解释工具。Cancer SIGVAR 可在 http://cancersigvar.bgi.com/ 上公开获取。