Cancer SIGVAR: A semi-automated interpretation tool for germline variants of hereditary cancer-related genes [article]

Hong Li, Shuixia Liu, Shuangying Wang, Quanlei Zeng, Yulan Chen, Ting Fang, Yi Zhang, Ying Zhou, Yu Zhang, Kaiyue Wang, Zhangwei Yan, Cuicui Qiang (+6 others)
2020 bioRxiv   pre-print
AbstractThe American College of Medical Genetics and Genomics and the Association for Molecular Pathology published guidelines in 2015 for the clinical interpretation of Mendelian disorder sequence variants based on 28 criteria. ClinGen Sequence Variant Interpretation (SVI) Working Groups have developed many adaptations or refinements of these guidelines to improve the consistency of interpretation. We combined the most recent adaptations to expand the criteria from 28 to 48 and developed a
more » ... and developed a tool called Cancer SIGVAR to help healthcare workers and genetic counselors interpret the clinical significance of cancer germline variants, which is critical for the clinical diagnosis and treatment of hereditary cancer. Our tool can accept VCF files as input and realize fully automated interpretation based on 21 criteria and semi-automated interpretation based on 48 criteria. We validated our tool on the ClinVar and CLINVITAE benchmark databases for the accuracy of fully automated interpretation, achieving an average consistency for pathogenic and benign assessment up to 93.40% and 82.54%, respectively. We compared Cancer SIGVAR with a similar tool, InterVar, and analyzed the main differences in criteria and implementation. In addition, to verify the performance of semi-automated interpretation based on 48 criteria, we selected 911 variants from two benchmark databases and reached an average classification consistency of 98.35%. Our findings highlight the need to optimize automated interpretation tools based on constantly updated guidelines.
doi:10.1101/2020.04.15.042283 fatcat:obq7qbqetjgsflbr6umfppduw4