Cancer SIGVAR: A semiautomated interpretation tool for germline variants of hereditary cancer‐related genes

Abstract

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/.

Towards Next-Generation Sequencing (NGS)-Based Newborn Screening: A Technical Study to Prepare for the Challenges Ahead

“…Secondly, the throughput time should be as short as possible because treatment should be started as soon as possible. Different automated and semiautomated tools have been developed to interpret genetic variants causing different kinds of disorders, including IMDs [47,48], to aid these decisions. In our experience, the different types of genetic databases used for NGS, especially WES/WGS, will provide large amounts of data that will need to be analyzed and interpreted correctly.…”

vaRHC: an R package for semi-automation of variant classification in hereditary cancer genes according to ACMG/AMP and gene-specific ClinGen guidelines

“…CDH1 and PTEN variant datasets were also analysed with Cancer-SIGVAR ( Li et al 2021 ) using default settings. The number of differences between both software was statistically evaluated using the Kappa test from vcd (v 1.4-10) CRAN package; P -value was adjusted using Benjamini–Hochberg correction for multiple comparisons ( Benjamini and Hochberg 1995 ).…”

“…Most tools are based on ACMG/AMP general rules, like InterVar ( Li and Wang 2017 ), PathoMAN ( Joseph et al 2017 ; Ravichandran et al 2019 ), ClinGen Pathogenicity Calculator ( Patel et al 2017 ), CharGer ( Scott et al 2019 ), Varsome ( Kopanos et al 2019 ), or Franklin ( https://franklin.genoox.com ). Other tools focus on a set of genes like CardioClassifier ( Whiffin et al 2018 ) and CardioVai ( Nicora et al 2018 ) for inherited cardiac conditions or Cancer Predisposition Sequencing Reporter (CPSR) ( Nakken et al 2021 ) and Cancer-SIGVAR ( Li et al 2021 ) for cancer predisposition genes. CPSR uses SherLoc algorithm ( Nykamp et al 2017 ) that provided several refinements to the original guidelines.…”

MARGINAL: An Automatic Classification of Variants in BRCA1 and BRCA2 Genes Using a Machine Learning Model

“…Many automatic tools that implement ACMG-AMP criteria and rules have been developed to address these challenges. For instance, InterVar is an automated tool that helps human reviewers to interpret the clinical significance of variants in any Mendelian gene [ 6 ], while Cancer SIGVAR contributes to the interpretation of hereditary cancer-associated germline variants [ 7 ], PathoMAN allows the automation of germline variant curation in clinical cancer genetics [ 8 ], CardioVAI enables variant interpretation in the diagnosis of cardiovascular diseases [ 9 ] and the GenOtoScope tool automatically classifies variants that may be associated to congenital hearing loss [ 10 ]. All these tools are based on the implementation of ACMG-AMP criteria, while the final classification of the variants depends on the number of verified criteria combined with the rules provided in the 2015 ACMG-AMP guidelines.…”