Variant Interpretation for Cancer (VIC): a computational tool for assessing clinical impacts of somatic variants

Abstract

Background 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. Results 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. Conclusions 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/ . Electronic supplementary material The online version of this article (10.1186/s13073-019-0664-4) contains supplementary material, which is available to authorized users.

Comparison of three commercial decision support platforms for matching of next-generation sequencing results with therapies in patients with cancer

“… 34 To this end, the abovementioned study, which compared six somatic cancer variant knowledge bases, harmonised variant interpretations from these databases and made them available via a freely accessible web interface (search.cancervariants.org). 13 Other tools, such as Variant Interpretation for Cancer, which acknowledges that it should be employed alongside human reviewers, as well as the NIH-funded Clinical Genome Resource (ClinGen) effort Minimal Variant Level Data framework, 35 36 have also contributed to minimising bias in the interpretation workflow. Our incomplete knowledge about how to optimally identify druggable targets and the lacking consensus in the processes delegating drug matching may be overcome by such cooperative and global efforts, in turn contributing to the realisation of the promising precision oncology concept.…”

CancerVar: an Artificial Intelligence empowered platform for clinical interpretation of somatic mutations in cancer

“…According to the AMP/ASCO/CAP 2017 guidelines, there are a total of 12 types of clinically derived evidence to predict the clinical significance for somatic variants, including therapies, mutation types, variant allele fraction (mosaic variant frequency (likely somatic), non-mosaic variant frequency (potential germline)), population databases, germline databases, somatic databases, predictive results of different computational algorithms, pathway involvement, and publications (8,11). As shown in Figure 1, CancerVar contains all the above 12 types of evidence, among which 10 of them are automatically generated, while the other two, including variant allele fraction and potential germline, require user input for manual adjustment.…”

“…To standardize clinical interpretation of somatic variants in cancer and support clinical decision making, the Association for Molecular Pathology (AMP), American Society of Clinical Oncology (ASCO), College of American Pathologists (CAP), jointly proposed standards and guidelines for interpretation and reporting of somatic variants, which classify somatic variants into four Tiers: strong clinical significance (Tier I), potential clinical significance (Tier II), uncertain significance (Tier III), and benign (Tier IV) (8). The AMP/ASCO/CAP 2017 guideline included 12 pieces of evidence, which are diagnostic, prognostic and therapeutic clinical evidences, mutation types, variant allele fraction (mosaic variant frequency (likely somatic), non-mosaic variant frequency (potential germline)), population databases, germline databases, somatic databases, predictive results of different computational algorithms, pathway involvement, and publications (8,9).…”

Keeping up with the genomes: scaling genomic variant interpretation

“…In this issue, Lever and colleagues [9] demonstrate a text-mining approach to gather data from the literature on thousands of biomarkers and to deposit the information in a publicly accessible database called CIViCmine. He and colleagues [10] apply computational approaches to consume pre-annotated files and to apply criteria for clinical assessment. Both approaches enable the prioritization of variants identified in tumors for further review.…”