ArticleGenome medicine2019
Variant Interpretation for Cancer (VIC): a computational tool for assessing clinical impacts of somatic variants.
Article in Genome medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Convergence of machine learning and genomics for precision oncology.Nature reviews. Cancer · 2026Review
- The promises and pitfalls of automated variant interpretation: a comprehensive review.Briefings in bioinformatics · 2025Review
- Integrated analysis of oral rinse-derived and plasma circulating tumour DNA for mutation profiling and outcome prediction with oral squamous cell carcinoma.NPJ precision oncology · 2025Article
- An Analytical Approach that Combines Knowledge from Germline and Somatic Mutations Enhances Tumor Genomic Reanalyses in Precision Oncology.Journal of computational biology : a journal of computational molecular cell biology · 2025Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges.International journal of molecular sciences · 2024Review
- Duodenal Fluid Analysis as a Rewarding Approach to Detect Low-Abundance Mutations in Biliopancreatic Cancers.International journal of molecular sciences · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- Prognostic analysis of mutated genes and insight into effects of DNA damage and repair on mutational strand asymmetries in gastric cancer.Biochemistry and biophysics reports · 2024Article
- Tri©DB: an integrated platform of knowledgebase and reporting system for cancer precision medicine.Journal of translational medicine · 2023Article
- AJCEM case reports · 2023Article
- The association of blood ctDNA levels to mutations of marker genes in colorectal cancer.Cancer reports (Hoboken, N.J.) · 2023Article
- LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution.BMC bioinformatics · 2023Article
- Article
- Unveiling the genomic landscape of possible metastatic malignant transformation of teratoma secondary to cisplatin-chemotherapy: a Tempus gene analysis-based case report literature review.Frontiers in oncology · 2023Article
- Article
- Targeted Sequencing of Plasma-Derived vs. Urinary cfDNA from Patients with Triple-Negative Breast Cancer.Cancers · 2022Article
- Unique Growth Pattern Presentation of a Papillary Renal Cell Carcinoma.Diagnostics (Basel, Switzerland) · 2022Article
- Targeted Next-Generation Sequencing of Thymic Epithelial Tumours Revealed Pathogenic Variants inCancers · 2022Article
- CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer.Science advances · 2022Article
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9 authors.
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Abstract
backgroundClinical 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.
resultsWe 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.
conclusionsAlthough 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/ .
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