Dataset Information

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/ .

SUBMITTER: He MM

PROVIDER: S-EPMC6708137 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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

He Max M MM Li Quan Q Yan Muqing M Cao Hui H Hu Yue Y He Karen Y KY Cao Kajia K Li Marilyn M MM Wang Kai K

Genome medicine 20190823 1

<h4>Background</h4>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 guide ...[more]

PMID: 31443733

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Project description:BackgroundGenetic variation databases provide invaluable information on the presence and frequency of genetic variants in the 'untargeted' human population, aggregated with the primary goal to facilitate the interpretation of clinically important variants. The presence of somatic variants in such databases can affect variant assessment in undiagnosed rare disease (RD) patients. Previously, the impact of somatic mosaicism was only considered in relation to two Mendelian disease-associated genes. Here, we expand the analyses to identify additional mosaicism-prone genes in blood-derived reference population databases.ResultsTo identify additional mosaicism-prone genes relevant to RDs, we focused on known/previously established ClinVar pathogenic and likely pathogenic single-nucleotide variants, residing in genes associated with early onset, severe autosomal dominant diseases. We asked whether any of these variants are present in a higher-than-expected frequency in the reference population databases and whether there is evidence of somatic origin (i.e., allelic imbalance) rather than germline heterozygosity (~ half of the reads supporting alternative allele). The mosaicism-prone genes identified were further categorized according to the processes they are involved in. Beyond the previously reported ASXL1 and DNMT3A, we identified 7 additional autosomal dominant RD-associated genes with known pathogenic single-nucleotide variants present in the reference population databases and good evidence of allelic imbalance: BRAF, CBL, FGFR3, IDH2, KRAS, PTPN11 and SETBP1. From this group of 9 genes, the majority (n = 7) was important for hematopoiesis. In addition, 4 of these genes were involved in cell proliferation. Further assessment of the known 156 hematopoietic genes led to identification of 48 genes (21 not yet associated with RDs) with at least some evidence of mosaicism detectable in reference population databases.ConclusionsThese results stress the importance of considering genes involved in hematopoiesis and cell proliferation when interpreting the presence and frequency of genetic variants in blood-derived reference population databases, both public and private. This is especially important when considering new variants of uncertain significance in known hematopoietic/cell proliferation RD genes and future novel gene-disease associations involving this class of genes.

Project description:Clinical interpretation of DNA sequence variants is a critical step in reporting clinical genetic testing results. Application of next-generation sequencing technology in molecular genetic testing has facilitated diagnoses of genetic disorders in clinical practice. However, the large number of DNA sequence variants detected in clinical specimens, many of which have never been seen before, make clinical interpretation challenging. Recommendations by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) have been widely adopted by clinical laboratories around the world to guide clinical interpretation of sequence variants. The ClinGen Sequence Variant Interpretation Working Group and various disease-specific variant curation expert panels have also developed specifications for the ACMG/AMP recommendations. Despite these efforts to standardize variant interpretation in clinical practice, different laboratories may subjectively use professional judgment to determine which criteria are applicable when classifying a variant. In addition, clinicians and researchers who are not familiar with the variant interpretation process may have difficulty understanding clinical genetic reports and communicating the clinical significance of genetic testing results. Here we provide a step-by-step protocol for clinical interpretation of sequence variants, including practical examples. By following this protocol, clinical laboratory geneticists can interpret the clinical significance of sequence variants according to the ACMG/AMP recommendations and ClinGen framework. Furthermore, this article will help clinicians and researchers to understand variant classification in clinical genetic testing reports and evaluate the quality of the reports. © 2020 by John Wiley & Sons, Inc. Basic Protocol: Interpreting the clinical significance of sequence variants Support Protocol: Reevaluating the clinical significance of sequence variants.

Dataset Information

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

Publications

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

Similar Datasets

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