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Poster Display & Cocktail

6P - vAO - Variant Annotator for OncoKB: A simplified interface to identify targeted therapies for somatic tumors

Date

03 Mar 2025

Session

Poster Display & Cocktail

Presenters

Vigneshwaran Gurunadan

Citation

Annals of Oncology (2025) 10 (suppl_2): 1-2. 10.1016/esmoop/esmoop104155

Authors

V. Gurunadan1, Q. Hasan2, A. Eranki1

Author affiliations

  • 1 Department Of Biomedical Engineering, IITH - Indian Institute of Technology Hyderabad, 502285 - Sangareddy/IN
  • 2 Department Of Genetics And Molecular Medicine, Kamineni Hospitals, 500068 - Hyderabad, Telangana/IN

Resources

This content is available to ESMO members and event participants.

Abstract 6P

Background

Genomic variants from somatic tumors play a vital role in both therapy response and resistance. Annotating and understanding such actionable variants becomes a crucial step in precision oncology research to plan targeted therapeutic strategies. We have built a user-friendly program, vAO: Variant Annotator for OncoKB, which performs an API-based identification of targetable variants in large datasets.

Methods

vAO is a standalone executable file deployable on any Windows operating system computer. It supports two annotation workflows: genomic coordinate-based and amino acid alteration-based annotation using user-provided files in VCF, and Microsoft Excel formats, respectively. The number of variants to be annotated is user-defined, ranging from a few variants to complete exome sequences in the input file. Users can opt for their preferred genome build (GRCh37/38) and annotation mode, input the OncoKB™ API token, and select files to perform the analysis with a simple and intuitive interface.

Results

The output of this interface is provided to the user as an Excel spreadsheet for easy exploration and downstream analysis. The output files provide insights into the oncogenicity of the variant, level of sensitivity, targeted drugs available, sites of impact, and description of the variants, irrespective of the annotation workflows. The genomic coordinate-based annotation provides additional information on the impacted genes and the amino acid consequences.

Conclusions

vAO eliminates the need for programming expertise, enabling rapid and efficient annotation of large genomic variant datasets from somatic tumors. The automated workflow facilitates the identification of targeted therapies based on an individual's genomic profile.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

Indian Institute of Technology Hyderabad.

Funding

Junior Research Fellowship (DBT/2020/IIT-H/1452) - Department of Biotechnology, Government of India and Indian Institute of Technology Hyderabad.

Disclosure

All authors have declared no conflicts of interest.

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