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Updated: Feb 13, 2026

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NeoHunter: Flexible software for systematically detecting neoantigens from sequencing data.

Tianxing Ma1, Zetong Zhao1, Haochen Li2

  • 1MOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST and Department of Automation Tsinghua University Beijing China.

Quantitative Biology (Beijing, China)
|February 12, 2026
PubMed
Summary

NeoHunter software accurately detects and prioritizes diverse cancer neoantigens, including those from gene fusions and aberrant splicing. This advancement aids in designing personalized cancer vaccines by improving neoantigen identification from sequencing data.

Keywords:
cancer vaccinemolecular alterationneoantigenneoantigen prioritization

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Area of Science:

  • Oncology
  • Immunology
  • Bioinformatics

Background:

  • Tumors harbor molecular alterations producing mutant peptides called neoantigens.
  • Neoantigens presented on tumor cells can trigger immune responses, crucial for personalized cancer vaccines.
  • Existing computational tools have limitations in detecting diverse neoantigens and prioritizing them effectively.

Purpose of the Study:

  • To develop a flexible software, NeoHunter, for comprehensive neoantigen detection and prioritization.
  • To enable detection of neoantigens derived from various sources, including SNVs, indels, gene fusions, and aberrant splicing.
  • To implement advanced strategies for neoantigen immunogenicity evaluation and prioritization.

Main Methods:

  • NeoHunter systematically analyzes sequencing data in various formats.
  • It detects neoantigens from single nucleotide variants (SNVs), insertions/deletions (indels), gene fusions, and aberrant splicing.
  • Prioritization employs direct and indirect immunogenicity evaluation using binding characteristics, biological data, and T-cell receptor specificity.

Main Results:

  • NeoHunter was applied to the TESLA dataset, including melanoma and non-small cell lung cancer patients.
  • The software detected 79% (27/34) of validated neoantigens.
  • SNV/indel-derived neoantigens comprised 90% of the top 100 candidates, with aberrant splicing contributing 9% and gene fusions detected in one patient.

Conclusions:

  • NeoHunter is a powerful, versatile tool for comprehensive neoantigen detection and prioritization.
  • Its ability to identify diverse neoantigen types enhances potential for personalized cancer vaccine development.
  • NeoHunter is freely available for academic use, facilitating further research in cancer immunotherapy.