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This review aims to evaluate how artificial intelligence enhances the discovery of neoantigens relevant for T cell therapies.

AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition

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Comprehensive review describes AI's transformative role in identifying tumor-specific neoantigens, enhancing immunotherapy outcomes.

This review aims to evaluate how artificial intelligence enhances the discovery of neoantigens relevant for T cell therapies.
Summarizes advances in AI workflows for neoantigen discovery from somatic variant calling to T cell recognition.
Discusses tools for predicting peptide presentation and immunogenicity based on integrated immunopeptidomics and transcriptomics data.
Highlights challenges including prediction accuracy and tumor heterogeneity in neoantigen breakthroughs.
AI models trained on eluted ligand datasets significantly outperform traditional affinity predictors for peptide presentation.
Integrative pipelines improve the identification of clinically relevant neoepitopes by reducing non-immunogenic candidates.
Ongoing challenges in accurate prediction for class II alleles and tumor heterogeneity are discussed.

Bakhshian et al. (2026) studied this question.

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View Full Paper

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Authors

Comprehensive review describes AI's transformative role in identifying tumor-specific neoantigens, enhancing immunotherapy outcomes.

This review aims to evaluate how artificial intelligence enhances the discovery of neoantigens relevant for T cell therapies.
Summarizes advances in AI workflows for neoantigen discovery from somatic variant calling to T cell recognition.
Discusses tools for predicting peptide presentation and immunogenicity based on integrated immunopeptidomics and transcriptomics data.
Highlights challenges including prediction accuracy and tumor heterogeneity in neoantigen breakthroughs.
AI models trained on eluted ligand datasets significantly outperform traditional affinity predictors for peptide presentation.
Integrative pipelines improve the identification of clinically relevant neoepitopes by reducing non-immunogenic candidates.
Ongoing challenges in accurate prediction for class II alleles and tumor heterogeneity are discussed.

Bakhshian et al. (2026) studied this question.

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