Transformers meets neoantigen detection: a systematic literature review

Area of Science:

• Oncology
• Immunology
• Artificial Intelligence
• Bioinformatics

Background:

• Cancer immunology presents a promising alternative to traditional treatments like chemotherapy and radiotherapy.
• Personalized cancer vaccines, based on cancer neoantigens, are an emerging therapeutic strategy.
• Transformers, a type of AI, have shown significant impact in natural language processing and are increasingly used in proteomics.

Purpose of the Study:

• To systematically review the application of Transformers in neoantigen detection.
• To map current bioinformatics pipelines for neoantigen identification.
• To examine clinical trial results for cancer vaccines.

Main Methods:

• Systematic literature review.
• Analysis of bioinformatics pipelines for neoantigen detection.
• Review of clinical trial data for cancer vaccines.

Main Results:

• Transformers are being applied across various stages of the neoantigen detection process.
• Current pipelines for neoantigen identification are being enhanced by AI.
• Clinical trials show varying outcomes for cancer vaccines, highlighting the need for optimized neoantigen identification.

Conclusions:

• Transformers show potential to revolutionize neoantigen detection for personalized cancer vaccines.
• Further research is needed to optimize AI-driven pipelines for improved vaccine efficacy.
• The integration of AI in cancer immunology is critical for advancing novel cancer therapies.