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Leveraging Artificial Intelligence for Neoantigen Prediction
Jing Zeng1, Zhengjun Lin1, Xianghong Zhang1
1Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, People's Republic of China.
Abstract:
Neoantigens represent a class of antigens within tumor microenvironments that arise from diverse somatic mutations and aberrations specific to tumorigenesis, holding substantial promise for advancing tumor immunotherapy. However, only a subset of neoantigens effectively elicits antitumor immune responses, and the specific neoantigens recognized by individual T-cell receptors (TCR) remain incompletely characterized. Therefore, substantial research has focused on screening immunogenic neoantigens, mainly through their major histocompatibility complex (MHC) presentation and TCR recognition specificity. Given the resource intensiveness and inefficiency of experimental validation, predictive models based on artificial intelligence (AI) have gradually become mainstream methods to discover immunogenic neoantigens. In this article, we provide a comprehensive summary of current AI methodologies for predicting neoantigens, with a particular focus on their capability to model peptide-MHC (pMHC) and pMHC-TCR binding. Furthermore, a thorough benchmarking analysis was conducted to assess the performance of antigen presentation predictors for scoring the immunogenicity of neoantigens. AI models have potential applications in the treatment of clinical diseases although several limitations must first be overcome to realize their full potential. Anticipated advancements in data accessibility, algorithmic refinement, platform enhancement, and comprehensive validation of immune processes are poised to enhance the precision and utility of neoantigen prediction methodologies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.
Insights
Artificial intelligence (AI) models are revolutionizing cancer immunotherapy by predicting tumor-specific neoantigens. These advanced AI tools enhance the discovery of immunogenic neoantigens for more effective cancer treatments.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Neoantigens, derived from tumor-specific mutations, are crucial targets for cancer immunotherapy.
- Identifying immunogenic neoantigens that elicit anti-tumor immune responses is challenging due to complex interactions.
- Experimental validation of neoantigens is resource-intensive and inefficient.
Purpose of the Study:
- To provide a comprehensive summary of current artificial intelligence (AI) methodologies for neoantigen prediction.
- To focus on AI's capability in modeling peptide-MHC (pMHC) and pMHC-TCR binding.
- To benchmark the performance of antigen presentation predictors for neoantigen immunogenicity scoring.
Main Methods:
- Review and summarization of existing AI methodologies for neoantigen prediction.
- Analysis of AI models focusing on peptide-MHC (pMHC) binding prediction.
- Benchmarking of AI-driven antigen presentation predictors for immunogenicity assessment.
Main Results:
- AI models are increasingly utilized for discovering immunogenic neoantigens, overcoming experimental limitations.
- The study assessed AI's effectiveness in modeling pMHC and pMHC-TCR interactions.
- Benchmarking analysis evaluated the performance of AI predictors in scoring neoantigen immunogenicity.
Conclusions:
- AI methodologies show significant potential for advancing tumor immunotherapy through precise neoantigen prediction.
- Overcoming current limitations in data, algorithms, and validation is essential for clinical translation.
- Future advancements in AI are expected to enhance the precision and utility of neoantigen discovery.
