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Computational Methods for Cancer Neoantigen Prediction
Andrea Moreno-Manuel1, Sotiris Ouzounis2, Marius Eidsaa3
1Cancer Heterogeneity and Immunomics (CHI) Group, University Hospital Lozano Blesa, Aragon Health Research Institute (IISA), Zaragoza, Spain.
Abstract:
Neoantigens are mutated peptides arising from tumor genomic alterations, which can be recognized and attacked by the immune system, leading to antitumor immune responses. In the last decades, many immunotherapeutic strategies have been developed, which has increased the interest in neoantigens. This led to the development of computational tools that facilitate neoantigen identification and prioritization, prior to their validation using experimental approaches. This chapter aims at explaining the key steps that need to be conducted to identify potential neoantigens in silico, including an example of the most frequently used tools. This is followed by a description and comparison of the cutting-edge tools and pipelines for neoantigen prediction both for human and mouse. The last aim of this chapter is to depict the technical challenges that limit neoantigen prediction using bioinformatics, as well as the expected improvements, given the current revolution of artificial intelligence, which is implemented in most of the neoantigen-related tools. As exposed in this book chapter, we believe that advances in immunomics and computational biology will be key to implement personalized cancer immunotherapy in the clinical practice, to improve outcomes of cancer patients.
Insights
Neoantigens, mutated peptides from tumors, are key targets for cancer immunotherapy. Computational tools are advancing to identify and prioritize these neoantigens for personalized treatments.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Neoantigens arise from tumor-specific mutations and are recognized by the immune system, driving antitumor responses.
- The rise of cancer immunotherapies has intensified the focus on neoantigens.
- Computational tools are crucial for identifying and prioritizing neoantigens for experimental validation.
Purpose of the Study:
- To detail the in silico identification and prioritization of potential neoantigens.
- To review and compare current bioinformatics tools and pipelines for neoantigen prediction in humans and mice.
- To discuss the technical challenges and future improvements in neoantigen prediction, particularly with AI integration.
Main Methods:
- In silico identification of neoantigens using computational tools.
- Comparison of leading bioinformatics pipelines for neoantigen prediction.
- Analysis of artificial intelligence applications in neoantigen discovery.
Main Results:
- Key steps for in silico neoantigen identification are outlined.
- A comparison of frequently used and cutting-edge neoantigen prediction tools is provided.
- Technical limitations and AI-driven advancements in neoantigen prediction are discussed.
Conclusions:
- Advances in immunomics and computational biology are essential for personalized cancer immunotherapy.
- Improved neoantigen prediction will enhance the clinical application of immunotherapies.
- AI integration promises significant improvements in neoantigen discovery and cancer patient outcomes.
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