GeNeo: A Bioinformatics Toolbox for Genomics-Guided Neoepitope Prediction

Al-okaily, Anas; Shcheglova, Tatiana; Sherafat, Elham; Alqahtani, Fahad; Hagymasi, Adam T.; Kaur, Anupinder; Srivastava, Pramod K.; Măndoiu, Ion

doi:10.1089/cmb.2022.0491

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…All studies predicting neoepitopes from mutations in cancer have used this algorithm to discriminate between the few mutations that are likely to be presented and be immunogenic over those that are not ( Figure 1 ) ( 25 , 27 , 28 ). A surprisingly large number of algorithms for predicting cancer neoepitopes have now been published ( 52 – 60 ); almost all algorithms share the critical principle that the neoepitopes must bind the relevant MHC I alleles with high affinity.…”

Section: The Complexity: Mechanistic Expectations Versus Realitymentioning

confidence: 99%

Cancer neoepitopes viewed through negative selection and peripheral tolerance: a new path to cancer vaccines

Srivastava

2024

Journal of Clinical Investigation

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A proportion of somatic mutations in tumors create neoepitopes that can prime T cell responses that target the MHC I–neoepitope complexes on tumor cells, mediating tumor control or rejection. Despite the compelling centrality of neoepitopes to cancer immunity, we know remarkably little about what constitutes a neoepitope that can mediate tumor control in vivo and what distinguishes such a neoepitope from the vast majority of similar candidate neoepitopes that are inefficacious in vivo. Studies in mice as well as clinical trials have begun to reveal the unexpected paradoxes in this area. Because cancer neoepitopes straddle that ambiguous ground between self and non-self, some rules that are fundamental to immunology of frankly non-self antigens, such as viral or model antigens, do not appear to apply to neoepitopes. Because neoepitopes are so similar to self-epitopes, with only small changes that render them non-self, immune response to them is regulated at least partially the way immune response to self is regulated. Therefore, neoepitopes are viewed and understood here through the clarifying lens of negative thymic selection. Here, the emergent questions in the biology and clinical applications of neoepitopes are discussed critically and a mechanistic and testable framework that explains the complexity and translational potential of these wonderful antigens is proposed.

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Section: The Complexity: Mechanistic Expectations Versus Realitymentioning

confidence: 99%

Cancer neoepitopes viewed through negative selection and peripheral tolerance: a new path to cancer vaccines

Srivastava

2024

Journal of Clinical Investigation

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Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

Bulashevska,

Nacsa,

Lang

et al. 2024

Front. Immunol.

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Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

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GeNeo: A Bioinformatics Toolbox for Genomics-Guided Neoepitope Prediction

Cited by 2 publications

References 38 publications

Cancer neoepitopes viewed through negative selection and peripheral tolerance: a new path to cancer vaccines

Cancer neoepitopes viewed through negative selection and peripheral tolerance: a new path to cancer vaccines

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

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