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Published on: November 17, 2018
Neoantigen identification: Technological advances and challenges
Ting Pu1, Allyson Peddle1, Jingjing Zhu2
1Digestive Oncology Unit, KULeuven, Leuven, Belgium.
Abstract:
Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.
Insights
Neoantigens, unique tumor targets, are crucial for cancer immunotherapies like vaccines. Improved prediction tools are needed, but validated data for immunogenic neoantigens remains a challenge.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Neoantigens are tumor-specific targets arising from genetic and non-genomic alterations.
- They are critical for developing personalized cancer vaccines and adoptive cell therapies.
- Advancements in technology facilitate the identification of immunogenic neoantigens.
Approach:
- Neoantigen prediction requires high-quality samples and sequencing data for mutation calling.
- Computational pipelines integrate tools for predicting expression, processing, binding, and recognition.
- The review summarizes current knowledge and limitations in neoantigen prediction and validation.
Key Points:
- Neoantigens encompass genomic mutations, post-transcriptomic variants, and viral oncoproteins.
- Accurate prediction relies on robust computational pipelines and validated datasets.
- Understanding neoantigen origin and biological roles enhances their therapeutic potential.
Conclusions:
- Neoantigen prediction and validation are essential for advancing cancer immunotherapy.
- Further development of computational tools is hampered by the lack of validated immunogenic neoantigen datasets.
- Harnessing neoantigens offers significant promise in the fight against cancer.
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