Abstract
Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.
Highlights
Neoantigens are tumor-specific mutated peptides that are key targets of the anti-cancer immune response, because neoantigens are not subject to immune tolerance [1–4]
Regardless of the vaccination strategy, all personalized neoantigen vaccines rely on accurate prediction of immunogenic neoantigens, neoantigens that are presented by MHC and elicit a T cell-mediated immune response
As for personalized neoantigen vaccines, adoptive T cell therapy specific to neoantigens relies on accurate prediction of immunogenic neoantigens
Summary
Neoantigens are tumor-specific mutated peptides that are key targets of the anti-cancer immune response, because neoantigens are not subject to immune tolerance (non-reactivity to self) [1–4]. Three classes of cancer therapies reliant on the neoantigen expression and presentation by MHC are personalized neoantigen vaccines, adoptive T cell therapy, and immune checkpoint inhibitors. Regardless of the vaccination strategy, all personalized neoantigen vaccines rely on accurate prediction of immunogenic neoantigens, neoantigens that are presented by MHC and elicit a T cell-mediated immune response. Prioritization of immunogenic neoantigens relies on a thorough understanding of the characteristics of a neoantigen and the optimal ways of combining these characteristics to predict the potential of the neoantigen to elicit an immune response For both MHC class I- and II-restricted neoantigens, characteristics that have been considered include expression of the neoantigen of interest, processing of the peptide including proteasomal cleavage and transport into the endoplasmic reticulum, binding of the neoantigen to MHC class I or II, and TCR recognition. We will summarize the available datasets and highlight ways to enhance future validation sets for continued improvement of neoantigen prediction and prioritization
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