Computational Prediction and Validation of Tumor-Associated Neoantigens.

ABSTRACT: Tumor progression is typically accompanied by an accumulation of driver and passenger somatic mutations. A handful of those mutations occur in protein coding genes which introduce non-synonymous polymorphisms. Certain substitutions may give rise to novel, tumor-associated antigens or neoantigens, presentable by cancer cells to the host adaptive immune system. As antigen recognition is the core of an effective immune response, the identification of patient tumor specific antigens derived from transformed cells is of importance for immunotherapeutic approaches. Recent technological advances in DNA sequencing of tumor genomes, advances in gene expression analysis, algorithm development for antigen predictions and methods for T-cell receptor (TCR) repertoire sequencing have facilitated the selection of candidate immunogenic neoantigens. In this regard, multiple research groups have reported encouraging results of neoantigen-based cancer vaccines that generate tumor antigen specific immune responses, both in mouse models and clinical trials. Additionally, both the quantity and quality of neoantigens has been shown to have predictive value for clinical outcomes in checkpoint-blockade immunotherapy in certain tumor types. Neoantigen recognition by vaccination or through adoptive T cell therapy may have unprecedented potential to advance cancer immunotherapy in combination with other approaches. In our review we discuss three parameters regarding neoantigens: computational methods for epitope prediction, experimental methods for epitope immunogenicity validation and future directions for improvement of those methods. Within each section, we will describe the advantages and limitations of existing methods as well as highlight pressing fundamental problems to be addressed.

SUBMITTER: Roudko V 

PROVIDER: S-EPMC7025577 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature