Main Strategies for the Identification of Neoantigens

Abstract

Genetic instability of tumors leads to the appearance of numerous tumor-specific somatic mutations that could potentially result in the production of mutated peptides that are presented on the cell surface by the MHC molecules. Peptides of this kind are commonly called neoantigens. Their presence on the cell surface specifically distinguishes tumors from healthy tissues. This feature makes neoantigens a promising target for immunotherapy. The rapid evolution of high-throughput genomics and proteomics makes it possible to implement these techniques in clinical practice. In particular, they provide useful tools for the investigation of neoantigens. The most valuable genomic approach to this problem is whole-exome sequencing coupled with RNA-seq. High-throughput mass-spectrometry is another option for direct identification of MHC-bound peptides, which is capable of revealing the entire MHC-bound peptidome. Finally, structure-based predictions could significantly improve the understanding of physicochemical and structural features that affect the immunogenicity of peptides. The development of pipelines combining such tools could improve the accuracy of the peptide selection process and decrease the required time. Here we present a review of the main existing approaches to investigating the neoantigens and suggest a possible ideal pipeline that takes into account all modern trends in the context of neoantigen discovery.

The contribution of uncharted RNA sequences to tumor identity in lung adenocarcinoma

“…Protocols for neoantigen discovery usually start from a list of nonsynonymous somatic mutations identified from WES or WGS libraries and whose expression is confirmed by RNA-seq. Candidate mutated peptides are then submitted to an epitope presentation prediction pipeline [55]. This protocol predicts potential neoantigens from annotated and mappable regions.…”

Cancer Vaccines: Antigen Selection Strategy

“…Simultaneous utilization of RNA-seq reveals information on gene expression level, allowing to predict which genes are more likely to be translated into proteins. Furthermore, RNA-seq can reveal other types of neoantigen sources, such as gene fusion, alternative splicing isoforms, and RNA editing events, that WES cannot [ 20 , 21 ]. Currently, for the identification and establishment of neoantigens in tumors, typically the following major steps are undertaken: (1) tumor biopsy sample and corresponding normal tissues obtainment; (2) whole-genome or -exome sequencing and analysis of mutated genes and abnormal proteins by comparison with normal tissues; (3) bioinformatic algorithms for predicting the most promising antigenic mutated protein; (4) verification of neoantigens via immunological analyses ( Figure 2 ) [ 22 , 23 , 24 ].…”

“…Another strategy named the mass spectrometry-based approach can make up for this defect by obtaining these insufficient data via affinity chromatography [ 30 , 31 ]. Furthermore, structure-based approaches and neoantigen peptide databases are also unignorable supplementary methods in neoantigen prediction [ 20 ].…”

Personalized immunotherapy in cancer precision medicine

“…Recently, HLA-peptide affinity prediction tools using several artificial-intelligence approaches have been also reported [63][64][65] . Mass spectrometry-based immunopeptidome analysis to identify peptides that are presenting on HLA molecules is one of the approaches to narrow down the candidate peptides, although improvement of sensitivity is critically essential to optimize the potential of immunopeptidome analysis for clinical application 66 .…”