Abstract

Simple SummaryThis review provides an overview of currently available approaches applied for neoantigens discovery—tumor-specific peptides that appeared due to the mutation process and distinguish tumors from normal tissues. Focusing on genomics-based approaches and computational pipelines, we cover all steps required for selecting appropriate candidate peptides starting from NGS-derived data. Moreover, additional approaches such as mass-spectrometry-based and structure-based methods are discussed highlighting their advantages and disadvantages. This review also provides a description of available complex bioinformatics pipelines ensuring automated data processing resulting in a list of neoantigens. We propose the possible ideal pipeline that could be implemented in the neoantigens identification process. We discuss the integration of results generated by different approaches to improve the accuracy of neoantigens selection.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.

Highlights

  • Cancer burden significantly impacts human health and quality of life

  • artificial neural networks (ANN)-based NetMHCpan 4.0-L (AUC = 0.977), NetMHCpan 4.0-B (AUC = 0.975) and MHCflurry-L (AUC = 0.973) were reported to achieve the best performance which was in general agreement with the results previously reported in [49]

  • It is considered an established fact that tumor mutation burden and, most notably, neoantigen burden exhibit a significant correlation with response to immune checkpoint therapy for certain cancer types, once again highlighting the effect of neoantigens on the activation of the immune response to tumors

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Summary

Introduction

Cancer burden significantly impacts human health and quality of life. It is one of the leading causes of death worldwide (9.6 million deaths in 2018, according to WHO) [1]. It was noticed that the efficacy of treatment with ICIs correlates with the tumor mutation burden (TMB) [20,21] This trend is only observed in particular cancer types (e.g., melanoma, NSCLC, etc.) [21]. Targeting of neoantigens (e.g., by peptide vaccination) probably leads to less side effects associated with the targeting of TAAs and CGAs, including autoimmune toxicity related to immune activation in non-target tissues [35], cytokine release syndrome [36], and others. High-confidence somatic mutation data that were identified based on WES and RNA-seq are commonly used in the isolation and ranking (or prioritization) of mutated peptide sequences aiming to detect peptides with the highest probability of being bound to the MHC and presented on the cell surface for TCR recognition [48,49]. It is meant to provide a bird’s eye view of the main trends in the context of neoantigen identification, present interactions between different approaches and propose possible improvements

Genomics-Based Approaches and Current Bioinformatics Pipelines
Mass Spectrometry-Based Approaches
Structure-Based Approaches
Neoantigen Peptide Databases
Findings
Conclusions
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