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ABSTRACT
Introduction
Proteogenomic techniques find applications in identifying novel cancer-specific peptides called neoantigens; they are non-self peptides derived from tumor-specific non-synonymous mutations. These peptides with MHCs are recognized by the T cells and induce an antitumor response. Due to their selective expression of tumor cells, neoantigens are considered attractive targets for cancer immunotherapy.
Areas Covered
In this review, we have discussed the proteogenomic strategies to identify neoantigens. We have also provided a neoantigen identification pipeline using data from whole-exome sequencing, RNA sequencing, and MHC peptidomics. Further, we have reviewed recent tools for neoantigen discovery.
Expert commentary
The limitations in instrument sensitivity and availability of bioinformatics tools have restricted the identification of neoantigens from tumor samples. Nonetheless, the recent improvement in genome sequencing, mass spectrometry technologies, and the development of reliable algorithms for epitope prediction provide hope for efficient identification of neoantigens. Translating this workflow on patient samples would represent a massive advancement in neoantigen identification methods, leading to the constitution of novel personalized neoantigen cancer vaccines.
Abbreviations
| ALL | = | Acute Lymphocytic Leukemia |
| APCs | = | Antigen Presenting Cells |
| BAM | = | Binary Alignment Map |
| CAR T cell | = | Chimeric Antigen Receptor T cell |
| CID | = | Collision Induced Dissociation |
| CNAs | = | Copy Number Alterations |
| COSMIC | = | Catalogue of Somatic Mutations in Cancer |
| CT | = | Cancer/Testis antigen |
| CTLA | = | 4 Cytotoxic T Lymphocyte Antigen 4 |
| DDA | = | Data-dependent acquisition |
| DIA | = | Data-independent acquisition |
| EM | = | Expectation Maximization |
| ER | = | Endoplasmic Reticulum |
| ERAP | = | ER resident AminoPeptidases |
| ETD | = | Electron Transfer Dissociation |
| FDA | = | Food and Drug Administration |
| FDR | = | False Discovery Rate |
| HLA | = | Human Leukocyte Antigen |
| HPV | = | Human Papilloma Virus |
| HTS | = | High Throughput Sequencing |
| ICGC | = | International Cancer Genome Consortium |
| ICIs | = | Immune Checkpoint Inhibitors |
| IEDB | = | Immune Epitope Data Base |
| IMAC | = | Immobilized Metal Affinity Chromatography |
| INDEL | = | Insertion-Deletion |
| IP | = | ImmunoPrecipitation |
| LC | = | Liquid Chromatography |
| LOH | = | Loss of Heterozygosity |
| MAE | = | Mild Acid Elution |
| MDSCs | = | Myeloid Derived Suppressor Cells |
| MHC | = | Major Histocompatibility Complex |
| NCBI | = | National Centre for Biotechnology Information |
| NGS | = | Next Generation Sequencing |
| NSCLC | = | Non Small Cell Lung Cancer |
| nsSNVs | = | non-synonymous Single-Nucleotide Variants |
| MAPs | = | Mutation Associated Peptides |
| MCMC | = | Markov Chain Monte Carlo |
| PCPS | = | Proteasome Catalyzed Peptide Splicing |
| PCR | = | Polymerase Chain Reaction |
| PSMs | = | Peptide to Spectrum Matches |
| PTM | = | Post Translational Modification |
| QC | = | Quality Check |
| SAAVs | = | Single Amino Acid Variants |
| SNV | = | Single-Nucleotide Variants |
| SWATH | = | MS Sequential Window Acquisition of all Theoretical Spectra |
| TAAs | = | Tumor Associated Antigens |
| TAP | = | Transporter Associated with antigen Processing |
| TSAs | = | Tumor Specific Antigens |
| TCRs | = | T Cell Receptors |
| TMB | = | Tumor Mutation Burden |
| VCF | = | Variant Calling Format |
| WXS | = | Whole-exome sequencing |
Supplemental material
Supplemental data for this article can be accessed here.
Declaration of interest
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
Reviewer disclosures
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Article highlights
Integration of genomics, transcriptomics, and proteomics data has facilitated the researchers to gain clear insights into the underlying mechanism of cancer biology.
Advancement in next-generation sequencing, mass spectrometry techniques, and the development of high-throughput bioinformatics tools have led to the identification of single amino acid variants from various sources.
The proteogenomic approach has helped to identify variants across the genomes and assess their effects on protein stability and functions.
Mutations in tumor cells’ genome give rise to several antigens, but neoantigens are the preferred target for cancer immunotherapy due to their selective expression in tumor cells.
Neoantigens are less susceptible to central immune tolerance mechanisms and have minimal chances of inducing autoimmunity, making them ideal targets for cancer immunotherapy.
Neoantigens undergo proteasomal degradation and are processed in the endoplasmic reticulum, where they combine with the MHC molecules. The complex is transported to the tumor cell’s plasma membrane, where they are recognized by the cytotoxic T cells to generate the antitumor response.
Conventional CAR T cell therapies are not so effective in solid tumors because of their inability to overcome the tumor-suppressive microenvironment.
Tumor mutational burden is shown to positively correlate with immune checkpoint inhibition therapy.
The multi-omics approach is the most efficient way to predict neoantigens as whole-exome sequencing (WXS) identifies SNVs and indels; transcriptomics sequencing identifies frameshift mutations and intron retentions; and proteomics data help to identify alternative PTM sites.
Sensitive and accurate bioinformatics tools shall facilitate the clinical translation of neoantigen-based personalized immunotherapy by identifying true neoantigens.