Current Perspectives on Mass Spectrometry-Based Immunopeptidomics: The Computational Angle to Tumor Antigen Discovery

Authors

Bing Zhang
Michal Bassani-Sternberg

Publication Date

10-1-2023

Journal

The Journal for ImmunoTherapy of Cancer

DOI

10.1136/jitc-2023-007073

PMID

37899131

PMCID

PMC10619091

PubMedCentral® Posted Date

10-29-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Histocompatibility Antigens Class I, Mass Spectrometry, Antigens, Neoplasm, Peptides, HLA Antigens, Neoplasms, Histocompatibility Antigens Class II, RNA, DNA, Antigens, Neoplasm; Computational Biology; Immunity

Abstract

Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.