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      Emerging potential of immunopeptidomics by mass spectrometry in cancer immunotherapy

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          Abstract

          With significant advances in analytical technologies, research in the field of cancer immunotherapy, such as adoptive T cell therapy, cancer vaccine, and immune checkpoint blockade (ICB), is currently gaining tremendous momentum. Since the efficacy of cancer immunotherapy is recognized only by a minority of patients, more potent tumor‐specific antigens (TSAs, also known as neoantigens) and predictive markers for treatment response are of great interest. In cancer immunity, immunopeptides, presented by human leukocyte antigen (HLA) class I, play a role as initiating mediators of immunogenicity. The latest advancement in the interdisciplinary multiomics approach has rapidly enlightened us about the identity of the “dark matter” of cancer and the associated immunopeptides. In this field, mass spectrometry (MS) is a viable option to select because of the naturally processed and actually presented TSA candidates in order to grasp the whole picture of the immunopeptidome. In the past few years the search space has been enlarged by the multiomics approach, the sensitivity of mass spectrometers has been improved, and deep/machine‐learning‐supported peptide search algorithms have taken immunopeptidomics to the next level. In this review, along with the introduction of key technical advancements in immunopeptidomics, the potential and further directions of immunopeptidomics will be reviewed from the perspective of cancer immunotherapy.

          Abstract

          In this review, we discuss the rapidly advancing field of immunopeptidomics research for “cancer's dark matter” and its utility in the field of cancer immunotherapy along with the latest developments in mass spectrometry technology.

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          Most cited references82

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          NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.

          Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway. Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide-MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.
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            MSFragger: ultrafast and comprehensive peptide identification in shotgun proteomics

            There is a need to better understand and handle the “dark matter” of proteomics – the vast diversity of post-translational and chemical modifications that are unaccounted in a typical analysis and thus remain unidentified. We present a novel fragment-ion indexing method, and its implementation in peptide identification tool MSFragger, that enables an over 100-fold improvement in speed over most existing tools. Using some of the largest proteomic datasets to date, we demonstrate how MSFragger empowers the open database search concept for comprehensive identification of peptides and all their modified forms, uncovering dramatic differences in the modification rates across experimental samples and conditions. We further illustrate its utility using protein-RNA crosslinked peptide data, and using affinity purification experiments where we observe on average a 300% increase in the number of identified spectra for enriched proteins. We also discuss the benefits of open searching for improved false discovery rate estimation in proteomics.
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              DIA-NN: Neural networks and interference correction enable deep proteome coverage in high throughput

              We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when employed in combination with fast chromatographic methods.
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                Author and article information

                Contributors
                yuriko.minegishi@jfcr.or.jp
                Journal
                Cancer Sci
                Cancer Sci
                10.1111/(ISSN)1349-7006
                CAS
                Cancer Science
                John Wiley and Sons Inc. (Hoboken )
                1347-9032
                1349-7006
                21 February 2024
                April 2024
                : 115
                : 4 ( doiID: 10.1111/cas.v115.4 )
                : 1048-1059
                Affiliations
                [ 1 ] Cancer Proteomics Group, Cancer Precision Medicine Center Japanese Foundation for Cancer Research Tokyo Japan
                Author notes
                [*] [* ] Correspondence

                Yuriko Minegishi, Cancer Proteomics Group, Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, 3‐8‐31 Ariake, Koto‐ku, Tokyo 135‐8550, Japan.

                Email: yuriko.minegishi@ 123456jfcr.or.jp

                Author information
                https://orcid.org/0009-0001-1655-1902
                https://orcid.org/0000-0001-7861-617X
                https://orcid.org/0000-0001-9066-4959
                Article
                CAS16118 CAS-RA-3158-2023.R1
                10.1111/cas.16118
                11007014
                38382459
                181a76ea-f9d3-4bbc-9e9e-5177f6d5acc9
                © 2024 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 02 February 2024
                : 18 December 2023
                : 07 February 2024
                Page count
                Figures: 5, Tables: 1, Pages: 12, Words: 7774
                Funding
                Funded by: Japan Agency for Medical Research and Development , doi 10.13039/100009619;
                Award ID: 20ae0101074s0302
                Funded by: Shimadzu Corporation , doi 10.13039/100016846;
                Funded by: Japan Society for the Promotion of Science , doi 10.13039/501100001691;
                Award ID: 23K04971
                Categories
                Review Article
                Review Articles
                Custom metadata
                2.0
                April 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.4.0 mode:remove_FC converted:11.04.2024

                Oncology & Radiotherapy
                cancer immunotherapy,immunopeptidomics,mass spectrometry,neoantigen
                Oncology & Radiotherapy
                cancer immunotherapy, immunopeptidomics, mass spectrometry, neoantigen

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