Author Correction: Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification.
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ABSTRACT:
SUBMITTER: Tretter C
PROVIDER: S-EPMC10943035 | biostudies-literature | 2024 Mar
REPOSITORIES: biostudies-literature
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Author Correction: Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification.
Tretter Celina C de Andrade Krätzig Niklas N Pecoraro Matteo M Lange Sebastian S Seifert Philipp P von Frankenberg Clara C Untch Johannes J Zuleger Gabriela G Wilhelm Mathias M Zolg Daniel P DP Dreyer Florian S FS Bräunlein Eva E Engleitner Thomas T Uhrig Sebastian S Boxberg Melanie M Steiger Katja K Slotta-Huspenina Julia J Ochsenreither Sebastian S von Bubnoff Nikolas N Bauer Sebastian S Boerries Melanie M Jost Philipp J PJ Schenck Kristina K Dresing Iska I Bassermann Florian F Friess Helmut H Reim Daniel D Grützmann Konrad K Pfütze Katrin K Klink Barbara B Schröck Evelin E Haller Bernhard B Kuster Bernhard B Mann Matthias M Weichert Wilko W Fröhling Stefan S Rad Roland R Hiltensperger Michael M Krackhardt Angela M AM
Nature communications 20240315 1
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Project description:Although CD8+ T cells recognize neoantigens that arise from somatic mutations in cancer, only a small fraction of nonsynonymous mutations give rise to clinically relevant neoantigens. In this study, HLA class I ligandomes of a panel of human colorectal cancer (CRC) and matched normal tissues were analyzed using mass spectrometry-based proteogenomic analysis. Neoantigen presentation was rare; however, the analysis detected a single neoantigen in a mismatch repair-deficient CRC (dMMR-CRC) tissue sample carrying 3967 nonsynonymous mutations, where abundant tumor-infiltrating lymphocytes (TILs) and inflamed gene expression status were observed in the tumor microenvironment (TME). Using the HLA class I ligandome data and gene expression profiles, a set of nonmutated tumor-associated antigen (TAA) candidates was concomitantly identified. Interestingly, CD8+ TILs predominantly recognized the detected neoantigen over the array of TAA candidates. Neoantigen-reactive CD8+ TILs showed PD-1 positivity and exhibited functional and specific responses. Moreover, T cell receptor (TCR) profiling identified the sequence of the neoantigen-reactive TCR clonotype and showed its expansion in the TME. Transduction of the sequenced TCR conferred neoantigen specificity and cytotoxicity to peripheral blood lymphocytes. The proteogenomic approach revealed the antigenic and reactive T cell landscape in dMMR-CRC, demonstrating the presence of an immunogenic neoantigen and its potential therapeutic applications.
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Project description:BackgroundNeoantigens can be differentially recognized by T cell receptor (TCR) as these sequences are derived from mutant proteins and are unique to the tumor. The discovery of neoantigens is the first key step for tumor-specific antigen (TSA) based immunotherapy. Based on high-throughput tumor genomic analysis, each missense mutation can potentially give rise to multiple neopeptides, resulting in a vast total number, but only a small percentage of these peptides may achieve immune-dominant status with a given major histocompatibility complex (MHC) class I allele. Specific identification of immunogenic candidate neoantigens is consequently a major challenge. Currently almost all neoantigen prediction tools are based on genomics data.ResultsHere we report the construction of proteogenomics prediction of neoantigen (ProGeo-neo) pipeline, which incorporates the following modules: mining tumor specific antigens from next-generation sequencing genomic and mRNA expression data, predicting the binding mutant peptides to class I MHC molecules by latest netMHCpan (v.4.0), verifying MHC-peptides by MaxQuant with mass spectrometry proteomics data searched against customized protein database, and checking potential immunogenicity of T-cell-recognization by additional screening methods. ProGeo-neo pipeline achieves proteogenomics strategy and the neopeptides identified were of much higher quality as compared to those identified using genomic data only.ConclusionsThe pipeline was constructed based on the genomics and proteomics data of Jurkat leukemia cell line but is generally applicable to other solid cancer research. With massively parallel sequencing and proteomics profiling increasing, this proteogenomics workflow should be useful for neoantigen oriented research and immunotherapy.
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