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Direct analysis of HLA-presented antigens by mass spectrometry provides a comprehensive view on the antigenic landscape of different tissues/malignancies and enables the identification of novel, pathophysiologically relevant T-cell epitopes. Here, we present a systematic and comparative study of the HLA class I and II presented, non mutant antigenome of multiple myeloma (MM).Quantification of HLA surface expression revealed elevated HLA molecule counts on malignant plasma cells compared with normal B cells, excluding relevant HLA downregulation in MM. Analyzing the presentation of established myeloma-associated T-cell antigenson the HLA ligandome level, we found a substantial proportion of antigens to be only infrequently presented on primary myelomas or to display suboptimal degrees of myeloma specificity. However, unsupervised analysis of our extensive HLA ligand data set delineated a panel of 58 highly specific myeloma-associated antigens (including multiple myeloma SET domain containing protein) which are characterized by frequent and exclusive presentation on myeloma samples. Functional characterization of these target antigens revealed peptide-specific, preexisting CD81 T-cell responses exclusively in myeloma patients, which is indicative of pathophysiological relevance. Furthermore, in vitro priming experiments revealed that peptide-specific T-cell responses can be induced in response-naive myeloma patients. Together, our results serve to guide antigen selection for T-cell-based immunotherapy of MM. [ABSTRACT FROM AUTHOR]

Abstract: Human cytomegalovirus (HCMV) reactivation poses a substantial risk to patients receiving tranplants. Effective risk stratification and vaccine development is hampered by a lack of HCMV-derived immunogenic peptides in patients with common HLA-A∗03:01 and HLA-B∗15:01 haplotypes. This study aimed to discover novel HCMV immunogenic peptides for these haplotypes by combining ribosome sequencing (Ribo-seq) and mass spectrometry with state-of-the-art computational tools, Peptide-PRISM and Probabilistic Inference of Codon Activities by an EM Algorithm. Furthermore, using machine learning, an algorithm was developed to predict immunogenicity based on translational activity, binding affinity, and peptide localization within small open reading frames to identify the most promising peptides for in vitro validation. Immunogenicity of these peptides was subsequently tested by analyzing peptide-specific T-cell responses of HCMV-seropositive and -seronegative healthy donors as well as patients with transplants. This resulted in the direct identification of 3 canonical and 1 cryptic HLA-A∗03-restricted immunogenic peptides as well as 5 canonical and 1 cryptic HLA-B∗15-restricted immunogenic peptide, with a specific interferon gamma-positive (IFN-γ+)/CD8+ T-cell response of ≥0.02%. High T-cell responses were detected against 2 HLA-A∗03-restricted and 3 HLA-B∗15-restricted canonical peptides with frequencies of up to 8.77% IFN-γ+/CD8+ T cells in patients after allogeneic stem cell transplantation. Therefore, our comprehensive strategy establishes a framework for efficient identification of novel immunogenic peptides from both existing and novel Ribo-seq data sets., (© 2024 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)