Exploring the Immunogenicity of Noncanonical HLA-I Tumor Ligands Identified through Proteogenomics

Immunogenicity; ProteogenomicsInmunogenicidad; ProteogenómicaImmunogenicitat; ProteogenòmicaPurpose: Tumor antigens are central to antitumor immunity. Recent evidence suggests that peptides from noncanonical (nonC) aberrantly translated proteins can be presented on HLA-I by tumor cells. Here, we investigated the immunogenicity of nonC tumor HLA-I ligands (nonC-TL) to better understand their contribution to cancer immunosurveillance and their therapeutic applicability. Experimental Design: Peptides presented on HLA-I were identified in 9 patient-derived tumor cell lines from melanoma, gynecologic, and head and neck cancer through proteogenomics. A total of 507 candidate tumor antigens, including nonC-TL, neoantigens, cancer-germline, or melanocyte differentiation antigens, were tested for T-cell recognition of preexisting responses in patients with cancer. Donor peripheral blood lymphocytes (PBL) were in vitro sensitized against 170 selected nonC-TL to isolate antigen-specific T-cell receptors (TCR) and evaluate their therapeutic potential. Results: We found no recognition of the 507 nonC-TL tested by autologous ex vivo expanded tumor-reactive T-cell cultures while the same cultures demonstrated reactivity to mutated, cancer-germline, or melanocyte differentiation antigens. However, in vitro sensitization of donor PBL against 170 selected nonC-TL, led to the identification of TCRs specific to three nonC-TL, two of which mapped to the 5′ UTR regions of HOXC13 and ZKSCAN1, and one mapping to a noncoding spliced variant of C5orf22C. T cells targeting these nonC-TL recognized cancer cell lines naturally presenting their corresponding antigens. Expression of the three immunogenic nonC-TL was shared across tumor types and barely or not detected in normal cells. Conclusions: Our findings predict a limited contribution of nonC-TL to cancer immunosurveillance but demonstrate they may be attractive novel targets for widely applicable immunotherapies.We thank the patients for their participation in this study, Steven A. Rosenberg for providing valuable reagents and support for NGS studies, R. Pujol for helpful scientific discussion, J. Gonzalez for bioinformatics support, CRG/UPF Flow Cytometry Unit for assistance with cell sorting, and CRG/UPF and IRB Proteomics Units for technical support. A. Gros and this work were funded by the Comprehensive Program of Cancer Immunotherapy & Immunology II (CAIMI-II) supported by the BBVA Foundation (53/2021), Institute Carlos III (MS15/00058 and PI17/01085), AECC (IDEAS197PORT), and La Fundació La Marató de TV3 (201919–30). We thank CERCA Programme / Generalitat de Catalunya for institutional support. M. Lozano-Rabella was supported by the Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR) (2018FI_B 00946). A. Garcia-Garijo was supported by Generalitat PERIS award (SLT017/20/000131). A. Yuste-Estevanez was supported by the Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR) (2021 FI_B 00365). J. Palomero was supported by the Beatriu de Pinós programme (BP 2018), cofounded by the Agency for Management of University and Research Grants (AGAUR) and European Union's Horizon 2020

Brewpitopes: a pipeline to refine B-cell epitope predictions during public health emergencies

The application of B-cell epitope identification to develop therapeutic antibodies and vaccine candidates is well established. However, the validation of epitopes is time-consuming and resource-intensive. To alleviate this, in recent years, multiple computational predictors have been developed in the immunoinformatics community. Brewpitopes is a pipeline that curates bioinformatic B-cell epitope predictions obtained by integrating different state-of-the-art tools. We used additional computational predictors to account for subcellular location, glycosylation status, and surface accessibility of the predicted epitopes. The implementation of these sets of rational filters optimizes in vivo antibody recognition properties of the candidate epitopes. To validate Brewpitopes, we performed a proteome-wide analysis of SARS-CoV-2 with a particular focus on S protein and its variants of concern. In the S protein, we obtained a fivefold enrichment in terms of predicted neutralization versus the epitopes identified by individual tools. We analyzed epitope landscape changes caused by mutations in the S protein of new viral variants that were linked to observed immune escape evidence in specific strains. In addition, we identified a set of epitopes with neutralizing potential in four SARS-CoV-2 proteins (R1AB, R1A, AP3A, and ORF9C). These epitopes and antigenic proteins are conserved targets for viral neutralization studies. In summary, Brewpitopes is a powerful pipeline that refines B-cell epitope bioinformatic predictions during public health emergencies in a high-throughput capacity to facilitate the optimization of experimental validation of therapeutic antibodies and candidate vaccines

Exploring the Immunogenicity of Noncanonical HLA-I Tumor Ligands Identified through Proteogenomics

Purpose: Tumor antigens are central to antitumor immunity. Recent evidence suggests that peptides from noncanonical (nonC) aberrantly translated proteins can be presented on HLA-I by tumor cells. Here, we investigated the immunogenicity of nonC tumor HLA-I ligands (nonC-TL) to better understand their contribution to cancer immunosurveillance and their therapeutic applicability. Experimental Design: Peptides presented on HLA-I were iden-tified in 9 patient-derived tumor cell lines from melanoma, gyneco-logic, and head and neck cancer through proteogenomics. A total of 507 candidate tumor antigens, including nonC-TL, neoantigens, cancer-germline, or melanocyte differentiation antigens, were tested for T-cell recognition of preexisting responses in patients with cancer. Donor peripheral blood lymphocytes (PBL) were in vitro sensitized against 170 selected nonC-TL to isolate antigen-specific T-cell recep-tors (TCR) and evaluate their therapeutic potential.Rudolf Virchow Center, Center for Integrative and Transla- tional Bioimaging, Julius-Maximilians-University Wueurorzburg, Wueurorzburg, German

Determination of immunogenic epitopes

Curs 2019-2020The identification of immunogenic epitopes (such as fragments of proteins, in particular peptides, that can trigger an immune response) is a fundamental need for immune-based therapies. A computational tool that could detect such immunogenic epitopes would have vast potential applications in biomedicine ranging, from vaccine design against viruses or bacteria to therapeutic vaccination of cancer patients. While there are several methods that predict whether a peptide will be shown to the immune system via the HLA proteins of a patient, most of them cannot predict whether such presentation will indeed trigger an immune response. The aim of this project is to build an immunogenicity predictor that discriminates immunogenic from non-immunogenic epitopes. After a careful study of the drivers of antigen processing and presentation on HLA class I molecules and an assessment of the physicochemical factors influencing epitope recognition by T-cell receptors (TCRs), we have used a selection of publicly available tools and in-house developed algorithms to identify the most relevant features that determine epitope immunogenicity. We then used these features to build an immunogenicity predictor (PredIG) modelled by logistic regression against immunogenically validated epitopes by the ImmunoEpitope DataBase (IEDB). Overall, our immunogenicity predictor shows a better performance in identifying immunogenic epitopes than other stateof- the-art metrics.Director/a: Serrat Jurado, Josep Mari

Brewpitopes : a pipeline to refine B-cell epitope predictions during public health emergencies

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. RF-D received support by a La Caixa Junior Leader Fellowship (LCF/BQ/PI18/11630003) from Fundación La Caixa. EP-P received support by a La Caixa Junior Leader Fellowship (LCF/BQ/PI18/11630003) from Fundación La Caixa and a Ramon y Cajal fellowship from the Spanish Ministry of Science (RYC2019-026415-I). LF-B and RL-A received support by Direcció General de Recerca i Inovació en Salut (DGRIS) and BIOCAT (https://www.biocat.cat/ca) (Code: BIOCAT_DGRIS_COVID19) awarded to AT and LF-B; ISCIII-FOS (FI19/00090) grant awarded to RL-A, CB 06/06/0028/CIBER de enfermedades respiratorias (Ciberes), Ciberes is an initiative of ISCIII. ICREA Academy/Institució Catalana de Recerca i Estudis Avançats awarded to AT; 2.603/IDIBAPS, SGR/Generalitat de Catalunya awarded to AT. Funders did not play any role in project design, data collection, data analysis, interpretation, or writing of the paper.The application of B-cell epitope identification to develop therapeutic antibodies and vaccine candidates is well established. However, the validation of epitopes is time-consuming and resource-intensive. To alleviate this, in recent years, multiple computational predictors have been developed in the immunoinformatics community. Brewpitopes is a pipeline that curates bioinformatic B-cell epitope predictions obtained by integrating different state-of-the-art tools. We used additional computational predictors to account for subcellular location, glycosylation status, and surface accessibility of the predicted epitopes. The implementation of these sets of rational filters optimizes in vivo antibody recognition properties of the candidate epitopes. To validate Brewpitopes, we performed a proteome-wide analysis of SARS-CoV-2 with a particular focus on S protein and its variants of concern. In the S protein, we obtained a fivefold enrichment in terms of predicted neutralization versus the epitopes identified by individual tools. We analyzed epitope landscape changes caused by mutations in the S protein of new viral variants that were linked to observed immune escape evidence in specific strains. In addition, we identified a set of epitopes with neutralizing potential in four SARS-CoV-2 proteins (R1AB, R1A, AP3A, and ORF9C). These epitopes and antigenic proteins are conserved targets for viral neutralization studies. In summary, Brewpitopes is a powerful pipeline that refines B-cell epitope bioinformatic predictions during public health emergencies in a high-throughput capacity to facilitate the optimization of experimental validation of therapeutic antibodies and candidate vaccines