| Autor: |
Carola Berking, Gerold Schuler, Olaf Wolkenhauer, Annelies W Turksma, Heiko Bruns, Julio Vera, Martin Eberhardt, Manuel Wiesinger, Beatrice Schuler-Thurner, Cindy Flamann, Markus Vincent Heppt, Shailendra Gupta, Christopher Lischer, Johannes Berges, Esther Güse, Anja Wessely, Adrian Weich, Jimmy Retzlaff, Jan Dörrie, Niels Schaft, Johannes März, Harald Knorr, Krishna Pal Singh, Elias Andreas Thomas Koch, Nadine D van Kleef, Julian J Freen-van Heeren, Bettina Hohberger |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Journal for ImmunoTherapy of Cancer, Vol 12, Iss 5 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2051-1426 |
| DOI: |
10.1136/jitc-2023-008104 |
| Popis: |
Background Tumor-associated antigens and their derived peptides constitute an opportunity to design off-the-shelf mainline or adjuvant anti-cancer immunotherapies for a broad array of patients. A performant and rational antigen selection pipeline would lay the foundation for immunotherapy trials with the potential to enhance treatment, tremendously benefiting patients suffering from rare, understudied cancers.Methods We present an experimentally validated, data-driven computational pipeline that selects and ranks antigens in a multipronged approach. In addition to minimizing the risk of immune-related adverse events by selecting antigens based on their expression profile in tumor biopsies and healthy tissues, we incorporated a network analysis-derived antigen indispensability index based on computational modeling results, and candidate immunogenicity predictions from a machine learning ensemble model relying on peptide physicochemical characteristics.Results In a model study of uveal melanoma, Human Leukocyte Antigen (HLA) docking simulations and experimental quantification of the peptide–major histocompatibility complex binding affinities confirmed that our approach discriminates between high-binding and low-binding affinity peptides with a performance similar to that of established methodologies. Blinded validation experiments with autologous T-cells yielded peptide stimulation-induced interferon-γ secretion and cytotoxic activity despite high interdonor variability. Dissecting the score contribution of the tested antigens revealed that peptides with the potential to induce cytotoxicity but unsuitable due to potential tissue damage or instability of expression were properly discarded by the computational pipeline.Conclusions In this study, we demonstrate the feasibility of the de novo computational selection of antigens with the capacity to induce an anti-tumor immune response and a predicted low risk of tissue damage. On translation to the clinic, our pipeline supports fast turn-around validation, for example, for adoptive T-cell transfer preparations, in both generalized and personalized antigen-directed immunotherapy settings. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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