Gene network-based and ensemble modeling-based selection of tumor-associated antigens with a predicted low risk of tissue damage for targeted immunotherapy.

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.

S100A8/A9 Is a Marker for the Release of Neutrophil Extracellular Traps and Induces Neutrophil Activation.

Neutrophils are the most abundant innate immune cells in the circulation and they are the first cells recruited to sites of infection or inflammation. Almost half of the intracellular protein content in neutrophils consists of S100A8 and S100A9, though there has been controversy about their actual localization. Once released extracellularly, these proteins are thought to act as damage-associated molecular patterns (DAMPs), though their mechanism of action is not well understood. These S100 proteins mainly form heterodimers (S100A8/A9, also known as calprotectin) and this heterocomplex is recognized as a useful biomarker for several inflammatory diseases. We observed that S100A8/A9 is highly present in the cytoplasmic fraction of neutrophils and is not part of the granule content. Furthermore, we found that S100A8/A9 was not released in parallel with granular content but upon the formation of neutrophil extracellular traps (NETs). Accordingly, neutrophils of patients with chronic granulomatous disease, who are deficient in phorbol 12-myristate 13-acetate (PMA)-induced NETosis, did not release S100A8/A9 upon PMA stimulation. Moreover, we purified S100A8/A9 from the cytoplasmic fraction of neutrophils and found that S100A8/A9 could induce neutrophil activation, including adhesion and CD11b upregulation, indicating that this DAMP might amplify neutrophil activation.