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.

Single-Molecule RNA Sequencing Reveals IFNγ-Induced Differential Expression of Immune Escape Genes in Merkel Cell Polyomavirus-Positive MCC Cell Lines.

Merkel cell carcinoma (MCC) is a rare and highly aggressive cancer, which is mainly caused by genomic integration of the Merkel cell polyomavirus and subsequent expression of a truncated form of its large T antigen. The resulting primary tumor is known to be immunogenic and under constant pressure to escape immune surveillance. Because interferon gamma (IFNγ), a key player of immune response, is secreted by many immune effector cells and has been shown to exert both anti-tumoral and pro-tumoral effects, we studied the transcriptomic response of MCC cells to IFNγ. In particular, immune modulatory effects that may help the tumor evade immune surveillance were of high interest to our investigation. The effect of IFNγ treatment on the transcriptomic program of three MCC cell lines (WaGa, MKL-1, and MKL-2) was analyzed using single-molecule sequencing via the Oxford Nanopore platform. A significant differential expression of several genes was detected across all three cell lines. Subsequent pathway analysis and manual annotation showed a clear upregulation of genes involved in the immune escape of tumor due to IFNγ treatment. The analysis of selected genes on protein level underlined our sequencing results. These findings contribute to a better understanding of immune escape of MCC and may help in clinical treatment of MCC patients. Furthermore, we demonstrate that single-molecule sequencing can be used to assess characteristics of large eukaryotic transcriptomes and thus contribute to a broader access to sequencing data in the community due to its low cost of entry.