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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.

Natural killer T (NKT) cells represent a cell subpopulation that combines characteristics of natural killer (NK) cells and T cells. Through their endogenous T-cell receptors (TCRs), they reveal a pronounced intrinsic anti-tumor activity. Thus, a NKT cell transfected with a chimeric antigen receptor (CAR), which recognizes a tumor-specific surface antigen, could attack tumor cells antigen-specifically via the CAR and additionally through its endogenous TCR. NKT cells were isolated from peripheral blood mononuclear cells (PBMCs), expanded, and electroporated with mRNA encoding a chondroitin sulfate proteoglycan 4 (CSPG4)-specific CAR. The CAR expression on NKT cells and their in vitro functionality were analyzed. A transfection efficiency of more than 80% was achieved. Upon stimulation with melanoma cells, CAR-NKT cells produced cytokines antigen-specifically. Compared with conventional CAR-T cells, cytokine secretion of CAR-NKT cells was generally lower. Specific cytotoxicity, however, was similar with CAR-NKT cells showing a trend towards improved cytotoxicity. Additionally, CAR-NKT cells could kill target cells through their endogenous TCRs. In summary, it is feasible to generate CAR-NKT cells by using mRNA electroporation. Their CAR-mediated cytotoxicity is at least equal to that of conventional CAR-T cells, while their intrinsic cytotoxic activity is maintained. Thus, CAR-NKT cells may represent a valuable alternative to conventional CAR-T cells for cancer immunotherapy.

Abstract While our understanding of long-term trends in material wealth inequality in prehistoric societies has expanded in recent decades, we know little about long-term trends in other dimensions of wealth and about social developments within particular societal segments. This paper provides the first evidence of inequality in relational wealth within the upper societal segment of a supra-regional network of communities in prehistoric Central Europe over the first four millennia BCE. To this end, we compiled a novel dataset of 5000 single-funeral burial mounds and employed burial mound volume as a proxy for the buried individual’s relational wealth. Our analysis reveals a consistently high level of inequality among the buried individuals, showing a wave-like pattern with an increasing trend over time. Additionally, our findings show temporal shifts in the size of the upper societal segment. Based on a review of archeological and paleo-environmental evidence, the temporal change in inequality may be explained by technological progress, climate and population dynamics, trade and social networks, and/or sociopolitical transformations.