Melanoma is the deadliest form of skin cancer, and improved non-invasive approaches to monitor tumor burden and immune dynamics are needed. Although extracellular vesicles (EVs) are increasingly explored as cancer biomarkers, how distinct EV subpopulations reflect dynamic tumor states induced by immune pressure remains insufficiently understood. Using proteomic analyses, we identified the melanoma-associated antigens gp100 (PMEL) and GPNMB in EVs derived from B16F10 melanoma cells and incorporated them into sandwich enzyme-linked immunosorbent assays (ELISAs) that capture total EVs while selectively detecting gp100 and GPNMB EV subpopulations. We subsequently evaluated these EV populations in murine models of anti-tumor vaccination and in plasma samples from melanoma patients. In vivo, melanoma-associated EV subpopulations increased in the serum of tumor-bearing mice and were further augmented following antigen-specific anti-tumor vaccination, whereas total CD81 EVs accumulated more gradually. Notably, gp100 EV levels, but not GPNMB EVs, correlated with tumor-infiltrating lymphocyte densities across multiple time points and treatment conditions. In vitro, TNF/IFN{gamma} stimulation preferentially increased total CD81 EV release, whereas gp100 EVs were promoted by IL-1{beta} stimulation. In contrast, GPNMB EVs accumulated more gradually and broadly across inflammatory, hypoxic, and cytotoxic stress conditions. Together, these findings indicate that immune and stress signals differentially remodel melanoma-associated EV composition. The assay translated to humans, revealing elevated gp100 EV levels in the plasma of melanoma patients compared with healthy donors, whereas GPNMB EVs identified a subset of melanoma patients. We describe a clinically feasible approach that enables direct detection of melanoma-associated EV subpopulations from blood without prior EV isolation. Conceptually, our findings suggest that immune and stress signals dynamically shape circulating EV composition, generating distinct EV signatures that reflect tumor state.
Sypka, M., Ghimire, A., Sole Casaramona, A., Bingi, T., Vogt, A.-C., Jie, H., Whiteside, T., Toledo, D., von Gunten, S., Bachmann, M., Mohsen, M., Engeroff, P.
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