Abstract
Uveal melanoma (UM), the primary intraocular tumor in adults, arises from eye melanocytes and poses a significant threat to vision and health. Despite its rarity, UM is concerning due to its high potential for liver metastasis, resulting in a median survival of about a year after detection. Unlike cutaneous melanoma, UM responds poorly to immune checkpoint inhibition (ICI) due to its low tumor mutational burden and PD-1/PD-L1 expression. Tebentafusp, a bispecific T cell engager (TCE) approved for metastatic UM, showed potential in clinical trials, but the objective response rate remains modest. To enhance TCE efficacy, we explored quantitative systems pharmacology (QSP) modeling in this study. By integrating a TCE module into an existing QSP model and using clinical data on UM and tebentafusp, we aimed to identify and rank potential predictive biomarkers for patient selection. We selected 30 important predictive biomarkers, including model parameters and cell concentrations in tumor and blood compartments. We investigated biomarkers using different methods, including comparison of median levels in responders and non-responders, and a cutoff-based biomarker testing algorithm. CD8+ T cell density in the tumor and blood, CD8+ T cell to regulatory T cell ratio in the tumor, and naïve CD4+ density in the blood are examples of key biomarkers identified. Quantification of predictive power suggested a limited predictive power for single pre-treatment biomarkers, which was improved by early on-treatment biomarkers and combination of predictive biomarkers. Ultimately, this QSP model could facilitate biomarker-guided patient selection, improving clinical trial efficiency and UM treatment outcomes.