Uveal Melanoma Zebrafish Xenograft Models Illustrate the Mutation Status-Dependent Effect of Compound Synergism or Antagonism

P URPOSE . Uveal melanoma (UM) is the most common primary intraocular malignancy with a high probability of metastatic disease. Although excellent treatment options for primary UM are available, therapy for metastatic disease remain limited. Drug discovery studies using mouse models have thus far failed to provide therapeutic solutions, highlighting the need for novel models. Here, we optimize zebrafish xenografts as a potential model for drug discovery by showcasing the behavior of multiple cell lines and novel findings on mutation-dependent compound synergism/antagonism using Z-Tada; an algorithm to objectively characterize output measurements. M ETHODS . Prognostic relevant primary (N N = 4) and metastatic UM (N N = 1) cell lines or healthy melanocytes (N N = 2) were inoculated at three distinct inoculation sites. Standardized quantifications independent of inoculation site were obtained using Z-Tada; an algorithm to measure tumor burden and the number, size, and distance of disseminated tumor cells. Sequentially, we utilized this model to validate combinatorial synergism or antagonism seen in vitro. R ESULTS . Detailed analysis of 691 zebrafish xenografts demonstrated perivitelline space inoculation provided robust data with high probability of cell dissemination. Cell lines with more invasive behavior (SF3B1mut mut and BAP1mut) mut ) behaved most aggressive in this model. Combinatorial drug treatment illustrated synergism or antagonism is mutation-dependent, which were confirmed in vivo. Combinatorial treatment differed per xenograft-model, as it either inhibited overall tumor burden or cell dissemination. C ONCLUSIONS . Perivitelline space inoculation provides robust zebrafish xenografts with the ability for high-throughput drug screening and robust data acquisition using Z-Tada. This model demonstrates that drug discovery for uveal melanoma must take mutational subclasses into account, especially in combinatorial treatment discoveries.