Abstract
Purpose :
Gene expression profiling (GEP) has been clinically validated for stratifying uveal melanoma (UM) patients into two prognostic classes: class 1 (low metastatic risk) and class 2 (high metastatic risk). However, performing GEP analysis requires an intraocular tumor biopsy, which may be limited by tumor heterogeneity and accessibility of the tumor tissue. As a less invasive alternative, specifically the eye-specific aqueous humor (AH) liquid biopsy, has emerged. Previous research in our lab has identified UM-specific differentially expressed proteins (DEPs) from AH that could be used to differentiate GEP classes. In this study, we aim to verify these results and develop a scoring system using a UM-specific DEP signature for predicting metastatic potential.
Methods :
The validation set consisted of thirty treatment-naive UM AH samples collected before plaque brachytherapy. Patients were subgrouped into GEP 1 (n=20) and GEP 2 (n=10) based on their GEP classes. Eighty microliters of AH samples were analyzed using the proximity extension assay-derived multiplexed Olink platform. Protein expression levels from the Olink® Explore 3,072 panel were assessed, and a UM-specific protein signature was compared between the GEP classes. Multiple logistic regression was used to calculate the predicted probability of the observation, and Youden's index was utilized to determine the optimal cut-off value.
Results :
Through a stepwise selection, we identified 15 the most significant proteins that could serve as potential biomarkers for GEP 2 UM. The combination of 6 DEPs as a panel demonstrated the best performance in differentiating GEP classes. The area under the curve from a receiver operating characteristic (ROC) curve is 0.935 and under an optimal cut-off, the sensitivity and the specificity for discriminating GEP class 2 is 100% and 80%, respectively. Pathway analysis indicated that these DEPs regulate metastatic-related processes.
Conclusions :
This study identified a unique AH UM protein signature that can differentiate between GEP class 1 and class 2 at the diagnostic stage, even when the tumor is too small to biopsy. Further verification will be necessary using a larger UM cohort.
This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.