Extracellular vesicle derived prognostic biomarkers for uveal melanoma
Uveal melanoma (UM), the most common ocular cancer in adults, metastasizes most commonly to the liver and lung (up to 50% of patients). UM can be divided into Class 1, which is characterized by low metastatic potential, and Class 2, which has a much higher propensity to metastasize and is distinguished by up-regulations of genes located on chromosome 3 and down-regulations of genes on chromosome 8q. Prior research has shown that tumor biopsy tissues taken directly from Class 1 patients express a three-gene signature (PHLDA1, FZD6, ENPP2) characteristic of UM at a greater level than those from Class 2 patients. Extracellular vesicles (EVs), which are excreted by healthy cells and tumor cells alike, have gained recognition in the past decade as a source of novel biomarkers. We sought to identify RNA transcripts from plasma EVs in patients with UM that would enable us to distinguish between UM Classes via expression of the three-gene signature. Additionally, we sought to optimize EV and RNA isolation and reverse transcription (RT) assays for generation of cDNA. We found that qEV size exclusion chromatography columns outperformed the exoEasy spin column-based filtration kit in EV isolation from plasma, while exoEasy yielded more RNA from EVs than did the qEV columns. The iScript RT kit outperformed the TaqMan RT kit by requiring less RNA input for cDNA generation. Following procedural optimization, samples were processed for expression of GAPDH, the housekeeping gene, and the three-gene signature. While the reference RNA sample (total RNA isolated from tumor cells) showed expression for all four genes, RNA samples from the EVs only showed expression for GAPDH. This implies that either EV-derived RNAs do not express the three-gene signature, or that there was not enough RNA to reveal expression of those genes. Therefore, continuing studies will involve re-running RT-PCR with a higher amount of RNA and performing sequencing on EV-derived RNA to potentially identify novel markers for UM prognosis.
