Purpose Early/initiating oncogenic mutations have been identified for many cancers, but such mutations remain unidentified in uveal melanoma (UM). An extensive search for such mutations was undertaken, focusing on the RAF/MEK/ERK pathway, which is often the target of initiating mutations in other types of cancer. Methods DNA samples from primary UMs were analyzed for mutations in 24 potential oncogenes that affect the RAF/MEK/ERK pathway. For GNAQ, a stimulatory αq G-protein subunit which was recently found to be mutated in uveal melanomas, re-sequencing was expanded to include 67 primary UMs and 22 peripheral blood samples. GNAQ status was analyzed for association with clinical, pathologic, chromosomal, immunohistochemical and transcriptional features. Results Activating mutations at codon 209 were identified in GNAQ in 33/67 (49%) primary UMs, including 2/9 (22%) iris melanomas and 31/58 (54%) posterior UMs. No mutations were found in the other 23 potential oncogenes. GNAQ mutations were not found in normal blood DNA samples. Consistent with GNAQ mutation being an early or initiating event, this mutation was not associated with any clinical, pathologic or molecular features associated with late tumor progression. Conclusions GNAQ mutations occur in about half of UMs, representing the most common known oncogenic mutation in this cancer. The presence of this mutation in tumors at all stages of malignant progression suggests that it is an early event in UM. Mutations in this G-protein provide new insights into UM pathogenesis and could lead to new therapeutic possibilities.

Uveal (ocular) melanoma is a highly aggressive cancer that leads to metastatic death in up to half of patients despite successful local therapy. Biomarkers of metastatic risk are critically needed to institute new adjuvant treatment strategies in high-risk patients. Previously, we showed that two prognostically significant molecular subtypes of uveal melanoma could be identified based on gene expression profiling of the primary tumor. In this study, we investigated the value of micro-RNA (miRNA) expression patterns in predicting metastatic risk. A genome-wide, microarray-based approach was used to screen for differentially expressed miRNAs using the Agilent miRNA microarray (Agilent Technologies, Foster City, California, USA) platform containing probes for 470 human miRNAs. Unsupervised analysis was performed using principal component analysis, and supervised analysis was performed using significance analysis of microarrays. Tumors readily clustered based on miRNA expression into two groups that corresponded to the gene expression-based subtypes: class 1 (low metastatic risk) and class 2 (high metastatic risk). The most significant discriminators were let-7b and miR-199a, and the expression of these miRNAs was validated by quantitative PCR. A classifier that included the top six miRNA discriminators accurately distinguished class 1 from class 2 tumors with 100% sensitivity and specificity. miRNA expression may represent a highly accurate biomarker for metastatic risk in uveal melanoma. In addition, these results may provide new insights into the role of miRNAs in tumor progression and the metastatic phenotype.