The metastatic risk of uveal melanoma (UM) is defined by a limited number of molecular lesions, somatic mutations (SF3B1 and BAP1), and copy number alterations (CNA): monosomy of chromosome 3 (M3), chr8q gain (8q), chr6p gain (6p), yet the sequence of events is not clear. We analyzed data from three datasets (TCGA-UVM, GSE27831, GSE51880) with information regarding M3, 8q, 6p, SF3B1, and BAP1 status. We confirm that BAP1 mutations are always associated with M3 in high-risk patients. All other features (6p, 8q, M3, SF3B1 mutation) were present independently from each other. Chr8q gain was frequently associated with chr3 disomy. Hierarchical clustering of gene expression data of samples with different binary combinations of aggressivity factors shows that patients with 8q|M3, BAP1|M3 form one cluster enriched in samples that developed metastases. Patients with 6p combined with either 8q or SF3B1 are mainly represented in the other, low-risk cluster. Several gene expression events that show a non-significant association with outcome when considering single features become significant when analyzing combinations of risk features indicating additive action. The independence of risk factors is consistent with a random risk model of UM metastasis without an obligatory sequence.

Uveal melanoma (UM) is the most common primary intraocular malignancy with a limited five-year survival for metastatic patients. Limited therapeutic treatments are currently available for metastatic disease, even if the genomics of this tumor has been deeply studied using next-generation sequencing (NGS) and functional experiments. The profound knowledge of the molecular features that characterize this tumor has not led to the development of efficacious therapies, and the survival of metastatic patients has not changed for decades. Several bioinformatics methods have been applied to mine NGS tumor data in order to unveil tumor biology and detect possible molecular targets for new therapies. Each application can be single domain based while others are more focused on data integration from multiple genomics domains (as gene expression and methylation data). Examples of single domain approaches include differentially expressed gene (DEG) analysis on gene expression data with statistical methods such as SAM (significance analysis of microarray) or gene prioritization with complex algorithms such as deep learning. Data fusion or integration methods merge multiple domains of information to define new clusters of patients or to detect relevant genes, according to multiple NGS data. In this work, we compare different strategies to detect relevant genes for metastatic disease prediction in the TCGA uveal melanoma (UVM) dataset. Detected targets are validated with multi-gene score analysis on a larger UM microarray dataset.

Non-canonical secondary structures in DNA are increasingly being revealed as critical players in DNA metabolism, including modulating the accessibility and activity of promoters. These structures comprise the so-called G-quadruplexes (G4s) that are formed from sequences rich in guanine bases. Using a well-defined transcriptional reporter system, we sought to systematically investigate the impact of the presence of G4 structures on transcription in yeast S. cerevisiae . To this aim, different G4 prone sequences were modeled to vary the chance of intramolecular G4 formation, analyzed in vitro by Thioflavin T binding test and circular dichroism and then placed at the yeast ADE2 locus on chromosome XV, downstream and adjacent to a P53 response element (RE) and upstream from a minimal CYC1 promoter and Luciferase 1 ( LUC1 ) reporter gene in isogenic strains. While the minimal CYC1 promoter provides for basal reporter activity, the P53 RE enables LUC1 transactivation under the control of the human P53 family proteins expressed under the inducible GAL1 promoter. Thus, the impact of the different G4 prone sequences on both basal and P53 family proteins dependent expression was measured after shifting the yeast cells onto galactose containing medium. The results showed that the presence of G4 prone sequences upstream of a yeast minimal promoter can increase its basal activity proportionally to their potential to form intramolecular G4 structures; consequently, this improved accessibility, when present near the target binding site of P53 family transcription factors can be exploited in order to regulate the transcriptional activity of P53, P63 and P73 proteins.