Uveal melanoma (UM) is the most common type of eye cancer in adults. This cancer is characterized by a high risk to develop metastasis, leading to an overall poor survival for the patient. This project aims to comprehensively analyze the NGS genomics data of a cohort of UM patients to improve personalized prognostic tests and optimize individualized treatment options. Our goal is to explore the genetic landscape of individual tumors to elucidate molecular mechanisms driving micro-metastasis and poor response to standard treatments. This project leverages comprehensive bioinformatics analysis of NGS data to identify genomics determinants of metastasis and response to treatment. We will estimate genetic variants, copy number alterations and structural rearrangements to define the genetic landscape and molecular signatures associated with high-risk disease. We will integrate the different levels of genomic information to define a feature matrix to be used to classify tumors into different subtypes. For every subtype we will compute the average signature exposure and will test association of tumor subtype vs mutational signature and CNV patterns. The available data consists of NGS genomics of 200 patients diagnosed with UM. The samples are from FFPE-embedded biopsies. The raw data delivered consists of FASTQ and BAM files.