||The advent of Next-Generation Sequencing (NGS) technologies in 2005 reduced DNA and RNA sequencing cost by orders of magnitude and significantly increased the amount of biological data produced, thus transforming genetics in a Big Data field.
In order to handle these huge amounts of new NGS data and to interpret them in a biologically meaningful manner, a novel bioinformatics approach has been designed and implemented to identify gene fusions potentially associated with cancer onset, progression, prognosis and outcome. Gene fusions are structural chromosome rearrangements that may result in the exchange of coding or regulatory DNA sequences between genes. Many such gene fusions are strong driver mutations in tumors and have provided fundamental insights into the disease mechanisms that are involved in tumorigenesis. Moreover, the close association between the type of gene fusion and the tumor phenotype makes gene fusions ideal for diagnostic purposes, enabling of tumor subclones classification. In addition, many gene fusions add important information for risk stratification, and increasing numbers of chimeric proteins encoded by the gene fusions serve as specific targets for treatment, resulting in dramatically improved patient outcomes.
The implemented approach applied to different tumor samples, from Gastro Intestinal Stromal Tumor (GIST), Diffuse Large B Cell Lymphoma (DLBCL), and High Grade Serous Ovarian Cancer (HGSC), showed promising results reporting novel fusions and fusions already well known in literature with high probability to be drivers or directly involved in the oncogenic progression of the disease.