LEVERAGING TRANSCRIPTOMIC ANALYSIS TO IDENTIFY TRANSCRIPTION FACTORS ORCHESTRATING CANCER PROGRESSION

Next generation sequencing (NGS) technology is currently employed to explore the molecular profiles associated to different biological contexts. The application of this technology provides at same time a high-resolution and global view of the genome and epigenome phenomena, enabling us to study the molecular events underlying many human diseases, including cancer. Our lab tries to exploit the utility of high throughput sequencing technologies generating genomic, transcriptomic and epigenomic data from patient's cohort to study the underlying molecular mechanisms that characterize the specific diseases and map the key regulators that can be critical targets for relevant therapeutic measures. I take the advantage of this technology to mainly understand two aggressive cancers: Ovarian Cancer (OC) and Glioblastoma multiforme (GBM). OC is a leading cause of cancer-related death for which no significant therapeutic progress has been made in the last decades. Also, in this case, despite multimodal treatment its prognosis remains extremely poor. This is due to the fact that the molecular mechanisms underlying OC tumorigenesis and progression are still poorly understood (Vaughan et al., 2011). GBM is the most common and aggressive primary brain malignancy with very poor prognosis (Frattini et al., 2013). The median survival rate is of 12-15 months (Singh et al., 2012) with 5-year survival that is less than 5% despite the multimodal treatment which include surgery, radiotherapy and chemotherapy. To this end, I will be integrating various genomic and transcriptomic analysis to define the key regulatory actors that characterize the disease progression paving. This integrated analysis has been devised in form of a computational workflow that gives way for a discovery pipeline for physiopathologically meaningful epigenetic targets that can lead to therapies.