USE OF NEXT-GENERATION SEQUENCING FOR GENOMIC ANALYSIS IN COMPLEX DISEASES

Since the early 1990s, Sanger method has been the gold standard methodology for sequencing analysis of DNA. Next-generation sequencing (NGS) approaches revolutionized the field of genomics over the last 5 years. These new sequencing technologies make feasible the direct and cost-effective sequencing of genomes at unprecedented scale and speed. Furthermore, the applications of these technologies are wide-spread and have been developed to explore the complex biological systems, among which RNA sequencing (RNA-seq) for transcriptomics and ChIP-seq for epigenomics. NGS has changed the way we think about scientific approaches in basic, applied and clinical research. However, the potentialities of NGS entail major bioinformatic challenges for managing and understanding this vast amount of data and also for demanding computational resources. During my PhD course, as bioinformatician, I was involved in the processing of NGS data and in the development of algorithms and pipelines for the analysis. I developed a bioinformatic tool, called GAMES (Genomic Analysis of Mutations Extracted by Sequencing) to annotate and prioritize all the variants detected from NGS. The aim is to highlight those genetic events, functionally involved in disease. I also developed a statistical method for copy number variants (CNV) detection that allows to identify structural abnormalities potentially related to pathogenetic mechanisms. The presented approaches and algorithms were applied in different projects and are widely used within the Molecular Genetics Laboratory at Hospital Papa Giovanni XXIII in Bergamo, Italy. In this thesis, I will present selected analyses performed by NGS in routinely diagnostics. In clinical settings in fact NGS is applied widely, frommolecular characterization of molecular markers for predicting therapy response in cancer patients, to the research of pathomechanism for cardiovascular disease, to multi-genes screening for differential diagnosis in pediatric age to optimize the therapeutic management. Predictive markers are factors that are associated with upfront response or resistance to a particular therapy. They are important in oncology since tumors of the same tissue vary widely in their response to therapies. Of all human cancers, colorectal cancer (CRC) remains one of principal cause of death in the world. Almost 25% of patients have stage IV (metastatic) disease at diagnosis, and their predicted 5-year survival is less than 10%. The most advanced treatment option for metastatic CRC (mCRC) patients are anti-epidermal growth factor receptor (EGFR) monoclonal antibodies (mAbs) that bind to and inhibit the activity of EGFR. The use of anti-EGFR mABs, such as cetuximab and panitumumab, have had great impact in the treatment of mCRC. However, their efficacy is dependent to small GTPase Kras genotyping. In diagnostic practice, I have applied NGS for Kras gene analysis, in order to detect specific Kras mutations for the identification of patients with advanced colorectal cancer unlikely to benefit from either cetuximab or panitumumab. To this aim, a large screening by NGS allows to analysis with high level of sensitivity the heterogeneity in somatic mutations in solid tumors or tumor cell lines. Besides the importance of Kras in the tumor pathway, germline mutations in this gene are known to be causative in genetic syndromes, called RASopathies. These represent one of the largest groups of multiple congenital anomaly syndromes known, caused by germline variants in various genes encoding components of the RAS/mitogen-activated protein kinase (MAPK) pathway. Proteins belonging to this network play key roles in cell proliferation, differentiation, survival, and death. The RASopathies have many overlapping characteristics, including characteristic facial features, cardiac defects, cutaneous abnormalities, neurocognitive delay and a predisposition to malignancies. These syndromes include Noonan syndrome, Costello syndrome, neurofibromatosis-1 and Leopard syndrome Differential diagnosis is challenging and to prevent the onset of complications and to establish the correct therapeutic agent is fundamental. By NGS, it is possible the molecular characterization of genotype and to reach a definitive diagnosis of rare genetic conditions characterized by high genetic heterogeneity and phenotypic overlap. In conclusion, the aim of my PhD thesis has been to provide bioinformatic pipeline and analysis for next-generation sequencing for deeper understanding of molecular causes of these disease. This is potentially useful in diagnosis for elucidation of phenotype and for clinical decision-making