COMPUTATIONAL FRAMEWORKS FOR THE IDENTIFICATION OF SOMATIC AND GERMLINE VARIANTS CONTRIBUTING TO CANCER PREDISPOSITION AND DEVELOPMENT

The most recent cancer classification from NIH includes ~200 types of tumor that originates from several tissue types (http://www.cancer.gov/types). Although macroscopic and microscopic characteristics varies significantly across subtypes, the starting point of every cancer is believed to be a single cell that acquires DNA somatic alterations that increases its fitness over the surrounding cells and makes it behave abnormally and proliferate uncontrollably. Somatic mutations are the consequence of many possible defective processes such as replication deficiencies, exposure to carcinogens, or DNA repair machinery faults. Mutation development is a random and mostly natural process that frequently happens in every cell of an individual. Only the acquisition of a series of subtype-specific alterations, including also larger aberrations such as translocations or deletions, can lead to the development of the disease and this is a long process for the majority of adult tumor types. However, genetic predisposition for certain cancer types is epidemiologically well established. In fact, several cancer predisposing genes where identified in the last 30 years with various technologies but they characterize only a small fraction of familial cases. This work will therefore cover two main steps of cancer genetics and genomics: the identification of the genes that somatically changes the behavior of a normal human cell to a cancer cell and the genetic variants that increase risk of cancer development. The use of publicly available datasets is common to all the three results sections that compose this work. In particular, we took advantage of several whole exome sequencing databases (WES) for the identification of both driver mutations and driver variants. In particular, the use of WES in cancer predisposition analysis represents one of the few attempts of performing such analysis on genome-wide sequencing germline data.