First name
Giorgio
Last name
Melloni
Year of Study
Research Center
Thesis Title
Computational frameworks for the identification of somatic and germline variants contributing to cancer predisposition and development
Thesis Abstract
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.
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