A driver-passenger stochastic model for cancer progression
Andrea Perin · 2018
Cancer progression is determined by the accumulation of a number of key mutations. These can be coarsely divided into driver and passenger mutations; this distinction is made on behalf of their effect on the cell's fitness. Advantageous mutations, which confer the cancer cell its hallmark features, are called driver, whereas mutations with neutral or deleterious effects are called passenger mutations. In this thesis, we focus on a stochastic model for cancer progression, based on the distinction between types of mutation. The key traits of such a model seem to be similar to those observed in cancer. We then model a network, loosely based on multiple myeloma bone disease, on which we study how a cancer population, the likes of which have been studied in the first part, evolves while interacting with healthy tissues.