Novel computational strategies for the identification of new therapeutic targets in melanoma and thyroid cancer.

Abstract

Cancer signaling pathways have been extensively investigated. However, how cross-talk processes and integrates pathway responses in cancer is still far from being completely elucidated. Genetic and epigenetic alterations lead cells to aberrant proliferation and escapement from physiological mechanism controlling cell growth, survival and migration. In this context, specific mutations transform cellular proto-oncogenes to oncogenes, triggering hyperactivation of signaling pathways, whereas inactivation of tumor suppressors removes critical negative regulators of signaling. MAPK and PI3K/AKT pathways often present mutated genes in different types of cancer, and are strongly involved in intensive cross-talk. There is an ever-increasing awareness that computational modeling and simulation are more than helpful in improving the understanding at cellular and molecular levels, in speeding-up the drug discovery process through the identification of alternative strategies with the aim to overcome drug resistance in cancer. The main objective of this thesis is to reveal biochemical and genetic mechanisms underlying drug resistance in melanoma and thyroid cancer through the application of ordinary differential equations based models coupled with algorithmic approaches. These tumors share both MAPK and PI3K/AKT signaling pathway, with the presence of BRAF V600E mutation. Computational approaches developed in this PhD project were demonstrated to be able to find novel therapeutic targets and prognostic biomarkers for a more effective treatment in melanoma and thyroid cancer.