Cellule staminali tumorali: esplorazione dei meccanismi di retroazione

Cancer research has been attracting researchers from all the scientific disciplines, in recent years mathematical approaches have been widely applied to address the complexity of cancer. One of the most studied subjects is the involvement of the Cancer Stem Cells (CSCs) in cancer progression. CSC are cancer cells that possess characteristics associated with normal stem cells. The CSC theory states that cancer cells populations are characterized by a hierarchical structure and that CSCs, which are found at the apex of this hierarchy, are essential for cancer propagation. The aim of our project is to study breast cancer growth by mean of a mathematical model describing cell population dynamics during cancer growth and to use this model to reproduce and explain experimental data. We started from a linear model describing cancer subpopulations evolution based on the CSC theory. Moreover, we added feedback mechanisms from the cell populations into themselves, in order to mimic micro-environment effects in cancer growth. We hypothesized two feedback mechanisms and we studied their effects both separately and combined. In this way we obtained three ODE systems that we validated using experimental data and that we analyzed using a sensitivity analysis approach. Our models are completely deterministic, so model outputs only depend on model inputs, i.e. on a particular set of biological parameter values and initial conditions. We tuned our model using data describing the total population and subpopulations evolution over time and we identified parameter values that best fit these data. The experimental data have been performed on TUBO Cancer cells line by the group of prof. Cavallo at Molecular Biotechnology Center. We further explored feedback effects, trying to understand which combination of feedback mechanisms better describe experimental data. Finally, in order to study how changes in parameter values influence model behaviors, we applied the work-flow proposed and described in a paper written by Fornari C., Cordero F. and coworkers, that uses both analytical and statistical techniques.