Tumor cells heterogeneity is one of the most important handicaps in fighting cancer. Such heterogeneity is mainly driven by genomic instability, epigenetics and different sources of stochasticity. Heterogeneity provides cancer cells with ways to escape to the action of the immune system or to anticancer therapies. An important, and not yet completely understood process, is the dynamics between cancer cells and immune system cells (such as Antigen Presenting Cells (APCs) and both regulatory and effector ...
Tumor cells heterogeneity is one of the most important handicaps in fighting cancer. Such heterogeneity is mainly driven by genomic instability, epigenetics and different sources of stochasticity. Heterogeneity provides cancer cells with ways to escape to the action of the immune system or to anticancer therapies. An important, and not yet completely understood process, is the dynamics between cancer cells and immune system cells (such as Antigen Presenting Cells (APCs) and both regulatory and effector T cells) for heterogeneous cancer cells with evolving immunogenicity. In this project thesis we will develop a computational model explicitly considering heterogeneity of tumor cell populations with the capacity to evolve immunogenicity, affecting the recognition by APCs and thus the activation of both effector and regulatory T cells. We will develop a stochastic, agent-based model implementing heterogeneity and differential, evolutionary immunogenicity by means of bit-strings simulations.
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