Cancer is the second major cause of death worldwide (after cardiovascular diseases), being recognized as a global threat. Researchers have made major advances in learning more details about how to prevent, diagnose, and treat cancer. However, many aspects of this complex and multifaceted disease remain unknown. Multidisciplinary research combining mathematical modelling with real data in this area becomes essential to understand how tumors grow, evolve, and evade selective pressures such as the immune system (IS) or therapies. The complexity of tumors brings about a large number of questions such as how heterogeneity and stochasticity affect cancer cells proliferation, the action of the IS on them, and the evasion of anticancer therapies. These features are addressed in this proposal within the framework of dynamical systems theory and multi-scale modelling applied to cancer evolution and anticancer therapies.

Heterogeneity is a primary cause of resistance to targeted treatments in cancer therapeutics. A diverse distribution of phenotypically distinct tumor cells prior to drug treatment predisposes to nonuniform responses, leading to the elimination of sensitive cancer cells whilst leaving resistant subpopulations unharmed. There is ample evidence of genetic diversity among cancer cells both within and across tumors, with genomic instability being the main engine of heterogeneity. However, it is now apparent that genetic variability alone does not account for the whole variation in responses to targeted anti-cancer drugs. Non-genetic heterogeneity has several sources: epigenetic, stochastic, and environmental. These sources of heterogeneity can produce phenotypic diversity even in genetically identical cells. Not surprisingly, the analysis of the consequences of non-genetic cell-to-cell variability in the cellular response to drugs and its potential impact for the treatment of human diseases including cancer has become a crucial issue to understanding drug resistance phenomena and developing new targeted agents.

The overarching aim of this project is to further develop our research on the role of heterogeneity in cancer, identifying routes to and mechanisms of resistance to treatment, and, finally, evaluate the rational design of combinatorial therapies involving existing drugs and prescribe new therapeutic strategies to overcome the effects of heterogeneity. To this end, we will apply a number of mathematical techniques, ranging from dynamical systems theory to multiscale hybrid modelling. These techniques will allow us to tackle issues related to the generation of heterogeneous behavior in tumors (degradation of epigenetic landscapes (ELs) and tumor-induced angiogenesis), its role in immunoediting, how heterogeneity in cancer cells affects the outcomes of immunotherapy and oncolytic virotherapy, and the effects of noise on autocatalytic processes in cancer.