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Cancer is no more a disease of cells than a traffic jam is a disease of cars. A lifetime of study of the internal-combustion engine would not help anyone understand our traffic problems.

-D. W. Smithers (1962)

Tumor heterogeneity is the result of the dynamic interplay of tumor and stromal/microenvironmental cells, which in turn is pivotal for therapy resistance. An example of cancer cell adaptation, particularly to nutrient fluctuations, is the migration/proliferation plasticity, or Go-or-Grow,  that I pioneered in exemplifying its impact  in terms of tumor invasion and therapy resistance, e.g. in [Hatzikirou et al, 2012, Boettger et al 2015, Alfonso et al 2016, Mascheroni et al 2019].  

A crucial part of the tumor’s microenvironment is composed by the immune cells. Immune cells exhibit a large degree of phenotypic plasticity resulting into a plethora of immune phenotypes. For instance, T-cells can be found either cytotoxic or regulatory. Similarly, phenotypic polarization of macrophages is prominent in tumors, spanning from anti- (M1) to pro-tumoral (M2) phenotypes. The balance between pro- and anti-tumoral immune populations is critical for the tumor fate. In the light of novel immune therapies, such as checkpoint inhibitors or CTLA4, is important to elucidate their dynamics in the tumor context. However, it not yet understood why these therapies fail and how could be combined with other therapeutic modalities. This lies in the lack of understanding the dynamic interplay of two phenotypically plastic populations, such as tumor and immune cells. To resolve the later, I will use the LEUP principle to develop a comprehensive predictive theory and translate this into designing efficient combinatorial therapies. Model parameters are typically informed by using medical imaging data, such as biopsies and radiological images, kinetic cell data and ex-vivo experiments.