Abstract: Cancer can be viewed as an evolutionary process and as such can be modeled and explained using evolutionary game theory. In the first part of the talk we will focus on hallmarks of cancer and various models of cancer as a game (e.g. nonspatial model based on differential equations, spatial model using diffusion equations, spatial in-silico model). Subsequently, we will focus on the cancer treatment. The current standard of care for cancer therapy is to kill the largest possible number of tumor cells by applying the maximum tolerated dose. While this approach is often initially successful at reducing tumor burden, it inevitably fails due to evolution and proliferation of resistant cancer phenotypes. Moreover, this highest tolerable dose regimen is typically very aggressive to the patient and very expensive. Shifting the goal of therapy from complete elimination of tumor burden to instead controlling the tumor burden (with much lower treatment doses) for a maximal period of time - the so-called adaptive therapy - can change the approach to therapy dramatically. We will show that (inverse) Stackelberg game theory can be very helpful in designing such an adaptive treatment.