We have extended an automaton model of brain tumor growth to study the effects of treatment. By varying three treatment parameters, we can simulate tumors that display clinically plausible survival times. Much of our work is dedicated to heterogeneous tumors with both treatment-sensitive and treatment-resistant cells. First, we investigate two-strain systems in which resistant cells are initialized within predominantly sensitive tumors. We find that when resistant cells are not confined to a particular location, they compete more effectively with the sensitive population. Moreover, in this case, the fraction of resistant cells within the tumor is a less important indicator of patient prognosis when compared to the case in which the resistant cells are scattered throughout the tumor. In additional simulations, we investigate tumors that are initially monoclonal and treatment-sensitive, but that undergo resistance-mutations in response to treatment. Here, the tumors with both very frequent and very infrequent mutations develop with more spherical geometries. Tumors with intermediate mutational responses exhibit multi-lobed geometries, as mutant strains develop at localized points on the tumors' surfaces.