Emergence of a subpopulation in a computational model of tumor growth

Malignant brain tumors consist of a number of distinct subclonal population s. Each of these subpopulations may be characterized by its own behaviors a nd properties. These subpopulations arise from the constant genetic and epi genetic alteration of existing cells in the rapidly growing tumor. However, since each single-cell mutation only leads to a small number of offspring initially, very few newly arisen subpopulations survive more than a short t ime. The present work quantifies "emergence", i.e, the likelihood of an iso lated subpopulation surviving for an extended period of time. Only competit ion between clones is considered; there are no cooperative effects included . The probability that a subpopulation emerges under these conditions is fo und to be a sigmoidal function of the degree of change in cell division rat es. This function has a non-zero value for mutations which confer no advant age in growth rate, which represents the emergence of a distinct subpopulat ion with an advantage that has yet to be selected for, such as hypoxia tole rance or treatment resistance. A logarithmic dependence on the size of the mutated population is also observed. A significant probability of emergence is observed for subpopulations with any growth advantage that comprise eve n 0.1% of the proliferative cells in a tumor. The impact of even two clonal populations within a tumor is shown to be sufficient such that a prognosis based on the assumption of a monoclonal tumor can be markedly inaccurate. (C) 2000 Academic Press.