The importance of spatial distribution of stemness and proliferation state in determining tumor radioresponse

Abstract

Tumor growth and progression is a complex phenomenon dependent on the interaction of multiple intrinsic and extrinsic factors. Necessary for tumor development is a small subpopulation of potent cells, so-called cancer stem cells, that can undergo an unlimited number of cell divisions and which are proposed to divide symmetrically with a small probability to produce more cancer stem cells. We show that the majority of cells in a tumor must indeed be non-stem cancer cells with limited life span and limited replicative potential. Tumor development is dependent as well on the proliferative potential and death of these cells, and on the migratory ability of all cancer cells. With increasing number of cells in the tumor, competition for space limits tumor progression, and in agreement with in vitro observation, the majority of cancer cells become quiescent, with proliferation primarily occurring on the outer rim where space is available. We present an agent-based model of early tumor development that captures the spatial heterogeneity of stemness and proliferation status. We apply the model to simulations of radiotherapy to predict treatment outcomes for tumors with different stem cell pool sizes and different quiescence radiosensitivities. We show by first presuming homogeneous radiosensitivity throughout the tumor, and then considering the greater resistance of quiescent cells, that stem cell pool size and stem cell repopulation during treatment determine treatment success. The results for tumor cure probabilities comprise upper bounds, as there is evidence that cancer stem cells are also more radioresistant than other tumor cells. Beyond just demonstrating the influence of mass effects of stem to non-stem cell ratios and proliferating to quiescent cell ratios, we show that the spatiotemporal evolution of the developing heterogeneous population plays a pivotal role in determining radioresponse and treatment optimization. © EDP Sciences, 2009.