Abstract B05: Harnessing heterogeneity to design better combination therapies

Field: Medicine (Health Sciences)

Abstract Background: Significant cellular, molecular, and tissue heterogeneity is widely observed between and within tumors and the potential clinical significance of these variations is increasingly recognized. The intricate dialogue between the tumor cells and their environment selects for tumor phenotypes that are best adapted to survive. However, this environment is temporally and spatially heterogeneous largely due to variations in blood flow which results in local fluctuations of nutrients, growth factors and other cellular populations. This variability can lead to a heterogeneous response of the tumor to a given therapy. Interactions that occur within the cancer ecosystem do so in a dynamic spatio-temporal manner that is almost impossible to dissect via experimentation alone, so we propose a theoretical framework to generate combination therapies that are sensitive to initial tumor heterogeneity. Methods: Using a hybrid multi-scale mathematical model of tumor growth in vascularized tissue, we investigate the selection pressures exerted by spatial and temporal variations in tumor microenvironment and the resulting phenotypic adaptations. A key component of this model is normal and tumor metabolism and its interaction with microenvironmental factors. The metabolic phenotype of tumor cells is plastic, and microenvironmental selection leads to increased tumor glycolysis and decreased pH. Once this phenotype emerges, the tumor dramatically changes its behavior due to acid-mediated invasion, an effect that depends on the heterogeneity of the tumor cell phenotypes and their spatial distribution within the tumor. The tumors grown within this in silico model display much phenotypic variation, and this heterogeneity depends on the conditions of the microenvironment and the plasticity of the tumor cells. Results: Using the model, we generate sets of tumors with different biological parameters, classify them according to their spatial and temporal heterogeneity, and then administer several therapies, including chemotherapy, vascular therapy, pH buffer therapy, and hypoxia-activated drugs. The model predicts that pH buffer therapy will only have a tumor-preventative effect if administered before the tumor acquires the heterogeneous state that leads to acid-mediated invasion. This is in agreement with experimental results from a spontaneous prostate tumor mouse model (TRAMP mouse). In general, the model predicts that the outcomes of each therapy are highly dependent on the initial tumor heterogeneity at the time of commencing treatment. We categorize the ‘signatures’ of each therapy outcome as a function of the heterogeneity class of the initial tumor. By understanding the signature of each drug in isolation, we implement drug combinations in a sequence that promotes synergistic response for a given class of tumor heterogeneity. The signature of the first drug in the sequence is used to pick the following complementary drug. This produces a more intelligent treatment regimen that can be designed to harness tumor heterogeneity and modulate its impact on treatment outcomes. Citation Format: Mark Robertson-Tessi, Robert J. Gillies, Robert A. Gatenby, Alexander RA Anderson. Harnessing heterogeneity to design better combination therapies. [abstract]. In: Abstracts: AACR Special Conference on Cellular Heterogeneity in the Tumor Microenvironment; 2014 Feb 26-Mar 1; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(1 Suppl):Abstract nr B05. doi:10.1158/1538-7445.CHTME14-B05