Abstract B214: Application of an evolutionary model of cancer cell response to dose-response viability curves to assess the potential for pre-existing resistance.

Abstract

Abstract In recent years, tumor progression in the clinic has been demonstrated to be polyclonal. A single tumor contains diverse subpopulations of cancer cells which under the selective pressure of drug treatment evolve to produce drug resistance. As well, clonal diversity and genomic instability exist in tissue culture cancer cell lines. In this work, we determine the levels of pre-existing resistant subpopulations in cancer cell lines through the application of an evolutionary model of cancer cell response to an in vitro time-course of dose-response viability curves. Methods: We simulated dose response curves versus exposure time for a range of heterogeneous (varying fractions of sensitive and resistance cells) populations of cancer cells with predefined PD parameters. We added Gaussian noise to our estimates and fitted the time-course dose response data with both a single population model and with a dual population model and compared the goodness of the fit, PD parameter and residual variability levels for each of the heterogeneous populations. Model fitting and simulations were performed using NONMEM. Cancer cells were assumed to grow exponentially and the drug effect was modeled as a sigmoidal Emax function. An analytical solution was derived to estimate the population fractions from the plateau levels of the dose response curves. We validated the model predictions against a previously published dataset (Sci. Transl. Med. 3, 90ra59, 2011) where the baseline fraction of sensitive and resistant cells was varied in a controlled manner. Finally, the model was used to predict the percentage of resistant cells in a pre-existing population in response to treatment with signal transduction inhibitors. These predictions were then validated directly using time-lapse microscopy on cells grown in plastic and on soft agar. Results: From the simulations, we determined the study design necessary for modeling dose response relationship for heterogeneous populations to be a time course (0, 24, 48, 72h) viability assay with a range of concentrations spanning low to saturable effect. The dual population model accurately fitted the dose response curves simulated from heterogeneous populations, whereas the single population model showed poor predictability and exhibited large unexplained residual variability. The dual population model also predicted well the PD parameters and growth kinetics of the sensitive and resistance populations when applied to the published dataset with single time course (72h) viability measurement performed on heterogeneous populations with varying levels of resistance sub-fractions. When applied to a prospective in vitro study, the dual population model adequately predicted the fraction of resistance populations preexisting in the cell line tested. Conclusion: Taken together, the modeling approach described here provides a novel evolutionary approach to the assessment of pre-existing resistance to drug treatment that can be rapidly applied for mechanistic investigations of single-agent and combination cancer therapeutics. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B214. Citation Format: Snehal Samant, Derek Blair, Andrew Chen, Jerome Mettetal, Wen Chyi Shyu, Mark Hixon, Jeffrey Ecsedy, Santhosh Palani, Arijit Chakravarty. Application of an evolutionary model of cancer cell response to dose-response viability curves to assess the potential for pre-existing resistance. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B214.