Abstract 5229: An integrated <i>in vitro</i>/<i>in silico</i> platform to study the evolutionary dynamics of resistance to cancer therapy .
Authors: Keisuke Kuida, Carl Boand, John Bradley, Jean Courtemanche, John Donovan, Doanh Mai, Jerome Mettetal, Santhosh Palani, Derek Blair, Jeffrey Ecsedy, Andy Dorner, Wen Chyi Shyu, Joe Bolen, Doug Bowman, Arijit Chakravarty
Journal: Cancer Research (2013)

Abstract Emerging clinical evidence suggests that tumor response to treatment occurs along stochastic, evolutionary trajectories, with the amplification of pre-existing clones of resistant cells being shown to be responsible for treatment failure in a number of cases. In this evolutionary view of cancer, pre-existing tumor heterogeneity forms a pool of candidate mutations for selection to act on. Thus, a clearer picture of the heterogeneity in cancer cell lines will facilitate a mechanistic understanding of the dynamics of resistance evolution. In this report, we describe the development of an in vitro system based on the soft agar clonogenic assay, to characterize the degree of heterogeneity in net growth rate (fitness) and the effects of this variation in fitness on the population-level response to drug treatment. The in vitro system was developed as a 24-well soft agar clonogenic assay, with sequential images of the growing colonies taken every day, over a period of two weeks. An automated system for plate preparation, imaging, and plate transfer from incubator to imager was developed to increase assay throughput. A modified greedy algorithm was developed in MATLAB to reliably track colonies as they grow and shift in the image frame from day to day. The individual growth rates of colonies in the pre-treatment colonies followed an exponential model, indicative of a clonal origin for the individual colonies, which was confirmed by microscopy. Next, we developed an in silico platform (based on a Monte Carlo stochastic simulation) to study this heterogeneity in growth rates, and derive insights that are relevant to the planning of therapeutic interventions. This simulation was also used to optimize the design of the experimental system and can provide guidance on the optimal seeding density to avoid merging colonies. We are now using this simulation platform to explore the impact of heterogeneity on population fitness and on drug resistance. This platform can also be extended to examine the individual colonies derived from drug treatment, and provides a rapid and simple means of isolation of resistant subclones for further study. Taken together, the combined in vitro/in silico platform presented here forms the basis of a Systems Biology approach for assessing the role of cancer cell heterogeneity in the evolution of resistance in response to treatment Citation Format: Keisuke Kuida, Carl Boand, John Bradley, Jean Courtemanche, John Donovan, Doanh Mai, Jerome Mettetal, Santhosh Palani, Derek Blair, Jeffrey Ecsedy, Andy Dorner, Wen Chyi Shyu, Joe Bolen, Doug Bowman, Arijit Chakravarty. An integrated in vitro/in silico platform to study the evolutionary dynamics of resistance to cancer therapy . [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5229. doi:10.1158/1538-7445.AM2013-5229