Abstract 4920: Integrative identification of stochastically activated signaling sub-networks in individual cancer cells

Abstract

Abstract Traditional studies on signal transduction are focused on measuring the average levels of proteins in populations of isogenic cells to determine the regulatory properties of cell signaling networks and design effective intervention strategies for cancer therapy. However, the average protein levels measured in populations of cells may be quite different from the protein levels measured in individual cells. Signaling differentiation in individual cells is often associated with stochastic events that may activate different cellular sub-networks at different times to generate diverse responses. It is therefore critical to elucidate these stochastic events to design therapeutic interventions that may effectively inhibit abnormal signaling sub-networks in individual cells. In this work, we analyzed the epidermal growth factor (EGF) signaling network in the MDA-MB468 breast cancer cell line and the insulin-like growth factor (IGF-1) signaling network in the MBA-MB231 breast cancer cell line. We measured the average changes in the phosphorylation of proteins after EGF or IGF-1 stimulation using reverse-phase protein arrays (RPPA). We developed a detailed mathematical model to predict the deterministic and stochastic dynamics of EGF and IGF-1 signaling networks. We used particle swarm optimization to train the deterministic model to predict the average transient changes in the phosphorylation of proteins measured by RPPA and infer the unknown model parameters. Using the estimated model parameters, we implemented the stochastic model to predict the transient changes in the phosphorylation of proteins in individual cells. The modeling results showed that protein phosphorylation in individual cells could attain levels that were several fold higher or lower that those attained by the average cell in the population. We used a multicolor fluorescence-activated cell sorter (FACS) to measure the distribution in the phosphorylation of protein after EGF or IGF-1 stimulation and experimentally validate the modeling predictions. We finally compared the overall signal transduction of individual cells whose protein phosphorylation attained higher and lower levels to computationally identify cellular sub-networks that were differentially activated. The integrative approach described here is therefore a useful tool for computationally predicting the stochastic events responsible for signaling differentiation in individual cells and optimizing experimental intervention strategies for cancer therapy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4920. doi:10.1158/1538-7445.AM2011-4920