Modeling MAPK activation and androgen action could help us understand how to treat androgen independent prostate cancer.

Prostate cancer is the most common cancer found in men in the United States with an estimated 192,000 new cases in 2009. A typical non-surgical treatment for metastatic prostate cancer is the removal or inhibition of androgens such as testosterone. Unfortunately, this treatment is not a permanent solution as the cancer eventually develops an androgen independent phenotype. Thus, the identification of effective treatments for androgen independent prostate cancer represents an unmet medical need. In this project, we constructed a dynamic mathematical model of one possible mechanism for the deregulation of androgen control in prostate epithelial cells. This mechanism involved crosstalk between the androgen sensing subsystem and the mitogen activated protein kinase cascade. The model was formulated as a set of ordinary differential equations which described 212 species and 384 interactions including both the mRNA and protein levels for key species. An ensemble approach was chosen to constrain model parameters and to estimate the impact of parametric uncertainty on model predictions. Model parameters were identified using 14 steady-state and dynamic data sets taken from multiple laboratories. An example set of training simulations constraining the relationship between MAPK and AR activation is shown in Fig 2.

Fig. 2 Simulation results for key species under androgen free conditions. A: Effect of HER2 and MEK overexpression on LNCaP C-33 steady state PSA levels. The inhibition of MEK blocks the effect HER2 overexpression. Experimental data adapted from Lee et al. (14). B: Effect of HER2 and MEK inhibition on LNCaP C-33 steady state PSA levels. The inhibition of either HER2 or MEK blocks high AIPC PSA levels. Experimental data adapted from Lee et al. (14). C: Effect of PAcP isoforms on LNCaP steady state cyclin D levels. Experimental data adapted from Lingappa and coworkers (Prosetta Corporation, unpublished data). D: Transient activation of ERK via ligand dependent EGF signaling (8nM EGF at t = 60s) in HeLa cells. The HeLa data was reproduced from (30). Inset: Simulated phosphorylated ETS (ETSp) levels following the addition of 8nM EGF in the presence and absence of Her2. Her2 activation drives a sustained MAPK signal which in turns sustained ETS activation. The shaded region denotes one standard deviation centered about the ensemble mean (line).

The model recapitulated the positive feedback between Her2 induced MAPK activa- tion and androgen action. Several studies have demonstrated that MAPK can activate AR in the absence of hormone stimulation. Activated AR transcriptionally down-regulates cPAcP expression which in turn increases Her2 activation. Both Her2 dimerization along with the traditional EGFR-growth factor pathway can activate MAPK, leading to a posi- tive feedback loop. However, typical growth factor induced MAPK activation is transient whereas de-regulated Her2 induced MAPK activation is persistent. The MAPK module in the model described both activation pathways. Growth factor dependent MAPK activation was constrained by dynamic measurements of phosphorylated ERK (ERKpp) levels fol- lowing stimulation of EGFR with 8nM EGF (Fig. 2D). The EGF induced ERKpp data was taken from HeLa cells (30). However, we expect transient EGF-induced MAPK activation in LNCaP cells will be qualitatively similar to HeLa given the conserved nature of mito- genic signaling. We constrained Her2 induced MAPK activation using cyclin D protein expression data in C-33 and C-81 cells without androgen following PAcP expression (Fig. 2C). Cyclin D expression was coupled to ERK through the ETS and AP1 transcription factors, both of which activate cyclin D expression (37). Her2 induced MAPK activation led to a persistent ETSp signal compared to ETS activation following EGFR-induced MAPK activation (Fig. 2D, inset). Nominally, C-33 cells have lower cyclin D expression compared to C-81 (Fig. 2C, lane 1 and 4). The difference in cyclin D expression between C-33 and C-81 cells was qualitatively consistent with increased C-81 proliferation (13). While the expression of cPAcP in C-81 reduced cyclin D levels (Fig. 2C, lane 2), sPAcP expression resulted in no change (Fig. 2C, lane 3). To further constrain the relationship between MAPK, Her2 and AR activation, we used the Her2 perturbation study of Lee et al. (14) in the ensemble calculations. Because the perturbation magnitudes were not reported, we assumed ±50% for all changes. Where possible, this assumption was validated by analyzing the corresponding Western blots using the GelEval software package (v1.22, Frog Dance Software). The ±50% perturbation magnitude was approximately consistent with the published blots. A 50% increase in Her2 led to an approximately 50% increase in PSA expression in C-33 without androgen (Fig. 2A, lanes 1 and 3). While a 50% decrease in Her2 in C-81 led to a similar decrease in PSA secretion (Fig. 2B, lanes 1 and 2). Further disruption of Her2 effectively blocked PSA expression in C-81 without androgen (Fig. 2B, lane 3). A 50% reduction of MEK, one of the three primary protein kinases in MAPK, resulted in reduced PSA expression in C-81 (Fig. 2B, lane 4). While a 50% increase of MEK in C-33 increased PSA expression by 5-fold (Fig. 2A, lane 2). The combination of MEK inhibition and Her2 activation (50% increase in Her2 and a 50% decrease in MEK) decreased PSA expression in C-33 (Fig. 2A, lane 4). Taken together, the model replicated qualitative features of the relationship between MAPK, AR activation and androgen action.