Androgen ablation
therapy is currently the primary treatment for metastatic
prostate cancer. Unfortunately, in nearly all cases,
androgen ablation fails to permanently arrest
cancer progression. As
androgens like
testosterone are withdrawn,
prostate cancer cells lose their
androgen sensitivity and begin to proliferate without
hormone growth factors. In this study, we constructed and analyzed a mathematical model of the integration between
hormone growth factor signaling,
androgen receptor activation, and the expression of
cyclin D and
Prostate-Specific Antigen in human LNCaP prostate
adenocarcinoma cells. The objective of the study was to investigate which signaling systems were important in the loss of
androgen dependence. 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 LNCaP data sets taken from literature sources. Alterations in the rate of
Prostatic Acid Phosphatase expression was sufficient to capture varying levels of
androgen dependence. Analysis of the model provided insight into the importance of network components as a function of
androgen dependence. The importance of
androgen receptor availability and the MAPK/Akt signaling axes was independent of
androgen status. Interestingly,
androgen receptor availability was important even in
androgen-independent LNCaP cells. Translation became progressively more important in
androgen-independent LNCaP cells. Further analysis suggested a positive synergy between the MAPK and Akt signaling axes and the translation of key proliferative markers like
cyclin D in
androgen-independent cells. Taken together, the results support the targeting of both the Akt and MAPK pathways. Moreover, the analysis suggested that direct targeting of the translational machinery, specifically
eIF4E, could be efficacious in
androgen-independent
prostate cancers.