Analysis of the Molecular Networks in Androgen Dependent and Independent Prostate Cancer Revealed Fragile and Robust Subsystems

Abstract

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 As androgens like testosterone are withdrawn, prostate cancer cells lose their androgen sensitivity and begin to proliferate without hormone growth factors.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.

Dynamic Modeling of the Human Coagulation Cascade Using Reduced Order Effective Kinetic Models

“…Instead, we estimated an ensemble of likely parameter sets (N = 20) from eight in vitro time-series coagulation data sets with and without the protein C pathway. Ensemble approaches have been used previously for other signal transduction models [43][44][45][46][47], and for metabolic models [48] to estimate the impact of poorly constrained parameter values or poorly understood network structure on simulation performance. Thus, ensemble approaches are common in the dynamic modeling community.…”

Fibroblast growth factor and ornithine decarboxylase 5′UTRs enable preferential expression in human prostate cancer cells and in prostate tumors of PTEN−/− transgenic mice

“…This finding is in keeping with recent studies, which have suggested that direct targeting of the translational machinery, specifically eIF4E, is required for efficient targeted therapies of androgen-independent prostate cancers. 3,32,33 Hence, the careful testing and selection of 5 0 UTRs such as that derived from FGF-2 may enable the construction of a virus suitable for gene therapy, in that its therapeutic proteins are selectively expressed only in tumors and not in normal tissues.…”

A multiscale road map of cancer spheroids – incorporating experimental and mathematical modelling to understand cancer progression

“…For prostate cancer, two mathematical approaches have been employed; a continuum model to quantitatively describe biomarker dynamics and a Monte Carlo strategy (probabilistic method) to identify hormone-dependent signalling parameters (Swanson et al, 2003;Tasseff et al, 2010). The continuum model combined the serum prostate-specific antigen level, which is linked to prostate cancer growth, and quantified this level in a simple equation that predicted its production and loss.…”