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Analysis of the molecular networks in androgen dependent and independent prostate cancer revealed fragile and robust subsystems.

Tasseff R, Nayak S, Salim S, Kaushik P, Rizvi N, Varner JD - PLoS ONE (2010)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, United States of America.

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 As androgens like testosterone are withdrawn, prostate cancer cells lose their androgen sensitivity and begin to proliferate without hormone growth factors.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

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Schematic overview of the interaction network used in modeling the androgen response in prostate epithelial cells.The model architecture was formulated by aggregating molecular modules into a single network (see insert for high level details). The model describes growth factor and hormone induced expression of cyclin D, PSA and the two forms of PAcP. The complete list of molecular interactions that comprise the model (along with kinetic parameter values) are given in Table S1.
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pone-0008864-g001: Schematic overview of the interaction network used in modeling the androgen response in prostate epithelial cells.The model architecture was formulated by aggregating molecular modules into a single network (see insert for high level details). The model describes growth factor and hormone induced expression of cyclin D, PSA and the two forms of PAcP. The complete list of molecular interactions that comprise the model (along with kinetic parameter values) are given in Table S1.

Mentions: The objective of this study was to determine which signaling components were important in AI versus AD LNCaP cells. Toward this objective, we constructed and analyzed a mechanistic mathematical model of the androgen response of three different LNCaP prostate adenocarcinoma sub-lines. We investigated MAPK-dependent outlaw activation of AR in AD (C-33), mid-range (C-51) and AI (C-81) LNCaP cells [13], [27]. Our network model included: nuclear hormone and transmembrane growth factor receptor activation; transcriptional activity via the MAPK subsystem [28]–[30] together with outlaw activation of AR via MAPK [15], [18]; PI3K/AKT/TOR mediated translation initiation [31], [32]; the transcriptional and translational regulation of PSA, cyclin D and PAcP expression [14], [20]; and the regulation of Her2 activity by PAcP [26] (Fig. 1). The network described 212 species and 384 interactions (Table S1). Transcription and translation were modeled using elementary reactions based on literature (supplemental materials). Constitutive and regulated expression of PSA, cyclin D and the two forms of PAcP were considered in the model. The total level of all other model proteins was constant. We modeled the molecular interactions using mass action kinetic processes within an ordinary differential equation (ODE) framework. ODEs are a common method of modeling biological pathways and have been used to model a range of signal transduction processes [29], [33]–[41]. Mass action kinetics have also been used extensively, for example, to model receptor tyrosine kinase signaling [41], blood coagulation [39], pain networks [40] or Toll like receptor signaling [42], [43]. They have also been a key component in the success of perturbation-response approaches which have shown that simple linear rules often govern the response behavior of biological networks [44]. The ODE model was deterministic and captured only population averaged behavior. While we assumed spatial homogeneity, we differentiated between cytosolic and membrane localized processes. We used mass-action kinetics to describe the rate of each molecular interaction. Thus, the 384 kinetic model parameters were mainly association, dissociation or catalytic rate constants. With one exception, model parameters were estimated and validated using LNCaP training data taken from literature sources (Table S2). However, we were unable to estimate unique model parameters. Instead, we estimated a family or ensemble of parameters that was consistent with the training data. The ensemble allowed us to estimate the model uncertainty associated with the many poorly characterized parameters. We analyzed the model ensemble to better understand which architectural features were important in androgen dependent versus independence cells.


Analysis of the molecular networks in androgen dependent and independent prostate cancer revealed fragile and robust subsystems.

Tasseff R, Nayak S, Salim S, Kaushik P, Rizvi N, Varner JD - PLoS ONE (2010)

Schematic overview of the interaction network used in modeling the androgen response in prostate epithelial cells.The model architecture was formulated by aggregating molecular modules into a single network (see insert for high level details). The model describes growth factor and hormone induced expression of cyclin D, PSA and the two forms of PAcP. The complete list of molecular interactions that comprise the model (along with kinetic parameter values) are given in Table S1.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2812491&req=5

pone-0008864-g001: Schematic overview of the interaction network used in modeling the androgen response in prostate epithelial cells.The model architecture was formulated by aggregating molecular modules into a single network (see insert for high level details). The model describes growth factor and hormone induced expression of cyclin D, PSA and the two forms of PAcP. The complete list of molecular interactions that comprise the model (along with kinetic parameter values) are given in Table S1.
Mentions: The objective of this study was to determine which signaling components were important in AI versus AD LNCaP cells. Toward this objective, we constructed and analyzed a mechanistic mathematical model of the androgen response of three different LNCaP prostate adenocarcinoma sub-lines. We investigated MAPK-dependent outlaw activation of AR in AD (C-33), mid-range (C-51) and AI (C-81) LNCaP cells [13], [27]. Our network model included: nuclear hormone and transmembrane growth factor receptor activation; transcriptional activity via the MAPK subsystem [28]–[30] together with outlaw activation of AR via MAPK [15], [18]; PI3K/AKT/TOR mediated translation initiation [31], [32]; the transcriptional and translational regulation of PSA, cyclin D and PAcP expression [14], [20]; and the regulation of Her2 activity by PAcP [26] (Fig. 1). The network described 212 species and 384 interactions (Table S1). Transcription and translation were modeled using elementary reactions based on literature (supplemental materials). Constitutive and regulated expression of PSA, cyclin D and the two forms of PAcP were considered in the model. The total level of all other model proteins was constant. We modeled the molecular interactions using mass action kinetic processes within an ordinary differential equation (ODE) framework. ODEs are a common method of modeling biological pathways and have been used to model a range of signal transduction processes [29], [33]–[41]. Mass action kinetics have also been used extensively, for example, to model receptor tyrosine kinase signaling [41], blood coagulation [39], pain networks [40] or Toll like receptor signaling [42], [43]. They have also been a key component in the success of perturbation-response approaches which have shown that simple linear rules often govern the response behavior of biological networks [44]. The ODE model was deterministic and captured only population averaged behavior. While we assumed spatial homogeneity, we differentiated between cytosolic and membrane localized processes. We used mass-action kinetics to describe the rate of each molecular interaction. Thus, the 384 kinetic model parameters were mainly association, dissociation or catalytic rate constants. With one exception, model parameters were estimated and validated using LNCaP training data taken from literature sources (Table S2). However, we were unable to estimate unique model parameters. Instead, we estimated a family or ensemble of parameters that was consistent with the training data. The ensemble allowed us to estimate the model uncertainty associated with the many poorly characterized parameters. We analyzed the model ensemble to better understand which architectural features were important in androgen dependent versus independence cells.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, United States of America.

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.

Show MeSH
Related in: MedlinePlus