(284a) Modeling the ErbB Signaling Network in MCF-7 Breast Cancer Cells and Analysis of Ligand-Dependent Responses

The ErbB signaling network controls diverse cell fates ranging from proliferation to differentiation and its deregulation contributes to the development of multiple cancer types [9]. Extracellular ligands, the four ErbB trans-membrane receptors, and cytoplasmic adapters, scaffolds, enzymes, and small molecules make up the network. Approximately 25% of breast cancers overexpress the ErbB2 receptor, and these patients have poor prognoses [3, 10]. Although essentially the same cytoplasmic machinery transduces network input signals, different inputs stimulate different activation kinetics, and lead to different cell fates. For example, in various cell types, stimulation with the ligand Heregulin (HRG) causes sustained network activation and leads to differentiation, while stimulation with the ligand Epidermal Growth Factor (EGF) causes transient network activation and leads to proliferation [6, 7]. While it is clear that there is a connection between activation kinetics and cell fate, there are currently no hard mechanisms for explaining these links. To understand how the ErbB signaling network controls cellular fate, however, we must first elucidate the mechanisms that control ligand-dependent activation kinetics. Similarly, understanding ligand-dependent signaling mechanisms is central to understanding how the ErbB network's deregulation contributes to tumorigenesis, potentially leading to more effective cancer treatment strategies. In this work, we investigate the short-term (£ 30 minutes) response of the ErbB signaling network to stimulation with the ligands EGF and HRG in MCF-7 breast cancer cells. We specifically focus on elucidating mechanisms that control ligand-dependent activation of the proteins ERK and Akt, which are regulators of cell proliferation and survival, respectively.

Approach

The ErbB signaling system is a highly interconnected, dynamic network containing multiple positive and negative feedback loops. As such, in this work we take a combined experimental and computational modeling based approach to understanding the ErbB network pioneered by Kholodenko et al. [5] and extended by Schoeberl et al. [8], Hatakeyama et al. [4], and many others. This approach employs mechanistic, ordinary differential equation (ODE) modeling for simulation in combination with quantitative immunoblotting for experimental measurements of signaling dynamics.

A main problem for mechanistic, differential equation modeling of biochemical signal transduction networks is combinatorial complexity, which arises when network entities have multiple sites and domains that can exist in multiple states [1]. For example, a mechanistic description of the ErbB1 receptor, which has a ligand binding domain, a dimerization site, a kinase domain, and ~10 phosphorylation sites, requires more than 106 differential equations. Because solutions to the problem of combinatorial complexity are only starting to be investigated, previous models of ErbB signaling [4, 5, 8] largely neglected the issue. These previous models were kept tractable by 1) limiting the number of entities considered and 2) introducing intuitively appealing assumptions whose mathematical consequences and physical bases were not clear. In this work, we consider all four ErbB receptors and two ligands, whereas the previous models of ErbB signaling were limited to a single ErbB receptor and ligand. We use recently developed theory [2] and create novel methods for reducing combinatorial complexity whose mathematical and physical bases are well-defined, and the details of these reduction techniques will be presented during the talk. The result of our approach is a tractable, ?pseudo-mechanistic? model based on well-defined assumptions, incorporating a greater number of ErbB network entities than any previous model.

Results

In this study we investigate how ErbB2 overexpression or the MEK inhibitor UO126 each affects ligand-dependent signaling phenomena. (MEK directly activates ERK). Our analyses provide evidence that 1) ErbB2 overexpression transforms transient, EGF-induced signaling into sustained signaling; 2) EGF- and HRG-induced ERK activation have different sensitivity to UO126; and 3) contrary to the current view, ERK is activated by not one, but two UO126-sensitive mechanisms, and activation of the two mechanisms is ligand-dependent. Additional analysis providing mechanistic insight into these three main findings will be discussed in the presentation.

Conclusions

Biological signal transduction networks are complex, dynamic systems, whose analyses are greatly facilitated by quantitative modeling in conjunction with traditional experimental techniques. In this work we use a combined computational and experimental approach to analyze ligand-dependent responses in the ErbB signaling network, and our results give insight into how 1) ErbB2 overexpression can lead to ErbB signaling network deregulation; and 2) the effect of ErbB signaling network inhibitors can be ligand-dependent. The results of this study, and further implementation of our approach in the ErbB and other signaling systems have the promise to inform the development of new, targeted pharmaceuticals for cancer treatment, and strategies to administer these pharmaceuticals.

References

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