An analysis of receptor tyrosine kinase signaling in cancer cells: identifying signaling network architectures in a triple negative breast cancer model

Zachary Joseph Speth

Receptor Tyrosine Kinases (RTKs) play an important role in inter and intra-cellular communication, and regulate physiological responses such as cell growth, replication and motility. Most growth factor receptors are RTKs. Growth factor receptor signaling pathways act through multiple signaling intermediates, usually kinases, to elicit their respective associated physiological responses. Kinases are activated or deactivated in response to growth factor receptor mediated activation and are subject to the activity of other kinases. Regulation of a kinase by other kinases is a component of cellular signaling known as cross-talk. In cancerous cells the cross-talk may exhibit significantly different regulatory activity than in normal cells. A significant problem facing the treatment of triple negative breast cancer patients is the ability of multiple receptors to contribute to survival and proliferation of cancer cells in spite of exposure to current cancer treatments. The Epidermal Growth Factor Receptor (EGFR) as well as the Type 1 Insulin-like Growth Factor Receptor (IGF-1R) are two particularly pernicious receptors when expressed in breast cancer cells. In this study the signaling network architecture of a model triple negative cell line expressing EGFR and IGF-1R was examined in response to activation by different families of growth factors which activate either of these receptors. Two computational approaches were used to predict signaling cross-talk, Bayesian Variable Selection Algorithm (BVSA) and Modular Response Analysis (MRA). These approaches complement each other in analysis of likelihoods and relative strengths of individual signaling cross-talk interactions. Results of the analysis show that similar cross-talk responses may be elicited through activation of either receptor, centered around ERK/JNK and Src/STAT3/Akt interaction groups, although other aspects of signaling architecture can vary. In the development of improved cancer treatments the variability of signaling architecture should be an important consideration. While this property of biochemical networks is a barrier hindering effectiveness of current paradigms in cancer treatment, advances could be made through identification of more robust cross-talk interactions. The use of BVSA and MRA can provide the computational firepower to identify the most significant of these interactions. In this study the algorithms predicted signaling interactions with diagnostic significance in triple negative breast cancer cells.

An analysis of receptor tyrosine kinase signaling in cancer cells: identifying signaling network architectures in a triple negative breast cancer model

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