Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 h. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences.
Insulin (Ins)1 and type I insulin-like growth factor (IGF1) receptors (InsR and IGF1R, respectively) are receptor tyrosine kinases that are expressed in almost all types of cells. Signaling through InsR and IGF1R initiates a phosphorylation cascade that drives cell growth and proliferation (1-8). Overexpression of these receptors is correlated with higher breast cancer risk (9 -15) and has been shown to influence tumorigenesis, metastasis, and resistance to existing forms of cancer therapy (10,16,17). IGF receptor blockade can slow tumor growth and metastasis, but the receptor has proven to be difficult to target specifically (18 -20). A confounding factor in developing therapies targeting these receptors is their high sequence (ϳ60%) and structural homology. IGF1R and InsR are able to form functional hybrids, and each can partially compensate for the loss or suppression of the other (1-4, 21-24). Moreover, it has been shown that IGF1R signaling is one mechanism of resistance to conventional hormonal therapy (19,(25)(26)(27)(28)(29). Understanding the relationships between IGF1R and InsR signaling cascades and their combinatorial role in cancer is crucial to developing better diagnostics and personalized treatments for cancer.