EGFR signaling pathways are wired differently in normal 184A1L5 human mammary epithelial and MDA-MB-231 breast cancer cells

Because of differences in the downstream signaling patterns of its pathways, the role of the human epidermal growth factor family of receptors (HER) in promoting cell growth and survival is cell line and context dependent. Using two model cell lines, we have studied how the regulatory interaction network among the key proteins of HER signaling pathways may be rewired upon normal to cancerous transformation. We in particular investigated how the transcription factor STAT3 and several key kinases’ involvement in cancer-related signaling processes differ between normal 184A1L5 human mammary epithelial (HME) and MDA-MB-231 breast cancer epithelial cells. Comparison of the responses in these cells showed that normal-to-cancerous cellular transformation causes a major re-wiring of the growth factor initiated signaling. In particular, we found that: i) regulatory interactions between Erk, p38, JNK and STAT3 are triangulated and tightly coupled in 184A1L5 HME cells, and ii) STAT3 is only weakly associated with the Erk-p38-JNK pathway in MDA-MB-231 cells. Utilizing the concept of pathway substitution, we predicted how the observed differences in the regulatory interactions may affect the proliferation/survival and motility responses of the 184A1L5 and MDA-MB-231 cells when exposed to various inhibitors. We then validated our predictions experimentally to complete the experiment-computation-experiment iteration loop. Validated differences in the regulatory interactions of the 184A1L5 and MDA-MB-231 cells indicated that instead of inhibiting STAT3, which has severe toxic side effects, simultaneous inhibition of JNK together with Erk or p38 could be a more effective strategy to impose cell death selectively to MDA-MB-231 cancer cells while considerably lowering the side effects to normal epithelial cells. Presented analysis establishes a framework with examples that would enable cell signaling researchers to identify the signaling network structures which can be used to predict the phenotypic responses in particular cell lines of interest.