Modeling HER2 Effects on Cell Behavior from Mass Spectrometry Phosphotyrosine Data

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Abstract
Cellular behavior in response to stimulatory cues is governed by information encoded within a complex intracellular signaling network. An understanding of how phenotype is determined requires the distributed characterization of signaling processes (e.g., phosphorylation states and kinase activities) in parallel with measures of resulting cell function. We previously applied quantitative mass spectrometry methods to characterize the dynamics of tyrosine phosphorylation in human mammary epithelial cells with varying human epidermal growth factor receptor 2 (HER2) expression levels after treatment with epidermal growth factor (EGF) or heregulin (HRG). We sought to identify potential mechanisms by which changes in tyrosine phosphorylation govern changes in cell migration or proliferation, two behaviors that we measured in the same cell system. Here, we describe the use of a computational linear mapping technique, partial least squares regression (PLSR), to detail and characterize signaling mechanisms responsible for HER2-mediated effects on migration and proliferation. PLSR model analysis via principal component inner products identified phosphotyrosine signals most strongly associated with control of migration and proliferation, as HER2 expression or ligand treatment were individually varied. Inspection of these signals revealed both previously identified and novel pathways that correlate with cell behavior. Furthermore, we isolated elements of the signaling network that differentially give rise to migration and proliferation. Finally, model analysis identified nine especially informative phosphorylation sites on six proteins that recapitulated the predictive capability of the full model. A model based on these nine sites and trained solely on data from a low HER2-expressing cell line a priori predicted migration and proliferation in a HER2-overexpressing cell line. We identify the nine signals as a “network gauge,” meaning that when interrogated together and integrated according to the quantitative rules of the model, these signals capture information content in the network sufficiently to predict cell migration and proliferation under diverse ligand treatments and receptor expression levels. Examination of the network gauge in the context of previous literature indicates that endocytosis and activation of phosphoinositide 3-kinase (PI3K)-mediated pathways together represent particularly strong loci for the integration of the multiple pathways mediating HER2′s control of mammary epithelial cell proliferation and migration. Thus, a PLSR modeling approach reveals critical signaling processes regulating HER2-mediated cell behavior. Cells in the human body interpret extracellular information to “decide” on the execution of particular behaviors such as migration, proliferation, and differentiation. Many diseases, such as cancer, occur when these decision-making processes are compromised. The transfer of extracellular information to the intracellular space is often accomplished through receptor proteins whose chemical properties are altered as extracellular conditions change. These receptors transfer information in the intracellular space through the transfer of phosphate groups from one molecule to another. In particular, the transfer of phosphate groups to tyrosine sites is critical for cellular signaling. How the cell decides to execute a particular behavior on the basis of many changing phosphorylation events, however, is not understood. Here, we apply a computational approach to understand and predict how cells make the decision to migrate and proliferate as extracellular information changes. In particular, we wanted to understand the basis of decision-making processes in cells overexpressing a receptor protein called human epidermal growth factor receptor 2 (HER2). This receptor is overexpressed in ∼30% of breast cancer patients and correlates with poor prognosis. Taking advantage of a recently published dataset that quantified tyrosine phosphorylation events in HER2-overexpressing cells, we created models to understand and to predict HER2-mediated changes in migration and proliferation. The model identified small subsets of measured phosphorylation events that are predictive of changes in behavior with HER2 overexpression. Analysis of the phosphorylated subset of proteins implicated certain cellular processes as being crucial for cellular decision making, and suggested potential biomarkers and targets for therapeutic use in HER2-overexpressing cancers. Further application of our technique should aid in the understanding of cellular decision processes from large sets of cell signal and behavior data.