A computational and experimental study of HER2-signaling effects on cellular migration and proliferation
Massachusetts Institute of Technology. Dept. of Chemical Engineering.
Douglas A. Lauffenburger.
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The fundamental question posed in this thesis is: how does a cell 'decide' to behave in a particular way? The human body is comprised of [approx.] 1014 cells that interpret extracellular information and respond with such behavior as migration, proliferation, apoptosis, or differentiation. Thirty years of research in the related fields of biochemistry, molecular biology, and genetics have demonstrated that, in most cases, the cellular decision-making process cannot be described or predicted by regulation of only one gene or one protein alone. Instead, it has become clear that cellular behavior is a function of information flow through multiple intracellular molecules. Furthermore, the molecules responsible for the control of cell behavior comprise a surprisingly short list, indicating that factors such as signaling dynamics and intensity coupled with combinatorial control are essential to produce the wide array of observed cell behavior. The identification of protein kinases as transducers of large amounts of intracellular information led us to pose the hypothesis that the quantitative regulation of key kinases governs cellular behavior. The goal of this thesis was to identify rules governing multi-kinase behavioral control and to then, on the basis of these rules, predict changes in cell function in response to changes in receptor expression, ligand treatment, and pharmacological intervention.(cont.) A human mammary epithelial cell (HMEC) system with varying levels of the human epidermal growth factor receptor 2 (HER2) was chosen to explore cell decision processes. HER2 overexpression is found in 30% of breast cancers and correlates with poor prognosis and increased metastasis. In particular, we investigated the effects of HER2 overexpression on signaling networks and resultant cell proliferation and migration in the presence of epidermal growth factor (EGF) or heregulin (HRG), two EGFR-family ligands that promote HER2 heterodimerization. To investigate HER2-mediated signaling and cell behavior we developed and applied high-throughput experimental techniques to measure kinase activity and phosphorylation as well as cell proliferation and migration. Measurement of -~100 different kinases downstream of HER2 resulted in the identification of network signaling mechanisms. Application of a novel high-throughput migration assay enabled the identification of HER2-mediated increases in cell migration due to increases in the directional persistence of movement. Linear mapping techniques related to partial least squares regression (PLSR) defined and predicted cell behavior in response to HER2 overexpression.(cont.) Combining quantitative datasets of both biological signals and behavior using PLSR, we identified subsets of kinase phosphorylation events that most critically regulate HER2-mediated migration and proliferation. Importantly, we demonstrated that our models provide predictive ability through a priori predictions of cell behavior in HER2-overexpressing cells. Application of linear models in response to pharmacological inhibition resulted in the a priori prediction of cell migration, and identified an EGFR kinase inhibitor Gefitinib as a potent inhibitor of HER2-mediated migration. In conclusion, the application of computational linear modeling to quantitative biological signaling and behavior datasets captured systems-level regulation of cell behavior and, based on this, predicted cell migration and proliferation in response to HER2 overexpression and pharmacological inhibition. Further application of quantitative measurement together with linear modeling should enable the identification of salient cell signal-cell response elements to understand how cells make decisions and to predict how those decisions can be therapeutically manipulated.
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, February 2007.Includes bibliographical references.
DepartmentMassachusetts Institute of Technology. Dept. of Chemical Engineering.
Massachusetts Institute of Technology