White Laboratory

The focus of research in the White lab is the quantitative analysis of protein phosphorylation events regulating signal transduction cascades associated with cancer and other biological processes. With our mass spectrometry-based technology, analysis of protein phosphorylation occurs on a global scale, allowing for quantitative mapping of complex signal transduction cascades in a variety of biological samples. Currently, we are applying this technology to understand signaling processes regulating biological response to exogenous stimuli in a variety of cancer models systems. Although a significant amount of work will be done in human cell lines, we will also analyze signaling networks in tumors derived from mouse cancer models, with the eventual goal of analyzing staged human clinical samples. Elucidation of signal transduction cascades involved in oncogenesis, cancer progression, and metastasis will generate both novel drug targets and a host of biological markers, allowing for early diagnosis and tracking of cancer progression. A variety of other applications will be pursued, including mapping the phosphorylation events associated with development of Type I and Type II diabetes.

The goal of research in the White lab is to understand how protein phosphorylation-mediated signaling networks drive biological responses to cellular stimulation. If we take a cue-signal-response view of biological systems, we can present the systems with different cues, such as agonists or antagonists, over-expression, mutation, or knock-down of components in the network and monitor biological responses including proliferation, cell motility, endocytosis, and invasiveness. Quantification of the signaling networks which result from each of these cues and drive the corresponding biological response should provide key information regarding the mechanism by which the cue relates to the response. A protein may have multiple phosphorylation sites which control different biological functions and show unique phosphorylation dynamics. A site-specific high-resolution map of the signaling network, with associated temporal dynamics, will enable improved computational modeling of the systems and provide predictive power for more effective targeted interventions in aberrant signaling networks.

Within this framework, a significant fraction of research in the group is centered on the Epidermal Growth Factor Receptor (EGFR) signaling network, quantifying temporal dynamics of protein phosphorylation within the EGFR network while monitoring changes in the network induced by perturbations at the ligand and receptor level. The goal of this research is to answer several questions in oncogenic signaling: how does the EGFR signaling network change when different ligands (e.g. EGF, TGF-alpha, heregulin) are used to stimulate EGFR or EGFR family members, how do mutations within EGFR or over-expression of EGFR family members affect the signaling network, and what role does the EGFR signaling network play in cancer progression?

T cell signaling is another focus area within the lab, specifically aimed (1) at the signaling networks involved in T cell response to peptide ligand stimulation, with the goal of identifying defective signaling processes which may lead to autoimmune disorders such as Type 1 diabetes, and (2) at signaling networks downstream of IL-2, IL-15, CD3, and CD28 stimulation, with the goal of monitoring the network response to combinations of cytokine stimulations.

To interrogate the signaling networks in these diverse biological systems, we use hybrid quadrupole time-of-flight mass spectrometers coupled with stable-isotope labeling, affinity chromatography, and LC-MS/MS to quantify temporal dynamics of tyrosine phosphorylation on hundreds of proteins simultaneously with site-specific resolution typically from several million cells. After gathering and analyzing the data, we are working with the Lauffenburger and Tidor labs in the Biological Engineering Division at MIT to develop better methods of computational analysis and modeling of signaling networks. These models will then be used to predict biological and signaling network responses to additional perturbations to the system.