Our lab is focused on development and application of proteomics-based methods for in-depth analysis of key molecular events in tumorogenesis. Given the highly dynamic nature of biological processes, we are particularly interested in novel and robust strategies for measuring relative changes in both protein expression levels and post-translational modification state of the proteome in response to perturbation. Limited dynamic range is a common obstacle encountered during proteomics-driven analyses of complex mixtures; often times, changes in low abundance proteins are critical to an observed phenotype but are undetectable by proteomics experiments performed in the context of a given biological milieu. We are pursuing a number of different strategies to overcome this hurdle, including: 1.) enrichment of protein classes of interest, either through isolation of cell compartments, or chromatographic- and affinity-based methods; 2.) improved mass spectrometry technology, through internal efforts and via industrial collaboration; 3.) development of novel data processing and bioinformatics algorithms to more efficiently analyze the vast quantity of data generated in global proteomics experiments.
We currently use cell lines as model systems to query relative protein expression and phosphorylation events critical to aberrant signaling, blocked differentiation, and other functional hallmarks of cancer. In principle proteomics-based methods provide a highly parallel readout of multiple biologically relevant events in a single experiment. Collectively these data provide a detailed view of key molecular mechanisms in cancer initiation and progression and can also facilitate drug target discovery and improved characterization of small molecule-based therapeutics. In the future we expect to leverage continued improvements in proteomics methods, in combination with other “-omics” technologies, to transition into analysis of clinical samples and development of new diagnostic protocols to better detect and characterize cancer onset and progression.
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