Proteins are assembled into complex networks through a variety of protein-protein interactions in both extracellular and intracellular microenvironments. The structural conformation of a protein and the subsequent access of interaction domains (e.g., SH2 and SH3 domains) enable a highly selective recognition between protein partners in a communication circuit of protein-protein interactions. Proteins can undergo conformational changes that functionally permit or prevent protein activity within networks. Conformational changes are largely dictated by posttranslational modifications that include phosphorylation, cleavage, acetylation, glycosylation, and ubiq-uitinylation. Such modifications functionally define regulated protein-protein interactions through specific domain binding, which then controls the information flow from the extracellular space to the nucleus. These signaling networks regulate key biologic processes defining cell function within larger tissue- and organ-specific contexts. In cancer, specific protein-signaling networks are typically deranged, resulting in unregulated proliferation, aberrant differentiation, and immortality, which ultimately underpins the malignant process. Aberrant activity through specific signaling pathways can be monitored by evaluating the phosphorylation of proteins within key nodes as a multiplexed kinase substrate assay. This can be achieved by using antibodies that recognize the active form (e.g., phosphorylated) of a protein versus the inactive form (e.g., unphosphorylated). Disruption of key regulated protein-protein interactions in diseased cells very often serves as an important indicator of drug-therapy targets .
Coupled with LCM, RPAs offer the advantage of allowing (a) the evaluation of native proteins in normal and diseased cells and (b) the posttranslational modifications associated with protein-protein interactions [16, 21], under the assumption that information flow through a specific "node" in the proteomic network requires the phosphorylation of a known protein at a specific amino acid sequence. By measuring the proportion of those protein molecules that are phosphorylated, we can infer the level of activity of the upstream kinase at that node. For example, we can infer MEK kinase activity by measuring phosphorylation of ERK. If we compare this measurement over time, or at stages of disease progression, or before and after treatment, a correlation can be made between the activity of the kinase and the biologic or disease state. The development of highly sensitive protein microarrays now makes it possible to profile the states of dozens of kinase substrates at once and to provide information about entire protein-signal pathways in tissue biopsies, aspirates, or body fluid samples.
The application of this technology to clinical molecular diagnostics is greatly enhanced by increasing the number of high-quality antibodies that are specific for the modification or activation state of target proteins within key pathways. Antibody specificity is particularly critical given the complex array of biological proteins at the vastly different concentrations contained within cell lysates. Given that there are no standard PCR-like direct amplification methods for proteins, the sensitivity of antibodies must be achieved in the near-femtomolar range. Moreover, the labeling and amplification method must be linear and reproducible. A cubic centimeter of biopsy tissue contains approximately 109 cells; in contrast, a needle biopsy or cell aspirate contains fewer than 100,000 cells. If the cell population of the specimen is heterogeneous, the final number of actual tumor cells microdissected or procured for analysis can be as low as a few thousand. Assuming that the proteins of interest, and their phosphorylated counterparts, exist in low abundance, the total concentration of analyte proteins in the sample will be very low. Newer generations of protein microarrays, combined with highly sensitive and specific validated antibodies, are now able to achieve adequate levels of sensitivity for analysis of clinical specimens.
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