Mass profiling-directed isolation and identification of a stage-specific serologic protein biomarker of advanced...

Carcinoma of the prostate (CaP) is the second leading cause of cancer-related mortality among American men. While high cure rates are associated with localized CaP, no cure exists for advanced recurrent disease. At present there are no known serologic biomarkers specific to this stage of the disease. Several groups have used mass spectrometry (MS) based mass profiling (MP) combined with multivariate analysis to identify diagnostically predictive protein peaks for CaP in serum and tissues. Nevertheless, an appreciable level of skepticism exists for MP attributed primarily to a lack of definitive protein characterization. To address this problem, we have applied an approach that combines MP with a whole-protein based top-down separation strategy for the identification of a stage-specific marker in a group comprising 16 patients with CaP (metastatic and localized disease) and 15 healthy individuals. MP, combined with multivariate analysis, yielded 17 serum proteins specific to metastatic disease. A single protein detected at m/z 7771 was found to be significantly decreased in the sera of all the metastatic CaP patients but not in localized CaP or healthy individuals. This protein was therefore chosen as the primary candidate for further analysis. The complex nature of the serologic proteome necessitated an isolation strategy that included a C18 prefractionation, followed by multidimensional liquid chromatography and, finally, two-dimensional gel electrophoresis. The separation process was monitored by UV-Vis and matrix-assisted laser desorption/ionization-time of flight MS analysis. This strategy was found to greatly facilitate subsequent MS characterization of the unknown protein, which was identified as platelet factor 4, a chemokine with prothrombolytic and antiangiogenic activities. Confirmation was achieved using both Western blot analysis and enzyme-linked immunosorbent assay. With the growing interest in using MP for patient classification and diagnosis, our approach and its variations should be powerful in the separation and characterization of proteins following MP.