Proteomic studies have generated numerous datasets of potential diagnostic, prognostic, and therapeutic significance in human cancer. Two key technologies underpinning these studies in cancer tissue are two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and mass spectrometry (MS). Although surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF)-MS is the mainstay for serum or plasma analysis, other methods including isotope-coded affinity tag technology, reverse-phase protein arrays, and antibody microarrays are emerging as alternative proteomic technologies. Because there is little overlap between studies conducted with these approaches, confirmation of these advanced technologies remains an elusive goal. This problem is further exacerbated by lack of uniform patient inclusion and exclusion criteria, low patient numbers, poor supporting clinical data, absence of standardized sample preparation, and limited analytical reproducibility (in particular of 2D-PAGE). Despite these problems, there is little doubt that the proteomic approach has the potential to identify novel diagnostic biomarkers in cancer. In therapeutic proteomics, the challenge is significant due to the complexity systems under investigation (i.e., cells generate over 10(5) different polypeptides). However, the most significant contribution of therapeutic proteomics research is expected to derive not from single experiments, but from the synthesis and comparison of large datasets obtained under different conditions (e.g., normal, inflammation, cancer) and in different tissues and organs. Thus, standardized processes for storing and retrieving data obtained with different technologies by different research groups will have to be developed. Shifting the emphasis of cancer proteomics from technology development and data generation to careful study design, data organization, formatting, and mining is crucial to answer clinical questions in cancer research.