Cancer proteomics encompasses the identification and quantitative analysis of differentially expressed proteins relative to healthy tissue counterparts at different stages of disease, from preneoplasia to neoplasia. Proteomic technologies have emerged as an important addition to the genomic and anti- body-based technologies for the diagnosis of cancer. Important technologies include 2-D gel electrophoresis, mass spectrometry, laser capture microdissection, detection of molec- ular markers of cancer and protein patterns. Proteomics holds great promise in contributing to the prevention and cure of cancer because it provides unique tools for discovery of biomarkers and therapeutic targets. As such, proteomics can help translate basic science discoveries into the clinical practice of personalized medicine. Genomics provides an overview of the complete set of genetic instructions provided by the DNA, while transcriptomics looks into gene expression patterns. Proteomics studies dynamic protein products and their interactions, while metabolomics is also an intermediate step in understanding organism's entire metabolism. Moreover, although studies focusing on detecting the differential expression of mRNA have been extremely informative, they do not necessarily correlate with the functional protein concentrations. Macromolecules, in general, and proteins, in particular, are highly dynamic molecules. Mechanistically, proteins can be subjected to extensive functional regulation by various processes such as proteolytic degradation, posttranslational modification, involvement in complex structures, and compartmentalization. Proteomics is concerned with studying the whole protein repertoire of a defined entity, be it a biological fluid, an organelle, a cell, a tissue, an organ, a system, or the whole organism. Therefore, in-depth studying of proteomics profiles of various biospecimens obtained from cancer patients are expected to increase our understanding of tumor pathogenesis, monitoring, and the identification of novel targets for cancer therapy. In addition, an essential goal for applying proteomics to study cancers is to adapt its high-throughput tools for regular use in clinical laboratories for the purpose of diagnostic and prognostic categorization of cancers, as well as in assessing various cancer therapeutic regimens.
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 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.