Details

Autor(en) / Beteiligte

• Ornstein, David K
• Petricoin, 3rd, Emmanuel F

Titel

Proteomics to diagnose human tumors and provide prognostic information

Ist Teil von

• Oncology (Williston Park, N.Y.), 2004-04, Vol.18 (4), p.521-9; discussion 529-32

Ort / Verlag

United States

Erscheinungsjahr

2004

Quelle

Electronic Journals Library

Beschreibungen/Notizen

• Proteomics is a rapidly emerging scientific discipline that holds great promise in identifying novel diagnostic and prognostic biomarkers for human cancer. Technologic improvements have made it possible to profile and compare the protein composition within defined populations of cells. Laser capture microdissection is a tool for procuring pure populations of cells from human tissue sections to be used for downstream proteomic analysis. Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) has been used traditionally to separate complex mixtures of proteins. Improvements in this technology have greatly enhanced resolution and sensitivity providing a more reproducible and comprehensive survey. Image analysis software and robotic instrumentation have been developed to facilitate comparisons of complex protein expression patterns and isolation of differentially expressed proteins spots. Differential in-gel electrophoresis (DIGE) facilitates protein expression by labeling different populations of proteins with fluorescent dyes. Isotope-coded affinity tagging (ICAT) uses mass spectroscopy for protein separation and different isotope tags for distinguishing populations of proteins. Although in the past proteomics has been primarily used for discovery, significant efforts are being made to develop proteomic technologies into clinical tools. Reverse-phase protein arrays offer a robust new method of quantitatively assessing expression levels and the activation status of a panel of proteins. Surface-enhanced laser-desorption/ionization time-of-flight (SELDI-TOF) mass spectroscopy rapidly assesses complex protein mixtures in tissue or serum. Combined with artificial intelligence-based pattern recognition algorithms, this emerging technology can generate highly accurate diagnostic information. It is likely that mass spectroscopy-based serum proteomics will evolve into useful clinical tools for the detection and treatment of human cancers.