Prediction in colorectal cancer using proteomics

Background: Colorectal cancer is the second leading cause of cancer related death. Current clinical practice in colorectal cancer screening (FOBT, Colonoscopy) has contributed to a reduction of mortality. However, despite these screening programs, about 70% of carcinomas are detected at advanced tumor stages (UICC III/IV) presenting poor patient prognosis. Thus, innovative tools and methodologies for early cancer detection can directly result in improving patient survival rates. Methods: Here we report our results of a comprehensive approach for colorectal cancer biomaker identification in tissue and blood with a main emphasis on two-dimensional gelelectrophoresis (2- DE) and mass-spectrometry analyses. Results: Proteomics-based technologies enable to identify single proteins and groups of proteins, including isoforms and post-translationally modified variants that are highly correlated to malignant transformation and clinical tumor aggressiveness. In addition, our 2-DE data strongly indicate that macroscopically and microscopically uneffected mucosa of patients later developing familial adenomatous polyposis (FAP) exhibit a specific protein expression pattern, which may allow to diagnose the disease at its earliest stage. This observation indicates for the first time a possibility to discriminate between clinically silent and clinically active hereditary disease. Furthermore, the detection of serum-based protein expression pattern that allow distinction of healthy and diseased patients in the peripheral blood can ameliorate existing screening programs in a minimally-invasive and patient friendly fashion. Conclusions: Proteomics-based technologies could greatly improve common classification systems, diagnostics and prediction. However, this progress has not yet transferred from bench to bedside but could open the door to a more accurate and target specific personalized medicine with improved patient survival.