In men, prostate carcinoma is the first cancer and the second cause of death by cancer. It is a slowly evolving disease with no prognostic marker of poor outcome.
Currently, the Prostate Specific Antigen (PSA) is the only available biological marker. It is a tissue marker and not a tumoral pathology control.
This is why new tissue markers are urgently needed to select patients with unfavourable evolution, in order to treat them rapidly by more effective methods such as chemotherapy, hormonotherapy or radiotherapy. This could improve survival time and quality of life.
Proteomic and clinical data comparison could point to new relevant molecules and permit the development of new biological tests for routine use.
SELDI-TOF-MS (Surface Enhanced Laser Desorption/Ionisation Mass Spectrometry) permits an extremely sensitive analysis of proteins. This method has been substantially ratified by the literature and a number of markers have already been identified, particularly for several cancer pathologies.
As far as prostate carcinomas are concerned, previous proteomic researches on serum have led to diagnostic parameters, differentiating healthy patients, patients with benign lesion and patients having malignant lesions. However, at present, no relevant protein has been identified. Moreover, no study has been carried out to characterize fast-developing lesions, in order to anticipate response to treatments.
The main objective of this study is to realise serum protein profiles for each patient undergoing a prostate biopsy and to identify relevant proteins.
The main judgement criteria will be intensity peaks in the protein profile (area and height) with reference to combined criteria (PSA rate, clinical stage, Gleason score).
Two groups will be compared:
* Group 1: Control (negative biopsy).
* Group 2: Prostate carcinoma (positive biopsy).
This group will be subdivided:
* Group 2a : favourable prognostic according to AMICO classification
* Group 2b : intermediate or unfavourable prognostic according to AMICO classification
This will contribute to setting up an aftercare database combining clinical data with biological data and protein profile.
