PURPOSE: This study was designed to evaluate whether patients with an unfavorable breast cancer prognosis could be identified by p53 protein overexpression detected by a quantitative enzyme-linked immunoabsorbent assay (ELISA). PATIENTS AND METHODS: Extracts from 998 breast carcinomas were assayed for p53 protein by an ELISA that used both DO-1 monoclonal and CM-1 polyclonal antibodies. Relative risks (RRs) for cancer relapse and death after 6 years of follow-up for patients with p53-positive tumors based on different dichotomization criteria were determined by multivariate Cox regression, adjusted for patient age, tumor size, S-phase fraction, estrogen (ER) and progesterone (PR) receptor concentrations, DNA ploidy, and lymph node metastases. Disease-free (DFS) and overall (OS) survival probabilities of p53-positive and p53-negative groups, using a median cutoff, were also estimated by the Kaplan-Meier method and the log-rank test. These analyses were performed for all patients and for subgroups defined by ER status, node status, and primary postoperative treatment. RESULTS: Univariate analysis showed that p53 concentrations that exceeded the median indicated significantly increased risks for relapse (P < .01) and death (P = .02). Multivariate analyses confirmed these observations (RR = 1.40; P = .02 for DFS and RR = 1.50; P < .01 for OS) and showed trends for increasing risks for relapse (P = .02) and death (P = .06) when p53 was considered as a four-level categoric variable, and identified p53 positivity as a significant predictor of outcome in node-positive patients (RR = 1.67; P < .01 and RR = 2.10; P < .01 for DFS and OS, respectively), ER-positive patients (RR = 1.45; P = .02 and RR = 1.50; P = .01 for DFS and OS, respectively), and in patients treated with chemotherapy (RR = 1.73; P = .04 for relapse and RR = 2.04; P = .03 for death). CONCLUSION: Assessment of p53 overexpression by ELISA, easily incorporated into the routine biochemical work-up of breast tumors, may be an independent predictor of reduced survival of breast cancer patients.
PURPOSE: This study was designed to evaluate whether patients with an unfavorable breast cancer prognosis could be identified by p53 protein overexpression detected by a quantitative enzyme-linked immunoabsorbent assay (ELISA). PATIENTS AND METHODS: Extracts from 998 breast carcinomas were assayed for p53 protein by an ELISA that used both DO-1 monoclonal and CM-1 polyclonal antibodies. Relative risks (RRs) for cancer relapse and death after 6 years of follow-up for patients with p53-positive tumors based on different dichotomization criteria were determined by multivariate Cox regression, adjusted for patient age, tumor size, S-phase fraction, estrogen (ER) and progesterone (PR) receptor concentrations, DNA ploidy, and lymph node metastases. Disease-free (DFS) and overall (OS) survival probabilities of p53-positive and p53-negative groups, using a median cutoff, were also estimated by the Kaplan-Meier method and the log-rank test. These analyses were performed for all patients and for subgroups defined by ER status, node status, and primary postoperative treatment. RESULTS: Univariate analysis showed that p53 concentrations that exceeded the median indicated significantly increased risks for relapse (P < .01) and death (P = .02). Multivariate analyses confirmed these observations (RR = 1.40; P = .02 for DFS and RR = 1.50; P < .01 for OS) and showed trends for increasing risks for relapse (P = .02) and death (P = .06) when p53 was considered as a four-level categoric variable, and identified p53 positivity as a significant predictor of outcome in node-positive patients (RR = 1.67; P < .01 and RR = 2.10; P < .01 for DFS and OS, respectively), ER-positive patients (RR = 1.45; P = .02 and RR = 1.50; P = .01 for DFS and OS, respectively), and in patients treated with chemotherapy (RR = 1.73; P = .04 for relapse and RR = 2.04; P = .03 for death). CONCLUSION: Assessment of p53 overexpression by ELISA, easily incorporated into the routine biochemical work-up of breast tumors, may be an independent predictor of reduced survival of breast cancerpatients.
Authors: V L Seewaldt; K Mrózek; R Sigle; E C Dietze; K Heine; D M Hockenbery; K B Hobbs; L E Caldwell Journal: J Cell Biol Date: 2001-10-22 Impact factor: 10.539
Authors: J M Ferrero; M C Etienne; J L Formento; M Francoual; P Rostagno; I Peyrottes; F Ettore; E Teissier; P Leblanc-Talent; M Namer; G Milano Journal: Br J Cancer Date: 2000-01 Impact factor: 7.640