The predictive value of ERICA in breast cancer recurrence. A univariate and multivariate analysis

Abstract

In breast cancer, primary tumor size (T), the number of lymph node metastases (#N), the biochemical estrogen (ER), and progesterone (PGR) receptor status have all been important prognostic variables. We evaluated the significance of the immunocytochemical measurement of estrogen receptors suing the ERICA method. To determine the relative prognostic value of these variables T, #N, ER, PGR, ERICA and adjuvant treatment, (ADJ), univariate and multivariate analyses of disease-free survival (DFS) were performed for 154 primary breast cancer patients who were diagnosed in 1985 to 1986 at Women's College Hospital and followed prospectively. We analyzed ERICA results using different classification systems, and assessed clinical cut points for the univariate and multivariate context. The variables consistently included in the best Cox stepwise regression are T, (p < 0.01), ADJ (p < 0.01), #N (p < 0.01), and ERICA (p < 0.01). There was weaker evidence of an association between DFS and the biochemically determined ER; ER was not included in the model with a cut point at 10 fmol mg of protein. This illustrates the value of the ERICA method in predicting outcome, and suggests the need to consider ERICA values for clinical decision making.