Quantitative imaging of immunocytochemical (pap) estrogen receptor staining patterns in breast cancer sections

Abstract

“Receptogram Analysis” has been developed as a pattern‐oriented approach for predicting endocrine response in breast cancer based upon quantification of the estrogen receptor immunocytochemical assay (ERICA), using a Quantimet Imaging System. Response prediction was evaluated in 58 stage III and IV patients receiving endocrine therapy (primarily Tamoxifen). The Receptogram is a composite of the univariate distributions of nuclear receptor content, IOD(S), and concentration (MOD), and their bivariate contour plot; where (S) is the calculated nuclear radius in section. MOD distributions were classified into four types based upon peak modality and kurtosis (I–IV), and contour plots were classified into four subtypes (A–D) based upon contour slope. Patients failing therapy were ERICA ‐ or their receptogram revealed co‐existent ER+ and ER− tumor cells (type II), highly skewed MOD distributions lacking defined peaks (type IV), or contours with nearly horizontal slope (type C). Response was realized in 9/16 type I patients, with a single positive MOD peak, and in 9/15 type III patients, with discrete, multimodal MOD peaks. In contrast, 0/8 type II, 0/12 type IV, and 0/10 type C patients were responders. Receptogram analysis was superior to cytosol assay (DCC) as a response discriminant: positive predictive value, 53% vs. 33%; negative predictive value, 100% vs. 75%; sensitivity, 100% vs. 83%; specificity, 68% vs. 23%; and accuracy, 78% vs. 41%, respectively. Alternately, patients were assigned to potentially responsive or nonresponsive groups based upon thresholded mean receptor parameters: field MOD, mean nuclear MOD (NMOD), and mean NMOD(PF) where PF is the ER+ nuclear fraction. While these parameters correlated with DCC (r = .72, 0.69, and 0.69), they were only marginally better in predictive value.