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Objectives Our aim was to explore the performance of TRPS1 as an immunohistochemical diagnostic marker; find the optimal conditions for its use in breast carcinomas, especially triple-negative breast cancers (TNBCs); and compare its results in carcinomas of a select few organ sites, with an emphasis on gynecologic tumors. Methods Tissue microarrays from breast carcinomas (n = 197), endometrial adenocarcinomas (n = 69), ovarian tumors (n = 250), vulvar squamous cell carcinomas (n = 97), pancreatic ductal adenocarcinomas (n = 20), and gastric adenocarcinomas (n = 12) were stained with TRPS1 using 2 different conditions (protocol 1: high pH; protocol 2: low pH). Breast carcinomas consisted of hormone receptor (HR)–positive/ERBB2 (formerly HER2 or HER2/neu)–negative (n = 53) samples, HR-positive/ERBB2-positive (n = 6) samples, and TNBCs (n = 138). Results Comparing TRPS1 results in breast carcinomas vs tumors from other organ sites, the sensitivity of TRPS1 was 91% and 87%, respectively, while the specificity was 66% and 74% for protocol 1 and 2, respectively. For TNBCs vs gynecologic tumors, the sensitivity of TRPS1 was 89% and 85%, respectively, while the specificity was 65% and 73%, respectively. Conclusions TRPS1 stains approximately 90% of breast carcinomas but also up to 71% of endometrial carcinomas, albeit with a weaker median expression. Our data show that although TRPS1 is a highly sensitive marker for TNBCs, it is not as highly specific as previously reported. [ABSTRACT FROM AUTHOR]

Segmenting a broad class of histological structures in transmitted light and/or fluorescence-based images is a prerequisite for determining the pathological basis of cancer, elucidating spatial interactions between histological structures in tumor microenvironments (e.g., tumor infiltrating lymphocytes), facilitating precision medicine studies with deep molecular profiling, and providing an exploratory tool for pathologists. This paper focuses on segmenting histological structures in hematoxylin- and eosin-stained images of breast tissues, e.g., invasive carcinoma, carcinoma in situ, atypical and normal ducts, adipose tissue, and lymphocytes. We propose two graph-theoretic segmentation methods based on local spatial color and nuclei neighborhood statistics. For benchmarking, we curated a data set of 232 high-power field breast tissue images together with expertly annotated ground truth. To accurately model the preference for histological structures (ducts, vessels, tumor nets, adipose, etc.) over the remaining connective tissue and non-tissue areas in ground truth annotations, we propose a new region-based score for evaluating segmentation algorithms. We demonstrate the improvement of our proposed methods over the state-of-the-art algorithms in both region- and boundary-based performance measures. [ABSTRACT FROM PUBLISHER]