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Hormone receptor status is an integral component of decision-making in breast cancer management. IHC4 score is an algorithm that combines hormone receptor, HER2, and Ki-67 status to provide a semiquantitative prognostic score for breast cancer. High accuracy and low interobserver variance are important to ensure the score is accurately calculated; however, few previous efforts have been made to measure or decrease interobserver variance. We developed a Web-based training tool, called "Score the Core" (STC) using tissue microarrays to train pathologists to visually score estrogen receptor (using the 300-point H score), progesterone receptor (percent positive), and Ki-67 (percent positive). STC used a reference score calculated from a reproducible manual counting method. Pathologists in the Athena Breast Health Network and pathology residents at associated institutions completed the exercise. By using STC, pathologists improved their estrogen receptor H score and progesterone receptor and Ki-67 proportion assessment and demonstrated a good correlation between pathologist and reference scores. In addition, we collected information about pathologist performance that allowed us to compare individual pathologists and measures of agreement. Pathologists' assessment of the proportion of positive cells was closer to the reference than their assessment of the relative intensity of positive cells. Careful training and assessment should be used to ensure the accuracy of breast biomarkers. This is particularly important as breast cancer diagnostics become increasingly quantitative and reproducible. Our training tool is a novel approach for pathologist training that can serve as an important component of ongoing quality assessment and can improve the accuracy of breast cancer prognostic biomarkers., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Although pathologic response to neoadjuvant chemotherapy is highly correlated with survival among patients with osteosarcoma, there are currently no established molecular markers to predict response to chemotherapy. The objective of this study was to investigate the relationship of P16 expression in pretreatment osteosarcoma tumors to pathologic necrotic response after neoadjuvant chemotherapy. A tissue microarray was created from paraffin-embedded pretreatment biopsy specimens of 40 patients with osteosarcoma. Immunohistochemistry was performed with commercially available P16 monoclonal mouse antibody. Expression of P16 was defined as nuclear staining in 30% or greater of cells. Percent tumor necrosis was measured in postchemotherapy resection specimens per established protocols, and 90% or greater tumor necrosis was considered "good." Data were abstracted on age, sex, tumor site, and histologic subtype. Univariate and multivariate analyses were performed. The median age was 15 years, 52% were female, and 35% of tumors were located in the femur. P16 expression was present in 62%. Median posttreatment tumor necrosis was 90%, and 55% of patients experienced "good" chemotherapy response (≥90% necrosis). On univariate analysis, P16 expression correlated positively with median percent necrosis and "good" chemotherapy response (P=.004 and .003, respectively). On logistic regression analysis, P16 expression was independently associated with chemotherapy response after controlling for age, subtype, sex, and location (odds ratio, 43.5; 95% confidence interval, 2.64-708.9; P=.008). In summary, immunohistochemical expression of P16 significantly correlates with chemotherapy response in osteosarcoma. P16 expression may be a useful biomarker to guide treatment selection., (Copyright © 2012 Elsevier Inc. All rights reserved.)