Breast cancer is noted for disparate clinical behaviors and patient outcomes, despite common histopathological features at diagnosis. Molecular pathogenesis studies suggest that breast cancer is a collection of diseases with variable molecular underpinnings that modulate therapeutic responses, disease-free intervals, and long-term survival. Traditional therapeutic strategies for individual patients are guided by the expression status of the estrogen and progesterone receptors (ER and PR) and human epidermal growth factor receptor 2 (HER2). Although such methods for clinical classification have utility in selection of targeted therapies, short-term patient responses and long-term survival remain difficult to predict. Molecular signatures of breast cancer based on complex gene expression patterns have utility in prediction of long-term patient outcomes, but are not yet used for guiding therapy. Examination of the correspondence between these methods for breast cancer classification reveals a lack of agreement affecting a significant percentage of cases. To realize true personalized breast cancer therapy, a more complete analysis and evaluation of the molecular characteristics of the disease in the individual patient is required, together with an understanding of the contributions of specific genetic and epigenetic alterations (and their combinations) to management of the patient. Here, we discuss the molecular and cellular heterogeneity of breast cancer, the impact of this heterogeneity on practical breast cancer classification, and the challenges for personalized breast cancer treatment.

us-map.html ¶ A list of severe manifestations of monkeypox can be found at https://emergency. cdc.gov/han/2022/han00475.asp. ** During the study period and as of October 21, 2022, CDC was notified by state and local jurisdictions of five decedents whose death certificates included monkeypox as a cause of death or contributing factor, six decedents whose cause of death is still under active investigation, and one decedent in whom the death was not monkeypox-related. Additional monkeypox cases involving severe disease or death might not be included in this report if CDC has not yet been notified about the case or if the case occurred outside of the study period.

In this study assessing the IOR of MSRSGC categories, fair agreement and slight agreement were found for the AUS and SUMP categories, respectively. Observers preferentially used the AUS or benign neoplasm category for SUMP cases, perhaps because of unfamiliarity with SUMP as a diagnostic option. The initial adoption of a new reporting system will require a quality assessment to ensure that the system is reliable and useful for clinicians. Cancer Cytopathol 2018;126:390-6. © 2018 American Cancer Society.

Supplementary data are available at Bioinformatics online.

Background Classification of breast cancer into intrinsic subtypes has clinical and epidemiologic importance. To examine accuracy of immunohistochemistry (IHC)-based methods for identifying intrinsic subtypes, a three-biomarker IHC panel was compared to the clinical record and RNA-based intrinsic (PAM50) subtypes. Methods Automated scoring of estrogen receptor (ER), progesterone receptor (PR) and HER2 was performed on IHC-stained tissue microarrays (TMAs) comprising 1,920 cases from the African American Breast Cancer Epidemiology and Risk (AMBER) consortium. Multiple cores (1–6/case) were collapsed to classify cases, and automated scoring was compared to the clinical record and to RNA-based subtyping. Results Automated analysis of the three-biomarker IHC panel produced high agreement with the clinical record (93% for ER and HER2, and 88% for PR). Cases with low tumor cellularity and smaller core size had reduced agreement with the clinical record. IHC-based definitions had high agreement with the clinical record regardless of hormone receptor positivity threshold (1% vs. 10%), but a 10% threshold produced highest agreement with RNA-based intrinsic subtypes. Using a 10% threshold, IHC-based definitions identified the basal-like intrinsic subtype with high sensitivity (86%), while sensitivity was lower for luminal A, luminal B and HER2-enriched subtypes (76%, 40% and 37%, respectively). Conclusion Three-biomarker IHC-based subtyping has reasonable accuracy for distinguishing basal-like from non-basal-like, while additional biomarkers are required for accurate classification of luminal A, luminal B and HER2-enriched cancers. Impact Epidemiologic studies relying on three-biomarker IHC status for subtype classification should use caution when distinguishing luminal A from luminal B and when interpreting findings for HER2-enriched cancers.