| Autor: |
Emma H. Allott, Joseph Geradts, Stephanie M. Cohen, Thaer Khoury, Gary R. Zirpoli, Wiam Bshara, Warren Davis, Angela Omilian, Priya Nair, Rochelle P. Ondracek, Ting-Yuan David Cheng, C. Ryan Miller, Helena Hwang, Leigh B. Thorne, Siobhan O’Connor, Traci N. Bethea, Mary E. Bell, Zhiyuan Hu, Yan Li, Erin L. Kirk, Xuezheng Sun, Edward A. Ruiz-Narvaez, Charles M. Perou, Julie R. Palmer, Andrew F. Olshan, Christine B. Ambrosone, Melissa A. Troester |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
Breast Cancer Research, Vol 20, Iss 1, Pp 1-9 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
1465-542X |
| DOI: |
10.1186/s13058-018-0939-5 |
| Popis: |
Abstract Background Breast cancer subtype can be classified using standard clinical markers (estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2)), supplemented with additional markers. However, automated biomarker scoring and classification schemes have not been standardized. The aim of this study was to optimize tumor classification using automated methods in order to describe subtype frequency in the African American Breast Cancer Epidemiology and Risk (AMBER) consortium. Methods Using immunohistochemistry (IHC), we quantified the expression of ER, PR, HER2, the proliferation marker Ki67, and two basal-like biomarkers, epidermal growth factor receptor (EGFR) and cytokeratin (CK)5/6, in 1381 invasive breast tumors from African American women. RNA-based (prediction analysis of microarray 50 (PAM50)) subtype, available for 574 (42%) cases, was used to optimize classification. Subtype frequency was calculated, and associations between subtype and tumor characteristics were estimated using logistic regression. Results Relative to ER, PR and HER2 from medical records, central IHC staining and the addition of Ki67 or combined tumor grade improved accuracy for classifying PAM50-based luminal subtypes. Few triple negative cases ( |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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