RESUMEN
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 (< 2%) lacked EGFR and CK5/6 expression, thereby providing little improvement in accuracy for identifying basal-like tumors. Relative to luminal A subtype, all other subtypes had higher combined grade and were larger, and ER-/HER2+ tumors were more often lymph node positive and late stage tumors. The frequency of basal-like tumors was 31%, exceeded only slightly by luminal A tumors (37%). CONCLUSIONS: Our findings indicate that automated IHC-based classification produces tumor subtype frequencies approximating those from PAM50-based classification and highlight high frequency of basal-like and low frequency of luminal A breast cancer in a large study of African American women.
Asunto(s)
Neoplasias de la Mama/genética , Receptor ErbB-2/genética , Receptores de Estrógenos/genética , Receptores de Progesterona/genética , Adulto , Negro o Afroamericano/genética , Anciano , Biomarcadores de Tumor/genética , Neoplasias de la Mama/clasificación , Neoplasias de la Mama/patología , Femenino , Regulación Neoplásica de la Expresión Génica , Humanos , Inmunohistoquímica/métodos , Antígeno Ki-67/genética , Persona de Mediana Edad , Clasificación del TumorRESUMEN
Nuclear morphometry is used to address subtleties of carcinogenesis; it has been proposed for evaluating chemoprevention. An important issue for morphometry concerns control for extraneous sources of variation: fixation, slide cutting and staining. A common strategy has been to standardize the morphometric measures. Morphometric variables--such features as mean nuclear size and staining intensity--are often combined into multivariate indices. In this paper, we consider these variables one by one; any index is to a significant degree dependent on the individual indicators. This paper considers the extent to which statistical adjustment adds to the informational utility of individual indicators. We consider 14 features of 934 prostatic nuclei diagnosed by a single pathologist (Rodolfo Montironi) within a region of either normal tissue or high-grade prostatic intraepithelial neoplasia (HGPIN). HGPIN, a precursor to prostate cancer (PC), has been suggested as a target for PC chemoprevention. We consider a range of adjustment methods: transforming variables into deviations from means or from expected values generated by regression analysis. Our major test of standardization utility is the ability of the variables to deemphasize interindividual differences within diagnostic categories but to distinguish between diagnostic categories.