Subtype classification for prediction of prognosis of breast cancer from a biomarker panel: correlations and indications

Chuang Chen,1 Jing-Ping Yuan,2,3 Wen Wei,1 Yi Tu,1 Feng Yao,1 Xue-Qin Yang,4 Jin-Zhong Sun,1 Sheng-Rong Sun,1 Yan Li2 1Department of Breast and Thyroid Surgery, Wuhan University, Renmin Hospital, Wuhan, 2Department of Oncology, Zhongnan Hospital of Wuhan University and Hubei Key Laboratory of Tumor...

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Autores principales:	Chen C, Yuan JP, Wei W, Tu Y, Yao F, Yang XQ, Sun JZ, Sun SR, Li Y
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2014
Materias:	Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/cea18547c8cc4e26988c6b8d2e2afa54
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spelling	oai:doaj.org-article:cea18547c8cc4e26988c6b8d2e2afa542021-12-02T08:06:23ZSubtype classification for prediction of prognosis of breast cancer from a biomarker panel: correlations and indications1178-2013https://doaj.org/article/cea18547c8cc4e26988c6b8d2e2afa542014-02-01T00:00:00Zhttp://www.dovepress.com/subtype-classification-for-prediction-of-prognosis-of-breast-cancer-fr-a15916https://doaj.org/toc/1178-2013 Chuang Chen,1 Jing-Ping Yuan,2,3 Wen Wei,1 Yi Tu,1 Feng Yao,1 Xue-Qin Yang,4 Jin-Zhong Sun,1 Sheng-Rong Sun,1 Yan Li2 1Department of Breast and Thyroid Surgery, Wuhan University, Renmin Hospital, Wuhan, 2Department of Oncology, Zhongnan Hospital of Wuhan University and Hubei Key Laboratory of Tumor Biological Behaviors and Hubei Cancer Clinical Study Center, Wuhan, 3Department of Pathology, The Central Hospital of Wuhan, Wuhan, 4Medical School of Jingchu University of Technology, Jingmen, People’s Republic of China Background: Hormone receptors, including the estrogen receptor and progesterone receptor, human epidermal growth factor receptor 2 (HER2), and other biomarkers like Ki67, epidermal growth factor receptor (EGFR, also known as HER1), the androgen receptor, and p53, are key molecules in breast cancer. This study evaluated the relationship between HER2 and hormone receptors and explored the additional prognostic value of Ki67, EGFR, the androgen receptor, and p53. Methods: Quantitative determination of HER2 and EGFR was performed in 240 invasive breast cancer tissue microarray specimens using quantum dot (QD)-based nanotechnology. We identified two subtypes of HER2, ie, high total HER2 load (HTH2) and low total HER2 load (LTH2), and three subtypes of hormone receptor, ie, high hormone receptor (HHR), low hormone receptor (LHR), and no hormone receptor (NHR). Therefore, breast cancer patients could be divided into five subtypes according to HER2 and hormone receptor status. Ki67, p53, and the androgen receptor were determined by traditional immunohistochemistry techniques. The relationship between hormone receptors and HER2 was investigated and the additional value of Ki67, EGFR, the androgen receptor, and p53 for prediction of 5-year disease-free survival was assessed. Results: In all patients, quantitative determination showed a statistically significant (P<0.001) negative correlation between HER2 and the hormone receptors and a significant positive correlation (P<0.001) between the estrogen receptor and the progesterone receptor (r=0.588), but a significant negative correlation (P<0.001, r=-0.618) with the HHR subtype. There were significant differences between the estrogen receptor, progesterone receptor, and HER2 subtypes with regard to total HER2 load and hormone receptor subtypes. The rates of androgen receptor and p53 positivity were 46.3% and 57.0%, respectively. Other than the androgen receptor, differences in expression of Ki67, EGFR, and p53 did not achieve statistical significance (P>0.05) between the five subtypes. EGFR and Ki67 had prognostic significance for 5-year disease-free survival in univariate analysis, but the androgen receptor and p53 did not. Multivariate analysis identified that EGFR expression had predictive significance for 5-year disease-free survival in hormone-receptor positive patients and in those with the lymph node-positive breast cancer subtype. Conclusion: Hormone receptor expression was indeed one of the molecular profiles in the subtypes identified by quantitative HER2 and vice versa. EGFR status may provide discriminative prognostic information in addition to HER2 and hormone receptor status, and should be integrated into routine practice to help formulate more specific prediction of the prognosis and appropriate individualized treatment. Keywords: quantum dots, breast cancer, molecular classification, prognosis, predictionChen CYuan JPWei WTu YYao FYang XQSun JZSun SRLi YDove Medical PressarticleMedicine (General)R5-920ENInternational Journal of Nanomedicine, Vol 2014, Iss Issue 1, Pp 1039-1048 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920
spellingShingle	Medicine (General) R5-920 Chen C Yuan JP Wei W Tu Y Yao F Yang XQ Sun JZ Sun SR Li Y Subtype classification for prediction of prognosis of breast cancer from a biomarker panel: correlations and indications
description	Chuang Chen,1 Jing-Ping Yuan,2,3 Wen Wei,1 Yi Tu,1 Feng Yao,1 Xue-Qin Yang,4 Jin-Zhong Sun,1 Sheng-Rong Sun,1 Yan Li2 1Department of Breast and Thyroid Surgery, Wuhan University, Renmin Hospital, Wuhan, 2Department of Oncology, Zhongnan Hospital of Wuhan University and Hubei Key Laboratory of Tumor Biological Behaviors and Hubei Cancer Clinical Study Center, Wuhan, 3Department of Pathology, The Central Hospital of Wuhan, Wuhan, 4Medical School of Jingchu University of Technology, Jingmen, People’s Republic of China Background: Hormone receptors, including the estrogen receptor and progesterone receptor, human epidermal growth factor receptor 2 (HER2), and other biomarkers like Ki67, epidermal growth factor receptor (EGFR, also known as HER1), the androgen receptor, and p53, are key molecules in breast cancer. This study evaluated the relationship between HER2 and hormone receptors and explored the additional prognostic value of Ki67, EGFR, the androgen receptor, and p53. Methods: Quantitative determination of HER2 and EGFR was performed in 240 invasive breast cancer tissue microarray specimens using quantum dot (QD)-based nanotechnology. We identified two subtypes of HER2, ie, high total HER2 load (HTH2) and low total HER2 load (LTH2), and three subtypes of hormone receptor, ie, high hormone receptor (HHR), low hormone receptor (LHR), and no hormone receptor (NHR). Therefore, breast cancer patients could be divided into five subtypes according to HER2 and hormone receptor status. Ki67, p53, and the androgen receptor were determined by traditional immunohistochemistry techniques. The relationship between hormone receptors and HER2 was investigated and the additional value of Ki67, EGFR, the androgen receptor, and p53 for prediction of 5-year disease-free survival was assessed. Results: In all patients, quantitative determination showed a statistically significant (P<0.001) negative correlation between HER2 and the hormone receptors and a significant positive correlation (P<0.001) between the estrogen receptor and the progesterone receptor (r=0.588), but a significant negative correlation (P<0.001, r=-0.618) with the HHR subtype. There were significant differences between the estrogen receptor, progesterone receptor, and HER2 subtypes with regard to total HER2 load and hormone receptor subtypes. The rates of androgen receptor and p53 positivity were 46.3% and 57.0%, respectively. Other than the androgen receptor, differences in expression of Ki67, EGFR, and p53 did not achieve statistical significance (P>0.05) between the five subtypes. EGFR and Ki67 had prognostic significance for 5-year disease-free survival in univariate analysis, but the androgen receptor and p53 did not. Multivariate analysis identified that EGFR expression had predictive significance for 5-year disease-free survival in hormone-receptor positive patients and in those with the lymph node-positive breast cancer subtype. Conclusion: Hormone receptor expression was indeed one of the molecular profiles in the subtypes identified by quantitative HER2 and vice versa. EGFR status may provide discriminative prognostic information in addition to HER2 and hormone receptor status, and should be integrated into routine practice to help formulate more specific prediction of the prognosis and appropriate individualized treatment. Keywords: quantum dots, breast cancer, molecular classification, prognosis, prediction
format	article
author	Chen C Yuan JP Wei W Tu Y Yao F Yang XQ Sun JZ Sun SR Li Y
author_facet	Chen C Yuan JP Wei W Tu Y Yao F Yang XQ Sun JZ Sun SR Li Y
author_sort	Chen C
title	Subtype classification for prediction of prognosis of breast cancer from a biomarker panel: correlations and indications
title_short	Subtype classification for prediction of prognosis of breast cancer from a biomarker panel: correlations and indications
title_full	Subtype classification for prediction of prognosis of breast cancer from a biomarker panel: correlations and indications
title_fullStr	Subtype classification for prediction of prognosis of breast cancer from a biomarker panel: correlations and indications
title_full_unstemmed	Subtype classification for prediction of prognosis of breast cancer from a biomarker panel: correlations and indications
title_sort	subtype classification for prediction of prognosis of breast cancer from a biomarker panel: correlations and indications
publisher	Dove Medical Press
publishDate	2014
url	https://doaj.org/article/cea18547c8cc4e26988c6b8d2e2afa54
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Subtype classification for prediction of prognosis of breast cancer from a biomarker panel: correlations and indications

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