Frequency of breast cancer subtypes among African American women in the AMBER consortium

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. KirkXuezheng Sun, Edward A. Ruiz-Narvaez, Charles M. Perou, Julie R. Palmer, Andrew F. Olshan, Christine B. Ambrosone, Melissa A. Troester

Research output: Contribution to journalArticle

3 Citations (Scopus)

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 (< 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.

Original languageEnglish (US)
Article number12
JournalBreast Cancer Research
Volume20
Issue number1
DOIs
StatePublished - Feb 6 2018

Fingerprint

African Americans
Epidemiology
Breast Neoplasms
Estrogen Receptors
Neoplasms
Progesterone Receptors
Microarray Analysis
Biomarkers
Immunohistochemistry
Epidermal Growth Factor Receptor
Keratin-6
Keratin-5
Medical Records
Logistic Models
Lymph Nodes
RNA
Staining and Labeling
human ERBB2 protein

Keywords

  • African American
  • Automated digital pathology
  • Basal-like
  • Immunohistochemistry
  • Luminal
  • PAM50

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Cite this

Allott, E. H., Geradts, J., Cohen, S. M., Khoury, T., Zirpoli, G. R., Bshara, W., ... Troester, M. A. (2018). Frequency of breast cancer subtypes among African American women in the AMBER consortium. Breast Cancer Research, 20(1), [12]. https://doi.org/10.1186/s13058-018-0939-5

Frequency of breast cancer subtypes among African American women in the AMBER consortium. / Allott, Emma H.; Geradts, Joseph; Cohen, Stephanie M.; Khoury, Thaer; Zirpoli, Gary R.; Bshara, Wiam; Davis, Warren; Omilian, Angela; Nair, Priya; Ondracek, Rochelle P.; Cheng, Ting Yuan David; Miller, C. Ryan; Hwang, Helena; Thorne, Leigh B.; O'Connor, Siobhan; Bethea, Traci N.; Bell, Mary E.; Hu, Zhiyuan; Li, Yan; Kirk, Erin L.; Sun, Xuezheng; Ruiz-Narvaez, Edward A.; Perou, Charles M.; Palmer, Julie R.; Olshan, Andrew F.; Ambrosone, Christine B.; Troester, Melissa A.

In: Breast Cancer Research, Vol. 20, No. 1, 12, 06.02.2018.

Research output: Contribution to journalArticle

Allott, EH, Geradts, J, Cohen, SM, Khoury, T, Zirpoli, GR, Bshara, W, Davis, W, Omilian, A, Nair, P, Ondracek, RP, Cheng, TYD, Miller, CR, Hwang, H, Thorne, LB, O'Connor, S, Bethea, TN, Bell, ME, Hu, Z, Li, Y, Kirk, EL, Sun, X, Ruiz-Narvaez, EA, Perou, CM, Palmer, JR, Olshan, AF, Ambrosone, CB & Troester, MA 2018, 'Frequency of breast cancer subtypes among African American women in the AMBER consortium', Breast Cancer Research, vol. 20, no. 1, 12. https://doi.org/10.1186/s13058-018-0939-5
Allott, Emma H. ; Geradts, Joseph ; Cohen, Stephanie M. ; Khoury, Thaer ; Zirpoli, Gary R. ; Bshara, Wiam ; Davis, Warren ; Omilian, Angela ; Nair, Priya ; Ondracek, Rochelle P. ; Cheng, Ting Yuan David ; Miller, C. Ryan ; Hwang, Helena ; Thorne, Leigh B. ; O'Connor, Siobhan ; Bethea, Traci N. ; Bell, Mary E. ; Hu, Zhiyuan ; Li, Yan ; Kirk, Erin L. ; Sun, Xuezheng ; Ruiz-Narvaez, Edward A. ; Perou, Charles M. ; Palmer, Julie R. ; Olshan, Andrew F. ; Ambrosone, Christine B. ; Troester, Melissa A. / Frequency of breast cancer subtypes among African American women in the AMBER consortium. In: Breast Cancer Research. 2018 ; Vol. 20, No. 1.
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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 (< 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.",
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T1 - Frequency of breast cancer subtypes among African American women in the AMBER consortium

AU - Allott, Emma H.

AU - Geradts, Joseph

AU - Cohen, Stephanie M.

AU - Khoury, Thaer

AU - Zirpoli, Gary R.

AU - Bshara, Wiam

AU - Davis, Warren

AU - Omilian, Angela

AU - Nair, Priya

AU - Ondracek, Rochelle P.

AU - Cheng, Ting Yuan David

AU - Miller, C. Ryan

AU - Hwang, Helena

AU - Thorne, Leigh B.

AU - O'Connor, Siobhan

AU - Bethea, Traci N.

AU - Bell, Mary E.

AU - Hu, Zhiyuan

AU - Li, Yan

AU - Kirk, Erin L.

AU - Sun, Xuezheng

AU - Ruiz-Narvaez, Edward A.

AU - Perou, Charles M.

AU - Palmer, Julie R.

AU - Olshan, Andrew F.

AU - Ambrosone, Christine B.

AU - Troester, Melissa A.

PY - 2018/2/6

Y1 - 2018/2/6

N2 - 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.

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