Intratumoral heterogeneity as a source of discordance in breast cancer biomarker classification

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  • Allott, Emma H
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Geradts, Joseph
    • Other Affiliation: Department of Pathology, Brigham & Women’s Hospital, Boston, MA, USA
  • Sun, Xuezheng
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Cohen, Stephanie M
    • Affiliation: N.C. Cancer Hospital, UNC Lineberger Comprehensive Cancer Center, School of Medicine
  • Zirpoli, Gary R
    • Other Affiliation: Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, USA
  • Khoury, Thaer
    • Other Affiliation: Department of Pathology, Roswell Park Cancer Institute, Buffalo, NY, USA
  • Bshara, Wiam
    • Other Affiliation: Department of Pathology, Roswell Park Cancer Institute, Buffalo, NY, USA
  • Chen, Mengjie
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics, School of Medicine, Department of Genetics
  • Sherman, Mark E
    • Other Affiliation: Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
  • Palmer, Julie R
    • Other Affiliation: Slone Epidemiology Center, Boston University, Boston, MA, USA
  • Ambrosone, Christine B
    • Other Affiliation: Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, USA
  • Olshan, Andrew
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Troester, Melissa
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
Abstract
  • Abstract Background Spatial heterogeneity in biomarker expression may impact breast cancer classification. The aims of this study were to estimate the frequency of spatial heterogeneity in biomarker expression within tumors, to identify technical and biological factors contributing to spatial heterogeneity, and to examine the impact of discordant biomarker status within tumors on clinical record agreement. Methods Tissue microarrays (TMAs) were constructed using two to four cores (1.0 mm) for each of 1085 invasive breast cancers from the Carolina Breast Cancer Study, which is part of the AMBER Consortium. Immunohistochemical staining for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) was quantified using automated digital imaging analysis. The biomarker status for each core and for each case was assigned using clinical thresholds. Cases with core-to-core biomarker discordance were manually reviewed to distinguish intratumoral biomarker heterogeneity from misclassification of biomarker status by the automated algorithm. The impact of core-to-core biomarker discordance on case-level agreement between TMAs and the clinical record was evaluated. Results On the basis of automated analysis, discordant biomarker status between TMA cores occurred in 9 %, 16 %, and 18 % of cases for ER, PR, and HER2, respectively. Misclassification of benign epithelium and/or ductal carcinoma in situ as invasive carcinoma by the automated algorithm was implicated in discordance among cores. However, manual review of discordant cases confirmed spatial heterogeneity as a source of discordant biomarker status between cores in 2 %, 7 %, and 8 % of cases for ER, PR, and HER2, respectively. Overall, agreement between TMA and clinical record was high for ER (94 %), PR (89 %), and HER2 (88 %), but it was reduced in cases with core-to-core discordance (agreement 70 % for ER, 61 % for PR, and 57 % for HER2). Conclusions Intratumoral biomarker heterogeneity may impact breast cancer classification accuracy, with implications for clinical management. Both manually confirmed biomarker heterogeneity and misclassification of biomarker status by automated image analysis contribute to discordant biomarker status between TMA cores. Given that manually confirmed heterogeneity is uncommon (<10 % of cases), large studies are needed to study the impact of heterogeneous biomarker expression on breast cancer classification and outcomes.
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  • Article
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  • In Copyright
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  • The Author(s).
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  • English
Bibliographic citation
  • Breast Cancer Research. 2016 Jun 28;18(1):68
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  • BioMed Central

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