Performance of Three-Biomarker Immunohistochemistry for Intrinsic Breast Cancer Subtyping in the AMBER Consortium

Background: Classification of breast cancer into intrinsic subtypes has clinical and epidemiologic importance. To examine accuracy of IHC-based methods for identifying intrinsic subtypes, a three-biomarker IHC panel was compared with the clinical record and RNA-based intrinsic (PAM50) subtypes. Methods: Automated scoring of estrogen receptor (ER), progesterone receptor (PR), and HER2 was performed on IHC-stained tissue microarrays comprising 1,920 cases from the African American Breast Cancer Epidemiology and Risk (AMBER) consortium. Multiple cores (1-6/case) were collapsed to classify cases, and automated scoring was compared with the clinical record and to RNA-based subtyping. Results: Automated analysis of the three-biomarker IHC panel produced high agreement with the clinical record (93% for ER and HER2, and 88% for PR). Cases with low tumor cellularity and smaller core size had reduced agreement with the clinical record. IHC-based definitions had high agreement with the clinical record regardless of hormone receptor positivity threshold (1% vs. 10%), but a 10% threshold produced highest agreement with RNA-based intrinsic subtypes. Using a 10% threshold, IHC-based definitions identified the basal-like intrinsic subtype with high sensitivity (86%), although sensitivity was lower for luminal A, luminal B, and HER2-enriched subtypes (76%, 40%, and 37%, respectively). Conclusion: Three-biomarker IHC-based subtyping has reasonable accuracy for distinguishing basal-like from nonbasal-like, although additional biomarkers are required for accurate classification of luminal A, luminal B, and HER2-enriched cancers.