Assessment of automated image analysis of breast cancer tissue microarrays for epidemiologic studies

Background: A major challenge in studies of etiologic heterogeneity in breast cancer has been the limited throughput, accuracy, and reproducibility of measuring tissue markers. Computerized image analysis systems may help address these concerns, but published reports of their use are limited. We assessed agreement between automated and pathologist scores of a diverse set of immunohistochemical assays done on breast cancer tissue microarrays (TMA). Methods: TMAs of 440 breast cancers previously stained for estrogen receptor (ER)-a, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), ER-β, and aromatase were independently scored by two pathologists and three automated systems (TMALab II, TMAx, and Ariol). Agreement between automated and pathologist scores of negative/positive was measured using the area under the receiver operating characteristics curve (AUC) and weighted κ tatistics for categorical scores. We also investigated the correlation between immunohistochemical scores and mRNA expression levels. Results: Agreement between pathologist and automated negative/positive and categorical scores was excellent for ER-a and PR (AUC range = 0.98-0.99; κ range = 0.86-0.91). Lower levels of agreement were seen for ER-β categorical scores (AUC = 0.99-1.0; κ = 0.80-0.86) and both negative/positive and categorical scores for aromatase (AUC = 0.85-0.96; κ = 0.41-0.67) and HER2 (AUC = 0.94-0.97; κ= 0.53-0.72). For ER-α and PR, there was a strong correlation between mRNA levels and automated (ρ = 0.67-0.74) and pathologist immunohistochemical scores (ρ = 0.67-0.77). HER2 mRNA levels were more strongly correlated with pathologist (ρ = 0.63) than automated immunohistochemical scores (ρ = 0.41-0.49). Conclusions: Automated analysis of immunohistochemical markers is a promising approach for scoring large numbers of breast cancer tissues in epidemiologic investigations. This would facilitate studies of etiologic heterogeneity, which ultimately may allow improved risk prediction and better prevention approaches