Abstract
BACKGROUND: Assessment of outcome using a single prognostic or predictive marker is the current basis of targeted therapy, but is inherently limited by its simplicity. Multiplexing has provided better classification, but has only been done quantitatively using RNA or DNA. Automated quantitative analysis is a new technology that allows quantitative in situ assessment of protein expression. The authors hypothesized that multiplexed quantitative measurement of ErbB receptor family proteins may allow better prediction of outcome. METHODS: The authors quantitatively assessed the expression of 6 proteins in 4 subcellular compartments in 676 patients using breast carcinoma tissue microarrays. Then using Cox proportional hazards modeling and unsupervised hierarchical clustering, they assessed the prognostic value of the expression singly and multiplexed. RESULTS: Epidermal growth factor receptor (EGFR), HER-2, and HER-3 expression were associated with decreased survival. Multivariate analysis showed high HER-2 and HER-3 expression maintained independence as prognostic markers. Hierarchical clustering of expression data defined a small class enriched for HER-2 expression with 40% 10-year survival, compared with 55% using conventional methods. Clustering also revealed a similarly poor-prognostic subgroup coexpressing EGFR and HER-3 (but low for estrogen receptor, progesterone receptor, and HER-2) with a 42% 10-year survival. CONCLUSIONS: This work shows that the combined analysis of protein expression improved prognostic classification, and that multiplexed models were superior to any single-marker-based method for prediction of 10-year survival. These methods illustrate a protein-based, multiplexed approach that could more accurately identify patients for targeted therapies.