11013 Background: While the TNM method for assessment of stage in breast cancer is simple and robust, molecular methods for limited patient subsets are gaining popularity (i.e Oncotype Dx or Mammaprint). We hypothesized that multiplexed quantitative measurement of proteins known to be involved in breast cancer signaling pathways of growth, proliferation, survival, and metastasis can enhance clinical methods of predicting prognosis in all patients. Methods: We assessed the expression of twenty-three proteins (ER, PR, EGFR, HER2, HER3, HER4, ERK, PTEN, PI3Kp85α, PI3Kp110α, p27/Kip1,EIF4E, FOXO3,AKT1, AKT2, AKT3, MYC, cyclinD1, FOXO1, mTOR, p70S6Kb, NFkB and BCL2) in four subcellular compartments by automated quantitative analysis of protein expression (AQUA) on tissue microarrays of the archival Yale breast cancer cohort (n=676). To project future performance of these markers including clinicopathological parameters, we constructed univariate and multivariate logistic regression models using leave-one-out cross-validation and calculated prediction error (PE) estimates of each model's value to predict a binary endpoint of 10 year survival. In addition, we constructed univariate and multivariate Cox models of ten year disease specific survival (DSS). Results: By Cox univariate analysis, ER, PR, PTEN, and BCL2 were directly correlated with DSS, while FOXO1, HER2, HER3, and PI3Kp110α were inversely correlated with DSS. A five-variable logistic regression model of 10 year survival including nuclear AKT1, BCL2, nuclear FOXO1, cytoplasmic mTOR, and nuclear p70S6Kb (prediction error= .274) surpasses performance of TNM staging (PE=.367) and the Nottingham Prognostic Index (PE=.326). The same model is associated with 10-year DSS by Cox proportional hazard (p=<.00001) independent of TNM stage and NPI. Conclusions: Our protein-based, multiplexed approach to prognostic classification was superior to traditional methods (TNM or NPI) and single biomarkers in this retrospective cohort. Current outcomes are influenced by modern therapies, limiting the direct impact of this analysis. However, molecular profiling of primary tumor linked to outcome paves the way for the incorporation of new prognostic models into prospective studies. [Table: see text]