Single markers are insufficient to accurately assess risk of relapse for adjuvant therapy guidance in operable breast cancer patients. In addition, the accuracy and interpretability of current multi-marker tests is generally limited by their simply additive algorithms and their overlap with clinicopathologic risks. Here, we report the development and validation of a nonlinear algorithm that combines protein (ER, PGR, ERBB2, BCL2 and TP53) and genomic (MYC/8q24) markers with standard clinicopathologic features (tumor size, tumor grade and nodal status) into a global risk assessment profile. The algorithm was trained using statistical pattern recognition in 200 stage I–III hormone receptor-positive patients treated with hormone therapy. Continuous risk scores (0–10+) were then generated for 232 independent patients. In hormone therapy-treated patients, the profile achieved a hazard ratio of 6.2 (95% confidence interval [CI], 1.8–20) in high- vs. low-risk groups for time to distant metastasis with the low-risk group having a 10-year metastasis rate of just 4% (95% CI, 0–8%). Similar results were achieved in untreated patients and for disease-specific survival. In multivariate analyses with standard prognostic factors and clinical practice guidelines, the profile was the only significant variable. Furthermore, the profile reclassified as low risk over half of node-negative patients at elevated risk according to the guidelines, which could have spared such patients from unnecessary cytotoxic chemotherapy. It also accurately identified a group of high-risk patients within a guideline low-risk group. In summary, the profile intelligently combines biologically relevant marker pathways and established clinicopathologic risks to help guide breast cancer patients to the most appropriate level of adjuvant therapy. © 2008 Wiley-Liss, Inc.