Clear identification among early-stage cancer patients of those at highest risk of having metastatic disease would be of great benefit in treatment planning and management. Considerable additional benefit would accrue to high-risk patients if their responses to specific therapeutic alternatives could be predicted. Molecular biomarkers in the form of gene expression profiles are proving to be more effective tools for both prognostic and predictive patient stratification than more traditional methods such as patient demographics and histopathology indicators. Such biomarkers must be clinically validated before they can be effectively used to manage patients in clinical studies or clinical practice. This can be most efficiently accomplished by analyzing archived clinical samples with well-characterized clinical outcomes. Doing studies of this type requires reoptimization of traditional molecular expression profiling techniques to analyze RNA from fixed paraffin-embedded tissues. We have modified molecular methods for RNA extraction, RNA quantification, reverse transcription, and quantitative PCR to work optimally in archived clinical samples in order to develop a clinically validated assay for breast cancer prognosis and prediction of patient response to hormonal and chemotherapy.