BREAST CANCER IS ONE OF THE MOST PREVALENT HUman cancers and ranks second as a cause of cancer death in women. In 2006, approximately 212 000 new cases of invasive breast cancer were diagnosed in the United States, and each year more than 40 000 women die of the disease. To date, therapeutic decisions for locally advanced breast cancer are mainly guided by clinicopathological parameters, such as patient age and functional status, comorbidities, estrogen receptor status, tumor grade, tumor size, and lymph node status. This results in inaccurate risk estimation in which some patients in the early stage of disease are overtreated and experience undeserved adverse effects. In addition, there is substantial variation in outcome among patients with similar clinicopathological disease characteristics. This medical challenge calls for better understanding of this disease and refined risk estimation to improve treatment efficacy and patient quality of life. The advent of microarrays allows for a simultaneous screen of gene expression patterns on a genome-wide scale and has become one of the most widely used technologies to study the molecular biology of human disease. The application of this methodology to cancer research has demonstrated its potential for refining cancer diagnosis and outcome prognostication at large. Breast cancer is one of the cancers for which gene expression profiling has identified distinct molecular entities associated with differential prognosis and response to cytotoxic agents. In this issue of JAMA, Acharya and colleagues report on the use of gene expression signatures to refine risk estimation and therapeutic decision making in a multiinstitutional panel of 964 breast cancer cases. This is one of the largest studies in human cancer showing the ability of gene expression profiles to improve risk stratification beyond established risk assessment algorithms that take into account clinicopathological variables. Distinct from other breast cancer microarray studies, the study by Acharya et al did not develop another novel gene signature. Instead, by combining gene expression data for a set of oncogenic and tumor microenvironment–related genes previously identified to be associated with poor prognosis in breast cancer, the investigators showed that molecular profiles can be useful to refine risk estimation in 3 risk subgroups (low, intermediate, and high), which they have defined deliberately based on established clinicopathological variables, by using the Adjuvant! Online scoring tool in an initial training set of 573 patients. Subsets of patients with comparably poor and good prognosis were identified for each clinicopathological subgroup. The prognostic power of the gene signature was subsequently validated in an independent test set of 391 patients. Close examination of expression patterns for each individual gene does not reveal a clear distinction between the various prognostic subgroups. However, as stated by the authors, it is the aggregation of the gene signature that provides the prognostic power. This observation is consistent with most other cancer microarray studies, which have commonly used so-called metagenes—patterns of gene expression—for risk classification. Although such metagenes lend support to the notion that in a complex human disease, it is not a single gene but rather multiple genes that account for the disease process, the risk associations of metagenes must be considered purely correlative and thus only of predictive value. Metagenes identify gene subsets without considering underlying mechanisms; therefore, there is low potential to aid biological interpretation. Molecular predictor models that place genes into pathways and networks allow moving from association to mechanism. The mechanistic insights that are gained are crucial for novel molecularly targeted therapies. In contrast with other microarray studies, Acharya et al have chosen to build on previous knowledge and to select a parsimonious multigene predictor that has been well characterized in breast cancer in terms of its oncogenic mechanisms. This gene set should thus represent an attractive target of future therapeutic modulation. Although the findings are encouraging, several factors may confound the interpretation of this study. One issue is why the authors chose to apply clinicopathological risk group-