Identification of a robust gene signature that predicts breast cancer outcome in independent data sets.

BACKGROUND: Breast cancer is a heterogeneous disease, presenting with a wide range of histologic, clinical, and genetic features. Microarray technology has shown promise in predicting outcome in these patients. METHODS: We profiled 162 breast tumors using expression microarrays to stratify tumors based on gene expression. A subset of 55 tumors with extensive follow-up was used to identify gene sets that predicted outcome. The predictive gene set was further tested in previously published data sets. RESULTS: We used different statistical methods to identify three gene sets associated with disease free survival. A fourth gene set, consisting of 21 genes in common to all three sets, also had the ability to predict patient outcome. To validate the predictive utility of this derived gene set, it was tested in two published data sets from other groups. This gene set resulted in significant separation of patients on the basis of survival in these data sets, correctly predicting outcome in 62-65% of patients. By comparing outcome prediction within subgroups based on ER status, grade, and nodal status, we found that our gene set was most effective in predicting outcome in ER positive and node negative tumors. CONCLUSION: This robust gene selection with extensive validation has identified a predictive gene set that may have clinical utility for outcome prediction in breast cancer patients.

Differentiation of lobular versus ductal breast carcinomas by expression microarray analysis.

Invasive lobular and ductal breast tumors have distinct histologies and clinical presentation. Other than altered expression of E-cadherin, little is known about the underlying biology that distinguishes the tumor subtypes. We used cDNA microarrays to identify genes differentially expressed between lobular and ductal tumors. Unsupervised clustering of tumors failed to distinguish between the two subtypes. Prediction analysis for microarrays (PAM) was able to predict tumor type with an accuracy of 93.7%. Genes that were significantly differentially expressed between the two groups were identified by MaxT permutation analysis using t tests (20 cDNA clones and 10 unique genes), significance analysis for microarrays (33 cDNA clones and 15 genes, at an estimated false discovery rate of 2%), and PAM (31 cDNAs and 15 genes). There were 8 genes identified by all three of these related methods (E-cadherin, survivin, cathepsin B, TPI1, SPRY1, SCYA14, TFAP2B, and thrombospondin 4), and an additional 3 that were identified by significance analysis for microarrays and PAM (osteopontin, HLA-G, and CHC1). To validate the differential expression of these genes, 7 of them were tested by real-time quantitative PCR, which verified that they were differentially expressed in lobular versus ductal tumors. In conclusion, specific changes in gene expression distinguish lobular from ductal breast carcinomas. These genes may be important in understanding the basis of phenotypic differences among breast cancers.