The significance of GATA3 expression in breast cancer: a 10-year follow-up study.

GATA3 is a transcription factor closely associated with estrogen receptor alpha in breast carcinoma, with a potential prognostic utility. This study investigated the immunohistochemical expression of GATA3 in estrogen receptor alpha-positive and estrogen receptor alpha-negative breast carcinomas. One hundred sixty-six cases of invasive breast carcinomas with 10-year follow-up information were analyzed. Positive GATA3 and estrogen receptor alpha cases were defined as greater than 20% of cells staining. Time to cancer recurrence and time to death were analyzed with survival methods. Of 166 patients, 40 were estrogen receptor alpha negative and 121 estrogen receptor alpha positive. Thirty-eight (23%) recurrences and 51 (31%) deaths were observed. In final multivariable analyses, GATA3-positive tumors had about two thirds the recurrence risk of GATA3-negative tumors (hazard ratio = 0.65, P = .395) and comparable mortality risk (hazard ratio = 0.86, P = .730). In prespecified subgroup analyses, the protective effect of GATA3 expression was most pronounced among estrogen receptor alpha-positive patients who received tamoxifen (hazard ratio = 0.57 for recurrence and 0.68 for death). We found no statistically significant differences in recurrence or survival rates between GATA3-positive and GATA3-negative tumors. However, there was a suggestion of a modest-to-strong protective effect of GATA3 expression among estrogen receptor alpha-positive patients receiving hormone therapy.

The molecular portraits of breast tumors are conserved across microarray platforms.

BACKGROUND: Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. RESULTS: A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. CONCLUSION: This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile.