Estrogen Receptor ß-Mediated Inhibition of Actin-Based Cell Migration Suppresses Metastasis of Inflammatory Breast Cancer.

Inflammatory breast cancer (IBC) is a highly metastatic breast carcinoma with high frequency of estrogen receptor α (ERα) negativity. Here we explored the role of the second ER subtype, ERß, and report expression in IBC tumors and its correlation with reduced metastasis. Ablation of ERß in IBC cells promoted cell migration and activated gene networks that control actin reorganization, including G-protein-coupled receptors and downstream effectors that activate Rho GTPases. Analysis of preclinical mouse models of IBC revealed decreased metastasis of IBC tumors when ERß was expressed or activated by chemical agonists. Our findings support a tumor-suppressive role of ERß by demonstrating the ability of the receptor to inhibit dissemination of IBC cells and prevent metastasis. On the basis of these findings, we propose ERß as a potentially novel biomarker and therapeutic target that can inhibit IBC metastasis and reduce its associated mortality. SIGNIFICANCE: These findings demonstrate the capacity of ERß to elicit antimetastatic effects in highly aggressive inflammatory breast cancer and propose ERß and the identified associated genes as potential therapeutic targets in this disease.

Efficient test-based variable selection for high-dimensional linear models.

Variable selection plays a fundamental role in high-dimensional data analysis. Various methods have been developed for variable selection in recent years. Well-known examples are forward stepwise regression (FSR) and least angle regression (LARS), among others. These methods typically add variables into the model one by one. For such selection procedures, it is crucial to find a stopping criterion that controls model complexity. One of the most commonly used techniques to this end is cross-validation (CV) which, in spite of its popularity, has two major drawbacks: expensive computational cost and lack of statistical interpretation. To overcome these drawbacks, we introduce a flexible and efficient test-based variable selection approach that can be incorporated into any sequential selection procedure. The test, which is on the overall signal in the remaining inactive variables, is based on the maximal absolute partial correlation between the inactive variables and the response given active variables. We develop the asymptotic null distribution of the proposed test statistic as the dimension tends to infinity uniformly in the sample size. We also show that the test is consistent. With this test, at each step of the selection, a new variable is included if and only if the p-value is below some pre-defined level. Numerical studies show that the proposed method delivers very competitive performance in terms of variable selection accuracy and computational complexity compared to CV.