Molecular basis for estrogen receptor alpha deficiency in BRCA1-linked breast cancer

Background BRCA1-mutant breast tumors are typically estrogen receptor alpha (ER alpha) negative, whereas most sporadic tumors express wild-type BRCA1 and are ER alpha positive. We examined a possible mechanism for the observed ER alpha-negative phenotype of BRCA1-mutant tumors.

Methods We used a breast cancer disease-specific microarray to identify transcripts that were differentially expressed between paraffin-embedded samples of 17 BRCA1-mutant and 14 sporadic breast tumors. We measured the mRNA levels of estrogen receptor 1 (ESR1) ( the gene encoding ER alpha), which was differentially expressed in the tumor samples, by quantitative polymerase chain reaction. Regulation of ESR1 mRNA and ER alpha protein expression was assessed in human breast cancer HCC1937 cells that were stably reconstituted with wild-type BRCA1 expression construct and in human breast cancer T47D and MCF-7 cells transiently transfected with BRCA1-specific short-interfering RNA ( siRNA). Chromatin immunoprecipitation assays were performed to determine if BRCA1 binds the ESR1 promoter and to identify other interacting proteins. Sensitivity to the antiestrogen drug fulvestrant was examined in T47D and MCF-7 cells transfected with BRCA1-specific siRNA. All statistical tests were two-sided.

Results Mean ESR1 gene expression was 5.4-fold lower in BRCA1-mutant tumors than in sporadic tumors ( 95% confidence interval [CI]=2.6-fold to 40.1-fold, P =.0019). The transcription factor Oct-1 recruited BRCA1 to the ESR1 promoter, and both BRCA1 and Oct-1 were required for ER alpha expression. BRCA1-depleted breast cancer cells expressing exogenous ER alpha were more sensitive to fulvestrant than BRCA1-depleted cells transfected with empty vector ( T47D cells, the mean concentration of fulvestrant that inhibited the growth of 40% of the cells [IC40] for empty vector versus ER alpha: > 10(-5) versus 8.0 x 10(-9) M [ 95% CI=3.1x10(-10) to 3.2 x 10(-6) M]; MCF-7 cells, mean IC40 for empty vector versus ER alpha : > 10(-5) versus 4.9 x 10(-8) M [ 95% CI=2.0 x 10(-9) to 3.9 x 10(-6) M]).

Conclusions BRCA1 alters the response of breast cancer cells to antiestrogen therapy by directly modulating ER alpha expression.

Monotonically normal topological vector spaces are stratifiable

We prove that for any Hausdorff topological vector space E over the field R there exists A subset of E such that E is homeomorphic to a subset of A x R and A x R is homeomorphic to a subset of E. Using this fact we prove that E is monotonically normal if and only if E is stratifiable.

Support vector machine classification for large data sets via minimum enclosing ball clustering

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.