No abstract

In females, X chromosome inactivation (XCI) begins with the expression of the XIST gene from the X chromosome destined to be inactivated (Xi) and the coating of XIST RNA in cis. It has recently been reported that this process is supported by the product of the BRCA1 tumor suppressor gene and that BRCA1 À/À À À / À À cancers show Xi chromatin structure defects, thus suggesting a role of XCI perturbation in BRCA1-mediated tumorigenesis. Using a combined genetic and epigenetic approach, we verified the occurrence of XCI in BRCA1À and BRCA1 wt breast cancer cell lines. It was ascertained that the Xi was lost in all cancer cell lines, irrespective of the BRCA1 status and that more than one active X (Xa) was present. In addition, no epigenetic silencing of genes normally subjected to XCI was observed. We also evaluated XIST expression and found that XIST may be occasionally transcribed also from Xa. Moreover, in one of the BRCA1 wt cell line the restoring of XIST expression using a histone deacetylase inhibitor, did not lead to XCI. To verify these findings in primary tumors, chromosome X behavior was investigated in a few BRCA1-associated and BRCA1-not associated primary noncultured breast carcinomas and the results mirrored those obtained in cancer cell lines. Our findings indicate that the lack of XCI may be a frequent phenomenon in breast tumorigenesis, which occurs independently of BRCA1 status and XIST expression and is due to the loss of Xi and replication of Xa and not to the reactivation of the native Xi. (Cancer Res 2005; 65(6): 2139-46)

No abstract

Data from microarray studies have been used to develop predictive models for treatment outcome in breast cancer, such as a recently proposed predictive model for antiestrogen response after tamoxifen treatment that was based on the expression ratio of two genes. We attempted to validate this model on an independent cohort of 58 patients with resectable estrogen receptor-positive breast cancer. We measured expression of the genes HOXB13 and IL17BR with real time-quantitative polymerase chain reaction and assessed the association between their expression and outcome by use of univariate logistic regression, area under the receiver-operating-characteristic curve (AUC), a two-sample t test, and a Mann-Whitney test. We also applied standard supervised methods to the original microarray dataset and to another independent dataset from similar patients to estimate the classification accuracy obtainable by using more than two genes in a microarray-based predictive model. We could not validate the performance of the two-gene predictor on our cohort of samples (relation between outcome and the following genes estimated by logistic regression: for HOXB13, odds ratio [OR] = 1.04, 95% confidence interval [CI] = 0.92 to 1.16, P = .54; for IL17BR, OR = 0.69, 95% CI = 0.40 to 1.20, P = .18; and for HOXB13/IL17BR, OR = 1.30, 95% CI = 0.88 to 1.93, P = .18). Similar results were obtained with the AUC, a two-sample two-sided t test, and a Mann-Whitney test. In addition, estimates of classification accuracies applied to two independent microarray datasets highlighted the poor performance of treatment-response predictive models that can be achieved with the sample sizes of patients and informative genes to date.