Breast cancer gene 1 (BRCA1) mutations predispose women to breast and ovarian cancers and men to increased risks for prostate cancer. We have previously showed BRCA1 splice variant BRCA1a/p110 to induce apoptosis of human breast cancer cells. In the current study, stable expression of BRCA1a/p110 resulted in inhibition of growth of estrogen receptor (ER)-positive and triplenegative (TN) human breast, ovarian, prostate and colon cancer cells and mouse fibroblast cells. Similar to wildtype BRCA1, only those cells with wild-type Rb were sensitive to BRCA1a-induced growth suppression and the status of p53 did not affect the ability of BRCA1a to suppress growth of tumor cells. BRCA1a also significantly inhibited tumor mass in nude mice bearing human CAL-51 TN breast cancer, ES-2 ovarian cancer and PC-3 prostate cancer xenografts. These results suggest that the majority of exon 11 sequences (residues 263-1365) are not required for the tumor suppressor function of BRCA1 proteins. This is the first report demonstrating antitumor activity of BRCA1a in human ER-positive and TN breast, hormoneindependent ovarian and prostate cancer cells. Currently, there are no effective treatments against TN breast cancers and results from these studies will provide new treatments for one of the biggest needs in breast cancer research.

Since the introduction of systemic adjuvant chemotherapy (ACT) and endocrine therapy in the early 1970s, the determination of risk of recurrence and death from breast cancer became a critical piece of information in the selection of the optimal postoperative treatment strategy. Classical histopathological prognostic factors included tumor size, regional lymph node metastases and number of axillary nodes involved, tumor grade, presence of lymphovascular invasion, and, more recently, estrogen receptor (ER) and progesterone receptor status, measurement of proliferative activity (S-phase fraction, mitotic index, Ki-67), and HER2 overexpression/amplification. As isolated factors, they have limited predictive ability in the case of individual patients. For that reason, prognostic indices were developed. The most successful is Adjuvant!Online, an online nomogram developed by Peter Ravdin. This nomogram incorporates tumor size, axillary nodal status, tumor grade, ER status, age and comorbidity. The nomogram will provide an assessment of recurrence and mortality rates at 10 years, including deaths due to comorbid conditions. In addition, the nomogram also calculates relative and absolute benefit from various adjuvant interventions: tamoxifen, aromatase inhibitors, and first-generation, secondgeneration and third-generation ACT regimens. The prognostic and predictive value of this nomogram has been externally validated, with a margin of error ≤1%. Over the past decade, high-throughput technologies have been developed based on gene expression profiling. These include between two and a couple of hundred genes, and have the ability to separate patients with excellent outcomes from those with higher risk. One of these prognostic profiles has been externally validated and is currently undergoing testing for clinical utility in a large, multicenter, prospective randomized trial (MINDACT). Another approach was based on prospectively identifying a set of genes from the literature and from the results of gene expression profiling. Mathematical modeling then led to the selection of 16 genes related to cell proliferation, ER-driven genes, HER2 and proteases, as well as five 'housekeeping' genes (OncotypeDx). This assay is based on RT-PCR, is reproducible and applicable to archival, paraffin-embedded material, and has been shown to predict prognosis in patients with lymph-nodenegative, ER-positive primary breast cancer. Further testing indicated that the assay might also predict sensitivity to tamoxifen, or firstgeneration adjuvant chemotherapy. This assay is also under evaluation for clinical utility in a large, multicenter, prospective randomized trial (TailoRx). Whether these multigene predictors of prognosis will have greater utility than Adjuvant!Online remains to be determined. In the meantime, exploratory analyses are ongoing to identify reliable predictors of response to individual drugs and modern combination drug regimens. These are expected to lead to individualized selection of treatment, or personalized medicine.