Regulation of Differentiation of HC11 Mouse Breast Epithelial Cells by the Signal Transducer and Activator of Transcription-3.

BACKGROUND/AIM: The differentiation of the mouse breast epithelial cell line HC11 is known to require confluence as well as the addition of hydrocortisone, insulin and prolactin. MATERIALS AND METHODS: Since confluence, which triggers the engagement of the cell-to-cell adhesion molecule E-cadherin, induces a dramatic increase in the activity of signal transducer and activator of transcription-3 (Stat3), we examined the role of Stat3 in HC11 cell differentiation. RESULTS: Stat3 inhibition abolished differentiation, indicating that Stat3 activity is critically required. However, expression of the mutationally activated form of Stat3 (Stat3C), rather than promoting, it was found to block cell differentiation, even when expressed in low levels, and in the absence of full neoplastic conversion. CONCLUSION: The strength of the E-cadherin/Stat3 signal is key for the outcome of the differentiation process.

Regulation of HC11 mouse breast epithelial cell differentiation by the E-cadherin/Rac axis.

It was previously demonstrated that differentiation of some established breast epithelial cell lines requires confluence and stimulation with hydrocortisone, insulin and prolactin inducers. We and others previously demonstrated that E-cadherin engagement, which is favored under conditions of confluence, increases the levels and activity of the Rac small GTPase. To investigate the functional relationship between the transforming ability of Rac and its role as an integral component of the differentiative E-cadherin signaling pathway, we introduced a mutationally activated form of Rac, RacV12, into the mouse breast epithelium-derived cell line, HC11. Our results demonstrate that the strength of the Rac signal is key for the outcome of the differentiation process; cRac1 is critically required for differentiation, and at low levels, mutationally activated RacV12 is able to increase differentiation, presumably reinforcing the E-cadherin/Rac differentiative signal. However, high RacV12 expression blocked differentiation concomitant with E-cadherin downregulation, while inducing neoplastic transformation. Therefore, the intensity of the Rac signal is a central determinant in the balance between cell proliferation vs differentiation, two fundamentally opposed processes, a finding which could also have important therapeutic implications.

Dataset of STAT5A status in breast cancer.

We analysed STAT5A gene expression in breast cancer using the Oncomine database. We exemplify four representative studies showing that STAT5A is generally downregulated in breast cancer.

STAT5A is regulated by DNA damage via the tumor suppressor p53.

Here we report that the STAT5A transcription factor is a direct p53 transcriptional target gene. STAT5A is well expressed in p53 wild type cells but not in p53-null cells. Inhibition of p53 reduces STAT5A expression. DNA damaging agents such as doxorubicin also induced STAT5A expression in a p53 dependent manner. Two p53 binding sites were mapped in the STAT5A gene and named PBS1 and PBS2; these sites were sufficient to confer p53 responsiveness in a luciferase reporter gene. Chromatin immunoprecipitation experiments revealed that PBS2 has constitutive p53 bound to it, while p53 binding to PBS1 required DNA damage. In normal human breast lobules, weak p53 staining correlated with regions of intense STAT5A staining. Interestingly, in a cohort of triple negative breast tumor tissues there was little correlation between regions of p53 and STAT5A staining, likely reflecting a high frequency of p53 mutations that stabilize the protein in these tumors. We thus reveal an unexpected connection between cytokine signaling and p53.

Automated Quantitative Analysis of p53, Cyclin D1, Ki67 and pERK Expression in Breast Carcinoma Does Not Differ from Expert Pathologist Scoring and Correlates with Clinico-Pathological Characteristics.

There is critical need for improved biomarker assessment platforms which integrate traditional pathological parameters (TNM stage, grade and ER/PR/HER2 status) with molecular profiling, to better define prognostic subgroups or systemic treatment response. One roadblock is the lack of semi-quantitative methods which reliably measure biomarker expression. Our study assesses reliability of automated immunohistochemistry (IHC) scoring compared to manual scoring of five selected biomarkers in a tissue microarray (TMA) of 63 human breast cancer cases, and correlates these markers with clinico-pathological data. TMA slides were scanned into an Ariol Imaging System, and histologic (H) scores (% positive tumor area x staining intensity 0-3) were calculated using trained algorithms. H scores for all five biomarkers concurred with pathologists' scores, based on Pearson correlation coefficients (0.80-0.90) for continuous data and Kappa statistics (0.55-0.92) for positive vs. negative stain. Using continuous data, significant association of pERK expression with absence of LVI (p = 0.005) and lymph node negativity (p = 0.002) was observed. p53 over-expression, characteristic of dysfunctional p53 in cancer, and Ki67 were associated with high grade (p = 0.032 and 0.0007, respectively). Cyclin D1 correlated inversely with ER/PR/HER2-ve (triple negative) tumors (p = 0.0002). Thus automated quantitation of immunostaining concurs with pathologists' scoring, and provides meaningful associations with clinico-pathological data.