The expression pattern of MUC1 (EMA) is related to tumour characteristics and clinical outcome of invasive ductal breast carcinoma

Aims: To clarify MUC1 patterns in invasive ductal breast carcinoma and to relate them to clinicopathological parameters, coexpression of other biological markers and prognosis. Methods and results: Samples from 243 consecutive patients with primary ductal carcinoma were incorporated into tissue microarrays (TMAs). Slides were stained for MUC1, oestrogen receptor (ER), progesterone receptor (PR), Her2/neu, p53 and cyclin D1. Apical membrane MUC1 expression was associated with smaller tumours (P = 0.001), lower tumour grades (P < 0.001), PR positivity (P = 0.003) and increased overall survival (OS; P = 0.030). Diffuse cytoplasmic MUC1 expression was associated with cyclin D1 positivity (P = 0.009) and increased relapse-free survival (RFS; P = 0.034). Negativity for MUC1 was associated with ER negativity (P = 0.004), PR negativity (P = 0.001) and cyclin D1 negativity (P = 0.009). In stepwise multivariate analysis MUC1 negativity was an independent predictor of both RFS [hazard ratio (HR) 3.5, 95% confidence interval (CI) 1.5, 8.5; P = 0.005] and OS (HR 14.7, 9 5% Cl 4.9, 44. 1; P < 0.001). Conclusions: The expression pattern of MUC1 in invasive ductal breast carcinoma is related to tumour characteristics and clinical outcome. In addition, negative MUC1 expression is an independent risk factor for poor RFS and OS, besides 'classical' prognostic indicators

Machine learning-based prediction of breast cancer growth rate in-vivo

BackgroundDetermining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the rate of in vivo tumour growth using a unique study cohort of BC patients who had two serial mammograms wherein the tumour, visible in the diagnostic mammogram, was missed in the first screen.MethodsA serial mammography-derived in vivo growth rate (SM-INVIGOR) index was developed using tumour volumes from two serial mammograms and time interval between measurements. We then developed a machine learning-based surrogate model called Surr-INVIGOR using routinely assessed biomarkers to predict in vivo rate of tumour growth and extend the utility of this approach to a larger patient population. Surr-INVIGOR was validated using an independent cohort.ResultsSM-INVIGOR stratified discovery cohort patients into fast-growing versus slow-growing tumour subgroups, wherein patients with fast-growing tumours experienced poorer BC-specific survival. Our clinically relevant Surr-INVIGOR stratified tumours in the discovery cohort and was concordant with SM-INVIGOR. In the validation cohort, Surr-INVIGOR uncovered significant survival differences between patients with fast-growing and slow-growing tumours.ConclusionOur Surr-INVIGOR model predicts in vivo BC growth rate during the pre-diagnostic stage and offers several useful applications

Partial Inhibition of Estrogen-Induced Mammary Carcinogenesis in Rats by Tamoxifen: Balance between Oxidant Stress and Estrogen Responsiveness

Epidemiological and experimental evidences strongly support the role of estrogens in breast tumor development. Both estrogen receptor (ER)-dependent and ER-independent mechanisms are implicated in estrogen-induced breast carcinogenesis. Tamoxifen, a selective estrogen receptor modulator is widely used as chemoprotectant in human breast cancer. It binds to ERs and interferes with normal binding of estrogen to ERs. In the present study, we examined the effect of long-term tamoxifen treatment in the prevention of estrogen-induced breast cancer. Female ACI rats were treated with 17β-estradiol (E2), tamoxifen or with a combination of E2 and tamoxifen for eight months. Tissue levels of oxidative stress markers 8-iso-Prostane F2α (8-isoPGF2α), superoxide dismutase (SOD), glutathione peroxidase (GPx), catalase, and oxidative DNA damage marker 8-hydroxydeoxyguanosine (8-OHdG) were quantified in the mammary tissues of all the treatment groups and compared with age-matched controls. Levels of tamoxifen metabolizing enzymes cytochrome P450s as well as estrogen responsive genes were also quantified. At necropsy, breast tumors were detected in 44% of rats co-treated with tamoxifen+E2. No tumors were detected in the sham or tamoxifen only treatment groups whereas in the E2 only treatment group, the tumor incidence was 82%. Co-treatment with tamoxifen decreased GPx and catalase levels; did not completely inhibit E2-mediated oxidative DNA damage and estrogen-responsive genes monoamine oxygenase B1 (MaoB1) and cell death inducing DFF45 like effector C (Cidec) but differentially affected the levels of tamoxifen metabolizing enzymes. In summary, our studies suggest that although tamoxifen treatment inhibits estrogen-induced breast tumor development and increases the latency of tumor development, it does not completely abrogate breast tumor development in a rat model of estrogen-induced breast cancer. The inability of tamoxifen to completely inhibit E2-induced breast carcinogenesis may be because of increased estrogen-mediated oxidant burden

Cyclin E expression and proliferation in breast cancer

Cyclin E is a part of the cell cycle machinery and aberrantly expressed in several malignancies including breast cancer. Since cyclin E is cell cycle specifically expressed, we wanted to examine the relation between proliferation and expression of cyclin E with special attention to tumours with overexpression of the protein. Seventy-four breast tumours were analysed for the expression of cyclin E by immunohistochemistry and Western blotting and related to the growth fraction determined by Ki-67. Significant correlations were obtained between the growth fraction, the percentage of cyclin E positive cells, the intensity of cyclin E and total amount of cyclin E determined by Western blotting. The majority of the tumours had less cyclin E than Ki-67 positive cells indicating a conserved cell cycle specific expression of the protein which further was supported by flow cytometric analysis of breast cancer cell lines. The cell cycle specificity of cyclin E was found even in tumours with inactivated retinoblastoma protein (pRB) demonstrating the existence of a pRB independent regulation of cyclin E. A fraction of the tumours had considerably elevated cyclin E levels that were not in relation to the proliferative activity as observed for the other tumours. These tumours were in general highly proliferative and considered to overexpress cyclin E. Patients with tumours of high proliferative activity, high total cyclin E levels or disproportionally elevated cyclin E expressions in relation to proliferation had significantly increased risk of death in breast cancer, whereas the intensity of the immunohistochemical cyclin E staining did not affect the survival