DUSP4 Silencing Enhances the Sensitivity of Breast Cancer Cells to Doxorubicin through the Activation of the JNK/c-Jun Signalling Pathway.

Doxorubicin (Dox) has limited efficiency in breast cancer (BC) due to drug-acquired resistance. The epithelial-mesenchymal transition (EMT) plays a major role in the survival and drug resistance of cancer cells. It was suggested that the JNK pathway was implicated in the response to Dox by regulating EMT. DUSP4/or MKP-2 is a well-known regulator of the JNK pathway and was found to be highly expressed in BC. However, its functional significance is not yet fully understood. In the present study, the possible involvement of MKP-2 in Dox-induced EMT was investigated in breast cancer cells. Immunohistochemistry for tissues obtained from BC patients (n = 108) revealed 71.3% of tissues stained positively for MKP-2 while only 28.7% stained negatively. However, MKP-2 protein expression exhibited no significant relationship between BC prognostic factors, such as histological grade, histological type, hormonal status, and Ki-67 marker, its expression was significantly correlated with age 40 or below. In MDA-MB-231 cells, Dox-induced phosphorylation of JNK was sufficiently enhanced in MKP-2 silenced cells. This resulted in the attenuation of Dox-induced EMT, cell cycle arrest, and ultimately accelerated apoptosis. It was confirmed that the acquisition of Dox sensitivity by MKP-2 silencing largely depends on the stimulation of the JNK pathway. Indeed, results showed that overexpressing MKP-2 in non-tumorigenic MCF-12A cells dramatically inhibited Dox-induced JNK activation and, subsequently, cell death. The present study, to our knowledge, is the first to provide evidence for the potential role of MKP-2 in chemoresistance to Dox through modulating the JNK pathway and enhancing EMT.

Immunoglobulin free light chains are biomarkers of poor prognosis in basal-like breast cancer and are potential targets in tumor-associated inflammation.

Inflammation is an important component of various cancers and its inflammatory cells and mediators have been shown to have prognostic potential. Tumor-infiltrating mast cells can promote tumor growth and angiogenesis, but the mechanism of mast cell activation is unclear. In earlier studies, we demonstrated that immunoglobulin free light chains (FLC) can trigger mast cells in an antigen-specific manner. Increased expression of FLC was observed within stroma of various human cancers including those of breast, colon, lung, pancreas, kidney and skin, and FLC expression co-localized with areas of mast cell infiltration. In a large cohort of breast cancer patients, FLC expression was shown associated with basal-like cancers with an aggressive phenotype. Moreover, lambda FLC was found expressed in areas of inflammatory infiltration and its expression was significantly associated with poor clinical outcome. Functional importance of FLCs was shown in a murine B16F10 melanoma model, where inhibition of FLC-mediated mast cell activation strongly reduced tumor growth. Collectively, this study identifies FLCs as a ligand in the pro-tumorigenic activation of mast cells. Blocking this pathway may open new avenues for the inhibition of tumor growth, while immunohistochemical staining of FLC may be helpful in the diagnosis and prognosis of cancer.

DACH1: its role as a classifier of long term good prognosis in luminal breast cancer.

BACKGROUND: Oestrogen receptor (ER) positive (luminal) tumours account for the largest proportion of females with breast cancer. Theirs is a heterogeneous disease presenting clinical challenges in managing their treatment. Three main biological luminal groups have been identified but clinically these can be distilled into two prognostic groups in which Luminal A are accorded good prognosis and Luminal B correlate with poor prognosis. Further biomarkers are needed to attain classification consensus. Machine learning approaches like Artificial Neural Networks (ANNs) have been used for classification and identification of biomarkers in breast cancer using high throughput data. In this study, we have used an artificial neural network (ANN) approach to identify DACH1 as a candidate luminal marker and its role in predicting clinical outcome in breast cancer is assessed. MATERIALS AND METHODS: A reiterative ANN approach incorporating a network inferencing algorithm was used to identify ER-associated biomarkers in a publically available cDNA microarray dataset. DACH1 was identified in having a strong influence on ER associated markers and a positive association with ER. Its clinical relevance in predicting breast cancer specific survival was investigated by statistically assessing protein expression levels after immunohistochemistry in a series of unselected breast cancers, formatted as a tissue microarray. RESULTS: Strong nuclear DACH1 staining is more prevalent in tubular and lobular breast cancer. Its expression correlated with ER-alpha positive tumours expressing PgR, epithelial cytokeratins (CK)18/19 and 'luminal-like' markers of good prognosis including FOXA1 and RERG (p<0.05). DACH1 is increased in patients showing longer cancer specific survival and disease free interval and reduced metastasis formation (p<0.001). Nuclear DACH1 showed a negative association with markers of aggressive growth and poor prognosis. CONCLUSION: Nuclear DACH1 expression appears to be a Luminal A biomarker predictive of good prognosis, but is not independent of clinical stage, tumour size, NPI status or systemic therapy.

A review of the biological and clinical characteristics of luminal-like oestrogen receptor-positive breast cancer.

Global gene expression profiling (GEP) studies of breast cancer have identified distinct biological classes with different clinical and therapeutic implications. Oestrogen receptor (ER) has been found to be a central marker of the molecular signature. GEP studies have consistently recognized a molecularly distinct class of tumours that is characterized by high-level expression of ER and other biomarkers recognized to be characteristic of normal luminal cells of the breast. This class is the largest of the GEP-defined molecular subclasses, comprising 60-70% of breast cancer cases. Moreover, it has been proposed that this group of tumours is composed of at least two subclasses distinguished by differing GEP profiles. At present, there is no consensus on the definition of the luminal subclasses and, in clinical practice, luminal-like tumours and ER-positive tumours are frequently considered to be the same. A better understanding of the biological features of luminal tumours could lead to their improved characterization and consistent identification. In this review, we explore the concept and definitions of the luminal-like class of breast carcinoma and their contribution to our understanding of their molecular features, clinical significance and therapeutic implications.

Proposal for a modified grading system based on mitotic index and Bcl2 provides objective determination of clinical outcome for patients with breast cancer.

We hypothesized that the interaction between mitotic index (M) and Bcl2 could accurately discriminate between low- and high-grade breast cancer (BC) and provide a more objective measure of clinical outcome than histological grade, especially for patients with intermediate histological grade (G2), small size or oestrogen receptor (ER)-negative cancers. A well-characterized series of 1650 BCs with long-term follow-up was subjected to immunohistochemical analysis for Bcl2. Mitotic index (M) was assessed according to Nottingham Grading System (NGS) guidelines: M1: < 10 mitoses; M2: 10-18 mitoses; M3: > 18 mitoses. Results were validated in an independent series of patients (n = 245) uniformly treated with adjuvant anthracycline-based chemotherapy. Subsequently, BCs were classified according to the combined M/Bcl2 profile and compared with NGS. Multivariate Cox regression models using validated prognostic factors demonstrated that the subgroups defined by M/Bcl2 profile remained significantly associated with patients' outcome but also performed better than lymph node status and tumour size. Incorporation of the M/Bcl2 profile into the Nottingham Prognostic Index (NPI) reclassified twice as many patients into the excellent prognosis group, potentially improving decision-making and sparing patients unneeded systemic adjuvant therapy. Patients with M2-3/Bcl2- and M3/Bcl2+ (high risk) had a two- to three-fold increased risk of recurrence when treated with either adjuvant hormone therapy or anthracycline-based chemotherapy compared with those with M1/Bcl2 ± and M2/Bcl2+ (low risk) [HR = 3.4 (2.8-5.6); p < 0.0001 and HR = 2.3 (1.2-4.3); p = 0.0009]. In conclusion, a grading system defined by mitotic counting and Bcl2 expression accurately reclassified patients with NGS-G2, small tumour size or ER-negative cancers into two groups: low risk (NGS-G1-like) versus high risk (NGS-G3-like) of BC mortality and recurrence, improving prognosis and therapeutic planning.

Beta-blocker drug therapy reduces secondary cancer formation in breast cancer and improves cancer specific survival.

Laboratory models show that the beta-blocker, propranolol, can inhibit norepinephrine-induced breast cancer cell migration. We hypothesised that breast cancer patients receiving beta-blockers for hypertension would show reduced metastasis and improved clinical outcome. Three patient subgroups were identified from the medical records of 466 consecutive female patients (median age 57, range 28-71) with operable breast cancer and follow-up (>10 years). Two subgroups comprised 43 and 49 hypertensive patients treated with beta-blockers or other antihypertensives respectively, prior to cancer diagnosis. 374 patients formed a non-hypertensive control group. Metastasis development, disease free interval, tumour recurrence and hazards risk were statistically compared between groups. Kaplan-Meier plots were used to model survival and DM. Beta-blocker treated patients showed a significant reduction in metastasis development (p=0.026), tumour recurrence (p=0.001), and longer disease free interval (p=0.01). In addition, there was a 57% reduced risk of metastasis (Hazards ratio=0.430; 95% CI=0.200-0.926, p=0.031), and a 71% reduction in breast cancer mortality after 10 years (Hazards ratio=0.291; 95% CI=0.119-0.715, p=0.007). This proof-of-principle study showed beta-blocker therapy significantly reduces distant metastases, cancer recurrence, and cancer-specific mortality in breast cancer patients suggesting a novel role for beta-blocker therapy. A larger epidemiological study leading to randomised clinical trials is needed for breast and other cancer types including colon, prostate and ovary.