Tumour-specific HMG-CoAR is an independent predictor of recurrence free survival in epithelial ovarian cancer

<p>Abstract</p> <p>Background</p> <p>Our group previously reported that tumour-specific expression of the rate-limiting enzyme in the mevalonate pathway, 3-hydroxy-3-methylglutharyl-coenzyme A reductase (HMG-CoAR) is associated with more favourable tumour parameters and a good prognosis in breast cancer. In the present study, the prognostic value of HMG-CoAR expression was examined in tumours from a cohort of patients with primary epithelial ovarian cancer.</p> <p>Methods</p> <p>HMG-CoAR expression was assessed using immunohistochemistry (IHC) on tissue microarrays (TMA) consisting of 76 ovarian cancer cases, analysed using automated algorithms to develop a quantitative scoring model. Kaplan Meier analysis and Cox proportional hazards modelling were used to estimate the risk of recurrence free survival (RFS).</p> <p>Results</p> <p>Seventy-two tumours were suitable for analysis. Cytoplasmic HMG-CoAR expression was present in 65% (n = 46) of tumours. No relationship was seen between HMG-CoAR and age, histological subtype, grade, disease stage, estrogen receptor or Ki-67 status. Patients with tumours expressing HMG-CoAR had a significantly prolonged RFS (p = 0.012). Multivariate Cox regression analysis revealed that HMG-CoAR expression was an independent predictor of improved RFS (RR = 0.49, 95% CI (0.25-0.93); p = 0.03) when adjusted for established prognostic factors such as residual disease, tumour stage and grade.</p> <p>Conclusion</p> <p>HMG-CoAR expression is an independent predictor of prolonged RFS in primary ovarian cancer. As HMG-CoAR inhibitors, also known as statins, have demonstrated anti-neoplastic effects <it>in vitro</it>, further studies are required to evaluate HMG-CoAR expression as a surrogate marker of response to statin treatment, especially in conjunction with current chemotherapeutic regimens.</p

Validation of cytoplasmic-to-nuclear ratio of survivin as an indicator of improved prognosis in breast cancer

<p>Abstract</p> <p>Background</p> <p>Conflicting data exist regarding the prognostic and predictive impact of survivin (BIRC5) in breast cancer. We previously reported survivin cytoplasmic-to-nuclear ratio (CNR) as an independent prognostic indicator in breast cancer. Here, we validate survivin CNR in a separate and extended cohort. Furthermore, we present new data suggesting that a low CNR may predict outcome in tamoxifen-treated patients.</p> <p>Methods</p> <p>Survin expression was assessed using immunhistochemistry on a breast cancer tissue microarray (TMA) containing 512 tumours. Whole slide digital images were captured using an Aperio XT scanner. Automated image analysis was used to identify tumour from stroma and then to quantify tumour-specific nuclear and cytoplasmic survivin. A decision tree model selected using a 10-fold cross-validation approach was used to identify prognostic subgroups based on nuclear and cytoplasmic survivin expression.</p> <p>Results</p> <p>Following optimisation of the staining procedure, it was possible to evaluate survivin protein expression in 70.1% (n = 359) of the 512 tumours represented on the TMA. Decision tree analysis predicted that nuclear, as opposed to cytoplasmic, survivin was the most important determinant of overall survival (OS) and breast cancer-specific survival (BCSS). The decision tree model confirmed CNR of 5 as the optimum threshold for survival analysis. Univariate analysis demonstrated an association between a high CNR (>5) and a prolonged BCSS (HR 0.49, 95% CI 0.29-0.81, p = 0.006). Multivariate analysis revealed a high CNR (>5) was an independent predictor of BCSS (HR 0.47, 95% CI 0.27-0.82, p = 0.008). An increased CNR was associated with ER positive (p = 0.045), low grade (p = 0.007), Ki-67 (p = 0.001) and Her2 (p = 0.026) negative tumours. Finally, a high CNR was an independent predictor of OS in tamoxifen-treated ER-positive patients (HR 0.44, 95% CI 0.23-0.87, p = 0.018).</p> <p>Conclusion</p> <p>Using the same threshold as our previous study, we have validated survivin CNR as a marker of good prognosis in breast cancer in a large independent cohort. These findings provide robust evidence of the importance of survivin CNR as a breast cancer biomarker, and its potential to predict outcome in tamoxifen-treated patients.</p

The tumour suppressor SOX11 is associated with improved survival among high grade epithelial ovarian cancers and is regulated by reversible promoter methylation

<p>Abstract</p> <p>Background</p> <p>The neural transcription factor SOX11 has been described as a prognostic marker in epithelial ovarian cancers (EOC), however its role in individual histological subtypes and tumour grade requires further clarification. Furthermore, methylation-dependent silencing of SOX11 has been reported for B cell lymphomas and indicates that epigenetic drugs may be used to re-express this tumour suppressor, but information on SOX11 promoter methylation in EOC is still lacking.</p> <p>Methods</p> <p>SOX11 expression and clinicopathological data was compared using χ²test in a cohort of 154 cases of primary invasive EOC. Kaplan-Meier analysis and the log rank test were applied to evaluate ovarian cancer-specific survival (OCSS) and overall survival (OS) in strata, according to SOX11 expression. Also, the methylation status of the SOX11 promoter was determined by sodium bisulfite sequencing and methylation specific PCR (MSP). Furthermore, the effect of ectopic overexpression of SOX11 on proliferation was studied through [3H]-thymidine incorporation.</p> <p>Results</p> <p>SOX11 expression was associated with an improved survival of patients with high grade EOC, although not independent of stage. Further analyses of EOC cell lines showed that SOX11 mRNA and protein were expressed in two of five cell lines, correlating with promoter methylation status. Demethylation was successfully performed using 5'-Aza-2'deoxycytidine (5-Aza-dC) resulting in SOX11 mRNA and protein expression in a previously negative EOC cell line. Furthermore, overexpression of SOX11 in EOC cell lines confirmed the growth regulatory role of SOX11.</p> <p>Conclusions</p> <p>SOX11 is a functionally associated protein in EOC with prognostic value for high-grade tumours. Re-expression of SOX11 in EOC indicates a potential use of epigenetic drugs to affect cellular growth in SOX11-negative tumours.</p

Identification of a myometrial molecular profile for dystocic labor

<p>Abstract</p> <p>Background</p> <p>The most common indication for cesarean section (CS) in nulliparous women is dystocia secondary to ineffective myometrial contractility. The aim of this study was to identify a molecular profile in myometrium associated with dystocic labor.</p> <p>Methods</p> <p>Myometrial biopsies were obtained from the upper incisional margins of nulliparous women undergoing lower segment CS for dystocia (n = 4) and control women undergoing CS in the second stage who had demonstrated efficient uterine action during the first stage of labor (n = 4). All patients were in spontaneous (non-induced) labor and had received intrapartum oxytocin to accelerate labor. RNA was extracted from biopsies and hybridized to Affymetrix HuGene U133A Plus 2 microarrays. Internal validation was performed using quantitative SYBR Green Real-Time PCR.</p> <p>Results</p> <p>Seventy genes were differentially expressed between the two groups. 58 genes were down-regulated in the dystocia group. Gene ontology analysis revealed 12 of the 58 down-regulated genes were involved in the immune response. These included (ERAP2, (8.67 fold change (FC)) HLA-DQB1 (7.88 FC) CD28 (2.60 FC), LILRA3 (2.87 FC) and TGFBR3 (2.1 FC)) Hierarchical clustering demonstrated a difference in global gene expression patterns between the samples from dystocic and non-dystocic labours. RT-PCR validation was performed on 4 genes ERAP2, CD28, LILRA3 and TGFBR3</p> <p>Conclusion</p> <p>These findings suggest an underlying molecular basis for dystocia in nulliparous women in spontaneous labor. Differentially expressed genes suggest an important role for the immune response in dystocic labor and may provide important indicators for new diagnostic assays and potential intrapartum therapeutic targets.</p

Novel image analysis approach for quantifying expression of nuclear proteins assessed by immunohistochemistry: application to measurement of oestrogen and progesterone receptor levels in breast cancer

INTRODUCTION: Manual interpretation of immunohistochemistry (IHC) is a subjective, time-consuming and variable process, with an inherent intra-observer and inter-observer variability. Automated image analysis approaches offer the possibility of developing rapid, uniform indicators of IHC staining. In the present article we describe the development of a novel approach for automatically quantifying oestrogen receptor (ER) and progesterone receptor (PR) protein expression assessed by IHC in primary breast cancer. METHODS: Two cohorts of breast cancer patients (n = 743) were used in the study. Digital images of breast cancer tissue microarrays were captured using the Aperio ScanScope XT slide scanner (Aperio Technologies, Vista, CA, USA). Image analysis algorithms were developed using MatLab 7 (MathWorks, Apple Hill Drive, MA, USA). A fully automated nuclear algorithm was developed to discriminate tumour from normal tissue and to quantify ER and PR expression in both cohorts. Random forest clustering was employed to identify optimum thresholds for survival analysis. RESULTS: The accuracy of the nuclear algorithm was initially confirmed by a histopathologist, who validated the output in 18 representative images. In these 18 samples, an excellent correlation was evident between the results obtained by manual and automated analysis (Spearman\u27s rho = 0.9, P \u3c 0.001). Optimum thresholds for survival analysis were identified using random forest clustering. This revealed 7% positive tumour cells as the optimum threshold for the ER and 5% positive tumour cells for the PR. Moreover, a 7% cutoff level for the ER predicted a better response to tamoxifen than the currently used 10% threshold. Finally, linear regression was employed to demonstrate a more homogeneous pattern of expression for the ER (R = 0.860) than for the PR (R = 0.681). CONCLUSIONS: In summary, we present data on the automated quantification of the ER and the PR in 743 primary breast tumours using a novel unsupervised image analysis algorithm. This novel approach provides a useful tool for the quantification of biomarkers on tissue specimens, as well as for objective identification of appropriate cutoff thresholds for biomarker positivity. It also offers the potential to identify proteins with a homogeneous pattern of expression