High expression of cyclin D1 is associated to high proliferation rate and increased risk of mortality in women with ER-positive but not in ER-negative breast cancers

Cyclin D1 has a central role in cell cycle control and is an important component of estrogen regulation of cell cycle progression. We have previously shown that high cyclin D expression is related to aggressive features of ER-positive but not ER-negative breast cancer. The aims of the present study were to validate this differential ER-related effect and furthermore explore the relationship between cyclin D overexpression and CCND1 gene amplification status in a node-negative breast cancer case-control study. Immunohistochemical nuclear expression of cyclin D1 (n = 364) and amplification of the gene CCND1 by fluorescent in situ hybridization (n = 255) was performed on tissue microarray sections from patients with T1-2N0M0 breast cancer. Patients given adjuvant chemotherapy were excluded. The primary event was defined as breast cancer death. Breast cancer-specific survival was analyzed in univariate and multivariable models using conditional logistic regression. Expression of cyclin D1 above the median (61.7%) in ER breast cancer was associated with an increased risk for breast cancer death (OR 3.2 95% CI 1.5-6.8) also when adjusted for tumor size and grade (OR 3.1). No significant prognostic impact of cyclin D1 expression was found among ER-negative cases. Cyclin D1 overexpression was significantly associated to high expression of the proliferation markers cyclins A (rho 0.19, p = 0.006) and B (rho 0.18, p = 0.003) in ER-positive tumors, but not in ER-negative cases. There was a significant association between CCND1 amplification and cyclin D1 expression (p = 0.003), but CCND1 amplification was not statistically significantly prognostic (HR 1.4, 95% CI 0.4-4.4). We confirmed our previous observation that high cyclin D1 expression is associated to high proliferation and a threefold higher risk of death from breast cancer in ER-positive breast cancer.Peer reviewe

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

Long-term survival of women with basal-like ductal carcinoma in situ of the breast : a population-based cohort study

Background: Microarray gene-profiling of invasive breast cancer has identified different subtypes including luminal A, luminal B, HER2-overexpressing and basal-like groups. Basal-like invasive breast cancer is associated with a worse prognosis. However, the prognosis of basal-like ductal carcinoma in situ (DCIS) is still unknown. Our aim was to study the prognosis of basal-like DCIS in a large population-based cohort. Methods: All 458 women with a primary DCIS diagnosed between 1986 and 2004, in Uppland and Vastmanland, Sweden were included. TMA blocks were constructed. To classify the DCIS tumors, we used immunohistochemical (IHC) markers (estrogen-, progesterone-, HER2, cytokeratin 5/6 and epidermal growth factor receptor) as a surrogate for the gene expression profiling. The association with prognosis was examined for basal-like DCIS and other subtypes using Kaplan-Meier survival analyses and Cox proportional hazards regression models. Results: IHC data were complete for 392 women. Thirty-two were basal-like (8.2%), 351 were luminal or HER2-positive (89.5%) and 9 unclassified (2.3%). Seventy-six women had a local recurrence of which 34 were invasive. Another 3 women had general metastases as first event. Basal-like DCIS showed a higher risk of local recurrence and invasive recurrence 1.8 (Confidence interval (CI) 95%, 0.8-4.2) and 1.9 (0.7-5.1), respectively. However, the difference was not statistically significant. Also, no statistically significant increased risk was seen for triple-negative or high grade DCIS. Conclusions: Basal-like DCIS showed about a doubled, however not statistically significant risk for local recurrence and developing invasive cancer compared with the other molecular subtypes. Molecular subtyping was a better prognostic parameter than histopathological grade.Peer reviewe

Additive clinical impact of epidermal growth factor receptor and podocalyxin-like protein expression in pancreatic and periampullary adenocarcinomas

The outcome of periampullary adenocarcinomas remains poor with few treatment options. Podocalyxin-like protein (PODXL) is an anti-adhesive protein, the high expression of which has been shown to confer a poor prognosis in numerous malignancies. A correlation and adverse prognostic synergy between PODXL and the epidermal growth factor receptor (EGFR) has been observed in colorectal cancer. Here, we investigated whether this also applies to periampullary adenocarcinomas. We analyzed the immunohistochemical expression of PODXL and EGFR in tissue microarrays with tumors from two patient cohorts; (Cohort 1, n=175) and (Cohort 2, n=189). The effect of TGF-beta -induced expression and siRNA-mediated knockdown of PODXL and EGFR, were investigated in pancreatic cancer cells (PANC-1) in vitro. We found a correlation between PODXL and EGFR in these cancers, and a synergistic adverse effect on survival. Furthermore, silencing PODXL in pancreatic cancer cells resulted in the down-regulation of EGFR, but not vice versa. Consequently, these findings suggest a functional link between PODXL and EGFR, and the potential combined utility as biomarkers possibly improving patient stratification. Further studies examining the mechanistic basis underlying these observations may open new avenues of targeted treatment options for subsets of patients affected by these particularly aggressive cancers.Peer reviewe

Increased serum levels of tumour-associated trypsin inhibitor independently predict a poor prognosis in colorectal cancer patients

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Molecular subtypes in ductal carcinoma in situ of the breast and their relation to prognosis: a population-based cohort study

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The prognostic role of HER2 expression in ductal breast carcinoma in situ (DCIS); a population-based cohort study

Background: HER2 is a well-established prognostic and predictive factor in invasive breast cancer. The role of HER2 in ductal breast carcinoma in situ (DCIS) is debated and recent data have suggested that HER2 is mainly related to in situ recurrences. Our aim was to study HER2 as a prognostic factor in a large population based cohort of DCIS with long-term follow-up. Methods: All 458 patients diagnosed with a primary DCIS 1986-2004 in two Swedish counties were included. Silver-enhanced in situ hybridisation (SISH) was used for detection of HER2 gene amplification and protein expression was assessed by immunohistochemistry (IHC) in tissue microarrays. HER2 positivity was defined as amplified HER2 gene and/or HER2 3+ by IHC. HER2 status in relation to new ipsilateral events (IBE) and Invasive Breast Cancer Recurrences, local or distant (IBCR) was assessed by Kaplan-Meier survival analyses and Cox proportional hazards regression models. Results: Primary DCIS was screening-detected in 75.5 % of cases. Breast conserving surgery (BCS) was performed in 78.6 % of whom 44.0 % received postoperative radiotherapy. No patients received adjuvant endocrine-or chemotherapy. The majority of DCIS could be HER2 classified (N = 420 (91.7 %)); 132 HER2 positive (31 %) and 288 HER2 negative (69 %)). HER2 positivity was related to large tumor size (P = 0.002), high grade (P <0.001) and ER-and PR negativity (P <0.001 for both). During follow-up (mean 184 months), 106 IBCRs and 105 IBEs were identified among all 458 cases corresponding to 54 in situ and 51 invasive recurrences. Eighteen women died from breast cancer and another 114 had died from other causes. The risk of IBCR was statistically significantly lower subsequent to a HER2 positive DCIS compared to a HER2 negative DCIS, (Log-Rank P = 0.03, (HR) 0.60 (95 % CI 0.38-0.94)). Remarkably, the curves did not separate until after 10 years. In ER-stratified analyses, HER2 positive DCIS was associated with lower risk of IBCR among women with ER negative DCIS (Log-Rank P = 0.003), but not for women with ER positive DCIS. Conclusions: Improved prognostic tools for DCIS patients are warranted to tailor adjuvant therapy. Here, we demonstrate that HER2 positive disease in the primary DCIS is associated with lower risk of recurrent invasive breast cancer.Peer reviewe

Molecular subtypes in ductal carcinoma in situ of the breast and their relation to prognosis: a population-based cohort study

Peer reviewe

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

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