HIF-1␣ (hypoxia-inducible factor 1␣) is a basic-helixloop-helix PAS (Per/Arnt/Sim) transcription factor that, under hypoxic conditions, dimerizes with a partner factor, the basic-helix-loop-helix/PAS protein Arnt, to recognize hypoxia-responsive elements of target genes. It has recently been demonstrated that HIF-1␣ protein but not mRNA levels are dramatically up-regulated in response to hypoxia. Here we show that inhibitors of 26 S proteasome activity produced a dramatic accumulation of endogenous as well as transfected HIF-1␣ protein under normoxic conditions, whereas the levels of Arnt protein were not affected. HIF-1␣ was polyubiquitinated in vivo under normoxic conditions, indicating rapid degradation via the ubiquitin-proteasome pathway. This degradation process appeared to target a region within the C terminus of HIF-1␣. Importantly, HIF-1␣ ubiquitination was drastically decreased under hypoxic conditions. Up-regulation of HIF-1␣ protein by proteasome inhibitors did not result in transcriptional activation of reporter genes, indicating either the requirement of additional regulatory steps to induce functional activity of HIF-1␣ or the inability of polyubiquitinated forms of HIF-1␣ to mediate hypoxic signal transduction. In support of both these notions, we demonstrate that HIF-1␣ showed hypoxia-dependent translocation from the cytoplasm to the nucleus and that this regulatory mechanism was severely impaired in the presence of proteasome inhibitors. Taken together, these data demonstrate that the mechanism of hypoxia-dependent activation of HIF-1␣ is a complex multistep process and that stabilization of HIF-1␣ protein levels is not sufficient to generate a functional form.Hypoxia-inducible factor 1 (HIF-1) 1 consists of a heterodimer of two basic helix-loop-helix PAS proteins, HIF-1␣ and Arnt. Upon decrease in oxygen tension, the activated HIF-1␣⅐Arnt complex functions as a transcription factor to control the expression of genes encoding products aimed at restoring cellular homeostasis such as erythropoietin, vascular endothelial growth factor, and several glycolytic enzymes (reviewed in Ref.

Hypoxia-inducible factor 1␣ (HIF-1␣) functions as a transcription factor that is activated by decreased cellular oxygen concentrations to induce expression of a network of genes involved in angiogenesis, erythropoiesis, and glucose homeostasis. Here we demonstrate that two members of the SRC-1/p160 family of transcriptional coactivators harboring histone acetyltransferase activity, SRC-1 and transcription intermediary factor 2 (TIF2), are able to interact with HIF-1␣ and enhance its transactivation potential in a hypoxiadependent manner. HIF-1␣ contains within its C terminus two transactivation domains. The hypoxia-inducible activity of both these domains was enhanced by either SRC-1 or the CREB-binding protein (

DNA microarrays have the potential to classify tumors according to their transcriptome. Tissue microarrays (TMAs) facilitate the validation of biomarkers by offering a high‐throughput approach to sample analysis. We reanalyzed a high profile breast cancer DNA microarray dataset containing 96 tumor samples using a powerful statistical approach, between group analyses. Among the genes we identified was centromere protein‐F (CENP‐F), a gene associated with poor prognosis. In a published follow‐up breast cancer DNA microarray study, comprising 295 tumour samples, we found that CENP‐F upregulation was significantly associated with worse overall survival (p < 0.001) and reduced metastasis‐free survival (p < 0.001). To validate and expand upon these findings, we used 2 independent breast cancer patient cohorts represented on TMAs. CENP‐F protein expression was evaluated by immunohistochemistry in 91 primary breast cancer samples from cohort I and 289 samples from cohort II. CENP‐F correlated with markers of aggressive tumor behavior including ER negativity and high tumor grade. In cohort I, CENP‐F was significantly associated with markers of CIN including cyclin E, increased telomerase activity, c‐Myc amplification and aneuploidy. In cohort II, CENP‐F correlated with VEGFR2, phosphorylated Ets‐2 and Ki67, and in multivariate analysis, was an independent predictor of worse breast cancer‐specific survival (p = 0.036) and overall survival (p = 0.040). In conclusion, we identified CENP‐F as a biomarker associated with poor outcome in breast cancer and showed several novel associations of biological significance. © 2006 Wiley‐Liss, Inc.

Purpose: Hypoxia in breast cancer is associated with poor prognosis and down-regulation of the estrogen receptor. Carbonic anhydrase IX (CA IX) is a hypoxia-inducible gene that has been associated with poor outcome in many epithelial cancers. Previous studies of CA IX in breast cancer have been carried out on mixed cohorts of premenopausal and postmenopausal patients with locally advanced disease and varying treatment regimens.We examined the potential prognostic and predictive role of CA IX in premenopausal breast cancer patients. Experimental Design: Using tissue microarrays, we analyzed CA IX expression in 400 stage II breast cancers from premenopausal women. The patients had previously participated in a randomized control trial comparing 2 years of tamoxifen to no systemic adjuvant treatment. Median follow-up was 13.9 years. Results: CA IX expression correlated positively with tumor size, grade, hypoxia-inducible factor 1a, Ki-67, cyclin E, and cyclin A2 expression. CA IX expression correlated negatively with cyclin D1, estrogen receptor, and progesterone receptor. CA IX expression was associated with a reduced relapse-free survival (P = 0.032), overall survival (P = 0.022), and breast cancerŝ pecific survival (P = 0.005). Multivariate analysis revealed that CA IX was an independent prognostic marker in untreated patients with one to three positive lymph nodes (hazard ratio, 3.2; 95% confidence interval, 1.15-9.13; P = 0.027). Conclusion: CA IX is marker of poor prognosis in premenopausal breast cancer patients and it is an independent predictor of survival in patients with one to three positive lymph nodes. As all these patients received locoregional radiation therapy, CA IX may be associated with resistance to radiotherapy.

Purpose: Survivin (BIRC5) is a promising tumor biomarker. Conflicting data exist on its prognostic effect in breast cancer. These data may at least be partly due to the manual interpretation of immunohistochemical staining, especially as survivin can be located in both the nucleus and cytoplasm. Quantitative determination of survivin expression using image analysis offers the opportunity to develop alternative scoring models for survivin immunohistochemistry. Here, we present such a model. Experimental Design: A breast cancer tissue microarray containing 102 tumors was stained with an anti-survivin antibody.Whole-slide scanning was used to capture high-resolution images. These images were analyzed using automated algorithms to quantify the staining. Results: Increased nuclear, but not cytoplasmic, survivin was associated with a reduced overall survival (OS; P = 0.038) and disease-specific survival (P = 0.0015). A high cytoplasmicto-nuclear ratio (CNR) of survivin was associated with improved OS (P = 0.005) and diseasespecific survival (P = 0.05). Multivariate analysis revealed that the survivin CNR was an independent predictor of OS (hazard ratio, 0.09; 95% confidence interval, 0.01-0.76; P = 0.027).A survivin CNR of >5 correlated positively with estrogen receptor (P = 0.019) and progesterone receptor (P = 0.033) levels, whereas it was negatively associated with Ki-67 expression (P = 0.04), p53 status (P = 0.005), and c-myc amplification (P = 0.016). Conclusion: Different prognostic information is supplied by nuclear and cytoplasmic survivin in breast cancer. Nuclear survivin is a poor prognostic marker in breast cancer. Moreover, CNR of survivin, as determined by image analysis, is an independent prognostic factor.

BackgroundConflicting 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.MethodsSurvin 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.ResultsFollowing 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).ConclusionUsing 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.

There are > 1.15 million cases of breast cancer diagnosed worldwide annually, and it is the second leading cause of cancer death in the European Union. The optimum management of patients with breast cancer requires accurate prognostic and predictive factors. At present, only a small number of such factors are used clinically. DNA microarrays have the potential to measure the expression of tens of thousands of genes simultaneously. Recent preliminary findings suggest that DNA microarray-based gene expression profiling can provide powerful and independent prognostic information in patients with newly diagnosed breast cancer. As well as providing prognostic information, emerging results suggest that DNA microarrays can also be used for predicting response or resistance to treatment, especially to neoadjuvant chemotherapy. Prior to clinical application, these preliminary findings must be validated using large-scale prospective studies. This article reviews these advances and also examines the role of DNA microarrays in reducing the number of patients who receive inappropriate chemotherapy. The most recent data supporting the integration of various publicly available data sets is also reviewed in detail.

A DNA microarray is a solid support such as a glass slide, silicon chip or nylon membrane on which DNA molecules are attached at precise locations. Using DNA microarrays, the expression of tens of thousands of genes in a biological sample can be detected in one experiment. Emerging data suggests that the use of DNA microarrays can aid the differentiation of tumors with similar morphological appearance, predict patient outcome independently of conventional prognostic factors and select for response or resistance to specific anti-cancer therapies. DNA microarray technology thus has the potential to supplement standard diagnostic procedures in oncology and permit a more individualized approach to patient management. Prior to clinical application, however, this methodology must be simplified, standardized, evaluated in external quality assessment schemes and made available at relatively low costs. Most importantly, the preliminary, but promising, early findings must be validated by high-level evidence studies, such as large prospective randomized trials or meta-analyses.