Low E2F1 transcript levels are a strong determinant of favorable breast cancer outcome

INTRODUCTION: We investigated whether mRNA levels of E2F1, a key transcription factor involved in proliferation, differentiation and apoptosis, could be used as a surrogate marker for the determination of breast cancer outcome. METHODS: E2F1 and other proliferation markers were measured by quantitative RT-PCR in 317 primary breast cancer patients from the Stiftung Tumorbank Basel. Correlations to one another as well as to the estrogen receptor and ERBB2 status and clinical outcome were investigated. Results were validated and further compared with expression-based prognostic profiles using The Netherlands Cancer Institute microarray data set reported by Fan and colleagues. RESULTS: E2F1 mRNA expression levels correlated strongly with the expression of other proliferation markers, and low values were mainly found in estrogen receptor-positive and ERBB2-negative phenotypes. Patients with low E2F1-expressing tumors were associated with favorable outcome (hazard ratio = 4.3 (95% confidence interval = 1.8-9.9), P = 0.001). These results were consistent in univariate and multivariate Cox analyses, and were successfully validated in The Netherlands Cancer Institute data set. Furthermore, E2F1 expression levels correlated well with the 70-gene signature displaying the ability of selecting a common subset of patients at good prognosis. Breast cancer patients' outcome was comparably predictable by E2F1 levels, by the 70-gene signature, by the intrinsic subtype gene classification, by the wound response signature and by the recurrence score. CONCLUSION: Assessment of E2F1 at the mRNA level in primary breast cancer is a strong determinant of breast cancer patient outcome. E2F1 expression identified patients at low risk of metastasis irrespective of the estrogen receptor and ERBB2 status, and demonstrated similar prognostic performance to different gene expression-based predictors

CDO1 Promoter Methylation is a Biomarker for Outcome Prediction of Anthracycline Treated, Estrogen Receptor-Positive, Lymph Node-Positive Breast Cancer Patients

<p>Abstract</p> <p>Background</p> <p>Various biomarkers for prediction of distant metastasis in lymph-node negative breast cancer have been described; however, predictive biomarkers for patients with lymph-node positive (LNP) disease in the context of distinct systemic therapies are still very much needed. DNA methylation is aberrant in breast cancer and is likely to play a major role in disease progression. In this study, the DNA methylation status of 202 candidate loci was screened to identify those loci that may predict outcome in LNP/estrogen receptor-positive (ER+) breast cancer patients with adjuvant anthracycline-based chemotherapy.</p> <p>Methods</p> <p>Quantitative bisulfite sequencing was used to analyze DNA methylation biomarker candidates in a retrospective cohort of 162 LNP/ER+ breast cancer patients, who received adjuvant anthracycline-based chemotherapy. First, twelve breast cancer specimens were analyzed for all 202 candidate loci to exclude genes that showed no differential methylation. To identify genes that predict distant metastasis, the remaining loci were analyzed in 84 selected cases, including the 12 initial ones. Significant loci were analyzed in the remaining 78 independent cases. Metastasis-free survival analysis was conducted by using Cox regression, time-dependent ROC analysis, and the Kaplan-Meier method. Pairwise multivariate regression analysis was performed by linear Cox Proportional Hazard models, testing the association between methylation scores and clinical parameters with respect to metastasis-free survival.</p> <p>Results</p> <p>Of the 202 loci analysed, 37 showed some indication of differential DNA methylation among the initial 12 patient samples tested. Of those, 6 loci were associated with outcome in the initial cohort (n = 84, log rank test, p < 0.05).</p> <p>Promoter DNA methylation of cysteine dioxygenase 1 (CDO1) was confirmed in univariate and in pairwise multivariate analysis adjusting for age at surgery, pathological T stage, progesterone receptor status, grade, and endocrine therapy as a strong and independent biomarker for outcome prediction in the independent validation set (log rank test p-value = 0.0010).</p> <p>Conclusions</p> <p>CDO1 methylation was shown to be a strong predictor for distant metastasis in retrospective cohorts of LNP/ER+ breast cancer patients, who had received adjuvant anthracycline-based chemotherapy.</p

Expression analysis of mitotic spindle checkpoint genes in breast carcinoma: role of NDC80/HEC1 in early breast tumorigenicity, and a two-gene signature for aneuploidy

<p>Abstract</p> <p>Background</p> <p>Aneuploidy and chromosomal instability (CIN) are common abnormalities in human cancer. Alterations of the mitotic spindle checkpoint are likely to contribute to these phenotypes, but little is known about somatic alterations of mitotic spindle checkpoint genes in breast cancer.</p> <p>Methods</p> <p>To obtain further insight into the molecular mechanisms underlying aneuploidy in breast cancer, we used real-time quantitative RT-PCR to quantify the mRNA expression of 76 selected mitotic spindle checkpoint genes in a large panel of breast tumor samples.</p> <p>Results</p> <p>The expression of 49 (64.5%) of the 76 genes was significantly dysregulated in breast tumors compared to normal breast tissues: 40 genes were upregulated and 9 were downregulated. Most of these changes in gene expression during malignant transformation were observed in epithelial cells.</p> <p>Alterations of nine of these genes, and particularly <it>NDC80</it>, were also detected in benign breast tumors, indicating that they may be involved in pre-neoplastic processes.</p> <p>We also identified a two-gene expression signature (<it>PLK1 </it>+ <it>AURKA</it>) which discriminated between DNA aneuploid and DNA diploid breast tumor samples. Interestingly, some DNA tetraploid tumor samples failed to cluster with DNA aneuploid breast tumors.</p> <p>Conclusion</p> <p>This study confirms the importance of previously characterized genes and identifies novel candidate genes that could be activated for aneuploidy to occur. Further functional analyses are required to clearly confirm the role of these new identified genes in the molecular mechanisms involved in breast cancer aneuploidy. The novel genes identified here, and/or the two-gene expression signature, might serve as diagnostic or prognostic markers and form the basis for novel therapeutic strategies.</p

Ki-67: level of evidence and methodological considerations for its role in the clinical management of breast cancer: analytical and critical review

Clinicians can use biomarkers to guide therapeutic decisions in estrogen receptor positive (ER+) breast cancer. One such biomarker is cellular proliferation as evaluated by Ki-67. This biomarker has been extensively studied and is easily assayed by histopathologists but it is not currently accepted as a standard. This review focuses on its prognostic and predictive value, and on methodological considerations for its measurement and the cut-points used for treatment decision. Data describing study design, patients’ characteristics, methods used and results were extracted from papers published between January 1990 and July 2010. In addition, the studies were assessed using the REMARK tool. Ki-67 is an independent prognostic factor for disease-free survival (HR 1.05–1.72) in multivariate analyses studies using samples from randomized clinical trials with secondary central analysis of the biomarker. The level of evidence (LOE) was judged to be I-B with the recently revised definition of Simon. However, standardization of the techniques and scoring methods are needed for the integration of this biomarker in everyday practice. Ki-67 was not found to be predictive for long-term follow-up after chemotherapy. Nevertheless, high KI-67 was found to be associated with immediate pathological complete response in the neoadjuvant setting, with an LOE of II-B. The REMARK score improved over time (with a range of 6–13/20 vs. 10–18/20, before and after 2005, respectively). KI-67 could be considered as a prognostic biomarker for therapeutic decision. It is assessed with a simple assay that could be standardized. However, international guidelines are needed for routine clinical use

Pooled Analysis of Prognostic Impact of Urokinase-Type Plasminogen Activator and Its Inhibitor PAI-1 in 8377 Breast Cancer Patients

Background: Urokinase-type plasminogen activator (uPA) and its inhibitor (PAI-1) play essential roles in tumor invasion and metastasis. High levels of both uPA and PAI-1 are associated with poor prognosis in breast cancer patients. To confirm the prognostic value of uPA and PAI-1 in primary breast cancer, we reanalyzed individual patient data provided by members of the European Organization for Research and Treatment of Cancer-Receptor and Biomarker Group (EORTC-RBG). Methods: The study included 18 datasets involving 8377 breast cancer patients. During follow-up (median 79 months), 35% of the patients relapsed and 27% died. Levels of uPA and PAI-1 in tumor tissue extracts were determined by different immunoassays; values were ranked within each dataset and divided by the number of patients in that dataset to produce fractional ranks that could be compared directly across datasets. Associations of ranks of uPA and PAI-1 levels with relapse-free survival (RFS) and overall survival (OS) were analyzed by Cox multivariable regression analysis stratified by dataset, including the following traditional prognostic variables: age, menopausal status, lymph node status, tumor size, histologic grade, and steroid hormone-receptor status. All P values were two-sided. Results: Apart from lymph node status, high levels of uPA and PAI-1 were the strongest predictors of both poor RFS and poor OS in the analyses of all patients. Moreover, in both lymph node-positive and lymph node-negative patients, higher uPA and PAI-1 values were independently associated with poor RFS and poor OS. For (untreated) lymph node-negative patients in particular, uPA and PAI-1 included together showed strong prognostic ability (all P<.001). Conclusions: This pooled analysis of the EORTC-RBG datasets confirmed the strong and independent prognostic value of uPA and PAI-1 in primary breast cancer. For patients with lymph node-negative breast cancer, uPA and PAI-1 measurements in primary tumors may be especially useful for designing individualized treatment strategie

Importance of pre-analytical steps for transcriptome and RT-qPCR analyses in the context of the phase II randomised multicentre trial REMAGUS02 of neoadjuvant chemotherapy in breast cancer patients

<p>Abstract</p> <p>Background</p> <p>Identification of predictive markers of response to treatment is a major objective in breast cancer. A major problem in clinical sampling is the variability of RNA templates, requiring accurate management of tumour material and subsequent analyses for future translation in clinical practice. Our aim was to establish the feasibility and reliability of high throughput RNA analysis in a prospective trial.</p> <p>Methods</p> <p>This study was conducted on RNA from initial biopsies, in a prospective trial of neoadjuvant chemotherapy in 327 patients with inoperable breast cancer. Four independent centres included patients and samples. Human U133 GeneChips plus 2.0 arrays for transcriptome analysis and quantitative RT-qPCR of 45 target genes and 6 reference genes were analysed on total RNA.</p> <p>Results</p> <p>Thirty seven samples were excluded because <it>i) </it>they contained less than 30% malignant cells, or <it>ii) </it>they provided RNA Integrity Number (RIN) of poor quality. Among the 290 remaining cases, taking into account strict quality control criteria initially defined to ensure good quality of sampling, 78% and 82% samples were eligible for transcriptome and RT-qPCR analyses, respectively. For RT-qPCR, efficiency was corrected by using standard curves for each gene and each plate. It was greater than 90% for all genes. Clustering analysis highlighted relevant breast cancer phenotypes for both techniques (ER+, PR+, HER2+, triple negative). Interestingly, clustering on trancriptome data also demonstrated a "centre effect", probably due to the sampling or extraction methods used in on of the centres. Conversely, the calibration of RT-qPCR analysis led to the centre effect withdrawing, allowing multicentre analysis of gene transcripts with high accuracy.</p> <p>Conclusions</p> <p>Our data showed that strict quality criteria for RNA integrity assessment and well calibrated and standardized RT-qPCR allows multicentre analysis of genes transcripts with high accuracy in the clinical context. More stringent criteria are needed for transcriptome analysis for clinical applications.</p