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      Down-Regulation of ECRG4, a Candidate Tumor Suppressor Gene, in Human Breast Cancer

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          Abstract

          Introduction

          ECRG4/C2ORF40 is a potential tumor suppressor gene (TSG) recently identified in esophageal carcinoma. Its expression, gene copy number and prognostic value have never been explored in breast cancer.

          Methods

          Using DNA microarray and array-based comparative genomic hybridization (aCGH), we examined ECRG4 mRNA expression and copy number alterations in 353 invasive breast cancer samples and normal breast (NB) samples. A meta-analysis was done on a large public retrospective gene expression dataset (n = 1,387) in search of correlations between ECRG4 expression and histo-clinical features including survival.

          Results

          ECRG4 was underexpressed in 94.3% of cancers when compared to NB. aCGH data revealed ECRG4 loss in 18% of tumors, suggesting that DNA loss is not the main mechanism of underexpression. Meta-analysis showed that ECRG4 expression was significantly higher in tumors displaying earlier stage, smaller size, negative axillary lymph node status, lower grade, and normal-like subtype. Higher expression was also associated with disease-free survival (DFS; HR = 0.84 [0.76–0.92], p = 0.0002) and overall survival (OS; HR = 0.72 [0.63–0.83], p = 5.0E-06). In multivariate analysis including the other histo-clinical prognostic features, ECRG4 expression remained the only prognostic factor for DFS and OS.

          Conclusions

          Our data suggest that ECRG4 is a candidate TSG in breast cancer, the expression of which may help improve the prognostication. If functional analyses confirm this TSG role, restoring ECRG4 expression in the tumor may represent a promising therapeutic approach.

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          Most cited references25

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          Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts

          Introduction Adjuvant breast cancer therapy significantly improves survival, but overtreatment and undertreatment are major problems. Breast cancer expression profiling has so far mainly been used to identify women with a poor prognosis as candidates for adjuvant therapy but without demonstrated value for therapy prediction. Methods We obtained the gene expression profiles of 159 population-derived breast cancer patients, and used hierarchical clustering to identify the signature associated with prognosis and impact of adjuvant therapies, defined as distant metastasis or death within 5 years. Independent datasets of 76 treated population-derived Swedish patients, 135 untreated population-derived Swedish patients and 78 Dutch patients were used for validation. The inclusion and exclusion criteria for the studies of population-derived Swedish patients were defined. Results Among the 159 patients, a subset of 64 genes was found to give an optimal separation of patients with good and poor outcomes. Hierarchical clustering revealed three subgroups: patients who did well with therapy, patients who did well without therapy, and patients that failed to benefit from given therapy. The expression profile gave significantly better prognostication (odds ratio, 4.19; P = 0.007) (breast cancer end-points odds ratio, 10.64) compared with the Elston–Ellis histological grading (odds ratio of grade 2 vs 1 and grade 3 vs 1, 2.81 and 3.32 respectively; P = 0.24 and 0.16), tumor stage (odds ratio of stage 2 vs 1 and stage 3 vs 1, 1.11 and 1.28; P = 0.83 and 0.68) and age (odds ratio, 0.11; P = 0.55). The risk groups were consistent and validated in the independent Swedish and Dutch data sets used with 211 and 78 patients, respectively. Conclusion We have identified discriminatory gene expression signatures working both on untreated and systematically treated primary breast cancer patients with the potential to spare them from adjuvant therapy.
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            Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer.

            Histologic grading of breast cancer defines morphologic subtypes informative of metastatic potential, although not without considerable interobserver disagreement and clinical heterogeneity particularly among the moderately differentiated grade 2 (G2) tumors. We posited that a gene expression signature capable of discerning tumors of grade 1 (G1) and grade 3 (G3) histology might provide a more objective measure of grade with prognostic benefit for patients with G2 disease. To this end, we studied the expression profiles of 347 primary invasive breast tumors analyzed on Affymetrix microarrays. Using class prediction algorithms, we identified 264 robust grade-associated markers, six of which could accurately classify G1 and G3 tumors, and separate G2 tumors into two highly discriminant classes (termed G2a and G2b genetic grades) with patient survival outcomes highly similar to those with G1 and G3 histology, respectively. Statistical analysis of conventional clinical variables further distinguished G2a and G2b subtypes from each other, but also from histologic G1 and G3 tumors. In multivariate analyses, genetic grade was consistently found to be an independent prognostic indicator of disease recurrence comparable with that of lymph node status and tumor size. When incorporated into the Nottingham prognostic index, genetic grade enhanced detection of patients with less harmful tumors, likely to benefit little from adjuvant therapy. Our findings show that a genetic grade signature can improve prognosis and therapeutic planning for breast cancer patients, and support the view that low- and high-grade disease, as defined genetically, reflect independent pathobiological entities rather than a continuum of cancer progression.
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              REporting recommendations for tumor MARKer prognostic studies (REMARK).

              Despite years of research and hundreds of reports on tumor markers in oncology, the number of markers that have emerged as clinically useful is pitifully small. Often initially reported studies of a marker show great promise, but subsequent studies on the same or related markers yield inconsistent conclusions or stand in direct contradiction to the promising results. It is imperative that we attempt to understand the reasons that multiple studies of the same marker lead to differing conclusions. A variety of methodologic problems have been cited to explain these discrepancies. Unfortunately, many tumor marker studies have not been reported in a rigorous fashion, and published articles often lack sufficient information to allow adequate assessment of the quality of the study or the generalizability of study results. The development of guidelines for the reporting of tumor marker studies was a major recommendation of the National Cancer Institute-European Organisation for Research and Treatment of Cancer (NCI-EORTC) First International Meeting on Cancer Diagnostics in 2000. As for the successful CONSORT initiative for randomized trials and for the STARD statement for diagnostic studies, we suggest guidelines to provide relevant information about the study design, pre-planned hypotheses, patient and specimen characteristics, assay methods, and statistical analysis methods. In addition, the guidelines suggest helpful presentations of data and important elements to include in discussions. The goal of these guidelines is to encourage transparent and complete reporting so that the relevant information will be available to others to help them to judge the usefulness of the data and understand the context in which the conclusions apply.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                16 November 2011
                : 6
                : 11
                : e27656
                Affiliations
                [1 ]Département d'Oncologie Moléculaire, Centre de Recherche en Cancérologie de Marseille, UMR891 INSERM and Institut Paoli-Calmettes Marseille, Marseille, France
                [2 ]Département d'Oncologie Médicale, Centre de Recherche en Cancérologie de Marseille, Institut Paoli-Calmettes Marseille, Marseille, France
                [3 ]Université de la Méditerranée, Marseille, France
                [4 ]Département de Polarité cellulaire, signalisation et cancer, Centre de Recherche en Cancérologie de Marseille, U891 INSERM and Institut Paoli-Calmettes Marseille, Marseille, France
                [5 ]SIB-Swiss Institute of Bioinformatics, Geneva, Switzerland
                [6 ]Department of Human Protein Science, University of Geneva, Geneva, Switzerland
                Institut de Génomique Fonctionnelle de Lyon, France
                Author notes

                Conceived and designed the experiments: FB RS LL DB J-PB. Performed the experiments: PF JA. Analyzed the data: FB RS PF AG. Contributed reagents/materials/analysis tools: FB RS PF JA AG MC. Wrote the paper: FB RS DB.

                Article
                PONE-D-11-15747
                10.1371/journal.pone.0027656
                3218004
                22110708
                dd9ba78e-b055-4592-96f6-db02580f5276
                Sabatier et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 11 August 2011
                : 21 October 2011
                Page count
                Pages: 8
                Categories
                Research Article
                Biology
                Computational Biology
                Molecular Genetics
                Gene Regulation
                Gene Expression
                Genetics
                Molecular Genetics
                Gene Identification and Analysis
                Cancer Genetics
                Genetics of Disease
                Medicine
                Oncology
                Basic Cancer Research
                Tumor Physiology
                Cancers and Neoplasms
                Breast Tumors

                Uncategorized
                Uncategorized

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