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      Necdin is a breast cancer metastasis suppressor that regulates the transcription of c-Myc

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          Abstract

          Metastasis is the primary cause of death in breast cancer. Earlier studies using a mammary tumorigenesis mouse model identified Necdin ( Ndn) as a germline modifier of metastasis. Differential expression of Ndn induces a gene-expression signature that predicts prognosis in human breast cancer. Additionally, a non-synonymous germline single nucleotide polymorphism (T50C; V17A) in Ndn distinguishes mouse strains with differing metastatic capacities. To better understand how hereditary factors influence metastasis in breast cancer, we characterized NDN-mediated transcription. Haplotype analysis in a well-characterized breast cancer cohort revealed that NDN germline variation is associated with both NDN expression levels and patient outcome. To examine the role of NDN in mammary tumor metastasis and transcriptional regulation, mouse mammary tumor cell lines stably over-expressing either the wildtype 50T or variant 50C Ndn allele were generated. Cells over-expressing Ndn 50T, but not Ndn 50C, exhibited significant decrease in cell invasiveness and pulmonary metastases compared to control cells. Transcriptome analyses identified a 71-gene expression signature that distinguishes cells over-expressing the two Ndn allelic variants. Furthermore, ChIP assays revealed c- Myc, a target gene of NDN, to be differentially regulated by the allelic variants. These data demonstrate that NDN and the T50C allele regulate gene expression and metastasis efficiency.

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          Breast cancer metastasis: markers and models.

          Breast cancer starts as a local disease, but it can metastasize to the lymph nodes and distant organs. At primary diagnosis, prognostic markers are used to assess whether the transition to systemic disease is likely to have occurred. The prevailing model of metastasis reflects this view--it suggests that metastatic capacity is a late, acquired event in tumorigenesis. Others have proposed the idea that breast cancer is intrinsically a systemic disease. New molecular technologies, such as DNA microarrays, support the idea that metastatic capacity might be an inherent feature of breast tumours. These data have important implications for prognosis prediction and our understanding of metastasis.
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            Modeling Survival Data: Extending the Cox Model

            This is a book for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyse multiple/correlated event data using marginal and random effects (frailty) models. It covers the use of residuals and diagnostic plots to identify influential or outlying observations, assess proportional hazards and examine other aspects of goodness of fit. Other topics include time-dependent covariates and strata, discontinuous intervals of risk, multiple time scales, smoothing and regression splines, and the computation of expected survival curves. A knowledge of counting processes and martingales is not assumed as the early chapters provide an introduction to this area. The focus of the book is on actual data examples, the analysis and interpretation of the results, and computation. The methods are now readily available in SAS and S-Plus and this book gives a hands-on introduction, showing how to implement them in both packages, with worked examples for many data sets. The authors call on their extensive experience and give practical advice, including pitfalls to be avoided. Terry Therneau is Head of the Section of Biostatistics, Mayo Clinic, Rochester, Minnesota. He is actively involved in medical consulting, with emphasis in the areas of chronic liver disease, physical medicine, hematology, and laboratory medicine, and is an author on numerous papers in medical and statistical journals. He wrote two of the original SAS procedures for survival analysis (coxregr and survtest), as well as the majority of the S-Plus survival functions. Patricia Grambsch is Associate Professor in the Division of Biostatistics, School of Public Health, University of Minnesota. She has collaborated extensively with physicians and public health researchers in chronic liver disease, cancer prevention, hypertension clinical trials and psychiatric research. She is a fellow the American Statistical Association and the author of many papers in medical and statistical journals.
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              Gene expression profiling in breast cancer: classification, prognostication, and prediction.

              Microarray-based gene expression profiling has had a major effect on our understanding of breast cancer. Breast cancer is now perceived as a heterogeneous group of different diseases characterised by distinct molecular aberrations, rather than one disease with varying histological features and clinical behaviour. Gene expression profiling studies have shown that oestrogen-receptor (ER)-positive and ER-negative breast cancers are distinct diseases at the transcriptomic level, that additional molecular subtypes might exist within these groups, and that the prognosis of patients with ER-positive disease is largely determined by the expression of proliferation-related genes. On the basis of these principles, a molecular classification system and prognostic multigene classifiers based on microarrays or derivative technologies have been developed and are being tested in randomised clinical trials and incorporated into clinical practice. In this review, we focus on the conceptual effect and potential clinical use of the molecular classification of breast cancer, and discuss prognostic and predictive multigene predictors. Copyright © 2011 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                13 October 2015
                19 August 2015
                : 6
                : 31
                : 31557-31568
                Affiliations
                1 Genetics and Molecular Biology Branch, National Human Genome Research Institute, NIH, Bethesda MD, USA
                2 Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Bethesda MD, USA
                3 NIH Intramural Sequencing Center, National Human Genome Research Institute, NIH, Bethesda MD, USA
                4 Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville MD, USA
                Author notes
                Correspondence to: Nigel P.S. Crawford, crawforn@ 123456mail.nih.gov
                Article
                4741624
                26384308
                3a58e083-89b4-4002-b487-82ceefa0dfc8
                Copyright: © 2015 Lee 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
                : 13 July 2015
                : 12 August 2015
                Categories
                Research Paper

                Oncology & Radiotherapy
                necdin,c-myc,germline polymorphisms,metastasis suppressors,breast cancer
                Oncology & Radiotherapy
                necdin, c-myc, germline polymorphisms, metastasis suppressors, breast cancer

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