Breast Cancer Research

official impact factor 5.79

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

Yudi Pawitan, Judith Bjöhle, Lukas Amler, Anna-Lena Borg, Suzanne Egyhazi, Per Hall, Xia Han, Lars Holmberg, Fei Huang, Sigrid Klaar, Edison T Liu, Lance Miller, Hans Nordgren, Alexander Ploner, Kerstin Sandelin, Peter M Shaw, Johanna Smeds, Lambert Skoog, Sara Wedrén and Jonas Bergh*

Breast Cancer Research 2005, 7:R953-R964 doi:10.1186/bcr1325

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Research   Open Access

Improving biomarker list stability by integration of biological knowledge in the learning process

Tiziana Sanavia, Fabio Aiolli, Giovanni Da San Martino, Andrea Bisognin, Barbara Di Camillo BMC Bioinformatics 2012, 13(Suppl 4):S22 (28 March 2012)

Research article   Open Access

Maximizing biomarker discovery by minimizing gene signatures

Chang Chang, Junwei Wang, Chen Zhao, Jennifer Fostel, Weida Tong, Pierre R Bushel, Youping Deng, Lajos Pusztai, W Fraser Symmans, Tieliu Shi BMC Genomics 2011, 12(Suppl 5):S6 (23 December 2011)

Research article   Open Access Highly Accessed

Variants on the promoter region of PTEN affect breast cancer progression and patient survival

Tuomas Heikkinen, Dario Greco, Liisa M Pelttari, Johanna Tommiska, Pia Vahteristo, Päivi Heikkilä, Carl Blomqvist, Kristiina Aittomäki, Heli Nevanlinna Breast Cancer Research 2011, 13:R130 (15 December 2011)

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A new gene expression signature, the ClinicoMolecular Triad Classification, may improve prediction and prognostication of breast cancer at the time of diagnosis

Dong-Yu Wang, Susan J Done, David R McCready, Scott Boerner, Supriya Kulkarni, Wey Leong Breast Cancer Research 2011, 13:R92 (22 September 2011)

Research article   Open Access

Breast tumors from CHEK2 1100delC-mutation carriers: genomic landscape and clinical implications

Taru A Muranen, Dario Greco, Rainer Fagerholm, Outi Kilpivaara, Kati Kämpjärvi, Kristiina Aittomäki, Carl Blomqvist, Päivi Heikkilä, Åke Borg, Heli Nevanlinna Breast Cancer Research 2011, 13:R90 (20 September 2011)

Research article   Open Access

Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response

Qiyuan Li, Aron C Eklund, Nicolai J Birkbak, Christine Desmedt, Benjamin Haibe-Kains, Christos Sotiriou, W Fraser Symmans, Lajos Pusztai, Søren Brunak, Andrea L Richardson, Zoltan Szallasi BMC Bioinformatics 2011, 12:310 (28 July 2011)

Software   Open Access

A simple method for assigning genomic grade to individual breast tumours

Kristian Wennmalm, Jonas Bergh BMC Cancer 2011, 11:306 (21 July 2011)

Research   Open Access

Kinome expression profiling and prognosis of basal breast cancers

Renaud Sabatier, Pascal Finetti, Emilie Mamessier, Stéphane Raynaud, Nathalie Cervera, Eric Lambaudie, Jocelyne Jacquemier, Patrice Viens, Daniel Birnbaum, François Bertucci Molecular Cancer 2011, 10:86 (21 July 2011)

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Protein expression based multimarker analysis of breast cancer samples

Angela P Presson, Nam K Yoon, Lora Bagryanova, Vei Mah, Mohammad Alavi, Erin L Maresh, Ayyappan K Rajasekaran, Lee Goodglick, David Chia, Steve Horvath BMC Cancer 2011, 11:230 (8 June 2011)

Research article   Open Access

An 8-gene qRT-PCR-based gene expression score that has prognostic value in early breast cancer

Iker Sánchez-Navarro, Angelo Gámez-Pozo, Álvaro Pinto, David Hardisson, Rosario Madero, Rocío López, Belén San José, Pilar Zamora, Andrés Redondo, Jaime Feliu, Paloma Cejas, Manuel González Barón, Juan Ángel Fresno Vara, Enrique Espinosa BMC Cancer 2010, 10:336 (28 June 2010)

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The expression level of HJURP has an independent prognostic impact and predicts the sensitivity to radiotherapy in breast cancer

Zhi Hu, Ge Huang, Anguraj Sadanandam, Shenda Gu, Marc E Lenburg, Melody Pai, Nora Bayani, Eleanor A Blakely, Joe W Gray, Jian-Hua Mao Breast Cancer Research 2010, 12:R18 (8 March 2010)

Method   Open Access

A fuzzy gene expression-based computational approach improves breast cancer prognostication

Benjamin Haibe-Kains, Christine Desmedt, Françoise Rothé, Martine Piccart, Christos Sotiriou, Gianluca Bontempi Genome Biology 2010, 11:R18 (15 February 2010)

A fuzzy computational approach that takes into account several molecular subtypes in order to provide more accurate breast cancer prognosis

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Cellular processes of v-Src transformation revealed by gene profiling of primary cells - Implications for human cancer

Bart M Maślikowski, Benjamin D Néel, Ying Wu, Lizhen Wang, Natalie A Rodrigues, Germain Gillet, Pierre-André Bédard BMC Cancer 2010, 10:41 (12 February 2010)

Research   Open Access

Bimodal gene expression patterns in breast cancer

Marina Bessarabova, Eugene Kirillov, Weiwei Shi, Andrej Bugrim, Yuri Nikolsky, Tatiana Nikolskaya BMC Genomics 2010, 11(Suppl 1):S8 (10 February 2010)

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A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability

Herman MJ Sontrop, Perry D Moerland, René van den Ham, Marcel JT Reinders, Wim FJ Verhaegh BMC Bioinformatics 2009, 10:389 (26 November 2009)

Research   Open Access

Survival Online: a web-based service for the analysis of correlations between gene expression and clinical and follow-up data

Luca Corradi, Valentina Mirisola, Ivan Porro, Livia Torterolo, Marco Fato, Paolo Romano, Ulrich Pfeffer BMC Bioinformatics 2009, 10(Suppl 12):S10 (15 October 2009)

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mRNA expression profiles show differential regulatory effects of microRNAs between estrogen receptor-positive and estrogen receptor-negative breast cancer

Chao Cheng, Xuping Fu, Pedro Alves, Mark Gerstein Genome Biology 2009, 10:R90 (1 September 2009)

Most microRNAs have a stronger inhibitory effect in estrogen receptor-negative than in estrogen receptor-positive breast cancers

Methodology article   Open Access Highly Accessed

CLEAN: CLustering Enrichment ANalysis

Johannes M Freudenberg, Vineet K Joshi, Zhen Hu, Mario Medvedovic BMC Bioinformatics 2009, 10:234 (29 July 2009)

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Effects of sample size on robustness and prediction accuracy of a prognostic gene signature

Seon-Young Kim BMC Bioinformatics 2009, 10:147 (16 May 2009)

Database   Open Access

PrognoScan: a new database for meta-analysis of the prognostic value of genes

Hideaki Mizuno, Kunio Kitada, Kenta Nakai, Akinori Sarai BMC Medical Genomics 2009, 2:18 (24 April 2009)

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T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers

Achim Rody, Uwe Holtrich, Laos Pusztai, Cornelia Liedtke, Regine Gaetje, Eugen Ruckhaeberle, Christine Solbach, Lars Hanker, Andre Ahr, Dirk Metzler, Knut Engels, Thomas Karn, Manfred Kaufmann Breast Cancer Research 2009, 11:R15 (9 March 2009)

The T-cell metagene LCK identifies ER negative breast cancers, commonly associated with poor disease free survival, and may also identify differences in prognosis within this tumor subtype.

Methodology article   Open Access Highly Accessed

Selecting control genes for RT-QPCR using public microarray data

Vlad Popovici, Darlene R Goldstein, Janine Antonov, Rolf Jaggi, Mauro Delorenzi, Pratyaksha Wirapati BMC Bioinformatics 2009, 10:42 (2 February 2009)

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Gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancer

Mads Thomassen, Qihua Tan, Torben A Kruse BMC Cancer 2008, 8:394 (30 December 2008)

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A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer

Fabien Reyal, Martin H van Vliet, Nicola J Armstrong, Hugo M Horlings, Karin E de Visser, Marlen Kok, Andrew E Teschendorff, Stella Mook, Laura van 't Veer, Carlos Caldas, Remy J Salmon, Marc Vijver, Lodewyk FA Wessels Breast Cancer Research 2008, 10:R93 (13 November 2008)

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Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells

Atsushi Niida, Andrew D Smith, Seiya Imoto, Shuichi Tsutsumi, Hiroyuki Aburatani, Michael Q Zhang, Tetsu Akiyama BMC Bioinformatics 2008, 9:404 (29 September 2008)

Research article   Open Access

The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis

Andrew H Sims, Graeme J Smethurst, Yvonne Hey, Michal J Okoniewski, Stuart D Pepper, Anthony Howell, Crispin J Miller, Robert B Clarke BMC Medical Genomics 2008, 1:42 (21 September 2008)

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Identification of a gene signature in cell cycle pathway for breast cancer prognosis using gene expression profiling data

Jiangang Liu, Andrew Campen, Shuguang Huang, Sheng-Bin Peng, Xiang Ye, Mathew Palakal, A Keith Dunker, Yuni Xia, Shuyu Li BMC Medical Genomics 2008, 1:39 (11 September 2008)

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Pooling breast cancer datasets has a synergetic effect on classification performance and improves signature stability

Martin H van Vliet, Fabien Reyal, Hugo M Horlings, Marc J van de Vijver, Marcel JT Reinders, Lodewyk FA Wessels BMC Genomics 2008, 9:375 (6 August 2008)

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Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures

Pratyaksha Wirapati, Christos Sotiriou, Susanne Kunkel, Pierre Farmer, Sylvain Pradervand, Benjamin Haibe-Kains, Christine Desmedt, Michail Ignatiadis, Thierry Sengstag, Frédéric Schütz, Darlene R Goldstein, Martine Piccart, Mauro Delorenzi Breast Cancer Research 2008, 10:R65 (28 July 2008)

Methodology article   Open Access Highly Accessed

A gene sets approach for identifying prognostic gene signatures for outcome prediction

Seon-Young Kim, Yong Sung Kim BMC Genomics 2008, 9:177 (16 April 2008)

Research article   Open Access Highly Accessed

Merging microarray data from separate breast cancer studies provides a robust prognostic test

Lei Xu, Aik Tan, Raimond L Winslow, Donald Geman BMC Bioinformatics 2008, 9:125 (27 February 2008)

Method   Open Access Highly Accessed

Correction of technical bias in clinical microarray data improves concordance with known biological information

Aron C Eklund, Zoltan Szallasi Genome Biology 2008, 9:R26 (4 February 2008)

A method is presented to correct for technical bias in clinical microarray data, which increases concordance with known biological relationships.

Research   Open Access Highly Accessed

An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer

Andrew E Teschendorff, Ahmad Miremadi, Sarah E Pinder, Ian O Ellis, Carlos Caldas Genome Biology 2007, 8:R157 (2 August 2007)

A feature selection method was used in an analysis of three major microarray expression datasets to identify molecular subclasses and prognostic markers in estrogen receptor-negative breast cancer, showing that it is a heterogeneous disease with at least four main subtypes.

Methodology article   Open Access

RNA quality in frozen breast cancer samples and the influence on gene expression analysis – a comparison of three evaluation methods using microcapillary electrophoresis traces

Carina Strand, Johan Enell, Ingrid Hedenfalk, Mårten Fernö BMC Molecular Biology 2007, 8:38 (22 May 2007)

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A consensus prognostic gene expression classifier for ER positive breast cancer

Andrew E Teschendorff, Ali Naderi, Nuno L Barbosa-Morais, Sarah E Pinder, Ian O Ellis, Sam Aparicio, James D Brenton, Carlos Caldas Genome Biology 2006, 7:R101 (31 October 2006)

A consensus prognostic classifier for estrogen receptor positive breast tumors has been developed and shown to be valid in nearly 900 samples across different microarray platforms.

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High-throughput genomic technology in research and clinical management of breast cancer. Exploiting the potential of gene expression profiling: is it ready for the clinic?

Andrew H Sims, Kai Ong, Robert B Clarke, Anthony Howell Breast Cancer Research 2006, 8:214 (11 October 2006)

This article is part of a collection on High-throughput genomic...

Research article   Open Access

Intrinsic molecular signature of breast cancer in a population-based cohort of 412 patients

Stefano Calza, Per Hall, Gert Auer, Judith Bjöhle, Sigrid Klaar, Ulrike Kronenwett, Edison T Liu, Lance Miller, Alexander Ploner, Johanna Smeds, Jonas Bergh, Yudi Pawitan Breast Cancer Research 2006, 8:R34 (17 July 2006)

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Hormone-replacement therapy influences gene expression profiles and is associated with breast-cancer prognosis: a cohort study

Per Hall, Alexander Ploner, Judith Bjöhle, Fei Huang, Chin-Yo Lin, Edison T Liu, Lance D Miller, Hans Nordgren, Yudi Pawitan, Peter Shaw, Lambert Skoog, Johanna Smeds, Sara Wedrén, John Öhd, Jonas Bergh BMC Medicine 2006, 4:16 (30 June 2006)

HRT influences breast cancer gene expression profiles in estrogen receptor positive tumors and may be associated with improved recurrence-free survival and a better prognosis despite lower estrogen receptor levels.

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A systems approach to clinical oncology: Focus on breast cancer

Mark Abramovitz, Brian Leyland-Jones Proteome Science 2006, 4:5 (4 April 2006)