- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Volume 2013 (2013), Article ID 924971, 12 pages
Gene Expression Profile Analysis of T1 and T2 Breast Cancer Reveals Different Activation Pathways
1Department of Surgery, Akershus University Hospital, 1478 Lørenskog, Norway
2Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway
3Department of Radiology, School of Medicine, Stanford Center for Cancer Systems Biology, Stanford University, Stanford, CA 94305-5488, USA
4Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway
5Department of Clinical Molecular Biology and Laboratory Sciences (EpiGen), Akershus University Hospital, 1478 Lørenskog, Norway
6Department of Pathology, Akershus University Hospital, 1478 Lørenskog, Norway
7Institute of Health Promotion, Akershus University Hospital, 1478 Lørenskog, Norway
Received 29 November 2012; Accepted 8 January 2013
Academic Editors: A. Abdollahi, Y. Ionov, and V. Lorusso
Copyright © 2013 Margit L. H. Riis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- J. Ferlay, H. Shin, F. Bray, et al., “GLOBOCAN 2008 v1.2, cancer incidence and mortality worldwide,” in IARC CancerBase, 2012, http://globocan.iarc.fr.
- A. Goldhirsch, J. H. Glick, R. D. Gelber et al., “Meeting highlights: international expert consensus on the primary therapy of early breast cancer 2005,” Annals of Oncology, vol. 16, no. 10, pp. 1569–1583, 2005.
- S. Ciatto, S. Cecchini, A. Iossa, and G. Grazzini, “‘T’ category and operable breast cancer prognosis,” Tumori, vol. 75, no. 1, pp. 18–22, 1989.
- A. H. Olsen, S. H. Njor, and E. Lynge, “Estimating the benefits of mammography screening: the impact of study design,” Epidemiology, vol. 18, no. 4, pp. 487–492, 2007.
- Early Breast Cancer Trialists' Collaborative Group (EBCTCG), “Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials,” The Lancet, vol. 365, no. 9472, pp. 1687–1717, 2005.
- D. B. Allison, X. Cui, G. P. Page, and M. Sabripour, “Microarray data analysis: from disarray to consolidation and consensus,” Nature Reviews Genetics, vol. 7, no. 1, pp. 55–65, 2006.
- L. D. Miller and E. T. Liu, “Expression genomics in breast cancer research: microarrays at the crossroads of biology and medicine,” Breast Cancer Research, vol. 9, no. 2, article 206, 2007.
- M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein, “Cluster analysis and display of genome-wide expression patterns,” Proceedings of the National Academy of Sciences of the United States of America, vol. 95, no. 25, pp. 14863–14868, 1998.
- C. M. Perou, T. Sørlie, M. B. Eisen, et al., “Molecular portraits of human breast tumours,” Nature, vol. 406, no. 6797, pp. 747–752, 2000.
- L. J. Van't Veer, H. Dai, M. J. van de Vijver et al., “Gene expression profiling predicts clinical outcome of breast cancer,” Nature, vol. 415, no. 6871, pp. 530–536, 2002.
- R. Tibshirani, T. Hastie, B. Narasimhan, and G. Chu, “Diagnosis of multiple cancer types by shrunken centroids of gene expression,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 10, pp. 6567–6572, 2002.
- T. Sørlie, “Molecular portraits of breast cancer: tumour subtypes as distinct disease entities,” European Journal of Cancer, vol. 40, no. 18, pp. 2667–2675, 2004.
- M. J. van de Vijver, Y. D. He, L. J. van't Veer et al., “A gene-expression signature as a predictor of survival in breast cancer,” The New England Journal of Medicine, vol. 347, no. 25, pp. 1999–2009, 2002.
- B. Dysvik and I. Jonassen, “J-Express: exploring gene expression data using Java,” Bioinformatics, vol. 17, no. 4, pp. 369–370, 2001.
- V. G. Tusher, R. Tibshirani, and G. Chu, “Significance analysis of microarrays applied to the ionizing radiation response,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 9, pp. 5116–5121, 2001.
- B. H. Mevik and R. Wehrens, “The pls package: principal component and partial least squares regression in R,” Journal of Statistical Software, vol. 18, no. 2, pp. 1–23, 2007.
- R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2010.
- D. W. Huang, B. T. Sherman, Q. Tan et al., “The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists,” Genome Biology, vol. 8, no. 9, article R183, 2007.
- D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources,” Nature Protocols, vol. 4, no. 1, pp. 44–57, 2009.
- X. Zhao, E. A. Rødeland, T. Sørlie, and H. G. Russnes, “Systematic assessment of prognostic gene signatures for breast cancer shows distinct influence of time and ER status,” under review.
- S. Loi, B. Haibe-Kains, C. Desmedt et al., “Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade,” Journal of Clinical Oncology, vol. 25, no. 10, pp. 1239–1246, 2007.
- L. D. Miller, J. Smeds, J. George et al., “An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 38, pp. 13550–13555, 2005.
- Y. Pawitan, J. Bjohle, L. Amler, et al., “Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts,” Breast Cancer Research, vol. 7, no. 6, pp. R953–R964, 2005.
- C. Desmedt, F. Piette, S. Loi et al., “Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series,” Clinical Cancer Research, vol. 13, no. 11, pp. 3207–3214, 2007.
- A. J. Minn, G. P. Gupta, P. M. Siegel et al., “Genes that mediate breast cancer metastasis to lung,” Nature, vol. 436, no. 7050, pp. 518–524, 2005.
- K. Chin, S. DeVries, J. Fridlyand et al., “Genomic and transcriptional aberrations linked to breast cancer pathophysiologies,” Cancer Cell, vol. 10, no. 6, pp. 529–541, 2006.
- R. A. Irizarry, B. M. Bolstad, F. Collin, L. M. Cope, B. Hobbs, and T. P. Speed, “Summaries of Affymetrix GeneChip probe level data,” Nucleic Acids Research, vol. 31, no. 4, article e15, 2003.
- A. H. Sims, G. J. Smethurst, Y. Hey, et al., “The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets—improving meta-analysis and prediction of prognosis,” BMC Medical Genomics, vol. 1, no. 1, article 42, 2008.
- C. M. Perou, S. S. Jeffrey, M. van de Rijn et al., “Distinctive gene expression patterns in human mammary epithelial cells and breast cancers,” Proceedings of the National Academy of Sciences of the United States of America, vol. 96, no. 16, pp. 9212–9217, 1999.
- T. Sørlie, C. M. Perou, R. Tibshirani et al., “Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 19, pp. 10869–10874, 2001.
- P. S. Bernard, J. S. Parker, M. Mullins et al., “Supervised risk predictor of breast cancer based on intrinsic subtypes,” Journal of Clinical Oncology, vol. 27, no. 8, pp. 1160–1167, 2009.
- M. Buyse, S. Loi, L. van't Veer et al., “Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer,” Journal of the National Cancer Institute, vol. 98, no. 17, pp. 1183–1192, 2006.
- Y. Wang, J. G. M. Klijn, Y. Zhang et al., “Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer,” The Lancet, vol. 365, no. 9460, pp. 671–679, 2005.
- C. Sotiriou, P. Wirapati, S. Loi et al., “Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis,” Journal of the National Cancer Institute, vol. 98, no. 4, pp. 262–272, 2006.
- S. Paik, S. Shak, G. Tang et al., “A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer,” The New England Journal of Medicine, vol. 351, no. 27, pp. 2817–2826, 2004.
- H. Y. Chang, D. S. A. Nuyten, J. B. Sneddon et al., “Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 10, pp. 3738–3743, 2005.
- H. Y. Chang, J. B. Sneddon, A. A. Alizadeh et al., “Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds,” PLoS Biology, vol. 2, no. 2, article E7, 2004.
- J. T. Chi, Z. Wang, D. S. A. Nuyten, et al., “Gene expression programs in response to hypoxia: cell type specificity and prognostic significance in human cancers,” PLoS Medicine, vol. 3, no. 3, article e47, 2006.
- D. S. A. Nuyten, T. Hastie, J. T. A. Chi, H. Y. Chang, and M. J. van de Vijver, “Combining biological gene expression signatures in predicting outcome in breast cancer: an alternative to supervised classification,” European Journal of Cancer, vol. 44, no. 15, pp. 2319–2329, 2008.
- M. Schemper, “The relative importance of prognostic factors in studies of survival,” Statistics in Medicine, vol. 12, no. 24, pp. 2377–2382, 1993.
- F. Harrell, K. Lee, and D. Mark, “Tutorial in biostatistics multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors,” Statistics in Medicine, vol. 15, pp. 361–387, 1996.
- H. Brenner, “Long-term survival rates of cancer patients achieved by the end of the 20th century: a period analysis,” The Lancet, vol. 360, no. 9340, pp. 1131–1135, 2002.
- X. Zhao, T. Sørlie, B. Naume et al., “Combining gene signatures improves prediction of breast cancer survival,” PLoS ONE, vol. 6, no. 3, Article ID e17845, 2011.
- C. Fan, D. S. Oh, L. Wessels et al., “Concordance among gene-expression-based predictors for breast cancer,” The New England Journal of Medicine, vol. 355, no. 6, pp. 560–569, 2006.
- P. Wirapati, C. Sotiriou, S. Kunkel et al., “Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures,” Breast Cancer Research, vol. 10, no. 4, article R65, 2008.
- M. Stendahl, S. Nilsson, C. Wigerup et al., “P27Kip1 is a predictive factor for tamoxifen treatment response but not a prognostic marker in premenopausal breast cancer patients,” International Journal of Cancer, vol. 127, no. 12, pp. 2851–2858, 2010.
- I. M. Chu, L. Hengst, and J. M. Slingerland, “The Cdk inhibitor p27 in human cancer: prognostic potential and relevance to anticancer therapy,” Nature Reviews Cancer, vol. 8, no. 4, pp. 253–267, 2008.
- P. L. Porter, W. E. Barlow, I. T. Yeh et al., “p27Kip1 and cyclin E expression and breast cancer survival after treatment with adjuvant chemotherapy,” Journal of the National Cancer Institute, vol. 98, no. 23, pp. 1723–1731, 2006.
- L. Newman, W. Xia, H. Y. Yang et al., “Correlation of p27 protein expression with HER-2/neu expression in breast cancer,” Molecular Carcinogenesis, vol. 30, no. 3, pp. 169–175, 2001.
- A. Giubellino, T. R. Burke, and D. P. Bottaro, “Grb2 signaling in cell motility and cancer,” Expert Opinion on Therapeutic Targets, vol. 12, no. 8, pp. 1021–1033, 2008.
- E. Wertheimer, A. Gutierrez-Uzquiza, C. Rosemblit, C. Lopez-Haber, M. S. Sosa, and M. G. Kazanietz, “Rac signaling in breast cancer: a tale of GEFs and GAPs,” Cellular Signalling, vol. 24, no. 2, pp. 353–362, 2012.