Free Access
Med Sci (Paris)
Volume 28, Number 5, Mai 2012
Page(s) 547 - 550
Section Forum
Published online 30 May 2012
  1. Van de Vijver MJ, He YD, van’t Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002 ; 347 : 1999–2009. [CrossRef] [PubMed] [Google Scholar]
  2. Tan PK, Downey TJ, Spitznagel EL Jr. Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res 2003 ; 31 : 5676–5684. [CrossRef] [PubMed] [Google Scholar]
  3. Jordan BR. How consistent are gene expression chip platforms? BioEssays 2004 ; 26 : 1236–1242. [CrossRef] [PubMed] [Google Scholar]
  4. Jordan B. Coup de tabac sur les puces. Med Sci (Paris) 2004 ; 20 : 487–490. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
  5. MAQC Consortium. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 2006 ; 24 : 1151–1161. [CrossRef] [PubMed] [Google Scholar]
  6. Cardoso F, Piccart-Gebhart M, Van’t Veer L, Rutgers E. TRANSBIG Consortium. The MINDACT trial: the first prospective clinical validation of a genomic tool. Mol Oncol 2007 ; 1 : 246–251. [CrossRef] [PubMed] [Google Scholar]
  7. Venet D, Dumont JE, Detours V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Comput Biol 2011 ; 7 : e 1002240. [CrossRef] [Google Scholar]
  8. Ein-Dor L, Zuk O, Domany E. Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc Natl Acad Sci USA 2006 ; 103 : 5923–5928. [CrossRef] [Google Scholar]
  9. Krishnan V, Han MH, Graham DL, et al. Molecular adaptations underlying susceptibility and resistance to social defeat in brain reward regions. Cell 2007 ; 131 : 391–404. [CrossRef] [PubMed] [Google Scholar]
  10. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004 ; 351 : 2817–2826. [CrossRef] [PubMed] [Google Scholar]
  11. Sotiriou C, Wirapati P, Loi S, et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 2006 ; 98 : 262–272. [CrossRef] [PubMed] [Google Scholar]
  12. Korkola JE, Blaveri E, DeVries, et al. Identification of a robust gene signature that predicts breast cancer outcome in independent data sets. BMC Cancer 2007 ; 7 : 61. [CrossRef] [PubMed] [Google Scholar]
  13. Taube JH, Herschkowitz JI, Komurov K, et al. Core epithelial-to-mesenchymal transition interactome gene-expression signature is associated with claudin-low and metaplastic breast cancer subtypes. Proc Natl Acad Sci USA 2010 ; 107 : 15449–15454. [CrossRef] [Google Scholar]
  14. Haibe-Kains B, Desmedt C, Loi S, et al. A three-gene model to robustly identify breast cancer molecular subtypes. J Natl Cancer Inst 2012 ; 104 : 311–325. [CrossRef] [PubMed] [Google Scholar]
  15. Detours V. Confounded cancer markers. The Scientist, December 7, 2011. [Google Scholar]
  16. Bertucci F, Birnbaum D. Génomique et recherche clinique en cancérologie mammaire. Med Sci (Paris) 2012 ; (Suppl 1) : 14–18. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
  17. Michiels S, Hill C. Défis statistiques posés par les biopuces : autant d’espoir que de faux positifs ? Med Sci (Paris) 2008 ; 24 : 317–319 [PubMed] [Google Scholar]

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