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Home All issues Volume 30 / No 6-7 (Juin–Juillet 2014) Med Sci (Paris), 30 6-7 (2014) 693-698 References
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Issue
Med Sci (Paris)
Volume 30, Number 6-7, Juin–Juillet 2014
Page(s) 693 - 698
Section Forum
DOI https://doi.org/10.1051/medsci/20143006023
Published online 11 July 2014
  1. Kandoth C, McLellan MD, Vandin F, et al. Mutational landscape and significance across 12 major cancer types. Nature 2013 ; 502 : 333–339. [CrossRef] [PubMed] [Google Scholar]
  2. Watson IR, Takahashi K, Futreal PA, Chin L. Emerging patterns of somatic mutations in cancer. Nat Rev Genet 2013 ; 14 : 703–718. [CrossRef] [PubMed] [Google Scholar]
  3. Alexandrov LB, Nik-Zainal S, Wedge DC, et al. Signatures of mutational processes in human cancer. Nature 2013 ; 500 : 415–421. [CrossRef] [PubMed] [Google Scholar]
  4. Vogelstein B, Papadopoulos N, Velculescu VE, et al. Cancer genome landscapes. Science 2013 ; 339 : 1546–1558. [Google Scholar]
  5. Stephens PJ, Tarpey PS, Davies H, et al. The landscape of cancer genes and mutational processes in breast cancer. Nature 2012 ; 486 : 400–404. [CrossRef] [PubMed] [Google Scholar]
  6. Shah SP, Roth A, Goya R, et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 2012 ; 486 : 395–399. [PubMed] [Google Scholar]
  7. Navin N, Kendall J, Troge J, et al. Tumour evolution inferred by single-cell sequencing. Nature 2011 ; 472 : 90–94. [CrossRef] [PubMed] [Google Scholar]
  8. Anderson K, Lutz C, van Delft FW, et al. Genetic variegation of clonal architecture and propagating cells in leukaemia. Nature 2010 ; 469 : 356–361. [Google Scholar]
  9. Xu X, Hou Y, Yin X, et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 2012 ; 148 : 886–895. [CrossRef] [PubMed] [Google Scholar]
  10. Feinberg AP, Ohlsson R, Henikoff S. The epigenetic progenitor origin of human cancer. Nat Rev Genet 2006 ; 7 : 21–33. [CrossRef] [PubMed] [Google Scholar]
  11. Plass C, Pfister SM, Lindroth AM, et al. Mutations in regulators of the epigenome and their connections to global chromatin patterns in cancer. Nat Rev Genet 2013 ; 14 : 765–780. [CrossRef] [PubMed] [Google Scholar]
  12. Shen H, Laird PW. Interplay between the cancer genome and epigenome. Cell 2013 ; 153 : 38–55. [CrossRef] [PubMed] [Google Scholar]
  13. Landan G, Cohen NM, Mukamel Z, et al. Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues. Nat Genet 2012 ; 44 : 1207–1214. [CrossRef] [PubMed] [Google Scholar]
  14. Efroni S, Duttagupta R, Cheng J, et al. Global transcription in pluripotent embryonic stem cells. Cell Stem Cell 2008 ; 2 : 437–447. [CrossRef] [PubMed] [Google Scholar]
  15. Brown CR, Mao C, Falkovskaia E, et al. Linking stochastic fluctuations in chromatin structure, gene expression. PLoS Biol 2013 ; 11 : e1001621. [CrossRef] [PubMed] [Google Scholar]
  16. Sanchez A, Golding I. Genetic determinants and cellular constraints in noisy gene expression. Science 2013 ; 342 : 1188–1193. [CrossRef] [PubMed] [Google Scholar]
  17. Kreso A, O’Brien CA, van Galen P, et al. Variable clonal repopulation dynamics influence chemotherapy response in colorectal cancer. Science 2012 ; 339 : 543–548. [Google Scholar]
  18. Gupta PB, Fillmore CM, Jiang G, et al. Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell 2011 ; 146 : 633–644. [CrossRef] [PubMed] [Google Scholar]
  19. Rosen JM, Jordan CT. The increasing complexity of the cancer stem cell paradigm. Science 2009 ; 324 : 1670–1673. [CrossRef] [PubMed] [Google Scholar]
  20. Quintana E, Shackleton M, Sabel MS, et al. Efficient tumour formation by single human melanoma cells. Nature 2008 ; 456 : 593–598. [CrossRef] [PubMed] [Google Scholar]
  21. Roesch A, Fukunaga-Kalabis M, Schmidt EC, et al. A temporarily distinct subpopulation of slow-cycling melanoma cells is required for continuous tumor growth. Cell 2010 ; 141 : 583–594. [CrossRef] [PubMed] [Google Scholar]
  22. Little SC, Tikhonov M, Gregor T. Precise developmental gene expression arises from globally stochastic transcriptional activity. Cell 2013 ; 154 : 789–800. [CrossRef] [PubMed] [Google Scholar]
  23. Ohnishi Y, Huber W, Tsumura A, et al. Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages. Nat Cell Biol 2014 ; 16 : 27–37. [CrossRef] [PubMed] [Google Scholar]
  24. Featherstone K, Harper CV, McNamara A, et al. Pulsatile patterns of pituitary hormone gene expression change during development. J Cell Sci 2011 ; 124 : 3484–3491. [CrossRef] [PubMed] [Google Scholar]
  25. MacArthur BD, Lemischka IR. Statistical mechanics of pluripotency. Cell 2013 ; 154 : 484–489. [CrossRef] [PubMed] [Google Scholar]
  26. Kupiec JJ. L’origine des individus. Paris : Fayard, 2008 : 315 p. [Google Scholar]
  27. Stergachis AB, Neph S, Reynolds A, et al. Developmental fate and cellular maturity encoded in human regulatory DNA landscapes. Cell 2013 ; 154 : 888–903. [CrossRef] [PubMed] [Google Scholar]
  28. Capp JP. Stochastic gene expression, disruption of tissue averaging effects and cancer as a disease of development. Bioessays 2005 ; 27 : 1277–1285. [CrossRef] [PubMed] [Google Scholar]
  29. Capp JP. Nouveau regard sur le cancer. Pour une révolution des traitements. Paris : Belin, 2012 : 256 p. [Google Scholar]
  30. Capp JP. Stochastic gene expression stabilization as a new therapeutic strategy for cancer. Bioessays 2012 ; 34 : 170–173. [CrossRef] [PubMed] [Google Scholar]
  31. Solary E. Une approche réductionniste du cancer. Med Sci (Paris) 2014 ; 30 : 683–687. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
  32. Kupiec JJ. Comment le hasard intervient-il dans le débat entre holisme et réductionnisme ? Conclusion du dossier Cancer/Haredhol. Med Sci (Paris) 2014 ; 30 : 699–700. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
  33. Sonnenschein C, Soto AM. Le cancer et ses gènes insaisissables. Med Sci (Paris) 2014 ; 30 : 688–692. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]

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