Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 7851789, 12 pages
http://dx.doi.org/10.1155/2016/7851789
Research Article

Towards the Design of a Patient-Specific Virtual Tumour

1Clinatec, INSERM UA01, 38054 Grenoble, France
2Université Grenoble Alpes, CNRS, TIMC-IMAG/DyCTIM2, 38041 Grenoble, France
3Université Grenoble Alpes, EA 7442 RSRM, ID17-ESRF, 38000 Grenoble, France

Received 8 September 2016; Accepted 21 November 2016

Academic Editor: Francesco Pappalardo

Copyright © 2016 Flavien Caraguel 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.

Linked References

  1. F. S. Collins and H. Varmus, “A new initiative on precision medicine,” New England Journal of Medicine, vol. 372, no. 9, pp. 793–795, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Katsnelson, “Momentum grows to make ‘personalized’ medicine more ‘precise’,” Nature Medicine, vol. 19, no. 3, article 249, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. A. R. Shaikh, A. J. Butte, S. D. Schully, W. S. Dalton, M. J. Khoury, and B. W. Hesse, “Collaborative biomedicine in the age of big data: the case of cancer,” Journal of Medical Internet Research, vol. 16, no. 4, article e101, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. J. U. Adams, “Genetics: big hopes for big data,” Nature, vol. 527, no. 7578, pp. S108–S109, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. E. Bender, “Big data in biomedicine: 4 big questions,” Nature, vol. 527, no. 7576, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Zhang, Q. Zhu, and H. Liu, “Next generation informatics for big data in precision medicine era,” BioData Mining, vol. 8, article 34, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. Q. Mi, N. Y.-K. Li, C. Ziraldo et al., “Translational systems biology of inflammation: potential applications to personalized medicine,” Personalized Medicine, vol. 7, no. 5, pp. 549–559, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. A. A. Friedman, A. Letai, D. E. Fisher, and K. T. Flaherty, “Precision medicine for cancer with next-generation functional diagnostics,” Nature Reviews Cancer, vol. 15, no. 12, pp. 747–756, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. L. Van Neste and W. Van Criekinge, “We are all individuals... bioinformatics in the personalized medicine era,” Cellular Oncology, vol. 38, no. 1, pp. 29–37, 2015. View at Publisher · View at Google Scholar
  10. Y. Louzoun, C. Xue, G. B. Lesinski, and A. Friedman, “A mathematical model for pancreatic cancer growth and treatments,” Journal of Theoretical Biology, vol. 351, pp. 74–82, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. G. G. Powathil, D. J. A. Adamson, and M. A. J. Chaplain, “Towards predicting the response of a solid tumour to chemotherapy and radiotherapy treatments: clinical insights from a computational model,” PLoS Computational Biology, vol. 9, no. 7, Article ID e1003120, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Robertson-Tessi, R. J. Gillies, R. A. Gatenby, and A. R. A. Anderson, “Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes,” Cancer Research, vol. 75, no. 8, pp. 1567–1579, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. O. Saut, J.-B. Lagaert, T. Colin, and H. M. Fathallah-Shaykh, “A Multilayer grow-or-go model for GBM: effects of invasive cells and anti-angiogenesis on growth,” Bulletin of Mathematical Biology, vol. 76, no. 9, pp. 2306–2333, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  14. L. Tang, A. L. van de Ven, D. Guo et al., “Computational modeling of 3D tumor growth and angiogenesis for chemotherapy evaluation,” PLoS ONE, vol. 9, no. 1, Article ID e83962, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. D. Hanahan and J. Folkman, “Patterns and emerging mechanisms of the angiogenic switch during tumorigenesis,” Cell, vol. 86, no. 3, pp. 353–364, 1996. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Pourtier-Manzanedo, C. Vercamer, E. Van Belle, V. Mattot, F. Mouquet, and B. Vandenbunder, “Expression of an Ets-1 dominant-negative mutant perturbs normal and tumor angiogenesis in a mouse ear model,” Oncogene, vol. 22, no. 12, pp. 1795–1806, 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. A.-C. Lesart, B. van der Sanden, L. Hamard, F. Estève, and A. Stéphanou, “On the importance of the submicrovascular network in a computational model of tumour growth,” Microvascular Research, vol. 84, no. 2, pp. 188–204, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. A. St{\'e}phanou, S. R. McDougall, A. R. Anderson, and M. A. Chaplain, “Mathematical modelling of flow in 2D and 3D Vascular networks: applications to anti-angiogenic and chemotherapeutic drug strategies,” Mathematical and Computer Modelling, vol. 41, no. 10, pp. 1137–1156, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. A. L. Bauer, T. L. Jackson, and Y. Jiang, “A cell-based model exhibiting branching and anastomosis during tumor-induced angiogenesis,” Biophysical Journal, vol. 92, no. 9, pp. 3105–3121, 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. D. Goldman and A. S. Popel, “A computational study of the effect of capillary network anastomoses and tortuosity on oxygen transport,” Journal of Theoretical Biology, vol. 206, no. 2, pp. 181–194, 2000. View at Publisher · View at Google Scholar · View at Scopus
  21. T. Alarcon, H. M. Byrne, and P. K. Maini, “A cellular automaton model for tumour growth in inhomogeneous environment,” Journal of Theoretical Biology, vol. 225, no. 2, pp. 257–274, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. M. Welter and H. Rieger, “Physical determinants of vascular network remodeling during tumor growth,” European Physical Journal E, vol. 33, no. 2, pp. 149–163, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. J.-C. Yen, F.-J. Chang, and S. Chang, “A new criterion for automatic multilevel thresholding,” IEEE Transactions on Image Processing, vol. 4, no. 3, pp. 370–378, 1995. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Pons-Salort, B. Van Der Sanden, A. Juhem, A. Popov, and A. Stéphanou, “A computational framework to assess the efficacy of cytotoxic molecules and vascular disrupting agents against solid tumours,” Mathematical Modelling of Natural Phenomena, vol. 7, no. 1, pp. 49–77, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  25. B. Bedessem and A. Stéphanou, “A mathematical model of HiF-1α-mediated response to hypoxia on the G1/S transition,” Mathematical Biosciences, vol. 248, pp. 31–39, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  26. S. Shimizu, Y. Eguchi, W. Kamiike et al., “Induction of apoptosis as well as necrosis by hypoxia and predominant prevention of apoptosis by Bcl-2 and Bcl-XL,” Cancer Research, vol. 56, no. 9, pp. 2161–2166, 1996. View at Google Scholar · View at Scopus
  27. M. R. Dowling, A. Kan, S. Heinzel et al., “Stretched cell cycle model for proliferating lymphocytes,” Proceedings of the National Academy of Sciences of the United States of America, vol. 111, no. 17, pp. 6377–6382, 2014. View at Publisher · View at Google Scholar
  28. R. Levayer, C. Dupont, and E. Moreno, “Tissue crowding induces caspase-dependent competition for space,” Current Biology, vol. 26, no. 5, pp. 670–677, 2016. View at Publisher · View at Google Scholar
  29. J. E. Koblinski, M. Ahram, and B. F. Sloane, “Unraveling the role of proteases in cancer,” Clinica Chimica Acta, vol. 291, no. 2, pp. 113–135, 2000. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Rakashanda, F. Rana, S. Rafiq, A. Masood, and S. Amin, “Role of proteases in cancer: a review,” Biotechnology and Molecular Biology Reviews, vol. 7, no. 4, pp. 90–101, 2012. View at Publisher · View at Google Scholar
  31. A. Stéphanou, S. R. McDougall, A. R. A. Anderson, and M. A. J. Chaplain, “Mathematical modelling of the influence of blood rheological properties upon adaptative tumour-induced angiogenesis,” Mathematical and Computer Modelling, vol. 44, no. 1-2, pp. 96–123, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  32. M. Fang, J. Yuan, C. Peng, and Y. Li, “Collagen as a double-edged sword in tumor progression,” Tumor Biology, vol. 35, no. 4, pp. 2871–2882, 2014. View at Publisher · View at Google Scholar · View at Scopus
  33. P. Lu, K. Takai, V. M. Weaver, and Z. Werb, “Extracellular matrix degradation and remodeling in development and disease,” Cold Spring Harbor Perspectives in Biology, vol. 3, no. 12, Article ID a005058, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. T. Stylianopoulos, J. D. Martin, V. P. Chauhan et al., “Causes, consequences, and remedies for growth-induced solid stress in murine and human tumors,” Proceedings of the National Academy of Sciences of the United States of America, vol. 109, no. 38, pp. 15101–15108, 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. F. Montel, M. Delarue, J. Elgeti, D. Vignjevic, G. Cappello, and J. Prost, “Isotropic stress reduces cell proliferation in tumor spheroids,” New Journal of Physics, vol. 14, Article ID 055008, 2012. View at Publisher · View at Google Scholar · View at Scopus