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BioMed Research International
Volume 2017, Article ID 8569328, 12 pages
https://doi.org/10.1155/2017/8569328
Research Article

In Vitro/In Silico Study on the Role of Doubling Time Heterogeneity among Primary Glioblastoma Cell Lines

1Department of Medicine, University of Crete, Heraklion, Greece
2Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece
3Neurosurgery Clinic, University General Hospital of Heraklion, Heraklion, Greece
4Gene Expression Laboratory, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Greece
5Department of Biology, University of Crete, Heraklion, Greece

Correspondence should be addressed to V. Sakkalis; rg.htrof.sci@silakkas

Received 5 May 2017; Accepted 18 September 2017; Published 31 October 2017

Academic Editor: Sara Piccirillo

Copyright © 2017 M.-E. Oraiopoulou 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. D. N. Louis, A. Perry, G. Reifenberger et al., “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary,” Acta Neuropathologica, vol. 131, no. 6, pp. 803–820, 2016. View at Publisher · View at Google Scholar
  2. D. Schiffer, Brain tumor pathology: current diagnostic hotspots and pitfalls, Springer, Dordrecht, Netherlands, 2006.
  3. D. Sturm et al., Hotspot Mutations in H3F3A And IDH1 Define Distinct Epigenetic And Biological Subgroups of Glioblastoma, vol. 22, no. 4, pp. 425–437, 2012.
  4. M.-D. Inda, R. Bonavia, and J. Seoane, “Glioblastoma multiforme:A look inside its heterogeneous nature,” Cancers, vol. 6, no. 1, pp. 226–239, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. M. L. Goodenberger and R. B. Jenkins, “Genetics of adult glioma,” Cancer Genetics, vol. 205, no. 12, pp. 613–621, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. F. Ali-Osman, “Brain tumors,” in Contemporary Cancer Research, p. 393, Humana Press, Totowa , NJ, USA, 2005. View at Google Scholar
  7. A. Sottoriva, I. Spiteri, S. G. M. Piccirillo et al., “Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 110, no. 10, pp. 4009–4014, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Atlasi, L. Looijenga, and R. Fodde, “Cancer Stem Cells, Pluripotency, and Cellular Heterogeneity. A WNTer Perspective.,” Current Topics in Developmental Biology, vol. 107, pp. 373–404, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Chen, R. M. McKay, and L. F. Parada, “Malignant glioma: Lessons from genomics, mouse models, and stem cells,” Cell, vol. 149, no. 1, pp. 36–47, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. M. J. Clark, N. Homer, B. D. O'Connor et al., “U87MG decoded: the genomic sequence of a cytogenetically aberrant human cancer cell line,” PLoS Genetics, vol. 6, no. 1, Article ID e1000832, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Allen, M. Bjerke, H. Edlund, S. Nelander, and B. Westermark, “Origin of the U87MG glioma cell line: Good news and bad news,” Science Translational Medicine, vol. 8, no. 354, Article ID 354re3, 2016. View at Publisher · View at Google Scholar · View at Scopus
  12. C. S. Mullins, B. Schneider, F. Stockhammer, M. Krohn, C. F. Classen, and M. Linnebacher, “Establishment and Characterization of Primary Glioblastoma Cell Lines from Fresh and Frozen Material: A Detailed Comparison,” PLoS ONE, vol. 8, no. 8, Article ID e71070, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. L. F. Pavon, L. C. Marti, T. T. Sibov et al., “In vitro analysis of neurospheres derived from glioblastoma primary culture: A novel methodology paradigm,” Frontiers in Neurology, vol. 4, Article ID Article 214, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. B. L. Carlson, J. L. Pokorny, M. A. Schroeder, and J. N. Sarkaria, “Establishment, maintenance and in vitro and in vivo applications of primary human glioblastoma multiforme (GBM) xenograft models for translational biology studies and drug discovery,” in Curr Protoc Pharmacol, vol. 14, Chapter 14, unit 14.16, 2011. View at Google Scholar
  15. K. M. Joo, J. Kim, J. Jin et al., “Patient-Specific Orthotopic Glioblastoma Xenograft Models Recapitulate the Histopathology and Biology of Human Glioblastomas In Situ,” Cell Reports, vol. 3, no. 1, pp. 260–273, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. R. Foty, “A simple hanging drop cell culture protocol for generation of 3D spheroids,” Journal of Visualized Experiments, no. 51, Article ID e2720, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. White paper, 5 Reasons Cancer Researchers Adopt 3D Cell Culture: A Review of Recent Literature, 3D Biomatrix Inc., Michigan, USA, 2013.
  18. J. Friedrich, C. Seidel, R. Ebner, and L. A. Kunz-Schughart, “Spheroid-based drug screen: considerations and practical approach,” Nature Protocols, vol. 4, no. 3, pp. 309–324, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Zanoni, F. Piccinini, C. Arienti et al., “3D tumor spheroid models for in vitro therapeutic screening: A systematic approach to enhance the biological relevance of data obtained,” Scientific Reports, vol. 6, Article ID 19103, 2016. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Vinci, S. Gowan, F. Boxall et al., “Advances in establishment and analysis of three-dimensional tumor spheroid-based functional assays for target validation and drug evaluation,” BMC Biology, vol. 10, article no. 29, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. M. A. Grotzer, A. Neve, and M. Baumgartner, “Dissecting brain tumor growth and metastasis in vitro and ex vivo,” Journal of Cancer Metastasis and Treatment, vol. 2, no. 5, pp. 149–162, 2016. View at Publisher · View at Google Scholar
  22. R. A. Morgan, “Human tumor xenografts: The good, the bad, and the ugly,” Molecular Therapy, vol. 20, no. 5, pp. 882–884, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. T. Voskoglou-Nomikos, J. L. Pater, and L. Seymour, “Clinical predictive value of the in vitro cell line, human xenograft, and mouse allograft preclinical cancer models,” Clinical Cancer Research, vol. 9, no. 11, pp. 4227–4239, 2003. View at Google Scholar · View at Scopus
  24. V. Sakkalis, S. Sfakianakis, E. Tzamali et al., “Web-based workflow planning platform supporting the design and execution of complex multiscale cancer models,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 3, pp. 824–831, 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. A. Roniotis, K. Marias, V. Sakkalis, G. D. Tsibidis, and M. Zervakis, “A complete mathematical study of a 3D model of heterogeneous and anisotropic glioma evolution,” in Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, pp. 2807–2810, usa, September 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. A. Gerisch and M. A. Chaplain, “Mathematical modelling of cancer cell invasion of tissue: local and non-local models and the effect of adhesion,” Journal of Theoretical Biology, vol. 250, no. 4, pp. 684–704, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  27. K. R. Swanson, R. C. Rockne, J. Claridge, M. A. Chaplain, E. C. Alvord Jr., and A. R. A. Anderson, “Quantifying the role of angiogenesis in malignant progression of gliomas: In Silico modeling integrates imaging and histology,” Cancer Research, vol. 71, no. 24, pp. 7366–7375, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. S. M. Wise, J. S. Lowengrub, H. B. Frieboes, and V. Cristini, “Three-dimensional multispecies nonlinear tumor growth—I: Model and numerical method,” Journal of Theoretical Biology, vol. 253, no. 3, pp. 524–543, 2008. View at Publisher · View at Google Scholar · View at MathSciNet
  29. A. R. A. Anderson, “A hybrid mathematical model of solid tumour invasion: the importance of cell adhesion,” Mathematical Medicine and Biology, vol. 22, no. 2, pp. 163–186, 2005. View at Publisher · View at Google Scholar · View at Scopus
  30. A. R. Anderson, K. A. Rejniak, P. Gerlee, and V. Quaranta, “Modelling of cancer growth, evolution and invasion: bridging scales and models,” Mathematical Modelling of Natural Phenomena, vol. 2, no. 3, pp. 1–29, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  31. F. Michor and K. Beal, “Improving Cancer Treatment via Mathematical Modeling: Surmounting the Challenges Is Worth the Effort,” Cell, vol. 163, no. 5, pp. 1059–1063, 2015. View at Publisher · View at Google Scholar · View at Scopus
  32. J. P. O’Connor et al., “Imaging biomarker roadmap for cancer studies,” Nature Reviews Clinical Oncology, 2016. View at Google Scholar
  33. A. L. Baldock, R. C. Rockne, A. D. Boone et al., “From patient-specific mathematical neuro-oncology to precision medicine,” Front Oncol, vol. 3, p. 62, 2013. View at Publisher · View at Google Scholar
  34. J. S. Lowengrub, H. B. Frieboes, F. Jin et al., “Nonlinear modelling of cancer: bridging the gap between cells and tumours,” Nonlinearity, vol. 23, no. 1, pp. R1–R91, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  35. M. G. McNamara, S. Sahebjam, and W. P. Mason, “Emerging biomarkers in glioblastoma,” Cancers, vol. 5, no. 3, pp. 1103–1119, 2013. View at Publisher · View at Google Scholar · View at Scopus
  36. J. C. Anderson, C. W. Duarte, K. Welaya et al., “Kinomic exploration of temozolomide and radiation resistance in Glioblastoma multiforme xenolines,” Radiotherapy & Oncology, vol. 111, no. 3, pp. 468–474, 2014. View at Publisher · View at Google Scholar · View at Scopus
  37. C. Athale, Y. Mansury, and T. S. Deisboeck, “Simulating the impact of a molecular 'decision-process' on cellular phenotype and multicellular patterns in brain tumors,” Journal of Theoretical Biology, vol. 233, no. 4, pp. 469–481, 2005. View at Publisher · View at Google Scholar · View at Scopus
  38. T. E. Yankeelov, N. Atuegwu, D. Hormuth et al., “Clinically relevant modeling of tumor growth and treatment response,” Science Translational Medicine, vol. 5, no. 187, p. 187ps9, 2013. View at Publisher · View at Google Scholar
  39. M. De Jong, J. Essers, and W. M. Van Weerden, “Imaging preclinical tumour models: Improving translational power,” Nature Reviews Cancer, vol. 14, no. 7, pp. 481–493, 2014. View at Publisher · View at Google Scholar · View at Scopus
  40. N. Jagiella, B. Müller, M. Müller, I. E. Vignon-Clementel, and D. Drasdo, “Inferring Growth Control Mechanisms in Growing Multi-cellular Spheroids of NSCLC Cells from Spatial-Temporal Image Data,” PLoS Computational Biology, vol. 12, no. 2, Article ID e1004412, 2016. View at Publisher · View at Google Scholar · View at Scopus
  41. B. Hegedüs, A. Czirók, I. Fazekas, T. Bábel, E. Madarász, and T. Vicsek, “Locomotion and proliferation of glioblastoma cells in vitro: Statistical evaluation of videomicroscopic observations,” Journal of Neurosurgery, vol. 92, no. 3, pp. 428–434, 2000. View at Publisher · View at Google Scholar · View at Scopus
  42. D. Hanahan and R. A. Weinberg, “Hallmarks of cancer: the next generation,” Cell, vol. 144, no. 5, pp. 646–674, 2011. View at Publisher · View at Google Scholar · View at Scopus
  43. A. Bertuzzi, A. Gandolfi, C. Sinisgalli, G. Starace, and P. Ubezio, “Cell loss and the concept of potential doubling time,” Cytometry, vol. 29, no. 1, pp. 34–40, 1997. View at Publisher · View at Google Scholar · View at Scopus
  44. P. Black, “Management of malignant glioma: role of surgery in relation to multimodality therapy,” Journal of NeuroVirology, vol. 4, no. 2, pp. 227–236, 1998. View at Publisher · View at Google Scholar
  45. N. C. Atuegwu, L. R. Arlinghaus, X. Li et al., “Parameterizing the logistic model of tumor growth by DW-MRI and DCE-MRI data to predict treatment response and changes in breast cancer cellularity during neoadjuvant chemotherapy,” Translational Oncology, vol. 6, no. 3, pp. 256–264, 2013. View at Publisher · View at Google Scholar · View at Scopus
  46. H. B. Frieboes, M. E. Edgerton, J. P. Fruehauf et al., “Prediction of drug response in breast cancer using integrative experimental/computational modeling,” Cancer Research, vol. 69, no. 10, pp. 4484–4492, 2009. View at Publisher · View at Google Scholar · View at Scopus
  47. Y.-C. Tung, A. Y. Hsiao, S. G. Allen, Y.-S. Torisawa, M. Ho, and S. Takayama, “High-throughput 3D spheroid culture and drug testing using a 384 hanging drop array,” Analyst, vol. 136, no. 3, pp. 473–478, 2011. View at Publisher · View at Google Scholar · View at Scopus
  48. C. A. Schneider, W. S. Rasband, and K. W. Eliceiri, “NIH Image to ImageJ: 25 years of image analysis,” Nature Methods, vol. 9, no. 7, pp. 671–675, 2012. View at Publisher · View at Google Scholar · View at Scopus
  49. G. Tzedakis, E. Tzamali, K. Marias, and V. Sakkalis, “The importance of neighborhood scheme selection in agent-based tumor growth modeling,” Cancer Informatics, vol. 14, pp. 67–81, 2015. View at Publisher · View at Google Scholar · View at Scopus
  50. G. Tzedakis, E. Liapis, E. Tzamali, G. Zacharakis, and V. Sakkalis, “A hybrid discrete-continuous model of in vitro spheroid tumor growth and drug response,” in Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, pp. 6142–6145, usa, August 2016. View at Publisher · View at Google Scholar · View at Scopus
  51. M. M. Melicow, “The three steps to cancer: a new concept of cancerigenesis,” Journal of Theoretical Biology, vol. 94, no. 2, pp. 471–511, 1982. View at Publisher · View at Google Scholar · View at Scopus
  52. D. A. Guertin and D. M. Sabatini, Cell Size Control, in in eLS2001, Hoboken, NJ, United States, John Wiley & Sons, in eLS2001, 2001.
  53. A. L. Stensjoen et al., “Growth dynamics of untreated glioblastomas in vivo,” Neuro-Oncology, vol. 17, no. 10, pp. 1402–1411, 2015. View at Publisher · View at Google Scholar
  54. E. Sandén, S. Eberstål, E. Visse, P. Siesjö, and A. Darabi, “A standardized and reproducible protocol for serum-free monolayer culturing of primary paediatric brain tumours to be utilized for therapeutic assays,” Scientific Reports, vol. 5, Article ID 12218, 2015. View at Publisher · View at Google Scholar · View at Scopus
  55. C. M. L. Machado, A. Schenka, J. Vassallo et al., “Morphological characterization of a human glioma cell line,” Cancer Cell International, vol. 5, article no. 13, 2005. View at Publisher · View at Google Scholar · View at Scopus
  56. J. Douglas, “Alternating direction methods for three space variables,” Numerische Mathematik, vol. 4, pp. 41–63, 1962. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  57. E. L. Wachspress and G. J. Habetler, “An alternating-direction-implicit iteration technique,” Journal of the Society for Industrial and Applied Mathematics, vol. 8, pp. 403–423, 1960. View at Google Scholar · View at MathSciNet
  58. P. Hinow, P. Gerlee, L. J. McCawley et al., “A spatial model of tumor-host interaction: application of chemotherapy,” Mathematical Biosciences and Engineering, vol. 6, no. 3, pp. 521–546, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  59. E. Skounakis et al., “DoctorEye: A clinically driven multifunctional platform, for accurate processing of tumors in medical images,” The Open Medical Informatics Journal, vol. 4, no. 3, pp. 105–115, 2010. View at Publisher · View at Google Scholar