New Challenges for Cancer Systems Biomedicine pp 355-375

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Top-Down Multiscale Simulation of Tumor Response to Treatment in the Context of In Silico Oncology. The Notion of Oncosimulator

  • Georgios Stamatakos

Abstract

The aim of this chapter is to provide a brief introduction into the basics of a top-down multilevel tumor dynamics modeling method primarily based on discrete entity consideration and manipulation. The method is clinically oriented, one of its major goals being to support patient individualized treatment optimization through experimentation in silico (= on the computer). Therefore, modeling of the treatment response of clinical tumors lies at the epicenter of the approach. Macroscopic data, including i.a. anatomic and metabolic tomographic images of the tumor, provide the framework for the integration of data and mechanisms pertaining to lower and lower biocomplexity levels such as clinically approved cellular and molecular biomarkers. The method also provides a powerful framework for the investigation of multilevel (multiscale) tumor biology in the generic investigational context. The Oncosimulator, a multiscale physics and biomedical engineering concept and construct tightly associated with the method and currently undergoing clinical adaptation, optimization and validation, is also sketched. A brief outline of the approach is provided in natural language. Two specific models of tumor response to chemotherapeutic and radiotherapeutic schemes are briefly outlined and indicative results are presented in order to exemplify the application potential of the method. The chapter concludes with a discussion of several important aspects of the method including i.a. numerical analysis aspects, technological issues, model extensions and validation within the framework of actual running clinico-genomic trials. Future perspectives and challenges are also addressed.

References

  1. 1.
    ACGT: Advancing Clinicogenomic Trials on Cancer: Open Grid Services for Improving Medical Knowledge Discovery. EC and Japan funded R&D project. (FP6-2005-IST- 026996) http://eu-acgt.org/acgt-for-you/researchers/in-silico-oncology/oncosimulator.html and http://www.eu-acgt.org/Google Scholar
  2. 2.
    Antipas, V.P., Stamatakos, G.S., Uzunoglu, N.K.: A patient-specific in vivo tumor and normal tissue model for prediction of the response to radiotherapy: a computer simulation approach. Meth. Inf. Med. 46, 367–375 (2007)Google Scholar
  3. 3.
    Antipas, V., Stamatakos, G. S., Uzunoglu, N.K., Dionysiou, D., Dale, R.: A spatiotemporal simulation model of the response of solid tumors to radiotherapy in vivo: parametric validation concerning oxygen enhancement ratio and cell cycle duration. Phys. Med. Biol. 49, 1–20 (2004)CrossRefGoogle Scholar
  4. 4.
    Bobola, M.S., Tseng, S.H., Blank, A., Berger, M.S., Silber, J.R.: Role of O6-methylguanine- DNA methyltransferase in resistance of human brain tumor cell lines to the clinically relevant methylating agents temozolomide and streptozotocin. Clin. Cancer Res. 2, 735–741 (1996)Google Scholar
  5. 5.
    Breward, C.J., Byrne, H.M., Lewis, C.E.: A multiphase model describing vascular tumour growth. Bull. Math. Biol. 65, 609–640 (2003)CrossRefGoogle Scholar
  6. 6.
    Clatz, O., Sermesant, M., Bondiau, P.Y., Delingette, H., Warfield, S.K., Malandain, G., Ayache, N.: Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation. IEEE Trans. Med. Imaging 24, 1334–1346 (2005)CrossRefGoogle Scholar
  7. 7.
    ContraCancrum: Clinically Oriented Translational Cancer Multilevel Modelling. EC funded R&D project (FP7-ICT-2007-2- 223979). www.contracancrum.euGoogle Scholar
  8. 8.
    Cristini, V., Frieboes, H.B., Gatenby, R., Caserta, S., Ferrari, M., Sinek, J.P.: Morphological instability and cancer invasion. Clin. Cancer Res. 11, 6772–6779 (2005)CrossRefGoogle Scholar
  9. 9.
    Deisboeck, T.S., Berens, M.E., Kansal, A.R., Torquato, S., Stemmer-Rachamimov, A.O., Chiocca, E.A.: Pattern of self-organization in tumour systems: Complex growth dynamics in a novel brain tumour spheroid model. Cell Prolif. 34, 115–134 (2001)CrossRefGoogle Scholar
  10. 10.
    Dionysiou, D.D., Stamatakos, G.S.: Applying a 4D multiscale in vivo tumor growth model to the exploration of radiotherapy scheduling: the effects of weekend treatment gaps and p53 gene status on the response of fast growing solid tumors. Cancer Inform. 2, 113–121 (2006)Google Scholar
  11. 11.
    Dionysiou, D.D., Stamatakos, G.S., Gintides, D., Uzunoglu, N., Kyriaki K.: Critical parameters determining standard radiotherapy treatment outcome for glioblastoma multiforme: a computer simulation. Open Biomed. Eng. J. 2, 43–51 (2008)CrossRefGoogle Scholar
  12. 12.
    Dionysiou, D.D., Stamatakos, G.S., Marias, K.: Simulating cancer radiotherapy on a multilevel basis: biology, oncology and image processing. Lect. Notes Comp. Sci. 4561, 569–575 (2007)CrossRefGoogle Scholar
  13. 13.
    Dionysiou, D.D., Stamatakos, G.S., Uzunoglu, N.K., Nikita K.S.: A computer simulation of in vivo tumour growth and response to radiotherapy: New algorithms and parametric results. Comp. Biol. Med. 36, 448–464 (2006)CrossRefGoogle Scholar
  14. 14.
    Dionysiou, D.D., Stamatakos, G.S., Uzunoglu, N.K., Nikita, K.S., Marioli, A.: A four- dimensional simulation model of tumour response to radiotherapy in vivo: parametric validation considering radiosensitivity, genetic profile and fractionation. J. Theor. Biol. 230, 1–20. (2004)CrossRefGoogle Scholar
  15. 15.
    d’Onofrio, A.: A general framework for modeling tumor-immune system competition and immunotherapy: analysis and medical inferences. Physica D 208, 220–235 (2005)MathSciNetMATHCrossRefGoogle Scholar
  16. 16.
    Duechting, W., Ulmer, W., Lehrig, R., Ginsberg, T., Dedeleit, E.: Computer simulation and modeling of tumor spheroid growth and their relevance for optimization of fractionated radiotherapy. Strahlenther Onkol 168, 354–360 (1992)Google Scholar
  17. 17.
    Duechting, W., Vogelsaenger, T.: Three-dimensional pattern generation applied to spheroidal tumor growth in a nutrient medium. Int. J. Biomed. Comput. 12, 377–392 (1981)CrossRefGoogle Scholar
  18. 18.
    Enderling, H., Chaplain, M.A.J., Anderson, A.R.A., Vaidya, J.S.: A mathematical model of breast cancer development, local treatment and recurrence. J. Theor. Biol. 246,245-259 (2007)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Frieboes, H.B., Zheng, X., Sun, C.H., Tromberg, B., Gatenby, R., Cristini, V.: An integrated computational/experimental model of tumor invasion. Cancer Res. 66, 1597–604 (2006)CrossRefGoogle Scholar
  20. 20.
    FDA, Center for Drug Evaluation and Research. Application Number: 21029, Drug Name: Temozolomide (Temodal), New Drug Application, Clinical Pharmacology and Biopharmaceutics Reviews, (Revision date: 2/2/1999), pp. 19–20 (1999)Google Scholar
  21. 21.
    Georgiadi, E.C., Stamatakos, G.S., Graf, N.M., Kolokotroni, E.A., Dionysiou, D.D., Hoppe, A., Uzunoglu, N.K.: Multilevel cancer modeling in the clinical environment: simulating the behavior of Wilms tumor in the context of the SIOP 2001/GP0H clinical trial and the ACGT Project. In: Proc. 8th IEEE International Conference on Bioinformatics and Bioengineering (BIBE 2008), Athens, Greece, October 8–10 2008, Paper No. BE-2.1.2. (2008)Google Scholar
  22. 22.
    Ginsberg, T., Ulmer, W., Duechting, W.: Computer simulation of fractionated radiotherapy: further results and their relevance to percutaneous irradiation and brachytherapy. Strahlenther Onkol 169, 304-310(1993)Google Scholar
  23. 23.
    Graf, N., Desmedt, C., Buffa, F., Kafetzopoulos, D., Forgo, N., Kollek, R., Hoppe, A., Stamatakos, G., Tsiknakis, M.: Post-genomic clinical trials - the perspective of ACGT. E-cancer Medical Science 2, (2008)Google Scholar
  24. 24.
    Graf, N., Desmedt, C., Hoppe, A., Tsiknakis, M., Dionysiou, D., Stamatakos, G.: Clinical requirements of “in silico oncology” as part of the integrated project ACGT (Advancing Clinico-Genomic Trials on Cancer). Eur. J. Cancer Suppl. 5, 83 (2007)CrossRefGoogle Scholar
  25. 25.
    Graf, N., Hoppe, A.: What are the expectations of a clinician from in silico oncology? In: Marias, K., Stamatakos, G.S. (eds.) Proc. 2nd International Advanced Research Workshop on In Silico Oncology, Kolympari -Chania, Greece, September 25–26, 2006, pp. 36–38 (2006)Google Scholar
  26. 26.
    Graf, N., Hoppe, A., Georgiadi, E., Belleman, R., Desmedt, C., Dionysiou, D., Erdt, M., Jacques, J., Kolokotroni, E., Lunzer, A., Tsiknakis, M., Stamatakos, G.: “In silico oncology” for clinical decision making in the context of nephroblastoma. Klin. Paediatr. 221, 141–149 (2009)CrossRefGoogle Scholar
  27. 27.
    Guiot, C., Delsanto, P.P., Carpinteri, A., Pugno, N., Mansury, Y., Deisboeck, T.S.: The dynamic evolution of the power exponent in a universal growth model of tumors. J. Theor. Biol. 240, 459–463 (2006)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Haas-Kogan, D.A., Yount, G., Haas, M., Levi, D. Kogan, S.S., Hu, L., Vidair, C., Deen, D.F., Dewey, W.C., Israel, M.A.: p53-dependent G1 arrest and p53 independent apoptosis influence the radiobiologic response of glioblastoma. Int. J. Radiat. Oncol. Biol. Phys. 36, 95–103 (1995)CrossRefGoogle Scholar
  29. 29.
    In Silico Oncology Group, Institute of Communications and Computer Systems, National Technical University of Athens. www.in-silico-oncology.iccs.ntua.grGoogle Scholar
  30. 30.
    Kansal, A.R., Torquato, S., Harsh, G.R., Chiocca, E.A., Deisboeck, T.S.: Simulated brain tumour growth dynamics using a three-dimensional cellular automaton. J. Theor. Biol. 203, 367–382 (2000)CrossRefGoogle Scholar
  31. 31.
    Katzung, B.G. (ed.): Basic and Clinical Pharmacology, 8th Ed. Lange Medical Books, McGraw-Hill, New York (2001)Google Scholar
  32. 32.
    Kolokotroni, E.A., Stamatakos, G.S., Dionysiou, D.D., Georgiadi, E.C., Desmedt, C., Graf N.M.: Translating multiscale cancer models into clinical trials: simulating breast cancer tumor dynamics within the framework of the “Trial of Principle” clinical trial and the ACGT Project. In: Proc. 8th IEEE International Conference on Bioinformatics and Bioengineering (BIBE 2008), Athens, Greece, 8–10 October 2008, Paper No. BE-2.1.1. (2008)Google Scholar
  33. 33.
    Marias, K., Dionysiou, D., Stamatakos, G.S., Zacharopoulou, F., Georgiadi, E., Maris, T.G., Tollis I.: Multi-level analysis and information extraction considerations for validating 4D models of human function. Lect. Notes Comput. Sci. 4561, 703–709 (2007)CrossRefGoogle Scholar
  34. 34.
    Murray, J.D.: Mathematical Biology II. Spatial Models and Biomedical Applications. 3rd Edition, pp. 543–546, Springer, Heidelberg, (2003)Google Scholar
  35. 35.
    Newlands, E.S., Blackledge, G.R., Slack, J.A., Rustin, G.J., Smith, D.B., Stuart, N.S., Quar- terman, C.P., Hoffman, R., Stevens, M.F., Brampton, M.H.: Phase I trial of temozolomide (CCRG 81045 M&B 39831 NSC 362856), Br. J. Cancer 65, 287–291 (1992)CrossRefGoogle Scholar
  36. 36.
    Perez, C., Brady, L.: Principles and Practice of Radiation Oncology, 3rd Ed.. Lippincott-Raven, Philadelphia. (1998)Google Scholar
  37. 37.
    Perry, M.C. (ed.): The Chemotherapy Source Book, 3rd Ed.. Lippincott Williams and Wilkins, Philadelphia (2001)Google Scholar
  38. 38.
    Ramis-Conde, I., Chaplain, M.A.J., Anderson, A.R.A.: Mathematical modelling of cancer cell invasion of tissue. Math. Comput. Model. 47, 533–545 (2008)MathSciNetMATHCrossRefGoogle Scholar
  39. 39.
    P-medicine - From data sharing and integration via VPH models to personalized medicine. EC and Japan funded R&D project (FP6-2005-IST-026996). www.p-medicine.eu/Google Scholar
  40. 40.
    Salmon, S.E., Sartorelli, A.C.: Cancer chemotherapy. In: Katzung, B.G. (ed.), Basic & Clinical Pharmacology, pp. 923–1044. Lange Medical Books/McGraw-Hill, International Edition (2001)Google Scholar
  41. 41.
    Stamatakos, G.S.: Spotlight on cancer informatics. Cancer Inform. 2, 83–86 (2006)Google Scholar
  42. 42.
    Stamatakos, G.S.: In silico oncology: a paradigm for clinically oriented living matter engineering. In: Stamatakos, G.S., Dionysiou, D. (eds.) Proc. 3rd International Advanced Research Workshop on In Silico Oncology, Istanbul, Turkey, September 23–24 2008, pp. 7–9. www.3rd- iarwiso.iccs.ntua.gr/procs.pdf (2008)Google Scholar
  43. 43.
    Stamatakos, G.S., Antipas, V.P., Uzunoglu, N.K.: A spatiotemporal, patient individualized simulation model of solid tumor response to chemotherapy in vivo: the paradigm of glioblas- toma multiforme treated by temozolomide. IEEE Trans. Biomed. Eng. 53, 1467–1477 (2006)CrossRefGoogle Scholar
  44. 44.
    Stamatakos, G.S., Antipas, V.P., Uzunoglu, N.K.: Simulating chemotherapeutic schemes in the individualized treatment context: the paradigm of glioblastoma multiforme treated by temo- zolomide in vivo. Comput. Biol. Med. 36, 1216–34 (2006)CrossRefGoogle Scholar
  45. 45.
    Stamatakos, G.S., Antipas, V.P., Uzunoglu, N.K., Dale, R.G.: A four dimensional computer simulation model of the in vivo response to radiotherapy of glioblastoma multiforme: studies on the effect of clonogenic cell density. Br. J. Radiol. 79, 389–400 (2006)CrossRefGoogle Scholar
  46. 46.
    Stamatakos, G.S., Dionysiou, D.: Introduction of hypermatrix and operator notation into a discrete mathematics simulation model of malignant tumour response to therapeutic schemes in vivo. Some operator properties. Cancer Inform. 7, 239–251 (2009)Google Scholar
  47. 47.
    Stamatakos, G.S., Dionysiou, D.D., Graf, N.M., Sofra, N.A., Desmedt, C., Hoppe, A., Uzunoglu, N., Tsiknakis, M. The Oncosimulator: a multilevel, clinically oriented simulation system of tumor growth and organism response to therapeutic schemes. Towards the clinical evaluation of in silico oncology. In: Proc. 29th Annual Intern Conf IEEE EMBS. Lyon, France, August 23–26 2007, Conf. Proc. IEEE Eng. Med. Biol. Soc., 6629–6632 (2007)Google Scholar
  48. 48.
    Stamatakos, G.S., Dionysiou, D.D., Nikita, K., Zamboglou, N., Baltas, D., Pissakas, G., Uzunoglu, N.: In vivo tumour growth and response to radiation therapy: a novel algorithmic description, Int. J. Radiat. Oncol. Biol. Phys 51, 240 (2001)Google Scholar
  49. 49.
    Stamatakos, G.S., Dionysiou, D.D., Uzunoglu N.K.: In silico radiation oncology: a platform for understanding cancer behavior and optimizing radiation therapy treatment. In: Akay, M., (ed.) Genomics and Proteomics Engineering in Medicine and Biology, pp. 131–156, Wiley- IEEE Press, Hoboken (2007)Google Scholar
  50. 50.
    Stamatakos, G.S., Dionysiou, D.D., Zacharaki, E.I., Mouravliansky, N.A., Nikita, K.S., Uzunoglu N.K.: In silico radiation oncology: combining novel simulation algorithms with current visualization techniques. Proc. IEEE. Special Issue on Bioinformatics: Advances and Challenges 90, 1764–1777 (2002)Google Scholar
  51. 51.
    Stamatakos, G.S., Kolokotroni, E., Dionysiou, D.D., Georgiadi, Giatili, S.: In silico oncology: a top-down multiscale simulator of cancer dynamics. Studying the effect of symmetric stem cell division on the cellular constitution of a tumour. In: Doessel, O., Schlegel, W.E. (eds.) Proc. World Congress Medical Physics and Biomedical Engineering, September 7–12 2012, Munich, Germany, pp. 1830–1833. Springer, Heidelberg (2009)Google Scholar
  52. 52.
    Stamatakos, G.S., Uzunoglu, N.K.: Computer simulation of tumour response to therapy. In: Nagl, S. (ed.) Cancer Bioinformatics: from therapy design to treatment, pp. 109–125. John Wiley & Sons Ltd, Chichester (2006)CrossRefGoogle Scholar
  53. 53.
    Stamatakos, G.S., Zacharaki, E.I., Makropoulou, M.I., Mouravliansky, N.A., Marsh, A., Nikita, K.S., Uzunoglu, N.K.: Modeling tumor growth and irradiation response in vitro- a combination of high-performance computing and web based technologies including VRML visualization. IEEE Trans. Inform. Technol. Biomed. 5, 279–289 (2001)CrossRefGoogle Scholar
  54. 54.
    Stamatakos, G.S., Zacharaki, E.I., Makropoulou, M.I., Mouravliansky, N.A., Marsh, A., Nikita, K.S., Uzunoglu, N.K.: Tumor growth and response to irradiation in vitro: a technologically advanced simulation model. Int. J. Radiat. Oncol. Biol. Phys. 51, Suppl. 1, 240–241 (2001)Google Scholar
  55. 55.
    Steel, G. (ed.): Basic Clinical Radiobiology, 3rd Ed.. Oxford University Press, Oxford (2002)Google Scholar
  56. 56.
    Stupp, R., Gander, M., Leyvraz, S., Newlands, E.: Current and future developments in the use of temozolomide for the treatment of brain tumours. Lancet Oncol. Rev. 2, 552–560 (2001)CrossRefGoogle Scholar
  57. 57.
    Swanson, K.R., Alvord, E.C., Murray, J.D.: Virtual brain tumours (gliomas) enhance the reality of medical imaging and highlight inadequacies of current therapy. Br. J. Cancer 86, 14–18 (2002)CrossRefGoogle Scholar
  58. 58.
    Werner-Wasik, M., Scott, C.B., Nelson, D.F., Gaspar, L.E., Murray, K.J., Fischbach, J.A., Nelson, J.S., Weinstein, A.S., Curran, W.J. Jr.: Curran. Final report of a phase I/II trial of hyper- fractionated and accelerated hyperfractionated radiation therapy with carmustine for adults with supratentorial malignant gliomas. Radiation Oncology Therapy Group Study 83–02. Cancer 77, 1535-43(1996)CrossRefGoogle Scholar
  59. 59.
    Zacharaki, E.I., Stamatakos, G.S., Nikita, K.S., Uzunoglu, N.K.: Simulating growth dynamics and radiation response of avascular tumour spheroid model validation in the case of an EMT6/Ro multicellular spheroid. Comput. Meth. Progr. Biomed. 76, 193–206 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Italia 2012

Authors and Affiliations

  • Georgios Stamatakos
    • 1
  1. 1.Silico Oncology Group. Laboratory of Microwaves and Fiber Optics. Institute of Communication and Computer SystemsNational Technical University of AthensZografosGreece

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