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

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New Challenges for Cancer Systems Biomedicine

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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.

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Notes

  1. 1.

    List of Abbreviations: A = apoptosis; ACGT = Advancing Clinico-Genomic Trials on Cancer; AHF = Accelerated Hyper-Fractionation; BIR = British Institute of Radiology; Cpav = Average Plasma Concentration; CT = Computerized Tomography; EC = European Commission, GBM = Glioblastoma Multiforme; Go = dormant phase; G1 = Gap 1 phase of the cell cycle; G2 = Gap 2 phase; HF = Hyper-Fractionation, IEEE = Institute of Electrical and Electronics Engineers; M = Mitosis; MRU = Magnetic Resonance Imaging; N = necrosis; PET = Positron Emission Tomography; R&D = Research and Development; RTOG = Radiation Therapy Oncology Group; S = DNA synthesis phase; TDS = Time Delay in the S phase compartment; TMZ = Temozolomide.

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Acknowledgements

This work has been supported in part by the European Commission under the projects “ACGT: Advancing Clinicogenomic Trials on Cancer” (FP6-2005-IST-026996), Contra Cancrum: Clinically Oriented Translational Cancer Multilevel Modelling” (FP7-ICT-2007-2- 223979), TUMOR: Transatlantic Tumor Model Repositories (FP7-ICT-2009.5.4-247754) and p- Medicine: Personalized Medicine (FP7-ICT-2009.5.3-270089).

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Authors and Affiliations

  1. Silico Oncology Group. Laboratory of Microwaves and Fiber Optics. Institute of Communication and Computer Systems, National Technical University of Athens, Iroon Polytechniou 9, Zografos, 15780, Greece

    Georgios Stamatakos

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  1. Georgios Stamatakos
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Editors and Affiliations

  1. Department of Experimental Oncology, European Institute of Oncology, Milan, Italy

    Alberto d’Onofrio

  2. Department of Mathematics, University of Pisa, Italy

    Paola Cerrai

  3. Istituto di Analisi dei Sistemi ed Informatica “A. Ruberti“ — CNR, Rome, Italy

    Alberto Gandolfi

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Stamatakos, G. (2012). Top-Down Multiscale Simulation of Tumor Response to Treatment in the Context of In Silico Oncology. The Notion of Oncosimulator. In: d’Onofrio, A., Cerrai, P., Gandolfi, A. (eds) New Challenges for Cancer Systems Biomedicine. SIMAI Springer Series. Springer, Milano. https://doi.org/10.1007/978-88-470-2571-4_19

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