Klin Padiatr 2009; 221(3): 141-149
DOI: 10.1055/s-0029-1216368
Review

© Georg Thieme Verlag KG Stuttgart · New York

‘In Silico’ Oncology for Clinical Decision Making in the Context of Nephroblastoma

Die Bedeutung von ,In silico‘-Onkologie zur klinischen Entscheidungsfindung am Beispiel des NephroblastomsN. Graf 1 , A. Hoppe 1 , E. Georgiadi 2 , R. Belleman 3 , C. Desmedt 4 , D. Dionysiou 2 , M. Erdt 5 , J. Jacques 6 , E. Kolokotroni 2 , A. Lunzer 7 , M. Tsiknakis 8 , G. Stamatakos 2
  • 1Universität des Saarlandes, Klinik für Pädiatrische Onkologie und Hämatologie, Homburg, Germany
  • 2National Technical University of Athens, Institute of Communications and Computer Systems, Laboratory of Microwaves and Fiber Optics, In Silico Oncology Group, Athens, Greece
  • 3Faculty of Science, Universiteit van Amsterdam, Section Computational Science, Informatics Institute, Amsterdam, The Netherlands
  • 4Institut Jules Bordet, Centre des Tumeurs, Bruxelles, Belgium
  • 5Fraunhofer Institute for Computer Graphics (IGD), Cognitive Computing & Medical Imaging, Darmstadt, Germany
  • 6IRISA-INRIA, Campus de Beaulieu, Rennes, France
  • 7Hokkaido University, Meme Media Laboratory, Sapporo, Japan
  • 8Foundation for Research and Technology Hellas, Institute of Computer Science, Heraklion, Greece
Further Information

Publication History

Publication Date:
12 May 2009 (online)

Abstract

The present paper outlines the initial version of the ACGT (Advancing Clinico-Genomic Trials) – an Integrated Project, partly funded by the EC (FP6-2005-IST-026996)I-Oncosimulator as an integrated software system simulating in vivo tumour response to therapeutic modalities within the clinical trials environment aiming to support clinical decision making in individual patients. Cancer treatment optimization is the main goal of the system. The document refers to the technology of the system and the clinical requirements and the types of medical data needed for exploitation in the case of nephroblastoma. The outcome of an initial step towards the clinical adaptation and validation of the system is presented and discussed. Use of anonymized real data before and after chemotherapeutic treatment for the case of the SIOP 2001/GPOH nephroblastoma clinical trial constitutes the basis of the clinical adaptation and validation process. By using real medical data concerning nephroblastoma for a single patient in conjunction with plausible values for the model parameters (based on available literature) a reasonable prediction of the actual tumour volume shrinkage has been made possible. Obviously as more and more sets of medical data are exploited the reliability of the model “tuning” is expected to increase. The successful performance of the initial combined ACGT Oncosimulator platform, although usable up to now only as a test of principle, has been a particularly encouraging step towards the clinical translation of the system, being the first of its kind worldwide.

Zusammenfassung

Die vorliegende Arbeit beschreibt die erste Version des ACGT (Advancing Clinico-Genomic Trials) – ein integriertes Projekt, z. T. finanziert über EC (FP6-2005-IST-026996)I-Oncosimulators als eine integrierte Software, die das In-vivo-Ansprechen eines Tumors auf therapeutische Maßnahmen innerhalb von klinischen Studien simuliert. Diese Software zielt darauf ab, klinische Entscheidungsprozesse bei individuellen Patienten zu unterstützen. Die Technologie des ACGT-Oncosimulators als auch die klinischen Voraussetzungen und notwendigen medizinischen Daten werden beschrieben, um Ergebnisse am Beispiel des Nephroblastoms zu generieren. Die Anpassung an klinische Gegebenheiten als auch die Validierung des Systems werden besprochen. Der Gebrauch von anonymisierten realen Daten vor und nach präoperativer Chemotherapie beim Nephroblastom innerhalb der SIOP-2001/GPOH-Studie stellen die Basis dieses Adaptations- und Validierungsprozesses dar. Es kann gezeigt werden, dass mit Hilfe realer klinischer Daten eines Patienten und plausiblen Parametern des vorgestellten Modells des ACGT-Oncosimulators eine gute Vorhersage des wirklichen Tumoransprechens möglich ist. Da es sich um ein lernendes System handelt, ist zu erwarten, dass sich mit der Zunahme durchgeführter Simulationen die Reliabilität verbessern wird. Die erfolgreiche Durchführung einer solchen Simulation durch den ACGT-Oncosimulator am Beispiel des Nephroblastoms kann als ,Test of Principle‘ angesehen werden und ist ein wichtiger Schritt hin zur klinischen Anwendung und der erste seiner Art weltweit.

Literatur

  • 1 Alarcon T. et al . A cellular automaton model for tumour growth in inhomogeneous environment.  J Theor Biol. 2003;  225 257-274
  • 2 Axelrod R. et al . Evolution of cooperation among tumor cells.  Proc Natl Acad Sci USA. 2006;  103 13474-13479
  • 3 Baish JW, Jain RK. Fractals and cancer.  Cancer Res. 2000;  60 3683-3688
  • 4 Bauer AL. et al . A cell-based model exhibiting branching and anastomosis during tumor-induced angiogenesis.  Biophys J. 2007;  92 3105-3121
  • 5 Belleman RG. et al . Interactive Simulation and Visualization for Cancer Treatment Planning with Grid-based Technology, ERCIM News, special theme: The Digital Patient.  . 2007;  69 22-23
  • 6 Bogaerts J. et al . Individual patient data analysis to assess modifications to the RECIST criteria.  EJC. 2009;  45 248-260
  • 7 Rajkumar B. Grid Computing: Making the Global Cyberinfrastructure for eScience a Reality.  CSI Communications (Mumbai, India: Computer Society of India (CSI)). 2005;  29 ((1)) , ISSN 0970-647X http://www.gridbus.org/∼raj/papers/csicommunicationsjuly2005.pdf , (last access: 25.2.2009)
  • 8 Castiglione F. et al . Computational modeling of the immune response to tumor antigens.  J Theor Biol. 2005;  237 390-400
  • 9 Cross SS. Fractals in pathology.  J Pathol. 1997;  182 1-8
  • 10 Deisboeck TS. et al . In silico cancer modeling: is it ready for prime time?.  Nat Clin Pract Oncol.. 2009;  6 34-42
  • 11 DeKraker J. et al . Reduction of postoperative chemotherapy in children with stage I intermediate-risk and anaplastic Wilms’ tumour (SIOP 93-01 trial): a randomised controlled trial.  Lancet. 2004;  364 1229-1235
  • 12 Dionysiou DD. et al . A four-dimensional simulation model of tumour response to radiotherapy in vivo: parametric validation considering radiosensitivity, genetic profile and fractionation.  J Theor Biol. 2004;  230 1-20
  • 13 Friedrich CM, Paterson TS. In silico predictions of target clinical efficacy.  Drug Disc Today. 2004;  3 216-222
  • 14 Gatenby RA, Vincent TL. An evolutionary model of carcinogenesis.  Cancer Res. 2003;  63 6212-6220
  • 15 Georgiadi ECh. et al . Multilevel Cancer Modeling in the Clinical Environment: Simulating the Behavior of Wilms Tumor in the Context of the SIOP 2001/GPOH Clinical Trial and the ACGT Project.  , Proceedings of the 8th IEEE International Conference on Bioinformatics and Bioengineering (BIBE 2008), Athens, Greece, 8–10 October 2008. IEEE Catalog Number: CFP08266, ISBN: 978-1-4244-2845-8, Library of Congress: 2008907441, Paper No. BE-2.1.2, length: 8 pages (in electronic format)
  • 16 Göbel U, Jürgens H. Translation der kliniknahen Grundlagenforschung in die pädiatrische Onkologie.  Klin Pädiatr. 2003;  215 289-290
  • 17 Göbel U, Witt O. Das Dilemma der klinischen Register in der pädiatrischen Onkologie und Hämatologie.  Klin Pädiatr. 2008;  220 129-133
  • 18 Goh KI. et al . The human disease network.  Proc Natl Acad Sci USA. 2007;  104 8685-8690
  • 19 Graf N. et al . The role of preoperative chemotherapy in the management of Wilms Tumor – The SIOP Studies.  Urol Clin North Am. 2000;  27 443-454
  • 20 Graf N, Göbel U. Therapieoptimierungsstudien der Gesellschaft für Pädiatrische Onkologie und Hämatologie (GPOH) und 12. Novelle des Arzneimittelgesetzes zur Umsetzung der EU-Richtlinie.  Klin Pädiatr. 2004;  216 129-131
  • 21 Graf N, Hoppe A. What are the expectations of a Clinician from In Silico Oncology?. In: Marias K, Stamatakos G, Hrsg. Proc. 2nd International Advanced Research Workshop on In Silico Oncology, Kolympari, Chania, Greece, 25–26 Sept. 2006: 36-38 http://(www.ics.forth.gr/bmi/2nd-iarwiso/) (last access: 25.2.2009)
  • 22 Graf N. et al . Post-genomic clinical trials: The perspective of ACGT.  , eCancerMedicalScience Journal, 2008 (online journal) 1, Article Num. 66, DOI:10.3332/eCMS.2007.66
  • 23 GridCafe . http://www.gridcafe.org/version1/whatisgrid/whatis.html , (last access: 25.2.2009)
  • 24 Guiot C. et al . Does tumor growth follow a “universal law”?.  J Theor Biol. 2003;  225 147-151
  • 25 Guiot C. et al . The dynamic evolution of the power exponent in a universal growth model of tumors.  J Theor Biol. 2006;  240 459-463
  • 26 Hero B. et al . Neuroblastoma preoperatively treated as nephroblastoma: does inadequate therapy worsen the prognosis.  Klin Pädiatr. 2002;  214 157-161
  • 27 Ho RL, Bartsell LT. Biosimulation software is changing research.  Biotechnol Annu Rev. 2004;  10 297-302
  • 28 Huff CA. et al . The paradox of response and survival in cancer therapeutics.  Blood. 2006;  107 431-434
  • 29 Lavenier D, Jacques J. Parallelizing the ACGT Oncosimulator.  , Proceedings of the 3rd International Advanced Research Workshop on In Silico Oncology, Zografeio Lyceum, Istanbul, Turkey, September 23/24, 2008; pp 38-40 http://www.3rd-iarwiso.iccs.ntua.gr/procs.pdf , (last access: 25.2.2009)
  • 30 Lemerle J, Voûte PA, Tournade MF. et al . Preoperative versus postoperative radiotherapy, single versus multiple courses of Actinomycin D, in the treatment of Wilms’ tumor. Preliminary results of a controlled clinical trial conducted by the International Society of Pediatric Oncology (SIOP).  Cancer. 1976;  38 647-654
  • 31 Lemerle J, Voûte PA, Tournade MF. et al . Effectiveness of Preoperative Chemotherapy in Wilms’Tumor: Results of an International Society of Paediatric Oncology (SIOP) Clinical Trial.  J Clin Oncol. 1983;  1 604-609
  • 32 Lodish D. et al . Molecular Cell Biology.  New York: Scientific American Books. 1995;  pp 1247-1294
  • 33 Lunzer A. et al .RecipeSheet: Creating, Combining and Controlling Information Processors. Proceedings of the 19th Annual ACM Symposium on User interface Software and Technology (UIST ’06). Montreux, Switzerland, ACM Press Oct 2006: 145-153
  • 34 Mansury Y. et al . Evolutionary game theory in an agent-based brain tumor model: exploring the ‘Genotype-Phenotype’ link.  J Theor Biol. 2006;  238 146-156
  • 35 Marusic M. et al . Analysis of growth of multicellular tumour spheroids by mathematical models.  Cell Prolif. 1994;  27 73-94
  • 36 McDougall SR. et al . Mathematical modelling of dynamic adaptive tumour-induced angiogenesis: clinical implications and therapeutic targeting strategies.  J Theor Biol. 2006;  241 564-589
  • 37 Michelson S. In silico prediction of clinical efficacy.  Current Opinion in Biotechnology. 2006;  17 666-670
  • 38 Noble D. Modeling the heart – from genes to cells to the whole organ.  Science. 2002;  295 1678-1682
  • 39 Norton L. Conceptual and practical implications of breast tissue geometry: toward a more effective, less toxic therapy.  Oncologist. 2005;  10 370-381
  • 40 Picci P. et al . Prognostic significance of histopathologic response to chemotherapy in nonmetastatic Ewing's sarcoma of the extremities.  J Clin Oncol. 1993;  11 1763-1769
  • 41 Plank MJ, Sleeman BD. Lattice and non lattice models of tumour angiogenesis.  Bull Math Biol. 2004;  66 1785-1819
  • 42 Reinhard H. et al . Results of the SIOP 93-01/GPOH trial and study for the treatment of patients with unilateral nonmetastatic Wilms Tumor.  Klin Pädiatr. 2004;  216 132-140
  • 43 Rosen G. Neoadjuvant chemotherapy for osteogenic sarcoma: a model for the treatment of other highly malignant neoplasms.  Recent Results Cancer Res. 1986;  103 148-157
  • 44 Sole RV, Deisboeck TS. An error catastrophe in cancer?.  J Theor Biol. 2004;  228 47-54
  • 45 Spencer SL. et al . Modelling somatic evolution in tumorigenesis.  PLoS Comput Biol. 2006;  2 e108
  • 46 Stamatakos GS. et al . In silico radiation oncology: combining novel simulation algorithms with current visualization techniques.  Proceedings of IEEE: Special Issue on Bioinformatics: Advances and Challenges. 2002;  90 1764-1777
  • 47 Stamatakos GS. et al . 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. 2006;  79 389-400
  • 48 Stamatakos GS. et al . A spatiotemporal, patient individualized simulation model of solid tumor response to chemotherapy in vivo: the paradigm of glioblastoma multiforme treated by temozolomide.  IEEE Transactions on Biomedical Engineering. 2006;  53 1467-1477
  • 49 Stamatakos GS. et al . 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. Proceedings of the 29th Annual International Conference of the IEEE EMBS, Lyon, France. 2007;  SuB07.1 6628-6631
  • 50 Therasse P. et al . New guidelines to evaluate the response to treatment in solid tumors.  J Natl Cancer Inst. 2000;  92 205-216
  • 51 Tournade MF. et al . Results of the Sixth International Society of Pediatric Oncology Wilms’ Tumor Trial and Study: A Risk-Adapted Therapeutic Approach in Wilms’ Tumor.  J Clin Oncol. 1993;  11 1014-1023
  • 52 Tournade MF. et al . Optimal duration of preoperative therapy in unilateral and non metastatic Wilms’ tumor in children over six months of age. Results of the 9th SIOP Wilms’ tumor trial and study.  J Clin Oncol. 2001;  19 488-500
  • 53 Tsiknakis M. et al . A semantic grid infrastructure enabling integrated access and analysis of multilevel biomedical data in support of post-genomic clinical trials on Cancer, IEEE Transactions on Information Technology in Biomedicine, Special issue on Bio-Grids.  2008;  12 191-204
  • 54 Vaidya VG, Alexandro  Jr  FJ. Evaluation of some mathematical models for tumor growth.  Int J Biomed Comput. 1982;  13 19-36
  • 55 Voûte PA, Tournade MF, Lemerle J. et al . Results of studies conducted by the International Society of Paediatric Oncology (SIOP) from 1971–1978 concerning Wilms’ tumor. Abstracts of the Tenth Meeting of the International Society of Paediatric Oncology (SIOP), Brussels, Belgium.  September 1978;  3-5 , (abstr 14)
  • 56 Winkler K, Bielack SS, Delling G. et al . Treatment of osteosarcoma: experience of the Cooperative Osteosarcoma Study Group (COSS).  Cancer Treat Res. 1993;  62 269-277
  • 57 Wu JT. et al . Analysis of a three-way race between tumor growth, a replication-competent virus and an immune response.  Bull Math Biol. 2004;  66 605-625

Correspondence

Prof. N. Graf

Universität des Saarlandes

Klinik für Pädiatrische Onkologie und Hämatologie

Campus Homburg

Homburg

Germany

66421

Phone: +6841/162/83 97

Fax: +6841/162/83 02

Email: graf@uks.eu

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