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

Kolokotroni, EA; Stamatakos, GS; Dionysiou, DD; Georgiadi, ECh; Desmedt, C; Graf, NM

dc.contributor.author	Kolokotroni, EA	en
dc.contributor.author	Stamatakos, GS	en
dc.contributor.author	Dionysiou, DD	en
dc.contributor.author	Georgiadi, ECh	en
dc.contributor.author	Desmedt, C	en
dc.contributor.author	Graf, NM	en
dc.date.accessioned	2014-03-01T02:45:50Z
dc.date.available	2014-03-01T02:45:50Z
dc.date.issued	2008	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32416
dc.subject	Cancer Treatment	en
dc.subject	Clinical Data	en
dc.subject	Clinical Practice	en
dc.subject	Clinical Trial	en
dc.subject	Computer Model	en
dc.subject	Dynamic Model	en
dc.subject	European Commission	en
dc.subject	in silico	en
dc.subject	Multilevel Model	en
dc.subject	Tumor Growth	en
dc.subject	Breast Cancer	en
dc.subject.other	Breast Cancer	en
dc.subject.other	Breast cancer tumors	en
dc.subject.other	Cancer models	en
dc.subject.other	Cancer treatment	en
dc.subject.other	Clinical data	en
dc.subject.other	Clinical information	en
dc.subject.other	Clinical practices	en
dc.subject.other	Clinical trial	en
dc.subject.other	Computer models	en
dc.subject.other	Dead cells	en
dc.subject.other	Discrete state	en
dc.subject.other	Dynamics models	en
dc.subject.other	Epirubicin	en
dc.subject.other	European Commission	en
dc.subject.other	In-silico	en
dc.subject.other	Multilevel modeling	en
dc.subject.other	Multiscale	en
dc.subject.other	Optimization process	en
dc.subject.other	Transition rates	en
dc.subject.other	Treatment optimization	en
dc.subject.other	Tumor growth	en
dc.subject.other	Bioinformatics	en
dc.subject.other	Dynamics	en
dc.subject.other	Optimization	en
dc.subject.other	Patient treatment	en
dc.subject.other	Tumors	en
dc.title	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	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/BIBE.2008.4696758	en
heal.identifier.secondary	http://dx.doi.org/10.1109/BIBE.2008.4696758	en
heal.identifier.secondary	4696758	en
heal.publicationDate	2008	en
heal.abstract	The potential of cancer multilevel modeling has been particularly emphasized over the past years. Integration of multiscale experimental and clinical information pertaining to cancer via advanced computer models seems to considerably accelerate optimization of cancer treatment in the patient individualized context. However, a sine qua non prerequisite for such models to reach clinical practice is to be thoroughly tested through clinical trials for validation and optimization purposes. This is one of the major goals of the European Commission funded ""Advancing Clinico-Genomic Trials on Cancer"" (ACGT) project. This paper presents a discrete state based, four dimensional, multiscale tumor dynamics model that has been specially developed by the In Silico Oncology Group in order to mimick the Trial Of Principle (TOP) clinical trial concerning breast cancer treated with epirubicin. The TOP trial constitutes one of the ACGT clinical trials. A substantial part of the model can address other tumor types as well. The actual pseudoanonymized imaging, histopathological, molecular and clinical data of the patient are exploited. Special emphasis is put on the effect of cancer stem/clonogenic, progenitor, differentiated and dead cells, the cell category transition rates and the cell category relative populations within the tumor from the treatment baseline onwards. The importance of adaptation of the cell category relative populations to the cell category transition rates for free tumor growth is revealed and the concept of a pertinent nomogram is introduced. A method which ensures adaptation of these two sets of entities at the beginning of the simulation execution is proposed and subsequently successfully applied. Convergence and code checking issues are addressed. Indicative parametric/sensitivity studies are presented along with specific numerical findings. The model's behavior substantiates its potential to serve as the basis of a treatment optimization system following an eventually succesful completion of the clinical validation and optimization process.	en
heal.journalName	8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008	en
dc.identifier.doi	10.1109/BIBE.2008.4696758	en