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.

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 ldquoadvancing clinico-genomic trials on cancerrdquo (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 modelpsilas behavior substantiates its potential to serve as the basis of a treatment optimization system fol- - lowing an eventually succesful completion of the clinical validation and optimization process.