Exploiting clinical trial data drastically narrows the window of possible solutions to the problem of clinical adaptation of a multiscale cancer model.

Georgios S Stamatakos,Norbert Graf,Dimitra D Dionysiou,Eleni C Georgiadi,Eleni A Kolokotroni,Lin Zhang

doi:10.1371/journal.pone.0017594

Abstract

The development of computational models for simulating tumor growth and response to treatment has gained significant momentum during the last few decades. At the dawn of the era of personalized medicine, providing insight into complex mechanisms involved in cancer and contributing to patient-specific therapy optimization constitute particularly inspiring pursuits. The in silico oncology community is facing the great challenge of effectively translating simulation models into clinical practice, which presupposes a thorough sensitivity analysis, adaptation and validation process based on real clinical data. In this paper, the behavior of a clinically-oriented, multiscale model of solid tumor response to chemotherapy is investigated, using the paradigm of nephroblastoma response to preoperative chemotherapy in the context of the SIOP/GPOH clinical trial. A sorting of the model's parameters according to the magnitude of their effect on the output has unveiled the relative importance of the corresponding biological mechanisms; major impact on the result of therapy is credited to the oxygenation and nutrient availability status of the tumor and the balance between the symmetric and asymmetric modes of stem cell division. The effect of a number of parameter combinations on the extent of chemotherapy-induced tumor shrinkage and on the tumor's growth rate are discussed. A real clinical case of nephroblastoma has served as a proof of principle study case, demonstrating the basics of an ongoing clinical adaptation and validation process. By using clinical data in conjunction with plausible values of model parameters, an excellent fit of the model to the available medical data of the selected nephroblastoma case has been achieved, in terms of both volume reduction and histological constitution of the tumor. In this context, the exploitation of multiscale clinical data drastically narrows the window of possible solutions to the clinical adaptation problem.

Highlights

The last few decades have witnessed an increased interest of the scientific community into the development of computational models for simulating tumor growth and response to treatment [1,2,3]
Ultimate goal of clinically-oriented cancer simulation models is their eventual translation into clinical practice, which entails a) thorough sensitivity analyses, in order to both comprehend and validate their behavior, and at the same time gain further insight into the simulated mechanisms, in a more quantitative way, and b) an adaptation and validation process based on real clinical data
This paper investigates the behavior of an actual clinical trialdriven model simulating the response of nephroblastoma tumors to preoperative chemotherapy

Summary

Introduction

The last few decades have witnessed an increased interest of the scientific community into the development of computational models for simulating tumor growth and response to treatment [1,2,3]. At the beginning of the era of personalized medicine, sophisticated multiscale models yield valuable quantitative insights into complex mechanisms involved in cancer and may contribute to patient-specific therapy optimization. Continuous models rely primarily on differential equations to describe processes such as diffusion of molecules, changes in tumor cell density and invasion of tumor cells into the surrounding tissue [4,5,6,7,8,9]. Ultimate goal of clinically-oriented cancer simulation models is their eventual translation into clinical practice, which entails a) thorough sensitivity analyses, in order to both comprehend and validate their behavior, and at the same time gain further insight into the simulated mechanisms, in a more quantitative way, and b) an adaptation and validation process based on real clinical data

Methods

Results

Conclusion