Top-Down Multilevel Simulation of Tumor Response to Treatment in the Context of In Silico Oncology

TL;DR

This paper introduces a top-down multilevel tumor modeling approach designed to support personalized cancer treatment planning through in silico experimentation, integrating clinical data with biological mechanisms.

Contribution

It presents a novel multilevel simulation framework, including the Oncosimulator, for modeling tumor response to therapy and supports clinical validation and application.

Findings

Demonstrated the model's ability to simulate tumor response to chemotherapy and radiotherapy.

Showed integration of clinical imaging and molecular data in tumor modeling.

Provided initial validation results within clinical trial contexts.

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.