Concepts in Cancer Modeling: A Brief History

Abstract

Modeling, an experimental approach to investigate complex biological systems, has significantly contributed to our understanding of cancer. Although extensive cancer research has been conducted utilizing animal models for elucidating mechanisms and developing therapeutics, the concepts in a good model design and its application have not been well elaborated. In this review, we discuss the theory underlying biological modeling and the process of producing a valuable and relevant animal model. Several renowned examples in the history of cancer research will be used to illustrate how modeling can be translatable to clinical applications. Finally, we will also discuss how the advances in cancer genomics and cancer modeling will influence each other going forward. Cancer Res; 76(20); 5921-5. ©2016 AACR.

Figure 1. Cancer modeling as a continuous cycle

1, Identification of the question. A good model design defines the question to be answered by the model. Determining the desired resolution (microscopic to macroscopic) given the complexity of the biological system (molecular level to population level) of study will aid in outlining the scope of the model (range of question) and its boundary (field of study), which will determine the parameters of inputs and outputs of the model. 2, Model building. To build a model, a physiologically or pathologically relevant system should be tailored to make the resolution of observation match the scope of the study. The driving factors of the system, which is consistent with the parameters in the identified question, should be able to be manipulated under experimentation. The system should produce a relevant and useful readout that properly addresses the endpoints of study. 3, Model testing. Testing of the model involves adjusting the parameters of the driving factors as input and generation of outputs that allow evaluation for comparison of real system. The study design should emphasize the importance of a well defined and controlled operation, ensuring statistical power for meaningful conclusions. 4, Outcome evaluation. The endpoint of the study should be compatible with that of the real system, allowing comparison between the two systems at translatable bases. The translation of modeling results to clinical outcomes depends on the resolution of the model: the high-resolution modeling (e.g. signaling pathway) allows more straightforward application of the results to clinics, while low-resolution (e.g. pathophysiological phenotypes) modeling results require consideration of genetic background and scaling. 5, Model improvement. Continual assessment should appraise the system for improvement and modifications, and the endpoints should be critiqued and evaluated within the context of the real biological system. This can inform the next test run and guide design improvements. A well-designed model can bridge the gap to translational studies and inform their design, with similar feedback from clinical studies informing the next model improvement. This interplay can advance fundamental knowledge and clinical therapies.