Cancer modeling: modern imaging applications in the generation of novel animal model systems to study cancer progression and therapy.

Abstract

Cancer is the result of a series of genetic and epigenetic mutations that evolve over years even decades and lead to the transformed phenotype. Paradoxically, most methods developed to study these changes are static and do not provide insights on the dynamics of the sequela of steps involved in tumorigenesis. This major shortcoming now can be overcome with the application of reporter genes and imaging technologies, which are providing tools to examine specific molecular events and their role in the carcinogenic process in single cells. In the last decade reporter-based biosensors were created to study gene transcription, protein/protein interactions, sub-cellular trafficking and protease activities; this wealth of systems enable to monitor intracellular signaling pathways at several key check points specifically involved in cancer cell development. The challenge is now to extend cell-based models to the generation of reporter mice, where non-invasive in vivo imaging technologies allow to follow single molecular events. When combined with murine models of cancer, these technologies will give an unprecedented opportunity to spatio-temporally investigate the molecular events resulting in neoplasia. The aim of the present review is to highlight the major changes occurring in this rapidly evolving field and their potential for increasing our knowledge in cancer biology and for the research of novel and more efficacious therapies.

Cite this paper

@article{Stell2007CancerMM, title={Cancer modeling: modern imaging applications in the generation of novel animal model systems to study cancer progression and therapy.}, author={Alessia Stell and Andrea Biserni and Sara Della Torre and Gianpaolo Rando and Balaji Ramachandran and L Ottobrini and Giovanni Lucignani and Adriana C Maggi and Paolo Ciana}, journal={The international journal of biochemistry & cell biology}, year={2007}, volume={39 7-8}, pages={1288-96} }