Genetically engineered mouse models for studying radiation biology.
Genetically engineered mouse models (GEMMs) are valuable research tools that have transformed our understanding of cancer. The first GEMMs generated in the 1980s and 1990s were knock-in and knock-out models of single oncogenes or tumor suppressors. The advances that made these models possible catalyzed both technological and conceptual shifts in the way cancer research was conducted. As a result, dozens of mouse models of cancer exist today, covering nearly every tissue type. The advantages inherent to GEMMs compared to in vitro and in vivo transplant models are compounded in preclinical radiobiology research for several reasons. First, they accurately and robustly recapitulate primary cancers anatomically, histopathologically, and genetically. Reliable models are a prerequisite for predictive preclinical studies. Second, they preserve the tumor microenvironment, including the immune, vascular, and stromal compartments, which enables the study of radiobiology at a systems biology level. Third, they provide exquisite control over the genetics and kinetics of tumor initiation, which enables the study of specific gene mutations on radiation response and functional genomics in vivo. Taken together, these facets allow researchers to utilize GEMMs for rigorous and reproducible preclinical research. In the three decades since the generation of the first GEMMs of cancer, advancements in modeling approaches have rapidly progressed and expanded the mouse modeling toolbox with techniques such as in vivo short hairpin RNA (shRNA) knockdown, inducible gene expression, site-specific recombinases, and dual recombinase systems. Our lab and many others have utilized these tools to study cancer and radiobiology. Recent advances in genome engineering with CRISPR/Cas9 technology have made GEMMs even more accessible to researchers. Here, we review current and future approaches to mouse modeling with a focus on applications in preclinical radiobiology research.
Castle, KD; Chen, M; Wisdom, AJ; Kirsch, DG
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