Lung cancer remains a major source of morbidity and mortality worldwide [1]. Accounting for over one million deaths each year, lung cancer causes more deaths worldwide than any other cancer type.
With the development of advanced therapies for lung cancer, outcomes are improving with 5-year survival rates between 60% and 77% depending on the stage of the cancer at the time of diagnosis. However, there are still some lung tumors that do not respond well to treatment.
Ongoing research aims to improve the diagnosis and treatment of lung cancer. The discovery of novel therapies to fight lung cancer has been greatly aided by the creation of genetically engineered mouse models of lung cancer. These mouse models have mutations in the K-ras oncogene and the p53 tumor suppressor gene, which leads to spontaneous development of adenocarcinomas in the mouse lung [2].
The tumors that develop in transgenic mouse models reflect more closely the lung cancers found in humans in terms of the stroma, vascularity, and immune infiltrate compared with the previously used xenograft models, in which tumor cell lines were implanted subcutaneously into immuno-compromised animals [3]. The data obtained when testing molecules with the potential for efficacy against lung tumors in these newer mouse models thus give a better indication of how a drug will function in the clinical setting.
Unfortunately, the detection of a therapeutic effect in transgenic mouse models has proved difficult by traditional means. Unlike subcutaneous xenografts, the lung tumors in mouse models cannot be measured using an external caliper. Although assessment can be made histologically, this is time-consuming, spatially limited within the selected slices, and does not give details of baseline tumor burden in individual animals before the treatment is started.
Recent technological advances have allowed rapid, cost-effective, in vivo imaging that provides the perfect tool for monitoring tumor burden in mice models of lung cancer.