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.

Tumor burden in mice models is determined by evaluation of the Body Condition Score, the size of tumor(s), anatomical location, incidence of multiple tumors, tumor appearance, and other clinical signs of well-being.

It is preferable to use serial non-invasive in vivo imaging of the animals during the treatment period to provide ongoing screening for any changes in tumor burden from the baseline evaluation. This has the benefit of enabling quantification of treatment effects on tumor burden over time within the same animals. In addition, the treated mice can be sub-grouped according to their baseline tumor burden.

In the clinical setting, the imaging technique widely used for the detection of lung tumors is computed tomography (CT). Image intensities in CT are quantitative and proportional to the tissue density, providing good contrast between solid tumors and the surrounding air space. Micro-CT imaging provides even greater resolution than can be achieved with clinical CT systems and has been successfully used for detecting and evaluating lung tumor burden in mouse lung cancer models [4]. However, differentiation between tissue types becomes increasingly challenging with increasing tumor burden. Although algorithms for interpreting micro-CT lung images are available [4], these typically exclude some areas of the lung, which usually amount to around 15% of the total lung area.

3D MRI for assessing tumor burden in mouse models

Magnetic resonance imaging (MRI) with possibilities such as diffusion weighting and 3D imaging enables tumor response evaluation without requiring the use of radiation.

Total tumor burden in a transgenic mouse model of non-small-cell lung cancer was recently determined using 3D ultrashort echo time MRI and short T2 images [5]. 3D MRI was performed using a Bruker BioSpec 94/20 MRI animal scanner. Short T2 component information was generated by subtraction of short and long echo time UTE images. For consensus identification of tumors manual segmentation was performed on this data by two independent investigators and compared with histological results.

The number of lung tumors and the total tumor burden determined from 3D MRI investigations correlated strongly with the results of histological investigations. Intra- and inter-user comparison showed highest correlations between the individual measurements for ultra-short TE MRI. 3D MRI thus facilitated the accurate identification of lung tumors in mice. Ultrashort echo-time MRI was identified as the superior 3D MR imaging strategy for the investigation of lung tumors.

References

[1] Jemal A, et al. Cancer statistics. Cancer J Clin 2010;60:277-300.

[2] Jackson EL, et al. The differential effects of mutant p53 alleles on advanced murine lung cancer. Cancer Res 2005;65:10280‑10288.

[3] Junttila MR and de Sauvage FJ. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature 2013;501:346‑354.

[4] Barck KH, et al. Quantification of Tumor Burden in a Genetically Engineered Mouse Model of Lung Cancer by Micro-CT and Automated Analysis. Translational Oncology 2015;8(2):126‑135.

[5] Müller A, et al. Three-Dimensional Ultrashort Echo Time MRI and Short T₂ Images Generated From Subtraction for Determination of Tumor Burden in Lung Cancer: Preclinical Investigation in Transgenic Mice. Magn Reson Med 2018;79:1052-1060.