A Quantitative Volumetric Micro-Computed Tomography Method to Analyze Lung Tumors in Genetically Engineered Mouse Models
Male
0303 health sciences
Lung Neoplasms
Dose-Response Relationship, Drug
Neovascularization, Pathologic
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Antineoplastic Agents
Mice, Transgenic
Adenocarcinoma
Cone-Beam Computed Tomography
Genes, p53
Adenoviridae
Tumor Burden
3. Good health
Erlotinib Hydrochloride
Mice
03 medical and health sciences
Genes, ras
Quinazolines
Animals
Female
RC254-282
Cell Proliferation
DOI:
10.1593/neo.81030
Publication Date:
2015-04-23T13:02:33Z
AUTHORS (11)
ABSTRACT
Two genetically engineered, conditional mouse models of lung tumor formation, K-ras(LSL-G12D) and K-ras(LSL-G12D)/p53(LSL-R270H), are commonly used to model human lung cancer. Developed by Tyler Jacks and colleagues, these models have been invaluable to study in vivo lung cancer initiation and progression in a genetically and physiologically relevant context. However, heterogeneity, multiplicity and complexity of tumor formation in these models make it challenging to monitor tumor growth in vivo and have limited the application of these models in oncology drug discovery. Here, we describe a novel analytical method to quantitatively measure total lung tumor burden in live animals using micro-computed tomography imaging. Applying this methodology, we studied the kinetics of tumor development and response to targeted therapy in vivo in K-ras and K-ras/p53 mice. Consistent with previous reports, lung tumors in both models developed in a time- and dose (Cre recombinase)-dependent manner. Furthermore, the compound K-ras(LSL-G12D)/p53(LSL-R270H) mice developed tumors faster and more robustly than mice harboring a single K-ras(LSL-G12D) oncogene, as expected. Erlotinib, a small molecule inhibitor of the epidermal growth factor receptor, significantly inhibited tumor growth in K-ras(LSL-G12D)/p53(LSL-R270H) mice. These results demonstrate that this novel imaging technique can be used to monitor both tumor progression and response to treatment and therefore supports a broader application of these genetically engineered mouse models in oncology drug discovery and development.
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