Quantification of Tumor Burden in a Genetically Engineered Mouse Model of Lung Cancer by Micro-CT and Automated Analysis

Kai H Barck; Hani Bou-Reslan; Ujjawal Rastogi; Timothy Sakhuja; Jason E Long; Rafael Molina; Anthony Lima; Patricia Hamilton; Melissa R Junttila; Leisa Johnson; Richard A D Carano

doi:10.1016/j.tranon.2015.03.003

Quantification of Tumor Burden in a Genetically Engineered Mouse Model of Lung Cancer by Micro-CT and Automated Analysis

Transl Oncol. 2015 Apr;8(2):126-35. doi: 10.1016/j.tranon.2015.03.003.

Affiliations

¹ Department of Biomedical Imaging, Genentech, Inc, South San Francisco, CA, USA.
² Department of Translational Oncology, Genentech, Inc, South San Francisco, CA, USA.
³ Department of Discovery Oncology, Genentech, Inc, South San Francisco, CA, USA.
⁴ Department of Biomedical Imaging, Genentech, Inc, South San Francisco, CA, USA. Electronic address: carano.richard@gene.com.

Abstract

Genetically engineered mouse models (GEMMs) of lung cancer closely recapitulate the human disease but suffer from the difficulty of evaluating tumor growth by conventional methods. Herein, a novel automated image analysis method for estimating the lung tumor burden from in vivo micro-computed tomography (micro-CT) data is described. The proposed tumor burden metric is the segmented soft tissue volume contained within a chest space region of interest, excluding an estimate of the heart volume. The method was validated by comparison with previously published manual analysis methods and applied in two therapeutic studies in a mutant K-ras GEMM of non-small cell lung carcinoma. Mice were imaged by micro-CT pre-treatment and stratified into four treatment groups: an antibody inhibiting vascular endothelial growth factor (anti-VEGF), chemotherapy, combination of anti-VEGF and chemotherapy, or control antibody. In the first study, post-treatment imaging was performed 4 weeks later. In the second study, mice were scanned serially on a high-throughput scanner every 2 weeks for 8 weeks during treatment. In both studies, the automated tumor burden estimates were well correlated with manual metrics (r value range: 0.83-0.93, P < .0001) and showed a similar, significant reduction in tumor growth in mice treated with anti-VEGF alone or in combination with chemotherapy. Given the fully automated nature of this technique, the proposed analysis method can provide a valuable tool in preclinical drug research for screening and randomizing animals into treatment groups and evaluating treatment efficacy in mouse models of lung cancer in a highly robust and efficient manner.