Time-serial Assessment of Drug Combination Interventions in a Mouse Model of Colorectal Carcinogenesis Using Optical Coherence Tomography

摘要

Optical coherence tomography (OCT) is a high-resolution, nondestructive imaging modality that enables time-serial assessment of adenoma development in the mouse model of colorectal cancer. In this study, OCT was utilized to evaluate the effectiveness of interventions with the experimental antitumor agent α-difluoromethylornithine (DFMO) and a nonsteroidal anti-inflammatory drug sulindac during early [chemoprevention (CP)] and late stages [chemotherapy (CT)] of colon tumorigenesis. Biological endpoints for drug interventions included OCT-generated tumor number and tumor burden. Immunochistochemistry was used to evaluate biochemical endpoints [Ki-67, cleaved caspase-3, cyclooxygenase (COX)-2, β-catenin]. K-Ras codon 12 mutations were studied with polymerase chain reaction-based technique. We demonstrated that OCT imaging significantly correlated with histological analysis of both tumor number and tumor burden for all experimental groups (P < 0.0001), but allows more accurate and full characterization of tumor number and burden growth rate because of its time-serial, nondestructive nature. DFMO alone or in combination with sulindac suppressed both the tumor number and tumor burden growth rate in the CP setting because of DFMO-mediated decrease in cell proliferation (Ki-67, P < 0.001) and K-RAS mutations frequency (P = 0.04). In the CT setting, sulindac alone and DFMO/sulindac combination were effective in reducing tumor number, but not tumor burden growth rate. A decrease in COX-2 staining in DFMO/sulindac CT groups (COX-2, P < 0.01) confirmed the treatment effect. Use of nondestructive OCT enabled repeated, quantitative evaluation of tumor number and burden, allowing changes in these parameters to be measured during CP and as a result of CT. In conclusion, OCT is a robust minimally invasive method for monitoring colorectal cancer disease and effectiveness of therapies in mouse models.