Quantification of mouse pulmonary cancer models by microcomputed tomography imaging
Quantification of mouse pulmonary cancer models by microcomputed tomography imaging
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DOI：
10.1111/j.1349-7006.2009.01199.x
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发表时间：
2009-08-01
期刊：
影响因子：
5.7
通讯作者：
Hayakawa, Yoshihiro
中科院分区：
文献类型：
--
作者：
Fushiki, Hiroshi;Kanoh-Azuma, Tomoko;Hayakawa, Yoshihiro
临床前癌症模型的进展，包括原位植入模型或基因工程小鼠癌症模型，使追求癌症疾病的分子机制，可能模仿人类的遗传和生物学过程。肺癌是癌症死亡的主要原因;因此，通过更好地了解疾病的复杂机制，有望改善肺癌的治疗和预防。在这项研究中，我们研究了两种不同的小鼠肺癌模型的量化，利用成像方式监测肿瘤进展和药物疗效评价。微计算机断层扫描（micro-CT）用于实时/非侵入性监测肺癌进展的实用性已通过结合生物发光成像和组织病理学分析得到证实。此外，我们通过利用K-ras（LSL-G12 D）/p53（LSL-R270 H）突变小鼠开发了一种临床上更相关的肺癌模型。利用micro-CT成像，我们监测了K-ras（LSL-G12 D）/p53（LSL-R270 H）突变小鼠中孤立性肺肿瘤的发生和进展，并进一步证实了抗癌药物治疗对肿瘤生长的抑制作用。这些结果清楚地表明，对临床相关性更高的肿瘤模型进行成像引导评价将改善新药发现的过程，并增加后续临床研究的成功概率。（Cancer Sci 2009）。
The advances in preclinical cancer models, including orthotopic implantation models or genetically engineered mouse models of cancer, enable pursuing the molecular mechanism of cancer disease that might mimic genetic and biological processes in humans. Lung cancer is the major cause of cancer deaths; therefore, the treatment and prevention of lung cancer are expected to be improved by a better understanding of the complex mechanism of disease. In this study, we have examined the quantification of two distinct mouse lung cancer models by utilizing imaging modalities for monitoring tumor progression and drug efficacy evaluation. The utility of microcomputed tomography (micro-CT) for real-time/non-invasive monitoring of lung cancer progression has been confirmed by combining bioluminescent imaging and histopathological analyses. Further, we have developed a more clinically relevant lung cancer model by utilizing K-ras(LSL-G12D)/p53(LSL-R270H) mutant mice. Using micro-CT imaging, we monitored the development and progression of solitary lung tumor in K-ras(LSL-G12D)/p53(LSL-R270H) mutant mouse, and further demonstrated tumor growth inhibition by anticancer drug treatment. These results clearly indicate that imaging-guided evaluation of more clinically relevant tumor models would improve the process of new drug discovery and increase the probability of success in subsequent clinical studies. (Cancer Sci 2009).