Gene expression analysis using human cancer xenografts to identify novel predictive marker genes for the efficacy of 5-fluorouracil-based drugs

使用人类癌症异种移植进行基因表达分析,以确定 5-氟尿嘧啶类药物疗效的新型预测标记基因

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作者:Akio Ooyama, Teiji Takechi, Etsuko Toda, Hideki Nagase, Yoshihiro Okayama, Kenji Kitazato, Yoshikazu Sugimoto, Toshinori Oka, Masakazu Fukushima

Abstract

The development of a diagnostic method for predicting the therapeutic efficacy or toxicity of anticancer drugs is a critical issue. We carried out a gene expression analysis to identify genes whose expression profiles were correlated with the sensitivity of 30 human tumor xenografts to 5-fluorouracil (5-FU)-based drugs (tegafur + uracil [UFT], tegafur + gimeracil + oteracil [S-1], 5'-deoxy-5-fluorouridine [5'-DFUR], and N4-pentyloxycarbonyl-5'-deoxy-5-fluorocytidine [capecitabine]), as well as three other drugs (cisplatin [CDDP], irinotecan hydrochloride [CPT-11], and paclitaxel) that have different modes of action. In the present study, we focused especially on the fluoropyrimidines. The efficacy of all anticancer drugs was assayed using human tumor xenografts in nude mice. The mRNA expression profile of each of these xenografts was analyzed using a Human Focus array. Correlation analysis between the gene expression profiles and the chemosensitivities of seven drugs identified 39 genes whose expression levels were correlated significantly with multidrug sensitivity, and we suggest that the angiogenic pathway plays a pivotal role in resistance to fluoropyrimidines. Furthermore, many genes showing specific correlations with each drug were also identified. Among the candidate genes associated with 5-FU resistance, the dihydropyrimidine dehydrogenase mRNA expression profiles of the tumors showed a significant negative correlation with chemosensitivity to all of the 5-FU based drugs except for S-1. Therefore, the administration of S-1 might be an effective strategy for the treatment of high dihydropyrimidine dehydrogenase-expressing tumors. The results of the present study may enhance the prediction of tumor response to anticancer drugs and contribute to the development of tailor-made chemotherapy.

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