Tanaka Tomotaka, Tanimoto Keiji, Otani Keiko, Satoh Kenichi, Ohtaki Megu, Yoshida Kazuhiro, Toge Tetsuya, Yahata Hiroshi, Tanaka Shinji, Chayama Kazuaki, Okazaki Yasushi, Hayashizaki Yoshihide, Hiyama Keiko, Nishiyama Masahiko
Department of Translational Cancer Research, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan.
Int J Cancer. 2004 Sep 10;111(4):617-26. doi: 10.1002/ijc.20289.
We developed concise, accurate prediction models of the in vitro activity for 8 anticancer drugs (5-FU, CDDP, MMC, DOX, CPT-11, SN-38, TXL and TXT), along with individual clinical responses to 5-FU using expression data of 12 genes. We first performed cDNA microarray analysis and MTT assay of 19 human cancer cell lines to sort out genes which were correlative in expression levels with cytotoxicities of the 8 drugs; we selected 13 genes with proven functional significance to drug sensitivity from a huge number of potent prediction marker genes. The correlation significance of each was confirmed using expression data quantified by real-time RT-PCR, and finally 12 genes (ABCB1, ABCG2, CYP2C8, CYP3A4, DPYD, GSTP1, MGMT, NQO1, POR, TOP2A, TUBB and TYMS) were selected as more reliable predictors of drug response. Using multiple regression analysis, we fixed 8 prediction formulae which embraced the variable expressions of the 12 genes and arranged them in order, to predict the efficacy of the drugs by referring to the value of Akaike's information criterion for each sample. These formulae appeared to accurately predict the in vitro efficacy of the drugs. For the first clinical application model, we fixed prediction formulae for individual clinical response to 5-FU in the same way using 41 clinical samples obtained from 30 gastric cancer patients and found to be of predictive value in terms of survival, time to treatment failure and tumor growth. None of the 12 selected genes alone could predict such clinical responses.
我们利用12个基因的表达数据，开发了8种抗癌药物（5-氟尿嘧啶、顺铂、丝裂霉素、阿霉素、伊立替康、SN-38、紫杉醇和多西他赛）体外活性以及对5-氟尿嘧啶个体临床反应的简洁、准确预测模型。我们首先对19种人类癌细胞系进行了cDNA微阵列分析和MTT试验，以筛选出表达水平与这8种药物细胞毒性相关的基因；我们从大量有效的预测标记基因中选择了13个对药物敏感性具有已证实功能意义的基因。使用实时RT-PCR定量的表达数据确认了每个基因的相关性显著性，最终选择了12个基因（ABCB1、ABCG2、CYP2C8、CYP3A4、DPYD、GSTP1、MGMT、NQO1、POR、TOP2A、TUBB和TYMS）作为更可靠的药物反应预测指标。通过多元回归分析，我们确定了8个包含这12个基因可变表达的预测公式，并按顺序排列，通过参考每个样本的赤池信息准则值来预测药物疗效。这些公式似乎能准确预测药物的体外疗效。对于第一个临床应用模型，我们以同样的方式使用从30名胃癌患者获得的41个临床样本确定了对5-氟尿嘧啶个体临床反应的预测公式，发现其在生存、治疗失败时间和肿瘤生长方面具有预测价值。单独的12个选定基因中没有一个能够预测这种临床反应。
