Shimokuni Tatsushi, Tanimoto Keiji, Hiyama Keiko, Otani Keiko, Ohtaki Megu, Hihara Jun, Yoshida Kazuhiro, Noguchi Tsuyoshi, Kawahara Katsunobu, Natsugoe Shoji, Aikou Takashi, Okazaki Yasushi, Hayashizaki Yoshihide, Sato Yuji, Todo Satoru, Hiyama Eiso, Nishiyama Masahiko
Department of Translational Cancer Research, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima 734-8553, Japan.
Int J Oncol. 2006 May;28(5):1153-62.
Esophageal cancer is a highly lethal disease and the optimal therapy remains unclear. Since adjuvant chemotherapy gives a better chance of survival, we attempted to develop a chemosensitivity prediction model to improve individual responses to therapy. Comprehensive gene expression analyses (cDNA and oligonucleotide microarrays) and MTT assay of 8 drugs in 20 KYSE squamous cell carcinoma cell lines were performed to distinguish candidate marker genes whose expression levels reproducibly correlated with cellular drug sensitivities. After confirmation with real-time RT-PCR, we performed multiple regression analyses to develop drug-sensitivity prediction formulae using the quantified expression data of selected marker genes. Using the same sets of genes, we also constructed prediction models for individual clinical responses to 5-FU-based chemotherapy using 18 cases. We selected 5 better marker genes, known as drug sensitivity determinants, identified 9 novel predictive genes for 4 of 8 anticancer drugs [5-FU, CDDP, DOX, and CPT-11 (SN-38)], and developed highly predictive formulae of in vitro sensitivities to the 4 drugs and clinical responses to 5-FU-based adjuvant chemotherapies in terms of overall and disease-free survivals. Our selected genes are likely to be effective drug-sensitivity markers and formulae using the 9 novel genes would provide advantages in prediction.
食管癌是一种致死率很高的疾病，最佳治疗方法仍不明确。由于辅助化疗能提供更好的生存机会，我们试图开发一种化疗敏感性预测模型，以改善个体对治疗的反应。对20种KYSE鳞状细胞癌细胞系进行了综合基因表达分析（cDNA和寡核苷酸微阵列）以及8种药物的MTT测定，以区分其表达水平与细胞药物敏感性可重复相关的候选标记基因。经实时RT-PCR确认后，我们使用选定标记基因的定量表达数据进行多元回归分析，以建立药物敏感性预测公式。使用相同的基因集，我们还利用18例病例构建了针对基于5-氟尿嘧啶化疗的个体临床反应的预测模型。我们选择了5个更好的标记基因，即已知的药物敏感性决定因素，为8种抗癌药物中的4种[5-氟尿嘧啶、顺铂、阿霉素和伊立替康（SN-38）]鉴定了9个新的预测基因，并开发了对这4种药物的体外敏感性以及基于5-氟尿嘧啶的辅助化疗在总生存期和无病生存期方面临床反应的高度预测公式。我们选择的基因可能是有效的药物敏感性标记物，使用这9个新基因的公式在预测方面将具有优势。
