Gene expression profiling of ATP-binding cassette (ABC) transporters as a predictor of the pathologic response to neoadjuvant chemotherapy in breast cancer patients
Gene expression profiling of ATP-binding cassette (ABC) transporters as a predictor of the pathologic response to neoadjuvant chemotherapy in breast cancer patients
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DOI：
10.1007/s10549-006-9175-2
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发表时间：
2006-09-01
影响因子：
3.8
通讯作者：
Fujiwara, Yasuhiro
中科院分区：
文献类型：
--
作者：
Park, Sarah;Shimizu, Chikako;Fujiwara, Yasuhiro
耐药性是化疗成功的主要障碍。已知几种ATP结合盒（ABC）转运蛋白ABCB 1、ABCC 1和ABCG 2是化疗耐药的重要介导物。使用寡核苷酸微阵列（HG-U133 Plus 2.0; Affyphin），我们分析了接受连续每周紫杉醇/FEC（5-氟尿嘧啶，表阿霉素和环磷酰胺）新辅助化疗的乳腺癌患者的ABC转运蛋白基因表达谱。我们比较了根据患者对新辅助化疗的病理学反应划分的两类预处理肿瘤样品之间的ABC转运蛋白表达谱（残留疾病[RD]与病理学完全反应[pCR]），ABCB 3、ABCC 7和ABCF 2在pCR中显示出显著高表达。几种ABC转运蛋白包括ABCC 5、ABCA 12、ABCA 1、ABCC 13、ABCB 6和ABCC 11在RD中显示显著增加的表达（p < 0.05）。我们评估了利用ABC转运蛋白基因表达谱建立新辅助化疗病理反应多基因预测模型的可行性。通过留一交叉验证（LOOCV）评估预测误差。一个多基因预测模型与ABC转运蛋白差异表达的两类（p
Drug resistance is a major obstacle to the successful chemotherapy. Several ATP-binding cassette (ABC) transporters including ABCB1, ABCC1 and ABCG2 have been known to be important mediators of chemoresistance. Using oligonucleotide microarrays (HG-U133 Plus 2.0; Affymetrix), we analyzed the ABC transporter gene expression profiles in breast cancer patients who underwent sequential weekly paclitaxel/FEC (5-fluorouracil, epirubicin and cyclophosphamide) neoadjuvant chemotherapy. We compared the ABC transporter expression profile between two classes of pretreatment tumor samples divided by the patients' pathological response to neoadjuvant chemotherapy (residual disease [RD] versus pathologic complete response [pCR]) ABCB3, ABCC7 and ABCF2 showed significantly high expression in the pCR. Several ABC transporters including ABCC5, ABCA12, ABCA1 ABCC13, ABCB6 and ABCC11 showed significantly increased expression in the RD (p < 0.05). We evaluated the feasibility of developing a multigene predictor model of pathologic response to neoadjuvant chemotherapy using gene expression profiles of ABC transporters. The prediction error was evaluated by leave-one-out cross-validation (LOOCV). A multigene predictor model with the ABC transporters differentially expressed between the two classes (p