Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival
Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival
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DOI：
10.1038/sj.onc.1208908
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发表时间：
2005-11-17
期刊：
影响因子：
8
通讯作者：
Schäfer, R
中科院分区：
文献类型：
--
作者：
Györffy, B;Serra, V;Schäfer, R
最新的临床试验预测癌症化疗反应是不可用的，个别标志物已显示出很小的预测价值。我们假设归因于化疗耐药细胞的基因表达模式可以预测反应和癌症预后。我们对比了13种不同的人肿瘤细胞系的表达谱，这些肿瘤细胞系来自胃（EPG 85 -257）、胰腺（EPP 85 -181）、结肠（HT 29）和乳腺（MCF 7和MDA-MB-231），以及它们对拓扑异构酶抑制剂柔红霉素、多柔比星或米托蒽醌耐药的对应物。我们用43000个cDNA克隆（类似于30000个独特的基因）询问cDNA阵列，以研究这些细胞系的表达模式。我们将基因表达谱分为两组：我们将柔红霉素/多柔比星耐药细胞系和米托蒽醌耐药细胞系的表达模式独立地与亲本细胞系进行比较。为了鉴定预测基因，使用微阵列预测分析算法。分析显示79个基因与阿霉素耐药最相关，70个基因与米托蒽醌耐药最相关。在一项独立的分类实验中，我们应用我们的耐药模型预测了44个先前表征的乳腺癌样本的敏感性。以基因表达为特征的患者组。与多柔比星敏感细胞系相似，耐药组（32.9 +/- 18.7个月，n = 23）的生存期较长（49.7 +/- 26.1个月，n = 21，P = 0.034）。多柔比星耐药和敏感细胞系基因表达特征的应用可以有效预测多柔比星单药治疗后的临床生存率。我们的方法证明了体外实验在开发癌症反应预测新策略中的重要性。
Up to date clinical tests for predicting cancer chemotherapy response are not available and individual markers have shown little predictive value. We hypothesized that gene expression patterns attributable to chemotherapy-resistant cells can predict response and cancer prognosis. We contrasted the expression profiles of 13 different human tumor cell lines of gastric (EPG85-257), pancreatic (EPP85-181), colon (HT29) and breast (MCF7 and MDA-MB-231) origin and their counterparts resistant to the topoisomerase inhibitors daunorubicin, doxorubicin or mitoxantrone. We interrogated cDNA arrays with 43 000 cDNA clones (similar to 30 000 unique genes) to study the expression pattern of these cell lines. We divided gene expression profiles into two sets: we compared the expression patterns of the daunorubicin/doxorubicin- resistant cell lines and the mitoxantrone-resistant cell lines independently to the parental cell lines. For identifying predictive genes, the Prediction Analysis for Mircorarrays algorithm was used. The analysis revealed 79 genes best correlated with doxorubicin resistance and 70 genes with mitoxantrone resistance. In an independent classification experiment, we applied our model of resistance for predicting the sensitivity of 44 previously characterized breast cancer samples. The patient group characterized by the gene expression pro. le similar to those of doxorubicin-sensitive cell lines exhibited longer survival (49.7 +/- 26.1 months, n = 21, P = 0.034) than the resistant group (32.9 +/- 18.7 months, n = 23). The application of gene expression signatures derived from doxorubicin-resistant and -sensitive cell lines allowed to predict effectively clinical survival after doxorubicin monotherapy. Our approach demonstrates the significance of in vitro experiments in the development of new strategies for cancer response prediction.