Györffy Balázs, Serra Violeta, Jürchott Karsten, Abdul-Ghani Rula, Garber Mitch, Stein Ulrike, Petersen Iver, Lage Hermann, Dietel Manfred, Schäfer Reinhold
Charité, Institute of Pathology, Humboldt University, Schumannstr. 20/21, Berlin D-10117, Germany.
Oncogene. 2005 Nov 17;24(51):7542-51. doi: 10.1038/sj.onc.1208908.
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 ( approximately 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 profile 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.
目前尚无用于预测癌症化疗反应的最新临床检测方法，单个标志物的预测价值也很小。我们假设，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种不同来源的人类肿瘤细胞系的表达谱，这些细胞系分别来自胃（EPG85 - 257）、胰腺（EPP85 - 181）、结肠（HT29）和乳腺（MCF7和MDA - MB - 231），以及它们对拓扑异构酶抑制剂柔红霉素、阿霉素或米托蒽醌耐药的对应细胞系。我们用43000个cDNA克隆（约30000个独特基因）检测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：我们分别将柔红霉素/阿霉素耐药细胞系和米托蒽醌耐药细胞系的表达模式与亲代细胞系进行比较。为了鉴定预测基因，使用了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，我们应用耐药模型预测44个先前已表征的乳腺癌样本的敏感性。基因表达谱与阿霉素敏感细胞系相似的患者组生存期（49.7±26.1个月，n = 21，P = 0.034）比耐药组（32.9±18.7个月，n = 23）更长。源自阿霉素耐药和敏感细胞系的基因表达特征的应用能够有效预测阿霉素单药治疗后的临床生存期。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。
