Gene expression patterns that predict sensitivity to epidermal growth factor receptor tyrosine kinase inhibitors in lung cancer cell lines and human lung tumors.
Gene expression patterns that predict sensitivity to epidermal growth factor receptor tyrosine kinase inhibitors in lung cancer cell lines and human lung tumors.
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DOI：
10.1186/1471-2164-7-289
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发表时间：
2006-11-10
期刊：
影响因子：
4.4
通讯作者：
Black EP
中科院分区：
文献类型：
--
作者：
Balko JM;Potti A;Saunders C;Stromberg A;Haura EB;Black EP
越来越多的关注点围绕着识别将从表皮生长因子受体（EGFR）酪氨酸激酶抑制剂（TKI）治疗中获益的晚期非小细胞肺癌（NSCLC）患者。EGFR突变、基因拷贝数、ErbB蛋白和配体的共表达以及上皮细胞向间质细胞转化标志物均与EGFR TKI敏感性相关，虽然使用任何一种标志物预测敏感性确实可以识别应答者，但由于患者间和肿瘤间变异性水平较高，单个标志物并不涵盖所有潜在应答者。我们假设，基于基因表达数据的EGFR TKI敏感性的多变量预测因子将提供一种临床上有用的方法，用于解释预测EGFR TKI应答时固有的变异性增加，并阐明异常EGFR信号传导的机制。此外，我们预计，这种方法将导致改善预测相比，单独的参数在体外和体内。使用源自对EGFR TKI（如厄洛替尼）表现出不同敏感性的细胞系的基因表达数据生成用于先验预测缓解的模型。EGFR TKI敏感性的基因表达特征在肺癌生物学中显示出显著的生物学相关性，因为相关信号传导分子和下游效应分子存在于特征中。对角线性判别分析，使用这种基因签名是非常有效的分类样本外的癌细胞系的敏感性EGFR抑制，更准确的分类比突变状态单独。使用相同的预测因子，我们对人肺腺癌进行了分类，并捕获了大多数具有高水平EGFR激活的肿瘤以及那些在激酶结构域中含有激活突变的肿瘤。我们已经证明，EGFR TKI敏感性的预测模型可以对样本外细胞系和肺腺癌进行分类。这些数据表明，EGFR TKI缓解的多变量预测因子具有临床应用潜力，可能提供EGFR TKI敏感性的稳健和准确的预测因子，这在非小细胞肺癌中无法通过单一生物标志物或临床特征实现。
Increased focus surrounds identifying patients with advanced non-small cell lung cancer (NSCLC) who will benefit from treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI). EGFR mutation, gene copy number, coexpression of ErbB proteins and ligands, and epithelial to mesenchymal transition markers all correlate with EGFR TKI sensitivity, and while prediction of sensitivity using any one of the markers does identify responders, individual markers do not encompass all potential responders due to high levels of inter-patient and inter-tumor variability. We hypothesized that a multivariate predictor of EGFR TKI sensitivity based on gene expression data would offer a clinically useful method of accounting for the increased variability inherent in predicting response to EGFR TKI and for elucidation of mechanisms of aberrant EGFR signalling. Furthermore, we anticipated that this methodology would result in improved predictions compared to single parameters alone both in vitro and in vivo. Gene expression data derived from cell lines that demonstrate differential sensitivity to EGFR TKI, such as erlotinib, were used to generate models for a priori prediction of response. The gene expression signature of EGFR TKI sensitivity displays significant biological relevance in lung cancer biology in that pertinent signalling molecules and downstream effector molecules are present in the signature. Diagonal linear discriminant analysis using this gene signature was highly effective in classifying out-of-sample cancer cell lines by sensitivity to EGFR inhibition, and was more accurate than classifying by mutational status alone. Using the same predictor, we classified human lung adenocarcinomas and captured the majority of tumors with high levels of EGFR activation as well as those harbouring activating mutations in the kinase domain. We have demonstrated that predictive models of EGFR TKI sensitivity can classify both out-of-sample cell lines and lung adenocarcinomas. These data suggest that multivariate predictors of response to EGFR TKI have potential for clinical use and likely provide a robust and accurate predictor of EGFR TKI sensitivity that is not achieved with single biomarkers or clinical characteristics in non-small cell lung cancers.
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影响因子：
158.5
作者：
Lynch, TJ;Bell, DW;Haber, DA
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Haber, DA
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