Zhang Jingxian, Jia Jia, Zhu Feng, Ma Xiaohua, Han Bucong, Wei Xiaona, Tan Chunyan, Jiang Yuyang, Chen Yuzong
The Guangdong Provincial Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen, China.
Mol Biosyst. 2012 Oct;8(10):2645-56. doi: 10.1039/c2mb25165e.
Some drugs, such as anticancer EGFR tyrosine kinase inhibitors, elicit markedly different clinical response rates due to differences in drug bypass signaling as well as genetic variations of drug target and downstream drug-resistant genes. The profiles of these bypass signaling are expected to be useful for improved drug response prediction, which have not been systematically explored previously. In this work, we searched and analyzed 16 literature-reported EGFR tyrosine kinase inhibitor bypass signaling routes in the EGFR pathway, which include 5 compensatory routes of EGFR transactivation by another receptor, and 11 alternative routes activated by another receptor. These 16 routes are reportedly regulated by 11 bypass genes. Their expression profiles together with the mutational, amplification and expression profiles of EGFR and 4 downstream drug-resistant genes, were used as new sets of biomarkers for identifying 53 NSCLC cell-lines sensitive or resistant to EGFR tyrosine kinase inhibitors gefitinib, erlotinib and lapatinib. The collective profiles of all 16 genes distinguish sensitive and resistant cell-lines are better than those of individual genes and the combined EGFR and downstream drug resistant genes, and their derived cell-line response rates are consistent with the reported clinical response rates of the three drugs. The usefulness of cell-line data for drug response studies was further analyzed by comparing the expression profiles of EGFR and bypass genes in NSCLC cell-lines and patient samples, and by using a machine learning feature selection method for selecting drug response biomarkers. Our study suggested that the profiles of drug bypass signaling are highly useful for improved drug response prediction.
一些药物，如抗癌表皮生长因子受体（EGFR）酪氨酸激酶抑制剂，由于药物旁路信号传导的差异以及药物靶点和下游耐药基因的基因变异，会引发明显不同的临床反应率。这些旁路信号传导的特征有望用于改进药物反应预测，而此前尚未对其进行系统研究。在这项研究中，我们搜索并分析了文献报道的表皮生长因子受体酪氨酸激酶抑制剂在EGFR通路中的16条旁路信号传导途径，其中包括由另一种受体介导的EGFR反式激活的5条补偿途径，以及由另一种受体激活的11条替代途径。据报道，这16条途径受11个旁路基因调控。它们的表达谱，连同EGFR和4个下游耐药基因的突变、扩增和表达谱，被用作新的生物标志物集，以鉴定对EGFR酪氨酸激酶抑制剂吉非替尼、厄洛替尼和拉帕替尼敏感或耐药的53种非小细胞肺癌细胞系。所有16个基因的综合特征区分敏感和耐药细胞系的能力优于单个基因以及EGFR和下游耐药基因组合的特征，并且它们得出的细胞系反应率与这三种药物报道的临床反应率一致。通过比较非小细胞肺癌细胞系和患者样本中EGFR和旁路基因的表达谱，并使用机器学习特征选择方法来选择药物反应生物标志物，进一步分析了细胞系数据在药物反应研究中的实用性。我们的研究表明，药物旁路信号传导的特征对于改进药物反应预测非常有用。
