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|Title:||Analysis of bypass signaling in EGFR pathway and profiling of bypass genes for predicting response to anticancer EGFR tyrosine kinase inhibitors|
|Source:||Zhang, J., Jia, J., Zhu, F., Ma, X., Han, B., Wei, X., Tan, C., Jiang, Y., Chen, Y. (2012-10). Analysis of bypass signaling in EGFR pathway and profiling of bypass genes for predicting response to anticancer EGFR tyrosine kinase inhibitors. Molecular BioSystems 8 (10) : 2645-2656. ScholarBank@NUS Repository. https://doi.org/10.1039/c2mb25165e|
|Abstract:||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. This journal is © 2012 The Royal Society of Chemistry.|
|Source Title:||Molecular BioSystems|
|Appears in Collections:||Staff Publications|
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