NDLI: Analysis of bypass signaling in EGFR pathway and profiling of bypass genes for predicting response to anticancer EGFR tyrosine kinase inhibitors.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Zhang, Jingxian Jia, Jia Zhu, Feng Ma, Xiaohua Han, Bucong Wei, Xiaona Tan, Chunyan Jiang, Yuyang Chen, Yuzong
Description	Country affiliation: China Author Affiliation: Zhang J ( The Guangdong Provincial Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen, China.)
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.
File Format	HTM / HTML
ISSN	1742206X
Issue Number	10
Volume Number	8
e-ISSN	17422051
Journal	Molecular BioSystems
Language	English
Publisher	Royal Society of Chemistry
Publisher Date	2012-10-01
Publisher Place	Great Britain (UK)
Access Restriction	Subscribed
Subject Keyword	Discipline Molecular Biology Discipline Biochemistry Antineoplastic Agents Pharmacology Carcinoma, Non-small-cell Lung Drug Therapy Gene Expression Regulation, Neoplastic Drug Effects Lung Neoplasms Protein Kinase Inhibitors Receptor, Epidermal Growth Factor Antagonists & Inhibitors Signal Transduction Biomarkers, Pharmacological Metabolism Genetics Pathology Drug Resistance, Neoplasm Erlotinib Hydrochloride Gene Expression Profiling Humans Mutation Predictive Value Of Tests Quinazolines Support Vector Machines Journal Article Research Support, Non-u.s. Gov't
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biotechnology

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