Characterization of epithelial-mesenchymal transition intermediate/hybrid phenotypes associated to resistance to EGFR inhibitors in non-small cell lung cancer cell lines
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Valentina Fustaino1,2,*, Dario Presutti1,*, Teresa Colombo2, Beatrice Cardinali1, Giuliana Papoff1, Rossella Brandi3, Paola Bertolazzi4,2, Giovanni Felici2,** and Giovina Ruberti1,**
1Institute of Cell Biology and Neurobiology, National Research Council (IBCN-CNR), Monterotondo, Rome, Italy
2Institute for Systems Analysis and Computer Science “Antonio Ruberti” National Research Council, (IASI-CNR), Rome, Italy
3Genomics facility of the European Brain Research Institute, “Rita Levi-Montalcini” (EBRI), Rome, Italy
4SYSBIO Center for Systems Biology, Milan, Italy
*These authors have contributed equally to this work
**Senior authors have contributed equally to this work
Giovina Ruberti, email: [email protected]
Giovanni Felici, email: [email protected]
Keywords: EMT intermediate/hybrid phenotype, NSCLC, EGFR, erlotinib, microarray data
Received: October 04, 2016 Accepted: August 23, 2017 Published: September 22, 2017
Increasing evidence points to a key role played by epithelial-mesenchymal transition (EMT) in cancer progression and drug resistance. In this study, we used wet and in silico approaches to investigate whether EMT phenotypes are associated to resistance to target therapy in a non-small cell lung cancer model system harboring activating mutations of the epidermal growth factor receptor. The combination of different analysis techniques allowed us to describe intermediate/hybrid and complete EMT phenotypes respectively in HCC827- and HCC4006-derived drug-resistant human cancer cell lines. Interestingly, intermediate/hybrid EMT phenotypes, a collective cell migration and increased stem-like ability associate to resistance to the epidermal growth factor receptor inhibitor, erlotinib, in HCC827 derived cell lines. Moreover, the use of three complementary approaches for gene expression analysis supported the identification of a small EMT-related gene list, which may have otherwise been overlooked by standard stand-alone methods for gene expression analysis.
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