An epithelial-mesenchymal transition (EMT) gene signature predicts resistance to EGFR and PI3K inhibitors and identifies Axl as a therapeutic target for overcoming EGFR inhibitor resistance
By L. Byers, L. Diao, Jing Wang, P. Saintigny, Luc Girard, M. Peyton, Li Shen, Youhong Fan, U. Giri, Praveen K. Tumula and 24 more. Clinical Cancer Research, 2012
934 citations recorded by the source corpus.
Purpose: Epithelial–mesenchymal transition (EMT) has been associated with metastatic spread and EGF receptor (EGFR) inhibitor resistance. We developed and validated a robust 76-gene EMT signature using gene expression profiles from four platforms using non–small cell lung carcinoma (NSCLC) cell lines and patients treated in the Biomarker-Integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) study. Experimental Design: We conducted an integrated gene expression, proteomic, and drug response analysis using cell lines and tumors from patients with NSCLC. A 76-gene EMT signature was developed and validated using gene expression profiles from four microarray platforms of NSCLC cell lines and patients treated in the BATTLE study, and potential therapeutic targets associated with EMT were identified. Results: Compared with epithelial cells, mesenchymal cells showed significantly greater resistance to EGFR and PI3K/Akt pathway inhibitors, independent of EGFR mutation status, but more sensitivity to certain chemotherapies. Mesenchymal cells also expressed increased levels of the receptor tyrosine kinase Axl and showed a trend toward greater sensitivity to the Axl inhibitor SGI-7079, whereas the combination of SGI-7079 with erlotinib reversed erlotinib resistance in mesenchymal lines expressing Axl and in a xenograft model of mesenchymal NSCLC. In patients with NSCLC, the EMT signature predicted 8-week disease control in patients receiving erlotinib but not other therapies. Conclusion: We have developed a robust EMT signature that predicts resistance to EGFR and PI3K/Akt inhibitors, highlights different patterns of drug responsiveness for epithelial and mesenchymal cells, and identifies Axl as a potential therapeutic target for overcoming EGFR inhibitor resistance associated with the mesenchymal phenotype. Clin Cancer Res; 19(1); 279–90. ©2012 AACR.
Subfields: Hepatology, Immunology, Oncology
View Paper: https://www.semanticscholar.org/paper/7669cd3f8491e49970cb54fbea766d1d3b74a65b
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Citations in the corpus
- Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines (2014, methodology): “Finally, we applied our approach to a dataset from the Biomarker-Integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) study (trial registration ID: NCT00409968) [17,34].”
- Co-occurring genomic alterations define major subsets of KRAS - mutant lung adenocarcinoma with distinct biology, immune profiles, and therapeutic vulnerabilities (2015, neutral): “RPPA analysis was performed as previously reported (12, 57).”
- Patterns of transcription factor programs and immune pathway activation define four major subtypes of SCLC with distinct therapeutic vulnerabilities (2021, methodology): “Lung-specific EMT score was calculated using gene expression values as previously described (Byers et al., 2013).”
- Epithelial-mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients (2014, positive): “Thus, to search for an association between EMT and chemother-apeutics, and to explore the potential therapeutic options for Epi and Mes cancers, we analysed drug sensitivity data from the SANGER/COSMIC (Garnett et al , 2012) inhibitor resistance in non-small cell lung cancer (NSCLC) (Byers et al , 2”
- Dynamic EMT: a multi‐tool for tumor progression (2021, positive): “…non-small-cell lung cancer (NSCLC), activation of the AXL receptor tyrosine kinase in the course of EMT provides resistance to EGFR and PI3K inhibition, dependent on sustained KRAS activity (Singh et al , 2009; Sequist et al , 2011; Zhang et al , 2012; Byers et al , 2013; Tulchinsky et al , 2019).”