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Published on: June 26, 2019
An EGFR signature predicts cell line and patient sensitivity to multiple tyrosine kinase inhibitors
Chao Cheng1,2,3, Yanding Zhao4, Evelien Schaafsma4
1Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.
Abstract:
EGFR is an oncogene with a high frequency of activating mutations in nonsmall cell lung cancer (NSCLC). EGFR inhibitors have been FDA-approved for NSCLC and have shown efficacy in patients with certain EGFR mutations. However, only 9% to 26% of these patients achieve objective responses. In our study, we developed an EGFR gene signature based on The Cancer Genome Atlas (TCGA) RNA-seq data of lung adenocarcinoma (LUAD) to direct the preselection of patients for more effective EGFR-targeted therapy. This signature infers baseline EGFR signaling pathway activity (denoted as EGFR score) in tumor samples, which is associated with tumor sensitivity to EGFR inhibitors and other tyrosine kinase inhibitors (TKIs). EGFR score predicted sensitivity of lung cancer cell lines to Erlotinib, Gefitinib and Sorafenib. Importantly, EGFR score calculated from pretreated samples was associated with patient response to Gefitinib and Sorafenib in lung cancer. Additionally, integration of the EGFR signature with TCGA LUAD data showed that it accurately predicted functional effects of different somatic EGFR mutations, and identified other mutations affecting EGFR pathway activity. Finally, using cancer cell line and clinical trial data, the EGFR score was associated with patient response to TKIs in liver cancer and other cancer types. The EGFR signature provides a useful biomarker that can expand the application of EGFR inhibitors or other TKIs and improve their treatment efficacy through patient stratification.
Insights
A new EGFR gene signature predicts tumor sensitivity to targeted therapies in lung cancer. This biomarker can improve patient selection for EGFR inhibitors and other tyrosine kinase inhibitors (TKIs).
Area of Science:
- Oncology
- Genomics
- Molecular Biology
Background:
- Epidermal Growth Factor Receptor (EGFR) is a key oncogene in non-small cell lung cancer (NSCLC).
- Current EGFR inhibitors show limited response rates (9-26%) even in patients with specific EGFR mutations.
- There is a need for biomarkers to better select patients for EGFR-targeted therapies.
Purpose of the Study:
- To develop an EGFR gene signature to predict tumor sensitivity to EGFR inhibitors and other tyrosine kinase inhibitors (TKIs).
- To validate the EGFR signature's ability to stratify patients for improved treatment efficacy.
Main Methods:
- Utilized The Cancer Genome Atlas (TCGA) RNA-seq data from lung adenocarcinoma (LUAD) to develop the EGFR signature.
- Calculated an "EGFR score" to infer baseline EGFR signaling pathway activity.
- Validated the EGFR score in lung cancer cell lines and patient samples treated with EGFR inhibitors (Erlotinib, Gefitinib) and Sorafenib.
Main Results:
- The EGFR score accurately predicted sensitivity of lung cancer cell lines to Erlotinib, Gefitinib, and Sorafenib.
- EGFR score from pre-treatment samples correlated with patient response to Gefitinib and Sorafenib in lung cancer.
- The signature identified functional effects of somatic EGFR mutations and other pathway-affecting mutations.
- EGFR score also showed association with TKI response in liver cancer and other cancer types.
Conclusions:
- The developed EGFR gene signature serves as a valuable biomarker for patient stratification.
- This signature can potentially expand the application of EGFR inhibitors and TKIs.
- Improved patient selection using the EGFR signature can enhance treatment efficacy in various cancers.
