EGFR TKI resistance in lung cancer cells using RNA sequencing and analytical bioinformatics tools

Abstract

Epidermal Growth Factor Receptor (EGFR) signaling and EGFR mutations play key roles in cancer pathogenesis, particularly in the development of drug resistance. For the ∼20% of all non-small cell lung cancer (NSCLC) patients that harbor an activating mutation, EGFR tyrosine kinase inhibitors (TKIs) provide initial clinical responses. However, long-term efficacy is not possible due to acquired drug resistance. Despite a gradually increasing knowledge of the mechanisms underpinning the development of resistance in tumors, there has been very little success in overcoming it and it is probable that many additional mechanisms are still unknown. Herein, publicly available RNASeq (RNA sequencing) datasets comparing lung cancer cell lines treated with EGFR TKIs until resistance developed with their corresponding parental cells and protein array data from our own EGFR TKI treated xenograft tumors, were analyzed for differential gene expression, with the intent to investigate the potential mechanisms of drug resistance to EGFR TKIs. Pathway analysis, as well as structural disorder analysis of proteins in these pathways, revealed several key proteins, including DUSP1, DUSP6, GAB2, and FOS, that could be targeted using novel combination therapies to overcome EGFR TKI resistance in lung cancer.

Keywords:

• EGFR TKI resistance
• lung cancer
• bioinformatics

Acknowledgements

This work is supported by Veterans Affairs Merit Review grant (BX003413) to Dr. Subhra Mohapatra, and Research Career Scientist Awards to Dr. Subhra Mohapatra (IK6BX004212) and Dr. Shyam Mohapatra (IK6 BX003778). Though this report is based upon work supported, in part, by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, the contents of this report do not represent the views of the Department of Veterans Affairs or the United States Government. This work is also supported by National Institute of Health (NIH) grant to Dr. Subhra Mohapatra (R01CA152005).

Authors’ contributions

Literature gathering and analysis: MH, RG; Experiments and data analysis: MH, RG, JC, GD, VU, SSM, SM; Wrote the manuscript: MH, RG, VU, SSM, SM; Figure illustration: MH, RG, GD, VU, SSM, SM; Funding; SM, SSM.

Availability of data and material

The raw and processed data used in this study was downloaded from the Gene Expression Omnibus (GEO) database and can be found using the indicated accession number (Table 1) for each dataset.

Consent for publication

All authors have read and agreed to the published version of the manuscript.

Disclosure statement

The authors declare no potential conflicts of interest.

Additional information

Funding

This work is supported by the Research Career Scientist Award IK6BX004212 to S.M. and IK6BX006032 to S.S.M., the Veterans Affairs Merit Review grant BX003413 to S.M. and S.S.M. and Public Service grant RO1CA152005 to S.S.M. and S.M. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs, the United States government, or the National Institute of Health.