Bioinformatics-driven discovery of rational combination for overcoming EGFR-mutant lung cancer resistance to EGFR therapy

Jihye Kim, Vihas T. Vasu, Rangnath Mishra, Katherine R. Singleton, Minjae Yoo, Sonia M. Leach, Eveline Farias-Hesson, Robert J. Mason, Jaewoo Kang, Preveen Ramamoorthy, Jeffrey A. Kern, Lynn E. Heasley, James H. Finigan, Aik-Choon Tan

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Motivation: Non-small-cell lung cancer (NSCLC) is the leading cause of cancer death in the United States. Targeted tyrosine kinase inhibitors (TKIs) directed against the epidermal growth factor receptor (EGFR) have been widely and successfully used in treating NSCLC patients with activating EGFR mutations. Unfortunately, the duration of response is short-lived, and all patients eventually relapse by acquiring resistance mechanisms. Result: We performed an integrative systems biology approach to determine essential kinases that drive EGFR-TKI resistance in cancer cell lines. We used a series of bioinformatics methods to analyze and integrate the functional genetics screen and RNA-seq data to identify a set of kinases that are critical in survival and proliferation in these TKI-resistant lines. By connecting the essential kinases to compounds using a novel kinase connectivity map (K-Map), we identified and validated bosutinib as an effective compound that could inhibit proliferation and induce apoptosis in TKI-resistant lines. A rational combination of bosutinib and gefitinib showed additive and synergistic effects in cancer cell lines resistant to EGFR TKI alone. Conclusions: We have demonstrated a bioinformatics-driven discovery roadmap for drug repurposing and development in overcoming resistance in EGFR-mutant NSCLC, which could be generalized to other cancer types in the era of personalized medicine..

Original languageEnglish
Pages (from-to)2393-2398
Number of pages6
JournalBioinformatics
Volume30
Issue number17
DOIs
Publication statusPublished - 2014 Sep 1

Fingerprint

Lung Cancer
Growth Factors
Bioinformatics
Computational Biology
Epidermal Growth Factor Receptor
Mutant
Protein-Tyrosine Kinases
Receptor
Inhibitor
Therapy
Lung Neoplasms
Cancer
Phosphotransferases
Non-Small Cell Lung Carcinoma
Cell
Line
Cells
Proliferation
Neoplasms
Drug Repositioning

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability

Cite this

Kim, J., Vasu, V. T., Mishra, R., Singleton, K. R., Yoo, M., Leach, S. M., ... Tan, A-C. (2014). Bioinformatics-driven discovery of rational combination for overcoming EGFR-mutant lung cancer resistance to EGFR therapy. Bioinformatics, 30(17), 2393-2398. https://doi.org/10.1093/bioinformatics/btu323

Bioinformatics-driven discovery of rational combination for overcoming EGFR-mutant lung cancer resistance to EGFR therapy. / Kim, Jihye; Vasu, Vihas T.; Mishra, Rangnath; Singleton, Katherine R.; Yoo, Minjae; Leach, Sonia M.; Farias-Hesson, Eveline; Mason, Robert J.; Kang, Jaewoo; Ramamoorthy, Preveen; Kern, Jeffrey A.; Heasley, Lynn E.; Finigan, James H.; Tan, Aik-Choon.

In: Bioinformatics, Vol. 30, No. 17, 01.09.2014, p. 2393-2398.

Research output: Contribution to journalArticle

Kim, J, Vasu, VT, Mishra, R, Singleton, KR, Yoo, M, Leach, SM, Farias-Hesson, E, Mason, RJ, Kang, J, Ramamoorthy, P, Kern, JA, Heasley, LE, Finigan, JH & Tan, A-C 2014, 'Bioinformatics-driven discovery of rational combination for overcoming EGFR-mutant lung cancer resistance to EGFR therapy', Bioinformatics, vol. 30, no. 17, pp. 2393-2398. https://doi.org/10.1093/bioinformatics/btu323
Kim, Jihye ; Vasu, Vihas T. ; Mishra, Rangnath ; Singleton, Katherine R. ; Yoo, Minjae ; Leach, Sonia M. ; Farias-Hesson, Eveline ; Mason, Robert J. ; Kang, Jaewoo ; Ramamoorthy, Preveen ; Kern, Jeffrey A. ; Heasley, Lynn E. ; Finigan, James H. ; Tan, Aik-Choon. / Bioinformatics-driven discovery of rational combination for overcoming EGFR-mutant lung cancer resistance to EGFR therapy. In: Bioinformatics. 2014 ; Vol. 30, No. 17. pp. 2393-2398.
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