Coevolution-based computational approach to detect resistance mechanism of epidermal growth factor receptor

Gyan Prakash Rai1, Asheesh Shanker1

  • 1Department of Bioinformatics, Central University of South Bihar, Gaya, Bihar 824236, India.

Insights

Compensatory mutations in epidermal growth factor receptor (EGFR) drive resistance to non-small cell lung cancer treatments. These EGFR mutations alter drug-binding pockets, impacting tyrosine kinase inhibitor efficacy.

Area of Science:

  • Biochemistry
  • Molecular Biology
  • Cancer Research

Background:

  • Epidermal growth factor receptor (EGFR) is crucial in non-small cell lung cancer (NSCLC) progression, driving metastasis, angiogenesis, and inhibiting apoptosis.
  • Tyrosine kinase inhibitors (TKIs) targeting EGFR show efficacy but are limited by acquired drug resistance, often within a year of treatment.
  • Acquired resistance to EGFR-TKIs in NSCLC is frequently mediated by secondary mutations within the EGFR gene.

Purpose of the Study:

  • To investigate the evolutionary mechanisms behind drug resistance in EGFR.
  • To identify compensatory mutations in EGFR that contribute to TKI resistance.
  • To understand how these mutations affect the drug-binding pocket and ligand interaction.

Main Methods:

  • Analysis of EGFR mutations in resistant NSCLC cases.
  • Structural analysis of EGFR protein with compensatory mutations.
  • In silico modeling of drug-ligand interactions (gefitinib, erlotinib) with mutated EGFR.

Main Results:

  • Compensatory mutations were detected in EGFR, suggesting an evolutionary adaptation to drug pressure.
  • These mutations were found to enlarge the drug-binding pocket of EGFR.
  • The enlarged pocket alters the binding orientation of TKIs like gefitinib and erlotinib, leading to reduced efficacy and drug resistance.

Conclusions:

  • Coevolutionary forces significantly influence EGFR structure, promoting resistance to targeted therapies.
  • Understanding evolution-induced structural changes in EGFR is key to overcoming drug resistance.
  • This research provides insights for designing next-generation TKIs with improved efficacy against resistant NSCLC.

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