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Updated: Apr 26, 2026

Establishing Dual Resistance to EGFR-TKI and MET-TKI in Lung Adenocarcinoma Cells In Vitro with a 2-step Dose-escalation Procedure
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Decoding the EGFR mutation-induced drug resistance in lung cancer treatment by local surface geometric properties

Lichun Ma1, Debby D Wang1, Yiqing Huang2

  • 1Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China.

Computers in Biology and Medicine
|July 19, 2014
PubMed

Insights

Local surface geometry of epidermal growth factor receptor (EGFR) mutants correlates with progression-free survival in lung cancer. These findings aid in predicting drug resistance for tyrosine kinase inhibitor therapies.

Area of Science:

  • Oncology
  • Structural Biology
  • Computational Chemistry

Background:

  • Drug resistance in lung cancer limits tyrosine kinase inhibitor (TKI) efficacy.
  • Epidermal growth factor receptor (EGFR) mutations are a key driver of resistance.
  • Predictive biomarkers for TKI resistance are crucial for effective lung cancer treatment.

Purpose of the Study:

  • To investigate the correlation between local surface geometric properties of EGFR mutants and progression-free survival (PFS).
  • To identify geometric features that predict drug resistance in EGFR-mutated lung cancer.

Main Methods:

  • Comparative analysis of local surface geometry between wild-type EGFR and EGFR mutants.
  • Quantification of four types of local surface changes and surface convexity.
  • Statistical analysis using Spearman's rank correlation to assess PFS association.

Main Results:

  • Three types of local surface properties showed significant positive correlations (Spearman's rho > 0.6, P < 0.05) with PFS.
  • The number of atoms within specific solid angle ranges, indicating surface convexity, strongly correlated with PFS.
  • These geometric characteristics serve as potential predictors of TKI resistance.

Conclusions:

  • Local surface geometry of EGFR mutants is a significant predictor of PFS in lung cancer patients treated with TKIs.
  • Geometric analysis of EGFR mutants offers a novel approach for predicting drug resistance.
  • The findings can be extended to predict resistance in other cancer types.
Keywords:
Alpha shapeDrug resistanceEGFR mutationLung cancerProtein surface geometric propertiesSolid angle

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