Personalized prediction of EGFR mutation-induced drug resistance in lung cancer

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

24 Scopus Citations
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Author(s)

  • Debby D. Wang
  • Weiqiang Zhou
  • Hong Yan
  • Maria Wong
  • Victor Lee

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Detail(s)

Original languageEnglish
Article number2855
Journal / PublicationScientific Reports
Volume3
Early online date4 Oct 2013
Publication statusPublished - 2013

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Abstract

EGFR mutation-induced drug resistance has significantly impaired the potency of small molecule tyrosine kinase inhibitors in lung cancer treatment. Computational approaches can provide powerful and efficient techniques in the investigation of drug resistance. In our work, the EGFR mutation feature is characterized by the energy components of binding free energy (concerning the mutant-inhibitor complex), and we combine it with specific personal features for 168 clinical subjects to construct a personalized drug resistance prediction model. The 3D structure of an EGFR mutant is computationally predicted from its protein sequence, after which the dynamics of the bound mutant-inhibitor complex is simulated via AMBER and the binding free energy of the complex is calculated based on the dynamics. The utilization of extreme learning machines and leave-one-out cross-validation promises a successful identification of resistant subjects with high accuracy. Overall, our study demonstrates advantages in the development of personalized medicine/therapy design and innovative drug discovery.

Research Area(s)

Citation Format(s)

Personalized prediction of EGFR mutation-induced drug resistance in lung cancer. / Wang, Debby D.; Zhou, Weiqiang; Yan, Hong; Wong, Maria; Lee, Victor.

In: Scientific Reports, Vol. 3, 2855, 2013.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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