Computational Analysis of EGFR Mutants and
                         Non-Small Cell Lung Cancer Drug Resistance


This is a list of our publications from the HMRF project.
Click here to see our project information on HMRF website.
Click here to see our project report on HMRF website.
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US Patent: Methods for modeling and analysis of interface between point patterns

Methods, systems, and articles of manufacture are described that facilitate computation of a model of an interface between two molecules and analyzing characteristics of the interface. The complex of the two molecules can be modeled, and location of the atoms on the surface can be determined. One of the two molecules can be similarly modeled, and the location of the atoms on the surface can be determined. An interface model utilizing atoms that are located in the same place on the complex and the molecule can be calculated. Properties of the interface can be utilized to analyze the interaction between the two molecules.
Deciphering mechanisms of acquired T790M mutation after EGFR inhibitors for NSCLC by computational simulations

We conducted computational studies and discovered that the EGFR mutation delE746_A750 had a lower stability around the residue T790 than delS752_I759 and L858R, which was consistent with our clinical observation that patients with delE746_A750 were more likely to acquire T790M mutation than those with delS752_I759 or L858R. [PDF file of this publication]
Identifying EGFR mutation-induced drug resistance based on alpha shape model analysis of the dynamics

We analyzed the EGFR mutation-induced drug resistance based on the motion trajectories obtained from MD simulation. We computed alpha shape model of each trajectory frame and we compare the predicted response with the clinically obtained drug response of the EGFR mutants collected from literature. [PDF file of this publication]
An eigen-binding site based method for the analysis of anti-EGFR drug resistance in lung cancer treatment

We explore the drug resistance mechanism in NSCLC treatment by characterizing the drug-binding site of a protein mutant based on local surface and energy features. These features are transformed to an eigen-binding site space and used for drug resistance level prediction and analysis. [PDF file of this publication]
Analysis of the Relationship between Lung Cancer Drug Response Level and Atom Connectivity Dynamics Based on Trimmed Delaunay Triangulation

We investigated the relationship between the number of EGFR residues connected with gefitinib and the response level for each EGFR mutation type based on three-dimensional trimmed Delaunay triangulation. We discovered that when the number of EGFR residues connected with gefitinib increases, the response level of the corresponding EGFR mutation tends to descend [PDF file of this publication]
A computational study of three frequent mutations of EGFR and their effects on protein dimer formation and non-small cell lung cancer drug resistance

We use computational method to predict the homo-dimers and hetero-dimers formation and compute the binding free energy of complexes (between drugs and proteins). Seven mutation types of protein-drug complexes have been analyzed and their corresponding affinities with the drug also have been evaluated based on the binding free energy between receptor and ligand. [PDF file of this publication]
Selectivity profile of afatinib for EGFR-mutated non-small-cell lung cancer

We profiled the correlations between afatinib potency and EGFR mutation status based on a cohort of patients with the EGFR-mutated NSCLC. We conducted a computational study to estimate the binding affinity in a mutant–afatinib system, based on molecular structural modeling and dynamics simulations, to bridge the mutation status with afatinib-related response or PFS. [PDF file of this publication]
Decoding the EGFR mutation-induced drug resistance in lung cancer treatment by local surface geometric properties

We explored the correlations between the local surface geometric properties of EGFR mutants and the progression-free survival (PFS). The results show that these characteristics can be efficiently applied to the prediction of drug resistance in lung cancer treatments, and easily extended to other cancer treatments. [PDF file of this publication]
Contribution of EGFR and ErbB-3 Heterodimerization to the EGFR Mutation-Induced Gefitinib- and Erlotinib-Resistance in Non-Small-Cell Lung Carcinoma Treatments

We separately investigated the EGFR and ErbB-3 heterodimerization, regarded as the origin of intracellular signaling pathways. We combined the molecular interaction in EGFR heterodimerization with that between the EGFR tyrosine kinase and its inhibitor. We also comparably examined the interactions between ErbB-3 and its partners (EGFR mutants, IGF-1R, ErbB-2 and c-Met). [PDF file of this publication]
EGFR Mutant Structural Database: Computationally predicted 3D structures and the corresponding binding free energies with gefitinib and erlotinib

Structural information is available for just a few EGFR mutants. In this study, we created an EGFR Mutant Structural Database for most known EGFR mutants, which can be used by other researchers to study NSCLC further and by medical doctors as reference for NSCLC treatment. [PDF file of this publication]
Personalized prediction of EGFR mutation-induced drug resistance in lung cancer

We combine the EGFR mutation features with specific personal features for 168 clinical subjects to construct a personalized drug resistance prediction model. The EGFR mutation feature is characterized by the energy components of binding free energy (concerning the mutant-inhibitor complex). [PDF file of this publication]