Computational Evaluation of EGFR Dynamic Characteristics in Mutation-Induced Drug Resistance Prediction

Recently, machine learning techniques have become an indispensable alternative for computational studies of cancers and efficient prediction of cancer-drug responses or drug resistance levels. Meanwhile, in cancer characterization, molecular dynamics (MD) simulations can greatly reveal the dynamic and functional features of cancer-related proteins. In our work, MD simulations were implemented to extract the EGFR TK mutation (dynamic) features of a non-small-cell lung cancer (NSCLC)-patient group. Specifically, the relative positions of a drug-binding site and a drug molecule in the dynamics-trajectory were calculated and used for characterizing the dynamic features. These derived features, couples with patient personal features, were subsequently handled by a model called SFABSRM, which combines Supervised Factor Analysis and Softmax Regression Model. SFABSRM first uses factor analysis to evaluate the contributions of the selected features, and in our analysis it suggested that dynamic features play an important role in correlating with the cancer-drug responses. Further, SFABSRM applies the regression model for a drug response prediction, which further verified the important contribution of dynamic characteristics to this prediction. The support vector machine (SVM) model was conducted as a comparison with SFABSRM, leading to an agreement with the earlier conclusion. Overall, these studies can greatly benefit the NSCLC studies and drug discovery.