Identifying EGFR mutation-induced drug resistance based on alpha shape model analysis of the dynamics

Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects

Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. The important EGFR mutants and the different generations of EGFR-TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. We explore limitations in the current methodologies, and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients.

Machine learning based personalized drug response prediction for lung cancer patients

Lung cancers with a mutated epidermal growth factor receptor (EGFR) are a major contributor to cancer fatalities globally. Targeted tyrosine kinase inhibitors (TKIs) have been developed against EGFR and show encouraging results for survival rate and quality of life. However, drug resistance may affect treatment plans and treatment efficacy may be lost after about a year. Predicting the response to EGFR-TKIs for EGFR-mutated lung cancer patients is a key research area. In this study, we propose a personalized drug response prediction model (PDRP), based on molecular dynamics simulations and machine learning, to predict the response of first generation FDA-approved small molecule EGFR-TKIs, Gefitinib/Erlotinib, in lung cancer patients. The patient's mutation status is taken into consideration in molecular dynamics (MD) simulation. Each patient's unique mutation status was modeled considering MD simulation to extract molecular-level geometric features. Moreover, additional clinical features were incorporated into machine learning model for drug response prediction. The complete feature set includes demographic and clinical information (DCI), geometrical properties of the drug-target binding site, and the binding free energy of the drug-target complex from the MD simulation. PDRP incorporates an XGBoost classifier, which achieves state-of-the-art performance with 97.5% accuracy, 93% recall, 96.5% precision, and 94% F1-score, for a 4-class drug response prediction task. We found that modeling the geometry of the binding pocket combined with binding free energy is a good predictor for drug response. However, we observed that clinical information had a little impact on the performance of the model. The proposed model could be tested on other types of cancers. We believe PDRP will support the planning of effective treatment regimes based on clinical-genomic information. The source code and related files are available on GitHub at:   https://github.com/rizwanqureshi123/PDRP/ .

Correlated Motions and Dynamics in Different Domains of Epidermal Growth Factor Receptor With L858R and T790M Mutations

Non-small cell lung cancer with an activating epidermal growth factor receptor (EGFR) mutation responds well to targeted drugs. In most cases, drug resistance appears after about a year. Several studies have been conducted on the kinase domain of EGFR to understand the drug resistance mechanism. Since EGFR is a multi-domain protein, mutation in the kinase domain may affect the other domains as well. In this study, we examine the complete structure of the multi-domain EGFR protein and its mutants. We performed molecular dynamics simulations for wildtype EGFR, EGFR with L858R mutation, and EGFR with L858R and T790M mutations. We applied normal mode analysis and complex network analysis to extract the correlated motions in the domains of EGFR. The normal modes are used to construct the dynamic cross-correlation map (DCCM). Simulation results show different patterns of correlated motions in each domain of EGFR mutants compared to the wildtype. In Domains 1 and 3 of the extracellular region, a small number of weak positively correlated motions are extracted. Domains 2 and 4 show large numbers of both positive and negative motions. However, the negatively correlated motions are stronger in mutant structures compared to the wildtype. In Domain 7, some residues showed a positive correlation around the main diagonal. We also identified different communities, nodes and crucial residues in the domains of the structures, which can be important for the function of EGFR. Moreover, hydrogen bond analysis is performed for the stability analysis. The mutant structures have fewer hydrogen bonds compared to the wildtype. Overall, these findings are useful for understanding the dynamics and communications in EGFR domains.

Genotype-determined EGFR-RTK heterodimerization and its effects on drug resistance in lung Cancer treatment revealed by molecular dynamics simulations

BACKGROUND

Epidermal growth factor receptor (EGFR) and its signaling pathways play a vital role in pathogenesis of lung cancer. By disturbing EGFR signaling, mutations of EGFR may lead to progression of cancer or the emergence of resistance to EGFR-targeted drugs.

RESULTS

We investigated the correlation between EGFR mutations and EGFR-receptor tyrosine kinase (RTK) crosstalk in the signaling network, in order to uncover the drug resistance mechanism induced by EGFR mutations. For several EGFR wild type (WT) or mutated proteins, we measured the EGFR-RTK interactions using several computational methods based on molecular dynamics (MD) simulations, including geometrical characterization of the interfaces and conventional estimation of free energy of binding. Geometrical properties, namely the matching rate of atomic solid angles in the interfaces and center-of-mass distances between interacting atoms, were extracted relying on Alpha Shape modeling. For a couple of RTK partners (c-Met, ErbB2 and IGF-1R), results have shown a looser EGFR-RTK crosstalk for the drug-sensitive EGFR mutant while a tighter crosstalk for the drug-resistant mutant. It guarantees the genotype-determined EGFR-RTK crosstalk, and further proposes a potential drug resistance mechanism by amplified EGFR-RTK crosstalk induced by EGFR mutations.

CONCLUSIONS

This study will lead to a deeper understanding of EGFR mutation-induced drug resistance mechanisms and promote the design of innovative drugs.

Visualization of Protein-Drug Interactions for the Analysis of Drug Resistance in Lung Cancer

Non-small cell lung cancer (NSCLC) caused by mutation of the epidermal growth factor receptor (EGFR) is a major cause of death worldwide. Tyrosine kinase inhibitors (TKIs) of EGFR have been developed and show promising results at the initial stage of therapy. However, in most cases, their efficacy becomes limited due to the emergence of secondary mutations causing drug resistance after about a year. In this work, we investigated the mechanism of drug resistance due to these mutations. We performed molecular dynamics (MD) simulations of EGFR-drug interactions to obtain Euclidean distance and binding free energy values to analyse drug resistance and visualize drug-protein interactions. A PCA-based method is proposed to find normal, rigid, flexible, and critical residues. We have established a systematic method for the visualization of protein-drug interactions, which provides an effective framework for the analysis of drug resistance in lung cancer at the atomic level.

Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods

Purpose

Mutation-induced variation of protein-ligand binding affinity is the key to many genetic diseases and the emergence of drug resistance, and therefore predicting such mutation impacts is of great importance. In this work, we aim to predict the mutation impacts on protein-ligand binding affinity using efficient structure-based, computational methods.

Methods

Relying on consolidated databases of experimentally determined data we characterize the affinity change upon mutation based on a number of local geometrical features and monitor such feature differences upon mutation during molecular dynamics (MD) simulations. The differences are quantified according to average difference, trajectory-wise distance or time-vary differences. Machine-learning methods are employed to predict the mutation impacts using the resulting conventional or time-series features. Predictions based on estimation of energy and based on investigation of molecular descriptors were conducted as benchmarks.

Results

Our method (machine-learning techniques using time-series features) outperformed the benchmark methods, especially in terms of the balanced F1 score. Particularly, deep-learning models led to the best prediction performance with distinct improvements in balanced F1 score and a sustained accuracy.

Conclusion

Our work highlights the effectiveness of the characterization of affinity change upon mutations. Furthermore, deep-learning techniques are well designed for handling the extracted time-series features. This study can lead to a deeper understanding of mutation-induced diseases and resistance, and further guide the development of innovative drug design.

AIMMS suite: a web server dedicated for prediction of drug resistance on protein mutation

Drug resistance is one of the most intractable issues for successful treatment in current clinical practice. Although many mutations contributing to drug resistance have been identified, the relationship between the mutations and the related pharmacological profile of drug candidates has yet to be fully elucidated, which is valuable both for the molecular dissection of drug resistance mechanisms and for suggestion of promising treatment strategies to counter resistant. Hence, effective prediction approach for estimating the sensitivity of mutations to agents is a new opportunity that counters drug resistance and creates a high interest in pharmaceutical research. However, this task is always hampered by limited known resistance training samples and accurately estimation of binding affinity. Upon this challenge, we successfully developed Auto In Silico Macromolecular Mutation Scanning (AIMMS), a web server for computer-aided de novo drug resistance prediction for any ligand-protein systems. AIMMS can qualitatively estimate the free energy consequences of any mutations through a fast mutagenesis scanning calculation based on a single molecular dynamics trajectory, which is differentiated with other web services by a statistical learning system. AIMMS suite is available at http://chemyang.ccnu.edu.cn/ccb/server/AIMMS/.

Prediction of sensitivity to gefitinib/erlotinib for EGFR mutations in NSCLC based on structural interaction fingerprints and multilinear principal component analysis

Background

Non-small cell lung cancer (NSCLC) with activating EGFR mutations, especially exon 19 deletions and the L858R point mutation, is particularly responsive to gefitinib and erlotinib. However, the sensitivity varies for less common and rare EGFR mutations. There are various explanations for the low sensitivity of EGFR exon 20 insertions and the exon 20 T790 M point mutation to gefitinib/erlotinib. However, few studies discuss, from a structural perspective, why less common mutations, like G719X and L861Q, have moderate sensitivity to gefitinib/erlotinib.

Results

To decode the drug sensitivity/selectivity of EGFR mutants, it is important to analyze the interaction between EGFR mutants and EGFR inhibitors. In this paper, the 30 most common EGFR mutants were selected and the technique of protein-ligand interaction fingerprint (IFP) was applied to analyze and compare the binding modes of EGFR mutant-gefitinib/erlotinib complexes. Molecular dynamics simulations were employed to obtain the dynamic trajectory and a matrix of IFPs for each EGFR mutant-inhibitor complex. Multilinear Principal Component Analysis (MPCA) was applied for dimensionality reduction and feature selection. The selected features were further analyzed for use as a drug sensitivity predictor. The results showed that the accuracy of prediction of drug sensitivity was very high for both gefitinib and erlotinib. Targeted Projection Pursuit (TPP) was used to show that the data points can be easily separated based on their sensitivities to gefetinib/erlotinib.

Conclusions

We can conclude that the IFP features of EGFR mutant-TKI complexes and the MPCA-based tensor object feature extraction are useful to predict the drug sensitivity of EGFR mutants. The findings provide new insights for studying and predicting drug resistance/sensitivity of EGFR mutations in NSCLC and can be beneficial to the design of future targeted therapies and innovative drug discovery.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2093-6) contains supplementary material, which is available to authorized users.

EGFR and Cortactin: Markers for potential double target therapy in oral squamous cell carcinoma

Survival periods of patients following surgical therapy of oral squamous cell carcinoma (OSCC) have previously been demonstrated to decrease over recent decades. Epidermal growth factor receptor (EGFR) and Cortactin are molecular markers that are important in tumour progression and development, and interact within the EGF pathway. Although EGFR antibody therapy exists, sufficient efforts for increased survival are still lacking due to the present limited response rates. The aim of the present study was to examine the association between EGFR and Cortactin expression on survival rates of OSCC patients and to determine whether EGFR and Cortactin expression levels are associated with advanced tumor sizes and lymphnode-metastases. In total, 222 OSCC patients were included in the study. EGFR and Cortactin expression in tumor tissue was evaluated by immunohistochemistry. Cox regression was used for survival analysis. Categories were tested for associations by using cross tabs (Chi-square test). Groups were compared by the non-parametric Mann Whitney U-test. Probabilities of less than 0.05 were considered significant and significant expression of Cortactin was observed in Advanced Union Internationale Contre le Cancer stage (P=0.032), including advanced tumour stage (P=0.021) and lymph node metastasis (P=0.049). High Cortactin expression was significantly associated with poorer survival rates (P=0.037). Further Cortactin expression was not associated with extracapsular spread, however EGFR exhibited a significant association (P=0.034). Neither EGFR nor Cortactin expression was correlated to grading. EGFR and Cortactin co-expression was demonstrated to be significantly associated with poorer survival rates in OSCC patients, suggesting that identification of predictive biomarkers for adjuvant therapies are of primary concern in OSCC. In particular, efficient dual-target therapy may act as an appropriate therapy to improve survival time for patients at advanced OSCC tumor stages.