- Open Access
Identifying EGFR mutation-induced drug resistance based on alpha shape model analysis of the dynamics
© The Author(s). 2016
- Received: 24 May 2016
- Accepted: 1 September 2016
- Published: 8 September 2016
Epidermal growth factor receptor (EGFR) mutation-induced drug resistance is a difficult problem in lung cancer treatment. Studying the molecular mechanisms of drug resistance can help to develop corresponding treatment strategies and benefit new drug design.
In this study, Rosetta was employed to model the EGFR mutant structures. Then Amber was carried out to conduct molecular dynamics (MD) simulation. Afterwards, we used Computational Geometry Algorithms Library (CGAL) to compute the alpha shape model of the mutants.
We analyzed the EGFR mutation-induced drug resistance based on the motion trajectories obtained from MD simulation. We computed alpha shape model of all the trajectory frames for each mutation type. Solid angle was used to characterize the curvature of the atoms at the drug binding site. We measured the knob level of the drug binding pocket of each mutant from two ways and analyzed its relationship with the drug response level. Results show that 90 % of the mutants can be grouped correctly by setting a certain knob level threshold.
There is a strong correlation between the geometric properties of the drug binding pocket of the EGFR mutants and the corresponding drug responses, which can be used to predict the response of a new EGFR mutant to a drug molecule.
- Epidermal growth factor receptor (EGFR)
- Non-small-cell lung cancer (NSCLC)
- Drug binding site
- Molecular dynamics
- Alpha shape modeling
- Solid angle
Lung cancer is the leading cause of cancer deaths worldwide [1–3]. Non-small-cell lung carcinoma (NSCLC) is the most common lung cancer type, accounting for over 80 % of all the lung cancer cases [4, 5]. Epidermal growth factor receptor (EGFR) is found to overexpress in about 60 % of NSCLC patients, making it a target of many treatment strategies [6–8]. EGFR, also called HER1 or ErbB-1, is a member of the ErbB family, which also contains HER2 (ErbB-2), HER3 (ErbB-3) and HER4 (ErbB-4) [9, 10]. It is a functional protein that can be activated through binding with cognate ligands, such as epidermal growth factor . On ligand binding, EGFR can form a homodimer (EGFR-EGFR) or heterodimers (dimerization with other family members), leading to the phosphorylation of specific residues at the intracellular tyrosine kinase (TK) domain. These phosphorylated residues act as docking sites for downstream proteins, triggering the downstream pathways that modulate cellular proliferation and survival [12, 13]. However, aberrant EGFR signaling, sometimes caused by mutations at the EGFR TK domain, can lead to tumor growth and progression in the lung [14–16]. Gefitinib is a commonly used drug to target the EGFR TK domain to block the activation of EGFR and its downstream signaling [17–22]. Nevertheless, after a period of treatment, drug resistance occurs usually due to a second mutation (e.g. T790M) or the activating of other receptor tyrosine kinases (e.g. c-Met) [23–26].
A lot of research has focused on drug resistance mechanisms in lung cancer [27–30]. Computational methods are successfully applied in these studies, benefiting from their advantages of low cost, easy implementation and capacity of processing large datasets [31–33]. Zhu et al. employed support vector machine based classifiers to correlate the collected features (clinicopathologic features and immunomarkers) and the overall survival of NSCLC patients . Wang et al. used the personal features of 168 NSCLC patients coupled with EGFR mutant-drug binding free energy features to build a classification model to predict the drug response levels, and obtained a best testing accuracy of 95.83 % .
To decode the EGFR mutation-induced drug resistance, it is very important to analyze the interaction between the EGFR mutants and a drug molecule. The geometric properties of the drug binding pocket can affect the binding affinity of two molecules. Intuitively, a concave shape may have a higher binding affinity with a drug molecule than a convex shape because of structural complementary characteristics. In addition, a shape with a low convex degree could bind more tightly on a drug molecule relative to a shape with high convex degree. Therefore, different from other studies which focused on personal features, energy features as well as immunomarkers, we use the geometric properties of the drug binding pocket of the EGFR mutants to identify the drug resistance mechanisms. Compared with the EGFR-drug binding free energy used by Wang et al. , which shows the overall binding affinity, the geometric features can provide specific structural information of each atom at the drug binding pocket. These structural information offers clues on how to modify the drug structure and the spatial relations of its atoms with EGFR to overcome the resistance problem.
In this work, we study the EGFR mutation-induced drug resistance by analyzing structural properties of the mutants in a dynamic form, based on the motion trajectories obtained from molecular dynamics (MD) simulation. The clinical data of EGFR mutation type and the drug response of patients were collected from Queen Mary Hospital in Hong Kong. We employed Rosetta to model the EGFR mutants based on the crystal structure of wild-type (WT) EGFR and the mutant sequences . Then Amber  was used to conduct MD simulation to show the dynamic evolution of the EGFR mutant-drug system. We extracted the trajectory frames of each mutant and computed the alpha shape model [38, 39] of each frame, in order to describe the mutant with geometric models. Solid angle [33, 40] was evaluated to characterize the curvature properties of the atoms at the drug binding site. Finally, we computed the knob level of the drug binding pocket of each mutant and analyzed its relationship with the drug response level. Results show that 90 % of the mutants can be grouped correctly by setting a certain knob level threshold.
Clinical data and ethics statement
Drug response of the mutants
Modeling of EGFR mutants
Molecular dynamics (MD) simulation
MD simulation can be used to study the interaction of molecules for a fixed period of time, and record the movements as trajectories which are determined by solving Newton’s equations of motion. We employed Amber 12  to conduct MD simulation to the EGFR mutant-drug complex. The complex was obtained by aligning the predicted mutant structure to the WT EGFR and appending drug molecule to it. Amber ff99SB force field is adopted to describe the forces between particles in the simulation. As the simulation was performed in a solvent environment, we solvated the complex into a periodic TIP3P water box, where molecules exit one side of the box will wrap to the other side. Then we conducted a series of steps (minimization, heating, density equilibration, and constant pressure equilibration) to equilibrate the system. At the environment of desired temperature, density and pressure, the production MD simulation was performed for 2 ns. We saved the motion trajectories every 10 ps and a total of 200 trajectory frames were collected for each mutant-drug complex.
Alpha shape modeling and solid angle calculation
After the motion trajectories of each mutant were obtained, we carried out alpha shape modeling for each trajectory frame, in order to show the surface geometric properties of the mutant structure. The alpha shape [38, 39] can provide effective approximation of the original shape of a molecule with a computational geometric model. We employed Computational Geometry Algorithms Library (CGAL)  to generate the weighted alpha shape models of the EGFR mutants. Figures 1c and d show the alpha shape models of the drug binding pocket of WT EGFR and the mutant delE746_A750insAP, respectively.
Then we used solid angle to characterize the geometric properties of each atom at the drug binding site. Solid angle describes the curvature by providing a value (in the range of [−1, 1]) to show the concave or convex properties of each surface atom. If the solid angle value falls in [−1, 0), the shape is defined as a concave one while a convex shape is obtained if the value is in (0, 1]. The detailed definition of solid angle can be found in [33, 40].
Based on the motion trajectories obtained from MD simulation, we carried out alpha shape modeling for all the 30 EGFR mutants. A total of 200 trajectory frames were collected in the production MD simulation process. Thus, the alpha shape model was built for 200 times for each mutant. Then we used solid angle to characterize the atoms at the drug binding site. In this study, the drug binding site of a mutant structure was defined as the amino acid residues at the drug binding site of the WT EGFR. Specifically, a total of 14 residues of 102 atoms are involved at the drug binding site of the WT EGFR. A few mutations locate just at the drug binding site, such as G719A_L858R, while the majority of mutations do not.
In this study, we analyzed the motion trajectories of the EGFR mutants obtained from MD simulation. The EGFR mutant structures were generated using Rosetta. Amber was employed to carry out MD simulation of the EGFR mutant-drug system. The motion trajectories were collected every 10 ps and a total of 200 frames were obtained for each mutant. Then we computed alpha shape model of each trajectory frame and characterized the curvature of the atoms at the drug binding site using the solid angle. In one aspect, we calculated the average convex degree at the drug binding site of each trajectory frame, and obtained the knob level of a mutant by computing the mean value of the convex degrees of the 200 frames. On the other hand, we calculated the mean of the solid angle value of each atom at the drug binding site from the 200 trajectory frames. In this way, an average drug binding site could be obtained, and the average convex degree of this binding site was calculated to measure the knob level of a mutant. Finally, we analyzed the relationship between the knob level of the drug binding site and the drug response level of the mutants. Results show that 90 % of the mutants can be grouped correctly by setting a certain knob level threshold.
To validate our model, we can compare the predicted response with the clinically obtained drug response of the EGFR mutants collected from literature. Meanwhile, the newly collected clinical data provide resources to refine our model to make further prediction more accurately. For example, L858R_T790M is a well-known mutant which is resistant to gefitinib. Then we can compute its alpha shape model and derive the knob level of the drug binding pocket in the aforementioned two ways. As the best grouping accuracy is achieved when the solid angle threshold equals to 0.01 for both the two way situations (Figs. 4b and b). We set the threshold to 0.01, and obtained the knob level of 0.5298 and 0.4964 with the two approaches, respectively. According to knob level thresholds of the drug Response mutants and the No-response ones in Figs. 4b and b, the mutant of L858R_T790M can be categorized to the No-response group, which is consistent with our knowledge.
By using the obtained relationships between knob level and drug response level of the EGFR mutants, we can have a general classification of a new EGFR mutant before measuring the drug response from clinical evaluation. We can model the 3D structure as well as the alpha shape of a new EGFR mutant to derive the knob level of the drug binding pocket. According to the obtained knob level threshold of the drug Response and No-response groups, we are able to predict the drug response of the new mutant. The results can be used for clinical guidance and can benefit the patients with a more effective therapy.
We thank Dr. Victor H.F. Lee and Dr. Maria P. Wong for providing the clinical data used in this study. We also acknowledge the support of Hong Kong Research Grants Council (CityU 11200715).
This work was supported by the Hong Kong Research Grants Council (CityU 11200715).
Availability of data and materials
The EGFR mutation types used in this work and the corresponding drug responses are shown in Table 1.
L.M. carried out the molecular dynamics simulations, performed structural analysis, and drafted the manuscript. B.Z. participated in the design of the study as well as the analysis of the results. H.Y. initiated the project, participated in the design of the study, and helped draft the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
This study was approved by Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. New Engl J Med. 2004;350:2129–39.View ArticlePubMedGoogle Scholar
- Paez JG, Jänne PA, Lee JC, Tracy S, Greulich H, Gabriel S, et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Sci. 2004;304:1497–500.View ArticleGoogle Scholar
- Jemal A, Bray F, Center MM, Ferlay J, Ward E, et al. Global cancer statistics. CA Cancer J Clin. 2011;61:69–90.View ArticlePubMedGoogle Scholar
- Hanna N, Shepherd FA, Fossella FV, Pereira JR, De Marinis F, von Pawel J, et al. Randomized phase III trial of pemetrexed versus docetaxel in patients with non-small-cell lung cancer previously treated with chemotherapy. J Clin Oncol. 2004;22:1589–97.View ArticlePubMedGoogle Scholar
- Fossella F, Pereira JR, von Pawel J, Pluzanska A, Gorbounova V, Kaukel E, et al. Randomized, multinational, phase III study of docetaxel plus platinum combinations versus vinorelbine plus cisplatin for advanced non-small-cell lung cancer: The TAX 326 study group. J Clin Oncol. 2003;21:3016–24.View ArticlePubMedGoogle Scholar
- Gazdar A. Activating and resistance mutations of EGFR in non-small-cell lung cancer: role in clinical response to EGFR tyrosine kinase inhibitors. Oncog. 2009;28:S24–31.View ArticleGoogle Scholar
- Sharma SV, Bell DW, Settleman J, Haber DA. Epidermal growth factor receptor mutations in lung cancer. Nat Rev Cancer. 2007;7:169–81.View ArticlePubMedGoogle Scholar
- Normanno N, De Luca A, Bianco C, Strizzi L, Mancino M, Maiello MR, et al. Epidermal growth factor receptor (EGFR) signaling in cancer. Gene. 2006;366:2–16.View ArticlePubMedGoogle Scholar
- Solca F, Dahl G, Zoephel A, Bader G, Sanderson M, Klein C, et al. Target binding properties and cellular activity of afatinib (BIBW 2992), an irreversible ErbB family blocker. J Pharmacol Exp Ther. 2012;343:342–50.View ArticlePubMedGoogle Scholar
- Tebbutt N, Pedersen MW, Johns TG. Targeting the ERBB family in cancer: couples therapy. Nat Rev Cancer. 2013;13:663–73.View ArticlePubMedGoogle Scholar
- Zhang Y, Wang L, Zhang M, Jin M, Bai C, Wang X. Potential mechanism of interleukin‐8 production from lung cancer cells: An involvement of EGF-EGFR-PI3K-Akt-Erk pathway. J Cell Physiol. 2012;227:35–43.View ArticlePubMedGoogle Scholar
- Rikova K, Guo A, Zeng Q, Possemato A, Yu J, Haack H, et al. Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell. 2007;131:1190–203.View ArticlePubMedGoogle Scholar
- Bar J, Onn A. Overcoming molecular mechanisms of resistance to first-generation epidermal growth factor receptor tyrosine kinase inhibitors. Clin Lung Cancer. 2012;13:267–79.View ArticlePubMedGoogle Scholar
- Kari C, Chan TO, de Quadros MR, Rodeck U. Targeting the Epidermal Growth Factor Receptor in Cancer Apoptosis Takes Center Stage. Cancer Res. 2003;63:1–5.PubMedGoogle Scholar
- Ono M, Kuwano M. Molecular mechanisms of epidermal growth factor receptor (EGFR) activation and response to gefitinib and other EGFR-targeting drugs. Clin Cancer Res. 2006;12:7242–51.View ArticlePubMedGoogle Scholar
- Hirata A, Ogawa SI, Kometani T, Kuwano T, Naito S, Kuwano M, Ono M. ZD1839 (Iressa) induces antiangiogenic effects through inhibition of epidermal growth factor receptor tyrosine kinase. Cancer Res. 2002;62:2554–60.PubMedGoogle Scholar
- Sordella R, Bell DW, Haber DA, Settleman J. Gefitinib-sensitizing EGFR mutations in lung cancer activate anti-apoptotic pathways. Sci. 2004;305:1163–7.View ArticleGoogle Scholar
- Maemondo M, Inoue A, Kobayashi K, Sugawara S, Oizumi S, Isobe H, et al. Gefitinib or chemotherapy for non-small-cell lung cancer with mutated EGFR. New Engl J Med. 2010;362:2380–8.View ArticlePubMedGoogle Scholar
- Sequist LV, Martins RG, Spigel D, Grunberg SM, Spira A, Jänne PA, et al. First-line gefitinib in patients with advanced non-small-cell lung cancer harboring somatic EGFR mutations. J Clin Oncol. 2008;26:2442–9.View ArticlePubMedGoogle Scholar
- Han SW, Kim TY, Hwang PG, Jeong S, Kim J, Choi IS, et al. Predictive and prognostic impact of epidermal growth factor receptor mutation in non-small-cell lung cancer patients treated with gefitinib. J Clin Oncol. 2005;23:2493–501.View ArticlePubMedGoogle Scholar
- Mitsudomi T, Morita S, Yatabe Y, Negoro S, Okamoto I, Tsurutani J, et al. Gefitinib versus cisplatin plus docetaxel in patients with non-small-cell lung cancer harbouring mutations of the epidermal growth factor receptor (WJTOG3405): an open label, randomised phase 3 trial. Lancet Oncol. 2010;11:121–8.View ArticlePubMedGoogle Scholar
- Kris MG, Natale RB, Herbst RS, Lynch Jr TJ, Prager D, Belani CP, et al. Efficacy of gefitinib, an inhibitor of the epidermal growth factor receptor tyrosine kinase, in symptomatic patients with non-small cell lung cancer: a randomized trial. Jama. 2003;290:2149–58.View ArticlePubMedGoogle Scholar
- Pao W, Miller VA, Politi KA, Ricly G, Somwar R, Zakowski MF, et al. Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain. PLoS Med. 2005;2:225.View ArticleGoogle Scholar
- Kobayashi S, Boggon TJ, Dayaram T, Jänne PA, Kocher O, Meyerson M, et al. EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. New Engl J Med. 2005;352:786–92.View ArticlePubMedGoogle Scholar
- Bean J, Brennan C, Shih JY, Riely G, Viale A, Wang L, et al. MET amplification occurs with or without T790M mutations in EGFR mutant lung tumors with acquired resistance to gefitinib or erlotinib. Proc Natl Acad Sci. 2007;104:20932–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Engelman JA, Zejnullahu K, Mitsudomi T, Song Y, Hyland C, Park JO, et al. MET amplification leads to gefitinib resistance in lung cancer by activating ERBB3 signaling. Sci. 2007;316:1039–43.View ArticleGoogle Scholar
- Bell DW, Gore I, Okimoto RA, Godin-Heymann N, Sordella R, Mulloy R, et al. Inherited susceptibility to lung cancer may be associated with the T790M drug resistance mutation in EGFR. Nat Genet. 2005;37:1315–6.View ArticlePubMedGoogle Scholar
- Regales L, Gong Y, Shen R, de Stanchina E, Vivanco I, Goel A, et al. Dual targeting of EGFR can overcome a major drug resistance mutation in mouse models of EGFR mutant lung cancer. J Clin Investig. 2009;119:3000.PubMedPubMed CentralGoogle Scholar
- Gottesman MM. Mechanisms of cancer drug resistance. Annual Rev Med. 2002;53:615–27.View ArticleGoogle Scholar
- Ozben T. Mechanisms and strategies to overcome multiple drug resistance in cancer. FEBS Lett. 2006;580:2903–9.View ArticlePubMedGoogle Scholar
- Cao ZW, Han LY, Zheng CJ, Ji ZL, Chen X, Lin HH, Chen YZ. Computer prediction of drug resistance mutations in proteins. Drug Discov Today. 2005;10:521–9.View ArticlePubMedGoogle Scholar
- Hao G, Yang G, Zhan C. Structure-based methods for predicting target mutation-induced drug resistance and rational drug design to overcome the problem. Drug Discov Today. 2012;17:1121–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Ma L, Wang DD, Huang Y, Wong MP, Lee VH, Yan H. Decoding the EGFR mutation-induced drug resistance in lung cancer treatment by local surface geometric properties. Comput Biol and Med. 2015;63:293–300.View ArticleGoogle Scholar
- Zhu Z, Sun B, Ma Y, Shao J, Long H, Zhang X, et al. Three immunomarker support vector machines-based prognostic classifiers for stage IB non-small-cell lung cancer. J Clin Oncol. 2009;27:1091–9.View ArticlePubMedGoogle Scholar
- Wang DD, Zhou W, Yan H, Wong M, Lee V. Personalized prediction of EGFR mutation-induced drug resistance in lung cancer. Sci Rep. 2013;3:1–8.Google Scholar
- Leaver-Fay A, Tyka M, Lewis SM, Lange OF, Thompson J, Jacak R, et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol. 2011;487:545–74.View ArticlePubMedPubMed CentralGoogle Scholar
- Case D, Darden T, Cheatham III TE, Simmerling C, Wang J, Duke R, et al. AMBER 12. San Francisco: University of California; 2012.Google Scholar
- Edelsbrunner H, Mücke EP. Three-dimensional alpha shapes. ACM Trans Gr (TOG). 1994;13:43–72.View ArticleGoogle Scholar
- Edelsbrunner H, Weighted alpha shapes. University of Illinois at Urbana-Champaign, Department of Computer Science. 1992.Google Scholar
- Zhou W, Yan H, Hao Q. Analysis of surface structures of hydrogen bonding in protein-ligand interactions using the alpha shape model. Chem Phys Lett. 2012;545:125–31.View ArticleGoogle Scholar
- The Protein Data Bank. [http://www.rcsb.org]. Accessed 23 Dec 2015.
- Kellogg EH, Leaver-Fay A, Baker D. Role of conformational sampling in computing mutation-induced changes in protein structure and stability. Proteins Struct Funct Bioinform. 2011;79:830–8.View ArticleGoogle Scholar
- Martí-Renom MA, Stuart AC, Fiser A, Sánchez R, Melo F, Šali A. Comparative protein structure modeling of genes and genomes. Annual Rev Biophys Biomol Struct. 2000;29:291–325.View ArticleGoogle Scholar
- Computational Geometry Algorithms Library. [https://www.cgal.org/]. Accessed 29 Dec 2015.