Identification of High-Risk Single Nucleotide Polymorphisms (SNPs) of Epidermal Growth Factor Receptor (EGFR) and Their Interaction with Various TKI Drugs

Ananya Sharma¹, Raman Thakur², Shikha Mittal¹, Jata Shankar¹

Show Less

¹ Genomic Laboratory, Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, India

² Department of Medical Laboratory Science, Lovely Professional University, Jalandhar, Punjab, India

EJMO 2023, 7(4), 334–344; https://doi.org/10.14744/ejmo.2023.33189

Submitted: 6 May 2023 | Revised: 24 July 2023 | Accepted: 30 July 2023 | Published: 29 December 2023

© 2023 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )

Download PDF

Cite

XML

Abstract

Objectives: Epidermal growth factor receptor (EGFR) is the membrane receptor of tyrosine kinase family that plays crucial role in cell growth, cell division and cell survival. Upregulation of EGFR gene has been seen in various cancer types due to mutations. As non-synonymous single nucleotide polymorphisms (nsSNPs) are responsible for half of the genetic variations that are responsible of human diseases, it is crucial to analyze putative functional nsSNPs. Therefore, we aimed to identify nsSNPs of EGFR gene and evaluate their effect on its protein receptor. We also docked the mutant type protein structures with common tyrosine kinase inhibitor (TKIs) to showcase their stability with one another.

Methods: We observed five novel nsSNPs (E330K, K745R, R962H, R675Q and S752Y) that are present in different domains of EGFR using various bioinformatics tools and simultaneously predicted their deleterious effects on EGFR. Furthermore, docking studies were carried out with three of the common TKIs (erlotinib, gefitinib, canertinib).

Results: Mutant type K745R was predicted to be potentially more damaging than other mutants due to its presence in highly conserved region of EGFR protein receptor and its ability to affect protein stability.

Conclusion: As this study is the first comprehensive study of these novel nsSNPs of EGFR, the results of this study would be crucial for future studies, drug discovery and development of personalized medicine. Although along with in-silico characterisation of nsSNPs clinical population-based studies are essential.

Keywords

Epidermal growth factor receptor (EGFR)

Single Nucleotide Polymorphism (SNP)

Tyrosine Kinase Inhibitors (TKIs)

Non-Small Cell Lung Cancer (NSCLCs)

Conflict of interest

None declared.

References

1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin 2022;72:7−33.
2. Guo XE, Ngo B, Modrek AS, Lee WH. Targeting tumor suppressor networks for cancer therapeutics. Curr Drug Targets 2014;15:2−16.
3. Sigismund S, Avanzato D, Lanzetti L. Emerging functions of the EGFR in cancer. Mol Oncol 2018;12:3−20.
4. Singh B, Carpenter G, Coffey RJ. EGF receptor ligands: recent advances. F1000Research 2016;5:F1000.
5. Wee P, Wang Z. Epidermal growth factor receptor cell proliferation signaling pathways. Cancers Basel 2017;9:52.
6. Malik PS, Raina V. Lung cancer: Prevalent trends & emerging concepts. Indian J Med Res 2015;141:5−7.
7. Li AR, Chitale D, Riely GJ, Pao W, Miller VA, Zakowski MF, et al. EGFR mutations in lung adenocarcinomas: Clinical testing experience and relationship to EGFR gene copy number and immunohistochemical expression. J Mol Diagn 2008;10:242−8.
8. Cargill M, Altshuler D, Ireland J, Sklar P, Ardlie K, Patil N, et al. Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat Genet 1999;22:231−8.
9. Thakur R, Shankar J. In-silico analysis revealed high-risk single nucleotide polymorphisms in human pentraxin-3 gene and their impact on innate immune response against microbial pathogens. Front Microbiol 2016;7:192.
10. Thakur R, Shankar J. Comprehensive in-silico analysis of highrisk non-synonymous snps in dectin-1 gene of human and their impact on protein structure. Curr Pharmacogenomics Pers Med 2017;15:144−55.
11. Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 2003;31:3812−4.
12. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods 2010;7:248−9.
13. Tang H, Thomas PD. PANTHER-PSEP: Predicting disease-causing genetic variants using position-specific evolutionary preservation. Bioinformatics 2016;32:2230−2.
14. Bromberg Y, Yachdav G, Rost B. SNAP predicts effect of mutations on protein function. Bioinformatics 2008;24:2397−8.
15. Bendl J, Stourac J, Salanda O, Pavelka A, Wieben ED, Zendulka J, et al. PredictSNP: Robust and accurate consensus classifier for prediction of disease-related mutations. PLoS Comput Biol 2014;10:e1003440.
16. Capriotti E, Fariselli P, Casadio R. I-Mutant2.0: Predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 2005;33(Web Server issue):W306-10.
17. Ashkenazy H, Erez E, Martz E, Pupko T, Ben-Tal N. ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic acids research. 2010;38(Web Server issue):529−33.
18. Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, et al. STRING 8 - A global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res 2009;37(Database issue):412−6.
19. Jorissen RN, Walker F, Pouliot N, Garrett TP, Ward CW, Burgess AW. Epidermal growth factor receptor: Mechanisms of activation and signalling. Exp Cell Res 2003;284:31−53.
20. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. 2015;10:845−58.
21. Land H, Humble MS. YASARA: A tool to obtain structural guidance in biocatalytic investigations. Methods Mol Biol 2018;1685:43−67.
22. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: A program to check the stereochemical quality of protein structures. J Appl Crystallogr 1993;26:283−91.
23. Sasaki T, Hiroki K, Yamashita Y. The role of epidermal growth factor receptor in cancer metastasis and microenvironment. BioMed Res Int 2013;2013:546318.
24. Marzouq M, Nairouz A, Ben Khalaf N, Bourguiba-Hachemi S, Quaddorah R, Ashoor D, et al. Genetic variants of the EGFR li-gand-binding domain and their association with structural alterations in Arab cancer patients. BMC Res Notes 2021;14:146.
25. Markóczy Z, Sárosi V, Kudaba I, Gálffy G, Turay ÜY, Demirkazik A, et al. Erlotinib as single agent first line treatment in locally advanced or metastatic activating EGFR mutationpositive lung adenocarcinoma (CEETAC): An open-label, nonrandomized, multicenter, phase IV clinical trial. BMC Cancer 2018;18:598.
26. Zhang Y, Li Y, Wang Q, Su B, Xu H, Sun Y, et al. Role of RASA1 in cancer: A review and update. Oncol Rep 2020;44:2386−96.
27. Rozario LT, Sharker T, Nila TA. In-silico analysis of deleterious SNPs of human MTUS1 gene and their impacts on subsequent protein structure and function. PloS One 2021;16:e0252932.
28. Hossain MS, Roy AS, Islam MS. In-silico analysis predicting effects of deleterious SNPs of human RASSF5 gene on its structure and functions. Sci Rep 2020;10:14542.
29. Ferguson KM. Structure-based view of epidermal growth factor receptor regulation. Ann Rev Biophys 2008;37:353−73.
30. Iwata KK, Beard SE, Haley JD. Epidermal growth factor receptor (EGFR) inhibitor for oncology: Discovery and development of erlotinib. In Metcalf BW, Dillon S, editors. Target Validation in Drug Discovery. Burlington: Academic Press; 2007. p. 155−78.
31. Thelemann A, Petti F, Griffin G, Iwata K, Hunt T, Settinari T, et al. Phosphotyrosine signaling networks in epidermal growth factor receptor overexpressing squamous carcinoma cells. Mol Cell Proteomics 2005;4:356−76.
32. Zhou C, Wu YL, Chen G, Feng J, Liu XQ, Wang C, et al. Erlotinib versus chemotherapy as first-line treatment for patients with advanced EGFR mutation-positive non-small-cell lung cancer (OPTIMAL, CTONG-0802): A multicentre, open-label, randomised, phase 3 study. Lancet Oncol 2011;12:735−42.
33. Hassan W, Chitcholtan K, Sykes P, Garrill A. A combination of two receptor tyrosine kinase inhibitors, canertinib and PHA665752 compromises ovarian cancer cell growth in 3D cell models. Oncol Ther 2016;4:257−74.
34. Jänne PA, von Pawel J, Cohen RB, Crino L, Butts CA, Olson SS, et al. Multicenter, randomized, phase II trial of CI-1033, an irreversible pan-ERBB inhibitor, for previously treated advanced non small-cell lung cancer. J Clin Oncol 2007;25:3936−44.
35. Rixe O, Franco SX, Yardley DA, Johnston SR, Martin M, Arun BK, et al. A randomized, phase II, dose-finding study of the panErbB receptor tyrosine-kinase inhibitor CI-1033 in patients with pretreated metastatic breast cancer. Cancer Chemother Pharmacol 2009;64:1139−48.
36. Campos S, Hamid O, Seiden MV, Oza A, Plante M, Potkul RK, et al. Multicenter, randomized phase II trial of oral CI-1033 for previously treated advanced ovarian cancer. J Clin Oncol 2005;23:5597−604.
37. Cassell A, Grandis JR. Investigational EGFR-targeted therapy in head and neck squamous cell carcinoma. Expert Opin Investig Drugs 2010;19:709−22.
38. Loeffler-Ragg J, Witsch-Baumgartner M, Tzankov A, Hilbe W, Schwentner I, Sprinzl GM, et al. Low incidence of mutations in EGFR kinase domain in Caucasian patients with head and neck squamous cell carcinoma. Eur J Cancer 2006;42:109−11.

Previous article in this issue

Next article in this issue

Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing