Survival Analysis of Lung Adenocarcinoma Patients with Exon 19 Del and 21 L858R Mutations Receiving EGFR-TKI Treatment
Downloads
Introduction: Patients with adenocarcinoma of the lung that have a common EGFR mutation, the Exon 19 Del mutation, survive better than those with the Exon 21 L858R mutation. This study examined whether there is a significant difference in prognosis between two common EGFR mutations, namely exon 19 Del and 21 L858R. This study compared OS (overall survival) and PFS (progression-free survival) in NSCLC patients with Exon 19 Del and Exon 21 L858R mutations who received EGFR-TKI targeted therapy at H. Adam Malik Hospital Medan.
Methods: This analysis study used a retrospective cohort design to evaluate the OS and PFS of NSCLC patients who underwent EGFR-TKI precision medicine at H. Adam Hospital Malik Medan between January 1, 2017, and December 31, 2020 and also had Exon 19 Del and Exon 21 L858R alterations.
Results: A total of 88 people were sampled. The majority of research subjects were male (60.2%). Median OS was eleven months (95 percent CI:9.594-12,406). According to the study's data, eight people (9.1%) survived until the study's ending. The median OS of Exon 19 Del Common Mutation was 11 months (95%CI 9,064-12,936). While Exon 21 L858R group had ten months (95%CI 4,546-15,454). The log-rank test identified no statistical difference in median OS between mutation types (p=0.562).
Conclusion: The findings of this study revealed that subjects with Exon 19 Del mutations had a longer median OS and PFS than those with Exon 21 L858R variants. Nevertheless, there was no significant difference in median OS and PFS between study subjects with mutation of Exon 19 Del and Exon 21 L858R, which received the targeted medication.
Bade BC, Cruz CSD. Lung cancer 2020: epidemiology, etiology, and prevention. Clinics in chest medicine. 2020;41(1):1-24.
Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA: a cancer journal for clinicians. 2021;71(1):7-33.
PNPK. Pedoman Nasional Pelayanan Kedokteran Kanker Paru. Jakarta: Kementerian Kesehatan Republik Indonesia. 2016:1-3.
Jusuf A, Wibawanto A, Icksan A, Syahruddin E, Juniarti, Endardjo S. KANKER PARU. 2015 ed. Jakarta: Penerbit Universitas Indonesia (UI-Press); 2018. 53 p.
Yoshioka H, Shimokawa M, Seto T, Morita S, Yatabe Y, Okamoto I, et al. Final overall survival results of WJTOG3405, a randomized phase III trial comparing gefitinib versus cisplatin with docetaxel as the first-line treatment for patients with stage IIIB/IV or postoperative recurrent EGFR mutation-positive non-small-cell lung cancer. Annals of Oncology. 2019;30(12):1978-84.
Jackman DM, Yeap BY, Sequist LV, Lindeman N, Holmes AJ, Joshi VA, et al. Exon 19 Deletion Mutations of Epidermal Growth Factor Receptor Are Associated with Prolonged Survival in Non–Small Cell Lung Cancer Patients Treated with Gefitinib or Erlotinib. Clinical Cancer Research. 2006;12(13):3908-14.
Won Y-W, Han J-Y, Lee GK, Park S-Y, Lim KY, Yoon K-A, et al. Comparison of clinical outcome of patients with non-small-cell lung cancer harbouring epidermal growth factor receptor exon 19 or exon 21 mutations. Journal of clinical pathology. 2011;64(11):947-52.
Jiang H, Zhu M, Li Y, Li Q. Association between EGFR exon 19 or exon 21 mutations and survival rates after first line EGFR TKI treatment in patients with non small cell lung cancer. Molecular and clinical oncology. 2019;11(3):301-8.
Wang P, Li Y, Reddy CK. Machine learning for survival analysis: A survey. ACM Computing Surveys (CSUR). 2019;51(6):1-36.
Faisal AR, Bustan MN, Annas S. Analisis Survival dengan Pemodelan Regresi Cox Proportional Hazard Menggunakan Pendekatan Bayesian (Studi Kasus: Pasien Rawat Inap Penderita Demam Tifoid di RSUD Haji Makassar). VARIANSI: Journal of Statistics and Its application on Teaching and Research. 2020;2(2):62-79.
Smuts M, Allison J. An overview of survival analysis with an application in the credit risk environment. ORiON. 2020;36(2).
Schober P, Vetter TR. Survival analysis and interpretation of time-to-event data: the tortoise and the hare. Anesthesia and analgesia. 2018;127(3):792.
Lee S, Lim H. Review of statistical methods for survival analysis using genomic data. Genomics & informatics. 2019;17(4).
Bender A, Rügamer D, Scheipl F, Bischl B, editors. A general machine learning framework for survival analysis. Joint European Conference on Machine Learning and Knowledge Discovery in Databases; 2020: Springer.
Candès EJ, Lei L, Ren Z. Conformalized survival analysis. arXiv preprint arXiv:210309763. 2021.
Dey T, Mukherjee A, Chakraborty S. A practical overview and reporting strategies for statistical analysis of survival studies. Chest. 2020;158(1):S39-S48.
Chakraborty S. A step-wise guide to performing survival analysis. Cancer Research, Statistics, and Treatment. 2018;1(1):41.
Fotso S. Deep neural networks for survival analysis based on a multi-task framework. arXiv preprint arXiv:180105512. 2018.
Lacny S, Wilson T, Clement F, Roberts DJ, Faris P, Ghali WA, et al. Kaplan–Meier survival analysis overestimates cumulative incidence of health-related events in competing risk settings: a meta-analysis. Journal of clinical epidemiology. 2018;93:25-35.
Ogłuszka M, Orzechowska M, Jędroszka D, Witas P, Bednarek AK. Evaluate Cutpoints: Adaptable continuous data distribution system for determining survival in Kaplan-Meier estimator. Computer Methods and Programs in Biomedicine. 2019;177:133-9.
In J, Lee DK. Survival analysis: Part I-analysis of time-to-event. Korean J Anesthesiol. 2018;71(3):182-91.
Emmert-Streib F, Dehmer M. Introduction to survival analysis in practice. Machine Learning and Knowledge Extraction. 2019;1(3):1013-38.
Bladt M, Furrer C. Expert Kaplan--Meier estimation. arXiv preprint arXiv:220613120. 2022.
Kasuma D, Soeroso N, Tarigan S. Penilaian Survival Rate pada Penderita Adenokarsinoma Paru Mutasi EGFR yang Mendapat Terapi Target di RSUP H. Adam Malik Medan Tahun 2014-2018. 2020.
Agustina TS, Wulandari L. Perbandingan respons terapi gefitinib pada pasien KPKBSK EGFR mutasi exon 19 dan exon 21. J Respir Indo. 2017;37(3):232-40.
Yu X, Si J, Wei J, Wang Y, Sun Y, Jin J, et al. The effect of EGFR"TKIs on survival in advanced non"small"cell lung cancer with EGFR mutations: A real"world study. Cancer Medicine. 2022.
Winfree KB, Molife C, Peterson PM, Chen Y, Visseren-Grul CM, Leusch MS, et al. Real-world characteristics and outcomes of advanced non-small-cell lung cancer patients with EGFR exon 19 deletions or exon 21 mutations. Future Oncology. 2021(0).
Choi YW, Jeon SY, Jeong GS, Lee HW, Jeong SH, Kang SY, et al. EGFR exon 19 deletion is associated with favorable overall survival after first-line gefitinib therapy in advanced non–small cell lung cancer patients. American journal of clinical oncology. 2018;41(4):385-90.
Liu Y, Wang H, Yang S, Yang Y, Wu Y, He Z, et al. EGFR mutation types and abundance were associated with the overall survival of advanced lung adenocarcinoma patients receiving first-line tyrosine kinase inhibitors. Journal of Thoracic Disease. 2022;14(6):2255.
Wang Z-F, Ren S-X, Li W, Gao G-H. Frequency of the acquired resistant mutation T790 M in non-small cell lung cancer patients with active exon 19Del and exon 21 L858R: a systematic review and meta-analysis. BMC cancer. 2018;18(1):1-7.
Stinchcombe TE, Jänne PA, Wang X, Bertino EM, Weiss J, Bazhenova L, et al. Effect of erlotinib plus bevacizumab vs erlotinib alone on progression-free survival in patients with advanced EGFR-mutant non–small cell lung cancer: A phase 2 randomized clinical trial. JAMA oncology. 2019;5(10):1448-55.
Chang W-Y, Wu Y-L, Su P-L, Yang S-C, Lin C-C, Su W-C. The impact of EGFR mutations on the incidence and survival of stages I to III NSCLC patients with subsequent brain metastasis. PloS one. 2018;13(2):e0192161.
Passaro A, Prelaj A, Bonanno L, Tiseo M, Tuzi A, Proto C, et al. Activity of EGFR TKIs in Caucasian patients with NSCLC harboring potentially sensitive uncommon EGFR mutations. Clinical lung cancer. 2019;20(2):e186-e94.
Hsu W-H, Yang J-H, Mok T, Loong H. Overview of current systemic management of EGFR-mutant NSCLC. Annals of Oncology. 2018;29:i3-i9.
Li S, Ding C, Zhang H, Song J, Wu L. Radiomics for the prediction of EGFR mutation subtypes in non"small cell lung cancer. Medical Physics. 2019;46(10):4545-52.
Cho JH, Lim SH, An HJ, Kim KH, Park KU, Kang EJ, et al. Osimertinib for patients with non–small-cell lung cancer harboring uncommon EGFR mutations: a multicenter, open-label, phase II trial (KCSG-LU15-09). Journal of Clinical Oncology. 2020;38(5):488.
Li X, Zhang L, Jiang D, Wang Y, Zang A, Ding C, et al. Routine-Dose and High-Dose Icotinib in Patients with Advanced Non–Small Cell Lung Cancer Harboring EGFR Exon 21-L858R Mutation: the Randomized, Phase II, INCREASE TrialHigh-Dose Icotinib in Advanced Non–Small Cell Lung Cancer. Clinical Cancer Research. 2020;26(13):3162-71.
Copyright (c) 2023 Stephen Johan Prasetyo, Noni Novisari Soeroso, Setia Putra Tarigan, Erna Mutiara, Novia Nurul Faizah
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
1. The journal allows the author to hold the copyright of the article without restrictions.
2. The journal allows the author(s) to retain publishing rights without restrictions.
3. The legal formal aspect of journal publication accessibility refers to Creative Commons Attribution Share-Alike (CC BY-SA).
4. The Creative Commons Attribution Share-Alike (CC BY-SA) license allows re-distribution and re-use of a licensed work on the conditions that the creator is appropriately credited and that any derivative work is made available under "the same, similar or a compatible license”. Other than the conditions mentioned above, the editorial board is not responsible for copyright violation.
JR (