Main Article Content
Lung cancer is one of the most commonly diagnosed cancers worldwide. It is the leading cause of cancer-related deaths in both men and women.
In 2020, there were an estimated 2.2 million new cases of lung cancer and 1.8 million deaths due to the disease.
Historically, lung cancer has been more common in men, but the gap has been closing.
Smoking tobacco is the leading cause of lung cancer. Survival rates for lung cancer vary greatly depending on the stage at diagnosis and other factors. Overall, the prognosis for lung cancer is often poor, with a relatively low five-year survival rate compared to some other cancers.
In this work we aim to show new paths in the diagnosis of lung cancer, through the study of several mutations and proteins, mostly detected by Next-generation sequencing (NGS) which has significantly transformed our understanding of cancer, by providing high-throughput and cost-effective methods for analyzing genomic information. In the context of lung cancer, NGS has played a crucial role in advancing our knowledge of the disease, improving diagnosis and treatment, and guiding personalized medicine approaches. key points highlighting the importance of next-generation sequencing in lung cancer:
Comprehensive Genomic Profiling
Identification of Driver Mutations
Stratification of Patients
Predicting Treatment Response
Monitoring Disease Progression
Clinical Trials and Drug Development
Early Detection and Prognosis
A large meta-analysis has been done, as well as a detailed study of 86 patients diagnosed with lung cancer in the ANALIZA laboratory. In this sense the most frequently implicated mutations in this tumor have been analyzed, ALK, ROS1 and EGFR, the positions they occupy in the genes, in addition to the programmed death ligand 1 (PD-L1), an immune control protein, which is expressed in activated immune cells and in tumor cells, and how its identification allows us to direct treatment in a more optimal way.
In summary, next-generation sequencing has revolutionized the field of lung cancer research and clinical practice. By providing detailed insights into the genomic landscape of tumors, NGS facilitates personalized treatment approaches, early detection, and ongoing monitoring, ultimately leading to improved patient outcomes.
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
2. Barta JA, Powell CA, Wisnivesky JP. Global Epidemiology of Lung Cancer. Ann Glob Health. 2019 Jan 22;85(1):8.
3. Sociedad Española de Oncología Médica. Las cifras del cáncer en España 2023. Madrid: SEOM; 2023. https://seom.org/images/Las_cifras_del_Cancer_en_Espana_2023.pdf
4. Estadística de defunciones según causa de muerte. INE – España Eurostat.2022.
5. Markowitz SB, Levin SM, Miller A and Morabia A. Asbestos, asbestosis, smoking, and lung cancer. New findings from the North American insulator cohort. Am J Respir Crit Care Med. 2013; 188(1): 90–96.
6. Schwartz AG, Prysak GM, Bock CH and Cote ML. The molecular epidemiology of lung cancer. Carcinogenesis. 2006; 28(3): 507–518.
7. Tan AC, Lai GGY, Tan GS, Poon SY, Doble B, Lim TH, Aung ZW, Takano A, Tan WL, Ang MK, Tan BS, Devanand A, Too CW, Gogna A, Ong BH, Koh TPT, Kanesvaran R, Ng QS, Jain A, Rajasekaran T, Lim AST, Lim WT, Toh CK, Tan EH, Lim TKH, Tan DSW. Utility of incorporating next-generation sequencing (NGS) in an Asian non-small cell lung cancer (NSCLC) population: Incremental yield of actionable alterations and cost-effectiveness analysis. Lung Cancer. 2020 Jan;139:207-215. doi: 10.1016/j.lungcan.2019.11.022. Epub 2019 Nov 26. PMID: 31835042.
8. Yatabe Y, Sunami K, Goto K, Nishio K, Aragane N, Ikeda S, Inoue A, Kinoshita I, Kimura H, Sakamoto T, Satouchi M, Shimizu J, Tsuta K, Toyooka S, Nishino K, Hatanaka Y, Matsumoto S, Mikubo M, Yokose T, Dosaka-Akita H. Multiplex gene-panel testing for lung cancer patients. Pathol Int. 2020 Dec;70(12):921-931
9. Lin C, Shi X, Yang S, Zhao J, He Q, Jin Y, Yu X. Comparison of ALK detection by FISH, IHC and NGS to predict benefit from crizotinib in advanced non-small-cell lung cancer. Lung Cancer. 2019 May;131:62-68.
10. Slatko BE, Gardner AF, Ausubel FM. Overview of Next-Generation Sequencing Technologies. Curr Protoc Mol Biol. 2018 Apr;122(1)
11. Mosele F, Remon J, Mateo J, Westphalen CB, Barlesi F, Lolkema MP, et al. Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: a report from the ESMO Precision Medicine Working Group. Ann Oncol. 2020 Nov;31(11):1491-1505.
12. Rizvi H, Sanchez-Vega F, La K, Chatila W, Jonsson P, Halpenny D, Plodkowski A, Long N, Sauter JL, Rekhtman N, Hollmann T, Schalper KA, Gainor JF, Shen R, Ni A, Arbour KC, Merghoub T, Wolchok J, Snyder A, Chaft JE, Kris MG, Rudin CM, Socci ND, Berger MF, Taylor BS, Zehir A, Solit DB, Arcila ME, Ladanyi M, Riely GJ, Schultz N, Hellmann MD. Molecular Determinants of Response to Anti-Programmed Cell Death (PD)-1 and Anti-Programmed Death-Ligand 1 (PD-L1) Blockade in Patients With Non-Small-Cell Lung Cancer Profiled With Targeted Next-Generation Sequencing. J Clin Oncol. 2018 Mar 1;36(7):633-641. doi: 10.1200/JCO.2017.75.3384. Epub 2018 Jan 16. Erratum in: J Clin Oncol. 2018 Jun 1;36(16):1645.
13. Gao J, Wu H, Shi X, Huo Z, Zhang J, Liang Z. Comparison of Next-Generation Sequencing, Quantitative PCR, and Sanger Sequencing for Mutation Profiling of EGFR, KRAS, PIK3CA and BRAF in Clinical Lung Tumors. Clin Lab. 2016;62(4):689-96. doi: 10.7754/clin.lab.2015.150837. PMID: 27215089
14. IDYLLA TM, Nothing is simple in oncology. Nothing but this [flyer]. Mechelen- Belgium. Biocartis; March 2023
15. Ou SH, Bartlett CH, Mino-Kenudson M, Cui J, Iafrate AJ. Crizotinib for the treatment of ALK-rearranged non-small cell lung cancer: a success story to usher in the second decade of molecular targeted therapy in oncology. Oncologist. 2012;17(11):1351-75.
16. Antes G, Oxman AD. The Cochrane Collaboration in the 20th century. In: Egger M, Smith GD, Altman DG, editors. Systematic reviews in health care. Meta-analysis in context. London: BMJ Publishing Group 2001; p. 447–58.
17. Egger M, Smith GD, Altman DG, editors. Systematic reviews in health care. Meta-analysis in context. 2nd edition. London: BMJ Publishing Group 2001.
18. Sami W, Alrukban MO, Waqas T, Asad MR, Afzal K. Sample Size Determination In Health Research. J Ayub Med Coll Abbottabad. 2018 Apr-Jun;30(2):308-311.
19. Turner SL, Karahalios A, Forbes AB, Taljaard M, Grimshaw JM, Cheng AC, Bero L, McKenzie JE. Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: a review. J Clin Epidemiol. 2020 Jun;122:1-11. doi: 10.1016/j.jclinepi.2020.02.006.
20. Fleiss J L. Statistical Methods for rates and proportions. 3rd ed. New York: John Wiley & Sons 2003.
21. Selvin S. Statistical Analysis of epidemiologic data. 3rd ed. New York: Oxford University Press 2004.
22. Altman DG. Practical statistics for medical research. London, Chapman & Hall 1991.
23. Armitage P, Berry G. Estadística para la investigación biomédica. Barcelona 1999: Harcourt Brace.
24. Hedges LV, Vevea JL.Fixed- and random-effects models in meta-analysis. Psychol Methods 1998, 3:486–504
25. Hernández Sampieri R, Fernández Collado C and Baptista Lucio P. Metodología de la investigación. McGraw-Hill. Mexico. 4th Edition.2006 ISBN: 970-10-5753-8.
26. Marín Martínez F, Sánchez Meca J and López López JA. El metaanálisis en el ámbito de las Ciencias de la Salud: una metodología imprescindible para la eficiente acumulación del conocimiento. Physiotherapy 2009 31(3):107–114.
27. Oxman AD.The Cochrane Collaboration in the 21st century: Ten challenges and one reason why they must be met. In: Egger M, Smith GD, Altman DG, editors. London: BMJ Publishing Group 2001, 459–73. Systematic reviews in health care. Meta-analysis in context.
28. Sanchez-Meca J, Marín-Martínez F. Testing continuous moderators in meta-analysis: A comparison of procedures. British Journal of Mathematical and Statistical Psychology 1998. 51:311–26.
29. Alvarez AM, alvarez AR, Artal CA. PD-L1 in resected lung adenocarcinoma and its relationship with molecular markers: prognostic implications. [Internet] 2016 [cited 22 may 2023]; Available in: https://dialnet.unirioja.es/servlet/tesis?codigo=78657
30. Pisapia P, Malapelle U, Troncone G. Liquid Biopsy and Lung Cancer. Acta Cytol. 2019;63(6):489-496. doi: 10.1159/000492710. Epub 2018 Dec 19. PMID: 30566947.
31. Sakata S, Otsubo K, Yoshida H, Ito K, Nakamura A, Teraoka S, et al. Real-world data on NGS using the Oncomine DxTT for detecting genetic alterations in non-small-cell lung cancer: WJOG13019L. Cancer Sci. 2022 Jan;113(1):221-228
32. Wen S, Dai L, Wang L, Wang W, Wu D, Wang K, He Z, Wang A, Chen H, Zhang P, Dong X, Dong YA, Wang K, Yao M, Wang M. Genomic Signature of Driver Genes Identified by Target Next-Generation Sequencing in Chinese Non-Small Cell Lung Cancer. Oncologist. 2019 Nov;24(11):e1070-e1081.
33. Esagian SM, Grigoriadou GΙ, Nikas IP, Boikou V, Sadow PM, Won JK, Economopoulos KP. Comparison of liquid-based to tissue-based biopsy analysis by targeted next generation sequencing in advanced non-small cell lung cancer: a comprehensive systematic review. J Cancer Res Clin Oncol. 2020 Aug;146(8):2051-2066.
34. Provencio M, Cobo M, Rodriguez-Abreu D, Calvo V, Carcereny E, Cantero A, et al. Determination of essential biomarkers in lung cancer: a real-world data study in Spain with demographic, clinical, epidemiological and pathological characteristics. BMC Cancer. 2022 Jul 5;22(1):732.
35. Liu Y, Wu A, Li X, Wang S, Fang S, Mo Y. A retrospective analysis of eleven gene mutations, PD-L1 expression and clinicopathological characteristics in non-small cell lung cancer patients. Asian J Surg. 2022 Jan;45(1):367-375