Molecular characterization of lung adenocarcinoma from Korean patients using next generation sequencing

English version

Autoři: You Jin Chun ^aff001; Jae Woo Choi ^aff002; Min Hee Hong ^aff001; Dongmin Jung ^aff002; Hyeonju Son ^aff004; Eun Kyung Cho ^aff005; Young Joo Min ^aff006; Sang-We Kim ^aff007; Keunchil Park ^aff008; Sung Sook Lee ^aff009; Sangwoo Kim ^aff004; Hye Ryun Kim ^aff001; Byoung Chul Cho ^aff001;
Působiště autorů: Division of Medical Oncology, Department of Internal Medicine, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Korea ^aff001; Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Korea ^aff002; Department of Pharmacology, Yonsei University College of Medicine, Seoul, Korea ^aff003; Department of Biomedical Systems Informatics, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Korea ^aff004; Division of Hematology-Oncology, Department of Internal Medicine, Gachon Medical School, Gil Medical Center, Incheon, Korea ^aff005; Division of Hematology and Oncology, Department of Internal Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea ^aff006; Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea ^aff007; Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea ^aff008; Department of Hematology-Oncology, Inje University Haeundae Paik Hospital, Busan, Korea ^aff009
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0224379

Souhrn

The treatment of Lung adenocarcinoma (LUAD) could benefit from the incorporation of precision medicine. This study was to identify cancer-related genetic alterations by next generation sequencing (NGS) in resected LUAD samples from Korean patients and to determine their associations with clinical features. A total of 201 tumors and their matched peripheral blood samples were analyzed using targeted sequencing via the Illumina HiSeq 2500 platform of 242 genes with a median depth of coverage greater than 500X. One hundred ninety-two tumors were amenable to data analysis. EGFR was the most frequently mutated gene, occurring in 106 (55%) patients, followed by TP53 (n = 67, 35%) and KRAS (n = 11, 6%). EGFR mutations were strongly increased in patients that were female and never-smokers. Smokers had a significantly higher tumor mutational burden (TMB) than never-smokers (average 4.84 non-synonymous mutations/megabase [mt/Mb] vs. 2.84 mt/Mb, p = 0.019). Somatic mutations of APC, CTNNB1, and AMER1 in the WNT signaling pathway were highly associated with shortened disease-free survival (DFS) compared to others (median DFS of 89 vs. 27 months, p = 0.018). Patients with low TMB, annotated as less than 2 mt/Mb, had longer DFS than those with high TMB (p = 0.041). A higher frequency of EGFR mutations and a lower of KRAS mutations were observed in Korean LUAD patients. Profiles of 242 genes mapped in this study were compared with whole exome sequencing genetic profiles generated in The Cancer Genome Atlas Lung Adenocarcinoma. NGS-based diagnostics can provide clinically relevant information such as mutations or TMB from readily available formalin-fixed paraffin-embedded tissue.

Klíčová slova:

Human genetics – Lung and intrathoracic tumors – Cancer genomics – Mutation databases – Next-generation sequencing – Nonsense mutation – Deletion mutation – Somatic mutation

Zdroje

1. Jemal A, Siegel R, Ward E, Hao Y, Xu J, Thun MJ. Cancer statistics, 2009. CA Cancer J Clin. 2009;59(4):225–49. Epub 2009/05/29. doi: 10.3322/caac.20006 19474385.

2. The Cancer Genome Atlas Research N. Comprehensive molecular profiling of lung adenocarcinoma. Nature. 2014;511:543. doi: 10.1038/nature13385 https://www.nature.com/articles/nature13385#supplementary-information. 25079552

3. 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. N Engl J Med. 2004;350(21):2129–39. Epub 2004/05/01. doi: 10.1056/NEJMoa040938 15118073.

4. Chan BA, Hughes BGM. Targeted therapy for non-small cell lung cancer: current standards and the promise of the future. Translational lung cancer research. 2015;4(1):36–54. doi: 10.3978/j.issn.2218-6751.2014.05.01 25806345.

5. Dearden S, Stevens J, Wu YL, Blowers D. Mutation incidence and coincidence in non small-cell lung cancer: meta-analyses by ethnicity and histology (mutMap). Annals of Oncology. 2013;24(9):2371–6. doi: 10.1093/annonc/mdt205 23723294

6. Soo RA, Kawaguchi T, Loh M, Ou SH, Shieh MP, Cho BC, et al. Differences in outcome and toxicity between Asian and caucasian patients with lung cancer treated with systemic therapy. Future Oncol. 2012;8(4):451–62. Epub 2012/04/21. doi: 10.2217/fon.12.25 22515448.

7. Lim SM, Kim HR, Cho EK, Min YJ, Ahn JS, Ahn M-J, et al. Targeted sequencing identifies genetic alterations that confer primary resistance to EGFR tyrosine kinase inhibitor (Korean Lung Cancer Consortium). Oncotarget. 2016;7(24):36311–20. doi: 10.18632/oncotarget.8904 27121209.

8. Ahn JW, Kim HS, Yoon J-K, Jang H, Han SM, Eun S, et al. Identification of somatic mutations in EGFR/KRAS/ALK-negative lung adenocarcinoma in never-smokers. Genome Medicine. 2014;6(2):18. doi: 10.1186/gm535 24576404

9. Joshi NA, Fass JN. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33) [Software]. 2011.

10. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60. Epub 2009/05/20. doi: 10.1093/bioinformatics/btp324 19451168; PubMed Central PMCID: PMC2705234.

11. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303. Epub 2010/07/21. doi: 10.1101/gr.107524.110 20644199; PubMed Central PMCID: PMC2928508.

12. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013;31(3):213–9. Epub 2013/02/12. doi: 10.1038/nbt.2514 23396013; PubMed Central PMCID: PMC3833702.

13. Narzisi G, O'Rawe JA, Iossifov I, Fang H, Lee YH, Wang ZH, et al. Accurate de novo and transmitted indel detection in exome-capture data using microassembly. Nature Methods. 2014;11(10):1033–6. doi: 10.1038/nmeth.3069 PubMed PMID: WOS:000342719100021. 25128977

14. Costello M, Pugh TJ, Fennell TJ, Stewart C, Lichtenstein L, Meldrim JC, et al. Discovery and characterization of artifactual mutations in deep coverage targeted capture sequencing data due to oxidative DNA damage during sample preparation. Nucleic Acids Research. 2013;41(6):e67–e. doi: 10.1093/nar/gks1443 PubMed PMID: PMC3616734. 23303777

15. Talevich E, Shain AH, Botton T, Bastian BC. CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing. PLoS Comput Biol. 2016;12(4):e1004873. Epub 2016/04/23. doi: 10.1371/journal.pcbi.1004873 27100738; PubMed Central PMCID: PMC4839673.

16. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164. Epub 2010/07/06. doi: 10.1093/nar/gkq603 20601685; PubMed Central PMCID: PMC2938201.

17. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536(7616):285–91. Epub 2016/08/19. doi: 10.1038/nature19057 27535533; PubMed Central PMCID: PMC5018207.

18. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32(18):2847–9. doi: 10.1093/bioinformatics/btw313 27207943

19. Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28(11):1747–56. Epub 2018/10/21. doi: 10.1101/gr.239244.118 30341162; PubMed Central PMCID: PMC6211645.

20. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4. Epub 2012/05/17. doi: 10.1158/2159-8290.CD-12-0095 22588877; PubMed Central PMCID: PMC3956037.

21. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1. Epub 2013/04/04. doi: 10.1126/scisignal.2004088 23550210; PubMed Central PMCID: PMC4160307.

22. Bahceci I, Dogrusoz U, La KC, Babur O, Gao J, Schultz N. PathwayMapper: a collaborative visual web editor for cancer pathways and genomic data. Bioinformatics. 2017;33(14):2238–40. Epub 2017/03/24. doi: 10.1093/bioinformatics/btx149 28334343; PubMed Central PMCID: PMC5859976.

23. Tamborero D, Rubio-Perez C, Deu-Pons J, Schroeder MP, Vivancos A, Rovira A, et al. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med. 2018;10(1):25. Epub 2018/03/30. doi: 10.1186/s13073-018-0531-8 29592813; PubMed Central PMCID: PMC5875005.

24. Oliveros JC. VENNY. An interactive tool for comparing lists with Venn diagrams. 2007.

25. Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC, et al. Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell. 2018;173(2):321-37.e10. Epub 2018/04/07. doi: 10.1016/j.cell.2018.03.035 29625050; PubMed Central PMCID: PMC6070353.

26. Van Allen EM, Wagle N, Stojanov P, Perrin DL, Cibulskis K, Marlow S, et al. Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine. Nat Med. 2014;20(6):682–8. Epub 2014/05/20. doi: 10.1038/nm.3559 24836576; PubMed Central PMCID: PMC4048335.

27. Griffith M, Spies NC, Krysiak K, McMichael JF, Coffman AC, Danos AM, et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat Genet. 2017;49(2):170–4. Epub 2017/02/01. doi: 10.1038/ng.3774 28138153; PubMed Central PMCID: PMC5367263.

28. Douville C, Carter H, Kim R, Niknafs N, Diekhans M, Stenson PD, et al. CRAVAT: cancer-related analysis of variants toolkit. Bioinformatics. 2013;29(5):647–8. Epub 2013/01/18. doi: 10.1093/bioinformatics/btt017 23325621; PubMed Central PMCID: PMC3582272.

29. Li S, Choi YL, Gong Z, Liu X, Lira M, Kan Z, et al. Comprehensive Characterization of Oncogenic Drivers in Asian Lung Adenocarcinoma. J Thorac Oncol. 2016;11(12):2129–40. Epub 2016/09/13. doi: 10.1016/j.jtho.2016.08.142 27615396.

30. Luo W, Tian P, Wang Y, Xu H, Chen L, Tang C, et al. Characteristics of genomic alterations of lung adenocarcinoma in young never-smokers. International Journal of Cancer. 2018;143(7):1696–705. doi: 10.1002/ijc.31542 29667179

31. Tsimberidou AM. Targeted therapy in cancer. Cancer Chemother Pharmacol. 2015;76(6):1113–32. Epub 2015/09/24. doi: 10.1007/s00280-015-2861-1 26391154; PubMed Central PMCID: PMC4998041.

32. Stewart DJ. Wnt signaling pathway in non-small cell lung cancer. J Natl Cancer Inst. 2014;106(1):djt356. Epub 2013/12/07. doi: 10.1093/jnci/djt356 24309006.

33. Rapp J, Jaromi L, Kvell K, Miskei G, Pongracz JE. WNT signaling–lung cancer is no exception. Respiratory Research. 2017;18:167. doi: 10.1186/s12931-017-0650-6 PubMed PMID: PMC5584342. 28870231

34. Owada-Ozaki Y, Muto S, Takagi H, Inoue T, Watanabe Y, Fukuhara M, et al. Prognostic Impact of Tumor Mutation Burden in Patients With Completely Resected Non-Small Cell Lung Cancer: Brief Report. J Thorac Oncol. 2018;13(8):1217–21. Epub 2018/04/15. doi: 10.1016/j.jtho.2018.04.003 29654927.

35. Devarakonda S, Rotolo F, Tsao M-S, Lanc I, Brambilla E, Masood A, et al. Tumor Mutation Burden as a Biomarker in Resected Non–Small-Cell Lung Cancer. Journal of Clinical Oncology. 2018;36(30):2995–3006. doi: 10.1200/JCO.2018.78.1963 30106638

36. 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. Am J Clin Oncol. 2018;41(4):385–90. Epub 2016/03/12. doi: 10.1097/COC.0000000000000282 26967328.

37. Wagner AH, Walsh B, Mayfield G, Tamborero D, Sonkin D, Krysiak K, et al. A harmonized meta-knowledgebase of clinical interpretations of cancer genomic variants. bioRxiv. 2018:366856. doi: 10.1101/366856

38. Lamichhane DK, Kim HC, Choi CM, Shin MH, Shim YM, Leem JH, et al. Lung Cancer Risk and Residential Exposure to Air Pollution: A Korean Population-Based Case-Control Study. Yonsei Med J. 2017;58(6):1111–8. Epub 2017/10/20. doi: 10.3349/ymj.2017.58.6.1111 29047234; PubMed Central PMCID: PMC5653475.

39. Consonni D, Carugno M, De Matteis S, Nordio F, Randi G, Bazzano M, et al. Outdoor particulate matter (PM10) exposure and lung cancer risk in the EAGLE study. PLoS One. 2018;13(9):e0203539. Epub 2018/09/15. doi: 10.1371/journal.pone.0203539 30216350; PubMed Central PMCID: PMC6157824.

40. Jin ZY, Wu M, Han RQ, Zhang XF, Wang XS, Liu AM, et al. Household ventilation may reduce effects of indoor air pollutants for prevention of lung cancer: a case-control study in a Chinese population. PLoS One. 2014;9(7):e102685. Epub 2014/07/16. doi: 10.1371/journal.pone.0102685 25019554; PubMed Central PMCID: PMC4097600.

Molecular characterization of lung adenocarcinoma from Korean patients using next generation sequencing

Souhrn

Klíčová slova:

Zdroje

PLOS One

Aktuální možnosti diagnostiky a léčby litiáz