Quantitative detection of ALK fusion breakpoints in plasma cell-free DNA from patients with non-small cell lung cancer using PCR-based target sequencing with a tiling primer set and two-step mapping/alignment

English version

Autoři: Kei Kunimasa ^aff001; Kikuya Kato ^aff002; Fumio Imamura ^aff001; Yoji Kukita ^aff002
Působiště autorů: Department of Thoracic Oncology, Osaka International Cancer Institute, Osaka, Osaka, Japan ^aff001; Laboratory of Medical Genomics, Nara Institute of Science and Technology, Ikoma, Nara, Japan ^aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222233

Souhrn

Background

Tyrosine kinase inhibitors targeted to anaplastic lymphoma kinase (ALK) have been demonstrated to be effective for lung cancer patients with an ALK fusion gene. Application of liquid biopsy, i.e., detection and quantitation of the fusion product in plasma cell-free DNA (cfDNA), could improve clinical practice. To detect ALK fusions, because fusion breakpoints occur somewhere in intron 19 of the ALK gene, sequencing of the entire intron is required to locate breakpoints.

Results

We constructed a target sequencing system using an adapter and a set of primers that cover the entire ALK intron 19. This system can amplify fragments, including breakpoints, regardless of fusion partners. The data analysis pipeline firstly detected fusions by alignment to selected target sequences, and then quantitated the fusion alleles aligning to the identified breakpoint sequences. Performance was validated using 20 cfDNA samples from ALK-positive non-small cell lung cancer patients and samples from 10 healthy volunteers. Sensitivity and specificity were 50 and 100%, respectively.

Conclusions

We demonstrated that PCR-based target sequencing using a tiling primer set and two-step mapping/alignment quantitatively detected ALK fusions in cfDNA from lung cancer patients. The system offers an alternative to existing approaches based on hybridization capture.

Klíčová slova:

Biology and life sciences – Cell biology – Genetics – Genomics – Genome analysis – Computational biology – Research and analysis methods – Molecular biology – Database and informatics methods – Bioinformatics – Sequence analysis – Sequence alignment – Molecular biology techniques – Anatomy – Medicine and health sciences – Physiology – Body fluids – Blood – Oncology – Cancers and neoplasms – Artificial gene amplification and extension – Polymerase chain reaction – Cell physiology – Lung and intrathoracic tumors – Sequencing techniques – Transcriptome analysis – Genome complexity – Introns – DNA sequencing – Next-generation sequencing – Cell fusion – Non-small cell lung cancer

Zdroje

1. Du X, Shao Y, Qin HF, Tai YH, Gao HJ. ALK-rearrangement in non-small-cell lung cancer (NSCLC). Thorac Cancer. 2018;9(4):423–430. doi: 10.1111/1759-7714.12613 29488330

2. Santarpia M, Daffina MG, D'Aveni A, Marabello G, Liguori A, Giovannetti E, et al. Spotlight on ceritinib in the treatment of ALK+ NSCLC: design, development and place in therapy. Drug Des Devel Ther. 2017;11:2047–2063. doi: 10.2147/DDDT.S113500 28740365

3. Soda M, Choi YL, Enomoto M, Takada S, Yamashita Y, Ishikawa S, et al. Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer. Nature. 2007;448(7153):561–566. 17625570

4. Sasaki T, Rodig SJ, Chirieac LR, Janne PA. The biology and treatment of EML4-ALK non-small cell lung cancer. Eur J Cancer. 2010;46(10):1773–1780. doi: 10.1016/j.ejca.2010.04.002 20418096

5. Mano H. Non-solid oncogenes in solid tumors: EML4-ALK fusion genes in lung cancer. Cancer Sci. 2008;99(12):2349–2355. doi: 10.1111/j.1349-7006.2008.00972.x 19032370

6. Sabir SR, Yeoh S, Jackson G, Bayliss R. EML4-ALK Variants: Biological and Molecular Properties, and the Implications for Patients. Cancers (Basel). 2017;9(9):118.

7. Guibert N, Hu Y, Feeney N, Kuang Y, Plagnol V, Jones G, et al. Amplicon-based next-generation sequencing of plasma cell-free DNA for detection of driver and resistance mutations in advanced non-small cell lung cancer. Ann Oncol. 2018;29(4):1049–1055. doi: 10.1093/annonc/mdy005 29325035

8. Lanman RB, Mortimer SA, Zill OA, Sebisanovic D, Lopez R, Blau S, et al. Analytical and Clinical Validation of a Digital Sequencing Panel for Quantitative, Highly Accurate Evaluation of Cell-Free Circulating Tumor DNA. PLoS One. 2015;10(10):e0140712. doi: 10.1371/journal.pone.0140712 26474073

9. Newman AM, Bratman SV, To J, Wynne JF, Eclov NC, Modlin LA, et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med. 2014;20(5):548–554. doi: 10.1038/nm.3519 24705333

10. Phallen J, Sausen M, Adleff V, Leal A, Hruban C, White J, et al. Direct detection of early-stage cancers using circulating tumor DNA. Sci Transl Med. 2017;9(403):eaan2415.

11. Wang Y, Tian PW, Wang WY, Wang K, Zhang Z, Chen BJ, et al. Noninvasive genotyping and monitoring of anaplastic lymphoma kinase (ALK) rearranged non-small cell lung cancer by capture-based next-generation sequencing. Oncotarget. 2016;7(40):65208–65217. doi: 10.18632/oncotarget.11569 27564104

12. Paweletz CP, Sacher AG, Raymond CK, Alden RS, O'Connell A, Mach SL, et al. Bias-Corrected Targeted Next-Generation Sequencing for Rapid, Multiplexed Detection of Actionable Alterations in Cell-Free DNA from Advanced Lung Cancer Patients. Clin Cancer Res. 2016;22(4):915–922. doi: 10.1158/1078-0432.CCR-15-1627-T 26459174

13. Sabina J, Leamon JH. Bias in Whole Genome Amplification: Causes and Considerations. Methods Mol Biol. 2015;1347:15–41. doi: 10.1007/978-1-4939-2990-0_2 26374307

14. Kukita Y, Ohkawa K, Takada R, Uehara H, Katayama K, Kato K. Selective identification of somatic mutations in pancreatic cancer cells through a combination of next-generation sequencing of plasma DNA using molecular barcodes and a bioinformatic variant filter. PLoS One. 2018;13(2):e0192611. doi: 10.1371/journal.pone.0192611 29451897

15. Kukita Y, Matoba R, Uchida J, Hamakawa T, Doki Y, Imamura F, et al. High-fidelity target sequencing of individual molecules identified using barcode sequences: de novo detection and absolute quantitation of mutations in plasma cell-free DNA from cancer patients. DNA Res. 2015;22(4):269–277. doi: 10.1093/dnares/dsv010 26126624

16. Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM; 2013. Preprint. Available from arXiv:1303.3997.

17. Tong Y, Zhao Z, Liu B, Bao A, Zheng H, Gu J, et al. 5'/ 3' imbalance strategy to detect ALK fusion genes in circulating tumor RNA from patients with non-small cell lung cancer. J Exp Clin Cancer Res. 2018;37(1):68. doi: 10.1186/s13046-018-0735-1 29587818

18. Nilsson RJ, Karachaliou N, Berenguer J, Gimenez-Capitan A, Schellen P, Teixido C, et al. Rearranged EML4-ALK fusion transcripts sequester in circulating blood platelets and enable blood-based crizotinib response monitoring in non-small-cell lung cancer. Oncotarget. 2016;7(1):1066–1075. doi: 10.18632/oncotarget.6279 26544515

19. Lin E, Li L, Guan Y, Soriano R, Rivers CS, Mohan S, et al. Exon array profiling detects EML4-ALK fusion in breast, colorectal, and non-small cell lung cancers. Mol Cancer Res. 2009;7(9):1466–1476. doi: 10.1158/1541-7786.MCR-08-0522 19737969

20. Krumbholz M, Woessmann W, Zierk J, Seniuk D, Ceppi P, Zimmermann M, et al. Characterization and diagnostic application of genomic NPM-ALK fusion sequences in anaplastic large-cell lymphoma. Oncotarget. 2018;9(41):26543–26555. doi: 10.18632/oncotarget.25489 29899875

21. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409(6822):860–921. 11237011

22. Quail MA, Smith M, Coupland P, Otto TD, Harris SR, Connor TR, et al. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics. 2012;13:341. doi: 10.1186/1471-2164-13-341 22827831

23. Junemann S, Sedlazeck FJ, Prior K, Albersmeier A, John U, Kalinowski J, et al. Updating benchtop sequencing performance comparison. Nat Biotechnol. 2013;31(4):294–296. doi: 10.1038/nbt.2522 23563421

24. Schmitt MW, Fox EJ, Prindle MJ, Reid-Bayliss KS, True LD, Radich JP et al. Sequencing small genomic targets with high efficiency and extreme accuracy. Nat Methods. 2015;12(5): 423–425. doi: 10.1038/nmeth.3351 25849638

25. Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med. 2014;6(224):224ra224.

26. Li BT, Janku F, Jung B, Hou C, Madwani K, Alden R, et al. Ultra-deep next-generation sequencing of plasma cell-free DNA in patients with advanced lung cancers: results from the Actionable Genome Consortium. Ann Oncol. 2019;30(4):597–603. doi: 10.1093/annonc/mdz046 30891595

27. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421. doi: 10.1186/1471-2105-10-421 20003500

28. Ruijter JM, Ramakers C, Hoogaars WM, Karlen Y, Bakker O, van den Hoff MJ, et al. Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Res. 2009;37(6):e45. doi: 10.1093/nar/gkp045 19237396