利用包含22个基因的二代测序panel分析骨髓增生性肿瘤的遗传变异

15 January 2022


Jaymi Tan, Yock Ping Chow, Norziha Zainul Abidin, Kian Meng Chang, Veena Selvaratnam, Nor Rafeah Tumian, Yang Ming Poh, Abhi Veerakumarasivam, Michael Arthur Laffan & Chieh Lee Wong

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摘要

Background

The Philadelphia (Ph)-negative myeloproliferative neoplasms (MPNs), namely essential thrombocythaemia (ET), polycythaemia vera (PV) and primary myelofibrosis (PMF), are a group of chronic clonal haematopoietic disorders that have the propensity to advance into bone marrow failure or acute myeloid leukaemia; often resulting in fatality. Although driver mutations have been identified in these MPNs, subtype-specific markers of the disease have yet to be discovered. Next-generation sequencing (NGS) technology can potentially improve the clinical management of MPNs by allowing for the simultaneous screening of many disease-associated genes.

Methods

The performance of a custom, in-house designed 22-gene NGS panel was technically validated using reference standards across two independent replicate runs. The panel was subsequently used to screen a total of 10 clinical MPN samples (ET n = 3, PV n = 3, PMF n = 4). The resulting NGS data was then analysed via a bioinformatics pipeline.

Results

The custom NGS panel had a detection limit of 1% variant allele frequency (VAF). A total of 20 unique variants with VAFs above 5% (4 of which were putatively novel variants with potential biological significance) and one pathogenic variant with a VAF of between 1 and 5% were identified across all of the clinical MPN samples. All single nucleotide variants with VAFs ≥ 15% were confirmed via Sanger sequencing.

Conclusions

The high fidelity of the NGS analysis and the identification of known and novel variants in this study cohort support its potential clinical utility in the management of MPNs. However, further optimisation is needed to avoid false negatives in regions with low sequencing coverage, especially for the detection of driver mutations in MPL.


参考资料

  1. Cervantes, F., Passamonti, F., & Barosi, G. (2008). Life expectancy and prognostic factors in the classic BCR/ABL-negative myeloproliferative disorders. Leukemia, 22(5), 905–914. https://doi.org/10.1038/leu.2008.11
  2. Vannucchi, A. M., Barbui, T., Cervantes, F., Harrison, C., Kiladjian, J. J., Kroger, N., Thiele, J., & Kvasnicka, H. M. (2015). Philadelphia chromosome-negative chronic myeloproliferative neoplasms: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Annals of Oncology, 26(Suppl 5), v85–v99. https://doi.org/10.1093/annonc/mdv203
  3. Mullally, A., Bruedigam, C., Poveromo, L., Heidel, F. H., Purdon, A., Vu, T., Austin, R., Hochhaus, A., Lane, S. W., & Ebert, B. L. (2013). Depletion of Jak2V617F myeloproliferative neoplasm-propagating stem cells by interferon-$\alpha$ in a murine model of polycythemia vera. Blood, 121(18), 3692–3702. https://doi.org/10.1182/blood-2012-05-432922
  4. Swerdlow, S. H., Campo, E., Harris, N. L., Jaffe, E., Pileri, S., Stein, H., Thiele, J., & Vardiman, J. W. (Eds.). (2017). WHO classification of tumours of haematopoietic and lymphoid tissues (4th ed.). International Agency for Research on Cancer Press.
  5. Arber, D. A., Orazi, A., Hasserjian, R., Thiele, J., Borowitz, M. J., Le Beau, M. M., Falini, B., Cazzola, M., & Vardiman, J. W. (2016). The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood, 127(20), 2391–2405. https://doi.org/10.1182/blood-2016-03-643544
  6. Buhr, T., Hebeda, K., Kaloutsi, V., Porwit, A., Van der Walt, J., & Kreipe, H. (2012). European Bone Marrow Working Group trial on reproducibility of World Health Organization criteria to discriminate essential thrombocythemia from prefibrotic primary myelofibrosis. Haematologica, 97(3), 360–365. https://doi.org/10.3324/haematol.2011.047837
  7. Wilkins, B. S., Erber, W. N., Bareford, D., Buck, G., Wheatley, K., East, C. L., Paul, B., Harrison, C. N., Green, A. R., & Campbell, P. J. (2008). Bone marrow pathology in essential thrombocythemia: Interobserver reliability and utility for identifying disease subtypes. Blood, 111(1), 60–70. https://doi.org/10.1182/blood-2007-05-091850
  8. Brousseau, M., Parot-Schinkel, E., Moles, M. P., Boyer, F., Hunault, M., & Rousselet, M. C. (2010). Practical application and clinical impact of the WHO histopathological criteria on bone marrow biopsy for the diagnosis of essential thrombocythemia versus prefibrotic primary myelofibrosis. Histopathology, 56(6), 758–767. https://doi.org/10.1111/j.1365-2559.2010.03541x
  9. Grinfeld, J., Nangalia, J., Baxter, E. J., Wedge, D. C., Angelopoulos, N., Cantrill, R., Godfrey, A. L., Papaemmanuil, E., Gundem, G., MacLean, C., Cook, J., O'Neil, L., O'Meara, S., Teague, J. W., Butler, A. P., Massie, C. E., Williams, N., Harrison, C. N., Mead, A. J., ... Green, A. R. (2018). Classification and personalized prognosis in myeloproliferative neoplasms. The New England Journal of Medicine, 379(15), 1416–1430. https://doi.org/10.1056/NEJMoa1716614
  10. Ye, K., Schulz, M. H., Long, Q., Apweiler, R., & Ning, Z. (2009). Pindel: A pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics, 25(21), 2865–2871. https://doi.org/10.1093/bioinformatics/btp394
  11. Dai, Y., Liang, S., Dong, X., Zhao, Y., Ren, H., Guan, Y., ... & Zhang, X. (2019). Whole exome sequencing identified a novel DAG1 mutation in a patient with rare, mild and late age of onset muscular dystrophy-dystroglycanopathy. Journal of Cellular and Molecular Medicine, 23(2), 811–818. https://doi.org/10.1111/jcmm.13979
  12. Zheng, Y., Xu, J., Liang, S., Lin, D., & Banerjee, S. (2018). Whole exome sequencing identified a novel heterozygous mutation in HMBS gene in a Chinese patient with acute intermittent porphyria with rare type of mild anemia. Frontiers in Genetics, 9, Article 129. https://doi.org/10.3389/fgene.2018.00129
  13. Sherry, S. T., Ward, M. H., Kholodov, M., Baker, J., Phan, L., Smigielski, EM., & Sirotkin, K. (2001). dbSNP: The NCBI database of genetic variation. Nucleic Acids Research, 29(1), 308–311. https://doi.org/10.1093/nar/29.1.308
  14. Landrum, M. J., Lee, J. M., Riley, G. R., Jang, W., Rubinstein, W. S., Church, D. M., ... & Maglott, D. R. (2014). ClinVar: Public archive of relationships among sequence variation and human phenotype. Nucleic Acids Research, 42(D1), D980–D985. https://doi.org/10.1093/nar/gkt1113
  15. Tate, J. G., Bamford, S., Jubb, H. C., Sondka, Z., Beare, D. M., Bindal, N., Boutselakis, H., Cole, C. G., Creatore, C., Dawson, E., Fish, P., Harsha, B., Hathaway, C., Jupe, S. C., Kok, C. Y., Noble, K., Ponting, L., Ward, G. R., Cockerill, P. A., ... Forbes, S. A. (2018). COSMIC: The catalogue of somatic mutations in cancer. Nucleic Acids Research, 47(D1), D941–D947. https://doi.org/10.1093/nar/gky1015
  16. Yates, A. D., Achuthan, P., Akanni, W., Allen, J., Allen, J., Alvarez-Jarreta, J., ... & Flicek, P. (2019). Ensembl 2020. Nucleic Acids Research, 48(D1), D682–D688. https://doi.org/10.1093/nar/gkz966
  17. Ye, J., Coulouris, G., Zaretskaya, I., Cutcutache, I., Rozen, S., & Madden, T. L. (2012). Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics, 13, Article 134. https://doi.org/10.1186/1471-2105-13-134
  18. Waterhouse, A. M., Procter, J. B., Martin, D. M. A., Clamp, M., & Barton, G. J. (2009). Jalview Version 2—a multiple sequence alignment editor and analysis workbench. Bioinformatics, 25(9), 1189–1191. https://doi.org/10.1093/bioinformatics/btp033
  19. Abdel-Wahab, O., Kilpivaara, O., Patel, J., Busque, L., & Levine, R. L. (2010). The most commonly reported variant in ASXL1 (c.1934dupG;p.Gly646TrpfsX12) is not a somatic alteration. Leukemia, 24(9), 1656–1657. https://doi.org/10.1038/leu.2010.129
  20. Nangalia, J., Massie, C. E., Baxter, E. J., Nice, F. L., Gundem, G., Wedge, D. C., ... & Green, A. R. (2013). Somatic CALR mutations in myeloproliferative neoplasms with nonmutated JAK2. The New England Journal of Medicine, 369(25), 2391–2405. https://doi.org/10.1056/NEJMoa1312542
  21. Papaemmanuil, E., Gerstung, M., Malcovati, L., Tauro, S., Gundem, G., Van Loo, P., ... & Campbell, P. J. (2013). Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood, 122(22), 3616–3627. https://doi.org/10.1182/blood-2013-05-505313
  22. Grossmann, V., Kohlmann, A., Eder, C., Haferlach, C., Kern, W., Cross, N. C., Haferlach, T., & Schnittger, S. (2011). Molecular profiling of chronic myelomonocytic leukemia reveals diverse mutations in $>80\%$ of patients with TET2 and EZH2 being of high prognostic relevance. Leukemia, 25(5), 877–879. https://doi.org/10.1038/leu.2011.10
  23. Pratcorona, M., Abbas, S., Sanders, M. A., Koenders, J. E., Kavelaars, F. G., Erpelinck-Verschueren, C. A., ... & Valk, P. J. (2012). Acquired mutations in ASXL1 in acute myeloid leukemia: Prevalence and prognostic value. Haematologica, 97(3), 388–392. https://doi.org/10.3324/haematol.2011.051268
  24. Traina, F., Visconte, V., Jankowska, A. M., Makishima, H., O’Keefe, C. L., Elson, P., ... & Maciejewski, J. P. (2012). Single nucleotide polymorphism array lesions, TET2, DNMT3A, ASXL1 and CBL mutations are present in systemic mastocytosis. PLoS ONE, 7(8), Article e43090. https://doi.org/10.1371/journal.pone.0043090
  25. Cui, Y., Li, B., Jiang, Q., Xu, Z., Qin, T., Zhang, P., ... & Xiao, Z. (2014). CSF3R, ASXL1, SETBP1, JAK2 V617F and CALR mutations in chronic neutrophilic leukemia. Zhonghua Xue Ye Xue Za Zhi, 35(12), 1069–1073. https://doi.org/10.3760/cma.j.issn.0253-2727.2014.12.003
  26. Ross, J. S., Gay, L. M., Wang, K., Ali, S. M., Chumsri, S., Elvin, J. A., ... & Stephens, P. J. (2016). Nonamplification ERBB2 genomic alterations in 5605 cases of recurrent and metastatic breast cancer: An emerging opportunity for anti-HER2 targeted therapies. Cancer, 122(17), 2654–2662. https://doi.org/10.1002/cncr.30102
  27. Shain, A. H., Garrido, M., Botton, T., Talevich, E., Yeh, I., Sanborn, J. Z., ... & Bastian, B. C. (2015). Exome sequencing of desmoplastic melanoma identifies recurrent NFKBIE promoter mutations and diverse activating mutations in the MAPK pathway. Nature Genetics, 47(10), 1194–1199. https://doi.org/10.1038/ng.3386
  28. Yap, Y. S., Singh, A. P., Lim, J. H. C., Ahn, J. H., Jung, K. H., Kim, J., ... & Arnedos, M. (2018). Elucidating therapeutic molecular targets in premenopausal Asian women with recurrent breast cancers. NPJ Breast Cancer, 4, Article 19. https://doi.org/10.1038/s41523-018-0070-x
  29. Shimoda, K., Shide, K., Kameda, T., Hidaka, T., Kubuki, Y., & Kamiunten, A. (2015). TET2 mutation in adult T-cell leukemia/lymphoma. Journal of Clinical and Experimental Hematopathology, 55(3), 145–149. https://doi.org/10.3960/jslrt.55.145
  30. Wang, J., Ai, X., Gale, R. P., Xu, Z., Qin, T., Fang, L., ... & Xiao, Z. (2013). TET2, ASXL1 and EZH2 mutations in Chinese with myelodysplastic syndromes. Leukemia Research, 37(3), 305–311. https://doi.org/10.1016/j.leukres.2012.11.016
  31. Metzeler, K. H., Maharry, K., Radmacher, M. D., Mrózek, K., Margeson, D., Becker, H., ... & Marcucci, G. (2011). TET2 mutations improve the new European LeukemiaNet risk classification of acute myeloid leukemia: A Cancer and Leukemia Group B study. Journal of Clinical Oncology, 29(10), 1373–1381. https://doi.org/10.1200/JCO.2010.32.4749
  32. Ha, J. S., Jeon, D. S., Kim, R. J., Ryoo, N. H., & Suh, J. S. (2014). Analysis of the ten-eleven translocation 2 (TET2) gene mutation in myeloproliferative neoplasms. Annals of Clinical & Laboratory Science, 44(2), 173–179.
  33. Odejide, O., Weigert, O., Lane, A. A., Toscano, D., Lunning, M. A., Kopp, N., ... & Weinstock, D. M. (2014). A targeted mutational landscape of angioimmunoblastic T-cell lymphoma. Blood, 123(9), 1293–1296. https://doi.org/10.1182/blood-2013-09-524744
  34. Roche-Lestienne, C., Marceau, A., Labis, E., Nibourel, O., Coiteux, V., Guilhot, J., ... & Preudhomme, C. (2011). Mutation analysis of TET2, IDH1, IDH2 and ASXL1 in chronic myeloid leukemia. Leukemia, 25(10), 1661–1664. https://doi.org/10.1038/leu.2011.136
  35. Graubert, T. A., Shen, D., Ding, L., Okeyo-Owuor, T., Lunn, C. L., Shao, J., ... & Walter, M. J. (2011). Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes. Nature Genetics, 44(1), 53–57. https://doi.org/10.1038/ng.1017
  36. Yoshida, K., Sanada, M., Shiraishi, Y., Nowak, D., Nagata, Y., Yamamoto, R., ... & Ogawa, S. (2011). Frequent pathway mutations of splicing machinery in myelodysplasia. Nature, 478(7367), 64–69. https://doi.org/10.1038/nature10496
  37. Papaemmanuil, E., Cazzola, M., Boultwood, J., Malcovati, L., Vyas, P., Bowen, D., ... & Campbell, P. J. (2011). Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. The New England Journal of Medicine, 365(15), 1384–1395. https://doi.org/10.1056/NEJMoa1103283
  38. Aird, D., Ross, M. G., Chen, W. S., Danielsson, M., Fennell, T., Russ, C., ... & Gnirke, A. (2011). Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Genome Biology, 12(2), Article R18. https://doi.org/10.1186/gb-2011-12-2-r18
  39. Benjamini, Y., & Speed, T. P. (2012). Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Research, 40(10), Article e72. https://doi.org/10.1093/nar/gks160
  40. Pereira, R., Oliveira, J., & Sousa, M. (2020). Bioinformatics and computational tools for next-generation sequencing analysis in clinical genetics. Journal of Clinical Medicine, 9(1), Article 132. https://doi.org/10.3390/jcm9010132
  41. Haferlach, T., Nagata, Y., Grossmann, V., Okuno, Y., Bacher, U., Nagae, G., ... & Ogawa, S. (2014). Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia, 28(2), 241–247. https://doi.org/10.1038/leu.2013.208
  42. Thol, F., Kade, S., Schlarmann, C., Löffeld, P., Morgan, M., Krauter, J., ... & Heuser, M. (2012). Frequency and prognostic impact of mutations in SRSF2, U2AF1, and ZRSR2 in patients with myelodysplastic syndromes. Blood, 119(15), 3578–3584. https://doi.org/10.1182/blood-2011-12-399337
  43. Lindsley, R. C., Saber, W., Mar, B. G., Redd, R., Wang, T., Haagenson, M. D., ... & Ebert, B. L. (2017). Prognostic mutations in myelodysplastic syndrome after stem-cell transplantation. The New England Journal of Medicine, 376(6), 536–547. https://doi.org/10.1056/NEJMoa1611604
  44. Alberti, M. O., Srivatsan, S. N., Shao, J., McNulty, S. N., Chang, G. S., Miller, C. A., ... & Duncavage, E. J. (2018). Discriminating a common somatic ASXL1 mutation (c.1934dup; p.G646Wfs*12) from artifact in myeloid malignancies using NGS. Leukemia, 32(8), 1874–1878. https://doi.org/10.1038/s41375-018-0077-x
  45. Grinfeld, J., Nangalia, J., & Green, A. R. (2017). Molecular determinants of pathogenesis and clinical phenotype in myeloproliferative neoplasms. Haematologica, 102(1), 7–17. https://doi.org/10.3324/haematol.2014.114025
  46. Papaemmanuil, E., Gerstung, M., Bullinger, L., Gaidzik, V. I., Paschka, P., Roberts, N. D., ... & Campbell, P. J. (2016). Genomic classification and prognosis in acute myeloid leukemia. The New England Journal of Medicine, 374(23), 2209–2221. https://doi.org/10.1056/NEJMoa1516192
  47. Chandrasekhar, C., Kumar, P. S., & Sarma, P. V. G. K. (2019). Novel mutations in the kinase domain of BCR-ABL gene causing imatinib resistance in chronic myeloid leukemia patients. Scientific Reports, 9(1), Article 2412. https://doi.org/10.1038/s41598-019-38859-0
  48. Soverini, S., Rosti, G., Iacobucci, I., Baccarani, M., & Martinelli, G. (2011). Choosing the best second-line tyrosine kinase inhibitor in imatinib-resistant chronic myeloid leukemia patients harboring Bcr-Abl kinase domain mutations: How reliable is the IC50? The Oncologist, 16(6), 868–876. https://doi.org/10.1634/theoncologist.2010-0382
  49. Wylie, A. A., Schoepfer, J., Jahnke, W., Cowan-Jacob, S. W., Loo, A., Furet, P., ... & Sellers, W. R. (2017). The allosteric inhibitor ABL001 enables dual targeting of BCR–ABL1. Nature, 543(7647), 733–737. https://doi.org/10.1038/nature21702
  50. Klco, J. M., Vij, R., Kreisel, F. H., Hassan, A., & Frater, J. L. (2010). Molecular pathology of myeloproliferative neoplasms. American Journal of Clinical Pathology, 133(4), 602–615. https://doi.org/10.1309/AJCP4G4OWYUMTBMK
  51. Ortmann, C. A., Kent, Dis. G., Nangalia, J., Silber, Y., Wedge, D. C., Grinfeld, J., ... & Green, A. R. (2015). Effect of mutation order on myeloproliferative neoplasms. The New England Journal of Medicine, 372(7), 601–612. https://doi.org/10.1056/NEJMoa1412098
  52. Nangalia, J., Nice, F. L., Wedge, D. C., Godfrey, A. L., Grinfeld, J., Thakker, C., ... & Green, A. R. (2015). DNMT3A mutations occur early or late in patients with myeloproliferative neoplasms and mutation order influences phenotype. Haematologica, 100(11), e438–e442. https://doi.org/10.3324/haematol.2015.128140
  53. Tan, J., Chow, Y. P., Abidin, N. Z., Veerakumarasivam, A., & Wong, C. L. (2021). From driver mutations to genomic classification: Current & future perspectives on myeloproliferative neoplasms. Malaysian Journal of Medicine and Health Sciences, 17(1), 170–183.
  54. Robevska, G., van den Bergen, J. A., Ohnesorg, T., Eggers, S., Hanna, C., Hersmus, R., ... & Sinclair, A. H. (2018). Functional characterization of novel NR5A1 variants reveals multiple complex roles in disorders of sex development. Human Mutation, 39(1), 124–139. https://doi.org/10.1002/humu.23352
  55. Wheway, G., Nazlamova, L., Meshad, N., Hunt, S., Jackson, N., & Churchill, A. (2019). A combined in silico, in vitro and clinical approach to characterize novel pathogenic missense variants in PRPF31 in retinitis pigmentosa. Frontiers in Genetics, 10, Article 248. https://doi.org/10.3389/fgene.2019.00248
  56. Beck, T. F., Mullikin, J. C., & Biesecker, L. G. (2016). Systematic evaluation of Sanger validation of next-generation sequencing variants. Clinical Chemistry, 62(4), 647–654. https://doi.org/10.1373/clinchem.2015.249623
  57. Alghasham, N., Alnouri, Y., Abalkhail, H., & Khalil, S. (2016). Detection of mutations in JAK2 exons 12–15 by Sanger sequencing. International Journal of Laboratory Hematology, 38(1), 34–41. https://doi.org/10.1111/ijlh.12423
  58. Braunholz, D., Obieglo, C., Parenti, I., Pozojevic, J., Eckhold, J., Reiz, B., ... & Kaiser, F. J. (2015). Hidden mutations in Cornelia de Lange syndrome limitations of Sanger sequencing in molecular diagnostics. Human Mutation, 36(1), 26–29. https://doi.org/10.1002/humu.22706
  59. Paparini, A., Gofton, A., Yang, R., White, N., Bunce, M., & Ryan, U. M. (2015). Comparison of Sanger and next generation sequencing performance for genotyping Cryptosporidium isolates at the 18S rRNA and actin loci. Experimental Parasitology, 151–152, 21–27. https://doi.org/10.1016/j.exppara.2015.01.006
  60. Tsiatis, A. C., Norris-Kirby, A., Rich, R. G., Hafez, M. J., Gocke, C. D., Eshleman, J. R., ... & Lin, M. T. (2010). Comparison of Sanger sequencing, pyrosequencing, and melting curve analysis for the detection of KRAS mutations: Diagnostic and clinical implications. The Journal of Molecular Diagnostics, 12(4), 425–432. https://doi.org/10.2353/jmoldx.2010.090181
  61. Kuo, F. C., Mar, B. G., Lindsley, R. C., & Lindeman, N. I. (2017). The relative utilities of genome-wide, gene panel, and individual gene sequencing in clinical practice. Blood, 130(4), 433–439. https://doi.org/10.1182/blood-2016-09-742742
  62. Słomka, M., Sobalska-Kwapis, M., Wachulec, M., Bartosz, G., & Strapagiel, D. (2017). High resolution melting (HRM) for high-throughput genotyping—Limitations and caveats in practical case studies. International Journal of Molecular Sciences, 18(11), Article 2316. https://doi.org/10.3390/ijms18112316
  63. Teer, J. K., Zhang, Y., Chen, L., Welsh, E. A., Cress, W. D., Eschrich, S. A., ... & Wang, J. (2017). Evaluating somatic tumor mutation detection without matched normal samples. Human Genomics, 11(1), Article 22. https://doi.org/10.1186/s40246-017-0118-2
  64. Baer, C., Walter, W., Hutter, S., Twardziok, S., Meggendorfer, M., Kern, W., Haferlach, C., & Haferlach, T. (2019). “Somatic” and “pathogenic”: Is the classification strategy applicable in times of large-scale sequencing? Haematologica, 104(8), 1515–1520. https://doi.org/10.3324/haematol.2018.204560

引用

Tan, J., Chow, Y.P., Zainul Abidin, N. et al. Analysis of genetic variants in myeloproliferative neoplasms using a 22-gene next-generation sequencing panel. BMC Med Genomics 15, 10 (2022). https://doi.org/10.1186/s12920-021-01145-0

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