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基因组测序作为髓系癌症细胞遗传学分析的替代方法
Genome Sequencing as an Alternative to Cytogenetic Analysis in Myeloid Cancers


Eric J. Duncavage ... 肿瘤 • 2021.03.11
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• 更深入、更快速地分析髓系恶性肿瘤 • 患者整个癌症病程中的基因组测序

摘要


背景

基因组分析对于急性髓系白血病(AML)或骨髓增生异常综合征(MDS)患者的风险分层至关重要。全基因组测序是常规细胞遗传学和测序方法的潜在替代方法,但其准确性、可行性和临床效用尚未得到证实。

 

方法

我们使用优化全基因组测序方法获得263例髓系癌症患者的基因组特征,其中包括235例成功进行细胞遗传学分析的患者。我们改进了样本准备、测序和分析方法,从而根据现有欧洲白血病网络(European Leukemia Network,ELN)指南检测用于风险分层的突变,并尽量缩短周转时间。我们比较了我们的结果与细胞遗传学分析和靶向测序的结果,旨在分析全基因组测序的性能。

 

结果

全基因组测序检出了细胞遗传学分析之前已识别出的全部40个反复出现的易位和91个拷贝数改变。此外,在235例患者中,我们发现40例(17.0%)有临床上可报告的新基因组事件。我们在中位5天内对连续117例患者的样本进行了前瞻性测序,并提供了29例(24.8%)患者的新遗传信息,从而改变了19例(16.2%)患者的风险类别。根据测序结果(而非细胞遗传学分析结果)定义的标准AML风险组与临床结局相关。我们还应用全基因组测序结果对细胞遗传学分析结果不确定的患者进行了分层,将其分入临床结局明显不同的风险组。

 

结论

在本研究中,我们发现全基因组测序快速且准确地检测出了AML或MDS患者的基因组特征。与常规细胞遗传学分析相比,该测序法的诊断率较高,并且根据标准风险类别将患者进行风险分层的效率较高(由斯特曼癌症研究基金会[Siteman Cancer Research Fund]等资助)。





作者信息

Eric J. Duncavage, M.D., Molly C. Schroeder, Ph.D., Michele O’Laughlin, B.S., Roxanne Wilson, B.S., Sandra MacMillan, B.S., Andrew Bohannon, B.S., Scott Kruchowski, B.S., John Garza, B.S., Feiyu Du, M.S., Andrew E.O. Hughes, M.D., Ph.D., Josh Robinson, B.A., Emma Hughes, B.S., Sharon E. Heath, Jack D. Baty, B.A., Julie Neidich, M.D., Matthew J. Christopher, M.D., Ph.D., Meagan A. Jacoby, M.D., Ph.D., Geoffrey L. Uy, M.D., Robert S. Fulton, M.S., Christopher A. Miller, Ph.D., Jacqueline E. Payton, M.D., Ph.D., Daniel C. Link, M.D., Matthew J. Walter, M.D., Peter Westervelt, M.D., Ph.D., John F. DiPersio, M.D., Ph.D., Timothy J. Ley, M.D., and David H. Spencer, M.D., Ph.D.
From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis. Address reprint requests to Dr. Spencer at the Division of Oncology, Department of Medicine, Washington University School of Medicine, Campus Box 8007, 660 South Euclid Ave., St. Louis, MO 63110, or at dspencer@wustl.edu.

 

参考文献

1. Nangalia J, Campbell PJ. Genome sequencing during a patient’s journey through cancer. N Engl J Med 2019;381:2145-2156.

2. Welch JS, Westervelt P, Ding L, et al. Use of whole-genome sequencing to diagnose a cryptic fusion oncogene. JAMA 2011;305:1577-1584.

3. Döhner H, Estey E, Grimwade D, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood 2017;129:424-447.

4. Schlenk RF, Döhner K, Krauter J, et al. Mutations and treatment outcome in cytogenetically normal acute myeloid leukemia. N Engl J Med 2008;358:1909-1918.

5. Schnittger S, Schoch C, Kern W, et al. Nucleophosmin gene mutations are predictors of favorable prognosis in acute myelogenous leukemia with a normal karyotype. Blood 2005;106:3733-3739.

6. Welch JS, Petti AA, Miller CA, et al. TP53 and decitabine in acute myeloid leukemia and myelodysplastic syndromes. N Engl J Med 2016;375:2023-2036.

7. Papaemmanuil E, Gerstung M, Bullinger L, et al. Genomic classification and prognosis in acute myeloid leukemia. N Engl J Med 2016;374:2209-2221.

8. National Comprehensive Cancer Network. NCCN guidelines for patients: acute myeloid leukemia (https://www.nccn.org/patients/guidelines/content/PDF/aml-patient.pdf. opens in new tab).

9. Greenberg PL, Tuechler H, Schanz J, et al. Revised international prognostic scoring system for myelodysplastic syndromes. Blood 2012;120:2454-2465.

10. Ley TJ, Mardis ER, Ding L, et al. DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 2008;456:66-72.

11. Ley TJ, Miller C, Ding L, et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med 2013;368:2059-2074.

12. Mardis ER. The $1,000 genome, the $100,000 analysis? Genome Med 2010;2:84-84.

13. Mardis ER, Ding L, Dooling DJ, et al. Recurring mutations found by sequencing an acute myeloid leukemia genome. N Engl J Med 2009;361:1058-1066.

14. Welch JS, Ley TJ, Link DC, et al. The origin and evolution of mutations in acute myeloid leukemia. Cell 2012;150:264-278.

15. Bolouri H, Farrar JE, Triche T Jr, et al. The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural alterations and age-specific mutational interactions. Nat Med 2018;24:103-112.

16. Rusch M, Nakitandwe J, Shurtleff S, et al. Clinical cancer genomic profiling by three-platform sequencing of whole genome, whole exome and transcriptome. Nat Commun 2018;9:3962-3962.

17. Duncavage EJ, Jacoby MA, Chang GS, et al. Mutation clearance after transplantation for myelodysplastic syndrome. N Engl J Med 2018;379:1028-1041.

18. Huret J-L, Ahmad M, Arsaban M, et al. Atlas of genetics and cytogenetics in oncology and haematology in 2013. Nucleic Acids Res 2013;41:(D1):D920-D924.

19. Arber DA, Orazi A, Hasserjian R, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood 2016;127:2391-2405.

20. Griffith M, Miller CA, Griffith OL, et al. Optimizing cancer genome sequencing and analysis. Cell Syst 2015;1:210-223.

21. Rack KA, van den Berg E, Haferlach C, et al. European recommendations and quality assurance for cytogenomic analysis of haematological neoplasms. Leukemia 2019;33:1851-1867.

22. Byrd JC, Mrózek K, Dodge RK, et al. Pretreatment cytogenetic abnormalities are predictive of induction success, cumulative incidence of relapse, and overall survival in adult patients with de novo acute myeloid leukemia: results from Cancer and Leukemia Group B (CALGB 8461). Blood 2002;100:4325-4336.

23. Lazarevic V, Hörstedt A-S, Johansson B, et al. Failure matters: unsuccessful cytogenetics and unperformed cytogenetics are associated with a poor prognosis in a population-based series of acute myeloid leukaemia. Eur J Haematol 2015;94:419-423.

24. Medeiros BC, Othus M, Estey EH, Fang M, Appelbaum FR. Unsuccessful diagnostic cytogenetic analysis is a poor prognostic feature in acute myeloid leukaemia. Br J Haematol 2014;164:245-250.

25. Wetterstrand KA. DNA sequencing costs: data from the NHGRI Genome Sequencing Program (GSP). National Human Genome Research Institute, 2020 (www.genome.gov/sequencingcostsdata. opens in new tab).

26. Cressman S, Karsan A, Hogge DE, et al. Economic impact of genomic diagnostics for intermediate-risk acute myeloid leukaemia. Br J Haematol 2016;174:526-535.

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