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Acute myeloid leukemia

DNMT3A mutant transcript levels persist in remission and do not predict outcome in patients with acute myeloid leukemia

Abstract

We investigated the prognostic impact of minimal residual disease (MRD) monitoring in acute myeloid leukemia patients harboring DNA methyltransferase 3A-R882H/-R882C mutations (DNMT3Amut). MRD was determined by real-time quantitative PCR (RQ-PCR) in 1494 samples of 181 DNMT3Amut patients. At the time of diagnosis, DNMT3Amut transcript levels did not correlate with presenting clinical characteristics and concurrent gene mutations as well as the survival end points. In Cox regression analyses, bone marrow (BM) DNMT3Amut transcript levels (log10-transformed continuous variable) were not associated with the rate of relapse or death. DNMT3Amut transcript levels were significantly higher in BM than in blood after induction I (P=0.01), induction II (P=0.05), consolidation I (P=0.004) and consolidation II (P=0.008). With regard to the clinically relevant MRD time points, after two cycles of induction and at the end of therapy, DNMT3Amut transcript levels had no impact on the end point remission duration and overall survival. Of note, only a minority of the patients achieved RQ-PCR negativity, whereas most had constantly high DNMT3Amut transcript levels, a finding which is consistent with the persistence of clonal hematopoiesis in hematological remission.

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References

  1. Döhner H, Estey E, Grimwade D, Amadori S, Appelbaum FR, Büchner T et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood 2017; 129: 424–447.

    Article  Google Scholar 

  2. Döhner H, Weisdorf DJ, Bloomfield CD . Acute myeloid leukemia. N Engl J Med 2015; 373: 1136–1152.

    Article  Google Scholar 

  3. Grimwade D, Freeman SD . Defining minimal residual disease in acute myeloid leukemia: which platforms are ready for ‘prime time’? Hematology Am Soc Hematol Educ Program 2014; 2014: 222–233.

    Article  Google Scholar 

  4. Bullinger L, Döhner K, Döhner H . Genomics of acute myeloid leukemia diagnosis and pathways. J Clin Oncol 2017; 35: 934–946.

    Article  CAS  Google Scholar 

  5. Inaba H, Coustan-Smith E, Cao X, Pounds SB, Shurtleff SA, Wang KY et al. Comparative analysis of different approaches to measure treatment response in acute myeloid leukemia. J Clin Oncol 2012; 30: 3625–3632.

    Article  Google Scholar 

  6. Haferlach T, Meggendorfer M, Schnittger S, Fasan A, Kern W, Haferlach C . Clinical impact of minimal residual disease (MRD) monitoring in AML with PML-Rara, CBFB-MYH11, and RUNX1-RUNX1T1: a study on 600 patients. Blood 2015; 126: (abstract 228).

  7. Sanz MA, Grimwade D, Tallman MS, Lowenberg B, Fenaux P, Estey EH et al. Management of acute promyelocytic leukemia: recommendations from an expert panel on behalf of the European LeukemiaNet. Blood 2009; 113: 1875–1891.

    Article  CAS  Google Scholar 

  8. Corbacioglu A, Scholl C, Schlenk RF, Eiwen K, Du J, Bullinger L et al. Prognostic impact of minimal residual disease in CBFB-MYH11-positive acute myeloid leukemia. J Clin Oncol 2010; 28: 3724–3729.

    Article  CAS  Google Scholar 

  9. Yin JA, O'Brien MA, Hills RK, Daly SB, Wheatley K, Burnett AK . Minimal residual disease monitoring by quantitative RT-PCR in core binding factor AML allows risk stratification and predicts relapse: results of the United Kingdom MRC AML-15 trial. Blood 2012; 120: 2826–2835.

    Article  CAS  Google Scholar 

  10. Jourdan E, Boissel N, Chevret S, Delabesse E, Renneville A, Cornillet P et al. Prospective evaluation of gene mutations and minimal residual disease in patients with core binding factor acute myeloid leukemia. Blood 2013; 121: 2213–2223.

    Article  CAS  Google Scholar 

  11. Krönke J, Schlenk RF, Jensen KO, Tschürtz F, Corbacioglu A, Gaidzik VI et al. Monitoring of minimal residual disease in NPM1-mutated acute myeloid leukemia: a study from the German-Austrian acute myeloid leukemia study group. J Clin Oncol 2011; 29: 2709–2716.

    Article  Google Scholar 

  12. Schnittger S, Kern W, Tschulik C, Weiss T, Dicker F, Falini B et al. Minimal residual disease levels assessed by NPM1 mutation-specific RQ-PCR provide important prognostic information in AML. Blood 2009; 114: 2220–2231.

    Article  CAS  Google Scholar 

  13. Ivey A, Hills RK, Simpson MA, Jovanovic JV, Gilkes A, Grech A et al. Assessment of minimal residual disease in standard-risk AML. N Engl J Med 2016; 374: 422–433.

    Article  CAS  Google Scholar 

  14. Krönke J, Bullinger L, Teleanu V, Tschürtz F, Gaidzik VI, Kühn MW et al. Clonal evolution in relapsed NPM1-mutated acute myeloid leukemia. Blood 2013; 122: 100–108.

    Article  Google Scholar 

  15. Gaidzik VI, Schlenk RF, Paschka P, Stölzle A, Späth D, Kuendgen A et al. Clinical impact of DNMT3A mutations in younger adult patients with acute myeloid leukemia: results of the AML Study Group (AMLSG). Blood 2013; 121: 4769–4777.

    Article  CAS  Google Scholar 

  16. Ley TJ, Ding L, Walter MJ, McLellan MD, Lamprecht T, Larson DE et al. DNMT3A mutations in acute myeloid leukemia. N Engl J Med 2010; 363: 2424–2433.

    Article  CAS  Google Scholar 

  17. Thol F, Damm F, Lüdeking A, Winschel C, Wagner K, Morgan M et al. Incidence and prognostic influence of DNMT3A mutations in acute myeloid leukemia. J Clin Oncol 2011; 29: 2889–2896.

    Article  CAS  Google Scholar 

  18. Marková J, Michková P, Burčková K, Březinová J, Michalová K, Dohnalová A et al. Prognostic impact of DNMT3A mutations in patients with intermediate cytogenetic risk profile acute myeloid leukemia. Eur J Haematol 2011; 88: 128–135.

    Article  Google Scholar 

  19. LaRochelle O, Bertoli S, Vergez F, Sarry JE, Mansat-De Mas V, Dobbelstein S et al. Do AML patients with DNMT3A exon 23 mutations benefit from idarubicin as compared to daunorubicin? A single center experience. Oncotarget 2011; 2: 850–861.

    Article  Google Scholar 

  20. Renneville A, Boissel N, Nibourel O, Berthon C, Helevaut N, Gardin C et al. Prognostic significance of DNA methyltransferase 3A mutations in cytogenetically normal acute myeloid leukemia: a study by the Acute Leukemia French Association. Leukemia 2012; 26: 1247–1254.

    Article  CAS  Google Scholar 

  21. Hou HA, Kuo YY, Liu CY, Chou WC, Lee MC, Chen CY et al. DNMT3A mutations in acute myeloid leukemia: stability during disease evolution and clinical implications. Blood 2011; 119: 559–568.

    Article  Google Scholar 

  22. Ribeiro AF, Pratcorona M, Erpelinck-Verschueren C, Rockova V, Sanders M, Abbas S et al. Mutant DNMT3A: a marker of poor prognosis in acute myeloid leukemia. Blood 2012; 119: 5824–5831.

    Article  CAS  Google Scholar 

  23. Marcucci G, Metzeler KH, Schwind S, Becker H, Maharry K, Mrózek K et al. Age-related prognostic impact of different types of DNMT3A mutations in adults with primary cytogenetically normal acute myeloid leukemia. J Clin Oncol 2012; 30: 742–750.

    Article  Google Scholar 

  24. Ostronoff F, Othus M, Ho PA, Kutny M, Geraghty DE, Petersdorf SH et al. Mutations in the DNMT3A exon 23 independently predict poor outcome in older patients with acute myeloid leukemia: a SWOG report. Leukemia 2013; 27: 238–241.

    Article  CAS  Google Scholar 

  25. Corces-Zimmerman MR, Hong WJ, Weissman IL, Medeiros BC, Majeti R . Preleukemic mutations in human acute myeloid leukemia affect epigenetic regulators and persist in remission. Proc Natl Acad Sci USA 2014; 111: 2548–2553.

    Article  CAS  Google Scholar 

  26. Jan M, Snyder TM, Corces-Zimmerman MR, Vyas P, Weissman IL, Quake SR et al. Clonal evolution of preleukemic hematopoietic stem cells precedes human acute myeloid leukemia. Sci Transl Med 2012; 4: 149ra118.

    Article  Google Scholar 

  27. Welch JS, Ley TJ, Link DC, Miller CA, Larson DE, Koboldt DC et al. The origin and evolution of mutations in acute myeloid leukemia. Cell 2012; 150: 264–278.

    Article  CAS  Google Scholar 

  28. Ding L, Ley TJ, Larson DE, Miller CA, Koboldt DC, Welch JS et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 2012; 481: 506–510.

    Article  CAS  Google Scholar 

  29. Shlush LI, Zandi S, Mitchell A, Chen WC, Brandwein JM, Gupta V et al. Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia. Nature 2014; 506: 328–333.

    Article  CAS  Google Scholar 

  30. Xie M, Lu C, Wang J, McLellan MD, Johnson KJ, Wendl MC et al. Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nat Med 2014; 20: 1472–1478.

    Article  CAS  Google Scholar 

  31. Genovese G, Kähler AK, Handsaker RE, Lindberg J, Rose SA, Bakhoum SF et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N Engl J Med 2014; 371: 2477–2487.

    Article  Google Scholar 

  32. Jaiswal S, Fontanillas P, Flannick J, Manning A, Grauman PV, Mar BG et al. Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med 2014; 371: 2488–2498.

    Article  Google Scholar 

  33. Schlenk RF, Döhner K, Mack S, Stoppel M, Király F, Götze K et al. Prospective evaluation of allogeneic hematopoietic stem-cell transplantation from matched related and matched unrelated donors in younger adults with high-risk acute myeloid leukemia: German-Austrian trial AMLHD98A. J Clin Oncol 2010; 28: 4642–4648.

    Article  Google Scholar 

  34. Schlenk RF, Lübbert M, Benner A, Lamparter A, Krauter J, Herr W et al. All-trans retinoic acid as adjunct to intensive treatment in younger adult patients with acute myeloid leukemia: results of the randomized AMLSG 07-04 study. Ann Hematol 2016; 95: 1931–1942.

    Article  CAS  Google Scholar 

  35. Simons A, Shaffer LG, Hastings RJ . Cytogenetic nomenclature: changes in the ISCN 2013 compared to the 2009 edition. Cytogenet Genome Res 2013; 141: 1–6.

    Article  CAS  Google Scholar 

  36. Schlenk RF, Döhner K, Krauter J, Fröhling S, Corbacioglu A, Bullinger L et al. Mutations and treatment outcome in cytogenetically normal acute myeloid leukemia. N Engl J Med 2008; 358: 1909–1918.

    Article  CAS  Google Scholar 

  37. Beillard E, Pallisgaard N, van der Velden VH, Bi W, Dee R, van der Schoot E et al. Evaluation of candidate control genes for diagnosis and residual disease detection in leukemic patients using 'real-time' quantitative reverse-transcriptase polymerase chain reaction (RQ-PCR)—a Europe against cancer program. Leukemia 2003; 17: 2474–2486.

    Article  CAS  Google Scholar 

  38. Korn EL . Censoring distributions as a measure of follow-up in survival analysis. Stat Med 1986; 5: 255–260.

    Article  CAS  Google Scholar 

  39. Kaplan E, Meier P . Nonparametric estimation from incomplete observations. J Am Stat Assoc 1958; 53: 457–481.

    Article  Google Scholar 

  40. Therneau TM, Grambusch PM . Modeling Survival Data: Extending the Cox Model. Springer Verlag: New York, NY, USA, 2000.

    Book  Google Scholar 

  41. Cox DR . Regression models and life tables (with discussion). J R Stat Soc B 1972; 34: 187–220.

    Google Scholar 

  42. Harrell FE . Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer Verlag: New York, NY, USA, 2001.

    Book  Google Scholar 

  43. R Development Core Team R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria, 2009.

  44. Debarri H, Lebon D, Roumier C, Cheok M, Marceau-Renaut A, Nibourel O et al. IDH1/2 but not DNMT3A mutations are suitable targets for minimal residual disease monitoring in acute myeloid leukemia patients: a study by the Acute Leukemia French Association. Oncotarget 2015; 6: 42345–42353.

    Article  Google Scholar 

  45. Jeziskova I, Musilova M, Culen M, Foltankova V, Dvorakova D, Mayer J et al. Distribution of mutations in DNMT3A gene and the suitability of mutations in R882 codon for MRD monitoring in patients with AML. Int J Hematol 2015; 102: 553–557.

    Article  CAS  Google Scholar 

  46. Pløen GG, Nederby L, Guldberg P, Hansen M, Ebbesen LH, Jensen UB et al. Persistence of DNMT3A mutations at long-term remission in adult patients with AML. Br J Haematol 2014; 167: 478–486.

    Article  Google Scholar 

  47. Bhatnagar B, Eisfeld AK, Nicolet D, Mrózek K, Blachly JS, Orwick S et al. Persistence of DNMT3A R882 mutations during remission does not adversely affect outcomes of patients with acute myeloid leukaemia. Br J Haematol 2016; 175: 226–236.

    Article  CAS  Google Scholar 

  48. Brambati C, Galbiati S, Xue E, Toffalori C, Crucitti L, Greco R et al. Droplet digital PCR for DNMT3A and IDH1/2 mutations to improve early detection of acute myeloid leukemia relapse after allogeneic hematopoietic stem cell transplantation. Haematologica 2016; 101: e157–e161.

    Article  CAS  Google Scholar 

  49. Berenstein R, Blau IW, Suckert N, Baldus C, Pezzutto A, Dörken B et al. Quantitative detection of DNMT3A R882H mutation in acute myeloid leukemia. J Exp Clin Cancer Res 2015; 34: 55.

    Article  Google Scholar 

  50. Göhring G, Thomay K, Schmidt G, Ripperger T, Xu M, Wittner N et al. A common ancestral DNMT3A-mutated preleukemic clone giving rise to AML and MDS in an adolescent girl. Leuk Lymphoma 2017; 58: 718–721.

    Article  Google Scholar 

  51. Thol F, Klesse S, Köhler L, Gabdoulline R, Kloos A, Liebich A et al. Acute myeloid leukemia derived from lympho-myeloid clonal hematopoiesis. Leukemia 2017; 31: 1286–1295.

    Article  CAS  Google Scholar 

  52. Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND et al. Genomic classification and prognosis in acute myeloid leukemia. N Engl J Med 2016; 374: 2209–2221.

    Article  CAS  Google Scholar 

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Acknowledgements

This work was supported in part by grants 01GI9981 and 01KG0605 from the German Bundesministerium für Bildung und Forschung (BMBF), grant 109675 from the Deutsche Krebshilfe and the Sonderforschungsbereich (SFB) 1074 funded by the Deutsche Forschungsgemeinschaft (SFB 1074, projects B3 and B4). VG is a grant recipient of the Else-Kröner-Forschungskolleg; LB and MH are Heisenberg Professors of the Deutsche Forschungsgemeinschaft (DFG, BU 1339/3-1 and HE 5240-6-1). AMLSG treatment trials were in part supported by Pfizer and Amgen. We are grateful to all members of the German-Austrian AML Study Group (AMLSG) for their participation in this study and providing patient samples; a list of participating institutions and investigators is provided in the Supplementary Appendix.

Author contributions

VIG, DW, RFS, KD and HD designed the research; VIG, PP, AK, SK, AC, JK and SKS performed experiments; VIG, VT, PP, AK, SK, AC, JK, SKS and KD analyzed the results; DW and RFS performed statistical analyses; DK, HAH, ISW, GH, AK, MR, KG, TK, WF, MW, LB, VT, BS, FT, MH, AG, RFS, HD and KD accrued patients and provided material; VIG, DW, HD and KD wrote the paper.

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Correspondence to K Döhner.

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Presented in abstract form at the 57th Annual Meeting of the American Society of Hematology, Orlando, FL, USA on 6 December 2015.

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Gaidzik, V., Weber, D., Paschka, P. et al. DNMT3A mutant transcript levels persist in remission and do not predict outcome in patients with acute myeloid leukemia. Leukemia 32, 30–37 (2018). https://doi.org/10.1038/leu.2017.200

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