Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Acute myeloid leukemia

CD34+CD38− leukemic stem cell frequency to predict outcome in acute myeloid leukemia

Leukemia volume 33, pages 1102–1112 (2019)Cite this article

Subjects

Abstract

Current risk algorithms are primarily based on pre-treatment factors and imperfectly predict outcome in acute myeloid leukemia (AML). We introduce and validate a post-treatment approach of leukemic stem cell (LSC) assessment for prediction of outcome. LSC containing CD34+CD38− fractions were measured using flow cytometry in an add-on study of the HOVON102/SAKK trial. Predefined cut-off levels were prospectively evaluated to assess CD34+CD38−LSC levels at diagnosis (n = 594), and, to identify LSClow/LSChigh (n = 302) and MRDlow/MRDhigh patients (n = 305) in bone marrow in morphological complete remission (CR). In 242 CR patients combined MRD and LSC results were available. At diagnosis the CD34+CD38− LSC frequency independently predicts overall survival (OS). After achieving CR, combining LSC and MRD showed reduced survival in MRDhigh/LSChigh patients (hazard ratio [HR] 3.62 for OS and 5.89 for cumulative incidence of relapse [CIR]) compared to MRDlow/LSChigh, MRDhigh/LSClow, and especially MRDlow/LSClow patients. Moreover, in the NPM1mutant positive sub-group, prognostic value of golden standard NPM1-MRD by qPCR can be improved by addition of flow cytometric approaches. This is the first prospective study demonstrating that LSC strongly improves prognostic impact of MRD detection, identifying a patient subgroup with an almost 100% treatment failure probability, warranting consideration of LSC measurement incorporation in future AML risk schemes.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Hokland P, Ommen HB, Mulè MP, Hourigan CS. Advancing the Mrd concept in acute myeloid. Semin Hematol. 2015;52:184–92.

    Article  Google Scholar 

  2. Terwijn M, van Putten WLJ, Kelder A, van der Velden VHJ, Brooimans RA, Pabst T, et al. High prognostic impact of flow cytometric minimal residual disease detection in acute myeloid leukemia: data from the HOVON/SAKK AML 42A study. J Clin Oncol. 2013;31:3889–97.

    Article  Google Scholar 

  3. Freeman SD, Virgo P, Couzens S, Grimwade D, Russell N, Hills RK, et al. Prognostic relevance of treatment response measured by flow cytometric residual disease detection in older patients with acute myeloid leukemia. J Clin Oncol. 2013;31:4123–31.

    Article  Google Scholar 

  4. Loken MR, Alonzo Ta, Pardo L, Gerbing RB, Raimondi SC, Hirsch BA, et al. Residual disease detected by multidimensional flow cytometry signifies high relapse risk in patients with de novo acute myeloid leukemia: a report from Children’s Oncology Group. Blood. 2012;120:1581–8.

    Article  Google Scholar 

  5. Zhu H, Zhang X, Qin Y, Liu D, Jiang H, Chen H, et al. MRD-directed risk stratification treatment may improve outcomes of t(8 ;21) AML in the first complete remission: results from the AML05 multicenter trial. Blood. 2013;121:4056–62.

    Article  CAS  Google Scholar 

  6. 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;374:422–33.

    Article  CAS  Google Scholar 

  7. Bonnet D, Dick JE. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat Med. 1997;3:730–7.

    Article  CAS  Google Scholar 

  8. Becker MW, Jordan CT. Leukemia stem cells in 2010: current understanding and future directions. Blood Rev. 2011;25:75–81.

    Article  CAS  Google Scholar 

  9. Taussig DC, Vargaftig J, Miraki-Moud F, Griessinger E, Sharrock K, Luke T, et al. Leukemia-initiating cells from some acute myeloid leukemia patients with mutated nucleophosmin reside in the CD34(-) fraction. Blood. 2010;115:1976–84.

    Article  CAS  Google Scholar 

  10. Terwijn M, Zeijlemaker W, Kelder A, Rutten AP, Snel AN, Scholten WJ, et al. Leukemic stem cell frequency: a strong biomarker for clinical outcome in acute Myeloid Leukemia. PLoS ONE. 2014;9:e107587.

    Article  Google Scholar 

  11. Goardon N, Marchi E, Atzberger A, Quek L, Schuh A, Soneji S, et al. Coexistence of LMPP-like and GMP-like leukemia stem cells in acute myeloid leukemia. Cancer Cell. 2011;19:138–52.

    Article  CAS  Google Scholar 

  12. Sarry J-E, Murphy K, Perry R, Sanchez PV, Secreto A, Keefer C, et al. Human acute myelogenous leukemia stem cells are rare and heterogeneous when assayed in NOD/SCID/IL2Rγc-deficient mice. J Clin Invest. 2011;121:384–95.

    Article  CAS  Google Scholar 

  13. Ng SWK, Mitchell A, Kennedy Ja, Chen WC, McLeod J, Ibrahimova N, et al. A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature. 2016;540:433–7.

    Article  CAS  Google Scholar 

  14. Ishikawa F, Yoshida S, Saito Y, Hijikata A, Kitamura H, Tanaka S, et al. Chemotherapy-resistant human AML stem cells home to and engraft within the bone-marrow endosteal region. Nat Biotechnol. 2007;25:1315–21.

    Article  CAS  Google Scholar 

  15. Costello RT, Mallet F, Gaugler B, Sainty D, Arnoulet C, Gastaut JA, et al. Human acute myeloid leukemia CD34+/CD38- progenitor cells have decreased sensitivity to chemotherapy and Fas-induced apoptosis, reduced immunogenicity, and impaired dendritic cell transformation capacities. Cancer Res. 2000;60:4403–11.

    CAS  PubMed  Google Scholar 

  16. Bradbury C, Houlton aE, Akiki S, Gregg R, Rindl M, Khan J, et al. Prognostic value of monitoring a candidate immunophenotypic leukaemic stem/progenitor cell population in patients allografted for acute myeloid leukaemia. Leukemia. 2014;9:1–4.

    Google Scholar 

  17. Jentzsch M, Bill M, Nicolet D, Leiblein S, Schubert K, Pless M, et al. Prognostic impact of the CD34+/CD38- cell burden in patients with acute myeloid leukemia receiving allogeneic stem cell transplantation. Am J Hematol. 2017;92:388–96.

    Article  CAS  Google Scholar 

  18. Feller N, van der Pol Ma, van Stijn A, Weijers GWD, Westra aH, Evertse BW, et al. MRD parameters using immunophenotypic detection methods are highly reliable in predicting survival in acute myeloid leukaemia. Leukemia. 2004;18:1380–90.

    Article  CAS  Google Scholar 

  19. Löwenberg B, Pabst T, Maertens J, van Norden Y, Biemond BJ, Schouten HC, et al. Therapeutic value of clofarabine in younger and middle aged (18 – 65 yrs) adults with newly diagnosed AML. Blood. 2017;129:1636–45.

    Article  Google Scholar 

  20. Zeijlemaker W, Kelder A, Wouters R, Valk PJM, Witte BI, Cloos J, et al. Absence of leukaemic CD34+ cells in acute myeloid leukaemia is of high prognostic value: a longstanding controversy deciphered. Br J Haematol. 2015;171:227–38.

    Article  CAS  Google Scholar 

  21. Cloos J, Harris J, Janssen JJ, Kelder A, Huang F, Sijm G, et al. Comprehensive protocol to sample and process bone marrow for measuring measurable residual disease and leukemic stem cells in acute myeloid leukemia. J Vis Exp. 2018; 133. https://doi.org/10.3791/56386.

  22. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94:496–509.

    Article  Google Scholar 

  23. Falini B, Mecucci C, Tiacci E, Alcalay M, Rosati R, Pasqualucci L, et al. Cytoplasmic nucleophosmin in acute myelogenous leukemia with a normal karyotype. N Engl J Med. 2005;352:254–66.

    Article  CAS  Google Scholar 

  24. Tsykunova G, Reikvam H, Hovland R, Bruserud Ø. The surface molecule signature of primary human acute myeloid leukemia (AML) cells is highly associated with NPM1 mutation status. Leukemia. 2012;26:557–9.

    Article  CAS  Google Scholar 

  25. Zeijlemaker W, Gratama JW, Schuurhuis GJ. Tumor heterogeneity makes AML a ‘moving target’ for detection of residual disease. Cytom B Clin Cytom. 2014;86:3–14.

    Article  CAS  Google Scholar 

  26. Chen X, Xie H, Wood BL, Walter RB, Pagel JM, Becker PS, et al. Relation of clinical response and minimal residual disease and their prognostic impact on outcome in acute myeloid leukemia. J Clin Oncol. 2015;33:1258–64.

    Article  Google Scholar 

  27. Schuurhuis GJ, Ossenkoppele G. Minimal residual disease in acute myeloid leukemia: already predicting a safe haven? Expert Rev Hematol. 2010;3:1–5.

    Article  Google Scholar 

  28. Khan N, Freeman SD, Virgo P, Couzens S, Richardson P, Thomas I, et al. An immunophenotypic pre-treatment predictor for poor response to induction chemotherapy in older acute myeloid leukaemia patients: blood frequency of CD34+CD38 low blasts. Br J Haematol. 2015;170:80–4.

    Article  CAS  Google Scholar 

  29. Van Rhenen A, Feller N, Kelder A, Westra AH, Rombouts E, Zweegman S, et al. High stem cell frequency in acute myeloid leukemia at diagnosis predicts high minimal residual disease and poor survival. Clin Cancer Res. 2005;11:6520–7.

    Article  Google Scholar 

  30. Witte K-E, Ahlers J, Schäfer I, André M, Kerst G, Scheel-Walter H-G, et al. High proportion of leukemic stem cells at diagnosis is correlated with unfavorable prognosis in childhood acute myeloid leukemia. Pediatr Hematol Oncol. 2011;28:91–9.

    Article  CAS  Google Scholar 

  31. Hwang K, Park C-J, Jang S, Chi H-S, Kim D-Y, Lee J-H, et al. Flow cytometric quantification and immunophenotyping of leukemic stem cells in acute myeloid leukemia. Ann Hematol. 2012;91:1541–6.

    Article  Google Scholar 

  32. Jongen-Lavrencic M, Grob T, Hanekamp D, Kavelaars FG, al Hinai A, Zeilemaker A, et al. Molecular minimal residual disease in acute myeloid leukemia. N Engl J Med. 2018;378:1189–99.

    Article  CAS  Google Scholar 

  33. Quek L, Otto GW, Garnett C, Lhermitte L, Karamitros D, Stoilova B, et al. Genetically distinct leukemic stem cells in human CD34 − acute myeloid leukemia are arrested at a hemopoietic precursor-like stage. J Exp Med. 2016;213:1513–35.

    Article  CAS  Google Scholar 

  34. Walter RB, Buckley SA, Pagel JM, Wood BL, Storer BE, Sandmaier BM, et al. Significance of minimal residual disease before myeloablative allogeneic hematopoietic cell transplantation for AML in first and second complete remission. Blood. 2013;122:1813–22.

    Article  CAS  Google Scholar 

  35. Ding L, Ley TJ, Larson DE, Miller CA, Koboldt DC, Welch JS, et al. Clonal evolution in relapsed acute myeloid leukemia revealed by whole genome sequencing. Nature. 2012;481:506–10.

    Article  CAS  Google Scholar 

  36. Bachas C, Schuurhuis GJ, Assaraf YG, Kwidama ZJ, Kelder A, Wouters F, et al. The role of minor subpopulations within the leukemic blast compartment of AML patients at initial diagnosis in the development of relapse. Leukemia. 2012;26:1313–20.

    Article  CAS  Google Scholar 

  37. Bachas C, Schuurhuis GJ, IHIM Hollink, Kwidama ZJ, Goemans BF, Zwaan CM, et al. High-frequency type I/II mutational shifts between diagnosis and relapse are associated with outcome in pediatric AML: implications for personalized medicine. Blood. 2010;116:2752–8.

    Article  CAS  Google Scholar 

  38. Wang ES, Sait SNJ, Gold D, Mashtare T, Starostik P, Ford LA, et al. Genomic, immunophenotypic, and NPM1/FLT3 mutational studies on 17 patients with normal karyotype acute myeloid leukemia (AML) followed by aberrant karyotype AML at relapse. Cancer Genet Cytogenet. 2010;202:101–7.

    Article  CAS  Google Scholar 

  39. Ottone T, Zaza S, Divona M, Hasan SK, Lavorgna S, Laterza S, et al. Identification of emerging FLT3 ITD-positive clones during clinical remission and kinetics of disease relapse in acute myeloid leukaemia with mutated nucleophosmin. Br J Haematol. 2013. https://doi.org/10.1111/bjh.12288.

    Article  CAS  Google Scholar 

  40. Zeijlemaker W, Kelder A, Oussoren-Brockhoff YJM, Scholten WJ, Snel aN, Veldhuizen D, et al. A simple one-tube assay for immunophenotypical quantification of leukemic stem cells in acute myeloid leukemia. Leukemia. 2016;30:439–46.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank all participating HOVON/SAKK study centers, and the patients herein.

Author information

Author notes

  1. These authors contributed equally: Tim Grob, Rosa Meijer, Diana Hanekamp

  2. These authors contributed equally: Peter J. M. Valk, Mojca Jongen-Lavrencic

  3. These authors contributed equally: Gert J. Ossenkoppele, Gerrit J. Schuurhuis

Authors and Affiliations

  1. Department of Hematology, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, The Netherlands

    Wendelien Zeijlemaker, Diana Hanekamp, Angèle Kelder, Jannemieke C. Carbaat-Ham, Yvonne J. M. Oussoren-Brockhoff, Alexander N. Snel, Dennis Veldhuizen, Willemijn J. Scholten, Jacqueline Cloos, Arjan A. van de Loosdrecht, Gert J. Ossenkoppele & Gerrit J. Schuurhuis

  2. Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands

    Tim Grob, Bob Löwenberg, Peter J. M. Valk & Mojca Jongen-Lavrencic

  3. Clinical trial Center- HOVON data center, Erasmus University Medical Center, Rotterdam, The Netherlands

    Rosa Meijer

  4. Department of Hematology, University Hospitals Leuven, Campus Gasthuisberg, Leuven, Belgium

    Johan Maertens

  5. Department of Hematology, Ziekenhuis Netwerk Antwerpen, Antwerp, Belgium

    Dimitri A. Breems

  6. Department of Hematology, Inselspital, Bern University Hospital, Bern, Switzerland

    Thomas Pabst

  7. Department of Hematology, University and University Hospital Zürich, Zürich, Switzerland

    Markus G. Manz

  8. Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands

    Vincent H. J. van der Velden

  9. Department of Clinical Chemistry, Medisch Spectrum Twente/Medlon, Enschede, The Netherlands

    Jennichjen Slomp

  10. Department of Laboratory Medicine – Laboratory for Hematology, Radboud University Nijmegen Medical Center, RUNMC, Nijmegen, The Netherlands

    Frank Preijers

  11. Department of Pediatric Oncology/Hematology, VU University Medical Center, Amsterdam, The Netherlands

    Jacqueline Cloos

Authors
  1. Wendelien Zeijlemaker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tim Grob
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rosa Meijer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Diana Hanekamp
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Angèle Kelder
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jannemieke C. Carbaat-Ham
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yvonne J. M. Oussoren-Brockhoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Alexander N. Snel
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Dennis Veldhuizen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Willemijn J. Scholten
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Johan Maertens
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Dimitri A. Breems
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Thomas Pabst
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Markus G. Manz
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Vincent H. J. van der Velden
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Jennichjen Slomp
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Frank Preijers
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Jacqueline Cloos
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Arjan A. van de Loosdrecht
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Bob Löwenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Peter J. M. Valk
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Mojca Jongen-Lavrencic
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Gert J. Ossenkoppele
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Gerrit J. Schuurhuis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gerrit J. Schuurhuis.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zeijlemaker, W., Grob, T., Meijer, R. et al. CD34+CD38− leukemic stem cell frequency to predict outcome in acute myeloid leukemia. Leukemia 33, 1102–1112 (2019). https://doi.org/10.1038/s41375-018-0326-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41375-018-0326-3

This article is cited by

Search

Advanced search

Quick links