SpringerLink
Log in

Predicting Chemotherapy Resistance in AML

  • Acute Myeloid Leukemias (H Erba, Section Editor)
  • Published:
Current Hematologic Malignancy Reports Aims and scope Submit manuscript

Abstract

Acute myeloid leukemia (AML) is the most common type of acute leukemia and is characterized by a wide variety of distinct cytogenetic and molecular subgroups that impact upon response and survival. Despite decades of trials trying to improve short- and long-term responses, outcomes have been stubbornly stable. Thus, the problem of chemotherapy resistance remains unsolved. Indeed, the use of novel targeted agents has uncovered new mechanisms of chemotherapy resistance. In this review, we will cover past work of primary treatment resistance, new resistance mechanisms in de novo AML, functional mechanism of resistance inherent to the leukemic blasts, and resistance mechanisms attributed to host factors and also review acquired mechanisms of resistance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Canada)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Kayser S, Zucknick M, Dohner K, et al. Monosomal karyotype in adult acute myeloid leukemia: prognostic impact and outcome after different treatment strategies. Blood. 2012;119(2):551–8.

    Article  CAS  PubMed  Google Scholar 

  2. Burnett AK, Russell NH, Hills RK, et al. Optimization of chemotherapy for younger patients with acute myeloid leukemia: results of the medical research council AML15 trial. J Clin Oncol. 2013;31(27):3360–8.

    Article  CAS  PubMed  Google Scholar 

  3. Othus M, Kantarjian H, Petersdorf S, et al. Declining rates of treatment-related mortality in patients with newly diagnosed AML given ‘intense’ induction regimens: a report from SWOG and MD Anderson. Leukemia. 2014;28(2):289–92.

    Article  CAS  PubMed  Google Scholar 

  4. Patel JP, Gonen M, Figueroa ME, et al. Prognostic relevance of integrated genetic profiling in acute myeloid leukemia. N Engl J Med. 2012;366(12):1079–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Dohner H, Estey EH, Amadori S, et al. Diagnosis and management of acute myeloid leukemia in adults: recommendations from an international expert panel, on behalf of the European LeukemiaNet. Blood. 2010;115(3):453–74.

    Article  PubMed  Google Scholar 

  6. Cheson BD, Bennett JM, Kopecky KJ, et al. Revised recommendations of the International Working Group for Diagnosis, standardization of response criteria, treatment outcomes, and reporting standards for therapeutic trials in acute myeloid leukemia. J Clin Oncol. 2003;21(24):4642–9.

    Article  PubMed  Google Scholar 

  7. Walter RB, Gyurkocza B, Storer BE, et al. Comparison of minimal residual disease as outcome predictor for AML patients in first complete remission undergoing myeloablative or nonmyeloablative allogeneic hematopoietic cell transplantation. Leukemia. 2015a;29(1):137–44.

    Article  CAS  PubMed  Google Scholar 

  8. Schneider T, Florcken A, Singh A, et al. Flow cytometric maturity score as a novel prognostic parameter in patients with acute myeloid leukemia. Ann Hematol. 2015;94(8):1337–45.

    Article  CAS  PubMed  Google Scholar 

  9. Debarri H, Lebon D, Roumier C, 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(39):42345–53.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Leung W, Pui CH, Coustan-Smith E, et al. Detectable minimal residual disease before hematopoietic cell transplantation is prognostic but does not preclude cure for children with very-high-risk leukemia. Blood. 2012;120(2):468–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Campana D, Leung W. Clinical significance of minimal residual disease in patients with acute leukaemia undergoing haematopoietic stem cell transplantation. Br J Haematol. 2013;162(2):147–61.

    Article  PubMed  Google Scholar 

  12. Damdinsuren A, Matsushita H, Ito M, et al. FLT3-ITD drives Ara-C resistance in leukemic cells via the induction of RUNX3. Leuk Res. 2015;39(12):1405–13.

    Article  CAS  PubMed  Google Scholar 

  13. Abe M, Pelus LM, Singh P, et al. Internal tandem duplication in FLT3 attenuates proliferation and regulates resistance to the FLT3 inhibitor AC220 by modulating p21Cdkn1a and Pbx1 in hematopoietic cells. PLoS One. 2016;11(7):e0158290.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kiyoi H. FLT3 inhibitors: recent advances and Problems for clinical Application. Nagoya J Med Sci. 2015;77(1–2):7–17.

    PubMed  PubMed Central  Google Scholar 

  15. Smith CC, Lasater EA, Zhu X, et al. Activity of ponatinib against clinically-relevant AC220-resistant kinase domain mutants of FLT3-ITD. Blood. 2013;121(16):3165–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zimmerman EI, Turner DC, Buaboonnam J, et al. Crenolanib is active against models of drug-resistant FLT3-ITD-positive acute myeloid leukemia. Blood. 2013;122(22):3607–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Man CH, Fung TK, Ho C, et al. Sorafenib treatment of FLT3-ITD(+) acute myeloid leukemia: favorable initial outcome and mechanisms of subsequent nonresponsiveness associated with the emergence of a D835 mutation. Blood. 2012;119(22):5133–43.

    Article  CAS  PubMed  Google Scholar 

  18. Smith CC, Wang Q, Chin CS, et al. Validation of ITD mutations in FLT3 as a therapeutic target in human acute myeloid leukaemia. Nature. 2012;485(7397):260–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Smith CC, Lin K, Stecula A, et al. FLT3 D835 mutations confer differential resistance to type II FLT3 inhibitors. Leukemia. 2015;29(12):2390–2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Cher CY, Leung GM, Au CH, et al. Next-generation sequencing with a myeloid gene panel in core-binding factor AML showed KIT activation loop and TET2 mutations predictive of outcome. Blood Cancer J. 2016;6(7):e442.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chen W, **e H, Wang H, et al. Prognostic significance of KIT mutations in core-binding factor acute myeloid leukemia: a systematic review and meta-analysis. PLoS One. 2016;11(1):e0146614.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Au CH, Wa A, Ho DN, et al. Clinical evaluation of panel testing by next-generation sequencing (NGS) for gene mutations in myeloid neoplasms. Diagn Pathol. 2016;11:11.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Bartels S, Schipper E, Hasemeier B, et al. Routine clinical mutation profiling using next generation sequencing and a customized gene panel improves diagnostic precision in myeloid neoplasms. Oncotarget. 2016.

  24. Kuo YH, Qi J, Cook GJ. Regain control of p53: targeting leukemia stem cells by isoform-specific HDAC inhibition. Exp Hematol. 2016;44(5):315–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhu HH, Qin YZ, Huang XJ. Resistance to arsenic therapy in acute promyelocytic leukemia. N Engl J Med. 2014;370(19):1864–6.

    Article  CAS  PubMed  Google Scholar 

  26. Lou Y, Ma Y, Sun J, et al. Evaluating frequency of PML-RARA mutations and conferring resistance to arsenic trioxide-based therapy in relapsed acute promyelocytic leukemia patients. Ann Hematol. 2015;94(11):1829–37.

    Article  CAS  PubMed  Google Scholar 

  27. Lehmann-Che J, Bally C, de The H. Resistance to therapy in acute promyelocytic leukemia. N Engl J Med. 2014;371(12):1170–2.

    Article  PubMed  Google Scholar 

  28. Zahreddine HA, Borden KL. Molecular pathways: GLI1-induced drug glucuronidation in resistant cancer cells. Clin Cancer Res. 2015;21(10):2207–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Mehta SV, Shukla SN, Vora HH. Overexpression of Bcl2 protein predicts chemoresistance in acute myeloid leukemia: its correlation with FLT3. Neoplasma. 2013;60(6):666–75.

    Article  CAS  PubMed  Google Scholar 

  30. Khan N, Hills RK, Knapper S, et al. Normal hematopoietic progenitor subsets have distinct reactive oxygen species, BCL2 and cell-cycle profiles that are decoupled from maturation in acute myeloid leukemia. PLoS One. 2016;11(9):e0163291.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Eckford PD, Sharom FJ. ABC efflux pump-based resistance to chemotherapy drugs. Chem Rev. 2009;109(7):2989–3011.

    Article  CAS  PubMed  Google Scholar 

  32. • Bartholomae S, Gruhn B, Debatin KM, et al. Coexpression of multiple ABC-transporters is strongly associated with treatment response in childhood acute myeloid leukemia. Pediatr Blood Cancer. 2016;63(2):242–7. In this study, 112 patient samples with AML were study and the authors showed a strong association between overexpression of ABC transporter genes and the inability for a sustained remission

    Article  CAS  PubMed  Google Scholar 

  33. Leith CP, Kopecky KJ, Godwin J, et al. Acute myeloid leukemia in the elderly: assessment of multidrug resistance (MDR1) and cytogenetics distinguishes biologic subgroups with remarkably distinct responses to standard chemotherapy. A Southwest Oncology Group study. Blood. 1997;89(9):3323–9.

    CAS  PubMed  Google Scholar 

  34. Khamenehfar A, Gandhi MK, Chen Y, et al. Dielectrophoretic microfluidic chip enables single-cell measurements for multidrug resistance in heterogeneous acute myeloid leukemia patient samples. Anal Chem. 2016;88(11):5680–8.

    Article  CAS  PubMed  Google Scholar 

  35. Wang CC, Liu HE, Lee YL, et al. MPT0B169, a novel tubulin inhibitor, induces apoptosis in taxol-resistant acute myeloid leukemia cells through mitochondrial dysfunction and Mcl-1 downregulation. Tumour Biol. 2016a;37(5):6065–72.

    Article  CAS  PubMed  Google Scholar 

  36. Chou WC, Chou SC, Liu CY, et al. TET2 mutation is an unfavorable prognostic factor in acute myeloid leukemia patients with intermediate-risk cytogenetics. Blood. 2011;118(14):3803–10.

    Article  CAS  PubMed  Google Scholar 

  37. Moran-Crusio K, Reavie L, Shih A, et al. Tet2 loss leads to increased hematopoietic stem cell self-renewal and myeloid transformation. Cancer Cell. 2011;20(1):11–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Liu H, Cheng EH, Hsieh JJ. MLL fusions: pathways to leukemia. Cancer Biol Ther. 2009;8(13):1204–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Shlush LI, Zandi S, Mitchell A, et al. Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia. Nature. 2014;506(7488):328–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Yu DD, Lv MM, Chen WX, et al. Role of miR-155 in drug resistance of breast cancer. Tumour Biol. 2015;36(3):1395–401.

    Article  CAS  PubMed  Google Scholar 

  41. Monsalve M, Olmos Y. The complex biology of FOXO. Curr Drug Targets. 2011;12(9):1322–50.

    Article  CAS  PubMed  Google Scholar 

  42. Palma CA, Al Sheikha D, Lim TK, et al. MicroRNA-155 as an inducer of apoptosis and cell differentiation in acute myeloid leukaemia. Mol Cancer. 2014;13:79.

    Article  PubMed  PubMed Central  Google Scholar 

  43. •• Walter RB, Othus M, Burnett AK, et al. Resistance prediction in AML: analysis of 4601 patients from MRC/NCRI, HOVON/SAKK, SWOG and MD Anderson Cancer Center. Leukemia. 2015b;29(2):312–20. In this study, the authors reviewed 4601 patients and used area under receiver operator characteristic curves (AUC) in an attempt to predict resistance. However, their data showed only moderate ability for prediction based on routinely available pre-treatment covariates

    Article  CAS  PubMed  Google Scholar 

  44. Tiribelli M, Fabbro D, Franzoni A, et al. Q141K polymorphism of ABCG2 protein is associated with poor prognosis in adult acute myeloid leukemia treated with idarubicin-based chemotherapy. Haematologica. 2013;98(3):e28–9.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Medina-Sanson A, Ramirez-Pacheco A, Moreno-Guerrero SS, et al. Role of genetic polymorphisms of deoxycytidine kinase and cytidine deaminase to predict risk of death in children with acute myeloid leukemia. Biomed Res Int. 2015;2015:309491.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Angelini S, Soverini S, Ravegnini G, et al. Association between imatinib transporters and metabolizing enzymes genotype and response in newly diagnosed chronic myeloid leukemia patients receiving imatinib therapy. Haematologica. 2013;98(2):193–200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Tabe Y, Konopleva M. Role of microenvironment in resistance to therapy in AML. Curr Hematol Malig Rep. 2015;10(2):96–103.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Lee GY, Kim JA, Oh IH. Stem cell niche as a prognostic factor in leukemia. BMB Rep. 2015;48(8):427–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Rashidi A, Uy GL. Targeting the microenvironment in acute myeloid leukemia. Curr Hematol Malig Rep. 2015;10(2):126–31.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Kojima K, McQueen T, Chen Y, et al. p53 activation of mesenchymal stromal cells partially abrogates microenvironment-mediated resistance to FLT3 inhibition in AML through HIF-1alpha-mediated down-regulation of CXCL12. Blood. 2011;118(16):4431–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Figueroa ME, Lugthart S, Li Y, et al. DNA methylation signatures identify biologically distinct subtypes in acute myeloid leukemia. Cancer Cell. 2010;17(1):13–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Spencer DH, Young MA, Lamprecht TL, et al. Epigenomic analysis of the HOX gene loci reveals mechanisms that may control canonical expression patterns in AML and normal hematopoietic cells. Leukemia. 2015;29(6):1279–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Marcucci G, Yan P, Maharry K, et al. Epigenetics meets genetics in acute myeloid leukemia: clinical impact of a novel seven-gene score. J Clin Oncol. 2014;32(6):548–56.

    Article  PubMed  Google Scholar 

  54. Yan B, Hu Y, Ng C, et al. Coverage analysis in a targeted amplicon-based next-generation sequencing panel for myeloid neoplasms. J Clin Pathol. 2016.

  55. Ding L, Ley TJ, Larson DE, et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature. 2012;481(7382):506–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Walter RB, Laszlo GS, Lionberger JM, et al. Heterogeneity of clonal expansion and maturation-linked mutation acquisition in hematopoietic progenitors in human acute myeloid leukemia. Leukemia. 2014;28(10):1969–77.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. • Paguirigan AL, Smith J, Meshinchi S, et al. Single-cell genoty** demonstrates complex clonal diversity in acute myeloid leukemia. Sci Transl Med. 2015;7(281):281re2. This study is one of the first to utilize single-cell sequencing to prove AML is even more complex and clonally diverse than previously assumed. This study also showed how mutational clonal evolution occured in comparative samples from diaognosis to relapse

    Article  PubMed  PubMed Central  Google Scholar 

  58. •• Daver N, Cortes J, Ravandi F, et al. Secondary mutations as mediators of resistance to targeted therapy in leukemia. Blood. 2015;125(21):3236–45. This is a very comprehensive review article concentrating on secondary mutations arising in AML as a result of targeted therapy

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Kindler T, Lipka DB, Fischer T. FLT3 as a therapeutic target in AML: still challenging after all these years. Blood. 2010;116(24):5089–102.

    Article  CAS  PubMed  Google Scholar 

  60. Ikezoe T, Kojima S, Furihata M, et al. Expression of p-JAK2 predicts clinical outcome and is a potential molecular target of acute myelogenous leukemia. Int J Cancer. 2011;129(10):2512–21.

    Article  CAS  PubMed  Google Scholar 

  61. Pauwels D, Sweron B, Cools J. The N676D and G697R mutations in the kinase domain of FLT3 confer resistance to the inhibitor AC220. Haematologica. 2012;97(11):1773–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Uras IZ, Walter GJ, Scheicher R, et al. Palbociclib treatment of FLT3-ITD+ AML cells uncovers a kinase-dependent transcriptional regulation of FLT3 and PIM1 by CDK6. Blood. 2016;127(23):2890–902.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Wang A, Wu H, Chen C, et al. Dual inhibition of AKT/FLT3-ITD by A674563 overcomes FLT3 ligand-induced drug resistance in FLT3-ITD positive AML. Oncotarget. 2016b.

Download references

Author information

Authors and Affiliations

  1. Clinical Research Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave North, G7-910, Seattle, WA, 98109, USA

    Cecilia C. S. Yeung & Jerald Radich

Authors
  1. Cecilia C. S. Yeung
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jerald Radich
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cecilia C. S. Yeung.

Ethics declarations

Conflict of Interest

Cecilia C. S. Yeung reports a grant from Gilead.

Jerald Radich reports a grant from Novartis and is a consultant for Novartis, Ariad, and Bristol-Meyer-Squibb.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

This article is part of the Topical collection on Acute Myeloid Leukemias

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yeung, C.C.S., Radich, J. Predicting Chemotherapy Resistance in AML. Curr Hematol Malig Rep 12, 530–536 (2017). https://doi.org/10.1007/s11899-017-0378-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11899-017-0378-x

Keywords

Search

Navigation