Stem cell biology

A novel method for detecting the cellular stemness state in normal and leukemic human hematopoietic cells can predict disease outcome and drug sensitivity


Acute leukemia is an aggressive blood malignancy with low survival rates. A high expression of stem-like programs in leukemias predicts poor prognosis and is assumed to act in an aberrant fashion in the phenotypically heterogeneous leukemia stem cell (LSC) population. A lack of suitable genome engineering tools that can isolate LSCs based on their stemness precludes their comprehensive examination and full characterization. We hypothesized that tagging endogenous stemness-regulatory regions could generate a genome reporter for the putative leukemia stemness-state. Our analysis revealed that the ERG +85 enhancer region can serve as a marker for stemness-state and a fluorescent lentiviral reporter was developed that can accurately recapitulate the endogenous activity. Using our novel reporter, we revealed cellular heterogeneity in several leukemia cell lines and patient-derived samples. Alterations in reporter activity were associated with transcriptomic and functional changes that were closely related to the hematopoietic stem cell (HSC) identity. Notably, the differentiation potential was skewed towards the erythro-megakaryocytic lineage. Moreover, an ERG +85High fraction of AML cells could regenerate the original cellular heterogeneity and was enriched for LSCs. RNA-seq analysis coupled with in silico drug-screen analysis identified 4HPR as an effective inhibitor of ERG +85High leukemia growth. We propose that further utilization of our novel molecular tool will identify crucial determinants of LSCs, thus providing a rationale for their therapeutic targeting.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. 1.

    Dohner H, Weisdorf DJ, Bloomfield CD. Acute Myeloid Leukemia. N Engl J Med. 2015;373:1136–52.

    Article  Google Scholar 

  2. 2.

    Vedi A, Santoro A, Dunant CF, Dick JE, Laurenti E. Molecular landscapes of human hematopoietic stem cells in health and leukemia. Ann N Y Acad Sci. 2016;1370:5–14.

    Article  Google Scholar 

  3. 3.

    Greaves M. Leukaemia ‘firsts’ in cancer research and treatment. Nat Rev Cancer. 2016;16:163–72.

    Article  Google Scholar 

  4. 4.

    Eppert K, Takenaka K, Lechman ER, Waldron L, Nilsson B, van Galen P, et al. Stem cell gene expression programs influence clinical outcome in human leukemia. Nat Med. 2011;17:1086–93.

    CAS  Article  Google Scholar 

  5. 5.

    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.

    CAS  Article  Google Scholar 

  6. 6.

    Shlush LI, Mitchell A, Heisler L, Abelson S, Ng SWK, Trotman-Grant A, et al. Tracing the origins of relapse in acute myeloid leukaemia to stem cells. Nature. 2017; 547:104–8.

    CAS  Article  Google Scholar 

  7. 7.

    Zhang J, Ding L, Holmfeldt L, Wu G, Heatley SL, Payne-Turner D, et al. The genetic basis of early T-cell precursor acute lymphoblastic leukaemia. Nature. 2012;481:157–63.

    CAS  Article  Google Scholar 

  8. 8.

    Coustan-Smith E, Mullighan CG, Onciu M, Behm FG, Raimondi SC, Pei D, et al. Early T-cell precursor leukaemia: a subtype of very high-risk acute lymphoblastic leukaemia. Lancet Oncol. 2009;10:147–56.

    CAS  Article  Google Scholar 

  9. 9.

    Thomas D, Majeti R. Biology and relevance of human acute myeloid leukemia stem cells. Blood. 2017;129:1577–85.

    CAS  Article  Google Scholar 

  10. 10.

    Pollyea DA, Jordan CT. Therapeutic targeting of acute myeloid leukemia stem cells. Blood. 2017;129:1627–35.

    CAS  Article  Google Scholar 

  11. 11.

    Gerber JM, Smith BD, Ngwang B, Zhang H, Vala MS, Morsberger L, et al. A clinically relevant population of leukemic CD34(+)CD38(-) cells in acute myeloid leukemia. Blood. 2012;119:3571–7.

    CAS  Article  Google Scholar 

  12. 12.

    Lagadinou ED, Sach A, Callahan K, Rossi RM, Neering SJ, Minhajuddin M, et al. BCL-2 inhibition targets oxidative phosphorylation and selectively eradicates quiescent human leukemia stem cells. Cell Stem Cell. 2013;12:329–41.

    CAS  Article  Google Scholar 

  13. 13.

    Guan Y, Gerhard B, Hogge DEE. Detection, isolation, and stimulation of quiescent primitive leukemic progenitor cells from patients with acute myeloid leukemia (AML). Blood. 2003;101:3142–9.

    CAS  Article  Google Scholar 

  14. 14.

    van Galen P, Mbong N, Kreso A, Schoof EM, Wagenblast E, Ng SWK, et al. Integrated stress response activity marks stem cells in normal hematopoiesis and leukemia. Cell Rep. 2018;25:1109–17.e5.

    Article  Google Scholar 

  15. 15.

    Lechman ER, Gentner B, Ng SWK, Schoof EM, van Galen P, Kennedy JA, et al. miR-126 Regulates distinct self-renewal outcomes in normal and malignant hematopoietic stem cells. Cancer Cell. 2016;29:602–6.

    CAS  Article  Google Scholar 

  16. 16.

    Ho T-C, LaMere M, Stevens BM, Ashton JM, Myers JR, ODwyer KM, et al. Evolution of acute myelogenous leukemia stem cell properties after treatment and progression. Blood. 2016;128:1671–8.

    CAS  Article  Google Scholar 

  17. 17.

    Klco JM, Spencer DH, Miller CA, Griffith M, Lamprecht TL, O’Laughlin M, et al. Functional heterogeneity of genetically defined subclones in acute myeloid leukemia. Cancer Cell. 2014;25:379–92.

    CAS  Article  Google Scholar 

  18. 18.

    Riddell J, Gazit R, Garrison BS, Guo G, Saadatpour A, Mandal PK, et al. Reprogramming committed murine blood cells to induced hematopoietic stem cells with defined factors. Cell. 2014;157:549–64.

    CAS  Article  Google Scholar 

  19. 19.

    Doulatov S, Vo LT, Chou SS, Kim PG, Arora N, Li H, et al. Induction of multipotential hematopoietic progenitors from human pluripotent stem cells via respecification of lineage-restricted precursors. Cell Stem Cell. 2013;13:459–70.

    CAS  Article  Google Scholar 

  20. 20.

    Wilkinson AC, Nakauchi H, Gottgens B. Mammalian Transcription FactorNetworks: Recent Advances in Interrogating Biological Complexity. Cell Syst. 2017;5:319–31.

    CAS  Article  Google Scholar 

  21. 21.

    Hnisz D, Abraham BJ, Lee TI, Lau A, Saint-André V, Sigova AA, et al. Super-enhancers in the control of cell identity and disease. Cell. 2013;155:934–47.

    CAS  Article  Google Scholar 

  22. 22.

    Loughran SJ, Kruse EA, Hacking DF, de Graaf CA, Hyland CD, Willson TA, et al. The transcription factor Erg is essential for definitive hematopoiesis and the function of adult hematopoietic stem cells. Nat Immunol. 2008;9:810–9.

    CAS  Article  Google Scholar 

  23. 23.

    Batta K, Florkowska M, Kouskoff V, Lacaud G. Direct reprogramming of murine fibroblasts to hematopoietic progenitor cells. Cell Rep. 2014;9:1871–84.

    CAS  Article  Google Scholar 

  24. 24.

    Marcucci G, Maharry K, Whitman SP, Vukosavljevic T, Paschka P, Langer C, et al. High expression levels of the ETS-related gene, ERG, predict adverse outcome and improve molecular risk-based classification of cytogenetically normal acute myeloid leukemia: a Cancer and Leukemia Group B Study. J Clin Oncol. 2007;25:3337–43.

    CAS  Article  Google Scholar 

  25. 25.

    Baldus CD, Burmeister T, Martus P, Schwartz S, Gökbuget N, Bloomfield CD, et al. High expression of the ETS transcription factor ERG predicts adverse outcome in acute T-lymphoblastic leukemia in adults. J Clin Oncol. 2006;24:4714–20.

    CAS  Article  Google Scholar 

  26. 26.

    Beck D, Thoms JAI, Perera D, Schutte J, Unnikrishnan A, Knezevic K, et al. Genome-wide analysis of transcriptional regulators in human HSPCs reveals a densely interconnected network of coding and noncoding genes. Blood. 2013;122:e12–22.

    CAS  Article  Google Scholar 

  27. 27.

    Diffner E, Beck D, Gudgin E, Thoms Ja, Knezevic K, Pridans C, et al. Activity of a heptad of transcription factors is associated with stem cell programs and clinical outcome in acute myeloid leukemia. Blood. 2013;121:2289–2300.

    CAS  Article  Google Scholar 

  28. 28.

    Andersson R, Gebhard C, Miguel-Escalada I, Hoof I, Bornholdt J, Boyd M, et al. An atlas of active enhancers across human cell types and tissues. Nature. 2014;507:455–61.

    CAS  Article  Google Scholar 

  29. 29.

    van Galen P, Viny AD, Ram O, Ryan RJH, Cotton MJ, Donohue L, et al. A multiplexed system for quantitative comparisons of chromatin landscapes. Mol Cell. 2016;61:170–80.

    Article  Google Scholar 

  30. 30.

    Wilson NK, Foster SD, Wang X, Knezevic K, Schütte J, Kaimakis P, et al. Combinatorial transcriptional control in blood stem/progenitor cells: genome-wide analysis of ten major transcriptional regulators. Cell Stem Cell. 2010;7:532–44.

    CAS  Article  Google Scholar 

  31. 31.

    Thoms JAI, Birger Y, Foster S, Knezevic K, Kirschenbaum Y, Chandrakanthan V, et al. ERG promotes T-acute lymphoblastic leukemia and is transcriptionally regulated in leukemic cells by a stem cell enhancer. Blood. 2011;117:7079–89.

    CAS  Article  Google Scholar 

  32. 32.

    McKeown MR, Corces MR, Eaton ML, Fiore C, Lee E, Lopez JT, et al. Superenhancer analysis defines novel epigenomic subtypes of non-APL AML, including an RARα dependency targetable by SY-1425, a potent and selective RARα agonist. Cancer Discov. 2017;7:1136–53.

    CAS  Article  Google Scholar 

  33. 33.

    Unnikrishnan A, Guan YF, Huang Y, Beck D, Thoms JA, Peirs S, et al. A quantitative proteomics approach identifies ETV6 and IKZF1 as new regulators of an ERG-driven transcriptional network. Nucleic Acids Res. 2016;44:10644–61.

    CAS  Article  Google Scholar 

  34. 34.

    Schutte J, Wang H, Antoniou S, Jarratt A, Wilson NK, Riepsaame J, et al. An experimentally validated network of nine haematopoietic transcription factors reveals mechanisms of cell state stability. eLife. 2016;5:e11469.

    Article  Google Scholar 

  35. 35.

    Moignard V, Woodhouse S, Haghverdi L, Lilly AJ, Tanaka Y, Wilkinson AC, et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat Biotechnol. 2015;33:269–76.

    CAS  Article  Google Scholar 

  36. 36.

    Jaatinen T, Hemmoranta H, Hautaniemi S, Niemi J, Nicorici D, Laine J, et al. Global gene expression profile of human cord blood-derived CD133+ cells. Stem Cells. 2006;24:631–41.

    CAS  Article  Google Scholar 

  37. 37.

    Pabst C, Bergeron A, Lavallée V-P, Yeh J, Gendron P, Norddahl GL, et al. GPR56 identifies primary human acute myeloid leukemia cells with high repopulating potential in vivo. Blood. 2016;127:2018–27.

    CAS  Article  Google Scholar 

  38. 38.

    Metzeler KH, Hummel M, Bloomfield CD, Spiekermann K, Braess J, Sauerland M-C, et al. An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia. Blood. 2008;112:4193–201.

    CAS  Article  Google Scholar 

  39. 39.

    Verhaak RGW, Wouters BJ, Erpelinck CAJ, Abbas S, Beverloo HB, Lugthart S, et al. Prediction of molecular subtypes in acute myeloid leukemia based on gene expression profiling. Haematologica. 2009;94:131–4.

    Article  Google Scholar 

  40. 40.

    Rucker FG, Sander S, Dohner K, Dohner H, Pollack JR, Bullinger L, et al. Molecular profiling reveals myeloid leukemia cell lines to be faithful model systems characterized by distinct genomic aberrations. Leukemia. 2006;20:994–1001.

    CAS  Article  Google Scholar 

  41. 41.

    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.

    CAS  Article  Google Scholar 

  42. 42.

    Levine JHH, Simonds EFF, Bendall SCC, Davis KLL, Amir ED, Tadmor MDD, et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell. 2015;162:184–97.

    CAS  Article  Google Scholar 

  43. 43.

    Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603–7.

    CAS  Article  Google Scholar 

  44. 44.

    Dorrell C, Gan OI, Pereira DS, Hawley RG, Dick JE. Expansion of human cord blood CD34+ CD38- cells in ex vivo culture during retroviral transduction without a corresponding increase in SCID repopulating cell (SRC) frequency: dissociation of SRC phenotype and function. Blood. 2000;95:102–10.

  45. 45.

    Magnusson M, Sierra MI, Sasidharan R, Prashad SL, Romero M, Saarikoski P, et al. Expansion on stromal cells preserves the undifferentiated state of human hematopoietic stem cells despite compromised reconstitution ability. PLoS ONE. 2013;8:e53912.

    CAS  Article  Google Scholar 

  46. 46.

    Carmichael CL, Metcalf D, Henley KJ, Kruse EA, Di Rago L, Mifsud S, et al. Hematopoietic overexpression of the transcription factor Erg induces lymphoid and erythro-megakaryocytic leukemia. Proc Natl Acad Sci USA. 2012;109:15437–42.

    CAS  Article  Google Scholar 

  47. 47.

    Pimkin M, Kossenkov AV, Mishra T, Morrissey CS, Wu W, Keller CA, et al. Divergent functions of hematopoietic transcription factors in lineage priming and differentiation during erythro-megakaryopoiesis. Genome Res. 2014;24:1932–44.

    CAS  Article  Google Scholar 

  48. 48.

    Hope KJJ, Jin L, Dick JEE. Acute myeloid leukemia originates from a hierarchy of leukemic stem cell classes that differ in self-renewal capacity. Nat Immunol. 2004;5:738–43.

    CAS  Article  Google Scholar 

  49. 49.

    Zhang H, Mi J-Q, Fang H, Wang Z, Wang C, Wu L, et al. Preferential eradication of acute myelogenous leukemia stem cells by fenretinide. Proc Natl Acad Sci. 2013;110:5606–11.

    CAS  Article  Google Scholar 

  50. 50.

    Cornet-Masana JM, Moreno-Martínez D, Lara-Castillo MC, Nomdedeu M, Etxabe A, Tesi N, et al. Emetine induces chemosensitivity and reduces clonogenicity of acute myeloid leukemia cells. Oncotarget. 2016;7:23239–50.

    Article  Google Scholar 

  51. 51.

    Baudet A, Ek F, Davidsson J, Soneji S, Olsson R, Magnusson M, et al. Small molecule screen identifies differentiation-promoting compounds targeting genetically diverse acute myeloid leukaemia. Br J Haematol. 2016;175:342–6.

    Article  Google Scholar 

  52. 52.

    Guzman ML, Rossi RM, Karnischky L, Li X, Peterson DR, Howard DS, et al. The sesquiterpene lactone parthenolide induces apoptosis of human acute myelogenous leukemia stem and progenitor cells. Blood. 2005;105:4163–9.

    CAS  Article  Google Scholar 

  53. 53.

    Farge T, Saland E, de Toni F, Aroua N, Hosseini M, Perry R, et al. Chemotherapy-resistant human acute myeloid leukemia cells are not enriched for leukemic stem cells but require oxidative metabolism. Cancer Discov. 2017;7:716–35.

    CAS  Article  Google Scholar 

  54. 54.

    Griessinger E, Anjos-Afonso F, Pizzitola I, Rouault-Pierre K, Vargaftig J, Taussig D, et al. A niche-like culture system allowing the maintenance of primary human acute myeloid leukemia-initiating cells: a new tool to decipher their chemoresistance and self-renewal mechanisms. Stem Cells Transl Med. 2014;3:520–9.

    CAS  Article  Google Scholar 

  55. 55.

    Boyd AL, Aslostovar L, Reid J, Ye W, Tanasijevic B, Porras DP, et al. Identification of chemotherapy-induced leukemic-regenerating cells reveals a transient vulnerability of human AML recurrence. Cancer Cell. 2018;34:483–498.e5.

    CAS  Article  Google Scholar 

  56. 56.

    Laurenti E, Frelin C, Xie S, Ferrari R, Dunant CF, Zandi S, et al. CDK6 levels regulate quiescence exit in human hematopoietic stem cells. Cell Stem Cell. 2015;16:302–13.

    CAS  Article  Google Scholar 

  57. 57.

    Challen GA, Sun D, Mayle A, Jeong M, Luo M, Rodriguez B, et al. Dnmt3a and Dnmt3b have overlapping and distinct functions in hematopoietic stem cells. Cell Stem Cell. 2014;15:350–64.

    CAS  Article  Google Scholar 

  58. 58.

    Yanagisawa B, Ghiaur G, Smith BD, Jones RJ. Translating leukemia stem cells into the clinical setting: Harmonizing the heterogeneity. Exp Hematol. 2016;44:1130–7.

    CAS  Article  Google Scholar 

  59. 59.

    Saland E, Boutzen H, Castellano R, Pouyet L, Griessinger E, Larrue C, et al. A robust and rapid xenograft model to assess efficacy of chemotherapeutic agents for human acute myeloid leukemia. Blood Cancer J. 2015;5:e297.

    CAS  Article  Google Scholar 

  60. 60.

    Tsuzuki S, Taguchi O, Seto M. Promotion and maintenance of leukemia by ERG. Blood. 2011;117:3858–68.

    CAS  Article  Google Scholar 

  61. 61.

    Weber S, Haferlach T, Haferlach C, Kern W. Comprehensive study on ERG gene expression in normal karyotype acute myeloid leukemia: ERG expression is of limited prognostic value, whereas the accumulation of adverse prognostic markers stepwise worsens the prognosis. Blood Cancer J. 2016;6:e507.

    CAS  Article  Google Scholar 

  62. 62.

    Schwartzman O, Savino AM, Gombert M, Palmi C, Cario G, Schrappe M, et al. Suppressors and activators of JAK-STAT signaling at diagnosis and relapse of acute lymphoblastic leukemia in Down syndrome. Proc Natl Acad Sci USA. 2017;114:E4030–39.

    CAS  Article  Google Scholar 

  63. 63.

    Laverdière I, Boileau M, Neumann AL, Frison H, Mitchell A, Ng SWK, et al. Leukemic stem cell signatures identify novel therapeutics targeting acute myeloid leukemia. Blood Cancer J. 2018;8:52.

    Article  Google Scholar 

  64. 64.

    Corces MR, Buenrostro JD, Wu B, Greenside PG, Chan SM, Koenig JL, et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat Genet. 2016;48:1193–203.

    CAS  Article  Google Scholar 

Download references


The authors thank Dr. J. E. Pimanda (Prince of Wales Clinical School, Australia) for providing the PGL2-ERG+85 Luciferase vector. Nour Ershaid (Tel-Aviv University) for helping with FACS analysis. This work was partially supported by Israel Science Foundation (ISF 1512/14, to MM), Varda and Boaz Dotan Research Center in Hemato-Oncology (to MM and SI) and Israel Cancer Research Fund (RCDA 14-171 to MM). MY is a recipient of Israel Council for Higher Education PhD scholarship for minorities. NA is a recipient of a PhD scholarship from State of Israel Ministry of Science, Technology and Space. This work was performed in partial fulfillment of the requirements for a PhD degree of Muhammad Yassin, Nasma Aqaqe and Eitan Kugler, the Dr. Miriam and Sheldon G. Adelson Graduate School of Medicine, Sackler Faculty of Medicine, Tel Aviv University, Israel.

Author information



Corresponding author

Correspondence to Michael Milyavsky.

Ethics declarations

Conflict of interest

BEB owns equity in Fulcrum Therapeutics, 1CellBio Inc, Nohla Therapeutics and HiFiBio Inc., and is an advisor for Fulcrum Therapeutics, HiFiBio Inc and Cell Signaling Technologies.The remaining authors declare that they have no conflict of interest.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yassin, M., Aqaqe, N., Yassin, A.A. et al. A novel method for detecting the cellular stemness state in normal and leukemic human hematopoietic cells can predict disease outcome and drug sensitivity. Leukemia 33, 2061–2077 (2019).

Download citation

Further reading