Stem cell gene expression programs influence clinical outcome in human leukemia

Journal name:
Nature Medicine
Volume:
17,
Pages:
1086–1093
Year published:
DOI:
doi:10.1038/nm.2415
Received
Accepted
Published online

Abstract

Xenograft studies indicate that some solid tumors and leukemias are organized as cellular hierarchies sustained by cancer stem cells (CSCs). Despite the promise of the CSC model, its relevance in humans remains uncertain. Here we show that acute myeloid leukemia (AML) follows a CSC model on the basis of sorting multiple populations from each of 16 primary human AML samples and identifying which contain leukemia stem cells (LSCs) using a sensitive xenograft assay. Analysis of gene expression from all functionally validated populations yielded an LSC-specific signature. Similarly, a hematopoietic stem cell (HSC) gene signature was established. Bioinformatic analysis identified a core transcriptional program shared by LSCs and HSCs, revealing the molecular machinery underlying 'stemness' properties. Both stem cell programs were highly significant independent predictors of patient survival and were found in existing prognostic signatures. Thus, determinants of stemness influence the clinical outcome of AML, establishing that LSCs are clinically relevant and not artifacts of xenotransplantation.

At a glance

Figures

  1. Strategy of transcriptional profiling of stem cell fractions identified by function.
    Figure 1: Strategy of transcriptional profiling of stem cell fractions identified by function.

    (a) Overview of experimental design. Cells were sorted on CD34 and CD38, with sort gates for AML and cord blood as well as FACS analysis of the resulting sorted fractions. Functional validation of sorted fractions was done in vivo and combined with gene expression profiling to generate stem cell–related gene expression profiles. (b) Surface marker profiles of AML are variable with respect to coexpression of CD34 and CD38. CD34 and CD38 marker profiles for 16 AML samples were sorted into four populations and assayed for LSCs.

  2. Correlation between LSC-R and HSC-R.
    Figure 2: Correlation between LSC-R and HSC-R.

    (a) Heat map of genes more highly expressed in LSC than in non-LSC populations (LSC-R gene signature). LSC and non-LSC represent sorted AML fractions with LSCs, as determined by an in vivo reconstitution assay, and no detected LSCs, respectively. (b) Heat map of genes more highly expressed in HSC populations than in those with no detectable HSCs (HSC-R gene signature) in four different sorted cord blood populations. Sorted fractions include two HSC fractions (HSC1, LinCD34+CD38; HSC2, LinCD34+CD38loCD36), a progenitor-enriched fraction (Prog, Lin CD34+CD38+) and unsorted cord blood cells (Lin+). (c) GSEA plot of enrichment of HSC-R gene signature (top) and common lineage–committed progenitor gene signature (bottom) in LSC versus non-LSC gene expression profile. NES denotes normalized enrichment score. (d) Heat map of HSC-R GSEA plot from c (top) showing core enriched HSC-R genes in LSC expression profile (CE-HSC-LSC). Genes separated by slashes are detected by the same probe set. (e) Representative protein-protein interaction network of core enriched genes (CE-HSC-LSC) from d, generated from known and interologous interactions from I2D. Large circles, proteins from core enriched gene list (CE-HSC-LSC); small squares, proteins that link proteins in core enriched list. Node color corresponds to GO protein function. Visualization was done using NAViGaTOR (Supplementary Data).

  3. LSC-R and HSC-R gene signatures are correlated with disease outcome.
    Figure 3: LSC-R and HSC-R gene signatures are correlated with disease outcome.

    Unsorted cytogenetically normal AML samples (160) were divided into two populations of 80 AML samples by expression of stem cell gene signatures. (a) Correlation of LSC-R and HSC-R signatures and overall survival. Red line, subjects whose AML cells expressed LSC-R (left) or HSC-R (right) signatures greater than the median; black line, those whose AML cells expressed respective stem cell signature less than the median. (b) Event-free survival of subjects stratified by expression of the LSC-R and HSC-R, as in a. (c) Additive correlation analysis of the LSC-R signature and overall survival. y axis, log-rank P value of each combination of probes. x axis, number of probes included in analysis, starting with top-ranked probe positively correlated with LSCs followed by the addition of each next ranked probe in the LSC-R gene profile (as determined by z-score in the LSC versus non-LSC t-test). (d) Correlation of an AML signature based on phenotypic markers (CD34+CD38, stem cell, versus CD34+CD38+, progenitor; 23 AML samples) and overall survival. Red line, subjects whose AML expressed the CD34+CD38 gene list greater than the median; black line, those who expressed the CD34+CD38 gene list less than the median.

  4. Correlation of LSC and HSC gene expression signatures and molecular risk status with overall survival in a cohort of cytogenetically normal AML samples.
    Figure 4: Correlation of LSC and HSC gene expression signatures and molecular risk status with overall survival in a cohort of cytogenetically normal AML samples.

    Overall survival curves of 159 cytogenetically normal AML samples divided by expression of the LSC-R (left) or HSC-R (right) signatures and molecular risk. LMR group, NPM1mut/FLT3wt cytogenetically normal AML; HMR group, NPM1wt or FLT3ITD cytogenetically normal AML.

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References

  1. Dick, J.E. Stem cell concepts renew cancer research. Blood 112, 47934807 (2008).
  2. Bao, S. et al. Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature 444, 756760 (2006).
  3. Diehn, M. et al. Association of reactive oxygen species levels and radioresistance in cancer stem cells. Nature 458, 780783 (2009).
  4. Li, X. et al. Intrinsic resistance of tumorigenic breast cancer cells to chemotherapy. J. Natl. Cancer Inst. 100, 672679 (2008).
  5. Ishikawa, F. et al. Chemotherapy-resistant human AML stem cells home to and engraft within the bone-marrow endosteal region. Nat. Biotechnol. 25, 13151321 (2007).
  6. Dylla, S.J. et al. Colorectal cancer stem cells are enriched in xenogeneic tumors following chemotherapy. PLoS ONE 3, e2428 (2008).
  7. Guzman, M.L. et al. Nuclear factor-kappaB is constitutively activated in primitive human acute myelogenous leukemia cells. Blood 98, 23012307 (2001).
  8. Kelly, P.N., Dakic, A., Adams, J.M., Nutt, S.L. & Strasser, A. Tumor growth need not be driven by rare cancer stem cells. Science 317, 337 (2007).
  9. Taussig, D.C. et al. Anti-CD38 antibody-mediated clearance of human repopulating cells masks the heterogeneity of leukemia-initiating cells. Blood 112, 568575 (2008).
  10. Taussig, D.C. et al. Leukemia-initiating cells from some acute myeloid leukemia patients with mutated nucleophosmin reside in the CD34(−) fraction. Blood 115, 19761984 (2010).
  11. Quintana, E. et al. Efficient tumour formation by single human melanoma cells. Nature 456, 593598 (2008).
  12. Lapidot, T. et al. A cell initiating human acute myeloid leukaemia after transplantation into SCID mice. Nature 367, 645648 (1994).
  13. Bonnet, D. & Dick, J.E. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat. Med. 3, 730737 (1997).
  14. Pearce, D.J. et al. AML engraftment in the NOD/SCID assay reflects the outcome of AML: implications for our understanding of the heterogeneity of AML. Blood 107, 11661173 (2006).
  15. van Rhenen, A. et al. High stem cell frequency in acute myeloid leukemia at diagnosis predicts high minimal residual disease and poor survival. Clin. Cancer Res. 11, 65206527 (2005).
  16. McKenzie, J.L., Gan, O.I., Doedens, M. & Dick, J.E. Human short-term repopulating stem cells are efficiently detected following intrafemoral transplantation into NOD/SCID recipients depleted of CD122+ cells. Blood 106, 12591261 (2005).
  17. McDermott, S.P., Eppert, K., Lechman, E., Doedens, M. & Dick, J.E. Comparison of human cord blood engraftment between immunocompromised mouse strains. Blood 116, 193200 (2010).
  18. Blair, A., Hogge, D.E., Ailles, L.E., Lansdorp, P.M. & Sutherland, H.J. Lack of expression of Thy-1 (CD90) on acute myeloid leukemia cells with long-term proliferative ability in vitro and in vivo. Blood 89, 31043112 (1997).
  19. Terpstra, W. et al. Fluoroucil selectively spares acute myeloid leukemia cells with long-term growth abilities in immunodeficient mice and in culture. Blood 88, 19441950 (1996).
  20. Sarry, J.E. et al. Human acute myelogenous leukemia stem cells are rare and heterogeneous when assayed in NOD/SCID/IL2Rgammac-deficient mice. J. Clin. Invest. 121, 384395 (2011).
  21. Novershtern, N. et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144, 296309 (2011).
  22. Georgantas, R.W. III et al. Microarray and serial analysis of gene expression analyses identify known and novel transcripts overexpressed in hematopoietic stem cells. Cancer Res. 64, 44344441 (2004).
  23. Shojaei, F. et al. Hierarchical and ontogenic positions serve to define the molecular basis of human hematopoietic stem cell behavior. Dev. Cell 8, 651663 (2005).
  24. Wagner, W. et al. Molecular evidence for stem cell function of the slow-dividing fraction among human hematopoietic progenitor cells by genome-wide analysis. Blood 104, 675686 (2004).
  25. Ivanova, N.B. et al. A stem cell molecular signature. Science 298, 601604 (2002).
  26. Notta, F. et al. Isolation of single human hematopoietic stem cells capable of long-term multilineage engraftment. Science 333, 218221 (2011).
  27. Mazurier, F., Doedens, M., Gan, O.I. & Dick, J.E. Rapid myeloerythroid repopulation after intrafemoral transplantation of NOD-SCID mice reveals a new class of human stem cells. Nat. Med. 9, 959963 (2003).
  28. Brown, K.R. & Jurisica, I. Online predicted human interaction database. Bioinformatics 21, 20762082 (2005).
  29. Brown, K.R. et al. NAViGaTOR: Network Analysis, Visualization and Graphing Toronto. Bioinformatics 25, 33273329 (2009).
  30. Oh, I.H. & Eaves, C.J. Overexpression of a dominant negative form of STAT3 selectively impairs hematopoietic stem cell activity. Oncogene 21, 47784787 (2002).
  31. Varnum-Finney, B. et al. Pluripotent, cytokine-dependent, hematopoietic stem cells are immortalized by constitutive Notch1 signaling. Nat. Med. 6, 12781281 (2000).
  32. Karanu, F.N. et al. The notch ligand jagged-1 represents a novel growth factor of human hematopoietic stem cells. J. Exp. Med. 192, 13651372 (2000).
  33. Somervaille, T.C. et al. Hierarchical maintenance of MLL myeloid leukemia stem cells employs a transcriptional program shared with embryonic rather than adult stem cells. Cell Stem Cell 4, 129140 (2009).
  34. Valk, P.J. et al. Prognostically useful gene-expression profiles in acute myeloid leukemia. N. Engl. J. Med. 350, 16171628 (2004).
  35. Verhaak, R.G. et al. Prediction of molecular subtypes in acute myeloid leukemia based on gene expression profiling. Haematologica 94, 131134 (2009).
  36. Metzeler, K.H. et al. An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia. Blood 112, 41934201 (2008).
  37. Schlenk, R.F. et al. Mutations and treatment outcome in cytogenetically normal acute myeloid leukemia. N. Engl. J. Med. 358, 19091918 (2008).
  38. Mrózek, K., Marcucci, G., Paschka, P., Whitman, S.P. & Bloomfield, C.D. Clinical relevance of mutations and gene-expression changes in adult acute myeloid leukemia with normal cytogenetics: are we ready for a prognostically prioritized molecular classification? Blood 109, 431448 (2007).
  39. Döhner, K. et al. Mutant nucleophosmin (NPM1) predicts favorable prognosis in younger adults with acute myeloid leukemia and normal cytogenetics: interaction with other gene mutations. Blood 106, 37403746 (2005).
  40. Schnittger, S. et al. Nucleophosmin gene mutations are predictors of favorable prognosis in acute myelogenous leukemia with a normal karyotype. Blood 106, 37333739 (2005).
  41. Thiede, C. et al. Prevalence and prognostic impact of NPM1 mutations in 1485 adult patients with acute myeloid leukemia (AML). Blood 107, 40114020 (2006).
  42. Marcucci, G. et al. Prognostic significance of, and gene and microRNA expression signatures associated with, CEBPA mutations in cytogenetically normal acute myeloid leukemia with high-risk molecular features: a Cancer and Leukemia Group B Study. J. Clin. Oncol. 26, 50785087 (2008).
  43. Bullinger, L. et al. Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. N. Engl. J. Med. 350, 16051616 (2004).
  44. Radmacher, M.D. et al. Independent confirmation of a prognostic gene-expression signature in adult acute myeloid leukemia with a normal karyotype: a Cancer and Leukemia Group B study. Blood 108, 16771683 (2006).
  45. Ben-Porath, I. et al. An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors. Nat. Genet. 40, 499507 (2008).
  46. Hassan, K.A., Chen, G., Kalemkerian, G.P., Wicha, M.S. & Beer, D.G. An embryonic stem cell-like signature identifies poorly differentiated lung adenocarcinoma but not squamous cell carcinoma. Clin. Cancer Res. 15, 63866390 (2009).
  47. Wong, D.J. et al. Module map of stem cell genes guides creation of epithelial cancer stem cells. Cell Stem Cell 2, 333344 (2008).
  48. Gal, H. et al. Gene expression profiles of AML derived stem cells; similarity to hematopoietic stem cells. Leukemia 20, 21472154 (2006).
  49. Gentles, A.J., Plevritis, S.K., Majeti, R. & Alizadeh, A.A. Association of a leukemic stem cell gene expression signature with clinical outcomes in acute myeloid leukemia. J. Am. Med. Assoc. 304, 27062715 (2010).
  50. Guzman, M.L. et al. Expression of tumor-suppressor genes interferon regulatory factor 1 and death-associated protein kinase in primitive acute myelogenous leukemia cells. Blood 97, 21772179 (2001).
  51. Saito, Y. et al. Identification of therapeutic targets for quiescent, chemotherapy-resistant human leukemia stem cells. Sci. Transl. Med. 2, 17ra9 (2010).
  52. Majeti, R. et al. Dysregulated gene expression networks in human acute myelogenous leukemia stem cells. Proc. Natl. Acad. Sci. USA 106, 33963401 (2009).
  53. Rosen, J.M. & Jordan, C.T. The increasing complexity of the cancer stem cell paradigm. Science 324, 16701673 (2009).
  54. Adams, J.M. & Strasser, A. Is tumor growth sustained by rare cancer stem cells or dominant clones? Cancer Res. 68, 40184021 (2008).
  55. Goyama, S. et al. Evi-1 is a critical regulator for hematopoietic stem cells and transformed leukemic cells. Cell Stem Cell 3, 207220 (2008).
  56. Simsek, T. et al. The distinct metabolic profile of hematopoietic stem cells reflects their location in a hypoxic niche. Cell Stem Cell 7, 380390 (2010).
  57. Björnsson, J.M. et al. Reduced proliferative capacity of hematopoietic stem cells deficient in Hoxb3 and Hoxb4. Mol. Cell. Biol. 23, 38723883 (2003).
  58. Loughran, S.J. et al. The transcription factor Erg is essential for definitive hematopoiesis and the function of adult hematopoietic stem cells. Nat. Immunol. 9, 810819 (2008).
  59. Barjesteh van Waalwijk van Doorn-Khosrovani, S. et al. High EVI1 expression predicts poor survival in acute myeloid leukemia: a study of 319 de novo AML patients. Blood 101, 837845 (2003).
  60. Wang, J.C. Good cells gone bad: the cellular origins of cancer. Trends Mol. Med. 16, 145151 (2010).
  61. Tenen, D.G. Disruption of differentiation in human cancer: AML shows the way. Nat. Rev. Cancer 3, 89101 (2003).
  62. Smyth, G.K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article3 (2004).
  63. Hu, Y. & Smyth, G.K. ELDA: extreme limiting dilution analysis for comparing depleted and enriched populations in stem cell and other assays. J. Immunol. Methods 347, 7078 (2009).

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Author information

  1. These authors contributed equally to this work.

    • Katsuto Takenaka,
    • Eric R Lechman,
    • Levi Waldron &
    • Björn Nilsson

Affiliations

  1. Division of Stem Cell and Developmental Biology, Campbell Family Institute for Cancer Research, Ontario Cancer Institute, University Health Network and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.

    • Kolja Eppert,
    • Eric R Lechman,
    • Peter van Galen,
    • Armando Poeppl &
    • John E Dick
  2. Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan.

    • Katsuto Takenaka
  3. Campbell Family Institute for Cancer Research, Ontario Cancer Institute, University Health Network and Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.

    • Levi Waldron &
    • Igor Jurisica
  4. Brigham and Women's Hospital, Boston, Massachusetts, USA.

    • Björn Nilsson &
    • Benjamin L Ebert
  5. Department of Internal Medicine III, Ludwig-Maximilians-Universität, Munich, Germany.

    • Klaus H Metzeler &
    • Stefan K Bohlander
  6. Population Health Sciences, Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada.

    • Vicki Ling &
    • Joseph Beyene
  7. Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada.

    • Angelo J Canty
  8. Program in Genetics and Genome Biology, Hospital for Sick Children and Department of Immunology and Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.

    • Jayne S Danska
  9. Institute of Experimental Cancer Research, Comprehensive Cancer Center, University Hospital of Ulm, Ulm, Germany.

    • Christian Buske
  10. Department of Medicine, University Health Network, Toronto, Ontario, Canada.

    • Mark D Minden
  11. Broad Institute, Cambridge, Massachusetts, USA.

    • Todd R Golub

Contributions

K.E., E.R.L., K.T., B.L.E. and J.E.D. designed the study. K.E., E.R.L., P.v.G., K.T. and A.P. carried out experiments. K.E., K.T., L.W., B.N., E.R.L., P.v.G., V.L. and I.J. analyzed and interpreted data. K.E., J.B., A.J.C., J.S.D., S.K.B., K.H.M., C.B., M.D.M., T.R.G., I.J., B.L.E. and J.E.D. provided research support and conceptual advice. M.D.M. provided samples. K.E. and J.E.D. wrote the paper. E.R.L., K.T., K.H.M., J.S.D., S.K.B., C.B., M.D.M., I.J. and B.L.E. revised the paper.

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The authors declare no competing financial interests.

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