• A Corrigendum to this article was published on 27 July 2011

This article has been updated

Abstract

The importance of individual microRNAs (miRNAs) has been established in specific cancers. However, a comprehensive analysis of the contribution of miRNAs to the pathogenesis of any specific cancer is lacking. Here we show that in T-cell acute lymphoblastic leukemia (T-ALL), a small set of miRNAs is responsible for the cooperative suppression of several tumor suppressor genes. Cross-comparison of miRNA expression profiles in human T-ALL with the results of an unbiased miRNA library screen allowed us to identify five miRNAs (miR-19b, miR-20a, miR-26a, miR-92 and miR-223) that are capable of promoting T-ALL development in a mouse model and which account for the majority of miRNA expression in human T-ALL. Moreover, these miRNAs produce overlapping and cooperative effects on tumor suppressor genes implicated in the pathogenesis of T-ALL, including IKAROS (also known as IKZF1), PTEN, BIM, PHF6, NF1 and FBXW7. Thus, a comprehensive and unbiased analysis of miRNA action in T-ALL reveals a striking pattern of miRNA-tumor suppressor gene interactions in this cancer.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Change history

  • 11 July 2011

    In the version of this article initially published, the name of author Manu Setty was incorrectly spelled as Manu Setti. The error has been corrected in the HTML and PDF versions of the article.

References

  1. 1.

    , & Molecular pathogenesis of T-cell leukaemia and lymphoma. Nat. Rev. Immunol. 8, 380–390 (2008).

  2. 2.

    et al. Activating mutations of NOTCH1 in human T cell acute lymphoblastic leukemia. Science 306, 269–271 (2004).

  3. 3.

    et al. Mutational loss of PTEN induces resistance to NOTCH1 inhibition in T-cell leukemia. Nat. Med. 13, 1203–1210 (2007).

  4. 4.

    et al. Leukemia-associated NF1 inactivation in patients with pediatric T-ALL and AML lacking evidence for neurofibromatosis. Blood 111, 4322–4328 (2008).

  5. 5.

    et al. PHF6 mutations in T-cell acute lymphoblastic leukemia. Nat. Genet. 42, 338–342 (2010).

  6. 6.

    et al. Deletion of the protein tyrosine phosphatase gene PTPN2 in T-cell acute lymphoblastic leukemia. Nat. Genet. 42, 530–535 (2010).

  7. 7.

    et al. Mutant Ikzf1, KrasG12D, and Notch1 cooperate in T lineage leukemogenesis and modulate responses to targeted agents. Proc. Natl. Acad. Sci. USA 107, 5106–5111 (2010).

  8. 8.

    , & A dominant mutation in the Ikaros gene leads to rapid development of leukemia and lymphoma. Cell 83, 289–299 (1995).

  9. 9.

    et al. Genetic inactivation of Ikaros is a rare event in human T-ALL. Leuk. Res. 34, 426–429 (2010).

  10. 10.

    et al. Expression of dominant-negative Ikaros isoforms in T-cell acute lymphoblastic leukemia. Clin. Cancer Res. 5, 2112–2120 (1999).

  11. 11.

    et al. Genome-wide analysis of genetic alterations in acute lymphoblastic leukaemia. Nature 446, 758–764 (2007).

  12. 12.

    et al. FBW7 mutations in leukemic cells mediate NOTCH pathway activation and resistance to gamma-secretase inhibitors. J. Exp. Med. 204, 1813–1824 (2007).

  13. 13.

    et al. The SCFFBW7 ubiquitin ligase complex as a tumor suppressor in T cell leukemia. J. Exp. Med. 204, 1825–1835 (2007).

  14. 14.

    , , & Oncogenic potential of the miR-106–363 cluster and its implication in human T-cell leukemia. Cancer Res. 67, 5699–5707 (2007).

  15. 15.

    et al. Genome-wide RNA-mediated interference screen identifies miR-19 targets in Notch-induced T-cell acute lymphoblastic leukaemia. Nat. Cell Biol. 12, 372–379 (2010).

  16. 16.

    et al. MicroRNA expression profiles classify human cancers. Nature 435, 834–838 (2005).

  17. 17.

    et al. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129, 1401–1414 (2007).

  18. 18.

    MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281–297 (2004).

  19. 19.

    et al. miR-181a is an intrinsic modulator of T cell sensitivity and selection. Cell 129, 147–161 (2007).

  20. 20.

    et al. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol. 10, R64 (2009).

  21. 21.

    , , , & Prediction of mammalian microRNA targets. Cell 115, 787–798 (2003).

  22. 22.

    , , & Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 19, 92–105 (2009).

  23. 23.

    et al. Induction of apoptosis in fibroblasts by c-myc protein. Cell 69, 119–128 (1992).

  24. 24.

    et al. NOTCH1 directly regulates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth. Proc. Natl. Acad. Sci. USA 103, 18261–18266 (2006).

  25. 25.

    et al. c-Myc is an important direct target of Notch1 in T-cell acute lymphoblastic leukemia/lymphoma. Genes Dev. 20, 2096–2109 (2006).

  26. 26.

    et al. Myc is a Notch1 transcriptional target and a requisite for Notch1-induced mammary tumorigenesis in mice. Proc. Natl. Acad. Sci. USA 103, 9262–9267 (2006).

  27. 27.

    & Perturbation of Ikaros isoform selection by MLV integration is a cooperative event in Notch(IC)-induced T cell leukemogenesis. Cancer Cell 3, 551–564 (2003).

  28. 28.

    et al. SCFFBW7 regulates cellular apoptosis by targeting MCL1 for ubiquitylation and destruction. Nature 471, 104–109 (2011).

  29. 29.

    et al. Sensitivity to antitubulin chemotherapeutics is regulated by MCL1 and FBW7. Nature 471, 110–114 (2011).

  30. 30.

    , & Genetic analysis of chemoresistance in primary murine lymphomas. Nat. Med. 6, 1029–1035 (2000).

  31. 31.

    et al. Dissecting eIF4E action in tumorigenesis. Genes Dev. 21, 3232–3237 (2007).

  32. 32.

    et al. Chromosomally unstable mouse tumours have genomic alterations similar to diverse human cancers. Nature 447, 966–971 (2007).

  33. 33.

    et al. An early decrease in Notch activation is required for human TCR-alphabeta lineage differentiation at the expense of TCR-gammadelta T cells. Blood 113, 2988–2998 (2009).

  34. 34.

    et al. Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res. 33, e179 (2005).

  35. 35.

    et al. High-throughput stem-loop RT-qPCR miRNA expression profiling using minute amounts of input RNA. Nucleic Acids Res. 36, e143 (2008).

  36. 36.

    et al. Tumorigenic activity and therapeutic inhibition of Rheb GTPase. Genes Dev. 22, 2178–2188 (2008).

  37. 37.

    , , , & Akt and Bcl-xL promote growth factor-independent survival through distinct effects on mitochondrial physiology. J. Biol. Chem. 276, 12041–12048 (2001).

  38. 38.

    et al. A microRNA polycistron as a potential human oncogene. Nature 435, 828–833 (2005).

  39. 39.

    et al. Exclusive development of T cell neoplasms in mice transplanted with bone marrow expressing activated Notch alleles. J. Exp. Med. 183, 2283–2291 (1996).

  40. 40.

    et al. Survival signaling by Akt and eIF4E in oncogenesis and cancer therapy. Nature 428, 332–337 (2004).

  41. 41.

    et al. The PTEN-regulating microRNA miR-26a is amplified in high-grade glioma and facilitates gliomagenesis in vivo. Genes Dev. 23, 1327–1337 (2009).

  42. 42.

    et al. Lymphoproliferative disease and autoimmunity in mice with increased miR-17–92 expression in lymphocytes. Nat. Immunol. 9, 405–414 (2008).

  43. 43.

    et al. NOTCH1 and/or FBXW7 mutations predict for initial good prednisone response but not for improved outcome in pediatric T-cell acute lymphoblastic leukemia patients treated on DCOG or COALL protocols. Leukemia 24, 2014–2022 (2010).

  44. 44.

    et al. NOTCH1/FBXW7 mutation identifies a large subgroup with favorable outcome in adult T-cell acute lymphoblastic leukemia (T-ALL): a Group for Research on Adult Acute Lymphoblastic Leukemia (GRAALL) study. Blood 113, 3918–3924 (2009).

Download references

Acknowledgements

We thank A.J. Capobianco, L. Beverly, A.A. Ferrando, J. Cools and W. Pear for reagents. The Memorial Sloan Kettering (MSK) animal facility and Research Animal Resource Center (RARC), A. Viale of the MSK Genomics Core, H. Zhao of Computational Biology (cBIO) program, K. Huberman of the Geoffrey Beene Translational Oncology Core Facility and J. Schatz for editorial advice. This work is supported by grants from the National Cancer Institute (NCI) (R01-CA142798-01) (H.-G.W.), and a P30 supplemental award (H.-G.W.), the Louis V. Gerstner Foundation (H.-G.W.), the William Lawrence and Blanche Hughes (WLBH) Foundation (H.-G.W.), the Society of MSKCC (H.-G.W.), the Geoffrey Beene Foundation (H.-G.W.), and May & Samuel Rudin Foundation Award (H.-G.W.); W.H. Goodwin and A. Goodwin and the Commonwealth Foundation for Cancer Research, The Experimental Therapeutics Center of Memorial Sloan-Kettering Cancer Center (H.-G.W.), the Fund for Scientific Research (FWO) Flanders (postdoctoral grants to T.T. and P.V.V., PhD grant to J.V.d.M., P.V.V. is a Senior Clinical Investigator of FWO-Vlaanderen, Odysseus program grant to T.T., and project grants G.0198.08 and G.0869.10N to F.S.); the GOA-UGent (grant no. 12051203); Stichting tegen Kanker, FOD ALL the Children Cancer Fund Ghent (F.S.); and the Belgian Program of Interuniversity Poles of Attraction and the Belgian Foundation Against Cancer.

Author information

Author notes

    • Konstantinos J Mavrakis
    • , Joni Van Der Meulen
    • , Frank Speleman
    •  & Hans-Guido Wendel

    These authors contributed equally to this work.

Affiliations

  1. Cancer Biology & Genetics Program, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

    • Konstantinos J Mavrakis
    • , Andrew L Wolfe
    • , Xiaoping Liu
    •  & Hans-Guido Wendel
  2. Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium.

    • Joni Van Der Meulen
    • , Evelien Mets
    • , Pieter Rondou
    • , Pieter Van Vlierberghe
    •  & Frank Speleman
  3. Weill Cornell Graduate School of Medical Sciences, New York, New York, USA.

    • Andrew L Wolfe
    • , Aly A Khan
    •  & Manu Setty
  4. Department of Clinical Chemistry, Microbiology and Immunology, Ghent University Hospital, Ghent, Belgium.

    • Tom Taghon
  5. Computational Biology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

    • Aly A Khan
    • , Manu Setty
    •  & Christina S Leslie
  6. Centre for Human Genetics, University Hospital Leuven, Leuven, Belgium.

    • Peter Vandenberghe
  7. INSERM U563, Toulouse, France.

    • Eric Delabesse
  8. Department of Pediatric Hematology-Oncology, Ghent University Hospital, Ghent, Belgium.

    • Yves Benoit
  9. Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

    • Nicholas B Socci
  10. Institute for Cancer Genetics, Columbia University, New York, New York, USA.

    • Pieter Van Vlierberghe

Authors

  1. Search for Konstantinos J Mavrakis in:

  2. Search for Joni Van Der Meulen in:

  3. Search for Andrew L Wolfe in:

  4. Search for Xiaoping Liu in:

  5. Search for Evelien Mets in:

  6. Search for Tom Taghon in:

  7. Search for Aly A Khan in:

  8. Search for Manu Setty in:

  9. Search for Pieter Rondou in:

  10. Search for Peter Vandenberghe in:

  11. Search for Eric Delabesse in:

  12. Search for Yves Benoit in:

  13. Search for Nicholas B Socci in:

  14. Search for Christina S Leslie in:

  15. Search for Pieter Van Vlierberghe in:

  16. Search for Frank Speleman in:

  17. Search for Hans-Guido Wendel in:

Contributions

K.J.M., A.L.W. and X.L. performed the screen, mouse model and data analysis. J.V.d.M. and P.V.V. performed miRNA profiling on T-ALL samples. E.M. and P.R. performed studies on miR-223 and FBXW7. T.T. performed cell sorting and miRNA profiling. P.V. and E.D. performed genetic analyses on T-ALL samples. Y.B. was the co-supervisor of the miRNA profiling project on childhood ALLs and integrated clinical data management. A.A.K., M.S., C.S.L. and N.D.S. performed computational analyses. F.S. supervised the miRNA expression analyses. H.G.W. designed the study and wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Hans-Guido Wendel.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–10 and Supplementary Tables 2–16.

Excel files

  1. 1.

    Supplementary Table 1

    miRNA expression in primary T-ALL samples

  2. 2.

    Supplementary Table 5

    miRNA expression in T-ALL cell lines

  3. 3.

    Supplementary Table 7

    miRNA expression in normal T-cells and progenitor populations

  4. 4.

    Supplementary Table 8

    Comparative analysis of miRNA expression between normal T-cells and progenitor populations and human T-ALL samples

  5. 5.

    Supplementary Table 9

    Computational target prediction (a: by total context score; b: by number of 7- and 8-mer sites)

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/ng.858

Further reading