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A cooperative microRNA-tumor suppressor gene network in acute T-cell lymphoblastic leukemia (T-ALL)

Nature Genetics volume 43, pages 673–678 (2011)Cite this article

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A Corrigendum to this article was published on 27 July 2011

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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.

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Figure 1: Comprehensive study of oncogenic miRNAs in T-ALL.
Figure 2: Pooled library screen for oncogenic miRNAs.
Figure 3: Candidate miRNAs act as oncogenes in a mouse T-ALL model.
Figure 4: miRNAs regulate the expression of tumor suppressor genes in mouse T-ALL.
Figure 5: Individual and cooperative miRNA effects on T-ALL suppressor genes.

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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.

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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

  1. Konstantinos J Mavrakis, Joni Van Der Meulen, Frank Speleman and Hans-Guido Wendel: These authors contributed equally to this work.

Authors and 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

    Joni Van Der Meulen, Evelien Mets, Pieter Rondou, Pieter Van Vlierberghe & Frank Speleman

  3. Belgium,

    Joni Van Der Meulen, Evelien Mets, Pieter Rondou, Pieter Van Vlierberghe & Frank Speleman

  4. Weill Cornell Graduate School of Medical Sciences, New York, New York, USA

    Andrew L Wolfe, Aly A Khan & Manu Setty

  5. Department of Clinical Chemistry, Microbiology and Immunology, Ghent University Hospital, Ghent, Belgium

    Tom Taghon

  6. Computational Biology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA

    Aly A Khan, Manu Setty & Christina S Leslie

  7. Centre for Human Genetics, University Hospital Leuven, Leuven, Belgium

    Peter Vandenberghe

  8. INSERM U563, Toulouse, France

    Eric Delabesse

  9. Department of Pediatric Hematology-Oncology, Ghent University Hospital, Ghent, Belgium

    Yves Benoit

  10. Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, USA

    Nicholas B Socci

  11. Institute for Cancer Genetics, Columbia University, New York, New York, USA

    Pieter Van Vlierberghe

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  1. Konstantinos J Mavrakis
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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.

Corresponding author

Correspondence to Hans-Guido Wendel.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10 and Supplementary Tables 2–16. (PDF 2785 kb)

Supplementary Table 1

miRNA expression in primary T-ALL samples (XLSX 190 kb)

Supplementary Table 5

miRNA expression in T-ALL cell lines (XLSX 225 kb)

Supplementary Table 7

miRNA expression in normal T-cells and progenitor populations (XLSX 145 kb)

Supplementary Table 8

Comparative analysis of miRNA expression between normal T-cells and progenitor populations and human T-ALL samples (XLSX 76 kb)

Supplementary Table 9

Computational target prediction (a: by total context score; b: by number of 7- and 8-mer sites) (XLSX 21 kb)

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Mavrakis, K., Van Der Meulen, J., Wolfe, A. et al. A cooperative microRNA-tumor suppressor gene network in acute T-cell lymphoblastic leukemia (T-ALL). Nat Genet 43, 673–678 (2011). https://doi.org/10.1038/ng.858

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