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Acute myeloid leukemia

A four-gene LincRNA expression signature predicts risk in multiple cohorts of acute myeloid leukemia patients

Leukemia volume 32, pages 263–272 (2018)Cite this article

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Abstract

Prognostic gene expression signatures have been proposed as clinical tools to clarify therapeutic options in acute myeloid leukemia (AML). However, these signatures rely on measuring large numbers of genes and often perform poorly when applied to independent cohorts or those with older patients. Long intergenic non-coding RNAs (lincRNAs) are emerging as important regulators of cell identity and oncogenesis, but knowledge of their utility as prognostic markers in AML is limited. Here we analyze transcriptomic data from multiple cohorts of clinically annotated AML patients and report that (i) microarrays designed for coding gene expression can be repurposed to yield robust lincRNA expression data, (ii) some lincRNA genes are located in close proximity to hematopoietic coding genes and show strong expression correlations in AML, (iii) lincRNA gene expression patterns distinguish cytogenetic and molecular subtypes of AML, (iv) lincRNA signatures composed of three or four genes are independent predictors of clinical outcome and further dichotomize survival in European Leukemia Net (ELN) risk groups and (v) an analytical tool based on logistic regression analysis of quantitative PCR measurement of four lincRNA genes (LINC4) can be used to determine risk in AML.

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Acknowledgements

This work was supported by research grants, fellowships and scholarships from the National Health and Medical Research Council of Australia (JP, JWHW, DB, RJD’A, HSS, LBT and IDL), Australian Research Council (JWHW), Cancer Institute NSW (DB), Cure Cancer Australia Foundation (DB), Anthony Rothe Memorial Trust (JAIT and DB), Ian Potter Foundation (DB), UNSW Australia (YH and DC), the Translational Cancer Research Network of the Cancer Institute of NSW (DB, YH and DC) and the Wilhelm-Sander-Stiftung (TH). Biospecimens and/ or clinical data were provided by the South Australian Cancer Research Biobank (SACRB), which is supported by the Cancer Council SA Beat Cancer Project, Medvet Laboratories Pty Ltd and the Government of South Australia. We thank Diana Iarossi, Silke Danner and Ing Soo Tiong for maintaining the Acute Leukaemia Laboratory AML database and Ruud Delwel and Peter Valk for helpful discussion and the provision of expression and clinical data from the Dutch-Belgian Hemato-Oncology Cooperative Group.

Author contributions

DB, JAIT, CP, TH, AS, JO, LB, YH, DC, AB, MB, MP, performed research, contributed to study design, analysed and interpreted data; CH, XZ, BJH, AS, J-H K, WEB, BW, TB, WH, SKB, LBT, HSS, IDL, RJD’A. provided vital reagents and data, DB, JWHW and JEP designed the study and wrote the paper.

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Author notes

  1. J A I Thoms and C Palu: These authors contributed equally to this work.

Authors and Affiliations

  1. Adult Cancer Program, Lowy Cancer Research Centre, Prince of Wales Clinical School, University of New South Wales, Sydney, New South Wales, Australia

    D Beck, J A I Thoms, C Palu, A Shah, Y Huang, D Chacon, R J D'Andrea, J W H Wong & J E Pimanda

  2. Centre for Health Technologies and the School of Software, University of Technology, Sydney, NSW, Australia

    D Beck & J E Pimanda

  3. Department of Internal Medicine III, University Hospital Grosshadern, Ludwig Maximilians Universität, Munich, Germany

    T Herold & W Hiddemann

  4. School of Mathematics, UNSW Australia, Sydney, NSW, Australia

    J Olivier & R J D'Andrea

  5. Department of Medicine, Faculty of Medicine, Imperial College London, London, UK

    L Boelen

  6. Centre for Cancer Biology, SA Pathology, University of South Australia, Adelaide,, SA, Australia

    A Brown, M Babic, C Hahn, M Perugini, H S Scott, I D Lewis & R J D'Andrea

  7. School of Medicine, Wake Forest University, Winston-Salem, NC, USA

    X Zhou

  8. Department of Haematology, Cambridge Institute for Medical Research, Cambridge, UK

    B J Huntly

  9. Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany

    A Schwarzer & J-H Klusmann

  10. Department of Medicine, Hematology and Oncology, University of Münster, Münster, Germany

    W E Berdel & T Büchner

  11. Department of Medicine, Hematology, Oncology, Tumor Immunology, Charité—Universitätsmedizin Berlin, Berlin, Germany

    B Wörmann

  12. Laboratory for Leukemia Diagnostics, Ludwig Maximilians Universität, Munich, Germany

    W Hiddemann

  13. Department of Molecular Medicine and Pathology, University of Auckland, New Zealand

    S K Bohlander

  14. Department of Haematology, SA Pathology, Royal Adelaide Hospital, Adelaide, SA, Australia

    L B To & I D Lewis

  15. Department of Haematology, Prince of Wales Hospital, Randwick, NSW, Australia

    J E Pimanda

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  1. D Beck
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Correspondence to D Beck, J W H Wong or J E Pimanda.

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Beck, D., Thoms, J., Palu, C. et al. A four-gene LincRNA expression signature predicts risk in multiple cohorts of acute myeloid leukemia patients. Leukemia 32, 263–272 (2018). https://doi.org/10.1038/leu.2017.210

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