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A 17-gene stemness score for rapid determination of risk in acute leukaemia

Nature volume 540, pages 433–437 (2016)Cite this article

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Abstract

Refractoriness to induction chemotherapy and relapse after achievement of remission are the main obstacles to cure in acute myeloid leukaemia (AML)1. After standard induction chemotherapy, patients are assigned to different post-remission strategies on the basis of cytogenetic and molecular abnormalities that broadly define adverse, intermediate and favourable risk categories2,3. However, some patients do not respond to induction therapy and another subset will eventually relapse despite the lack of adverse risk factors4. There is an urgent need for better biomarkers to identify these high-risk patients before starting induction chemotherapy, to enable testing of alternative induction strategies in clinical trials5. The high rate of relapse in AML has been attributed to the persistence of leukaemia stem cells (LSCs), which possess a number of stem cell properties, including quiescence, that are linked to therapy resistance6,7,8,9,10. Here, to develop predictive and/or prognostic biomarkers related to stemness, we generated a list of genes that are differentially expressed between 138 LSC+ and 89 LSC− cell fractions from 78 AML patients validated by xenotransplantation. To extract the core transcriptional components of stemness relevant to clinical outcomes, we performed sparse regression analysis of LSC gene expression against survival in a large training cohort, generating a 17-gene LSC score (LSC17). The LSC17 score was highly prognostic in five independent cohorts comprising patients of diverse AML subtypes (n = 908) and contributed greatly to accurate prediction of initial therapy resistance. Patients with high LSC17 scores had poor outcomes with current treatments including allogeneic stem cell transplantation. The LSC17 score provides clinicians with a rapid and powerful tool to identify AML patients who do not benefit from standard therapy and who should be enrolled in trials evaluating novel upfront or post-remission strategies.

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Figure 1: Analysis of LSC-specific GE identifies an optimal 17-gene prognostic signature.
Figure 2: LSC signature scores are associated with OS in multiple independent AML cohorts across different GE measurement platforms.
Figure 3: Impact of aSCT on patient outcome.
Figure 4: LSC17 score predicts therapy response.

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Acknowledgements

This work was supported by grants from the Ontario Institute for Cancer Research with funds from the province of Ontario, the Cancer Stem Cell Consortium with funding from the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-047), and the Canadian Institutes of Health Research (CSC-105367), Canadian Cancer Society, Terry Fox Foundation, a Canada Research Chair to J.E.D., the Philip S. Orsino Chair in Leukemia Research to M.D.M., and a Collaborative Translational Cancer Research Grant from the Princess Margaret Cancer Centre (formerly Ontario Cancer Institute). This research was funded in part by the Leukemia & Lymphoma Society of Canada (493946) and the Stem Cell Network (492019), Ontario Graduate Scholarships, and the Ontario Ministry of Health and Long Term Care (OMOHLTC). The views expressed do not necessarily reflect those of the OMOHLTC. L.B. was supported in part by the Deutsche Forschungsgemeinschaft (Heisenberg-Professur BU 1339/8-1). T.H. was supported by the Wilhelm-Sander-Stiftung (grant 2013.086.1). K.M. and W.H. received grant support from Deutsche Forschungsgemeinschaft (DFG SFB 1243). We thank The Centre for Applied Genomics (Hospital for Sick Children) and the Princess Margaret Genomics Centre for the generation of GE data for the PM sorted cell fractions and validation cohort. We thank M. Pintilie for discussions regarding time-dependent covariates in survival analysis. We thank S. Geffroy for technical support and running the microarrays for the ALFA-0701 trial cohort.

Author information

Author notes

  1. Stanley W. K. Ng, Amanda Mitchell and James A. Kennedy: These authors contributed equally to this work.

  2. Thomas Büchner: Deceased.

  3. Mark D. Minden, John E. Dick and Jean C. Y. Wang: These authors jointly supervised this work.

Authors and Affiliations

  1. Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, M5G 1A1, Ontario, Canada

    Stanley W. K. Ng & Peter W. Zandstra

  2. Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 2M9, Ontario, Canada

    Amanda Mitchell, James A. Kennedy, Weihsu C. Chen, Jessica McLeod, Narmin Ibrahimova, Andrea Arruda, Andreea Popescu, Vikas Gupta, Aaron D. Schimmer, Andre C. Schuh, Karen W. Yee, Mark D. Minden, John E. Dick & Jean C. Y. Wang

  3. Division of Medical Oncology and Hematology, Department of Medicine, University Health Network, Toronto, M5G 2M9, Ontario, Canada

    James A. Kennedy, Vikas Gupta, Aaron D. Schimmer, Andre C. Schuh, Karen W. Yee, Mark D. Minden & Jean C. Y. Wang

  4. Department of Medicine, University of Toronto, Toronto, M5G 1A1, Ontario, Canada

    James A. Kennedy, Vikas Gupta, Aaron D. Schimmer, Andre C. Schuh, Karen W. Yee, Mark D. Minden & Jean C. Y. Wang

  5. Department of Medical Biophysics, University of Toronto, Toronto, M5G 1A1, Ontario, Canada

    Aaron D. Schimmer & Mark D. Minden

  6. Department of Internal Medicine III, University Hospital of Ulm, Ulm, 89081, Germany

    Lars Bullinger

  7. Department of Internal Medicine III, University of Munich, Munich, 81377, Germany

    Tobias Herold, Wolfgang Hiddemann & Klaus Metzeler

  8. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany

    Tobias Herold, Wolfgang Hiddemann & Klaus Metzeler

  9. Institute of Biostatistics and Clinical Research, University of Münster, Münster, 48149, Germany

    Dennis Görlich

  10. Department of Medicine, Hematology and Oncology, University of Münster, Münster, 48149, Germany

    Thomas Büchner & Wolfgang E. Berdel

  11. Department of Hematology, Oncology and Tumor Immunology, Charité University Medicine, Campus Virchow, Berlin, 10117, Germany

    Bernhard Wörmann

  12. Jean-Pierre AUBERT Research Center UMR-S 1172, Institute for Cancer Research Lille, Lille, 59045, France

    Meyling Cheok

  13. University Hospital of Lille, Center of Pathology, Laboratory of Hematology, Lille, 59037, France

    Claude Preudhomme

  14. Department of Hematology, Saint-Louis Hospital, University of Paris Diderot, Paris, 75010, France

    Hervé Dombret

  15. Comprehensive Cancer Center Ulm, Institute of Experimental Cancer Research, University Hospital of Ulm, Ulm, 89081, Germany

    Christian Buske

  16. Department of Hematology, Erasmus University Medical Centre, Rotterdam, 3015 CE, the Netherlands

    Bob Löwenberg & Peter J. M. Valk

  17. Department of Molecular Genetics, University of Toronto, Toronto, M5G 1A1, Ontario, Canada

    John E. Dick

Authors
  1. Stanley W. K. Ng
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Contributions

S.W.K.N. developed the signature derivation workflow, identified, refined and validated prognostic and predictive signatures, designed the custom NanoString assay, processed and analysed GE data, and performed statistical analyses and bioinformatics. A.M., W.C.C., J.M. and A.P. carried out functional xenograft transplantation, RNA extraction for GE analysis, and provided technical support for experiments. J.A.K., N.I., A.A., V.G., A.D.S., A.C.S., K.W.Y. and M.D.M. provided clinical annotations for the PM AML cohort. M.D.M. provided PM AML samples. S.W.K.N., J.C.Y.W., J.E.D. and M.D.M. interpreted the data. W.H., W.E.B., B.W., T.B., D.G., L.B., K.M., T.H. and C.B. provided clinical annotations for the GSE15434 and GSE12417 data sets. M.C., C.P. and H.D. provided GE and clinical data for the ALFA-0701 trial cohort. P.J.M.V. and B.L. provided clinical annotations for the GSE6891 data set. J.C.Y.W. and J.E.D. supervised the study. S.W.K.N. and J.C.Y.W. wrote the paper. A.M., J.A.K., P.W.Z., J.E.D. and M.D.M. revised the paper.

Corresponding author

Correspondence to Jean C. Y. Wang.

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Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks F. Holstege, G. Schuurhuis and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Overview of LSC signature training and testing.

a, Clinical characteristics of the 78 patients analysed by xenotransplantation and microarray GE analysis. CMML, chronic myelomonocytic leukaemia; t-AML, therapy-associated AML; CN, cytogenetically normal. b, Schematic of the experimental protocol. c, d, Summary of functionally defined LSC+ and LSC− fractions in each phenotypic cell population as a whole (c) and for each patient (d). Red and blue denote LSC+ and LSC−, respectively. In d, each row represents fractions sorted from one patient sample. White boxes denote fractions that were not included in the analysis due to insufficient cell numbers for xenotransplantation and/or insufficient RNA. e, Strategy used to identify and test the 17 LSC signature genes. f, Key clinical characteristics of the GSE6891 signature training cohort. *P value calculated using the Wilcoxon rank-sum test; †P value calculated using the Student’s t-test; ‡P value calculated using Pearson’s chi-squared test; §P value calculated using log-rank test; ||P value calculated using Fisher’s exact test; ¶cytogenetic risk groups were defined as per GSE6891 investigators15.

Extended Data Figure 2 LSC17 and LSC3 scores are associated with survival in multiple AML cohorts.

a–n, q, Kaplan–Meier estimates of OS, EFS or RFS according to LSC17 scores in various patient cohorts, as indicated. In c, patients were also analysed according to whether or not CR was achieved after initial treatment (no CR, dotted lines; CR, solid lines). i, The subset of patients in the TCGA AML cohort with no clear genomic classification as defined previously21. o, Simon and Makuch estimates of OS, according to LSC17 scores and whether or not patients received aSCT (no aSCT, dotted lines; aSCT, solid lines). p, Kaplan–Meier estimates of OS of CN-LMR patients, according to LSC3 scores. In a–q, patients with scores above and below the median in each cohort are shown by red and blue lines, respectively. r, s, Kaplan–Meier estimates of RFS for patients with high (r) or low (s) LSC17 scores treated with standard chemotherapy with (red lines) or without (blue lines) addition of GO.

Extended Data Table 1 List of 104 DE LSC genes
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Extended Data Table 2 Clinical characteristics of the TCGA AML cohort
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Extended Data Table 3 Clinical characteristics of the GSE12417 CN-AML cohorts
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Extended Data Table 4 Multivariate survival analysis of LSC17 and LSC3 scores
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Extended Data Table 5 The LSC17 score refines genomic classifications
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Extended Data Table 6 The LSC17 score improves survival association compared to other LSC signatures
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Extended Data Table 7 Clinical characteristics of the PM AML and GSE15434 CN-LMR AML cohorts
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Extended Data Table 8 Clinical characteristics and multivariate survival analysis of the ALFA-0701 AML cohort
Full size table

Supplementary information

Supplementary Information

This file contains a Supplementary Discussion of the 17 LSC signature genes. (PDF 84 kb)

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Ng, S., Mitchell, A., Kennedy, J. et al. A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature 540, 433–437 (2016). https://doi.org/10.1038/nature20598

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