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The branched-chain amino acid (BCAA) pathway and high levels of BCAA transaminase 1 (BCAT1) have recently been associated with aggressiveness in several cancer entities1,2,3,4,5,6. However, the mechanistic role of BCAT1 in this process remains largely uncertain. Here, by performing high-resolution proteomic analysis of human acute myeloid leukaemia (AML) stem-cell and non-stem-cell populations, we find the BCAA pathway enriched and BCAT1 protein and transcripts overexpressed in leukaemia stem cells. We show that BCAT1, which transfers α-amino groups from BCAAs to α-ketoglutarate (αKG), is a critical regulator of intracellular αKG homeostasis. Further to its role in the tricarboxylic acid cycle, αKG is an essential cofactor for αKG-dependent dioxygenases such as Egl-9 family hypoxia inducible factor 1 (EGLN1) and the ten-eleven translocation (TET) family of DNA demethylases7,8,9,10. Knockdown of BCAT1 in leukaemia cells caused accumulation of αKG, leading to EGLN1-mediated HIF1α protein degradation. This resulted in a growth and survival defect and abrogated leukaemia-initiating potential. By contrast, overexpression of BCAT1 in leukaemia cells decreased intracellular αKG levels and caused DNA hypermethylation through altered TET activity. AML with high levels of BCAT1 (BCAT1high) displayed a DNA hypermethylation phenotype similar to cases carrying a mutant isocitrate dehydrogenase (IDHmut), in which TET2 is inhibited by the oncometabolite 2-hydroxyglutarate11,12. High levels of BCAT1 strongly correlate with shorter overall survival in IDHWTTET2WT, but not IDHmut or TET2mut AML. Gene sets characteristic for IDHmut AML13 were enriched in samples from patients with an IDHWTTET2WTBCAT1high status. BCAT1high AML showed robust enrichment for leukaemia stem-cell signatures14,15, and paired sample analysis showed a significant increase in BCAT1 levels upon disease relapse. In summary, by limiting intracellular αKG, BCAT1 links BCAA catabolism to HIF1α stability and regulation of the epigenomic landscape, mimicking the effects of IDH mutations. Our results suggest the BCAA–BCAT1–αKG pathway as a therapeutic target to compromise leukaemia stem-cell function in patients with IDHWTTET2WT AML.

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Change history

  • 01 August 2018

    In Extended Data Fig. 1a of this Article, the FACS plot depicting the surface phenotype of AML sample DD08 was a duplicate of the plot for AML sample DD06. Supplementary Data 4 has been added to the Supplementary Information of the original Letter to clarify the proteome data acquisition and presentation. The original Letter has been corrected online.


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We thank all members of HI-STEM for discussions, M. Milsom and S. Haas for reading the manuscript, A. Ehninger for help with AML sample acquisition, the members of the Central Animal Laboratory at DKFZ for animal husbandry, the members of the DKFZ Flow Cytometry Core Facility for expertise and support, R. Delwel, P. Valk and B. Lowenberg for providing patient survival data for the Erasmus GSE14468 dataset, and A. Lenze for processing cord blood samples. We thank the EMBL Proteomics Core Facility for assistance with mass spectrometry analysis, the microarray unit of the DKFZ Genomics and Proteomics Core Facility for support, and the Metabolomics Core Technology Platform of the Excellence Cluster CellNetworks for support with ultra-performance liquid chromatography-based metabolite quantification. This work was supported by the SFB873 funded by the Deutsche Forschungsgemeinschaft (DFG) (C.L., C.S., and A.T.), the SyTASC consortium funded by the Deutsche Krebshilfe (A.T.) and the Dietmar Hopp Foundation (A.T.), by grant ZUK 49/2 from the DFG (G.P.), and the DFG Heisenberg-Professorship BU 1339/8-1 (L.B.).

Author information

Author notes

    • Simon Raffel
    • , Mattia Falcone
    •  & Niclas Kneisel

    These authors contributed equally to this work.

    • Bernhard Radlwimmer
    •  & Andreas Trumpp

    These authors jointly supervised this work.


  1. Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH), 69120 Heidelberg, Germany

    • Simon Raffel
    • , Mattia Falcone
    • , Andrea Barnert
    • , Carsten Bahr
    • , Petra Zeisberger
    • , Adriana Przybylla
    • , Markus Sohn
    •  & Andreas Trumpp
  2. Division of Stem Cells and Cancer, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120 Heidelberg, Germany

    • Simon Raffel
    • , Mattia Falcone
    • , Andrea Barnert
    • , Carsten Bahr
    • , Petra Zeisberger
    • , Adriana Przybylla
    • , Markus Sohn
    •  & Andreas Trumpp
  3. Department of Internal Medicine V, Heidelberg University Hospital, 69120 Heidelberg, Germany

    • Simon Raffel
    • , Christoph Lutz
    • , Patrick Wuchter
    •  & Anthony D. Ho
  4. Division of Molecular Genetics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany

    • Niclas Kneisel
    • , Wei Wang
    • , Martje Tönjes
    • , Peter Lichter
    •  & Bernhard Radlwimmer
  5. Genome Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany

    • Jenny Hansson
    •  & Jeroen Krijgsveld
  6. Department of Internal Medicine III, University Hospital Ulm, 89081 Ulm, Germany

    • Lars Bullinger
    •  & Sibylle Cocciardi
  7. Centre for Organismal Studies (COS), University of Heidelberg, 69120 Heidelberg, Germany

    • Gernot Poschet
    •  & Rüdiger Hell
  8. Department of Bioinfomatics and Biochemistry and Braunschweig Integrated Center of Systems Biology (BRICS), Technical University Braunschweig, 38106 Braunschweig, Germany

    • Yannic Nonnenmacher
    •  & Karsten Hiller
  9. Luxemburg Centre for Systems Biomedicine, University of Luxemburg, L-4367 Belvaux, Luxemburg

    • Yannic Nonnenmacher
    •  & Karsten Hiller
  10. Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel

    • Ayelet Erez
    •  & Lital Adler
  11. Department of Translational Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany

    • Patrizia Jensen
    •  & Stefan Fröhling
  12. Division of Applied Functional Genomics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany

    • Claudia Scholl
  13. Section for Personalized Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany

    • Stefan Fröhling
  14. Institute of Transfusion Medicine and Immunology Mannheim, Medical Faculty Mannheim, Heidelberg University, German Red Cross Blood Service Baden-Württemberg-Hessen, 68167 Mannheim, Germany

    • Patrick Wuchter
  15. Medical Department 1, University Hospital Carl Gustav Carus, 01307 Dresden, Germany

    • Christian Thiede
  16. Department of Hematology, Oncology and Tumor Immunology; Charité-University Medicine Berlin, Campus Virchow Klinikum, 13353 Berlin, Germany

    • Anne Flörcken
    • , Jörg Westermann
    •  & Gerhard Ehninger
  17. Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany

    • Carl Herrmann
  18. Institute of Pharmacy and Molecular Biotechnology, and Bioquant Center, University of Heidelberg, 69120 Heidelberg, Germany

    • Carl Herrmann
  19. German Cancer Consortium (DKTK), DKFZ, 69120 Heidelberg, Germany

    • Claudia Scholl
    • , Stefan Fröhling
    • , Peter Lichter
    • , Bernhard Radlwimmer
    •  & Andreas Trumpp


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S.R. designed the study and performed experiments; M.F. performed experiments and bioinformatic analyses with conceptual input from S.R. and C.H.; N.K. performed tracing experiments with the help of Y.N. and K.H.; J.H. and J.K. generated and analysed the proteome data; W.W. helped with cloning, generated growth curves and colony-forming unit assays on HL-60 KD cells, and performed western blotting on primary samples; C.L. and A.D.H. provided AML samples, clinical data, and conceptual input; L.B. provided the GSE16432 dataset and conceptual input; G.P. and R.H. performed targeted metabolomics; A.B., C.B., P.Z., A.P., and M.S. helped with mouse and in vitro experiments; M.T., A.E., L.A., P.J., C.S., and S.F. gave conceptual input; S.C. and L.B. performed RNA-sequencing of paired diagnosis/relapse samples; C.T., A.F., J.W., and G.E. provided AML samples, P.L. financial support, and P.W. healthy HSPC samples; A.T. designed with S.R. the overall study and supervised it. B.R. helped to design and supervise parts of the study; S.R., M.F., N.K., B.R., and A.T. interpreted the results; and S.R. wrote the manuscript with M.F., N.K., B.R., and A.T.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Bernhard Radlwimmer or Andreas Trumpp.

Reviewer Information Nature thanks M. G. Vander Heiden, R. Levine and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Supplementary information

PDF files

  1. 1.

    Life Sciences Reporting Summary

  2. 2.

    Supplementary Figure 1

    This file contains uncropped scans of western blots with size marker indication.

  3. 3.

    Supplementary Table 1

    This file contains patient characteristics and leukaemia-initiating potential of sorted populations after transplantation into NSG mice.

Excel files

  1. 1.

    Supplementary Data 1

    This file contains differentially expressed proteins between LSC and non-LSC populations for all six patients used for proteomic analysis.

  2. 2.

    Supplementary Data 2

    This file contains GSEA for c2cp and hallmark gene sets comparing LSC to non-LSC populations.

  3. 3.

    Supplementary Data 3

    Differentially methylated CpGs comparing IDHmut to IDHwtBCAT1Q4 and IDHwtBCAT1Q1 to IDHwtBCAT1Q4 AML patients in the TCGA dataset and overlap to BCAT1-overexpressing AML cell lines.

  4. 4.

    Supplementary Data 4

    This file indicates sampling and labelling strategies and lists all identified proteins and their differential expression in LSC compared to non-LSC.

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