Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

ACUTE MYELOID LEUKEMIA

Mapping AML heterogeneity - multi-cohort transcriptomic analysis identifies novel clusters and divergent ex-vivo drug responses

Leukemia volume 38, pages 751–761 (2024)Cite this article

Subjects

Abstract

Subtyping of acute myeloid leukaemia (AML) is predominantly based on recurrent genetic abnormalities, but recent literature indicates that transcriptomic phenotyping holds immense potential to further refine AML classification. Here we integrated five AML transcriptomic datasets with corresponding genetic information to provide an overview (n = 1224) of the transcriptomic AML landscape. Consensus clustering identified 17 robust patient clusters which improved identification of CEBPA-mutated patients with favourable outcomes, and uncovered transcriptomic subtypes for KMT2A rearrangements (2), NPM1 mutations (5), and AML with myelodysplasia-related changes (AML-MRC) (5). Transcriptomic subtypes of KMT2A, NPM1 and AML-MRC showed distinct mutational profiles, cell type differentiation arrests and immune properties, suggesting differences in underlying disease biology. Moreover, our transcriptomic clusters show differences in ex-vivo drug responses, even when corrected for differentiation arrest and superiorly capture differences in drug response compared to genetic classification. In conclusion, our findings underscore the importance of transcriptomics in AML subtyping and offer a basis for future research and personalised treatment strategies. Our transcriptomic compendium is publicly available and we supply an R package to project clusters to new transcriptomic studies.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Transcriptomic analysis further stratifies AML.
Fig. 2: Transcriptome analysis identifies two KMT2A-related clusters.
Fig. 3: The CEBPAT cluster indicates a favourable prognosis.
Fig. 4: Gene expression profiling identifies five transcriptional NPM1-related clusters.
Fig. 5: Gene expression profiling identifies five transcriptional AML-MRC-related clusters.
Fig. 6: AML clusters exhibit cell type-independent differences in ex-vivo drug responses.

Similar content being viewed by others

Data availability

The datasets generated and/or analysed during the current study are available from https://osf.io/wq7gx (https://doi.org/10.17605/OSF.IO/WQ7GX).

Code availability

All code used to generate results is available on reasonable request. The predictor is available from https://github.com/jeppeseverens/AMLmapR as an R package.

References

  1. The Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013;368:2059–74.

    Article  PubMed Central  Google Scholar 

  2. Farrar JE, Schuback HL, Ries RE, Wai D, Hampton OA, Trevino LR, et al. Genomic profiling of pediatric acute myeloid leukemia reveals a changing mutational landscape from disease diagnosis to relapse. Cancer Res. 2016;76:2197–205.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Tyner JW, Tognon CE, Bottomly D, Wilmot B, Kurtz SE, Savage SL, et al. Functional genomic landscape of acute myeloid leukaemia. Nature. 2018;562:526–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND, et al. Genomic classification and prognosis in acute myeloid leukemia. N Engl J Med. 2016;374:2209–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Tazi Y, Arango-Ossa JE, Zhou Y, Bernard E, Thomas I, Gilkes A, et al. Unified classification and risk-stratification in Acute Myeloid Leukemia. Nat Commun. 2022;13:4622.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Khoury JD, Solary E, Abla O, Akkari Y, Alaggio R, Apperley JF, et al. The 5th edition of the World Health Organization classification of haematolymphoid tumours: myeloid and histiocytic/dendritic neoplasms. Leukemia. 2022;36:1703–19.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Arber DA, Orazi A, Hasserjian RP, Borowitz MJ, Calvo KR, Kvasnicka H-M, et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data. Blood. 2022;140:1200–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Döhner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H, et al. Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood. 2022;140:1345–77.

    Article  PubMed  Google Scholar 

  9. Burd A, Levine RL, Ruppert AS, Mims AS, Borate U, Stein EM, et al. Precision medicine treatment in acute myeloid leukemia using prospective genomic profiling: feasibility and preliminary efficacy of the Beat AML Master Trial. Nat Med. 2020;26:1852–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Valk PJM, Verhaak RGW, Beijen MA, Erpelinck CAJ, van Doorn-Khosrovani SB, van W, et al. Prognostically useful gene-expression profiles in acute myeloid leukemia. N Engl J Med. 2004;350:1617–28.

    Article  CAS  PubMed  Google Scholar 

  11. Lavallée V-P, Baccelli I, Krosl J, Wilhelm B, Barabé F, Gendron P, et al. The transcriptomic landscape and directed chemical interrogation of MLL-rearranged acute myeloid leukemias. Nat Genet. 2015;47:1030–7.

    Article  PubMed  Google Scholar 

  12. Mou T, Pawitan Y, Stahl M, Vesterlund M, Deng W, Jafari R, et al. The transcriptome-wide landscape of molecular subtype-specific mRNA expression profiles in acute myeloid leukemia. Am J Hematol. 2021;96:580–8.

    Article  CAS  PubMed  Google Scholar 

  13. Wouters BJ, Löwenberg B, Erpelinck-Verschueren CAJ, Van Putten WLJ, Valk PJM, Delwel R. Double CEBPA mutations, but not single CEBPA mutations, define a subgroup of acute myeloid leukemia with a distinctive gene expression profile that is uniquely associated with a favorable outcome. Blood. 2009;113:3088–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Taskesen E, Bullinger L, Corbacioglu A, Sanders MA, Erpelinck CAJ, Wouters BJ, et al. Prognostic impact, concurrent genetic mutations, and gene expression features of AML with CEBPA mutations in a cohort of 1182 cytogenetically normal AML patients: further evidence for CEBPA double mutant AML as a distinctive disease entity. Blood. 2011;117:2469–75.

    Article  CAS  PubMed  Google Scholar 

  15. Mer AS, Heath EM, Madani Tonekaboni SA, Dogan-Artun N, Nair SK, Murison A, et al. Biological and therapeutic implications of a unique subtype of NPM1 mutated AML. Nat Commun. 2021;12:1054.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Cheng W-Y, Li J-F, Zhu Y-M, Lin X-J, Wen L-J, Zhang F, et al. Transcriptome-based molecular subtypes and differentiation hierarchies improve the classification framework of acute myeloid leukemia. Proc Natl Acad Sci. 2022;119:e2211429119.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. de Leeuw DC, Ossenkoppele GJ, Janssen JJWM. Older patients with acute myeloid leukemia deserve individualized treatment. Curr Oncol Rep. 2022;24:1387–400.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Improved relative survival in older patients with acute myeloid leukemia over a 30-year period in the Netherlands: a long haul is needed to change nothing into something. Leukemia, https://www.nature.com/articles/s41375-021-01503-y. Accessed 25 October 2022.

  19. Zeng AGX, Bansal S, Jin L, Mitchell A, Chen WC, Abbas HA, et al. A cellular hierarchy framework for understanding heterogeneity and predicting drug response in acute myeloid leukemia. Nat Med. 2022;28:1212–23.

    Article  CAS  PubMed  Google Scholar 

  20. Bottomly D, Long N, Schultz AR, Kurtz SE, Tognon CE, Johnson K, et al. Integrative analysis of drug response and clinical outcome in acute myeloid leukemia. Cancer Cell. 2022;40:850–64.e9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Macrae T, Sargeant T, Lemieux S, Hébert J, Deneault E, Sauvageau G. RNA-Seq reveals spliceosome and proteasome genes as most consistent transcripts in human cancer cells. PloS One. 2013;8:e72884.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Lavallée V-P, Lemieux S, Boucher G, Gendron P, Boivin I, Armstrong RN, et al. RNA-sequencing analysis of core binding factor AML identifies recurrent ZBTB7A mutations and defines RUNX1-CBFA2T3 fusion signature. Blood. 2016;127:2498–501.

    Article  PubMed  Google Scholar 

  23. Pabst C, Bergeron A, Lavallée V-P, Yeh J, Gendron P, Norddahl GL, et al. GPR56 identifies primary human acute myeloid leukemia cells with high repopulating potential in vivo. Blood. 2016;127:2018–27.

    Article  CAS  PubMed  Google Scholar 

  24. Arindrarto W, Borràs DM, de Groen RAL, van den Berg RR, Locher IJ, van Diessen SAME, et al. Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing. Leukemia. 2020;35:47–61.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinforma Oxf Engl. 2013;29:15–21.

    Article  CAS  Google Scholar 

  26. Genome Reference Consortium, https://www.ncbi.nlm.nih.gov/grc. Accessed 15 August 2023.

  27. Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, et al. GENCODE 2021. Nucleic Acids Res. 2021;49:D916–23.

    Article  CAS  PubMed  Google Scholar 

  28. Zhang Y, Parmigiani G, Johnson WE. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genomics Bioinforma. 2020;2:lqaa078.

    Article  Google Scholar 

  29. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Büttner M, Miao Z, Wolf FA, Teichmann SA, Theis FJ. A test metric for assessing single-cell RNA-seq batch correction. Nat Methods. 2019;16:43–9.

    Article  PubMed  Google Scholar 

  31. Uhrig S, Ellermann J, Walther T, Burkhardt P, Fröhlich M, Hutter B, et al. Accurate and efficient detection of gene fusions from RNA sequencing data. Genome Res. 2021;31:448–60.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn. 2003;52:91–118.

    Article  Google Scholar 

  33. Jeub LGS, Sporns O, Fortunato S. Multiresolution consensus clustering in networks. Sci Rep. 2018;8:3259.

    Article  PubMed  PubMed Central  Google Scholar 

  34. McInnes L, Healy J, Melville J. UMAP: uniform manifold approximation and projection for dimension reduction. Epub ahead of print 17 September 2020. https://doi.org/10.48550/arXiv.1802.03426.

  35. Traag VA, Waltman L, van Eck NJ. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep. 2019;9:5233.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lambert SA, Jolma A, Campitelli LF, Das PK, Yin Y, Albu M, et al. The human transcription factors. Cell. 2018;172:650–65.

    Article  CAS  PubMed  Google Scholar 

  37. Bausch-Fluck D, Hofmann A, Bock T, Frei AP, Cerciello F, Jacobs A, et al. A mass spectrometric-derived cell surface protein atlas. PLOS One. 2015;10:e0121314.

    Article  PubMed  PubMed Central  Google Scholar 

  38. van Galen P, Hovestadt V, Wadsworth MH II, Hughes TK, Griffin GK, Battaglia S, et al. Single-cell RNA-seq reveals AML hierarchies relevant to disease progression and immunity. Cell. 2019;176:1265–81.e24.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Dufva O, Pölönen P, Brück O, Keränen MAI, Klievink J, Mehtonen J, et al. Immunogenomic landscape of hematological malignancies. Cancer Cell. 2020;38:380–99.e13.

    Article  CAS  PubMed  Google Scholar 

  40. Patel JP, Gönen M, Figueroa ME, Fernandez H, Sun Z, Racevskis J, et al. Prognostic relevance of integrated genetic profiling in acute myeloid leukemia. N Engl J Med. 2012;366:1079–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Gracia-Maldonado G, Clark J, Mulloy JC, Kumar AR. LAMP5 - a novel target of MLL-fusion proteins is required for the propagation of leukemia. Blood. 2016;128:1512.

    Article  Google Scholar 

  42. Milan T, Celton M, Lagacé K, Roques É, Safa-Tahar-Henni S, Bresson E, et al. Epigenetic changes in human model KMT2A leukemias highlight early events during leukemogenesis. Haematologica. 2022;107:86–99.

    Article  CAS  PubMed  Google Scholar 

  43. Wakita S, Sakaguchi M, Oh I, Kako S, Toya T, Najima Y, et al. Prognostic impact of CEBPA bZIP domain mutation in acute myeloid leukemia. Blood Adv. 2022;6:238–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Brunetti L, Gundry MC, Sorcini D, Guzman AG, Huang Y-H, Ramabadran R, et al. Mutant NPM1 maintains the leukemic state through HOX expression. Cancer Cell. 2018;34:499–512.e9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Falini B, Bigerna B, Pucciarini A, Tiacci E, Mecucci C, Morris SW, et al. Aberrant subcellular expression of nucleophosmin and NPM-MLF1 fusion protein in acute myeloid leukaemia carrying t(3;5): a comparison with NPMc+ AML. Leukemia. 2006;20:368–71.

    Article  CAS  PubMed  Google Scholar 

  46. Aguilo F, Avagyan S, Labar A, Sevilla A, Lee D-F, Kumar P, et al. Prdm16 is a physiologic regulator of hematopoietic stem cells. Blood. 2011;117:5057.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Imperato MR, Cauchy P, Obier N, Bonifer C. The RUNX1–PU.1 axis in the control of hematopoiesis. Int J Hematol. 2015;101:319–29.

    Article  CAS  PubMed  Google Scholar 

  48. Hollox EJ, Louzada S. Genetic variation of glycophorins and infectious disease. Immunogenetics. 2022. https://doi.org/10.1007/s00251-022-01280-7.

  49. Greaves MF, Sieff C, Edwards PAW. Monoclonal antiglycophorin as a probe for erythroleukemias. Blood. 1983;61:645–51.

    Article  CAS  PubMed  Google Scholar 

  50. Andersson LC, Jokinen M, Gahmberg CG. Induction of erythroid differentiation in the human leukaemia cell line K562. Nature. 1979;278:364–5.

    Article  CAS  PubMed  Google Scholar 

  51. Hollink IHIM, van den Heuvel-Eibrink MM, Arentsen-Peters STCJM, Zimmermann M, Peeters JK, Valk PJM, et al. Characterization of CEBPA mutations and promoter hypermethylation in pediatric acute myeloid leukemia. Haematologica. 2011;96:384–92.

    Article  CAS  PubMed  Google Scholar 

  52. Mohan M, Lin C, Guest E, Shilatifard A. Licensed to elongate: a molecular mechanism for MLL-based leukaemogenesis. Nat Rev Cancer. 2010;10:721–8.

    Article  CAS  PubMed  Google Scholar 

  53. Liedtke M, Ayton PM, Somervaille TCP, Smith KS, Cleary ML. Self-association mediated by the Ras association 1 domain of AF6 activates the oncogenic potential of MLL-AF6. Blood. 2010;116:63–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Meyer C, Larghero P, Almeida Lopes B, Burmeister T, Gröger D, Sutton R, et al. The KMT2A recombinome of acute leukemias in 2023. Leukemia. 2023;37:988–1005.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Mason EF, Kuo FC, Hasserjian RP, Seegmiller AC, Pozdnyakova O. A distinct immunophenotype identifies a subset of NPM1-mutated AML with TET2 or IDH1/2 mutations and improved outcome. Am J Hematol. 2018;93:504–10.

    Article  CAS  PubMed  Google Scholar 

  56. Martelli MP, Rossi R, Venanzi A, Meggendorfer M, Perriello VM, Martino G, et al. Novel NPM1 exon 5 mutations and gene fusions leading to aberrant cytoplasmic nucleophosmin in AML. Blood. 2021;138:2696–701.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Figueroa ME, Wahab OA, Lu C, Ward PS, Patel J, Shih A, et al. Leukemic IDH1 and IDH2 mutations result in a hypermethylation phenotype, disrupt TET2 function, and impair hematopoietic differentiation. Cancer Cell. 2010;18:553–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Morita K, Wang F, Jahn K, Hu T, Tanaka T, Sasaki Y, et al. Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics. Nat Commun. 2020;11:5327.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Krivtsov AV, Figueroa ME, Sinha AU, Stubbs MC, Feng Z, Valk PJM, et al. Cell of origin determines clinically relevant subtypes of MLL-rearranged AML. Leukemia. 2013;27:852–60.

    Article  CAS  PubMed  Google Scholar 

  60. Willemsen AECAB, Krausz S, Ligtenberg MJL, Grünberg K, Groen HJM, Voest EE, et al. Molecular tumour boards and molecular diagnostics for patients with cancer in the Netherlands: experiences, challenges, and aspirations. Br J Cancer. 2019;121:34–6.

Download references

Acknowledgements

This project was funded by the Dutch Cancer Society (project number 15152) and by a strategic investment of the Leiden University Medical Center, embedded within the Leiden Oncology Center, and executed within the Leiden Center for Computational Oncology. EBA was funded by a personal grant from the Dutch Research Council (NWO; VENI: 09150161810095). The funding bodies had no role in the study design, the collection, analysis, and interpretation of data, the writing of the manuscript, and the decision to submit the manuscript for publication.

Author information

Authors and Affiliations

  1. Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

    Jeppe F. Severens, E. Onur Karakaslar, Marcel J. T. Reinders & Erik B. van den Akker

  2. Pattern Recognition & Bioinformatics, Delft University of Technology, Delft, The Netherlands

    Jeppe F. Severens, E. Onur Karakaslar, Marcel J. T. Reinders & Erik B. van den Akker

  3. Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, The Netherlands

    Jeppe F. Severens, E. Onur Karakaslar, Elena Sánchez-López, Hendrik Veelken, Marcel J. T. Reinders & Erik B. van den Akker

  4. Laboratory of Hematology, Department of Laboratory Medicine, Radboud University Medical Center, Nijmegen, The Netherlands

    Bert A. van der Reijden

  5. Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands

    Elena Sánchez-López

  6. Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands

    Redmar R. van den Berg & Marcel J. T. Reinders

  7. Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands

    Redmar R. van den Berg, Constantijn J. M. Halkes, Peter van Balen, Hendrik Veelken & Marieke Griffioen

Authors
  1. Jeppe F. Severens
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Onur Karakaslar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bert A. van der Reijden
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elena Sánchez-López
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Redmar R. van den Berg
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Constantijn J. M. Halkes
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Peter van Balen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hendrik Veelken
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Marcel J. T. Reinders
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Marieke Griffioen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Erik B. van den Akker
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MJR, MG, and EBA conceived and designed the project; EBA acquired funding; EBA performed project administration; MG, EBA, HV, RRB, CJMH, PB performed oversight and management of resources (data generation, collection, transfer, infrastructure, data processing); JFS performed computational and statistical analyses; JFS, EBA, MG, EOK, ES-L performed analyses and interpretation; JFS performed and structured data visualisation; MJR, MG and EBA provided supervision and scientific direction; JFS wrote the manuscript; and all authors critically reviewed the manuscript and figures.

Corresponding author

Correspondence to Erik B. van den Akker.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Severens, J.F., Karakaslar, E.O., van der Reijden, B.A. et al. Mapping AML heterogeneity - multi-cohort transcriptomic analysis identifies novel clusters and divergent ex-vivo drug responses. Leukemia 38, 751–761 (2024). https://doi.org/10.1038/s41375-024-02137-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41375-024-02137-6

Search

Advanced search

Quick links