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ACUTE MYELOID LEUKEMIA

Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity

Leukemia (2024)Cite this article

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Abstract

Acute myeloid leukemia (AML) has a poor prognosis and a heterogeneous mutation landscape. Although common mutations are well-studied, little research has characterized how the sequence of mutations relates to clinical features. Using published, single-cell DNA sequencing data from three institutions, we compared clonal evolution patterns in AML to patient characteristics, disease phenotype, and outcomes. Mutation trees, which represent the order of select mutations, were created for 207 patients from targeted panel sequencing data using 1 639 162 cells, 823 mutations, and 275 samples. In 224 distinct orderings of mutated genes, mutations related to DNA methylation typically preceded those related to cell signaling, but signaling-first cases did occur, and had higher peripheral cell counts, increased signaling mutation homozygosity, and younger patient age. Serial sample analysis suggested that NPM1 and DNA methylation mutations provide an advantage to signaling mutations in AML. Interestingly, WT1 mutation evolution shared features with signaling mutations, such as WT1-early being proliferative and occurring in younger individuals, trends that remained in multivariable regression. Some mutation orderings had a worse prognosis, but this was mediated by unfavorable mutations, not mutation order. These findings add a dimension to the mutation landscape of AML, identifying uncommon patterns of leukemogenesis and shedding light on heterogeneous phenotypes.

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Fig. 1: Evolutionary mutation trees summarized and merged.
Fig. 2: Clonal co-occurrence and mutation order across all genes and samples.
Fig. 3: Signaling mutation clone size varies with mutation order.
Fig. 4: Signaling and WTI mutations were frequently gained at relapse.
Fig. 5: Earlier DNA methylation mutations were associated with higher bone marrow blast percentages while earlier signaling mutations were associated with higher peripheral myeloid cell counts.
Fig. 6: Signaling mutation order was associated with mutation zygosity and patient age.

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Data availability

Genomic data that were created for this study are available on dbGaP with accession phs002049.v1.p1 and on Sequence Read Archive with NCBI BioProject ID PRJNA648656. Data from Stanford are being submitted to dbGaP. Clinical data are available on request.

Code availability

Code and trees are available on Github at https://github.com/mattschwede/aml-mutation-order.

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Acknowledgements

LAM was supported by the National Cancer Institute (NCI) grant R00CA252005. RLB was supported by grant 5R00CA248460 from the NCI. RM was supported by National Institutes of Health (NIH) Grant 1R01CA251331 and the Stanford Ludwig Center for Cancer Stem Cell Research and Medicine. TR was supported by the American Society for Hematology Graduate Hematology Award. MS was supported by the Leukemia & Lymphoma Society Fellow Award, the Chan-Zuckerberg Physician Scientist Award, and the Stanford Biomedical Informatics NLM training grant T15 LM007033-40. AE was supported by the NCI under award F32CA250304, the Advanced Residency Training Program at Stanford, and the American Society of Hematology Scholar Award. BB was supported by the Blavatnik Family Foundation and NIH training grant 5T32CA9302-40. RLL is supported by a Cycle For Survival Innovation Grant, NCI grant R35 CA197594, and the NIH/NCI Cancer Center support grant P30 CA008748. NB and JK were partially supported by SNSF Grant 310030 179518 (http://www.snf.ch). KT was supported by the AML/MDS Moonshot Grant from MD Anderson and the Leukemia Lymphoma Society Scholar Award.

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Author notes

  1. These authors contributed equally: Matthew Schwede, Katharina Jahn.

Authors and Affiliations

  1. Department of Medicine, Division of Hematology, Stanford University, Stanford, CA, USA

    Matthew Schwede, Asiri Ediriwickrema & Ravindra Majeti

  2. Department of Biomedical Data Science, Stanford University, School of Medicine, Stanford, CA, USA

    Matthew Schwede & Andrew J. Gentles

  3. Biomedical Data Science, Institute for Computer Science, Free University of Berlin, Berlin, Germany

    Katharina Jahn

  4. Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland

    Katharina Jahn, Jack Kuipers & Niko Beerenwinkel

  5. SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Katharina Jahn, Jack Kuipers & Niko Beerenwinkel

  6. Division of Experimental Hematology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Linde A. Miles

  7. Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA

    Linde A. Miles

  8. Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Robert L. Bowman

  9. Human Oncology and Pathogenesis Program, Molecular Cancer Medicine Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Troy Robinson & Ross Levine

  10. Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Troy Robinson

  11. Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Ken Furudate, Hidetaka Uryu, Tomoyuki Tanaka, Yuya Sasaki & Koichi Takahashi

  12. Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA

    Asiri Ediriwickrema & Ravindra Majeti

  13. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Asiri Ediriwickrema & Ravindra Majeti

  14. Department of Pathology, Stanford University, Stanford, CA, USA

    Brooks Benard & Andrew J. Gentles

  15. Department of Medicine, Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA

    Andrew J. Gentles

  16. Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Ross Levine

  17. Leukemia Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Ross Levine

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Contributions

MS, BB, AE, AG, RM, KT, KJ, JK, and NB conceived the project, designed analyses, and analyzed data. KF, KT, HU, TT, and YS designed sequencing data pipelines and analyzed and processed raw sequencing data. MS aggregated clinical and sequencing data, created figures and tables, implemented analyses, and wrote the initial draft of the manuscript. JK and KJ also implemented analyses for the project and advised on granular aspects of data analyses. LAM, RLL, RL, and TR also designed analyses and assisted with merging datasets. All authors assisted in revising the manuscript.

Corresponding authors

Correspondence to Niko Beerenwinkel, Koichi Takahashi or Ravindra Majeti.

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Competing interests

LAM has received honoraria from Mission Bio and has served on their Speakers’ Bureau (2020-2021). RM is on the Advisory Boards of Kodikaz Therapeutic Solutions, Orbital Therapeutics, Pheast Therapeutics, and 858 Therapeutics. RM is a co-founder and equity holder of Pheast Therapeutics, MyeloGene, and Orbital Therapeutics. RLL is on the supervisory board of QIAGEN and is a scientific advisor to Imago, Mission Bio, Syndax, Zentalis, Ajax, Bakx, Auron, Prelude, C4 Therapeutics, and Isoplexis for which he receives equity support. RLL receives research support from Ajax and Abbvie and has consulted for Incyte, Janssen, Morphosys, and Novartis. RLL has received honoraria from Astra Zeneca and Kura for invited lectures and from Gilead for grant reviews. KT has received honoraria from Mission Bio and Illumina Inc. and received scientific advisory fees from Symbio Pharmaceuticals.

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Schwede, M., Jahn, K., Kuipers, J. et al. Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity. Leukemia (2024). https://doi.org/10.1038/s41375-024-02211-z

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