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A clinical solution for tracking clonal evolution of acute myeloid leukemia after allogeneic transplantation using bulk next generation sequencing

Bone Marrow Transplantation volume 60, pages 1083–1091 (2025)Cite this article

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Abstract

Clinical next generation sequencing (NGS) typically relies on limited gene panels run on bulk marrow or blood. Current computational tools for inferring clonal relationships is generally limited by the use of a small panel of pathogenic mutations to define clones. We developed an online software (CloneTracker) that uses ‘incidentally-sequenced’ single nucleotide polymorphisms (SNPs) in the regions of recurrent somatic mutations in addition to conventional mutation data from bulk NGS gene panels to provide detailed visualizations of clonal evolution during cancer treatment, alongside clinical data. Tested on 29 patients who underwent non-myeloablative transplantation for AML, CloneTracker successfully reconstructed the evolutionary dynamics of donor engraftment from bulk NGS and rendered intuitive visualizations of residual patient-derived hematopoiesis and relapsing malignant clones. The software does not require sequencing donor samples, as donor-derived clones are identifiable from post-HCT SNP data. This manuscript aims to introduce CloneTracker to the BMT community and make it available for those who would ascertain its clinical utility, e.g, in BMT trials leveraging molecular minimal residual disease (MRD) monitoring and targeted interventions to pre-empt relapse.

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Fig. 1: Schematic of CloneTracker core functions.
Fig. 2: Clonal dynamics reflect predicted donor and patient-derived clones, confirmed with donor sequencing.
Fig. 3: Illustrative cases.

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Data availability

For original data and the code to reproduce all analyses, or to inquire about hosting CloneTracker on your own server or joining the CloneTracker development project, please contact Dr. Krakow.

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Acknowledgements

Sources of funding included the Archer Reagent Challenge Grant (CY), P30 CA015704, and P01 CA078902 (BS).

Author information

Author notes

  1. Nathan Lee, Cecilia CS Yeung & Ivana Bozic

    Present address: Berlin Institute for the Foundations of Learning and Data (BIFOLD) & Institute for Computational Cancer Biology, Cologne, Germany

  2. These authors contributed equally: Elizabeth F. Krakow, Nathan Lee.

  3. These authors jointly supervised this work: Cecilia CS Yeung, Ivana Bozic.

Authors and Affiliations

  1. Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Elizabeth F. Krakow & Bryce Fukuda

  2. Division of Hematology-Oncology, University of Washington, Seattle, WA, USA

    Elizabeth F. Krakow, Jerald P. Radich & Brenda M. Sandmaier

  3. Department of Applied Mathematics, University of Washington, Seattle, WA, USA

    Nathan Lee & Ivana Bozic

  4. Department of Biostatistics, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Isaac Jenkins

  5. Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Olga Sala-Torra, Lan Beppu, Jerald P. Radich, Brenda M. Sandmaier & Cecilia CS Yeung

  6. Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA

    Cecilia CS Yeung

  7. Translational Data Science Integrated Research Core, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Ivana Bozic

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Contributions

EFK identified the clinical need and conceived the CloneTracker platform as detailed in Fig. 1 (that she created), assembled and led the research team, obtained IRB approval for donor sample sequencing, collected additional clinical data through chart review, and drafted the manuscript. CY and IB conceived of using incidentally-sequenced SNPs from the targeted gene panel sequencing output to identify clones. IB supervised PhD student NL, who evaluated, compared, selected and refined algorithms for clustering gene variants and inferring phylogenetic trees, and incorporated information from sex chromosomes in sex-matched and sex-mismatched transplant settings. NL performed statistical analysis comparing clonal prevalence in relapsed to non-relapsed patients, and created Fig. 2. EFK performed statistical analysis in Table 1 and created Fig. 3. LB and OST performed the Archer sequencing in the lab of JR. CY and OST evaluated the accuracy and pathogenicity of each putative gene variant, performed quality control, and established the filtering parameters with input from NL and IB, and they were implemented by NL. OST developed the harmonized vcf template with input from IB, CY, JR and EFK. IB provided simple criteria to assign donor origin to a clone. IJ developed the fishplot code and the visualization dashboard in R-Shiny. BF refined the dashboard, implemented the overall workflow (pipeline), and annotated the code in preparation for sharing it with future collaborators. BS designed, obtained IRB approval, and led the clinical trial in which serial patient samples were collected, and provided clinical data. OST, CY, NL and EFK drafted the eMethods supplement. OST prepared the supplemental Excel files. EFK, NL, CY, JR and IB interpreted the clonal dynamics in the context of the clinical treatment-and-response trajectories, patient-by-patient. All authors reviewed and edited the manuscript and supplemental files. Funding was provided by EFK, IB, CY, JR and BS.

Corresponding authors

Correspondence to Cecilia CS Yeung or Ivana Bozic.

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Competing interests

Authors EFK, NL, IB, IJ, JR, OST and CY are inventors of patent applications filed by FHCC directed to the system for tracking cancer clones alongside clinical data as described in this manuscript.

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Krakow, E.F., Lee, N., Jenkins, I. et al. A clinical solution for tracking clonal evolution of acute myeloid leukemia after allogeneic transplantation using bulk next generation sequencing. Bone Marrow Transplant 60, 1083–1091 (2025). https://doi.org/10.1038/s41409-025-02602-5

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