Abstract

Mixed phenotype acute leukaemia (MPAL) is a high-risk subtype of leukaemia with myeloid and lymphoid features, limited genetic characterization, and a lack of consensus regarding appropriate therapy. Here we show that the two principal subtypes of MPAL, T/myeloid (T/M) and B/myeloid (B/M), are genetically distinct. Rearrangement of ZNF384 is common in B/M MPAL, and biallelic WT1 alterations are common in T/M MPAL, which shares genomic features with early T-cell precursor acute lymphoblastic leukaemia. We show that the intratumoral immunophenotypic heterogeneity characteristic of MPAL is independent of somatic genetic variation, that founding lesions arise in primitive haematopoietic progenitors, and that individual phenotypic subpopulations can reconstitute the immunophenotypic diversity in vivo. These findings indicate that the cell of origin and founding lesions, rather than an accumulation of distinct genomic alterations, prime tumour cells for lineage promiscuity. Moreover, these findings position MPAL in the spectrum of immature leukaemias and provide a genetically informed framework for future clinical trials of potential treatments for MPAL.

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Acknowledgements

We thank the Biorepository, the Genome Sequencing Facility of the Hartwell Center for Bioinformatics and Biotechnology, and the Flow Cytometry and Cell Sorting core facility and Cytogenetics core facility of St. Jude Children’s Research Hospital (SJCRH). This work was supported in part by the American Lebanese Syrian Associated Charities of SJCRH, Cookies for Kids Cancer (to H.I.), St. Baldrick’s Foundation Robert J. Arceci Innovation Award and Henry Schueler 41&9 Foundation (to C.G.M.), SJCRH Physician Scientist Training Program Fellowship (to T.B.A.), the National Cancer Institute grants P30 CA021765 (SJCRH Cancer Center Support Grant), Chair’s grant and supplement to support the COG ALL TARGET project), U10 CA98413 (to the COG Statistical Center), U24 CA114766 (to COG; Specimen Banking), and Outstanding Investigator Award R35 CA197695 (to C.G.M.). The results published here are in part based upon data generated by the Therapeutically Applicable Research to Generate Effective Treatments initiative of the NCI (http://ocg.cancer.gov/programs/target). This project has been funded in part with Federal funds from the National Cancer Institute, National Institutes of Health, under contract No. HHSN261200800001E (to C.G.M. and Michael Smith Genome Sciences Centre). The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government. We acknowledge Canada's Michael Smith Genome Sciences Centre, Vancouver, Canada for library construction and sequencing. A full list of funders of infrastructure and research supporting the services accessed is available at www.bcgsc.ca/about/funding_support.

Reviewer information

Nature thanks R. Levine and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors contributed equally: Thomas B. Alexander, Zhaohui Gu, Ilaria Iacobucci

Affiliations

  1. Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    • Thomas B. Alexander
    • , Jeffrey E. Rubnitz
    • , Ching-Hon Pui
    • , Kim E. Nichols
    •  & Hiroto Inaba
  2. Department of Pediatrics, University of North Carolina, Chapel Hill, NC, USA

    • Thomas B. Alexander
  3. Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    • Zhaohui Gu
    • , Ilaria Iacobucci
    • , Kirsten Dickerson
    • , John K. Choi
    • , Debbie Payne-Turner
    • , Hiroki Yoshihara
    • , Laura J. Janke
    • , James R. Downing
    •  & Charles G. Mullighan
  4. Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    • Beisi Xu
    • , Liang Ding
    • , Yu Liu
    • , Jinghui Zhang
    •  & Scott Newman
  5. Department of Pediatrics, Benioff Children’s Hospital and the Helen Diller Family Comprehensive Cancer Center, University of California at San Francisco, San Francisco, CA, USA

    • Mignon L. Loh
  6. Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta and Emory University School of Medicine, Department of Pediatrics, Atlanta, GA, USA

    • John Horan
  7. Department of Women and Child Health, Hemato-Oncology Division, University of Padova, Padova, Italy

    • Barbara Buldini
    •  & Giuseppe Basso
  8. Pediatric Hematology-Oncology, Schneider Children’s Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Israel

    • Sarah Elitzur
  9. Prinses Maxima Centre, Utrecht, The Netherlands

    • Valerie de Haas
    •  & C. Michel Zwaan
  10. Department of Pediatric Oncology, Erasmus MC-Sophia, Rotterdam, The Netherlands

    • C. Michel Zwaan
  11. Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    • Allen Yeoh
  12. Universitäts-Klinikum, Essen, Germany

    • Dirk Reinhardt
  13. Division of Leukemia and Lymphoma, Children’s Cancer Center, National Center for Child Health and Development, Tokyo, Japan

    • Daisuke Tomizawa
  14. Department of Pediatric Hematology and Oncology Research, National Research Institute for Child Health and Development, Tokyo, Japan

    • Nobutaka Kiyokawa
  15. Department of Pediatric Hematology-Oncology and Stem Cell Transplantation, Ghent University Hospital, Ghent, Belgium

    • Tim Lammens
    •  & Barbara De Moerloose
  16. The Tumour Bank CCRU, The Kids Research Institute, The Children’s Hospital at Westmead, Westmead, New South Wales, Australia

    • Daniel Catchpoole
  17. Department of Pediatrics, Mie University, Tsu, Japan

    • Hiroki Hori
  18. Wolfson Childhood Cancer Centre, Northern Institute for Cancer Research, Newcastle University, Newcastle-upon-Tyne, UK

    • Anthony Moorman
  19. The University of Queensland Diamantina Institute & Children’s Health, Brisbane, Queensland, Australia

    • Andrew S. Moore
  20. Department of Paediatric Haematology and Oncology, 2nd Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic

    • Ondrej Hrusak
  21. Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, WA, USA

    • Soheil Meshinchi
  22. Children’s Oncology Group, Arcadia, CA, USA

    • Soheil Meshinchi
  23. Children’s Center for Cancer and Blood Disease, Children’s Hospital Los Angeles, Los Angeles, CA, USA

    • Etan Orgel
  24. University of Florida, Gainesville, FL, USA

    • Meenakshi Devidas
  25. Johns Hopkins Medical Institutions, Baltimore, MD, USA

    • Michael Borowitz
  26. University of Washington, Seattle, WA, USA

    • Brent Wood
  27. The Ohio State University School of Medicine, Columbus, OH, USA

    • Nyla A. Heerema
  28. University of Alabama at Birmingham, Birmingham, AL, USA

    • Andrew Carrol
  29. Department of Laboratory Medicine and Pediatrics, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan

    • Yung-Li Yang
  30. Cancer Therapy Evaluation Program, National Cancer Institute, Bethesda, MD, USA

    • Malcolm A. Smith
  31. Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA

    • Tanja M. Davidsen
  32. Office of Cancer Genomics, National Cancer Institute, Bethesda, MD, USA

    • Leandro C. Hermida
    • , Patee Gesuwan
    • , Jaime M. Guidry Auvil
    •  & Daniela S. Gerhard
  33. Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada

    • Marco A. Marra
    • , Yussanne Ma
    • , Andrew J. Mungall
    • , Richard A. Moore
    •  & Steven J. M. Jones
  34. Cytogenetics Shared Resource, St. Jude Children’s Research Hospital, Memphis, TN, USA

    • Marcus Valentine
  35. Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN, USA

    • Xueyuan Cao
    • , Lei Shi
    • , Stanley Pounds
    •  & Deqing Pei
  36. Division of Oncology and Center for Childhood Cancer Research, Children’s Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    • Stephen P. Hunger

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Contributions

T.B.A.: study design, flow analysis and sorting, data analysis, and manuscript writing. Z.G.: genomic data analysis. I.I.: genomic and mouse experiments, data analysis, data interpretation and manuscript preparation. K.D.: ZNF384r modelling. J.K.C.: central review of immunophenotype. B.X.: ChIP–seq and RNA-seq data analysis. D.P.-T. and H.Y.: performed experiments. M.L.L. and S.P.H.: led and contributed to Children’s Oncology Group ALL studies and the ALL TARGET project. M.B. and B.W.: reviewed flow cytometry. M.D., N.A.H., and A.C.: provided clinical data. J.H., E.O., B.B, G.B., S.E., V.d.H., C.M.Z., A.Y., D.R., D.T., N.K., T.L., B.D.M., D.C., H.H., A.M., A.S.M., O.H., K.E.N., J.R.D., and J.Z.: patient samples and clinical data. S.M.: data for comparison cohort. Y.-L.Y.: flow analysis. M.A.S., T.M.D., L.C.H., P.G., M.A.M., Y.M., A.J.M., R.A.M., S.J.M.J., and J.M.G.A.: genomic sequencing, analysis, and support. M.V.: performed FISH. L.J.J.: necropsy and histology on xenograft models. J.E.R. and C.-H.P.: patient samples and clinical data. D.S.G.: support for genomic analysis and manuscript editing. L.D. and Y.L.: genomic analysis. X.C., L.S., S.P. and D.P.: statistical analysis. S.N.: somatic and germline variant analysis. H.I.: acquisition of patient samples and clinical data. C.G.M.: designed and oversaw the study, analysed data and wrote the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Hiroto Inaba or Charles G. Mullighan.

Extended data figures and tables

  1. Extended Data Fig. 1 Criteria for diagnosis of ALAL.

    a, Subtypes of ALAL according to the WHO 2008 criteria and consistent with minor revisions of WHO 2016 criteria6. b, Antigen requirements for lineage assignment for MPAL according to WHO 2008 criteria. The 2016 revisions to the WHO classification for ALAL did not change the above categories or requirements. Rather, the revision emphasized that care should be taken before making a diagnosis of B/M MPAL when low-intensity myeloperoxidase is the only myeloid-associated feature. Additionally, the revision emphasized that in cases in which it is possible to resolve two distinct blast populations, it is not necessary that the specific markers be present, but only that each population would meet the criteria for B, T, or myeloid leukaemia64. c, Proposed update to WHO ALAL subtypes incorporating critical newer genomic information (new subtypes in red). d, Flow chart of ALAL cohort showing reasons for exclusion and initial diagnosis in cases for which initial ALAL diagnosis occurred at relapse.

  2. Extended Data Fig. 2 Illustrative immunophenotype and overall survival.

    ae, Representative flow cytometry pseudocolour dot plots and contour plots for five different MPAL cases gated on blast area from CD45 and side scatter area (SSC-A). There are a wide variety of immunophenotypic patterns, including classic bilineal phenotype (a), classic biphenotypic case (b), myeloid predominance (c), lymphoid predominance (d) and complex phenotype with more than two immunophenotypic clones (e). f, g, Morphology of cells from two patients with MPAL showing both lymphoid (orange arrow) and myeloid (black arrow) morphology. f, Bone marrow aspirate stained with myeloperoxidase from a patient with T/M MPAL showing multiple blasts with moderate MPO positivity along with one normal granulocyte. g, Peripheral blood haematoxylin and eosin stain from a patient with B/M MPAL. ho, Kaplan–Meier survival curves with overall survival (OS) distributions of patients whose initial diagnosis was MPAL or AUL compared using log-rank tests. At risk numbers for each analysis are provided in the figures. Outcome associations were analysed with the log-rank test. Overall survival according to WHO 2016 subtype (h), initial therapy (i), WT1 status within the T/M MPAL cohort (j), ZNF384 status within the B/M MPAL cohort (k), Ras pathway alteration within the entire cohort (l) and FLT3 alteration within the entire cohort (m). n, Overall survival according to initial therapy for patients with B/M MPAL with ZNF384r. o, Overall survival according to initial therapy for patients with B/M MPAL without ZNF384r. Patients included in this cohort were collected from a range of treatment eras, treatment locations, treatment regimens, and include a range of ages and genomic subtype, limiting the conclusions that can be drawn from these analyses.

  3. Extended Data Fig. 3 Copy number alterations and mutation burden in ALAL.

    a, Map showing spectrum of CNAs, visually recapitulating the data shown in Supplementary Table 10. Twenty-seven patients had SNP arrays for multiple subpopulations, annotated by stars. b, CNA and non-silent SNVs or indels in ALAL subtypes according the WHO 2016 classification. (CNA, T/M MPAL n = 36, B/M MPAL n = 34, KMT2Ar MPAL n = 15, MPAL NOS n = 7, AUL n = 5, Ph+ MPAL n = 1; SNV/indel, T/M MPAL n = 46, B/M MPAL n = 35, KMT2Ar MPAL n = 15, MPAL NOS n = 7, AUL n = 5, Ph+ MPAL n = 1.) Patients with KMT2Ar MPAL have a lower mutation burden than those with T/M MPAL or B/M MPAL. c, CNAs and non-silent SNVs or indels in our proposed updated classification system. (CNA, T/M MPAL NOS n = 24, T/M MPAL with WT1 alteration n = 12, B/M MPAL NOS n = 17, B/M MPAL with ZNF384r n = 15, KMT2Ar MPAL/AUL n = 17, MPAL/AUL NOS n = 9, Ph+/Ph-like MPAL/AUL n = 4; SNV/indel, T/M MPAL NOS n = 27, T/M MPAL with WT1 alteration, n = 19, B/M MPAL NOS n = 18, B/M MPAL with ZNF384r n = 15, KMT2Ar MPAL/AUL n = 17, MPAL/AUL NOS n = 9, Ph+/Ph-like MPAL/AUL n = 4.) Data shown as median ± 95% confidence interval. Comparisons assessed by two-sided unpaired t-test. One data point is outside the SNV/indel graph for the B/M NOS subtype (1 patient with 167 SNV/indels). SNV/indels per case are shown for cases with completed DNA sequencing analyses. Source Data

  4. Extended Data Fig. 4 Complete ALAL mutation oncoprint.

    Mutation spectrum of ALAL.

  5. Extended Data Fig. 5 Features of MPAL genomic analysis.

    a, WT1 alterations were observed in 28 patients, commonly as frameshift mutations (31/47 mutations) in exon 7 (29/47 mutations) and were frequently biallelic. In 16 patients, two clonal alterations were detected, and in 9 patients the locations of the alteration were encompassed by the same sequencing read, providing definitive demonstration that the mutations were in trans. Additionally, one patient (SJMPAL043773) had a frameshift mutation and copy number loss of the second allele, while another had a frameshift mutation with copy-neutral loss of heterozygosity (SJMPAL040036). Data are shown for two representative patients with MPAL, showing double-hit mutations on WT1. The read alignment view was generated by Samtools24. The reference human genome is on the first row and sequence reads are aligned below, with matched nucleotides as dots (forward strand match) and commas (reverse strand match) and mismatched ones showing the differences. Alignment gaps are shown as asterisks. Adjacent mutations are shown on different sequence reads, indicating that the mutations are on different alleles. b, Frequency of alteration by pathway analysis and MPAL subtype. The similarity of somatic alteration prevalence in different leukaemia subtypes was evaluated by two-sided Fisher’s exact test (n = 100 biologically independent cases). See also Supplementary Tables 12, 13 for numbers and P values for each gene and pathway. c, Schematic representation of ZNF384r observed in B/M MPAL. NLS, nuclear localization signal; TAZ1, transcriptional adaptor zinc-binding; LZ, leucine rich domain; QA, glycine/alanine repeat. d, FACS schema in a representative case with a ZNF384r, and VAF of SNVs/indels present in the respective sorted subpopulations, demonstrating genomic similarity of the sorted populations. e, t-SNE plot of RNA-seq gene expression of all patients with ZNF384r show no clear segregation of B/M MPAL and B-ALL cases. f, FLT3 gene expression in subtypes of ALAL showing that patients with ZNF384r B/M MPAL have high levels of FLT3 expression. As in patients with KMT2Ar, this occurs in the absence of FLT3 alteration in most cases. By contrast, high levels of FLT3 expression in T/M MPAL appears to be driven by FLT3 alterations. Data shown as median ± 95% confidence interval. Comparisons assessed by unpaired t-test, two sided. T/M MPAL FLT3 wild type n = 18, B/M MPAL NOS n = 10, T/M MPAL with FLT3 alteration n = 16, B/M MPAL NOS n = 17, B/M MPAL with ZNF384r n = 15, KMT2Ar MPAL/AUL n = 11, MPAL/AUL NOS n = 7, Ph+/Ph-like MPAL/AUL n = 5, KMT2A-like MPAL/AUL n = 8. Source Data

  6. Extended Data Fig. 6 ZNF384r leukaemia analysis and T/M MPAL mutation comparisons.

    a, GSEA of ZNF384r B/M MPAL versus non-ZNF384r B/M MPAL. HSC gene sets are negatively enriched, supporting the proposed update to MPAL subtypes in which ZNF384r leukaemia has distinct biology compared with other B/M MPAL cases20,65,66. b, GSEA of all ZNF384r cases versus other B-ALL cases indicates immaturity of this subtype compared to B-ALL, with positive enrichment for genes upregulated in ETP-ALL (a stem cell leukaemia), and negative enrichment for genes upregulated in Ph-like ALL in other B-ALL cases. ZNF384r acute leukaemia is also enriched for genes upregulated in patients with detectable minimal residual disease at end of induction10,51,67. c, Western blot analysis to validate expression of ZNF384, TAF15–ZNF384, and TCF3–ZNF384 in transduced Arf−/− pre-B cells. Proteins contain an HA epitope tag and are detected by anti-HA antibody. d, Heat map showing the ChIP–seq signal, centred on ZNF384 peaks, of wild-type (WT) ZNF384 compared to TAF15–ZNF384 and TCF3–ZNF384. Middle, peaks with increased binding of fusion proteins compared to wild-type proteins. Bottom, peaks with decreased binding of the fusion proteins compared to wild-type proteins. e, GSEA showing enrichment of genes whose promoters exhibit increased binding by ZNF384 fusions in the GEP of ZNF384r versus wild-type pre-B cells. f, GSEA showing similarity of the GEP of mouse pre-B cells expressing ZNF384r to the GEP of human ZNF384r leukaemia cells, supporting the notion that perturbation of ZNF384 binding contributes to deregulated gene expression in human ZNF384r leukaemia. g, Oncoprint of mutations in transcription factor genes across T/M MPAL (n = 49), ETP-ALL (n = 19) and T-ALL (other) (n = 245), showing lack of TAL1 alterations in T/M MPAL and few core T-ALL transcription factor alterations in T/M MPAL or ETP-ALL. The association of leukaemia subtype with individual transcription factor alterations was evaluated using two-sided Fisher exact test. Act, activating mutation; LoF, loss-of-function mutation. h, Gene pathway analyses showing similarity of ETP-ALL and T/M MPAL, specifically in frequency of mutations in pathways regulating cell cycle or apoptosis, transcriptional regulation, and signalling pathways. The similarity of somatic alteration prevalence in different leukaemia subtypes was evaluated by two sided Fisher’s exact tests in these four subtypes (T/M MPAL n = 49, ETP-ALL n = 19, non-ETP T-ALL n = 245, AML n = 197). Source Data

  7. Extended Data Fig. 7 MPAL subpopulation analysis and methylation analysis.

    a, Results of genomic analysis of the 50 patients with sorted subpopulations with WGS or WES results. Listed here are all genes with mutations that were either recurrent in the ALAL cohort or were in known cancer census genes68. *CNA results also available for sorted subpopulations in these cases. bd, Methylation analysis of MPAL, comparison with acute leukaemia and normal lymphocytes. The top 5,000 probes with highest mean absolute deviation were used to assess the clustering through a 2D t-SNE plot and heat map with Pearson correlation clustering. See Supplementary Table 37 for sample details. b, Heat map of all samples used for methylation analysis showing the general alignment of samples by leukaemia phenotype with B/M cases clustering with B-ALL, T/M MPAL, ETP-ALL cases together, and AML cases clustering separately. c, t-SNE analysis of the same samples as in the top heat map, showing general alignment by leukaemia phenotype with B/M cases clustering with B-ALL, T/M MPAL, ETP-ALL cases together, and AML cases clustering separately. d, Heat map of all MPAL cases, again showing some clustering by phenotype between B/M and T/M cases. Subpopulations sorted by distinct immunophenotype in MPAL cases clustered tightly with samples from the same patient, rather than with samples with similar phenotype from a different patient. e, Methylation analysis of sorted subpopulations from 11 patients with MPAL, demonstrating that methylation profiles cluster by patient and not by immunophenotype lineage.

  8. Extended Data Fig. 8 Xenograft analysis.

    a, Flow cytometry analysis of bulk leukaemic cells from patient SJMPAL011911 before sorting, and cytospins from bone marrow samples from representative primary recipient mice transplanted with different leukaemia subpopulations or bulk, confirming the presence of leukaemic blasts from each engrafted population. Scale bars, 10 μm. b, Phenotypic subpopulations from JIH-5 cells in the first column were sorted and injected into NSG-SGM3 mice. Remaining plots show the immunophenotypes of engrafted leukaemia propagated from each sorted subpopulation, demonstrating recapitulation of biphenotypic leukaemia from each. c, Flow cytometry analysis of bulk JIH-5 cells prior to sorting (left) and haematoxylin and eosin staining and IHC labelling for human CD45, CD19, CD33, MPO, CD34 and CD3 in sternum samples from representative primary recipient mice transplanted with different leukaemia subpopulations or bulk. Scale bars, 20 μm. d, Phenotypic subpopulations from patient SJMPAL012424 were sorted (left) and injected into irradiated NSG-SGM3 mice. Remaining plots show the immunophenotypes of engrafted leukaemia from each starting subpopulation, demonstrating recapitulation of mixed phenotype leukaemia from two sorted subpopulations. e, Flow cytometry analyses of bone marrow cells from an engrafted primary mouse transplanted with leukaemia cells from a patient with T/M MPAL (SJMPAL040036). f, g, Flow cytometry analyses of representative engrafted secondary recipient mice transplanted with leukaemia cells from the mouse in e showing lineage plasticity with mice developing an emerging CD19+CD33+ population (f) and other mice recapitulating the immunophenotype in the primary recipient (g). h, IHC labelling for human CD45, CD19, CD33, MPO and CD34 from harvested and fixed spleen cells from a representative secondary recipient mouse showing high expression of CD19 and CD33 and thus confirming the leukaemic lineage plasticity. Scale bars, 20 μm.

  9. Extended Data Fig. 9 Haematopoietic progenitor cell analysis.

    a, Progenitor cell sorting scheme for diagnosis sample from patient SJMPAL040028. Progenitor populations were all gated on CD19CD33CD34+ and sorted into HSC (CD38CD34+CD90+CD45RA; 2 replicates: HSC_1 and HSC_2); MPP (CD38CD34+CD90CD45RA); MLP (CD38CD34+CD45RA+); megakaryocyte erythroid progenitors/common myeloid progenitors (MEP/CMP; CD38+CD34+CD7CD10CD45RA); and granulocyte monocyte progenitor (GMP; CD38+CD34+CD7CD10CD45RA+) populations. b, Blast cell sorting scheme for diagnosis sample from patient SJMPAL040028. Cells were gated on CD45dim and sorted into four different immunophenotypic populations (CD33+CD19+CD10; CD33+CD19modCD10; CD33+CD19CD10; and CD33CD19). c, Sanger sequencing electropherograms for the mutational status of DNAH17, NDST2 and MYCN and for the fusion TCF3–ZNF384 in isolated progenitor and blast populations from patient SJMPAL040028 at diagnosis. The identification of somatic missense mutations and TCF3–ZNF384 fusion in early haematopoietic progenitors indicate that the ambiguous phenotype of MPAL is the result of the acquisition of alterations within an immature haematopoietic progenitor cells.

  10. Extended Data Fig. 10 Phenotypic and genotypic evolution from diagnosis to relapse.

    Patients for whom diagnosis and relapse pairs with matching non-tumour controls are available show recapitulation of the diagnostic multilineage phenotype in some cases and phenotype plasticity in others. The first column shows the case ID, the leukaemia subtype at diagnosis and then subsequent relapse, the in-frame fusion if present, and initial therapy received by the patient. Flow plots are shown of cells gated on CD45dim versus SSC-Alow. The diagram depicts the inferred clonal evolution based on WES and/or WGS and SNP array data (where available). Mutated genes (either recurrent in ALAL cohort or known cancer census genes68) are listed. The genes beside the initial diagnostic cell cluster remained present at relapse. The grey cells represent clones that were extinguished with therapy. The genes in the relapse column represent mutations that were gained at relapse.

Supplementary information

  1. Supplementary Information

    This file contains a Western Blot providing support for Extended Data Figure 6c.

  2. Reporting Summary

  3. Supplementary Information

    This file contains descriptions of Supplementary Tables 1-41 and Supplementary Results.

  4. Supplementary Tables

    This file contains Supplementary Tables 1-41 (see separate PDF file for Table Descriptions).

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https://doi.org/10.1038/s41586-018-0436-0

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