CoChat
Sign Up
Cited by 155Acute Myeloid Leukemia ResearchAcute Lymphoblastic Leukemia researchRetinoids in leukemia and cellular processes

Genomic subtyping and therapeutic targeting of acute erythroleukemia

Open access
Iacobucci, Ilaria, Wen, Ji, Meggendorfer, Manja, Choi, John K., Shi, Lei, Pounds, Stanley B., Carmichael, Catherine L., Masih, Katherine E. +40 more·2019·Nature Genetics
DOI 10.1038/s41588-019-0375-1PMC PMC6828160
Open this paper in CoChat — free account

Abstract

Acute erythroid leukemia (AEL) is a high-risk leukemia of poorly understood genetic basis, with controversy regarding diagnosis in the spectrum of myelodysplasia and myeloid leukemia. We compared genomic features of 159 childhood and adult AEL cases with non-AEL myeloid disorders and defined five age-related subgroups with distinct transcriptional profiles: adult, TP53 mutated; NPM1 mutated; KMT2A mutated/rearranged; adult, DDX41 mutated; and pediatric, NUP98 rearranged. Genomic features influenced outcome, with NPM1 mutations and HOXB9 overexpression being associated with a favorable prognosis and TP53, FLT3 or RB1 alterations associated with poor survival. Targetable signaling mutations were present in 45% of cases and included recurrent mutations of ALK and NTRK1, the latter of which drives erythroid leukemogenesis sensitive to TRK inhibition. This genomic landscape of AEL provides the framework for accurate diagnosis and risk stratification of this disease, and the rationale for testing targeted therapies in this high-risk leukemia.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41588-019-0375-1

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Three distinct clusters of HSPCs were identified in TP53-sAML, including one characterized by overexpression of erythroid genes.

    “Three distinct clusters of HSPCs were identified in TP53-sAML, including one characterized by overexpression of erythroid genes, of particular note as erythroleukemia is a rare entity, associated with adverse outcomes and TP53 mutation43,44.”

    From Single-cell multi-omics identifies chronic inflammation as a driver of TP53-mutant leukemic evolution (2023) · cites this paper as [44]

    The Results sections on TP53 alterations and genomic determinants of outcome link acute erythroleukemia to TP53 mutations and poor prognosis. Our copy does not report three HSPC clusters in TP53-sAML or establish that erythroleukemia is rare. In the citing sentence, the cluster finding appears to be the citing authors’ own result; the citations follow the background statement about erythroleukemia. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 79

  1. [1]Cancer Genome Atlas Research, N. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med 368, 2059– 74 (2013). 23634996 PMC3767041
  2. [2]Papaemmanuil E Genomic Classification and Prognosis in Acute Myeloid Leukemia. N Engl J Med 374, 2209– 21 (2016). 27276561 PMC4979995
  3. [3]Cervera N Molecular characterization of acute erythroid leukemia (M6-AML) using targeted next-generation sequencing. Leukemia 30, 966– 70 (2016). 26202927
  4. [4]Grossmann V Acute erythroid leukemia (AEL) can be separated into distinct prognostic subsets based on cytogenetic and molecular genetic characteristics. Leukemia 27, 1940– 3 (2013). 23648669
  5. [5]Ping N Exome sequencing identifies highly recurrent somatic GATA2 and CEBPA mutations in acute erythroid leukemia. Leukemia 31, 195– 202 (2017). 27389056
  6. [6]Liu W Pure erythroid leukemia: a reassessment of the entity using the 2008 World Health Organization classification. Mod Pathol 24, 375– 83 (2011). 21102413
  7. [7]Almeida AM Clinical Outcomes of 217 Patients with Acute Erythroleukemia According to Treatment Type and Line: A Retrospective Multinational Study. Int J Mol Sci 18(2017). PMC5412421 28420120
  8. [8]Dameshek W & Baldini M The Di Guglielmo syndrome. Blood 13, 192– 4 (1958). 13510296
  9. [9]Bennett JM Proposed revised criteria for the classification of acute myeloid leukemia. A report of the French-American-British Cooperative Group. Ann Intern Med 103, 620– 5 (1985). 3862359
  10. [10]Vardiman JW The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood 114, 937– 51 (2009). 19357394
  11. [12]Kowal-Vern A Diagnosis and characterization of acute erythroleukemia subsets by determining the percentages of myeloblasts and proerythroblasts in 69 cases. Am J Hematol 65, 5– 13 (2000). 10936857
  12. [13]Lichtman MA The disappearance of acute erythroid leukemia: An act of legerdemain at the World Health Organization. Blood Cells Mol Dis 61, 54– 7 (2016). 27667167
  13. [14]Arber DA Revisiting erythroleukemia. Curr Opin Hematol 24, 146– 151 (2017). 27875373
  14. [15]Wang SA Acute erythroid leukemia with <20% bone marrow blasts is clinically and biologically similar to myelodysplastic syndrome with excess blasts. Mod Pathol 29, 1221– 31 (2016). 27443511
  15. [16]Hasserjian RP Acute erythroid leukemia: a reassessment using criteria refined in the 2008 WHO classification. Blood 115, 1985– 92 (2010). 20040759 PMC2942006
  16. [17]Greenberg PL Revised international prognostic scoring system for myelodysplastic syndromes. Blood 120, 2454– 65 (2012). 22740453 PMC4425443
  17. [18]Pounds S A genomic random interval model for statistical analysis of genomic lesion data. Bioinformatics 29, 2088– 95 (2013). 23842812 PMC3740633
  18. [19]Forbes SA COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res 45, D777– D783 (2017). 27899578 PMC5210583
  19. [21]Xie M Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nat Med 20, 1472– 8 (2014). 25326804 PMC4313872
  20. [22]Genovese G Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N Engl J Med 371, 2477– 87 (2014). 25426838 PMC4290021
  21. [23]Stephens PJ Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144, 27– 40 (2011). 21215367 PMC3065307
  22. [24]Rausch T Genome sequencing of pediatric medulloblastoma links catastrophic DNA rearrangements with TP53 mutations. Cell 148, 59– 71 (2012). 22265402 PMC3332216
  23. [25]Abaigar M Chromothripsis Is a Recurrent Genomic Abnormality in High-Risk Myelodysplastic Syndromes. PLoS One 11, e0164370 (2016). 27741277 PMC5065168
  24. [26]Yang CT Activation of KLF1 Enhances the Differentiation and Maturation of Red Blood Cells from Human Pluripotent Stem Cells. Stem Cells 35, 886– 897 (2017). 28026072 PMC5396323
  25. [27]Holmfeldt P Nfix is a novel regulator of murine hematopoietic stem and progenitor cell survival. Blood 122, 2987– 96 (2013). 24041575 PMC3811173
  26. [28]Panagopoulos I Fusion of ZMYND8 and RELA genes in acute erythroid leukemia. PLoS One 8, e63663 (2013). 23667654 PMC3646816
  27. [29]Quelen C Identification of a transforming MYB-GATA1 fusion gene in acute basophilic leukemia: a new entity in male infants. Blood 117, 5719– 22 (2011). 21474671
  28. [30]Wang GG Haematopoietic malignancies caused by dysregulation of a chromatin-binding PHD finger. Nature 459, 847– 51 (2009). 19430464 PMC2697266
  29. [31]Majeti R Dysregulated gene expression networks in human acute myelogenous leukemia stem cells. Proc Natl Acad Sci U S A 106, 3396– 401 (2009). 19218430 PMC2642659
  30. [32]Andersson A, Eden P, Olofsson T & Fioretos T Gene expression signatures in childhood acute leukemias are largely unique and distinct from those of normal tissues and other malignancies. BMC Med Genomics 3, 6 (2010). 20211010 PMC2845086
  31. [33]Gatalica Z, Xiu J, Swensen J & Vranic S Molecular characterization of cancers with NTRK gene fusions. Mod Pathol (2018). 30171197
  32. [34]Taylor J Oncogenic TRK fusions are amenable to inhibition in hematologic malignancies. J Clin Invest 128, 3819– 3825 (2018). 29920189 PMC6118587
  33. [35]Roberts KG Targetable kinase-activating lesions in Ph-like acute lymphoblastic leukemia. N Engl J Med 371, 1005– 15 (2014). 25207766 PMC4191900
  34. [36]Roberts KG ETV6-NTRK3 induces aggressive acute lymphoblastic leukemia highly sensitive to selective TRK inhibition. Blood 132, 861– 865 (2018). 29880614 PMC6107883
  35. [37]Lang GA Gain of function of a p53 hot spot mutation in a mouse model of Li-Fraumeni syndrome. Cell 119, 861– 72 (2004). 15607981
  36. [38]Grunewald TG Chimeric EWSR1-FLI1 regulates the Ewing sarcoma susceptibility gene EGR2 via a GGAA microsatellite. Nat Genet 47, 1073– 8 (2015). 26214589 PMC4591073
  37. [39]Doebele RC An Oncogenic NTRK Fusion in a Patient with Soft-Tissue Sarcoma with Response to the Tropomyosin-Related Kinase Inhibitor LOXO-101. Cancer Discov 5, 1049– 57 (2015). 26216294 PMC4635026
  38. [40]Khotskaya YB Targeting TRK family proteins in cancer. Pharmacol Ther 173, 58– 66 (2017). 28174090
  39. [41]Dohner H Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood 129, 424– 447 (2017). 27895058 PMC5291965
  40. [42]Welch JS TP53 and Decitabine in Acute Myeloid Leukemia and Myelodysplastic Syndromes. N Engl J Med 375, 2023– 2036 (2016). 27959731 PMC5217532
  41. [43]Bertrand T The crystal structures of TrkA and TrkB suggest key regions for achieving selective inhibition. J Mol Biol 423, 439– 53 (2012). 22902478
  42. [44]Bertrand T Crystal Structures of Neurotrophin Receptors Kinase Domain. Vitam Horm 104, 1– 18 (2017). 28215291
  43. [45]Pession A Results of the AIEOP AML 2002/01 multicenter prospective trial for the treatment of children with acute myeloid leukemia. Blood 122, 170– 8 (2013). 23673857
  44. [46]Tomizawa D Excess treatment reduction including anthracyclines results in higher incidence of relapse in core binding factor acute myeloid leukemia in children. Leukemia 27, 2413– 6 (2013). 23677335
  45. [47]Schlenk RF All-trans retinoic acid as adjunct to intensive treatment in younger adult patients with acute myeloid leukemia: results of the randomized AMLSG 07–04 study. Ann Hematol 95, 1931– 1942 (2016). 27696203 PMC5093206
  46. [48]Lange BJ Outcomes in CCG-2961, a children’s oncology group phase 3 trial for untreated pediatric acute myeloid leukemia: a report from the children’s oncology group. Blood 111, 1044– 53 (2008). 18000167 PMC2214754
  47. [49]Buchner T Acute Myeloid Leukemia (AML): different treatment strategies versus a common standard arm--combined prospective analysis by the German AML Intergroup. J Clin Oncol 30, 3604– 10 (2012). 22965967
  48. [50]Farrar JE Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse. Cancer Res 76, 2197– 205 (2016). 26941285 PMC4873364
  49. [51]Cancer Genome Atlas Research, N. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med 368, 2059– 74 (2013). 23634996 PMC3767041
  50. [52]Bolouri H The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural alterations and age-specific mutational interactions. Nat Med 24, 103– 112 (2018). 29227476 PMC5907936
  51. [53]Lindsley RC Prognostic Mutations in Myelodysplastic Syndrome after Stem-Cell Transplantation. N Engl J Med 376, 536– 547 (2017). 28177873 PMC5438571
  52. [54]Kitamura T Establishment and characterization of a unique human cell line that proliferates dependently on GM-CSF, IL-3, or erythropoietin. J Cell Physiol 140, 323– 34 (1989). 2663885
  53. [55]Martin P & Papayannopoulou T HEL cells: a new human erythroleukemia cell line with spontaneous and induced globin expression. Science 216, 1233– 5 (1982). 6177045
  54. [57]Liu Y The genomic landscape of pediatric and young adult T-lineage acute lymphoblastic leukemia. Nat Genet 49, 1211– 1218 (2017). 28671688 PMC5535770
  55. [59]Zhang J SNPdetector: a software tool for sensitive and accurate SNP detection. PLoS Comput Biol 1, e53 (2005). 16261194 PMC1274293
  56. [60]Edmonson MN Bambino: a variant detector and alignment viewer for next-generation sequencing data in the SAM/BAM format. Bioinformatics 27, 865– 6 (2011). 21278191 PMC3051333
  57. [61]Downing JR The Pediatric Cancer Genome Project. Nat Genet 44, 619– 22 (2012). 22641210 PMC3619412
  58. [62]Roberts KG High Frequency and Poor Outcome of Philadelphia Chromosome-Like Acute Lymphoblastic Leukemia in Adults. J Clin Oncol 35, 394– 401 (2017). 27870571 PMC5455698
  59. [63]Dobin A STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15– 21 (2013). 23104886 PMC3530905
  60. [64]Li H & Durbin R Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754– 60 (2009). 19451168 PMC2705234
  61. [65]Robinson MD, McCarthy DJ & Smyth GK edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139– 40 (2010). 19910308 PMC2796818
  62. [66]Ritchie ME limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43, e47 (2015). 25605792 PMC4402510
  63. [67]Pawlikowska I Dunn Index Bootstrap (DIBS): A procedure to empirically select a cluster analysis method that identifies biologically and clinically relevant molecular disease subgroups. BMC Bioinformatics 16, P2 (2015).
  64. [68]Rousseeuw PJ Least Median of Squares Regression. Journal of the American Statistical Association 79, 871– 880 (1984).
  65. [69]He J Integrated genomic DNA/RNA profiling of hematologic malignancies in the clinical setting. Blood 127, 3004– 14 (2016). 26966091 PMC4968346
  66. [70]Kohlmann A The Interlaboratory RObustness of Next-generation sequencing (IRON) study: a deep sequencing investigation of TET2, CBL and KRAS mutations by an international consortium involving 10 laboratories. Leukemia 25, 1840– 8 (2011). 21681191
  67. [71]Delic S Application of an NGS-based 28-gene panel in myeloproliferative neoplasms reveals distinct mutation patterns in essential thrombocythaemia, primary myelofibrosis and polycythaemia vera. Br J Haematol 175, 419– 426 (2016). 27447873
  68. [72]Weisser M Risk assessment by monitoring expression levels of partial tandem duplications in the MLL gene in acute myeloid leukemia during therapy. Haematologica 90, 881– 9 (2005). 15996925
  69. [73]Schnittger S Analysis of FLT3 length mutations in 1003 patients with acute myeloid leukemia: correlation to cytogenetics, FAB subtype, and prognosis in the AMLCG study and usefulness as a marker for the detection of minimal residual disease. Blood 100, 59– 66 (2002). 12070009
  70. [74]Mullighan CG Genome-wide analysis of genetic alterations in acute lymphoblastic leukaemia. Nature 446, 758– 64 (2007). 17344859
  71. [75]Lin M dChipSNP: significance curve and clustering of SNP-array-based loss-of-heterozygosity data. Bioinformatics 20, 1233– 40 (2004). 14871870
  72. [76]Venkatraman ES & Olshen AB A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23, 657– 63 (2007). 17234643
  73. [78]Storey JD & Tibshirani R Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 100, 9440– 5 (2003). 12883005 PMC170937
  74. [79]Gao J Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6, pl1 (2013). 23550210 PMC4160307
  75. [80]Huntley RP The GOA database: gene Ontology annotation updates for 2015. Nucleic Acids Res 43, D1057– 63 (2015). 25378336 PMC4383930
  76. [81]Huether R The landscape of somatic mutations in epigenetic regulators across 1,000 paediatric cancer genomes. Nat Commun 5, 3630 (2014). 24710217 PMC4119022
  77. [83]Iacobucci I Truncating Erythropoietin Receptor Rearrangements in Acute Lymphoblastic Leukemia. Cancer Cell 29, 186– 200 (2016). 26859458 PMC4750652
  78. [85]Mantel N Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother Rep 50, 163– 70 (1966). 5910392
  79. [88]Zhou X Exploring genomic alteration in pediatric cancer using ProteinPaint. Nat Genet 48, 4– 6 (2016). 26711108 PMC4892362

Cited by

Sorted by citations

Authors
Iacobucci, I., Wen, J., Meggendorfer, M., Choi, J. K., Shi, L., Pounds, S. B., Carmichael, C. L., Masih, K. E., Morris, S. M., Lindsley, R. C., Janke, L. J., Alexander, T. B., Song, G., Qu, C., Li, Y., Payne-Turner, D., Tomizawa, D., Kiyokawa, N., Valentine, M., Valentine, V., Basso, G., Locatelli, F., Enemark, E. J., Kham, S. K. Y., Yeoh, A. E. J., Ma, X., Zhou, X., Sioson, E., Rusch, M., Ries, R. E., Stieglitz, E., Hunger, S. P., Wei, A. H., To, L. B., Lewis, I. D., D’Andrea, R. J., Kile, B. T., Brown, A. L., Scott, H. S., Hahn, C. N., Marlton, P., Pei, D., Cheng, C., Loh, M. L., Ebert, B. L., Meshinchi, S., Haferlach, T., & Mullighan, C. G.
Affiliations
  • St. Jude Children's Research Hospital (US)
  • Munich Leukemia Laboratory (Germany) (DE)
  • Monash University (AU)
  • Dana-Farber Cancer Institute (US)
  • National Center For Child Health and Development (JP)
  • National Research Institute for Child Health and Development (JP)
  • University of Padua (IT)
  • Bambino Gesù Children's Hospital (IT)
  • National University of Singapore (SG)
  • Fred Hutch Cancer Center (US)
  • University of California, San Francisco (US)
  • Children's Hospital of Philadelphia (US)
  • The Alfred Hospital (AU)
  • South Australia Pathology (AU)
  • The University of Adelaide (AU)
  • Walter and Eliza Hall Institute of Medical Research (AU)
  • The University of Queensland (AU)
Corresponding author
Mullighan, Charles G.
ORCID
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2019-03-29
Volume
51
Issue
4
Pages
694-704
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
DOI
10.1038/s41588-019-0375-1
PMC
PMC6828160
OpenAlex
W2932573403
Cited by
155
References
7
Citations by year
2026: 4 · 2025: 19 · 2024: 29 · 2023: 19 · 2022: 29 · 2021: 29 · 2020: 18 · 2019: 8
FWCI
14.84
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature Genetics (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
Funders
National Cancer Institute (T32CA009302, R25CA023944, P01CA066996, R35CA197695, T32CA128583, P30CA021765)
Topics
  • Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Acute Lymphoblastic Leukemia research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Retinoids in leukemia and cellular processes
Keywords
erythroleukemia, therapeutic targeting, NPM1 mutation, TP53 mutation, MLL rearrangement, NUP98 rearrangement, ALK mutations, risk stratification, prognosis, myelodysplastic syndrome, pediatric leukemia, adult leukemia
MeSH terms
Nucleophosmin, Adolescent, Adult, Child, Child, Preschool, Leukemia, Erythroblastic, Acute, Female, Humans, Infant, Infant, Newborn, Male, Mutation, Nuclear Proteins, Prognosis, Tumor Suppressor Protein p53, Homeodomain Proteins, Genomics, Myeloid-Lymphoid Leukemia Protein, fms-Like Tyrosine Kinase 3, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review