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Genomic subtyping and therapeutic targeting of acute erythroleukemia

Nature Genetics volume 51, pages 694–704 (2019)Cite this article

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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.

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Fig. 1: Demographic, clinical and genomic patient characteristics.
Fig. 2: Mutation rates in patients with AEL (WHO 2008), non-erythroid AML and MDS.
Fig. 3: Genomic classification of AEL.
Fig. 4: Association with clinical outcome.
Fig. 5: NTRK1 mutations in AEL.

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Data availability

Genomic data including both sequencing and copy number data have been deposited in the European Genome-phenome Archive (EGA), accession EGAS00001002537. This includes the following datasets: whole-exome sequencing data (EGAD00001003413), RNA-sequencing data (EGAD00001003412) and SNP6 Affymetrix copy number data (EGAD00010001443). Moreover, genomic data including nonsilent SNV, indels, ITD sequence mutations, in-frame gene fusions, structural variations, copy number aberrations and gene expression data can be explored interactively at the St. Jude PeCan Data Portal88 (https://pecan.stjude.cloud/proteinpaint/study/ael).

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Acknowledgements

This work was supported in part by the American Lebanese Syrian Associated Charities of St. Jude Children’s Research Hospital; a Stand Up to Cancer Innovative Research Grant and a St. Baldrick’s Foundation Robert J. Arceci Innovation Award (to C.G.M.); a Leukemia and Lymphoma Society Specialized Center of Research grant (to C.G.M.); the Henry Schueler 41&9 Foundation (to C.G.M.); a Lady Tata Memorial Trust Award (to I.I.); St. Jude Children’s Research Hospital Hematological Malignancies Program Garwood Fellowship (to I.I.); Italian Scientists and Scholars in North America Foundation (ISSNAF) to I.I.; a Leukemia and Lymphoma Society Translational Research Program (to C.G.M.); a National Cancer Institute Outstanding Investigator Award R35 CA197695 (to C.G.M.); the R25CA23944 from the National Cancer Institute (to St. Jude Pediatric Oncology Education program); a St. Jude Summer Plus Fellowship, Rhodes College (to S.M.M.); NIH Cancer Center Support Grant P30 CA21765 (to C.G.M.); Fondazione Cariparo (to G.B.) and AIRC (to G.B.); AIRC 5 × 1.000 ‘Immunity in Cancer Spreading and Metastasis’ (to F.L.); National Medical Research Council, Singapore (NMRC/CSA/0053/2013) (to A.E.J.Y.); and Cancer Science Institute of Singapore (to A.E.J.Y.). This work was also supported by a Project Grant (516726 to B.T.K.) and Program Grants (1016647 and 1113577 to B.T.K.), a Fellowship (1063008 to B.T.K.), and an Independent Research Institutes Infrastructure Support Scheme Grant (361646) from the Australian National Health and Medical Research Council (to B.T.K.); the Leukaemia Foundation of Australia (to C.L.C. and B.T.K.); the Sylvia & Charles Viertel Foundation (to B.T.K.); the Australian Cancer Research Fund (to B.T.K.); Cancer Council of South Australia Beat Cancer Project (1145385, to A.L.B., H.S.S. and C.N.H.) and a Victorian State Government Operational Infrastructure Support Grant (to B.T.K.). We thank the ALLG Tissue Bank at the Princess Alexandra Hospital, Brisbane (now the Cancer Collaborative Biobank), for providing samples. The ALLG Tissue Bank received funding support from the Leukaemia Foundation (to P.M.), the National Health and Medical Research Council (to P.M.), and Queensland Health and Pathology Queensland for the ALLG Tissue Bank (to P.M.). The South Australian Cancer Research Biobank was supported by the Cancer Council SA Beat Cancer Project, University of Adelaide, University of South Australia, South Australian Health and Medical Research Institute, SA Health, Health Service Gifts and Charitable Board of the Central Adelaide Local Health Network, Medvet Laboratories Pty Ltd and the Government of South Australia (to L.B.T., R.D.A., H.S. and I.L.). We thank the staff of the Biorepository, the Hartwell Centre for Bioinformatics and Biotechnology, the Flow Cytometry and Cell Sorting Core Facility, the Cell and Tissue Imaging Facility, the Animal Resources Center and the Small Animal Imaging Center, the Compound Management Center and the Department of Chemical Biology & Therapeutics of St. Jude Children’s Research Hospital. We thank Loxo Oncology, Inc. for providing larotrectinib and support in dosing.

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Authors and Affiliations

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

    Ilaria Iacobucci, Ji Wen, John K. Choi, Katherine E. Masih, Sarah M. Morris, Laura J. Janke, Guangchun Song, Chunxu Qu, Debbie Payne-Turner & Charles G. Mullighan

  2. MLL Munich Leukemia Laboratory, Munich, Germany

    Manja Meggendorfer & Torsten Haferlach

  3. Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Lei Shi, Stanley B. Pounds, Deqing Pei & Cheng Cheng

  4. The Australian Centre for Blood Diseases, Monash University, Melbourne, Victoria, Australia

    Catherine L. Carmichael & Andrew H. Wei

  5. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    R. Coleman Lindsley & Benjamin L. Ebert

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

    Thomas B. Alexander

  7. Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Yongjin Li, Xiaotu Ma, Xin Zhou, Edgar Sioson & Michael Rusch

  8. Division of Leukemia and Lymphoma, Children’s Cancer Center, National Center for Child Health and Development, Tokyo, Japan

    Daisuke Tomizawa

  9. Department of Pediatric Hematology and Oncology Research, National Research Institute for Child Health and Development, Tokyo, Japan

    Nobutaka Kiyokawa

  10. Cytogenetics Shared Resource, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Marcus Valentine & Virginia Valentine

  11. Clinic of Paediatric Haematology and Oncology, Department for Children’s and Women’s Health, University of Padua, Padua, Italy

    Giuseppe Basso

  12. Italian Institute for Genomic Medicine, Turin, Italy

    Giuseppe Basso

  13. Department of Gynecology/Obstetrics and Pediatrics, Sapienza University of Rome, Rome, Italy

    Franco Locatelli

  14. Department of Pediatric Hematology and Oncology, IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy

    Franco Locatelli

  15. Department of Structural Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Eric J. Enemark

  16. Centre for Translational Research in Acute Leukaemia, Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Shirley K. Y. Kham & Allen E. J. Yeoh

  17. Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore

    Allen E. J. Yeoh

  18. Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Rhonda E. Ries & Soheil Meshinchi

  19. Department of Pediatrics, Benioff Children’s Hospital, and the Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA

    Elliot Stieglitz & Mignon L. Loh

  20. 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

  21. Department of Clinical Haematology, The Alfred Hospital, Melbourne, Victoria, Australia

    Andrew H. Wei

  22. Department of Pathology, The Alfred Hospital, Melbourne, Victoria, Australia

    Andrew H. Wei

  23. Departments of Haematology, SA Pathology and Royal Adelaide Hospital, Adelaide, South Australia, Australia

    L Bik To

  24. Faculty of Health Sciences, University of Adelaide, Adelaide, South Australia, Australia

    L Bik To & Ian D. Lewis

  25. Centre for Cancer Biology, University of South Australia and SA Pathology, Adelaide, South Australia, Australia

    Richard J. D’Andrea, Anna L. Brown, Hamish S. Scott & Christopher N. Hahn

  26. The Walter & Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Benjamin T. Kile

  27. Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia

    Benjamin T. Kile

  28. Department of Genetics and Molecular Pathology, SA Pathology, Adelaide, South Australia, Australia

    Anna L. Brown, Hamish S. Scott & Christopher N. Hahn

  29. Princess Alexandra Hospital and University of Queensland School of Clinical Medicine, Brisbane, Queensland, Australia

    Paula Marlton

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Contributions

I.I. led the collaboration, coordination and processing of patient samples, analyzed genomic data, performed experiments and wrote the manuscript. J.W. analyzed sequencing data. J.K.C. performed central review of immunophenotypic data. L.J.J. performed histopathology analyses. K.E.M., S.M.M., T.B.A., D.P.-T., M.V. and V.V. performed experiments. G.S. analyzed copy number data. E.J.E. performed structure modeling. M.M, C.L.C., D.T., N.K., G.B., F.L., S.K.Y.K., A.E.J.Y., R.E.R., E.S., A.H.W., L.B.T., I.D.L., R.J.D.A., B.T.K., A.L.B., H.S.S., C.N.H., P.M., S.M., M.L.L. and T.H. provided patient samples and clinical data. R.C.L. and B.L.E. shared data for the MDS comparison cohort. Y.L., C.Q., X.M., X.Z., E.S., S.P.H. and M.R. performed genomic sequencing, analysis and support. L.S., S.B.P., D.P. and C.C. performed statistical analyses. C.G.M. designed and oversaw the study, analyzed genomic data and wrote the manuscript. All the authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Charles G. Mullighan.

Ethics declarations

Competing interests

M.M., employed by MLL Munich Leukemia Laboratory; R.C.L., research funding from MedImmune and Jazz, and consulting fees from Takeda; B.L.E., research funding from Celgene and Deerfield, and consulting fees from GRAIL; T.H., equity ownership of MLL Munich Leukemia Laboratory; C.G.M., research funding from Loxo Oncology for TRK inhibitors in acute lymphoblastic leukemia, and Abbvie and Pfizer for studies unrelated to those described herein. C.G.M. has received honoraria and travel support from Pfizer and Amgen.

Additional information

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

Integrated supplementary information

Supplementary Figure 1 Mutation distribution.

(a) Genetic variants including non-silent SNVs, indels or ITD sequence mutations, in-frame gene fusions, structural variations and focal copy number aberrations in AEL patients (n = 159 biologically independent samples). (b) Variant burden according to age (P value is from one-way ANOVA), IPSS-R cytogenetic risk (adjusted P values are from one-way ANOVA with post-hoc Tukey Honestly Significant Difference Test Calculator) and WHO 2008 (two-sided P value is from t test) and revised WHO 2016 diagnosis criteria (P value is from one-way ANOVA) (n = 159 biologically independent samples). Mean and S.D. are shown. (c) Proportion of nucleotide transition and transversion mutations according to age (n = 142 biologically independent samples with WES). Abbreviations: MAF, Mutant allele frequency.

Supplementary Figure 2 Mutations in DNA methylation genes and tumor suppressor genes.

(a) Heat map showing the distribution of mutations in DNA methylation genes and (b) tumor suppressor genes across age, IPSS-R cytogenetic risk groups and leukemia subtypes (WHO 2008 and WHO 2016). (c) Frequency of TP53 mutations according to the indicated age groups (n = 159 biologically independent samples), IPSS-R cytogenetic risk (n = 158 biologically independent samples), WHO 2008 (n = 150 biologically independent samples) and WHO 2016 (n = 149 biologically independent samples) criteria. Two-side P values are from Chi-square test (see Supplementary Table 9 for numbers for each group). Mean and S.D. are shown. Differences were identified according to age and cytogenetic risk. (d) Distribution of TP53 variant allele frequency in adult (n = 7) and older adult patients (n = 41). No significant differences (t test, two-sided P value = 0.7763) were identified. Mean and S.D. are shown. (e) Pie chart showing the co-occurrence of two TP53 mutations (MUT/MUT), a mutation and a loss of the other allele (MUT/LOSS) or a mutation and copy neutral loss of heterozygosis (LOH) (MUT/LOH). The percentages are calculated on the total mutated TP53 AEL cases (n = 51). (f) ProteinPaint visualization of TP53 (see URLs). (g) FISH with ZBTB4 5´ and ZBTB4 3´ showing one allele missing and the other allele broken apart (left). Addition of TP53 5´ shows pairing with ZBTB4 3´ (right). (h) Sanger sequencing electropherograms showing the rearrangement involving TP53 intron 1 and ZBTB4 intron 1 in SJAEL011863 (upper) and TP53 intron 1 and an intragenic region on chromosome 17 in SJAEL015002 (lower). This latter alteration was identifiable only by whole genome sequencing. (i) Log2 counts per million reads (CPM) for ZBTB4 from RNA-seq in the case with the TP53-ZBTB4 rearrangement (SJAEL011863) in red and the remaining AEL cases (total cases, n = 139). (j) Log2CPM values for TP53 from RNA-seq data (n = 139 biologically independent samples). Interestingly, the samples with rearrangements in intron 1 of TP53 had the lowest expression values for TP53 among the entire AEL cohort.

Supplementary Figure 3 Mutations in signaling genes.

(a) Pie charts showing the distribution of the different signaling pathway mutations in the whole AEL cohort (n = 159 biologically independent samples). (b) Matrix showing the distribution of mutations in genes involved in signaling pathways across age, IPSS-R cytogenetic risk groups and leukemia subtypes (WHO 2008 and WHO 2016). (c) Heatmap showing the variant allele frequency (VAF) for genes in the different signaling pathways. Each column is a patient. (d) Variant allele frequency boxplots comparing the VAF ranges in samples with one Ras pathway mutation (n = 16) and in those with at least 2 Ras pathway mutations (n = 13). Two-sided P value is from t test.

Supplementary Figure 4 Copy number alteration events detected by using SNP6 microarrays.

(a) Patients (n = 137 independent biological samples) are grouped by cytogenetic risk according to IPSS-R. (b) Correlation of copy number alterations with IPSS-R cytogenetic risk (Very good/Good: n = 59; Intermediate: n = 21; Poor: n = 6 and Very poor: n = 51; adjusted P values are from one-way ANOVA with post-hoc Tukey Honestly Significant Difference Test Calculator), TP53 mutations (TP53 wild-type, WT: n = 87; TP53 mutated, MUT: n = 50; P value is from t test), age (Pediatric: n = 20; Young adult: n = 6; Adult: n = 29; Older Adult: n = 82; adjusted P values are from one-way ANOVA with post-hoc Tukey Honestly Significant Difference Test Calculator) and M6 phenotype (M6a: n = 126; M6b: n = 11; two-sided P value is from t test). Mean and S.D. are shown. Correlation of loss of chromosomes 5 (c) and 7 (d) with age (left panel) and IPSS-R cytogenetic risk (right panel). Two-sided P values are from Chi-square test. Independent biological samples for each group are shown in the figure. (e) Number of CNAs in each chromosome. (f) Chromosomes affected by chromothripsis.

Supplementary Figure 5 Chromothripsis.

(a) Views by UCSC Genome Browser of chromosome 19 zoomed on the common deleted region. The Y axis shows the log2 copy number ratio. (b) Pie charts show the distribution of the recurrently mutated pathways in patients with chromothripsis.

Supplementary Figure 6 Comparison of mutation rates in pediatric and adult AEL cases according to WHO 2008 and reclassified as MDS according to WHO 2016 criteria (referred as AEL/MDS) and MDS.

(a) Comparison of mutation rates in pediatric (0-20 years) AEL/MDS (n = 11 biologically independent samples) and MDS (n= 104 biologically independent samples). (b) Comparison of mutation rates in adult AEL/MDS (n = 111 biologically independent samples) and MDS (n = 1410 biologically independent samples). Only cases for which sequencing data were available for both cohorts are reported. Data are from non-synonymous SNVs and indels. P values are from Fisher’s test (see Supplementary Table 19 for numbers for each group).

Supplementary Figure 7 Chimeric in-frame gene fusions in pediatric and adult AEL.

ProteinPaint schematic representation of chimeric gene fusions (right: a, MYB-GATA1; b, ASNS-PTPN1; c, ZMYND8-RELA; d, APLP2-EPOR; e, PFN1-SCHIP; f, NPM1-MLF1) and Sanger sequencing electropherograms showing the gene fusions (left). Abbreviations. LMSTEN, Leucine- Methionine- Serine- Threonine- Glutamic Acid- Asparagine motif in Myb proteins; Asn, Asparagine synthetase; PTPc, protein tyrosine phosphatase catalytic domain; PTTP, Pro-Trp-Trp-Pro motif; MYND finger, myeloid, Nervy, and DEAF-1 domain; RHD-n_RelA, Rel homology domain of RelA; IPT_NFkappaB, Immunoglobulin-like fold, Plexins, Transcription factors domain of NFkappaB; BPTI/Kunitz domain, bovine pancreatic trypsin inhibitor/Kunitz domain; APP, amyloid precursor protein; IL6Ra-bindInterleukin-6 receptor alpha chain, binding; FN3Fibronectin type 3 domain; SCHIP-1, Schwannomin-interacting protein 1; Mlf1IP, Myelodysplasia-myeloid leukemia factor 1-interacting protein. (g) Fluorescence in situ hybridization (FISH) for MYB-GATA1. MYB 5′ and GATA1 3′ (left) followed by addition of GATA1 5′ (right) showing insertion of GATA1 3′ into MYB 5′ in SJAEL011884. One hundred cells were analyzed and 96 (96%) were positive for MYB-GATA1 fusion. (h) FISH for ZMYND8-RELA fusion. ZMYND8 5′ and ZMYND8 3′ (left) followed by addition of RELA 3′ (right) demonstrating the fusions of ZMYND8 to RELA in SJAEL011882. One hundred cells were analyzed and 75 (75%) had the ZMYND8-RELA rearrangement.

Supplementary Figure 8 Pairwise associations between gene mutations.

Top rows indicate grouping of genes according to pathway from 159 patients. Left/upper triangular half of the figure displays the number of cases having alterations of both genes. Right/lower half of the figure displays the odds ratio (represented by color of square) and P value from the Fisher exact test (represented by size of black dots) testing the association between two genes. Odds ratio (OR) represents the odds of genomic event in one gene, compared to odds of genomic event in the other gene. OR>1 indicates a positive association between two genes. Conversely, OR<1 indicates a negative association. When there is at least one cell with zero in the 2*2 contingency table, the corresponding cell color in the co-occurrence matrix is set to white (which is not indicated in the figure legend) since the calculated odd ratio is either zero or infinite.

Supplementary Figure 9 Overall survival according to genomic features.

Kaplan–Meier survival curves with overall survival distributions according to gene expression clusters (n = 119), chromothripsis (n = 125 biologically independent samples), number of driver gene mutations in the same sample (1-2, 3-4, 5-6, 7-8 or > 8) (n = 147), functional pathway (cell cycle regulation, cohesin, DNA methylation, DNA repair, epigenomic, NPM1, signaling, RNA/processing/splicing and transcription regulation) (n = 147 biologically independent samples) and mutated genes (TP53, FLT3 and RB1) (n = 147 biologically independent samples). Survival curves were estimated for each group, considered separately, using the Kaplan–Meier method and compared statistically using the log rank test. Two-side P values were reported, multiple comparisons were not adjusted. See also Supplementary Table 25. Y: mutated; N: wild-type.

Supplementary Figure 10 NUP98 fusions in AEL.

(a) Schematic visualization of NUP98 fusions. Abbreviations: JmjN, Jumonji N-terminal domain; JmjC, Jumonji C-terminal domain. (b) Supervised hierarchical clustering analysis (using the top 500 differentially expressed coding and non-coding RNAs in pediatric cases (0-20 years) with/without NUP98 fusions (n = 20 biologically independent samples) (see also Supplementary Table 29 for the complete list of genes). (c) Volcano plot showing the differentially expressed genes between NUP98-fusion positive AEL and NUP98-fusion negative AEL cases (n = 20 biologically independent samples). Two-sided P values were identified by Limma Voom method. (d) Colony forming unit-granulocyte, macrophage (CFU-GM) and burst forming unit-erythroid (BFU-E) of lineage-negative mouse fetal liver cells expressing a fusion gene or transduced with empty vector (MIG). Columns show means of three replicates ± S.D. The experiment was performed independently twice with similar results. (e) Bioluminescent images from sublethally C57BL/6 mice transplanted with fetal liver lin-negative HSPCs expressing NUP98-KDM5A fusion or empty vector and captured after 12 weeks following transplantation from each group. (f) Splenic cells harvested from moribund primary recipients (median latency of 119 days) were GFP-sorted and injected into sublethally irradiated secondary recipient mice (shorter latency with a median of 90 days, P = 0.018) and Kaplan–Meier survival curves are shown for each group (Primary: n = 5; Secondary: n = 12). The Kaplan–Meier survival curve from primary mice (n = 5) transplanted with lin-negative HSPCs transduced with empty vector is shown as comparison. Outcome associations were analyzed with the log-rank test. Spleen weight in primary and secondary recipient mice expressing NUP98-KDM5A is reported. The mean expression is shown by the horizontal line in the scatter dot plot and the error bars represent the S.D. (g)(left) Representative histology showing multisystemic infiltration of NUP98-KDM5A induced leukemia in a primary mouse recipient. Scale bars represent 100 μm (bone marrow and uterus), 250 μm (meninges, skin-subcutis, kidney and liver) and 500 μm (pancreatic lymph node and spinal cord). Necropsy revealed moderate to marked leukemia infiltrates in multiple organs assessed including bone marrow, adrenal gland, brain, kidneys, liver, lymph nodes, salivary gland liver, skin, stomach and uterus confirming the systemic nature of the disease. (g)(right) Hematoxylin and eosin staining and MPO, B220, PAX5, CD41, GATA1, RUNX1 and GlyA labeling of lymph node from a representative mouse with NUP98-KDM5A induced leukemia. Scale bars are 50 μm. An erythroid phenotype (left) was excluded and the myeloid phenotype supported by the positive expression of RUNX1 and the negative expression of GATA1 and Glycophorin A; a megakaryocytic phenotype was ruled out by the negative expression of CD41. This analysis was performed in additional three independent primary mouse tumors and in two secondary recipient mice, obtaining similar results. (h) Flow cytometry of bone marrow cells harvested from a representative moribund primary (left) and secondary (right) recipient mouse with NUP98-KDM5A leukemia is shown. Cells were gated based on GFP expression. This analysis was performed on all mice in study that developed leukemia obtaining similar results.

Supplementary Figure 11 NTRK1 mutations in AEL.

(a) NTRK1 expression from RNA-seq and expressed as log2 counts per million, CPM, mapped reads according to NTRK1 mutational status (left panel, P value is from unpaired t test) and according to genomic subgroups (right panel, P value is from one-way ANOVA). The number of independent samples for each group is reported in each panel. Mean and S.D. are shown. (b) Immunofluorescence for NTRK1 expression in NIH 3T3 cells expressing WT or mutated NTRK1 (H498R, G617D and H766R) tagged at the C-terminus with a 6X-histidine sequence. Scale bars represent 10 μm. (c) Focus formation assay in NIH/ 3T3 cells expressing WT or mutated NTRK1 and treated with entrectinib or vehicle (CTRL). Number of foci are from 2 week culture and two biological replicates. Mean and S.D. are shown. (d) Representative histology showing infiltration of the lung and sternal bone marrow by NTRK1H498R/TP53R172H induced leukemia in a primary mouse recipient. Scale bars are 50 μm. This experiment was repeated in an independent mouse with NTRK1H498R/TP53R172H obtaining the same result. (e) Flow cytometry of bone marrow cells harvested from moribund representative primary recipient mice with NTRK1/TP53R172H, NTRK1H498R/TP53R172H, NTRK1G617D/TP53R172H or NTRK1H766R/TP53R172H induced leukemia is shown. Cells were gated based on GFP expression. Leukemia cells from NTRK/TP53 induced tumors were positive for the erythroid markers Ter119 and CD71 and negative for myeloid (Mac1, Gr1), megakaryoblastic (CD41) and lymphoid (B220, CD19, CD3) markers (data not shown). This analysis was performed on all mice in study that developed leukemia obtaining similar results. (f) Unsupervised hierarchical clustering analysis. (g) Kaplan–Meier survival curves and spleen weight from sublethally irradiated C57BL/6 primary recipients transplanted with bone marrow TP53R172H lin- HSPC expressing WT NTRK1 or mutated NTRK1 (G617D or H766R) are shown. The Kaplan–Meier survival curve from WT NTRK1 primary mice is from Fig. 5d. The number of independent mice is reported in each panel. Outcome associations were analyzed with the log-rank test. In the spleen weight graph the mean expression is shown by the horizontal line in the scatter dot plot and the error bars represent the S.D. (h) Hematoxylin and eosin staining and positive IHC labeling for GlyA, Ter119, mCD45, GATA1 and human TRKA in liver from mice with NTRK1/TP53R172H, NTRK1G617D/TP53R172H or NTRK1H766R/TP53R172H. All cells were strongly positive for human TRKA. This experiment was repeated in an independent mouse with NTRK1/TP53R172H obtaining the same result. (i) SKY karyotype of a representative mouse with NTRK1G617D /Tp53R172H mutated erythroid leukemia. The image shows the spectral (RGB) color image on the left and the classified (pseudocolor) chromosome after per-pixel classification of the spectral data on the right. The image has the following karyotype: 44~45,XX,+3[3],i(3)[1],+8[3],cf(8;14)[3],+11[3],+11[2]+15[3],+15[3][cp3]. The number of cells with each alteration (composite karyotype, cp) is shown by providing the number of cells in square brackets after each alteration. Abbreviation: cf: centric fusion. (j) Copy number alteration events detected by whole exome sequencing and showing perfect overlap with data generated from SKY karyotype. In red are copy number gains, in blues copy number losses and white is diploid copy number state. Data are from 11 independent mice. (k) Kaplan–Meier survival curves in mice with NTRK1G617D/TP53R172H induced leukemia treated with larotrectinib (n = 5) or vehicle (n = 5). Outcome associations were analyzed with the log-rank test. In the drug-treated group larotrectinib was stopped after 31 days.

Supplementary Figure 12 Pipelines for sequencing analysis.

(a) Analysis of whole exome sequencing (WES) for diagnosis/relapse samples with paired germline samples. (b) Analysis of WES for diagnosis/relapse samples without paired germline samples. (c) Pipeline for RNA-seq analysis. Abbreviations: SNV, single nucleotide variation; PCGP, Pediatric Cancer Genome Project; NHLBI ESP, NHLBI GO Exome Sequencing Project.

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Iacobucci, I., Wen, J., Meggendorfer, M. et al. Genomic subtyping and therapeutic targeting of acute erythroleukemia. Nat Genet 51, 694–704 (2019). https://doi.org/10.1038/s41588-019-0375-1

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