The genomic basis of childhood t-lineage acute lymphoblastic leukaemia

ABSTRACT T-lineage acute lymphoblastic leukaemia (T-ALL) is a high-risk tumour1 that has eluded comprehensive genomic characterization, which is partly due to the high frequency of noncoding

genomic alterations that result in oncogene deregulation2,3. Here we report an integrated analysis of genome and transcriptome sequencing of tumour and remission samples from more than

1,300 uniformly treated children with T-ALL, coupled with epigenomic and single-cell analyses of malignant and normal T cell precursors. This approach identified 15 subtypes with distinct

genomic drivers, gene expression patterns, developmental states and outcomes. Analyses of chromatin topology revealed multiple mechanisms of enhancer deregulation that involve enhancers and

genes in a subtype-specific manner, thereby demonstrating widespread involvement of the noncoding genome. We show that the immunophenotypically described, high-risk entity of early T cell

precursor ALL is superseded by a broader category of ‘early T cell precursor-like’ leukaemia. This category has a variable immunophenotype and diverse genomic alterations of a core set of

genes that encode regulators of hematopoietic stem cell development. Using multivariable outcome models, we show that genetic subtypes, driver and concomitant genetic alterations

independently predict treatment failure and survival. These findings provide a roadmap for the classification, risk stratification and mechanistic understanding of this disease. Access

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T-CELL ACUTE LYMPHOBLASTIC LEUKAEMIA: SUBTYPE PREVALENCE, CLINICAL OUTCOME, AND EMERGING TARGETED TREATMENTS Article Open access 17 April 2025 THE GENOMIC LANDSCAPE OF PEDIATRIC ACUTE

LYMPHOBLASTIC LEUKEMIA Article 01 September 2022 ONCOGENETIC LANDSCAPE OF T-CELL LYMPHOBLASTIC LYMPHOMAS COMPARED TO T-CELL ACUTE LYMPHOBLASTIC LEUKEMIA Article 13 May 2022 DATA AVAILABILITY

Primary sample whole-genome, exome and transcriptome data are available from the database of Genotypes and Phenotypes (dbGaP) under accession number phs002276.v2.p1 (phs000218 and phs000464

for T-ALL TARGET samples) and the Kids First data portal (https://portal.kidsfirstdrc.org/dashboard). Clinical data, processed genomic data and statistical analysis results can be found in

the Supplementary Tables. HiChIP, isoseq and ATAC–seq data are available from the European Genome Phenome (EGA) data portal under accession number EGAS50000000016. Processed gene expression

data can be accessed from Synapse under accession number syn54032669. Somatic alterations, recurrent mutations and HiChIP and ATAC–seq tracks can also be explored interactively using

ProteinPaint39 and GenomePaint40 on the St Jude Cloud at https://viz.stjude.cloud/mullighan-lab/collection/the-genomic-basis-of-childhood-t-lineage-acute-lymphoblastic-leukemia~29. Recurrent

mutation plots and oncoprints are provided as Supplementary Data 1 and 2, respectively. CODE AVAILABILITY This study did not involve the development of software. Code to reproduce key parts

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leukemia study. _J. Clin. Oncol._ 31, 4333–4342 (2013). Article  CAS  PubMed  Google Scholar  Download references ACKNOWLEDGEMENTS We would like to thank staff at the Gabriella Miller Kids

First Pediatric Data Research Program and Data Resource Center, including M. Fournier, J. Coulombe, E. Boja, J. G. Auvil, V. Cotton and D, Higgen; staff at the Biopathology Center at

Nationwide Children’s Hospital, including A. Cameron, Y. Moyer and T. Jones; staff at the COG operations, including S. Vargas, M. B. Sullivan, M. Thomas and C. Sauni; COG leadership

including D. Hawkins, L. Gore and P. Adamson; staff at the CTEP, including M. Smith; staff at the Hudson Alpha Genomics Project Management, including S. Kuhafa-Hall; staff at the Flow

Cytometry Core at the Children’s Hospital of Philadelphia, including F. Tunic and J. Murray; P. Raess; staff at the Oregon Health and Science University for facilitating SPI1 case

sequencing; and mentors from the Computational Biology Training in Hematology programme. The AALL0434 clinical trial was supported by Novartis. Funding included National Institutes of Health

Gabriella Miller Kids First Pediatric Research X01HD100702 (D.T.T., C.G.M., P.P., M.L.L., S.P.H., S.W., E.A.R., B.L.W., M.D., S.P.B., K.P.D. and J.J.Y.), R03CA256550 (D.T.T., C.G.M., P.P.,

M.L.L., S.P.H., S.W., E.A.R., B.L.W., M.D., S.P.B., K.P.D. and J.J.Y.), R01CA193776 (D.T.T., B.W., K.T., C.G.M., S.P.H., J.J.Y., R.S. and M.D.), U10CA180886 (D.T.T. and M.L.L.), R01CA264837

(D.T.T., J.J.Y., C.G.M., K.T., B.L.W. and R.S.), U10CA180899 (M.D.), U24CA114766 (D.T.T. and M.L.L.), U24CA196173 (D.T.T.), R01GM115634 (R.W.K.), U54CA243124 (R.W.K. and C.G.M.), P30CA021765

(C.G.M. and G.W.), R35CA197695 (C.G.M.), T32CA236748 (C.G.M), F32CA254140 (L.E.M.), K12CA076931 (C.D.); the Aiden Everett Davies Innovation Fund (D.T.T); Alex’s Lemonade Stand Foundation

(D.T.T., K.T. and S.P.H.); American Lebanese and Syrian Associated Charities of St Jude Children’s Research Hospital, Canadian Institutes of Health Research Young Investigator Award (C.D);

the Harrison Willing Memorial Research Fund (D.T.T); The Henry Schueler 41&9 Foundation (P.G. and C.G.M.); Hyundai Hope of Wheels (D.T.T., K.T. and R.S.); the Invisible Prince Foundation

(D.T.T); the Leukemia and Lymphoma Society (D.T.T.); the Pennsylvania Department of Health (D.T.T.); St Baldricks Foundation (D.T.T); the St Jude Children’s Research Hospital Chromatin

Collaborative, and the St Jude Children’s Hospital Hematological Malignancies Program Garwood Fellowship (S.K.). This research was supported in part by the National Cancer Institute grants.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. AUTHOR INFORMATION Author notes * These

authors contributed equally: Danika Di Giacomo, Anna Eames Seffernick * These authors jointly supervised this work: Charles G. Mullighan, David T. Teachey AUTHORS AND AFFILIATIONS *

Department of Pathology, St Jude Children’s Research Hospital, Memphis, TN, USA Petri Pölönen, Danika Di Giacomo, Abdelrahman Elsayed, Shunsuke Kimura, Francesca Benini, Lindsey E.

Montefiori, Ilaria Iacobucci, Yunchao Chang, Yaqi Zhao, Pankaj S. Ghate & Charles G. Mullighan * Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, Italy

Danika Di Giacomo * Department of Biostatistics, St Jude Children’s Research Hospital, Memphis, TN, USA Anna Eames Seffernick, Abdelrahman Elsayed & Stanley B. Pounds * Department of

Maternal Infantile and Urological Sciences, Sapienza University of Rome, Rome, Italy Francesca Benini * Department of Pediatric Hematology and Oncology, Bambino Gesù Children’s Research

Hospital, Rome, Italy Francesca Benini * Children’s Hospital Los Angeles, Laboratory Medicine, Los Angeles, CA, USA Brent L. Wood * Perelman School of Medicine, University of Pennsylvania,

Philadelphia, PA, USA Jason Xu, Jonathan Sussman, Caroline Diorio, David Frank, Kathrin M. Bernt, Stephen P. Hunger, Kai Tan & David T. Teachey * Division of Oncology and Center for

Childhood Cancer Research, Children’s Hospital of Philadelphia, Philadelphia, PA, USA Changya Chen, Elizabeth Li, Caroline Diorio, Lahari Uppuluri, Alexander Li, Kathrin M. Bernt, Tiffaney

L. Vincent, Stephen P. Hunger, Kai Tan & David T. Teachey * Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA Changya Chen *

Center for Applied Bioinformatics, St Jude Children’s Research Hospital, Memphis, TN, USA Zhongshan Cheng, Jason Myers, Dale Hedges, Yawei Hui, Yiping Fan, Evadnie Rampersaud, Ti-Cheng Chang

 & Gang Wu * Department of Pediatrics and the Center for Childhood Cancer Research, Children’s Hospital of Philadelphia, Philadelphia, PA, USA Haley Newman, Caroline Diorio, Rawan

Shraim, Kathrin M. Bernt, Stephen P. Hunger & David T. Teachey * Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, WA, USA Soheil Meshinchi & Rhonda Ries *

Global Pediatric Medicine, St Jude Children’s Research Hospital, Memphis, TN, USA Meenakshi Devidas * Research Institute and Cancer and Blood Disorders Program, Children’s Minnesota,

Minneapolis, MN, USA Stuart S. Winter * Division of Oncology, University of Virginia Children’s Hospital, Charlottesville, VA, USA Kimberly P. Dunsmore * Department of Oncology, St Jude

Children’s Research Hospital, Memphis, TN, USA Hiroto Inaba * Division of Pediatric Hematology Oncology, Department of Pediatrics, NYU Langone Health, New York, NY, USA William L. Carroll 

& Elizabeth Raetz * Department of Pediatrics and Pathology, Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA William L. Carroll & Elizabeth Raetz * Department of

Pathology and Laboratory Medicine, Nationwide Children’s Hospital, Columbus, OH, USA Nilsa C. Ramirez * Department of Structural Biology, St Jude Children’s Research Hospital, Memphis, TN,

USA Aaron H. Phillips & Richard W. Kriwacki * Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Sciences Center, Memphis, TN, USA Richard W.

Kriwacki * Department of Pharmaceutical Sciences, St Jude Children’s Research Hospital, Memphis, TN, USA Jun J. Yang * Department of Computational Biology, St Jude Children’s Research

Hospital, Memphis, TN, USA Jian Wang, Colleen Reilly & Xin Zhou * Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands Mathijs A. Sanders * Cancer, Ageing

and Somatic Mutation (CASM), Wellcome Sanger Institute, Hinxton, UK Mathijs A. Sanders * Department of Pediatrics, Graduate School of Medicine Kyoto University, Kyoto, Japan Junko Takita *

Department of Pediatrics, Tokyo University, Tokyo, Japan Motohiro Kato & Nao Takasugi * Department of Pediatrics, Knight Cancer Institute, Oregon Health and Science University, Portland,

OR, USA Bill H. Chang * Department of Pathology, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA Richard D. Press * Department of Pediatrics and the Ben

Towne Center for Childhood Cancer Research, Seattle Children’s Hospital, University of Washington, Seattle, WA, USA Mignon Loh * Center for Single Cell Biology, Children’s Hospital of

Philadelphia, Philadelphia, PA, USA Kai Tan Authors * Petri Pölönen View author publications You can also search for this author inPubMed Google Scholar * Danika Di Giacomo View author

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T. Teachey View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS M.L., E. Raetz, S.P.H., J.J.Y., M.D., H.I., S.W., K.D., W.L.C., N.C.R., K.B.,

C.D., L.U., D.F., S.M., R.R., A.L., T.L.V., P.S.G., M.A.S., J.T., M.K., N.T., B.H.C., R.D.P. and Y.Z. provided patient samples and collected data. P.P., Z.C., J.M., Y.H., Y.F., D.H. and E. 

Rampersaud preprocessed data. P.P., T.-C.C., G.W., H.N. and R.S. analysed genomic data. P.P., A.E.S., A.E. and S.B.P. performed statistical analyses. P.P., J.X., C.C., E.L., J.S., A.L. and

K.T. analysed scRNA data. P.P., J.W., C.R. and X.Z. generated cloud-based visualizations. R.R., S.M. and T.V. isolated DNA and RNA. J.X. and C.C. performed single cell library preparation

and sequencing. D.D.G. and L.E.M. performed HiChIP and ATAC–seq. R.D.P. performed targeted amplicon sequencing. F.B. and S.K. performed long-read sequencing. S.K. engineered _MED12_ KO

cells, Y.C. performed western blotting. F.B., I.I. and S.K. performed luciferase report assays. B.L.W. analysed flow cytometry data. A.H.P. and R.W.K. analyzed AlphaFold2 models. P.P.,

C.G.M. and D.T.T. designed the study and wrote the manuscript. CORRESPONDING AUTHORS Correspondence to Charles G. Mullighan or David T. Teachey. ETHICS DECLARATIONS COMPETING INTERESTS

D.T.T. has received research funding from BEAM Therapeutics, NeoImmune Tech and serves on advisory boards for BEAM Therapeutics, Janssen, Servier, Sobi, and Jazz. D.T.T. has multiple patents

pending on CAR-T. C.G.M. serves on the scientific advisory board for and has received honoraria from Illumina, and has received research funding from Pfizer, equity from Amgen and royalties

from Cyrus. E. Raetz has received institutional research funding from Pfizer and serves on a Data and Safety Monitoring Board for Bristol Myers Squibb. I.I. has received travel and

accommodation expenses reimbursed by Mission Bio. I.I. and P.P. have received consultancy fees from Arima Genomics. K.M.B. has received research funding from Syndax. PEER REVIEW PEER REVIEW

INFORMATION _Nature_ thanks Charley de Bock, Shai Izraeli and Seishi Ogawa for their contribution to the peer review of this work. Peer reviewer reports are available. ADDITIONAL INFORMATION

PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. EXTENDED DATA FIGURES AND TABLES EXTENDED DATA FIG. 1

T-ALL COHORT COMPOSITION AND SUBTYPING. A, Schematic depiction of the cohort composition, analysis framework, and study outcomes; B, UMAP scatter plots illustrating Leiden algorithm

clusters at 0.1 resolution, annotated by subtype; C, Leiden algorithm clusters at 0.5 resolution, further delineating distinct groups; D. UMAP plot showing types of TCR loci involved in

structural variants (SV) that lead to oncogene activation by enhancer hijacking; E, Leiden algorithm clusters showing oncogene expression patterns; F, Bar plot presenting blast percentage

distribution proportions, categorized by subtypes; G, Dot plot of marker genes from Fig. 2 in normal cell scRNA data, depicting average (mean) expression (dot color) and detection percentage

(dot size); H, UMAP plot showing the identification of clonal TCR rearrangements in cancer cells; I, UMAP plot of ETP status; J, Bar plot of ETP status proportions for cases with known

immunophenotype, categorized by subtypes, see panel I for legend (two-tailed Chi-square test _P_ = 3.25e-104, see N for each subtype from L, whole cohort N = 1309); K, Bar plot showing age

distribution proportions, categorized by subtypes and ordered by median age; L, Box plot, as defined in Fig. 3d legend, presenting age distributions per subtypes, with _P_ value from one-way

ANOVA. (two-sided pairwise _t_-test Holm-adjusted _P_ for significant pairs using TAL1 DP-like as comparison group: BCL11B _P_ = 0.0053, ETP-like _P_ = 0.00016, HOXA TCR _P_ = 0.019, NKX2-1

_P_ = 0.0092, NKX2-5 _P_ = 0.0014, SPI1 _P_ = 0.049, STAG2/LMO2 _P_ = 0.00023). Median age in the cohort is shown as red dotted line; M, Bar plot depicting gender distribution proportions,

categorized by subtypes (two-tailed Chi-square test _P_ = 0.000000417, see N for each subtype from L, whole cohort N = 1309). EXTENDED DATA FIG. 2 ALTERATIONS IN T-ALL AND SUBTYPE-ASSOCIATED

GENETIC CHANGES. A, Stacked bar plot depicting proportions of alteration types for significantly altered genes (q < 0.05 and altered in >1% of cases). Left: frequency of alteration

types. Right: alteration frequencies; B, Dot plot illustrating altered genes, broad CNVs, and pathways significantly associated with subtypes, displaying odds ratio as dot color and -log10

FDR as dot size. Odds ratios and -log10 FDR were truncated at 20. Genes/lesions are ordered by significance within each subtype. EXTENDED DATA FIG. 3 GENOMIC CHARACTERIZATION OF ENHANCER

HIJACKING EVENTS. A, Complex chromosome 7 rearrangements: Depiction of H3K27ac HiChIP, H3K27ac coverage, diagnostic sample, and germline WGS coverage in a T-ALL patient, showing interactions

between _HOXA13_ and TCRβ loci after chromosome 7 rearrangements. Arcs represent interactions between genomic loci, with color coding denoting interaction strength at 500 kb resolution.

Breakpoints are annotated by orange lines. Left: Boxplot, as defined in Fig. 3d legend, of _HOXA13_ gene expression and _P_ value for TCR::_HOXA13_ mutated (mut) vs _HOXA13_ unaltered wild

type (wt) samples (two-tailed Student _t_-test, _P_ values were not adjusted for multiple comparison), with PAVHBC sample labeled in cyan. The intensity of each arc represents contacts at 5 

kb resolution between a pair of loci. Maximum intensity is indicated in the color scale; B, TCR::_HOXA9_ Inversion: Similar representation as A; C, TCR::_LMO3_ translocation: similar

representation as A; D, _BCL11B_::_LMO2_ translocation: Similar representation as A; E, _HOXA13 BCL11B_ translocation: similar representation as A; F, _NKX2-1_ Chromosome 14 chromothripsis

and _BCL11B_ locus rearrangement: similar representation as A; G, _BCL11B_::_HOXB13_ translocation: similar representation as A; H, _LINC00592_::_TLX1_ enhancer hijacking by 10 Mb intergenic

loss. WGS and RNA coverage for the representative cases, normal double positive and CD34+ HSPCs H3K27ac and CTCF coverage is shown, with putative enhancers highlighted in blue. Top: Heatmap

showing normal double positive and CD34+ HSPCs H3K27ac HiChIP raw interactions as heatmaps, with topologically associated domains annotated by black dotted triangles. Left: boxplot as in A;

I, _NFKBIA_::_NKX2-1_ enhancer hijacking: similar representation as A; J, _MIR2117HG_::_LMO2_ translocation: Similar representation as A; K, _DHX9::TAL1_ inversion: Similar representation

as A; L, _CELF1::LMO2_ intergenic loss: Similar representation as A; M, _CAPRIN1::LMO2_ enhancer hijacking is shown as in H, with additional highlight of CTCF boundary by blue rectangle; N,

_MIR181A1HG::HOXA13_ Translocation: similar representation as A; O, _MIR181A1HG_::_LMO2_ translocation: similar representation as A; Abbreviations: diagnostic and germline/remission sample

WGS (WGS-D, WGS-G). Nucleosome-free and nucleosome cut fragments (ATAC-free, ATAC-nuc). EXTENDED DATA FIG. 4 GENOMIC CHARACTERIZATION OF ENHANCER AND PROMOTER ALTERATIONS. A, H3K27ac HiChIP,

H3K27ac coverage, cancer (WGS-D) and remission/germline (WGS-G) coverages in T-ALL patient, showing interactions between _MYC_ and the amplified N-me enhancer. Color scale bar showing

interaction strength at 5 kb resolution. The intensity of each arc represents contacts at 5 kb resolution between a pair of loci. Maximum intensity is indicated in the color scale; B, WGS

coverage tracks showing recurrent _LINC00649_ losses in T-ALL patients. H3K27ac HiChIP, H3K27ac and CTCF coverage shown as in A. _LINC00649_ is a putative regulatory region for _RUNX1_ in

CD34+ cells; C, Illustration of representative cases featuring WGS coverage tracks in _FTO_/_IRX_ loci, along with two WGS germline control samples. Color scale bar showing interaction

strength at 5 kb resolution. Boxplots, as defined in Fig. 3d legend, showing _IRX3_ and _IRX5_ gene expression comparing FTO/IRX loci deletions and wild type (wt) (two-tailed Student

_t_-test, not adjusted for multiple comparisons). Allelic expression denoted by red dots. Top: H3K27ac HiChIP, H3K27ac and CTCF coverage in CD34+ cells, showing interactions between _FTO_

enhancers and _IRX3_ and _IRX5_. Arcs represent interactions between genomic loci, with color coding denoting strength of interaction; D, Similar representation as C for _ZNF219_ loci with

annotation of TCRδ loci; E, WGS, H3K27ac HiChIP, H3K27ac and CTCF coverage in CD34+ cells, showing interactions between the _HNRNPC_ promoter and enhancers and _ZNF219_ loci. Below, WGS and

RNA coverage showing copy number loss at _ZNF219_ loci and revealing RNA coverage match with breakpoint location (red) and reads clip with _HNRNPC_ promoter (not shown); F, Gene expression

boxplot by _LMO2_ variant type with samples with monoallelic expression annotated as red dots; G, WGS, H3K27ac HiChIP, ATAC seq, WTS coverage tracks are shown for T-ALL patient PASPLG with 6

 bp enhancer deletion (light blue line) and PAWGYX 85 bp intronic gain (red line), revealing high levels of H3K27ac marking neoenhancer formation for PASPLG and neomorphic promoter

generation for PAWGYX; H, RNA coverage tracks for samples with _LMO2_ intronic gains (red), intronic SNV/indel (blue) and intergenic SNV/indels (light blue), showing generation of

alternative transcript start site for intronic gains and intronic SNV/indel, but not intergenic SNV/indels. Top: junction reads for representative case PARMMV are shown; I, WGS coverage

tracks for _TAL1_ enhancer gains (cyan line) are shown in 9 patient samples and one germline control (PATSIL_G) sample; J, WGS coverage for T-ALL patient and remission/germline samples are

shown with four RNA isoform sequencing reads visualized in IGV, capturing the 89 bp gain of the _TAL1_ enhancer. Orange rectangle: highlight of the increased WGS coverage matching 89 bp

gain/insert sequence size. Right: boxplot as in C of _TAL1_ gene expression comparing _TAL1_ enhancer gains with _TAL1_ wt samples with PASXMF sample labeled in cyan. EXTENDED DATA FIG. 5

ENHANCER HIJACKING IS ASSOCIATED WITH DIFFERENTIATION STAGE. A, H3K27ac HiChIP, H3K27ac, WGS and ATAC-seq coverages in T-ALL sample PASLPH showing _CD1E::TAL1_ intergenic inversion. Arcs

depict interaction between genomic loci, with colors indicating interaction strength. The intensity of each arc represents contacts at 5 kb resolution between a pair of loci. Maximum

intensity is indicated in the color scale. Left: boxplot, as defined in Fig. 3d legend, of _TAL1_ gene expression for _CD1E::TAL1_ and _TAL1_ wild type (wt) samples, with the PASLPH sample

labeled in cyan. Right: heatmap showing H3K27ac HiChIP interactions for normal double positive (DP) and CD34+ HSPCs as heatmaps, with H3K27ac coverage shown below. Lesion breakpoints are

shown as black lines; hijacked enhancers color coded; topologically associated domains annotated by black dotted triangles; B, H3K27ac coverage tracks of _CD1E::TAL1_ enhancer hijacking

(marked by cyan line) is shown in different thymic T-cells and in CD34+ cells. Top: heatmap showing H3K27ac HiChIP raw interactions for normal double positive and CD34+ HSPCs; C, same as B

for _RAG2::LMO2_ enhancer hijacking; D, same as B for _SOX4/CASC15::HOXA13_ enhancer hijacking; E, same as B for _MIR181A1::HOXA13_ enhancer hijacking; F, Expression of enhancer

hijacking-driven leukemia gene expression signatures in normal BM/thymic scRNA data: Dot plots depict mean gene set score of each gene expression signature by normal cell type (color) and

mean gene set detection percentage (dot size). Left: subtype annotation for each alteration. Top: _RAG1_, _RAG2_ gene expression is shown, with mean expression (dot color) and detection

percentage (dot size). Right: _RAG1_/_RAG2_ gene expression log2 fold change (FC) of for each alteration (altered vs. wild type) is shown as a heatmap. Right to the heatmap, horizontal bar

plots show the percentage of each enhancer hijacking structural variant (SV) event predicted to be RAG mediated. Bonferroni adjusted -log10 _P_ value is shown (one-tailed Fisher’s exact

test, comparing RAG mediated breakpoints compared to non-RAG mediated breakpoints for each lesion category). EXTENDED DATA FIG. 6 INTRAGENIC AND INTERGENIC NON-CODING ALTERATIONS. A, NOTCH1

amino acid position, mRNA bp position, exon number, and previously described41 protein domains are shown. Heterodimerization domain (HD) and transmembrane (TM), TM-connector domains are

color coded and locations of the proteolytic cleavage sites (S2 and S3) that lead to intracellular domain release from the membrane are shown as black lines. Below, exons 27-28 are shown,

with _NOTCH1_ intronic SNV location marked by a red line, with mutation position and types (forward strand C-A or C-G), resulting 3’ splice site sequences (TAG and CAG mutated splice site,

GAG wild type (wt)) are shown. The mutated splice site results in 129 bp or 43 amino acid insertion (green rectangle) at exon 28 and the insert sequence is shown below; B, _NOTCH1_

intergenic SNV validation by RT-PCR. Two patient samples, SJTALL031662 and SJTALL031904, have longer 538 bp transcript due to the intronic SNV, compared to _NOTCH1_ wt 409 bp transcript for

PEER and LOUCY cell lines, primers at Exon 26 (9:136504848-9:136504827), Exon 28 (9:136502426-9:136502405). This experiment was not repeated, but variant was confirmed using Sanger

sequencing; C, _NOTCH1_ exon 28 33 bp duplication sites are shown using tumor sample (D) and matched germline/remission sample (G) WGS and LR isoform sequencing. Duplicated site sequence

match with increased DNA coverage, highlighted in blue; D, _NOTCH1_ locus showing WGS coverage tracks and intragenic deletions between exon 16-27 and 3-27 in 11 representative cases and

matched non-tumor sample controls; E, _NOTCH1_ splicing plots for T-ALL patients with exon 16-27 and 3-27 deletions, compared to wt control, are visualized. Atypical splicing reads are

highlighted in red; F, Top: Coverage tracks for _IL7R_ diagnostic and matched non-tumor WGS and WTS coverage, along with SNPs. Below: Isoform sequencing reads, indicating the loss of one

allele due to TSS loss. Right: _IL7R_ and _PRLR_ loci deletion is shown for representative cases, accompanied by _IL7R_ and _PRLR_ gene expression and _P_ value (two-tailed, Student

_t_-test); G, Similar representation as F for _CCND3_: showing the loss of the long isoform (red) of _CCND3_ and _TAF8_ allele, while both alleles of short isoforms (blue) are expressed;

WGS-D, whole genome sequencing of diagnosis sample, WGS-G, matched non-tumor sample. Nucleosome-free (ATAC-free) and nucleosome cut fragments (ATAC-nuc). EXTENDED DATA FIG. 7 EFFECT OF

_CCND3/TAF8_ TSS DELETION AND SNV/INDELS ON PROTEIN STRUCTURE. A, the distribution of detected isoforms, showing that ENST00000372991 (short isoform of _CCND3)_ is predominantly expressed;

B, boxplots, as defined in Fig. 3d legend, depict the gene expression comparisons of _CCND3_, _TAF8_, short isoform (ENST00000372991), and long isoforms (ENST00000372988, ENST00000415497),

with _P_ value (two-tailed Student _t_-test) shown for comparison between _CCND3/TAF8_ TSS loss (N = 27) and _CCND3_ wild type (wt, N = 1193). Additional control groups were samples with

_CCND3_ SNV/indel (N = 79), and _CCND3_ wt TAL1 DP-like (N = 253), and TAL1 αβ-like (N = 206) subtypes (these subtypes are enriched for _CCND3/TAF8_ TSS loss). _CCND3/TAF8_ TSS loss is

associated with decreased long isoform expression while total _CCND3_ expression remains constant due to high short isoform expression; C, Exon composition, CDD/PFAM protein domains, and

amino acid residue positions of _CCND3_ SNV/indel for different isoforms; D, AlphaFold2 model of the long (blue) and short (yellow) isoforms of CCND3 superimposed onto the crystal structure

of the CDK4/CCND3 (PDB code 3G-33); E, Detailed views of the contacts between α1 and α2 of CCND3 showing that the long isoform of CCND3 lacks the first two α-helices of the short isoform

protein and α1 and α2 engage in essential hydrophobic contacts within the core of CCND3. FoldX predicted that the protein encoded by the long isoform of CCDN3 is unstable, with an estimated

destabilization of 100 kcal/mol, likely leading to an unfolded protein; F, SNV/indel at the C-terminus of CCND3 is unlikely to affect the interaction with CDK4. Mutations at the C-terminus

may affect CCND3 stability by interfering with CCND3 protein homeostasis; there is a conserved TPTDV motif at the C-terminus of CCND3 that recruits the E3 ligase AMBRA1. The TPTDV is

maintained in both the K268R and 264_274del mutants, but not in the R217fs variant. EXTENDED DATA FIG. 8 SUBTYPING AND GENOMIC ANALYSIS OF TLX3 AND NKX2-1 SUBGROUPS. A, Oncoprint showing

alterations that are significantly different in frequency between TLX3 DP-like and Immature subgroups. Left: -Log10 FDR (two-tailed Fisher’s exact test); TCR and ETP status are shown above

lesions; B, UMAP plots representing TLX3 DP-like and Immature subgroups; C, UMAP plot displaying TCR rearrangement status for TLX3 cases; D, UMAP plot illustrating ETP status for TLX3 cases;

E, Oncoprint of _NUP214::ABL1_ fusion and JAK/STAT or RAS pathway genes. Gene expression of differentially expressed (FDR < 0.01) surface markers and kinase genes are shown above; F, Dot

plot depicting significantly altered genes, broad CNVs, and pathways associated with TLX3 DP-like and Immature subgroups. Dot color represents odds ratio, and dot size reflects -log10 FDR

values. Odds ratio values are values are truncated at a maximum of 10. Genes are ordered by significance within each subtype; G, UMAP plots showing selected gene and lesion alterations

(green) for TLX3 samples; H, Dot plot visualizing expression of TLX3 DP-like and Immature gene expression signatures in normal BM/thymic single cell RNA data. Detection percentage is

represented by dot size, while mean signature enrichment by normal cell type is indicated by color. The TLX3 subgroup is further divided based on ETP status and TCR rearrangement status; I,

Similar to A, showing significantly different lesions between NKX2-1 TCR and Other subgroups; J, UMAP plot illustrating labels for NKX2-1 TCR and Other subgroups; K, WGS coverage tracks in

chromosome 14 showing chromothripsis and annotation for the _NKX2-1_ locus; L, similar as G, Dot plot depicting significantly altered co-lesion genes, broad CNVs, and pathways associated

with NKX2-1 TCR and Other subgroups; M, UMAP plots displaying selected gene and lesion alterations (green) for NKX2-1 samples. EXTENDED DATA FIG. 9 SUBTYPING AND GENOMIC ANALYSIS OF

TAL1/LMO2 GENOMIC SUBGROUPS. A, Oncoprint representing driver alteration prevalence between TAL1 DP-like and αβ-like subtypes. Left: -log10 FDR significance levels are shown (Two-tailed

Fisher’s exact test); B, Oncoprint of co-lesion prevalence as in A; C, Heatmap showing mutually exclusive and co-occurring lesions within the TAL1 DP-like and αβ-like subtypes. Color

represents log2 odds ratio. Significant pairs are indicated by stars (one-sided Fisher’s exact test, *** = FDR < 0.001, ** = FDR < 0.01, * = FDR < 0.05); D, UMAP plots representing

the alterations associated with the TAL1 groups; E, Oncoprint of genetic subgroup-associated alterations categorized by genes, lesions, and pathways. The subdivisions are based on subtypes

and further divided by genetic subgroup. Oncogene gene expression and expression of _MYC, MYB_ and _MYCN_ are represented as Z scores; F, UMAP plots illustrating the distribution of TAL1

genetic subgroups and LMO2 γδ-like and STAG2/LMO2 subgroups. TCR rearrangement status and ETP status are indicated on the right; G, Volcano plot depicting differential flow cytometry markers

between TAL1 αβ-like and DP-like subtypes (TAL1 αβ-like upregulated markers log2 fold change positive and downregulated negative); Significantly altered markers (two-tailed Wilcoxon

rank-sum test) are labeled by protein name, with _P_ value (0.1) and log2 fold change cutoffs (0.25) are shown as black dotted lines; H, As in G, volcano plot comparing LMO2 γδ-like samples

to the rest of the samples, showing differential flow cytometry markers. I, As in G, Volcano plot showing differential flow cytometry markers between LMO2 γδ-like and TAL1 αβ-like subtypes;

J, Similar to G, volcano plot comparing flow cytometry markers between LMO2 γδ-like and TAL1 DP-like subtypes; K, Similar to G, differential flow cytometry markers in STAG2/LMO2 group; L,

Similar to G, differential flow cytometry markers between STAG2/LMO2 and TAL1 DP-like subtypes. EXTENDED DATA FIG. 10 ETP/ETP-LIKE GENOMICS, IMMUNOPHENOTYPE AND CLINICAL ASSOCIATIONS. A,

UMAP plot revealing distinct _MLLT10_ and _KMT2A_ fusion partners; B, Dot plot showing significant gene, CNV (gain/amplification/loss/deletion), and pathway alterations linked with ETP-like

genetic subtypes, with dot color indicating odds ratio and dot size reflecting -log10 FDR. Odds ratio values are truncated at a maximum of 10, with genes/lesions ordered by significance

within each subtype; C, Location of MED12 SNV/indel amino acid position in T-ALL; D, Western blot of MED12 in knockout in PER117 and LOUCY cell line. These uncropped images were obtained

from LI-COR odyssey by defining the area to be scanned. Assay was repeated three times with consistent results; E, Dot plot displaying GSEA enrichment result for significant pathways in

MED12KO vs. wild type (wt) and ETP-like MED12 subgroup, where dot size is -log10 FDR and dot color is Normalized Enrichment Score (NES); F, Volcano plot illustrating significantly

differentially expressed genes (two-tailed Wald test) between LOUCY _MED12_ knockout (KO) and wt samples (LOUCY _MED12_ KO upregulated markers log2 fold change positive and downregulated

negative). The analysis is restricted to genes that exhibited differential expression in ETP-like MED12 subgroup; G, Dot plot visualizing the gene set detection percentage (dot size) and

mean gene set enrichment by normal cell type (color) for MED12 knockout and MED12 subtype upregulated (N = 60) and downregulated (N = 104) genes in normal BM/thymic scRNA data. Below is a

dot plot of select significantly differentially expressed genes (as in Fig. 4b) in normal cell scRNA, depicting mean expression (dot color) and detection percentage (dot size); H, Heatmap of

HOXA gene expression, samples ordered by distance to _HOXA9_ TSS, and driver/alteration type annotations on top; I, Oncoprint of ETP-like group altered drivers and pathways, divided by ETP

status, with day 29 MRD bar plot, clinical characteristics, morphological response, relapse, and TCR rearrangement status annotations. FDR between ETP categories and genomics indicated on

the left; J, Volcano plot depicting differential expression of cell surface markers detected by flow cytometry between ETP-like cases without TCR rearrangement (no TCR-R) versus those with

TCR γδ rearrangement (ETP-like TCRγδ) upregulated markers log2 fold change positive and downregulated negative, with _P_ value (0.1) and log2 fold change (0.25) cutoffs shown as black dotted

lines), with significantly altered (from two-tailed Wilcoxon rank-sum test) markers labeled by protein names; K, Similar to J, this plot compares flow cytometry markers between ETP-like

MLLT10 cases and other MLLT10 cases. EXTENDED DATA FIG. 11 T-ALL GENOMIC RISK FACTORS AND CLINICAL OUTCOMES CONSIDERING MRD, DFS AND OS. A, Proportions of induction failure and residual

disease cases by genetic subgroup; B, Oncoprint of variants significantly associated with MRD. Day 29 MRD displayed as a bar plot on top with other clinical annotations. Genomic data was

divided to residual disease (day 29 MRD ≥ 0.01%), and negative groups (day 29 MRD < 0.01%). Left: number of present (N) vs. absent lesions, -log10 q value from Firth penalized logistic

regression, log odds ratios (OR) and then forest plot with center box presenting log OR and error bars indicating 95% confidence intervals (CI); C, Forest plot showing hazard ratios (HR) and

95% CI comparing cases with subtype or genetic subtype present (N) vs. absent, considering DFS and OS as outcomes. Significant associations (two-tailed, Firth-penalized Cox models that also

adjusted for MRD (MRD.adj), _P_ < 0.1) are highlighted in red. These _P_ values were not adjusted for multiple comparisons. EXTENDED DATA FIG. 12 GENOMIC RISK FACTORS AND CUMULATIVE

INCIDENCE OF RELAPSE IN T-ALL. A, Left: number of present vs. absent (N) lesions, -log10 P value, hazard ratios (HR). Right: forest plot with center box presenting HR and error bars

indicating 95% confidence intervals (CI) for variants that were present vs. absent considering EFS, DFS, and OS as outcomes. Significant associations (two-tailed, Firth-penalized Cox models

that also adjusted for MRD, _P_ < 0.1) are highlighted in red. These _P_ values were not adjusted for multiple comparisons; B, The Kaplan-Meier curve of NOTCH pathway mutated vs. not

mutated cases, stratified by MRD percentage (%), with _P_ value from two-tailed Log-rank test; C, Cumulative incidence of relapse plot, revealing distinct risk between PI3K (NOTCH wild type

(wt)) and NOTCH (PI3K wt) pathway alterations, with _P_ value from two-tailed Holm-adjusted Gray’s test; D, as in C, Comparison of _PTEN_ deletions to other _PTEN_ alterations in terms of

cumulative relapse incidence; E, as in C Comparison of _NOTCH1_ intragenic loss to other _NOTCH1_ alterations regarding cumulative relapse incidence; F, as in C, Comparison of TCR::_MYC_

alterations to other _MYC_ alterations in relation to cumulative relapse incidence; G, as in C, Comparison of _LMO2_ intergenic loss to other _LMO2_ alterations with respect to cumulative

relapse incidence; H, as in C, Comparison of _TAL1_ upstream enhancer Indel to other _TAL1_ alterations in terms of cumulative relapse incidence; I, Comparison of TAL1 genetic subtypes in

relation to cumulative relapse incidence with _P_ value from two-sided global Gray’s test; J, Contingency table comparing _TAL1_ upstream enhancer Indel and TCR::_LMO1_ to induction failure,

demonstrating no association (_P_ = 0.91 and _P_ = 0.92, one-tailed Fisher’s exact test); K, Scatter plot of variant allele frequency (VAF) of all alterations in TALL0223 case, with T-ALL

shown on the y axis and Langerhans cell histiocytosis (LCH) in the x axis with annotations for T-ALL driver mutations and clusters denoting three distinct clones; L, analysis of

differentially expressed genes (two-tailed Wald test) between the T-ALL SPI1 subtype (N = 11) and the TALL023 Langerhans cell histiocytosis (LCH) case (N = 1) reveals the downregulation of

T-cell genes and the upregulation of the LCH marker CD207; M, A heatmap showcases select SPI1 subtype markers (_HLA-D, CD7, CD5, CD1A, CD3E, CD2, PTPRC/CD38_), LCH markers (_CD207_)34,

dendritic cell progenitor marker (_IRF8_)34, T-cell markers (_ZAP70, LCK, GATA3, RAG1, CD4, CD8A, TRDC, TRGC1, TRBC1, TRAC_), and myeloid markers (_CD33, CD14_) for the SPI1 subtype and the

TALL023 T-ALL and LCH cases. Right: same markers are shown in a normal cell scRNA, showing mean expression (dot color) and detection percentage (dot size). EXTENDED DATA FIG. 13

MULTIVARIABLE OUTCOME MODEL EVALUATION. A, The mean validation concordance and associated 95% confidence intervals (CI) are shown, derived from 100 random splits (70% training, N = 915, 30%

validation, N = 394) of the data for three distinct survival models. These models were fitted using different data types, with binary Day 29 MRD ( ≥ 0.1%) employed as the baseline, shown as

red line. The baseline concordance results are denoted by grey rectangles, where binary MRD, numeric MRD, and a combination of clinical features (MRD, sex, WBC, CNS) and

_NOTCH1/FBXW7/RAS/PTEN_ classifier42 are shown. The blue rectangle represents models fitted using single data types, including numeric MRD, Sex, WBC, and CNS as predictors. The pink

rectangles denote combinations of genomic and clinical features. Model coefficient numbers are shown on top of the figure as bar plots for penalized Cox regression and survival Trees. Model

concordance is shown on top of the best performing model for each algorithm and highest concordance annotated as blue line. Select subtype denotes data where the main subtype is further

divided into genetic subtypes when available; B, The stacked bar plot portrays a four-node survival tree that was generated from 1000 bootstrap resamples. The proportion of bootstrap samples

in which each subtype was classified into various risk groups is shown; C, The Kaplan-Meier curve illustrates the risk score (divided into four quartiles) derived from a penalized Cox

regression multivariable model, limited to ETP cases, showing models ability to risk-stratify patients within ETP group. Log-rank test _P_ value is shown; D, Same as C, for Near-ETP cases;

E, Same as C, for ETP and Near-ETP cases; F, Same as C, for Non-ETP cases; G, The Kaplan-Meier curve dividing ETP-like group by ETP status, revealing no difference in outcomes; H, Same as C,

for ETP-like subtype ETP cases; I, Same as C, for ETP-like subtype ETP and Near-ETP cases; J, Same as C, for ETP-like subtype Non-ETP cases; K, Dot plot illustrating altered pathways and

pathway genes significantly associated with subtypes, displaying odds ratio as dot color and -log10 FDR as dot size. Odds ratio and -log10 FDR were truncated at maximum of 20. SUPPLEMENTARY

INFORMATION SUPPLEMENTARY INFORMATION Supplementary Methods, Supplementary Results, titles for Supplementary Tables 1–56, and Supplementary References 43–94. REPORTING SUMMARY PEER REVIEW

FILE SUPPLEMENTARY TABLES Supplementary Tables 1–56. SUPPLEMENTARY DATA 1 Lollipop plots of point mutations for 127 significantly mutated genes. SUPPLEMENTARY DATA 2 Oncoprints showing

PI3K–JAK–RAS signalling pathway and other key pathway alterations for each subtype. RIGHTS AND PERMISSIONS Springer Nature or its licensor (e.g. a society or other partner) holds exclusive

rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed

by the terms of such publishing agreement and applicable law. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Pölönen, P., Di Giacomo, D., Seffernick, A.E. _et al._ The genomic

basis of childhood T-lineage acute lymphoblastic leukaemia. _Nature_ 632, 1082–1091 (2024). https://doi.org/10.1038/s41586-024-07807-0 Download citation * Received: 24 October 2023 *

Accepted: 09 July 2024 * Published: 14 August 2024 * Issue Date: 29 August 2024 * DOI: https://doi.org/10.1038/s41586-024-07807-0 SHARE THIS ARTICLE Anyone you share the following link with

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