Deregulation of DUX4 and ERG in acute lymphoblastic leukemia


Chromosomal rearrangements deregulating hematopoietic transcription factors are common in acute lymphoblastic leukemia (ALL). Here we show that deregulation of the homeobox transcription factor gene DUX4 and the ETS transcription factor gene ERG is a hallmark of a subtype of B-progenitor ALL that comprises up to 7% of B-ALL. DUX4 rearrangement and overexpression was present in all cases and was accompanied by transcriptional deregulation of ERG, expression of a novel ERG isoform, ERGalt, and frequent ERG deletion. ERGalt uses a non-canonical first exon whose transcription was initiated by DUX4 binding. ERGalt retains the DNA-binding and transactivation domains of ERG, but it inhibits wild-type ERG transcriptional activity and is transforming. These results illustrate a unique paradigm of transcription factor deregulation in leukemia in which DUX4 deregulation results in loss of function of ERG, either by deletion or induced expression of an isoform that is a dominant-negative inhibitor of wild-type ERG function.

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Figure 1: Gene expression profile and ERG deletions in DUX4/ERG ALL.
Figure 2: Rearrangement of DUX4.
Figure 3: Structural and sequence alterations in DUX4/ERG ALL.
Figure 4: Expression of ERGalt in DUX4/ERG ALL.
Figure 5: DUX4 induces deregulation of ERG.
Figure 6: Expression of ERGalt induces ALL.

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  1. 1

    Hunger, S.P. & Mullighan, C.G. Acute lymphoblastic leukemia in children. N. Engl. J. Med. 373, 1541–1552 (2015).

    Article  CAS  Google Scholar 

  2. 2

    Mullighan, C.G. Genomic characterization of childhood acute lymphoblastic leukemia. Semin. Hematol. 50, 314–324 (2013).

    Article  CAS  Google Scholar 

  3. 3

    Yeoh, E.J. et al. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 1, 133–143 (2002).

    Article  CAS  Google Scholar 

  4. 4

    Harvey, R.C. et al. Identification of novel cluster groups in pediatric high-risk B-precursor acute lymphoblastic leukemia with gene expression profiling: correlation with genome-wide DNA copy number alterations, clinical characteristics, and outcome. Blood 116, 4874–4884 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

    Mullighan, C.G. et al. Genome-wide analysis of genetic alterations in acute lymphoblastic leukaemia. Nature 446, 758–764 (2007).

    Article  CAS  Google Scholar 

  6. 6

    Reddy, E.S. & Rao, V.N. ERG, an ETS-related gene, codes for sequence-specific transcriptional activators. Oncogene 6, 2285–2289 (1991).

    CAS  PubMed  Google Scholar 

  7. 7

    Bartel, F.O., Higuchi, T. & Spyropoulos, D.D. Mouse models in the study of the Ets family of transcription factors. Oncogene 19, 6443–6454 (2000).

    Article  CAS  Google Scholar 

  8. 8

    Kruse, E.A. et al. Dual requirement for the ETS transcription factors Fli-1 and Erg in hematopoietic stem cells and the megakaryocyte lineage. Proc. Natl. Acad. Sci. USA 106, 13814–13819 (2009).

    Article  Google Scholar 

  9. 9

    Loughran, S.J. et al. The transcription factor Erg is essential for definitive hematopoiesis and the function of adult hematopoietic stem cells. Nat. Immunol. 9, 810–819 (2008).

    Article  CAS  Google Scholar 

  10. 10

    Salek-Ardakani, S. et al. ERG is a megakaryocytic oncogene. Cancer Res. 69, 4665–4673 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Rainis, L. et al. The proto-oncogene ERG in megakaryoblastic leukemias. Cancer Res. 65, 7596–7602 (2005).

    Article  CAS  Google Scholar 

  12. 12

    Ng, A.P. et al. Trisomy of Erg is required for myeloproliferation in a mouse model of Down syndrome. Blood 115, 3966–3969 (2010).

    Article  CAS  Google Scholar 

  13. 13

    Tomlins, S.A. et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310, 644–648 (2005).

    Article  CAS  Google Scholar 

  14. 14

    Prasad, D.D., Ouchida, M., Lee, L., Rao, V.N. & Reddy, E.S. TLS/FUS fusion domain of TLS/FUS-ERG chimeric protein resulting from the t(16;21) chromosomal translocation in human myeloid leukemia functions as a transcriptional activation domain. Oncogene 9, 3717–3729 (1994).

    CAS  PubMed  Google Scholar 

  15. 15

    Marcucci, G. et al. Overexpression of the ETS-related gene, ERG, predicts a worse outcome in acute myeloid leukemia with normal karyotype: a Cancer and Leukemia Group B study. J. Clin. Oncol. 23, 9234–9242 (2005).

    Article  CAS  Google Scholar 

  16. 16

    Rao, V.N., Papas, T.S. & Reddy, E.S. ERG, a human ETS-related gene on chromosome 21: alternative splicing, polyadenylation, and translation. Science 237, 635–639 (1987).

    Article  CAS  Google Scholar 

  17. 17

    Dixit, M. et al. DUX4, a candidate gene of facioscapulohumeral muscular dystrophy, encodes a transcriptional activator of PITX1. Proc. Natl. Acad. Sci. USA 104, 18157–18162 (2007).

    Article  Google Scholar 

  18. 18

    Italiano, A. et al. High prevalence of CIC fusion with double-homeobox (DUX4) transcription factors in EWSR1-negative undifferentiated small blue round cell sarcomas. Genes Chromosom. Cancer 51, 207–218 (2012).

    Article  CAS  Google Scholar 

  19. 19

    Kawamura-Saito, M. et al. Fusion between CIC and DUX4 up-regulates PEA3 family genes in Ewing-like sarcomas with t(4;19)(q35;q13) translocation. Hum. Mol. Genet. 15, 2125–2137 (2006).

    Article  CAS  Google Scholar 

  20. 20

    Yasuda, T. et al. Recurrent DUX4 fusions in B cell acute lymphoblastic leukemia of adolescents and young adults. Nat. Genet. 48, 569–574 (2016).

    Article  CAS  Google Scholar 

  21. 21

    van der Veer, A. et al. IKZF1 status as a prognostic feature in BCR-ABL1–positive childhood ALL. Blood 123, 1691–1698 (2014).

    Article  CAS  Google Scholar 

  22. 22

    Mullighan, C.G. et al. Deletion of IKZF1 and prognosis in acute lymphoblastic leukemia. N. Engl. J. Med. 360, 470–480 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Young, J.M. et al. DUX4 binding to retroelements creates promoters that are active in FSHD muscle and testis. PLoS Genet. 9, e1003947 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Zou, J. et al. The oncogenic TLS-ERG fusion protein exerts different effects in hematopoietic cells and fibroblasts. Mol. Cell. Biol. 25, 6235–6246 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Carmichael, C.L. et al. Hematopoietic overexpression of the transcription factor Erg induces lymphoid and erythro-megakaryocytic leukemia. Proc. Natl. Acad. Sci. USA 109, 15437–15442 (2012).

    Article  Google Scholar 

  26. 26

    Russell, L.J. et al. Deregulated expression of cytokine receptor gene, CRLF2, is involved in lymphoid transformation in B-cell precursor acute lymphoblastic leukemia. Blood 114, 2688–2698 (2009).

    Article  CAS  Google Scholar 

  27. 27

    Mullighan, C.G. et al. Rearrangement of CRLF2 in B-progenitor- and Down syndrome–associated acute lymphoblastic leukemia. Nat. Genet. 41, 1243–1246 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Iacobucci, I. et al. Truncating erythropoietin receptor rearrangements in acute lymphoblastic leukemia. Cancer Cell 29, 186–200 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Russell, L.J. et al. IGH@ translocations are prevalent in teenagers and young adults with acute lymphoblastic leukemia and are associated with a poor outcome. J. Clin. Oncol. 32, 1453–1462 (2014).

    Article  Google Scholar 

  30. 30

    Zaliova, M. et al. ERG deletion is associated with CD2 and attenuates the negative impact of IKZF1 deletion in childhood acute lymphoblastic leukemia. Leukemia 28, 182–185 (2014).

    Article  CAS  Google Scholar 

  31. 31

    Clappier, E. et al. An intragenic ERG deletion is a marker of an oncogenic subtype of B-cell precursor acute lymphoblastic leukemia with a favorable outcome despite frequent IKZF1 deletions. Leukemia 28, 70–77 (2014).

    Article  CAS  Google Scholar 

  32. 32

    Lilljebjörn, H. et al. Identification of ETV6-RUNX1–like and DUX4-rearranged subtypes in paediatric B-cell precursor acute lymphoblastic leukaemia. Nat. Commun. 7, 11790 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33

    Roberts, K.G. et al. Targetable kinase-activating lesions in Ph-like acute lymphoblastic leukemia. N. Engl. J. Med. 371, 1005–1015 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Zhou, X. et al. Exploring genomic alteration in pediatric cancer using ProteinPaint. Nat. Genet. 48, 4–6 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Holmfeldt, L. et al. The genomic landscape of hypodiploid acute lymphoblastic leukemia. Nat. Genet. 45, 242–252 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Zhang, J. et al. The genetic basis of early T-cell precursor acute lymphoblastic leukaemia. Nature 481, 157–163 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Anders, S., Pyl, P.T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  Google Scholar 

  38. 38

    Smyth, G.K. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article3 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  39. 39

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  40. 40

    Buenrostro, J.D., Giresi, P.G., Zaba, L.C., Chang, H.Y. & Greenleaf, W.J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Zhang, Y. et al. Model-based analysis of ChIP–Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Shultz, L.D. et al. Human lymphoid and myeloid cell development in NOD/LtSz-scid IL2Rγnull mice engrafted with mobilized human hemopoietic stem cells. J. Immunol. 174, 6477–6489 (2005).

    Article  CAS  Google Scholar 

  43. 43

    Kneissl, S. et al. Measles virus glycoprotein-based lentiviral targeting vectors that avoid neutralizing antibodies. PLoS One 7, e46667 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Churchman, M.L. et al. Efficacy of retinoids in IKZF1-mutated BCR-ABL1 acute lymphoblastic leukemia. Cancer Cell 28, 343–356 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 46

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

    Kharchenko, P.V., Tolstorukov, M.Y. & Park, P.J. Design and analysis of ChIP–seq experiments for DNA-binding proteins. Nat. Biotechnol. 26, 1351–1359 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Robinson, J.T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Bailey, T.L. & Gribskov, M. Combining evidence using P-values: application to sequence homology searches. Bioinformatics 14, 48–54 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    Bailey, T.L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    Mathelier, A. et al. JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 44 D1, D110–D115 (2016).

    Article  CAS  Google Scholar 

  52. 52

    Mullighan, C.G. et al. BCR-ABL1 lymphoblastic leukaemia is characterized by the deletion of Ikaros. Nature 453, 110–114 (2008).

    Article  CAS  Google Scholar 

  53. 53

    Kamijo, T. et al. Tumor suppression at the mouse INK4a locus mediated by the alternative reading frame product p19ARF. Cell 91, 649–659 (1997).

    Article  CAS  Google Scholar 

  54. 54

    Mantel, N. Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother. Rep. 50, 163–170 (1966).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Peto, R. et al. Design and analysis of randomized clinical trials requiring prolonged observation of each patient. II. Analysis and examples. Br. J. Cancer 35, 1–39 (1977).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Gray, R.J. A class of K-sample tests for comparing the cumulative incidence of a competing risk. Ann. Stat. 16, 1141–1154 (1988).

    Article  Google Scholar 

  57. 57

    Fine, J.P. & Gray, R.J. A proportional hazards model for the subdistribution of a competing risk. J. Am. Stat. Assoc. 94, 496–509 (1999).

    Article  Google Scholar 

  58. 58

    R Development Core Team. R. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2009).

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We thank L. Yang (University of Washington, Seattle) for the gpIX reporter construct and the Genome Sequencing Facility, the Hartwell Center for Bioinformatics and Biotechnology, the Flow Cytometry and Cell Sorting core facility and the Biorepository of St. Jude Children's Research Hospital.

This work was supported in part by the American Lebanese Syrian Associated Charities of St. Jude Children's Research Hospital, by a Stand Up to Cancer Innovative Research Grant and a St. Baldrick's Foundation Scholar Award (to C.G.M.), by a St. Baldrick's Consortium Award (to S.P.H.), by a Leukemia and Lymphoma Society Specialized Center of Research grant (to S.P.H. and C.G.M.), by a Lady Tata Memorial Trust Award (to I.I.), by a Leukemia and Lymphoma Society Special Fellow Award and Alex's Lemonade Stand Foundation Young Investigator Awards (to K.G.R.), by American Society of Hematology Scholar Awards (to C.G.M., P.N. and K.G.R.), by Dutch Cancer Society Fellowship KUN2012-5366 (to E. Waanders), by a St. Luke's Life Science Institute grant (to H.Y.), by National Cancer Institute grants P30 CA021765 (St. Jude Cancer Center Support Grant), U10 CA180820 (ECOG-ACRIN Operations), and CA180827 and CA196172 (to E.P.); U10 CA180861 (to C.D.B. and G.M.); U24 CA196171 (The Alliance NCTN Biorepository and Biospecimen Resource); CA145707 (to C.L.W. and C.G.M.); U01 CA157937 (to C.L.W. and S.P.H.), R00 CA188293 (to P.N.); and grants to the Children's Oncology Group: U10 CA98543 (Chair's grant and supplement to support the COG ALL TARGET project), U10 CA98413 (Statistical Center) and U24 CA114766 (Specimen Banking); and by National Institute of General Medical Sciences grant P50 GM115279 (to J.Z., J.Y., W.E.E., M.V.R., M.L.L. and C.G.M.). This project has been funded in whole or in part by federal funds from the National Cancer Institute, US National Institutes of Health, under contract HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the US Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the US government.

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J.Z., B.X., G.W., Yu Liu, L.W., Y. Li, C.Q., J. Wen, M.E., J.M., G.S., X.C., S.N., X.M., M.R., P.G., L.D., C.L., K.G.R., Y.T., R.C.H. and C.G.M. analyzed genomic data. K. McCastlain, I.I., H.Y., Y.C., D.P.-T., M.L.C., K.B.K., S.T., E. Waanders, E. Wienholds, P.N., S.B., J. Wang, I.A., K.G.R., J.E., H.L.M., K.B., B.V., J.D., Yanling Liu, M.L.V., R.C.H. and I.-M.L.C. performed experiments. R.S.F., L.F., K.O., E.R.M., R.K.W. and J.R.D. performed genome sequencing. M.D., D.P. and C.C. performed biostatistical analysis. J.Y., W.E.E., M.V.R., C.-H.P., S.J., C.L.W., G.M., C.D.B., J.K., K. Mrózek, E.P., M.S.T., W.S., M.C.F., J.R., J.M.R., S.L., S.M.K., S.A.S., S.C.R., S.P.H., M.L.L. and J.R.D. provided patient samples and data. J.E.D. provided reagents. J.Z., J.E.D. and C.G.M. designed experiments. J.Z. and C.G.M. wrote the manuscript.

Corresponding authors

Correspondence to Jinghui Zhang or Charles G Mullighan.

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The authors declare no competing financial interests.

Additional information

A list of members appears in the Supplementary Note.

Integrated supplementary information

Supplementary Figure 1 ERG is temporally regulated during B cell development.

(a) Schema of Hardy stages of mouse B cell maturation. (b) Representative schema for FACS of B cell progenitor populations. (c) Heat map of genes upregulated at the Hardy A to B transition, showing upregulation of Erg as well as key genes in B cell development, including Ebf1, Pax5 and Rag1. (d) Heat map of the genes significantly upregulated at each developmental transition. Data were first reported in Holmfeldt et al.35.

Supplementary Figure 2 Unsupervised clustering of Affymetrix U133A microarray data identifies a distinct subtype of B-ALL.

Unsupervised clustering of 16,395 U133A expression probe sets passing the Affymetrix MAS5 absent call filter for 199 ALL cases, showing clustering of the novel (DUX4/ERG) subtype of ALL. Clustering of arrays and probe sets was performed in Genemaths XT 2.1 using a Pearson similarity coefficient and the UPGMA clustering algorithm.

Supplementary Figure 3 NALM-6 cells exhibit the gene expression profile of DUX4/ERG ALL.

Clustering of the top 100 differentially expressed Affymetrix U133A/B gene expression probe sets for DUX4/ERG ALL in 32 acute leukemia cell lines examined in duplicate.

Supplementary Figure 4 Mapping and verification of IGHDUX4 rearrangement breakpoints.

Mapping of IGH breakpoints from whole-genome sequencing data. (a) The number of cases with discordant paired-end reads between chromosome 14 and one of the chromosomes with a DUX array (e.g., chromosomes 4 and 10). (b) CNVs identified by CONSERTING77 at the IGH locus. (c) Histone 3 lysine 27 acetylation data from ENCODE indicating the locations for super-enhancers. (d) IGH gene annotation. Representative sequencing electropherograms from genomic PCR in a and RT–PCR and capillary sequencing in b from two cases showing the C-terminal breakpoint of DUX4 rearranged to IGH, with intervening non-template nucleotides arising from RAG-mediated recombination and read-through into the IGH locus.

Supplementary Figure 5 Clinical outcome of DUX4/ERG ALL.

Kaplan–Meier plots of event-free survival (EFS) for children with ALL treated on St. Jude Children’s Research Hospital and Children’s Oncology Group protocols, showing the excellent outcome of DUX4/ERG cases (red lines). (a,b) Stratification by ALL subtype. (c,d) EFS for DUX4/ERG cases stratified by the presence or absence of IKZF1 alterations.

Supplementary Figure 6 Mutational spectrum of DUX4/ERG ALL.

Representative protein-domain plots showing the location of mutations for selected genes.

Supplementary Figure 7 RT–PCR and immunoblotting for ERG isoforms.

(a) RT–PCR for ERG transcripts using primers specific for exons 1 and 10, showing amplification of internally deleted transcripts. (b) Immunoblotting using an antibody specific to the C terminus showing the aberrant ERG peptide fragment (approximately 28 kDa) in novel cases in the test cohort, but not in non-novel B- and T-lineage ALL cases. (c) Immunoblotting of lysates from HEK293T cells transfected with MSCV-IRES-GFP vector expressing ERG alt (lane 2), several DUX4/ERG samples with (lanes 3 and 4) or without (lanes 5–7) deletion, and non-DUX4/ERG ALL samples (lanes 8 and 9). The size of the protein expressed from the cloned ERGalt transcript is identical to that of the protein expressed in patients, confirming that this transcript encodes the aberrant C-terminal ERG protein fragment observed in leukemic cells.

Supplementary Figure 8 Detailed analysis of ERG exon 6 alt, the alternative first exon in the ERGalt transcript.

RNA–seq read coverage, the region mapped by RACE and RT–PCR, and read pair mapping at the ERGalt first exon and canonical exon 7 in SJERG026. Coverage for RNA–seq is shown at the top, with the black line marking the exon boundary determined by RACE and RT–PCR. RNA–seq read pairs were sorted by ERGalt exon start site. The forward and reverse reads in a read pair are shown in red and blue, respectively. Cyan color indicates a ‘skipped’ region, i.e., a spliced intron, while gray color indicates a region where a forward read overlaps with a reverse read in a read pair. The black arrow points to the transcription initiation site determined on the basis of RACE that shows a strong bias for reverse reads, as expected from a first exon. The initiation site also matches the highest RNA–seq coverage peak in this region. A small proportion of reads aligned in forward orientation were from unspliced read pairs for which the reverse reads clustered to two additional potential alternative transcription start sites for ERGalt (marked by gray arrows).

Supplementary Figure 9 ERG transcriptional levels and intron retention in DUX4/ERG cases.

RNA–seq coverage (y axis) in the genomic region encoding ERG exons 5–10 (RefSeq accession NM_182918) for three tumors with ERG alt expression (top) and three tumors without ERG alt expression (bottom). The location of the ERGalt exon is marked in red and indicated by an arrow. Samples appended with an asterisk (e.g., SJERG016 above and SJERG031 below) are those that harbor ERG focal deletion. Aberrant expression of intron 6 is higher in ERGalt-negative samples (bottom) than in ERGalt-positive samples (top).

Supplementary Figure 10 Identification, expression and localization of ALE, a long noncoding RNA in DUX4/ERG ALL.

(a) UCSC WGL plot of transcriptome sequencing data at ERG for a representative case, SJERG003, showing peaks of coverage representing ERG exons, and a region of coverage proximal to the ERG isoform 1 (NM_182918) locus. (b,c) Stranded total RNA–seq data for the same case. (b) The sense strand shows coverage corresponding to a four-exon noncoding RNA, with exon junctions shown below the WGL plot as red bars and the numbers to the left of each bar corresponding to the number of exon–exon junctions. (c,d) Antisense sequencing data corresponding to coding ERG transcripts (c) and the number of annotated (green) and non-annotated (red) junctions shown as bars (d). Note the high number of reads corresponding to ERG alt (exon 6 alt to exon 7) junctions. (e) RT–PCR for expression of ALE (antisense long noncoding RNA associated with ERG) showing two spliced isoforms. (f) Sequential RNA and DNA FISH showing that expressed ALE transcripts are retained at the ERG locus, in contrast to coding ERG transcripts, which are dispersed throughout the nucleus.

Supplementary Figure 11 Expression of ERGalt and ALE in ALL and pediatric tumors.

(ad) Expression levels of ERGalt (a,b) and ALE (c,d) across ALL subtypes (a,c) and pediatric tumors (c,d). Expression of these transcripts was uncommon outside of DUX4/ERG ALL and at a lower level.

Supplementary Figure 12 DUX4 induces ERGalt expression.

(a) Analysis of previously published myoblast DUX4 ChIP–seq and transcriptome sequencing data23, depicting the region of ERG flanking the first exon of ERGalt and showing expression of canonical ERG transcripts in cells not expressing DUX4 and expression of ERGalt transcripts (red bracket) exclusively in cells expressing DUX4. (b) ChIP–PCR data confirming binding of DUX4 at ERG exon 6 alt in NALM-6 but not Reh cells. Primers for PCR at the DUX4 binding site amplify chr. 21: 39,764,747–39,764,881 (hg19), and negative-control primers amplify chr. 21: 39,769,872–39,769,942 (primers are listed in Supplementary Table 16). (c) DUX4 ChIP–seq and ATAC–seq data at CD200R1 in NALM-6 and Reh cells, showing peaks of DUX4 binding corresponding to regions of open chromatin (identified by ATAC–seq) in the DUX4/ERG cell line NALM-6 but not the ETV6-RUNX1 cell line Reh. This corresponds to overexpression of this gene in DUX4/ERG ALL. (d) Lentiviral expression of the rearranged DUX4 allele encoding Glu415*, but not empty vector, results in expression of ERGalt in three individual cord blood pools, as demonstrated by RT–PCR. Representative Sanger sequencing electropherograms confirming expression of ERGalt are shown below the RT–PCR gel image.

Supplementary Figure 13 Binding of DUX4 to the first exon of ERGalt.

(a) The ERG locus and ATAC–seq data for a representative DUX4/ERG case with expression of ERGalt, SJERG000016, showing an ATAC–seq peak (red arrow) not observed in the Reh cell line (ETV6-RUNX1 positive). (b) The genomic region highlighted in the red box in a, showing the overlapping ATAC–seq peak, DUX4 ChIP–seq peak, and two DUX4 binding motifs (TAAATCAATCA and TAATCTCATCA) at the first exon of ERGalt

Supplementary Figure 14 Nuclear localization and transactivation activity of ERGalt.

(a) Immunofluorescence of Arf–/– pre-B cells transduced with retroviral vectors with no ERG construct (MIG), wild-type ERG (MIG-ERG WT) or ERGalt (MIG-ERG e6alt) showing nuclear localization of ERG. Scale bars, 10 μm. (b) Competition assay to determine the transcriptional activity of wild-type (WT) ERG and ERGalt (MT) supplemented with empty vector (MIG) determined using the pGL3-gpIX luciferase reporter in HEK293T cells. Bars show means ± s.e.m. luciferase activity was derived from two individual experiments with triplicate measurements. (c) Immunoblotting of HEK293T cells transfected with empty vector or ERG plasmids confirming the expression shown in b.

Supplementary Figure 15 ERG and ERGalt colony-forming assays.

(a) Schema for colony assays. Mouse Arf–/– lineage-negative bone marrow was cotransduced with GFP-expressing bi- or tricistronic vectors also expressing NRAS Gly12Asp and/or wild-type ERG or ERGalt together with RFP-expressing empty vector or IK6-expressing vector. Flow-sorted GFP+RFP+ cells were plated in methylcellulose and lymphoid cytokines (IL-7, FLT3L and SCF), and colonies were counted and scored after 7–10 d, collected and replated. (b) Cells expressing NRAS Gly12Asp or WT ERG result in non-sustained replating in the absence of the dominant-negative form of IKZF1, IK6, but ERGalt results in sustained replating particularly in the presence of NRAS Gly12Asp with or without IK6. Representative colony morphology (showing GFP and RFP positivity) and immunophenotyping data are depicted showing pre-B immunophenotype.

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Zhang, J., McCastlain, K., Yoshihara, H. et al. Deregulation of DUX4 and ERG in acute lymphoblastic leukemia. Nat Genet 48, 1481–1489 (2016).

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