An epigenetic mechanism of resistance to targeted therapy in T cell acute lymphoblastic leukemia (original) (raw)

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References

1. Weng, A.P. et al. Activating mutations of NOTCH1 in human T cell acute lymphoblastic leukemia. Science 306, 269–271 (2004).
   Article CAS Google Scholar
2. Ellisen, L.W. et al. TAN-1, the human homolog of the Drosophila notch gene, is broken by chromosomal translocations in T lymphoblastic neoplasms. Cell 66, 649–661 (1991).
   Article CAS Google Scholar
3. Pui, C.H. & Evans, W.E. Treatment of acute lymphoblastic leukemia. N. Engl. J. Med. 354, 166–178 (2006).
   Article CAS Google Scholar
4. Rao, S.S. et al. Inhibition of NOTCH signaling by γ secretase inhibitor engages the RB pathway and elicits cell cycle exit in T-cell acute lymphoblastic leukemia cells. Cancer Res. 69, 3060–3068 (2009).
   Article CAS Google Scholar
5. Palomero, T. & Ferrando, A. Therapeutic targeting of NOTCH1 signaling in T-cell acute lymphoblastic leukemia. Clin. Lymphoma Myeloma 9 (suppl. 3), S205–S210 (2009).
   Article CAS Google Scholar
6. Guruharsha, K.G., Kankel, M.W. & Artavanis-Tsakonas, S. The Notch signalling system: recent insights into the complexity of a conserved pathway. Nat. Rev. Genet. 13, 654–666 (2012).
   Article CAS Google Scholar
7. Weng, A.P. et al. c-Myc is an important direct target of Notch1 in T-cell acute lymphoblastic leukemia/lymphoma. Genes Dev. 20, 2096–2109 (2006).
   Article CAS Google Scholar
8. Palomero, T. et al. NOTCH1 directly regulates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth. Proc. Natl. Acad. Sci. USA 103, 18261–18266 (2006).
   Article CAS Google Scholar
9. Sharma, V.M. et al. Notch1 contributes to mouse T-cell leukemia by directly inducing the expression of c-myc. Mol. Cell. Biol. 26, 8022–8031 (2006).
   Article CAS Google Scholar
10. Gutierrez, A. et al. High frequency of PTEN, PI3K, and AKT abnormalities in T-cell acute lymphoblastic leukemia. Blood 114, 647–650 (2009).
    Article CAS Google Scholar
11. Palomero, T. et al. Mutational loss of PTEN induces resistance to NOTCH1 inhibition in T-cell leukemia. Nat. Med. 13, 1203–1210 (2007).
    Article CAS Google Scholar
12. Chan, S.M., Weng, A.P., Tibshirani, R., Aster, J.C. & Utz, P.J. Notch signals positively regulate activity of the mTOR pathway in T-cell acute lymphoblastic leukemia. Blood 110, 278–286 (2007).
    Article CAS Google Scholar
13. Kalaitzidis, D. et al. mTOR complex 1 plays critical roles in hematopoiesis and _Pten_-loss-evoked leukemogenesis. Cell Stem Cell 11, 429–439 (2012).
    Article CAS Google Scholar
14. Dawson, M.A. & Kouzarides, T. Cancer epigenetics: from mechanism to therapy. Cell 150, 12–27 (2012).
    Article CAS Google Scholar
15. Zhao, R., Nakamura, T., Fu, Y., Lazar, Z. & Spector, D.L. Gene bookmarking accelerates the kinetics of post-mitotic transcriptional re-activation. Nat. Cell Biol. 13, 1295–1304 (2011).
    Article CAS Google Scholar
16. Blobel, G.A., Kalota, A., Sanchez, P.V. & Carroll, M. Short hairpin RNA screen reveals bromodomain proteins as novel targets in acute myeloid leukemia. Cancer Cell 20, 287–288 (2011).
    Article CAS Google Scholar
17. Zuber, J. et al. RNAi screen identifies Brd4 as a therapeutic target in acute myeloid leukaemia. Nature 478, 524–528 (2011).
    Article CAS Google Scholar
18. Filippakopoulos, P. et al. Selective inhibition of BET bromodomains. Nature 468, 1067–1073 (2010).
    Article CAS Google Scholar
19. Nicodeme, E. et al. Suppression of inflammation by a synthetic histone mimic. Nature 468, 1119–1123 (2010).
    Article CAS Google Scholar
20. Zhou, V.W., Goren, A. & Bernstein, B.E. Charting histone modifications and the functional organization of mammalian genomes. Nat. Rev. Genet. 12, 7–18 (2011).
    Article Google Scholar
21. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
22. Whyte, W.A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).
    Article CAS Google Scholar
23. Voigt, P. & Reinberg, D. BRD4 jump-starts transcription after mitotic silencing. Genome Biol. 12, 133 (2011).
    Article CAS Google Scholar
24. Dawson, M.A. et al. Inhibition of BET recruitment to chromatin as an effective treatment for _MLL_-fusion leukaemia. Nature 478, 529–533 (2011).
    Article CAS Google Scholar
25. Haber, D.A., Gray, N.S. & Baselga, J. The evolving war on cancer. Cell 145, 19–24 (2011).
    Article CAS Google Scholar
26. Sharma, S.V. et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell 141, 69–80 (2010).
    Article CAS Google Scholar
27. Ku, M. et al. Genomewide analysis of PRC1 and PRC2 occupancy identifies two classes of bivalent domains. PLoS Genet. 4, e1000242 (2008).
    Article Google Scholar
28. Sarcinella, E., Zuzarte, P.C., Lau, P.N., Draker, R. & Cheung, P. Monoubiquitylation of H2A.Z distinguishes its association with euchromatin or facultative heterochromatin. Mol. Cell. Biol. 27, 6457–6468 (2007).
    Article CAS Google Scholar
29. Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).
    Article CAS Google Scholar
30. Li, R. et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25, 1966–1967 (2009).
    Article CAS Google Scholar
31. Zhu, J. et al. Genome-wide chromatin state transitions associated with developmental and environmental cues. Cell 152, 642–654 (2013).
    Article CAS Google Scholar
32. Reich, M. et al. GenePattern 2.0. Nat. Genet. 38, 500–501 (2006).
    Article CAS Google Scholar
33. Mootha, V.K. et al. PGC-1α–responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).
    Article CAS Google Scholar
34. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).
    Article CAS Google Scholar
35. Root, D.E., Hacohen, N., Hahn, W.C., Lander, E.S. & Sabatini, D.M. Genome-scale loss-of-function screening with a lentiviral RNAi library. Nat. Methods 3, 715–719 (2006).
    Article CAS Google Scholar
36. Pear, W.S., Scott, M.L. & Nolan, G.P. in Methods in Molecular Biology: Methods in Gene Therapy (ed. Robbins, P.) 41–58 (Humana Press, Tonawa, NJ, 1997).
37. Boehm, J.S. et al. Integrative genomic approaches identify IKBKE as a breast cancer oncogene. Cell 129, 1065–1079 (2007).
    Article CAS Google Scholar
38. Moffat, J. et al. A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell 124, 1283–1298 (2006).
    Article CAS Google Scholar
39. Malo, N., Hanley, J.A., Cerquozzi, S., Pelletier, J. & Nadon, R. Statistical practice in high-throughput screening data analysis. Nat. Biotechnol. 24, 167–175 (2006).
    Article CAS Google Scholar
40. Luo, B. et al. Highly parallel identification of essential genes in cancer cells. Proc. Natl. Acad. Sci. USA 105, 20380–20385 (2008).
    Article CAS Google Scholar
41. Choi, Y.J. et al. The requirement for cyclin D function in tumor maintenance. Cancer Cell 22, 438–451 (2012).
    Article CAS Google Scholar
42. Kluk, M.J. et al. Immunohistochemical detection of MYC-driven diffuse large B-cell lymphomas. PLoS ONE 7, e33813 (2012).
    Article CAS Google Scholar
43. Kluk, M.J. et al. Gauging NOTCH1 activation in cancer using immunohistochemistry. PLoS ONE 8, e67306 (2013).
    Article CAS Google Scholar

Download references

Acknowledgements

We are grateful to T. Look, M. Harris, L. Silverman and S. Sallan for providing the pediatric T-ALL samples. We thank the Flow Cytometry Core of the Harvard Stem Cell Institute and the Center for Regenerative Medicine and Technology for excellent flow sorting. We thank E. Rheinbay, M. Suva, R. Ryan, N. Riggi, A. Goren, O. Ram, J. Wu, L. Pan, W. Pear and M. Rivera for helpful discussions; V. Mootha for critical comments on the manuscript; S. Muller-Knapp and the Structural Genomics Consortium (Oxford, UK) for their assistance with inhibitor experiments; L. Hamm and the Broad RNAi Platform for help with the shRNA screen; R. Issner, X. Zhang, C. Epstein, N. Shoresh, T. Durham and the Broad Genome Sequencing Platform for technical assistance; A. Christie for help with mouse experiments; L. Gaffney for help with illustrations; and O. Weigert for providing the BCL2 ORF. B.K. was supported by a US National Institutes of Health (NIH) T32 training grant (HL007574-30) and by a St. Baldrick's fellowship. J.E.R. was supported by a US NIH T32 training grant (CA130807) and by a postdoctoral fellowship from the American Cancer Society (125087-PF-13-247-01-LIB). H.W. is supported by a US NIH T32 training grant (HL007627). This work was supported by the National Human Genome Research Institute (ENCODE U54 HG004570 to B.E.B.), the NIH Common Fund for Epigenomics (U01 ES017155 to B.E.B.), the NIH/NCI (CA096899 to M.A.K. and 5P01 CA109901-10 to J.E.B.), the Howard Hughes Medical Institute (B.E.B.) and the Starr Cancer Consortium (B.E.B.). The Leukemia & Lymphoma Society Specialized Center of Research Program supports B.E.B., J.C.A., J.E.B., K.S. and A.L.K.

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Author notes

1. Birgit Knoechel and Justine E Roderick: These authors contributed equally to this work.

Authors and Affiliations

1. Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
   Birgit Knoechel, Kaylyn E Williamson, Jiang Zhu, Matthew J Cotton, Shawn M Gillespie, Manching Ku & Bradley E Bernstein
2. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
   Birgit Knoechel, Kaylyn E Williamson, Jiang Zhu, Jens G Lohr, Matthew J Cotton, Shawn M Gillespie, Daniel Fernandez, Manching Ku, Federica Piccioni, Serena J Silver, Mohit Jain, David E Root, James E Bradner & Bradley E Bernstein
3. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
   Birgit Knoechel, Alejandro Gutierrez, Kimberly Stegmaier & Andrew L Kung
4. Division of Hematology/Oncology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
   Birgit Knoechel, Daniel Pearson, Alejandro Gutierrez, Kimberly Stegmaier & Andrew L Kung
5. Department of Cancer Biology, University of Massachusetts Medical School, Worcester, Massachusetts, USA
   Justine E Roderick & Michelle A Kelliher
6. Center for Cancer Research, Massachusetts General Hospital, Boston, Massachusetts, USA
   Kaylyn E Williamson, Jiang Zhu, Matthew J Cotton, Shawn M Gillespie, Manching Ku & Bradley E Bernstein
7. Howard Hughes Medical Institute, Chevy Chase, Maryland, USA
   Kaylyn E Williamson, Jiang Zhu, Matthew J Cotton, Shawn M Gillespie & Bradley E Bernstein
8. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
   Jens G Lohr, Christopher J Ott & James E Bradner
9. Biostatistics Graduate Program, Harvard University, Cambridge, Massachusetts, USA
   Daniel Fernandez
10. Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
    Hongfang Wang, Michael J Kluk & Jon C Aster
11. Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA
    Mohit Jain
12. Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, USA
    Mohit Jain
13. Biological and Biomedical Sciences Graduate Program, Harvard Medical School, Boston, Massachusetts, USA
    Daniel Pearson
14. Jackson Laboratory, Bar Harbor, Maine, USA
    Leonard D Shultz
15. Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA
    Michael A Brehm & Dale L Greiner

Authors

1. Birgit Knoechel
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2. Justine E Roderick
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3. Kaylyn E Williamson
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4. Jiang Zhu
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5. Jens G Lohr
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6. Matthew J Cotton
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7. Shawn M Gillespie
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8. Daniel Fernandez
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9. Manching Ku
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10. Hongfang Wang
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11. Federica Piccioni
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12. Serena J Silver
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13. Mohit Jain
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14. Daniel Pearson
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15. Michael J Kluk
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16. Christopher J Ott
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17. Leonard D Shultz
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18. Michael A Brehm
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19. Dale L Greiner
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20. Alejandro Gutierrez
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21. Kimberly Stegmaier
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22. Andrew L Kung
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23. David E Root
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24. James E Bradner
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25. Jon C Aster
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26. Michelle A Kelliher
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27. Bradley E Bernstein
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Contributions

B.K. and J.E.R. designed and performed experiments and analyzed the data. M.A.K. and B.E.B. designed the experimental strategy and supervised the study and analysis. J.Z. and D.F. carried out computational analyses. B.K., J.E.R., M.A.K. and B.E.B. drafted the manuscript. K.E.W., S.M.G., M.J.C., J.G.L., M.K., H.W., F.P., S.J.S., M.J., D.P., M.J.K. and C.J.O. contributed to experiments and data analysis. L.D.S., M.A.B., D.L.G., A.G., K.S., A.L.K., D.E.R., J.E.B. and J.C.A. provided reagents, contributed to analysis and gave conceptual advice. All authors discussed the results and implications and reviewed the manuscript.

Corresponding authors

Correspondence toMichelle A Kelliher or Bradley E Bernstein.

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Competing interests

D.L.G. and M.A.B. are consultants for the Jackson Laboratory. J.E.B. is a scientific founder of Tensha Therapeutics, which has licensed drug-like derivatives of the JQ1 bromodomain inhibitor from the Dana-Farber Cancer Institute. The remaining authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Isolation and characterization of persister T-ALL cells.

a. Proliferation over time is shown for naive DND-41 T-ALL cells exposed to 1 μM GSI. b. NOTCH1 target gene expression is shown for naive cells (N), persister cells in 1 μM GSI (P), reversed persister cells removed from GSI for 2 weeks (Rev) and reversed cells re-exposed to 1 μM GSI for 5 d (Rev tx) (2 replicates, error bars reflect s.d.). c. DTX1 and HP1γ gene expression is shown for naive cells 4 weeks after transfection with dominant-negative mastermind-like 1 (DN-MAML) (3 replicates, error bars reflect s.d.). d. DTX1 and HP1γ gene expression is shown for naive and persister cells and for persister cells after GSI washout at the indicated time points (3 replicates, error bars reflect s.d.). e. Protein blots for total, phosphorylated PTEN (pPTEN) and tubulin in naive and persister cells (left). Persister cells show decreased PTEN activity. Proliferation of naive and persister cells treated with the indicated doses of the AKT inhibitor MK-2206 for 6 d (right; 4 replicates, error bars reflect s.d.). f. Proliferation of naive and persister cells treated with the indicated doses of doxorubicin (2 replicates, error bars reflect s.d.) and vorinostat (2 replicates, error bars reflect s.d.) for 6 d. (Data shown are for DND-41 cells.)

Supplementary Figure 2 Altered signaling and drug sensitivities in KOPT-K1 persister cells.

a. Proliferation of naive and persister KOPT-K1 T-ALL cells treated with the indicated doses of NOTCH inhibitor for 6 d (2 replicates, error bars reflect s.d.). b. Protein blot shows activated intracellular NOTCH1 (ICN1) and MYC levels in naive KOPT-K1 cells (N), short-term treated cells (ST, 5 d with 1 μM GSI), persister cells in 1 μM GSI (P), reversed persister cells removed from GSI for 2 weeks (Rev) and reversed cells re-exposed to GSI (Rev tx, 5 d). c. Protein blots show phospho-mTOR (p2481), total mTOR and tubulin in naive (N) and persister (P) KOPT-K1 cells (left). Proliferation of naive and persister cells treated with the indicated concentrations of rapamycin for 9 d is shown to the right (2 replicates, error bars reflect s.d.). d. Protein blots for total, phosphorylated PTEN (pPTEN) and tubulin in naive and persister KOPT-K1 cells (left). Proliferation of naive and persister cells treated with the indicated doses of the AKT inhibitor MK-2206 for 9 d (right; 2 replicates, error bars reflect s.d.). e. Proliferation of naive and persister cells treated with the indicated doses of doxorubicin, vorinostat and JQ1 for 6 d (2–3 replicates, error bars reflect s.d.). These data confirm that KOPT-K1 cells give rise to a persister phenotype similar to DND-41 cells (Fig. 1).

Supplementary Figure 3 Chromatin state alterations in KOPT-K1 persister cells.

a. Forward scatter analysis indicates size distributions of naive (blue) and persister (red) KOPT-K1 T-ALL cells. b. Size of naive (left) and persister (right) KOPT-K1 cell nuclei is shown by DAPI stain and quantified in box plot (right; naive n = 316, persister n = 396; P value < 1 × 10−4). c,d. Protein blots for HP1γ (c) and H3K27ac and total H3 (d) in naive (N) and persister (P) KOPT-K1 cells. e. Bar plot indicates relative levels of repressive histone modifications per ELISA of bulk histones from naive, short-term treated (3 d) and persister cells (2 replicates, error bars reflect s.d., *P < 0.05, **P < 0.01). f. BRD4 expression is shown for naive (N), short-term treated (ST, 5 d) and persister (P) KOPT-K1 cells. These data show that persister KOPT-K1 cells exhibit chromatin state changes similar to DND-41 cells (Fig. 2). g. Gel electrophoresis images depict size distribution of DNA after MNase digestion of chromatin from naive (N) or persister (P) KOPT-K1 cells (left). Plot depicts size distribution of mononucleosomal DNA fragments after MNase digestion, as measured by capillary electrophoresis (right). Protected regions are larger in persister compared to naive cells, consistent with greater chromatin compaction. h. Normalized H3K27me3 signal distribution over euchromatic H3K4me1-marked loci in naive and persister KOPT-K1 cells (P < 1 × 10−15). i. DTX1, HP1γ, BRD4 and BCL2 expression are shown for KOPT-K1 cells 4 weeks post-transfection with DN-MAML or empty vector (EV), (2 replicates, error bars reflect s.d., *P < 0.05, ***P < 0.001). These data confirm that KOPT-K1 persister cells adopt an altered chromatin state similar to DND-41 persister cells (Fig. 2).

Supplementary Figure 4 Persister T-ALL cells have increased dependency on BRD4.

a. Protein blots show BRD4 expression after knockdown with lentiviral shRNA in naive and persister DND-41 cells after 5 d of puromycin selection (2 replicates, error bars reflect s.d.). b. Proliferative response of naive and persister cells infected with BRD4 or control hairpins after 8 d of puromycin selection. c. Proliferative response of naive and persister cells after 6 d of treatment with inactive JQ1 enantiomer (3 replicates, error bars reflect s.d.).

Supplementary Figure 5 BRD4 binds enhancers near key regulatory genes in DND-41 T-ALL cells.

a. Heat map shows enrichment signals for BRD4, H3K27ac and H3K4me1 over 19,386 H3K4me1-marked distal sites (rows; 10-kb regions, centered on H3K4me1 peaks, ranked by overall signal intensities of BRD4 and H3K27ac) in DND-41 persister cells. b. Tracks show BRD4 binding and H3K36me3 enrichment (marking transcribed regions) over the CDK6 and ETV6 loci, both of which contain BRD4-bound superenhancers. c. Tracks show BRD4 binding and H3K27ac enrichment across the DTX1 (left) and LGALS9 (right) loci in naive and persister DND-41 cells. d. Tracks show BRD4 binding and H3K36me3 enrichment across the BCL2 locus in naive and persister DND-41 cells (left). BRD4 enrichment by ChIP-qPCR over BRD4 peaks in the BCL2 locus in naive and persister DND-41 cells (right; 2 replicates, error bars reflect s.d.). e. Protein blots show BCL2 expression after lentiviral infection with empty vector (EV) or BCL2 ORF in persister cells. f. Tracks show BRD4 binding and H3K36me3 enrichment across the MYC locus in naive and persister DND-41 cells (left). BRD4 enrichment by ChIP-qPCR over BRD4 peaks in the MYC locus in naive and persister DND-41 cells (right; 2 replicates, error bars reflect s.d.). g. MYC expression in persister cells infected with empty vector (EV) or MYC-overexpressing retrovirus (MYC; 2 replicates, error bars reflect s.d.). (Data shown are for DND-41 cells; data for KOPT-K1 cells are shown in Figure 3.)

Supplementary Figure 6 Proposed model for the increased BRD4 dependency in persister cells.

Enhancer elements with transcription factor (TF) binding, acetylated chromatin and BRD4 are flanked by compact chromatin. The tendency of persister cell chromatin (right) for greater compaction renders enhancers more dependent on BRD4 for their maintenance.

Supplementary Figure 7 Effects of combination therapy targeting NOTCH and BRD4 in vivo.

a. Bioluminescence readings in NSG mice engrafted with KOPT-K1 T-ALL cells expressing luciferase that were treated with vehicle, short-term DBZ (3 doses) or long-term DBZ (with dosing every other day; short-term and vehicle treated mice sacrificed after 5 d (3 doses DBZ), long-term treated mice after 3 weeks (11 doses DBZ); Online Methods). Data are averaged from 5 mice per group, error bars reflect s.d. b. Protein blots show intracellular NOTCH1 (ICN1), MYC, PTEN and actin control in three primary T-ALL samples (T-ALL-x-9, T-ALL-x-11 and T-ALL-x-14). c. Bar plot indicates relative levels of H3K27me3 per ELISA on bulk histones from vehicle (Veh) or GSI-treated mice engrafted with T-ALL-x-9 after 3 weeks of treatment (4 mice per group with 2 replicates each, error bars reflect s.d., *P < 0.05).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7, Supplementary Tables 4 and 5, and Supplementary Note (PDF 3536 kb)

Supplementary Table 1

Gene set enrichment analysis of persister DND-41 and KOPT-K1 cells compared to naive DND-41 and KOPT-K1 T-ALL cells. (XLSX 169 kb)

Supplementary Table 2

_Z_-scores of hairpins (TRC ID = Name) in naive (N1 and N2) and persister (P1 and P2) DND-41 cells. (XLSX 85 kb)

Supplementary Table 3

Genes associated with top-ranked BRD4 peaks in persister DND-41 (sumSignal_DND-41_Persister) and KOPT-K1 (sumSignal_KOPT-K1_Persister) cells. (XLSX 78 kb)

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Knoechel, B., Roderick, J., Williamson, K. et al. An epigenetic mechanism of resistance to targeted therapy in T cell acute lymphoblastic leukemia.Nat Genet 46, 364–370 (2014). https://doi.org/10.1038/ng.2913

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• Received: 12 August 2013
• Accepted: 06 February 2014
• Published: 02 March 2014
• Issue Date: April 2014
• DOI: https://doi.org/10.1038/ng.2913