Mutant-IDH1-dependent chromatin state reprogramming, reversibility, and persistence (original) (raw)

References

1. Parsons, D. W. et al. An integrated genomic analysis of human glioblastoma multiforme. Science 321, 1807–1812 (2008).
   CAS PubMed PubMed Central Google Scholar
2. Yan, H. et al. IDH1 and IDH2 mutations in gliomas. N. Engl. J. Med. 360, 765–773 (2009).
   CAS PubMed PubMed Central Google Scholar
3. Dang, L. et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462, 739–744 (2009).
   CAS PubMed PubMed Central Google Scholar
4. Lu, C. et al. IDH mutation impairs histone demethylation and results in a block to cell differentiation. Nature 483, 474–478 (2012).
   CAS PubMed PubMed Central Google Scholar
5. Turcan, S. et al. IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature 483, 479–483 (2012).
   CAS PubMed PubMed Central Google Scholar
6. Duncan, C. G. et al. A heterozygous IDH1 R132H/WT mutation induces genome-wide alterations in DNA methylation. Genome Res. 22, 2339–2355 (2012).
   CAS PubMed PubMed Central Google Scholar
7. Rohle, D. et al. An inhibitor of mutant IDH1 delays growth and promotes differentiation of glioma cells. Science 340, 626–630 (2013).
   CAS PubMed PubMed Central Google Scholar
8. Tateishi, K. et al. Extreme vulnerability of IDH1 mutant cancers to NAD+ depletion. Cancer Cell 28, 773–784 (2015).
   CAS PubMed PubMed Central Google Scholar
9. Bao, S. et al. Targeting cancer stem cells through L1CAM suppresses glioma growth. Cancer Res. 68, 6043–6048 (2008).
   CAS PubMed PubMed Central Google Scholar
10. Ducray, F. et al. Anaplastic oligodendrogliomas with 1p19q codeletion have a proneural gene expression profile. Mol. Cancer 7, 41 (2008).
    PubMed PubMed Central Google Scholar
11. Izumoto, S. et al. Gene expression of neural cell adhesion molecule L1 in malignant gliomas and biological significance of L1 in glioma invasion. Cancer Res. 56, 1440–1444 (1996).
    CAS PubMed Google Scholar
12. Maness, P. F. & Schachner, M. Neural recognition molecules of the immunoglobulin superfamily: signaling transducers of axon guidance and neuronal migration. Nat. Neurosci. 10, 19–26 (2007).
    CAS PubMed Google Scholar
13. Mimeault, M. & Batra, S. K. Molecular biomarkers of cancer stem/progenitor cells associated with progression, metastases, and treatment resistance of aggressive cancers. Cancer Epidemiol. Biomarkers Prev. 23, 234–254 (2014).
    CAS PubMed Google Scholar
14. Fang, X., Zheng, P., Tang, J. & Liu, Y. CD24: from A to Z. Cell. Mol. Immunol. 7, 100–103 (2010).
    CAS PubMed PubMed Central Google Scholar
15. Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).
    CAS PubMed PubMed Central Google Scholar
16. Mohanan, V., Temburni, M. K., Kappes, J. C. & Galileo, D.S. L1CAM stimulates glioma cell motility and proliferation through the fibroblast growth factor receptor. Clin. Exp. Metastasis 30, 507–520 (2013).
    CAS PubMed Google Scholar
17. Kleene, R., Yang, H., Kutsche, M. & Schachner, M. The neural recognition molecule L1 is a sialic acid–binding lectin for CD24, which induces promotion and inhibition of neurite outgrowth. J. Biol. Chem. 276, 21656–21663 (2001).
    CAS PubMed Google Scholar
18. Anders, S. Visualization of genomic data with the Hilbert curve. Bioinformatics 25, 1231–1235 (2009).
    CAS PubMed PubMed Central Google Scholar
19. Verhaak, R. G. et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98–110 (2010).
    CAS PubMed PubMed Central Google Scholar
20. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).
    CAS PubMed PubMed Central Google Scholar
21. Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
    PubMed Google Scholar
22. Bailey, S. D. et al. ZNF143 provides sequence specificity to secure chromatin interactions at gene promoters. Nat. Commun. 2, 6186 (2015).
    PubMed Google Scholar
23. Bai, H. et al. Integrated genomic characterization of _IDH1_-mutant glioma malignant progression. Nat. Genet. 48, 59–66 (2016).
    CAS PubMed Google Scholar
24. Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).
    CAS PubMed PubMed Central Google Scholar
25. Chiappinelli, K. B. et al. Inhibiting DNA methylation causes an interferon response in cancer via dsRNA including endogenous retroviruses. Cell 164, 1073 (2016).
    CAS PubMed Google Scholar
26. Roulois, D. et al. DNA-demethylating agents target colorectal cancer cells by inducing viral mimicry by endogenous transcripts. Cell 162, 961–973 (2015).
    CAS PubMed PubMed Central Google Scholar
27. Scheie, D. et al. Prognostic variables in oligodendroglial tumors: a single-institution study of 95 cases. Neuro Oncol. 13, 1225–1233 (2011).
    PubMed PubMed Central Google Scholar
28. Koivunen, P. et al. Transformation by the (R)-enantiomer of 2-hydroxyglutarate linked to EGLN activation. Nature 483, 484–488 (2012).
    CAS PubMed PubMed Central Google Scholar
29. Kim, Y. et al. Mapping social behavior–induced brain activation at cellular resolution in the mouse. Cell Rep. 10, 292–305 (2015).
    CAS PubMed Google Scholar
30. Sunkin, S. M. et al. Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res. 41, D996–D1008 (2013).
    CAS PubMed Google Scholar
31. Turcan, S. et al. Efficient induction of differentiation and growth inhibition in IDH1 mutant glioma cells by the DNMT inhibitor decitabine. Oncotarget 4, 1729–1736 (2013).
    PubMed PubMed Central Google Scholar
32. Flavahan, W. A. et al. Insulator dysfunction and oncogene activation in IDH mutant gliomas. Nature 529, 110–114 (2016).
    CAS PubMed Google Scholar
33. Sonoda, Y. et al. Formation of intracranial tumors by genetically modified human astrocytes defines four pathways critical in the development of human anaplastic astrocytoma. Cancer Res. 61, 4956–4960 (2001).
    CAS PubMed Google Scholar
34. Silber, J. et al. miR-34a repression in proneural malignant gliomas upregulates expression of its target PDGFRA and promotes tumorigenesis. PLoS One 7, e33844 (2012).
    CAS PubMed PubMed Central Google Scholar
35. Zheng, Y., Huang, X. & Kelleher, N. L. Epiproteomics: quantitative analysis of histone marks and codes by mass spectrometry. Curr. Opin. Chem. Biol. 33, 142–150 (2016).
    CAS PubMed PubMed Central Google Scholar
36. Garcia, B. A. et al. Chemical derivatization of histones for facilitated analysis by mass spectrometry. Nat. Protoc. 2, 933–938 (2007).
    CAS PubMed PubMed Central Google Scholar
37. Zheng, Y., Tipton, J. D., Thomas, P. M., Kelleher, N. L. & Sweet, S. M. Site-specific human histone H3 methylation stability: fast K4me3 turnover. Proteomics 14, 2190–2199 (2014).
    CAS PubMed PubMed Central Google Scholar
38. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).
    CAS PubMed PubMed Central Google Scholar
39. R Development Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, Austria, 2008).
    PubMed Google Scholar
40. Morris, T. J. et al. ChAMP: 450K chip analysis methylation pipeline. Bioinformatics 30, 428–430 (2014).
    CAS PubMed Google Scholar
41. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
    PubMed Google Scholar
42. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
    CAS PubMed PubMed Central Google Scholar
43. Feber, A. et al. Using high-density DNA methylation arrays to profile copy number alterations. Genome Biol. 15, R30 (2014).
    PubMed PubMed Central Google Scholar
44. Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).
    PubMed PubMed Central Google Scholar
45. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
    Google Scholar
46. Dobin, A. & Gingeras, T. R. Mapping RNA-seq reads with STAR. Curr. Protoc. Bioinformatics 51, 11.14.1–11.14.19 (2015).
    Google Scholar
47. Engström, P. G. et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat. Methods 10, 1185–1191 (2013).
    PubMed PubMed Central Google Scholar
48. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
    CAS PubMed Google Scholar
49. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
    CAS PubMed PubMed Central Google Scholar
50. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
    CAS PubMed PubMed Central Google Scholar
51. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
    CAS PubMed PubMed Central Google Scholar
52. Feng, J., Liu, T., Qin, B., Zhang, Y. & Liu, X. S. Identifying ChIP–seq enrichment using MACS. Nat. Protoc. 7, 1728–1740 (2012).
    CAS PubMed Google Scholar
53. Zang, C. et al. A clustering approach for identification of enriched domains from histone modification ChIP–Seq data. Bioinformatics 25, 1952–1958 (2009).
    CAS PubMed PubMed Central Google Scholar
54. Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).
    PubMed PubMed Central Google Scholar
55. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
    CAS PubMed PubMed Central Google Scholar
56. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
    CAS PubMed PubMed Central Google Scholar
57. 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).
    CAS PubMed PubMed Central Google Scholar
58. Reich, M. et al. GenePattern 2.0. Nat. Genet. 38, 500–501 (2006).
    CAS PubMed Google Scholar
59. Ragan, T. et al. Serial two-photon tomography for automated ex vivo mouse brain imaging. Nat. Methods 9, 255–258 (2012).
    CAS PubMed PubMed Central Google Scholar
60. Turaga, S. C. et al. Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Comput. 22, 511–538 (2010).
    PubMed Google Scholar
61. Klein, S., Staring, M., Murphy, K., Viergever, M. A. & Pluim, J. P. elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med.Imaging 29, 196–205 (2010).
    PubMed Google Scholar

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Acknowledgements

We thank the members of the Chan and Thompson laboratories for helpful discussions. This work was supported in part by the US National Institutes of Health (NIH; R01 CA177828) (T.A.C. and C.B.T.), the MSKCC Brain Tumor Center (S.T. and T.A.C.), the Sontag Foundation (T.A.C.), the PaineWebber Chair Endowment (T.A.C.), NIH T32 grant 5T32CA160001 (S.T.), the MSKCC Society (T.A.C.), the NIH (R01 MH096946) (P.O.), and NIH Cancer Center Support Grant P30CA008748 (G.N.). This research was carried out in collaboration with the National Resource for Translational and Developmental Proteomics under grant P41 GM108569 (N.L.K.) from the National Institute of General Medical Sciences, NIH.

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Author notes

1. Sevin Turcan
   Present address: Department of Neurology, University Hospital Heidelberg, Heidelberg, Germany
2. Armida W. M. Fabius
   Present address: Department of Ophthalmology, VU Medical Center, Amsterdam, The Netherlands

Authors and Affiliations

1. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
   Sevin Turcan, Vladimir Makarov, Yuxiang Wang, Armida W. M. Fabius, Wei Wu, Sara Haddock, Jason T. Huse & Timothy A. Chan
2. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
   Julian Taranda, Nour El-Amine & Pavel Osten
3. Sanofi Genzyme, Waltham, MA, USA
   Yupeng Zheng
4. Weill Cornell School of Medicine, New York, NY, USA
   Sara Haddock & Timothy A. Chan
5. Molecular Cytogenetics Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY, USA
   Gouri Nanjangud
6. Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
   H. Carl LeKaye
7. Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
   Cameron Brennan
8. Donald B. and Catherine C. Marron Cancer Metabolism Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
   Justin Cross
9. Department of Chemistry, Northwestern University, Evanston, IL, USA
   Neil L. Kelleher
10. Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
    Craig B. Thompson
11. Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA
    Timothy A. Chan

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1. Sevin Turcan
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2. Vladimir Makarov
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3. Julian Taranda
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4. Yuxiang Wang
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6. Wei Wu
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7. Yupeng Zheng
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Contributions

S.T. and T.A.C. conceived of the study. S.T., V.M., J.T., Y.W., A.W.M.F., W.W., Y.Z., N.E.-A., S.H., G.N., H.C.L., C.B., J.C., and J.T.H. performed the experiments. S.T., V.M., J.T., Y.W., A.W.M.F., Y.Z., N.E.-A., S.H., G.N., H.C.L., C.B., J.C., J.T.H., N.L.K., P.O., and T.A.C. analyzed the results. T.A.C. and C.B.T. supervised the project. All authors contributed to the writing or editing of the manuscript.

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Correspondence toSevin Turcan or Timothy A. Chan.

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Supplementary Figure 1 Gene expression kinetics in control immortalized human astrocytes (doxycycline-inducible expression of empty vector)

a, Hierarchical clustering of global gene expression profiles in inducible empty vector IHAs. b, The y axis shows upregulated probes (gene expression) in IDH1 R132H IHAs; the x axis shows the corresponding probes in doxycycline-inducible empty vector control IHAs. Probes that were either upregulated >1-fold (red line, top) or downregulated by <–1-fold (blue line, bottom) in control astrocytes were filtered from subsequent analyses. c, RSEM values from LGG TCGA RNA-seq data for L1CAM and MEOX2 in IDH-mutant versus IDH-wild-type tumors. d, Scatterplot of upregulated (top) and downregulated (bottom) genes (Dox+ versus Dox– at baseline) binned by fold change across transient, gradual, and persistent expression clusters.

Supplementary Figure 2 Comparison of mutant-IDH1-induced gene expression and methylation changes in inducible astrocytes and lower-grade gliomas

a, Pathway enrichment for down- or upregulated genes across all passages in inducible IDH1 R132H astrocytes (Dox+ compared to Dox–) and down- or upregulated genes in primary lower-grade gliomas (CIMP+ versus CIMP–; n = 52)5. b, Hypermethylated probes (Dox+ versus Dox– Δ_β_ > 0.1) at each passage following baseline were calculated for IDH1 R132H and empty vector inducible IHAs. Box plots show the methylation β values of these hypermethylated probes for CIMP– and CIMP+ primary LGGs5. P, passage. c, Log2-transformed fold change for Affymetrix probes corresponding to CD24 across all passages after baseline (comparing IDH1 R132H Dox+ versus Dox–). d, Scatterplot of CD24 expression in CIMP+ and CIMP– lower-grade gliomas (n = 52)5. e, Soft agar colony-formation assays of IDH1 R132HCD24– and IDH1 R132HCD24+ cells (derived from Dox+ IDH1 R132H cells at passage 50). All experiments were performed in triplicate. f, Quantification of macroscopic soft agar colonies in (i) (*P < 0.01). Error bars, s.d.; n = 3. g, Gene set enrichment analysis of IDH1 R132HCD24+ versus IDH1 R132HCD24– cells. FDR, false discovery rate; NES, normalized enrichment score.

Supplementary Figure 3 Histone mark landscape in immortalized human astrocytes

a, Overlapping Hilbert curves (chromosome 1) for H3K36me3, H4K20me3, and H3K9me3 at successive passages of parental IHA peaks (green pixels), IDH1 R132H IHA peaks (red pixels; constitutive overexpression), and overlapping peaks (yellow). b, HOMER annotation of clusters derived from the union of all H3K4me3 peaks in stable astrocytes. The color scale indicates relative enrichment. c, Normalized H3K9me3 signal across clusters of non-overlapping 5-kb genomic windows; n indicates the number of regions per cluster. d, Normalized H3K36me3 signal across clusters of non-overlapping 5-kb genomic windows; Par, parental; Mut, IDH1 mutant. e, IGV snapshot of input-normalized H3K9me3 ChIP signal (log2) across a 20-Mb region highlighting localized gain in H3K9me3 (red box); Par, parental; Mut, IDH1 mutant.

Supplementary Figure 4 Characterization of the histone mark landscape in IDH1-mutant astrocytes

a, HOMER-derived annotation of genomic loci covered by H3K27me3, H3K36me3, H3K9me3, or H4K20me3 peaks in parental and IDH1 R132H IHAs at passages 2, 10, and 40. TTS, transcription termination site; TSS, transcription start site. b, Quantitative liquid chromatography–mass spectrometry analysis of histone variants and their modifications. Fold change indicates the quantitative change in IDH1 R132H Dox+ samples compared to IDH1 R132H Dox– samples at 1, 10, and 20 passages after baseline (passage 30). P, passage.

Supplementary Figure 5 Methylation dynamics of inducible immortalized human astrocytes

a, β_-value distribution of 27,734 probes on TS603 glioma tumorspheres with endogenous expression of IDH1 R132H. Probes were derived from hypermethylated probes in IDH1 R132H Dox+ IHAs compared to Dox– IHAs. 18,023 probes display 70% methylation in TS603 cells. b, Random sets of genes (fixed size of 27,734) were chosen from Illumina 450K probes, and the median β value was calculated for TS603 cells. The distribution shows the median β value for 10,000 independent random trials, and the observed β value from the hypermethylated loci (median β value = 0.872) is marked with a red line (P < 0.0001). **c**, Hierarchical clustering of the global methylation profiles in inducible empty vector IHAs. **d**, The _y_ axis indicates probes with absolute Δ_β_ >0.1 at the baseline passage (passage 30); the x axis indicates the corresponding probes in inducible empty vector control IHAs. Probes that display absolute Δ_β >0.1 in control astrocytes were filtered from subsequent analyses (right). e, Differential methylation analyses across all passages comparing IDH1 R132H Dox+ versus Dox– IHAs (left) or empty vector Dox+ versus Dox– IHAs (right). Blue dots indicate hypomethylated sites, and red dots indicate hypermethylated sites.

Supplementary Figure 6 Characteristics of the persistently methylated cluster following doxycycline withdrawal

a, The persistently methylated cluster is divided into equally sized bins of five groups based on the β value of the Dox– sample (bin ranges are indicated above the box plots). b, Pie chart indicating the number of loci per bin as described in a. c, Histogram of Δ_β_ values (Doxoff 40 passages as compared to Dox–) in the persistent cluster. The median and mean Δ_β_ values are indicated on the histogram.

Supplementary Figure 7 Characteristics of the persistently methylated cluster following doxycycline withdrawal

a, β values of loci with Dox– β value <0.1 and Δ_β_ value (Doxoff 40 passages as compared to Dox–) >0.3 across all time points. b, Annotations of the probes in a.

Supplementary Figure 8 ChIP–seq profiles of IDH1-mutant inducible IHAs

a, Log2-transformed input-normalized H3K4me3, H3K27me3, H3K36me3, and H3K9me3 ChIP signal profiles ±1 kb around all CpGs on the Illumina HumanMethylation 450K array. b, Log2-transformed input-normalized H3K4me3, H3K27me3, H3K36me3, and H3K9me3 ChIP signal profiles (top) and heat maps (bottom) ±1 kb around the hypermethylated transient, gradual, and persistent clusters for Dox–, Dox+, and Doxoff IHAs.

Supplementary Figure 9 Epigenetic regulation in IDH1 R132H inducible immortalized human astrocytes

a, All genes are sorted in ascending order based on their abundance (FPKM; top) as measured by RNA-seq. H3K4me3 enrichment at the TSSs of FPKM-ranked genes is plotted in the bottom panel. b, Average profiles of enrichment plots for regions with increased H3K27me3, H3K36me3, or H3K9me3 in IDH1 R132H Dox+ IHAs in Dox– (green), Dox+ (orange), and Doxoff (blue) IHAs. c, _k_-means clustering of log2-transformed input-normalized H3K4me3 ChIP signal ±1 kb around H3K4me3 Dox+ peaks for all inducible IHAs. d, Overlap of genes with persistent changes in H3K4me3 and methylation at their TSSs despite long-term withdrawal of doxycycline in IDH1 R132H IHAs. e, PCA plot of significantly differentially methylated H3K4me3 peaks (1,150 regions) across IDH1 R132H cells (IDH1 R132H stable IHAs at passage 40, Dox+, and TS603) as compared to IDH-wild-type cells (parental at passage 40, Dox–, TS543, and TS667).

Supplementary Figure 10 Copy number alterations derived from Illumina HumanMethylation450K data

a, Heat map of RSEM values for the viral defense signature genes25 in LGGs (downloaded from Broad GDAC Firehose). The presence (turquoise) or absence (light red) of IDH mutation is indicated as an annotation. b, GISTIC scores and FDR values (q values; the vertical green line is the 0.25 cutoff for significance) for amplifications (red lines; left) and deletions (blue lines; right) for IDH1 R132H IHAs are plotted for all chromosomes. b, GISTIC scores and FDR values for amplifications (left) and deletions (right) for empty vector IHAs are plotted for all chromosomes. No alterations were detected.

Supplementary Figure 11 Characteristics of representative brains orthotopically implanted with IDH1 R132H inducible IHAs

a,b, Representative T2-weighted MRI images (axial brain slices, six mice per group) for control IHAs a and IDH1 R132H IHAs b 5 months post injection. c, Representative Ki67 staining for an IDH1 R132H Dox+ and a Doxoff (doxycycline withdrawn) brain. d, Representative STP slice for a control mouse (Dox–) without discernible tumors on MRI but cellular growth as evidenced by green fluorescence (ZsGreen) in the hindbrain (white arrows). e, Representative STP slice for Dox+ mice showing cellular growth in the hindbrain region (green channel).

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Turcan, S., Makarov, V., Taranda, J. et al. Mutant-IDH1-dependent chromatin state reprogramming, reversibility, and persistence.Nat Genet 50, 62–72 (2018). https://doi.org/10.1038/s41588-017-0001-z

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• Received: 11 November 2016
• Accepted: 25 October 2017
• Published: 27 November 2017
• Issue Date: January 2018
• DOI: https://doi.org/10.1038/s41588-017-0001-z