Abstract
Mutations in IDH1 and IDH2 (encoding isocitrate dehydrogenase 1 and 2) drive the development of gliomas and other human malignancies. Mutant IDH1 induces epigenetic changes that promote tumorigenesis, but the scale and reversibility of these changes are unknown. Here, using human astrocyte and glioma tumorsphere systems, we generate a large-scale atlas of mutant-IDH1-induced epigenomic reprogramming. We characterize the reversibility of the alterations in DNA methylation, the histone landscape, and transcriptional reprogramming that occur following IDH1 mutation. We discover genome-wide coordinate changes in the localization and intensity of multiple histone marks and chromatin states. Mutant IDH1 establishes a CD24+ population with a proliferative advantage and stem-like transcriptional features. Strikingly, prolonged exposure to mutant IDH1 results in irreversible genomic and epigenetic alterations. Together, these observations provide unprecedented high-resolution molecular portraits of mutant-IDH1-dependent epigenomic reprogramming. These findings have substantial implications for understanding of mutant IDH function and for optimizing therapeutic approaches to targeting IDH-mutant tumors.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Parsons, D. W. et al. An integrated genomic analysis of human glioblastoma multiforme. Science 321, 1807–1812 (2008).
Yan, H. et al. IDH1 and IDH2 mutations in gliomas. N. Engl. J. Med. 360, 765–773 (2009).
Dang, L. et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462, 739–744 (2009).
Lu, C. et al. IDH mutation impairs histone demethylation and results in a block to cell differentiation. Nature 483, 474–478 (2012).
Turcan, S. et al. IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature 483, 479–483 (2012).
Duncan, C. G. et al. A heterozygous IDH1 R132H/WT mutation induces genome-wide alterations in DNA methylation. Genome Res. 22, 2339–2355 (2012).
Rohle, D. et al. An inhibitor of mutant IDH1 delays growth and promotes differentiation of glioma cells. Science 340, 626–630 (2013).
Tateishi, K. et al. Extreme vulnerability of IDH1 mutant cancers to NAD+ depletion. Cancer Cell 28, 773–784 (2015).
Bao, S. et al. Targeting cancer stem cells through L1CAM suppresses glioma growth. Cancer Res. 68, 6043–6048 (2008).
Ducray, F. et al. Anaplastic oligodendrogliomas with 1p19q codeletion have a proneural gene expression profile. Mol. Cancer 7, 41 (2008).
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).
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).
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).
Fang, X., Zheng, P., Tang, J. & Liu, Y. CD24: from A to Z. Cell. Mol. Immunol. 7, 100–103 (2010).
Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).
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).
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).
Anders, S. Visualization of genomic data with the Hilbert curve. Bioinformatics 25, 1231–1235 (2009).
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).
Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).
Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Bailey, S. D. et al. ZNF143 provides sequence specificity to secure chromatin interactions at gene promoters. Nat. Commun. 2, 6186 (2015).
Bai, H. et al. Integrated genomic characterization of IDH1-mutant glioma malignant progression. Nat. Genet. 48, 59–66 (2016).
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).
Chiappinelli, K. B. et al. Inhibiting DNA methylation causes an interferon response in cancer via dsRNA including endogenous retroviruses. Cell 164, 1073 (2016).
Roulois, D. et al. DNA-demethylating agents target colorectal cancer cells by inducing viral mimicry by endogenous transcripts. Cell 162, 961–973 (2015).
Scheie, D. et al. Prognostic variables in oligodendroglial tumors: a single-institution study of 95 cases. Neuro Oncol. 13, 1225–1233 (2011).
Koivunen, P. et al. Transformation by the (R)-enantiomer of 2-hydroxyglutarate linked to EGLN activation. Nature 483, 484–488 (2012).
Kim, Y. et al. Mapping social behavior–induced brain activation at cellular resolution in the mouse. Cell Rep. 10, 292–305 (2015).
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).
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).
Flavahan, W. A. et al. Insulator dysfunction and oncogene activation in IDH mutant gliomas. Nature 529, 110–114 (2016).
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).
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).
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).
Garcia, B. A. et al. Chemical derivatization of histones for facilitated analysis by mass spectrometry. Nat. Protoc. 2, 933–938 (2007).
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).
MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).
R Development Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, Austria, 2008).
Morris, T. J. et al. ChAMP: 450K chip analysis methylation pipeline. Bioinformatics 30, 428–430 (2014).
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
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).
Feber, A. et al. Using high-density DNA methylation arrays to profile copy number alterations. Genome Biol. 15, R30 (2014).
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).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Dobin, A. & Gingeras, T. R. Mapping RNA-seq reads with STAR. Curr. Protoc. Bioinformatics 51, 11.14.1–11.14.19 (2015).
Engström, P. G. et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat. Methods 10, 1185–1191 (2013).
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).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
Feng, J., Liu, T., Qin, B., Zhang, Y. & Liu, X. S. Identifying ChIP–seq enrichment using MACS. Nat. Protoc. 7, 1728–1740 (2012).
Zang, C. et al. A clustering approach for identification of enriched domains from histone modification ChIP–Seq data. Bioinformatics 25, 1952–1958 (2009).
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).
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
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).
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).
Reich, M. et al. GenePattern 2.0. Nat. Genet. 38, 500–501 (2006).
Ragan, T. et al. Serial two-photon tomography for automated ex vivo mouse brain imaging. Nat. Methods 9, 255–258 (2012).
Turaga, S. C. et al. Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Comput. 22, 511–538 (2010).
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).
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.
Author information
Authors and Affiliations
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.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Additional information
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
Supplementary Figure 1 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– Δβ > 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 Δβ >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 Δβ >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 Δβ values (Doxoff 40 passages as compared to Dox–) in the persistent cluster. The median and mean Δβ 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 Δβ 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).
Supplementary information
Combined Supplementary Information
Supplementary Figures 1–11
Supplementary Video 1
Whole-brain surface reconstruction (Dox–) from 280 brain sections imaged with STP tomography with 20% resolution of raw data
Supplementary Video 2
Whole-brain surface and 3D tumor reconstruction (Dox+) from 280 brain sections imaged with STP tomography with 20% resolution of raw data
Supplementary Video 3
Whole-brain surface and 3D tumor reconstruction (Doxoff) from 280 brain sections imaged with STP tomography with 20% resolution of raw data
Supplementary Table 1
Up- and downregulated gene expression clusters following doxycycline withdrawal
Supplementary Table 2
Differentially expressed genes in IDH1 R132H Dox+ inducible IHAs sorted by CD24 expression (CD24+ versus CD24–)
Supplementary Table 3
Hyper- and hypomethylated clusters following doxycycline withdrawal
Supplementary Table 4
H3K4me3 enrichment at the transcription start sites (TSSs) of Dox– and Dox+ IDH1 R132H IHAs
Supplementary Table 5
FPKM values of RNA-seq data from inducible astrocytes and tumorspheres
Supplementary Table 6
Top 11,445 significantly H3K4me3-enriched regions in Dox+ IHAs
Supplementary Table 7
Primers used for qPCR analysis of selected ERVs
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41588-017-0001-z
This article is cited by
-
Comprehensive analysis of the REST transcription factor regulatory networks in IDH mutant and IDH wild-type glioma cell lines and tumors
Acta Neuropathologica Communications (2024)
-
Mechanistic insights and the clinical prospects of targeted therapies for glioblastoma: a comprehensive review
Experimental Hematology & Oncology (2024)
-
Valproic acid targets IDH1 mutants through alteration of lipid metabolism
npj Metabolic Health and Disease (2024)
-
Treatment of IDH-mutant glioma in the INDIGO era
npj Precision Oncology (2024)
-
IDH inhibition in gliomas: from preclinical models to clinical trials
Nature Reviews Neurology (2024)