Abstract
Glioblastoma is a universally lethal cancer with a median survival time of approximately 15 months1. Despite substantial efforts to define druggable targets, there are no therapeutic options that notably extend the lifespan of patients with glioblastoma. While previous work has largely focused on in vitro cellular models, here we demonstrate a more physiologically relevant approach to target discovery in glioblastoma. We adapted pooled RNA interference (RNAi) screening technology2,3,4 for use in orthotopic patient-derived xenograft models, creating a high-throughput negative-selection screening platform in a functional in vivo tumour microenvironment. Using this approach, we performed parallel in vivo and in vitro screens and discovered that the chromatin and transcriptional regulators needed for cell survival in vivo are non-overlapping with those required in vitro. We identified transcription pause–release and elongation factors as one set of in vivo-specific cancer dependencies, and determined that these factors are necessary for enhancer-mediated transcriptional adaptations that enable cells to survive the tumour microenvironment. Our lead hit, JMJD6, mediates the upregulation of in vivo stress and stimulus response pathways through enhancer-mediated transcriptional pause–release, promoting cell survival specifically in vivo. Targeting JMJD6 or other identified elongation factors extends survival in orthotopic xenograft mouse models, suggesting that targeting transcription elongation machinery may be an effective therapeutic strategy for glioblastoma. More broadly, this study demonstrates the power of in vivo phenotypic screening to identify new classes of ‘cancer dependencies’ not identified by previous in vitro approaches, and could supply new opportunities for therapeutic intervention.
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Change history
06 March 2018
In the HTML version of this Letter, several of the figures were numbered incorrectly: Fig. 4 displayed incorrectly as Extended Data Fig. 2; Extended Data Fig 1 displayed incorrectly as Extended Data Fig. 3; Extended Data Fig. 2 displayed incorrectly as Extended Data Fig. 5; Extended Data Fig. 3 displayed incorrectly as Extended Data Fig. 6; Extended Data Fig. 4 displayed incorrectly as Extended Data Fig. 7; Extended Data Fig. 5 displayed incorrectly as Extended Data Fig. 8; Extended Data Fig. 6 displayed incorrectly as Extended Data Fig. 9; and Extended Data Fig. 10 displayed incorrectly as Extended Data Fig. 4. This has been corrected online.
References
Stupp, R. et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol. 10, 459–466 (2009)
Zuber, J. et al. Toolkit for evaluating genes required for proliferation and survival using tetracycline-regulated RNAi. Nat. Biotechnol. 29, 79–83 (2011)
Zuber, J. et al. RNAi screen identifies Brd4 as a therapeutic target in acute myeloid leukaemia. Nature 478, 524–528 (2011)
Fellmann, C. et al. An optimized microRNA backbone for effective single-copy RNAi. Cell Reports 5, 1704–1713 (2013)
Dawson, M. A. & Kouzarides, T. Cancer epigenetics: from mechanism to therapy. Cell 150, 12–27 (2012)
Helin, K. & Dhanak, D. Chromatin proteins and modifications as drug targets. Nature 502, 480–488 (2013)
Polak, P. et al. Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature 518, 360–364 (2015)
Adam, R. C. et al. Pioneer factors govern super-enhancer dynamics in stem cell plasticity and lineage choice. Nature 521, 366–370 (2015)
Brown, J. D. et al. NF-κB directs dynamic super enhancer formation in inflammation and atherogenesis. Mol. Cell 56, 219–231 (2014)
Suvà, M. L., Riggi, N. & Bernstein, B. E. Epigenetic reprogramming in cancer. Science 339, 1567–1570 (2013)
Akhtar-Zaidi, B. et al. Epigenomic enhancer profiling defines a signature of colon cancer. Science 336, 736–739 (2012)
Liu, W. et al. Brd4 and JMJD6-associated anti-pause enhancers in regulation of transcriptional pause release. Cell 155, 1581–1595 (2013)
Jonkers, I. & Lis, J. T. Getting up to speed with transcription elongation by RNA polymerase II. Nat. Rev. Mol. Cell Biol. 16, 167–177 (2015)
Wu, L., Li, L., Zhou, B., Qin, Z. & Dou, Y. H2B ubiquitylation promotes RNA Pol II processivity via PAF1 and pTEFb. Mol. Cell 54, 920–931 (2014)
Lee, J. et al. Tumor stem cells derived from glioblastomas cultured in bFGF and EGF more closely mirror the phenotype and genotype of primary tumors than do serum-cultured cell lines. Cancer Cell 9, 391–403 (2006)
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)
Merico, D., Isserlin, R., Stueker, O., Emili, A. & Bader, G. D. Enrichment Map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 5, e13984 (2010)
Williams, L. H. et al. Pausing of RNA polymerase II regulates mammalian developmental potential through control of signaling networks. Mol. Cell 58, 311–322 (2015)
Adelman, K. & Lis, J. T. Promoter-proximal pausing of RNA polymerase II: emerging roles in metazoans. Nat. Rev. Genet. 13, 720–731 (2012)
Gilchrist, D. A. et al. Regulating the regulators: the pervasive effects of Pol II pausing on stimulus-responsive gene networks. Genes Dev. 26, 933–944 (2012)
Galbraith, M. D. et al. HIF1A employs CDK8-mediator to stimulate RNAPII elongation in response to hypoxia. Cell 153, 1327–1339 (2013)
Muse, G. W. et al. RNA polymerase is poised for activation across the genome. Nat. Genet. 39, 1507–1511 (2007)
Schröder, S. et al. Acetylation of RNA polymerase II regulates growth-factor-induced gene transcription in mammalian cells. Mol. Cell 52, 314–324 (2013)
Shah, S., Feng, X., Lankerovich, M., Puchalski, R. B. & Keogh, B. Data from Ivy GAP. The Cancer Imaging Archive http://dx.doi.org/10.7937/K9/TCIA.2016.XLwaN6nL (2016)
Kim, T. K. et al. Widespread transcription at neuronal activity-regulated enhancers. Nature 465, 182–187 (2010)
Luo, B. et al. Highly parallel identification of essential genes in cancer cells. Proc. Natl Acad. Sci. USA 105, 20380–20385 (2008)
Bao, S. et al. Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature 444, 756–760 (2006)
Goecks, J., Nekrutenko, A., Taylor, J. & The Galaxy Team. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 11, R86 (2010)
Reimand, J., Arak, T. & Vilo, J. g:Profiler—a web server for functional interpretation of gene lists (2011 update). Nucleic Acids Res. 39, W307–W315 (2011)
Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009)
Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010)
Suvà, M. L. et al. Reconstructing and reprogramming the tumor-propagating potential of glioblastoma stem-like cells. Cell 157, 580–594 (2014)
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009)
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)
Liu, T. et al. Cistrome: an integrative platform for transcriptional regulation studies. Genome Biol. 12, R83 (2011)
Miller, T. E. et al. Lgr5 marks post-mitotic, lineage restricted cerebellar granule neurons during postnatal development. PLoS One 9, e114433 (2014)
Gilbert, M. R. et al. Autophagy and oxidative stress in gliomas with IDH1 mutations. Acta Neuropathol. 127, 221–233 (2014)
Wan, Y. W., Allen, G. I. & Liu, Z. TCGA2STAT: simple TCGA data access for integrated statistical analysis in R. Bioinformatics 32, 952–954 (2016)
Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009)
Acknowledgements
The authors thank the Hemann, Paddison, and Zuber laboratories, along with K. Alazem for technical help with RNAi screening. We thank B. Kristensen and A. M. Rosager for input on histological analysis and A. Saiakhova, E. R. Chan, M. Squatrito and W. Liu for bioinformatic support. Additional support was provided by the Cytometry & Imaging Microscopy and Genomics core facilities of the Case Comprehensive Cancer Center at Cleveland Clinic and Case Western (P30CA043703), respectively. We also thank members of the Rich, Tesar and Scacheri laboratories for input on the project and discussion about the manuscript. This work was supported by Velosano (J.N.R.); New York Stem Cell Foundation-Robertson Investigator Award and philanthropic support from the Goodman family (P.J.T.); CIHR Banting Fellowship (S.C.M.); National Institutes of Health grants CA183510 (T.E.M.); GM007250 (T.E.M., A.R.M., L.J.Y.K., J.J.M.); CA189647 (C.G.H.); CA154130, CA169117, CA197718, CA171652, NS087913 and NS089272 (J.N.R.).
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Authors and Affiliations
Contributions
T.E.M., P.J.T. and J.N.R designed the overall screening strategy. T.H., J.Z., P.J.P. and M.T.H provided technical expertise, unpublished reagents and methods for the screen. T.E.M., S.C.M., C.G.H. and Q.W. performed the screen. T.E.M., L.C.W. and S.C.M. produced and sequenced screening libraries. T.E.M., P.J.T. and J.N.R. processed and analysed screening results. T.E.M., L.C.W. and D.C.F. generated and analysed RNA-seq data. T.E.M., B.B.L., S.M.G., R.T., B.E.B., L.C.W. and S.C.M. generated ChIP–seq data. T.E.M., B.B.L, A.R.M., B.E.B., J.J.M. and P.C.S. analysed and interpreted ChIP–seq data. T.E.M., L.J.Y.K. and W.A.F. performed bioinformatic analysis of published expression datasets. T.E.M., D.D., Q.X., L.C.W. and Q.W. performed functional knockdown and CRISPR-knockout studies. C.M.H. created and analysed tissue microarray for JMJD6 expression. T.E.M., P.J.T. and J.N.R. analysed all data and wrote the paper. All authors provided intellectual input, edited and approved the final manuscript.
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Reviewer Information Nature thanks K. Adelman, P. Northcott and the other anonymous reviewer(s) for their contribution to the peer review of this work.
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Extended data figures and tables
Extended Data Figure 1 shRNA delivery vector performance.
a, Top, schematic of the RT3REVIR shRNA delivery vector. Middle, once integrated into cells, a constitutive PGK promoter drives Venus-fluorescent reporter and rtTA through an IRES element, creating an all-in-one inducible vector. Bottom, when doxycycline is introduced to cells, it binds to rtTA and drives activity of the third generation TET-inducible promoter. This drives the dsRED fluorescent reporter and shRNA expression. In addition, it drives higher rtTA transcription through the IRES element, creating a positive-feedback loop that increases rtTA expression in the cell resulting in higher expression of inducible elements. b, The inducible shRNA delivery vector displays almost no unintended induction. Representative FACS plots from the parallel screen of cells infected with RT3REVIR with and without doxycycline treatment in vitro (left) and in vivo (right). c, RT3REVIR robustly expresses shRNAs and depletes cells expressing cell-lethal shRNAs in a competitive proliferation assay. Left, representative FACS plots over time of cells infected with a positive control shRNA against RPA3 and induced. Right, quantification of fluorescent cells in the representative competitive proliferation assay. Empty denotes cells with vector that had no shRNA; REN.713 denotes cells with vector containing negative-control shRNA targeting the Renilla protein. The percentage of cells actively expressing the shRNA within the total infected population from a single dish is shown.
Extended Data Figure 2 Importance of combining multiple mice to achieve increased reproducibility.
a, Correlation (r) values between individual mice (41 pairwise comparisons for induced mice and 20 pairwise comparisons for control uninduced mice) or of grouped replicates containing multiple mice (3 pairwise comparisons for triplicate induced arm replicates and 1 pairwise comparison for the duplicate control uninduced replicates). b, Positive control gene RPA3 was effectively depleted from cell populations in both in vivo and in vitro screens using grouped replicates for the in vivo screen. Four shRNAs targeting RPA3 were included in the shRNA screening library. At least two out of four shRNAs achieved a RIGER depletion score of 2.0 or greater.
Extended Data Figure 3 Validation of in vivo screen results.
a, b, Average mRNA expression of intracranial-specific (a) or cell culture-specific (b) hits in vivo and in vitro. P values calculated by two-sided Mann–Whitney U-test and were greater than 0.05. c, d, Screen hits were analysed for enrichment of Gene Ontology gene sets, using the screened library gene list as background to control for bias towards chromatin modifiers. c, All results with an FDR value of 0.05 or lower for in vivo-specific hits are presented. d, Top 10 results with an FDRvalue of 0.05 or lower for in vitro-specific hits are presented. Significance calculated by Benjamini–Hochberg FDR. e–h, Validation of elongation factor hits by in vivo survival assays. shRNAs from the primary screen were used to transduce GBM528 cells using a constitutive expression vector. Primary screen hits that led to an increase survival with knockdown, with at least 2 out of 4 mice surviving longer than all 9 negative control mice, were considered validated. e, f, Target mRNA knockdown by qRT–PCR (e) and Kaplan–Meier survival curve (f) of validated hits. Black lines and bars represent two independent negative-control shRNAs. Purple and pink lines and bars represent validated in vivo-specific hits; blue lines and bars represent validated common hits found in both in vivo and in vitro screen. g, h, Target mRNA knockdown by qRT–PCR (g) and Kaplan–Meier survival curve (h) of primary screen hits that did not validate (green lines and bars).
Extended Data Figure 4 Independent models confirm stimulus-controlled and stress response programs upregulated in vivo and in primary tumours.
a, b, Genes with an average expression change of more than 2.5-fold between conditions in GBM528 (a) and GBM3565 (b) cells, as determined by RNA-seq. GBM528 heatmap associated with Fig. 2b, c. c, d, Cellular programs enriched by GSEA in cells grown in each condition. c, Representation of all enriched programs in vitro and in vivo using Enrichment Map17. d, Example GSEA enrichment plots for cells grown in vitro and in vivo. FDR values calculated by GSEA software. d–g, As in b–d, GSEA was performed using data generated from two independent models by Lee and colleagues15 on cells grown in vitro versus in vivo intracranial xenograft tumours (e, g), or in vitro versus the primary glioblastoma from which the cells were derived (f, h). Cellular programs enriched in cells grown in each condition are presented using Enrichment Map. FDR values calculated by GSEA software.
Extended Data Figure 5 Transcription factors and signalling molecules that drive stimulus-controlled programs consistently upregulated in vivo.
a, Transcription factors upregulated in GBM528 cells upon growth in vivo. Values are mean FPKM ± s.d. from biological duplicates. b, Cell signalling programs regulated by pause control that are enriched for upregulated transcription factors in a. FDR values calculated by MSigDB for enrichment against all genes. c, 55 genes upregulated more than 2.5-fold in both GBM528 and GBM3565 upon growth in vivo. d, Expression of those 55 genes in vivo and in vitro in GBM528 and GBM3565 compared to expression in primary glioblastoma tumours from the TCGA RNaseqV2 database. Data were FPKM quantile normalized across all datasets before plotting. e, All genes in datasets shown to confirm normalization.
Extended Data Figure 6 Epigenomic regulation of glioblastoma cells is microenvironment-specific.
a–h, Global enhancer landscape of GBM528 (a) and GBM3565 (d) cells in both conditions and microenvironment-specific enhancers. b–h, Browser track examples (b, e), aggregate plots (c, f), and gene expression fold change of target genes (nearest expressed gene) (g, h) of microenvironment-specific enhancer loci from (a, d). i, Super-enhancers identified in vivo in GBM528. j, Browser track examples of condition-specific super-enhancers. k, Super-enhancers specific to each condition were identified. l, Expression of condition-specific super-enhancer target genes. Box plot P values calculated by two-sided Mann–Whitney U-test.
Extended Data Figure 7 Prioritization of JMJD6 as lead target.
a, Top, expression across all Ivy GAP samples of gene signature of 55 genes upregulated in vivo in both PDX models. Middle, elongation factor hits. Bottom, corresponding histological tumour structure and TCGA molecular subtype of each sample represented. The signature and JMJD6 expression is highest in hypoxic regions, which also corresponds to more mesenchymal-like regions of the tumour. b, Expression correlation dot plot for JMJD6 data represented in a. c, d, Expression correlation of each elongation factor hit with gene signature of 55 genes upregulated in vivo in both PDX models across all REMBRANDT (c) and TCGA (d) glioblastoma tumours (bulk tumour expression. Ivy GAP data in a and Fig. 3a is microenvironment-specific expression. P value (a–d) by FDR-adjusted Benjamini–Hochberg procedure. e, JMJD6 mRNA-seq correlation with each gene across TCGA glioblastoma tumours. f, Example plots from GSEA using the gene correlations in e and a pre-rank list. g, Representative images from tissue microarray analysis of JMJD6 protein expression in Fig. 3c. h, Primary screen results for the four shRNAs targeting JMJD6. Only two out of the four shRNAs were represented in the library at appreciable levels, and both led to a RIGER depletion score of greater than 2. Values are median RPM ± s.d. of three biological replicates for induced populations and two biological replicates for the uninduced population.
Extended Data Figure 8 JMJD6 regulates enhancer mediated pause–release in glioblastoma.
a, Known role of JMJD6 in transcription pause–release. In HEK293T and HeLa cells, JMJD6 acts with BRD4 as a key activator of enhancer-mediated pause–release at genes controlled by Pol II pausing12. Upon enhancer activation, JMJD6 demethylates 7SK RNA releasing positive transcription elongation factor (P-TEFb) inhibition from the 7SK/HEXIM complex12. b, Browser tracks of JMJD6 at enhancers and TSSs. c, Left, global distribution of genomic elements as determined by the hg19 reference genome and H3K27ac ChIP–seq. Right, global distribution of JMJD6 binding peaks per genomic element as determined by JMJD6 ChIP–seq. Enrichments shown in Fig. 3d. d–g, GSEA enrichment plots of genes with JMJD6-bound enhancers in the GBM528 (d) or GBM3565 (e) PDX model against differential expression of genes between in vivo and in vitro conditions (expression from Fig. 2b for GBM528 and Extended Data Fig. 4a for GBM3565). GSEA enrichment plots of genes with JMJD6-bound enhancers in the GBM528 (f) or GBM3565 (g) PDX model against gene correlations with JMJD6 in TCGA tumours (correlations from Extended Data Fig. 7e). h, i, Distribution of the pausing index of the common in vivo upregulated genes from Extended Data Fig. 5c for which the pausing index could be determined in GBM528 (h) and GBM3565 (i). All P values calculated by two-sided Mann–Whitney U-test.
Extended Data Figure 9 Validation of JMJD6 and other hits in multiple PDX models of glioblastoma.
a, JMJD6 mRNA expression by qRT–PCR after inducible shRNA knockdown of JMJD6. b, c, In vitro proliferation (b) and in vivo survival (c) compared to uninduced and induced non-targeting controls. Data are mean ± s.d. of three technical replicates. d, Endpoint tumours collected from the induced arm of c stained to show human tumour cells (human nuclear antigen) that harbour a JMJD6 shRNA (Venus+) or harbour and express a JMJD6 shRNA (Venus+dsRED+). The vast majority of tumour cells at endpoint had silenced the shRNA (Venus+dsRED−). Scale bar, 200 μm. e, CRISPR-mediated knockout of JMJD6 in a bulk population of GBMcw1919 cells in vitro. f, g, Parallel in vitro proliferation assay (f) and in vivo survival assay (g) of cells from e. h–l, Constitutive shRNA knockdown of DOT1L and DPY30 in vitro. m–v, Parallel in vitro proliferation assays (m–q) and in vivo survival assays (r–v) of cells from h–l, respectively. Data are mean ± s.d. of at least triplicates.
Extended Data Figure 10 Summary figure.
Overview summary of the results. The in vivo tumour microenvironment, both in primary glioblastoma tumours and intracranial xenograft tumours, is complex and stressful for cells. Tumour cells must appropriately interact with and respond to a large number of other cells, both cancerous and non-cancerous, to survive. They also must activate response pathways to survive in the face of reduced nutrient availability, including hypoxic and low glucose conditions, and in the face of increased cell stress due to immune regulators, and debris and signalling from apoptotic cells. Thus, slower growth is seen as the cells expend energy on responding to these microenvironmental stimuli to survive. Owing to the large number of pause-controlled genes needed to respond to the cell stresses in vivo appropriately, cells are dependent on transcriptional pause–release and elongation. By contrast, cell culture conditions are optimized to reduce cell stress and drive growth by providing a surplus of all required nutrients for cell growth. Cells are largely homogenous and cancerous. Together, these in vitro conditions lead to rapid cell growth and little need for pause-controlled pathways that respond to environmental stimuli and stress. Therefore, in vitro cells are not as dependent on transcriptional pause–release and elongation for growth and survival. We discovered this by conducting parallel screens in the in vivo and in vitro environments, and found that there were many genes necessary for survival in vivo that were not necessary, or less important, in cell culture. We focused on the transcriptional pause–release and elongation factors as those fell out as enriched, but there are many other targets to explore.
Supplementary information
Supplementary Table 1
PDX Model Characteristics. For each of the three main PDX models used in the manuscript, we provide the molecular subtype based on Veerhaak et. al. 2010 classification, IDH1/2 mutation status, Glioma-CpG island methylation phenotype (G-CIMP) status, mutation status for commonly mutated genes and copy number variations for commonly amplified or deleted genes in glioblastoma. Specific mutations are characterized below. (PDF 95 kb)
Supplementary Table 2
Primary Screen Results. Worksheet 1 contains the normalized RPM (reads per million) values for each hairpin for each replicate from the in vivo screen (13-14 individual animals per replicate). Median RPM value for each replicate was used for analysis. Signal to Noise of replicates was used to calculate individual shRNA score based on their ability to deplete cells in the induced arm compared to the control arm. Worksheet 2 contains the ranked list of genes from in vivo screen. 2nd-Best shRNA score from each expressed gene was used to rank genes for hits. 2nd-Best shRNA score of 2 or greater was considered a hit. Worksheet 3 and 4 contain the same information for the in vitro screen. (XLSX 245 kb)
Supplementary Table 3
RNAseq Results. RNA sequencing results for GBM528 and GBM3565 from in vivo and in vitro conditions. All expressed genes listed. FPKM (Fragments Per Kilobase of transcript per Million mapped reads) values for known genes were calculated using Cufflinks. FPKM values were quantile normalized. To generate an expressed genes list, an average of replicates for each condition was calculated and genes with FPKM value >0.25 in either intracranial sample or culture sample were considered expressed. Genes that did not meet this expression cut-off (Replicate average FPKM <0.25 in Intracranial and in Culture conditions) were removed as not expressed. Expressed genes were tabled by converting FPKMs <0.25 to 0.25. (XLSX 1924 kb)
Supplementary Table 4
Gene Ontology Enrichment Values. Raw outputs for gene ontology enrichments. Each worksheet contains program, settings, and files used to generate output. Last worksheet contains the GSEA genesets analyzed when using GSEA. (XLSX 4712 kb)
Supplementary Table 5
Super-Enhancer List for GBM528. Lists of all Super-Enhancers detected in in vivo intracranial condition (IC) (worksheet 1) and in the in vitro culture condition (worksheet 2). In vivo and in vitro SEs were identified using the dynamic Enhancer software (retrieved from https://github.com/BradnerLab/pipeline). H3K27Ac peaks identified as enhancers and separated by less than 12.5 kb were stitched together. All stitched peaks were then ranked by the density of H3K27Ac minus input. Peaks higher than the inflection point on the density curve were designated SEs. Worksheet 3 contains called condition-specific enhancers. To call condition-specific SEs, SEs from in vivo and in vitro conditions were merged and H3K27Ac signals for all merged SEs in each cell line were calculated. To be considered specific to in vivo or in vitro, SEs had to be only called a SE in a single condition and had to have at least 1.5-fold change in H3K27Ac signal between conditions. (XLSX 84 kb)
Supplementary Table 6
JMJD6 correlation with TCGA genes. List of all gene expression correlations with JMJD6 in TCGA primary tumours. To test correlation of all genes with JMJD6 across glioblastoma tumours, TCGA RNAseqV2 data were downloaded and analysed. All glioblastoma tumours in the TCGA with RNA-seq data available were used. (XLSX 403 kb)
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Miller, T., Liau, B., Wallace, L. et al. Transcription elongation factors represent in vivo cancer dependencies in glioblastoma. Nature 547, 355–359 (2017). https://doi.org/10.1038/nature23000
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