Genomic sequencing has driven precision-based oncology therapy; however, the genetic drivers of many malignancies remain unknown or non-targetable, so alternative approaches to the identification of therapeutic leads are necessary. Ependymomas are chemotherapy-resistant brain tumours, which, despite genomic sequencing, lack effective molecular targets. Intracranial ependymomas are segregated on the basis of anatomical location (supratentorial region or posterior fossa) and further divided into distinct molecular subgroups that reflect differences in the age of onset, gender predominance and response to therapy1,2,3. The most common and aggressive subgroup, posterior fossa ependymoma group A (PF-EPN-A), occurs in young children and appears to lack recurrent somatic mutations2. Conversely, posterior fossa ependymoma group B (PF-EPN-B) tumours display frequent large-scale copy number gains and losses but have favourable clinical outcomes1,3. More than 70% of supratentorial ependymomas are defined by highly recurrent gene fusions in the NF-κB subunit gene RELA (ST-EPN-RELA), and a smaller number involve fusion of the gene encoding the transcriptional activator YAP1 (ST-EPN-YAP1)1,3,4. Subependymomas, a distinct histologic variant, can also be found within the supratetorial and posterior fossa compartments, and account for the majority of tumours in the molecular subgroups ST-EPN-SE and PF-EPN-SE. Here we describe mapping of active chromatin landscapes in 42 primary ependymomas in two non-overlapping primary ependymoma cohorts, with the goal of identifying essential super-enhancer-associated genes on which tumour cells depend. Enhancer regions revealed putative oncogenes, molecular targets and pathways; inhibition of these targets with small molecule inhibitors or short hairpin RNA diminished the proliferation of patient-derived neurospheres and increased survival in mouse models of ependymomas. Through profiling of transcriptional enhancers, our study provides a framework for target and drug discovery in other cancers that lack known genetic drivers and are therefore difficult to treat.
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This work was supported by an Alex's Lemonade Stand Young Investigator Award (S.C.M.), The CIHR Banting Fellowship (S.C.M.), The Cancer Prevention Research Institute of Texas (S.C.M., RR170023), Sibylle Assmus Award for Neurooncology (K.W.P.), the DKFZ-MOST (Ministry of Science, Technology & Space, Israel) program in cancer research (H.W.), James S. McDonnell Foundation (J.N.R.) and NIH grants: CA154130 (J.N.R.), R01 CA169117 (J.N.R.), R01 CA171652 (J.N.R.), R01 NS087913 (J.N.R.) and R01 NS089272 (J.N.R.). R.C.G. is supported by NIH grants T32GM00725 and F30CA217065. M.D.T. is supported by The Garron Family Chair in Childhood Cancer Research, and grants from the Pediatric Brain Tumour Foundation, Grand Challenge Award from CureSearch for Children’s Cancer, the National Institutes of Health (R01CA148699, R01CA159859), The Terry Fox Research Institute and Brainchild. M.D.T. is also supported by a Stand Up To Cancer St. Baldrick’s Pediatric Dream Team Translational Research Grant (SU2C-AACR-DT1113). Stand Up To Cancer is a program of the Entertainment Industry Foundation administered by the American Association for Cancer Research. We thank S. Archer for technical writing and editing expertise. In addition, we thank the High-Throughput Sequencing Unit of the DKFZ Genomics and Proteomics Core Facility for technical support and acknowledge technical assistance by M. Mauermann, T. Wedig, A. Wittmann and L. Siebert. Additional support came from the ICGC DE-Mining grant (#01KU1505). We thank The Children’s Hospital at Westmead (CHW) Tumour Bank for support of tumour samples (H.W.). We thank D. Schumick (Cleveland Clinic Art Department) and G. Hsu (https://www.hsubiomedicalvisual.com) for their assistance with creative artwork.
The authors declare no competing financial interests.
Reviewer Information Nature thanks S. Pomeroy, W. Weiss and the other anonymous reviewer(s) for their contribution to the peer review of this work.
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, b, Unsupervised clustering of ChIP–seq, RNA-seq, WES, WGS, and Illumina DNA methylation profiles with genotypes that have an average heterozygosity score greater than 0.25 in the Heidelberg (n = 25 independent samples) (a) and Toronto cohorts (n = 18 independent samples) (b).
a, Number of somatic single nucleotide variants (SNVs) detected per ependymoma sample. b, Frequency of somatic mutations detected across the Heidelberg ependymoma cohort (n = 24 independent samples). c, Unsupervised hierarchical clustering of copy number alterations detected by WGS in primary ependymoma samples (n = 24 independent samples).
a, b, Box plots of H3K27ac enhancer profiles (n = 556,676 enhancer loci evaluated per sample) before quantile normalization for both Heidelberg (n = 24 independent samples) (a) and Toronto (n = 18 independent samples) (b) cohorts compared to Roadmap Epigenomics and ENCODE cohorts (n = 98 independent samples). Box plots are shown with the centre (median), upper and lower quartile range, and dotted line indicating minima and maxima per sample. c, d, Box plots of H3K27ac enhancers after quantile normalization for both Heidelberg (n = 24 independent samples) (c) and Toronto (n = 18 independent samples) (d) cohorts compared to the Roadmap Epigenomics cohort (n = 98 independent samples). e, f, Unsupervised hierarchical clustering of enhancer profiles as measured using the top 10,000 variant enhancer loci identified in the Roadmap Epigenomics cohort with the Heidelberg (n = 122 independent samples) (e) and Toronto cohorts (n = 116 samples) (f) and compared in a pair-wise fashion using a Spearman correlation.
a, Number of unique H3K27ac peaks detected by MACS1.4 (P < 1 × 10−9 cut-off) with increasing sample number in the Heidelberg cohort (n = 24 independent samples). b, Box plot of gene expression values comparing typical enhancer (n = 9,826 genes) versus super enhancer (n = 1,682 genes) associated genes. Statistical analysis was assessed using a two-sided Wilcoxon rank-sum test. Box plots show the centre (median), upper and lower quartile range, and dotted line indicating minima and maxima. c, Frequency of enhancer and super enhancer regions as a function of size in base pairs. d, Dot plots illustrating the numbers of super enhancers detected in the Heidelberg (n = 24 independent samples), Toronto (n = 18 independent samples) and normal brain (n = 7 independent samples) cohorts. The horizontal bar indicates the mean. e, Heatmap illustrating significant gained and lost enhancer loci in both ependymoma cohorts compared to normal brain samples. Comparisons were evaluated using a two-sided Wilcoxon rank-sum test with FDR correction and a cut-off of FDR <0.05. f, Example plots of normalized and scaled H3K27ac RPKM profiles at example ependymoma candidate genes in Heidelberg ependymomas and normal brain (NB) (n = 32 independent samples). g, Comparison of gene expression of ependymoma super-enhancer-associated genes derived from ref. 11 (n = 83 independent samples) with normal brain (n = 172 independent samples). Statistical analysis was assessed using a two-sided Wilcoxon rank-sum test. h, Table comparing the number and per cent confirmation between the Heidelberg (n = 24 independent samples) and Toronto ependymoma cohorts (n = 18 independent samples). i, G-Profiler pathway-enrichment analysis of ependymoma-specific super-enhancer-associated genes in the Toronto cohort (n = 18 independent samples), with statistical significance determined using a hypergeometric test. j, Overlap analysis measured by a two-sided binomial test between tumour-specific ependymoma super enhancers and cancer census genes from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. k, Classification of tumour-specific ependymoma super enhancer genes also found in the COSMIC database29 as tumour suppressor genes (n = 12), oncogenes (n = 26), or unknown (n = 21).
a, b, Heatmap of all subgroup-specific active enhancers detected in ependymomas in independent samples in the Heidelberg (n = 24 independent samples) (a) and Toronto (n = 18 independent samples) (b) cohorts. c, Box plot of gene expression for ependymoma SE-SSEA-associated genes in the Heidelberg cohort (n = 24 independent samples). Comparisons were made using a two-sided Wilcoxon rank-sum test. Box plots show the centre (median), upper and lower quartile range, and dotted lines indicate minima and maxima. d–f, Venn diagrams of the number and percentage of subgroup-specific super-enhancer-associated loci validated between the Heidelberg and Toronto cohorts. g, h, Non-negative factorization of ependymoma super enhancer profiles in the Heidelberg (n = 24 independent samples) and Toronto (n = 18 independent samples) cohorts. i, Normalized H3K27ac profiles for subgroup-specific genomic example loci in the Heidelberg cohort with at least three biological replicates per subgroup, with the exception of ST-EPN-SE, shown as a biological duplicate. j, G-Profiler pathway-enrichment analysis of ependymoma subgroup-specific super-enhancer-associated genes in the Heidelberg cohort (n = 24 independent samples) with statistical significance determined using a hypergeometric test. k–n, H3K27ac profiles surrounding the EPHB2 (k) and CCND1 (m) loci in the Heidelberg cohort with at least three biological replicates per subgroup, with the exception of ST-EPN-SE, shown as a biological duplicate. EPHB2 (l) and CCND1 (n) expression by RNA-seq across ependymoma subgroups in the Heidelberg cohort with horizontal bars indicating the median value and each dot representing an independent ependymoma sample (n = 24 independent samples).
Extended Data Figure 6 Workflow describing the functional validation of ependymoma super enhancer genes.
a, Workflow of super-enhancer target-gene prioritization for functional evaluation. b, Bar chart comparing the top-ranked super-enhancer-associated genes against top-ranked genes detected by RNA-seq defined as significantly increased or overexpressed compared to normal brain controls across all ependymoma samples (n = 42 independent samples). Significant genes were identified by a two-sided Wilcoxon rank-sum test with FDR correction and ranked by FDR corrected P value with a cut-off of less than 0.05.
a, Individual shRNA time-course knockdown experiments in EP1-NS (ST-EPN-RELA) cells, using two shRNA constructs (shRNA.1 and shRNA.2) compared to two controls (shCONTROL.1 and shCONTROL.2). Shown are time-course experiments for 19 genes performed in six technical replicates. b, Ependymoma cell viability (EP1-NS) following treatment with shRNAs targeting super-enhancer-associated genes over a seven-day time course (in alphabetical order). Cell viability data for treatment with non-targeting controls: shCONTROL.1 (black), shCONTROL.2 (grey), and for two gene-specific shRNA constructs: shRNA.1 (red) and shRNA.2 (pink).
a, H3K27ac profiles at the ependymoma-specific super enhancer locus IGF2BP1 in the Heidelberg cohort (n = 24 independent samples) with at least three biological replicates per subgroup, with the exception of ST-EPN-SE, which is shown as a biological duplicate. b, IGF2BP1 gene expression derived from RNA-seq data for the Heidelberg cohort (n = 24 independent samples) with a horizontal bar for each subgroup indicating the mean. c, d, Normalized survival of PF-EPN-A (S15) primary cultures (c) and EP1-NS cell cultures (d) following shRNA knockdown of IGF2BP1 with two independent non-overlapping shRNA constructs compared to shCONTROL.1. Experiments performed as six technical replicates and independently validated in three biological replicates. Horizontal bars indicates mean values. e, H3K27ac profiles at the ependymoma-specific super enhancer locus CACNA1H in the Heidelberg cohort with at least three biological replicates per subgroup, with the exception of ST-EPN-SE, which is shown as a biological duplicate. f, H3K27ac profiles surrounding the CACNA1H locus in a ST-EPN-RELA model (EP1-NS), a PF-EPN-A model (S15) and a normal neural stem cell control performed in biological duplicates. g, CACNA1H gene expression derived from RNA-seq data for the Heidelberg cohort (n = 24 independent samples) with a horizontal bar for each subgroup indicating indicating the mean. h, i, Normalized survival of PF-EPN-A (S15) primary cultures (h) and EP1-NS (i) cell cultures following shRNA knockdown of CACNA1H with two shRNA constructs compared to shCONTROL.1. Experiments performed as four technical replicates and independently validated in three biological replicates. Horizontal bars indicate mean values. j, Normalized cell survival of EP1-NS, S15, and NSC194 cells treated with increasing concentrations of mibefradil. Shown are technical triplicates, results replicated in biological triplicates. k, Overlay of ATAC-seq and H3K27ac-seq data centred upon ATAC-seq peak regions identified in the ST-EPN-RELA cell culture EP1-NS. l, CRISPR–dCAS9 targeting of CACNA1H active enhancers impairs CACNA1H expression. H3K27ac-seq (top) and ATAC-seq (bottom) surrounding the CACNA1H locus, indicating regions targeted by CRISPR–dCAS9 sgRNA complexes. Region 1 (R1) indicates a negative control region devoid of H3K27ac (green), while regions 2–4 (R2–R4) indicate experimental regions under evaluation. Experiments replicated in biological duplicates. m, Gene expression for various sgRNA constructs relative to a ‘dummy’ targeting control (D103), negative control (green), and uninfected control. All group comparisons were made using a two-sided Wilcoxon rank-sum test; error bars show s.d. and horizontal bars indicate mean value. Experiments were replicated in biological triplicates.
a, b, Gene expression of ‘high activity’ transcription factors (ranked <50) (a) and ‘low activity’ transcription factors (ranked >50) (b) in ependymoma (n = 83 independent samples) versus normal brain tissue (n = 172 independent samples). Box plots showing median value (horizontal bar), interquartile range and dotted line representing the data range. Comparison between groups was assessed using a two-sided Wilcoxon rank-sum test. c, Constituent enhancer activity in the central nervous system (CNS) of developing zebrafish embryos derived from subgroup-specific super enhancers identified in ependymomas.
Extended Data Figure 10 Putative cell lineage programs of origin uncovered by transcription factor mapping.
a–c, Immunohistochemical staining of Foxj1 at day 13.5 of mouse embryonic development (E13.5). Staining in discrete regions encompassing the choroid plexus and ependymal layer are shown in the forebrain (b) and hindbrain (c). d, log2 normalized gene expression of FOXJ1 in ependymoma (n = 83 independent samples) compared to independent sample cohorts of the following tissue types: normal brain (n = 172), paediatric glioma (n = 53), glioblastoma (n = 84), atypical rhabdoid teratoid tumours (n = 18), medulloblastoma (n = 62) and pilocytic astrocytoma (n = 41). Horizontal bar indicates the mean value. e, Subgroup-specific gene expression of FOXJ1 derived from ref. 1 (n = 209 independent samples). Error bars indicate s.d. and interquartile range; horizontal bar indicates median. f, Gene set enrichment analysis30 demonstrating significant enrichment of the FOXJ1 transcriptional program derived from E14.5 mouse embryos specifically in PF-EPN-B tumours (n = 209 independent samples). FDR corrected significance evaluated by gene set enrichment analysis. g, Significant FOXJ1 gene-expression correlations with proteins known to regulate cilia assembly and function. P values for significant positive or negative correlations have been corrected for multiple testing using the Bonferroni method. h–m, FOXJ1 gene set enrichment plots of PF-EPN-A (h), PF-EPN-B (i), PF-EPN-SE (j), ST-EPN-RELA (k), ST-EPN-YAP1 (l) and ST-EPN-SE (m) ependymomas. FDR-corrected significance evaluated by gene set enrichment analysis, n = 209 independent samples.
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Mack, S., Pajtler, K., Chavez, L. et al. Therapeutic targeting of ependymoma as informed by oncogenic enhancer profiling. Nature 553, 101–105 (2018). https://doi.org/10.1038/nature25169
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