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The molecular landscape of ETMR at diagnosis and relapse


Embryonal tumours with multilayered rosettes (ETMRs) are aggressive paediatric embryonal brain tumours with a universally poor prognosis1. Here we collected 193 primary ETMRs and 23 matched relapse samples to investigate the genomic landscape of this distinct tumour type. We found that patients with tumours in which the proposed driver C19MC2,3,4 was not amplified frequently had germline mutations in DICER1 or other microRNA-related aberrations such as somatic amplification of miR-17-92 (also known as MIR17HG). Whole-genome sequencing revealed that tumours had an overall low recurrence of single-nucleotide variants (SNVs), but showed prevalent genomic instability caused by widespread occurrence of R-loop structures. We show that R-loop-associated chromosomal instability can be induced by the loss of DICER1 function. Comparison of primary tumours and matched relapse samples showed a strong conservation of structural variants, but low conservation of SNVs. Moreover, many newly acquired SNVs are associated with a mutational signature related to cisplatin treatment. Finally, we show that targeting R-loops with topoisomerase and PARP inhibitors might be an effective treatment strategy for this deadly disease.

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Fig. 1: ETMRs regardless of C19MC amplification show high molecular similarity.
Fig. 2: ETMRs without C19MC amplification recurrently have miRNA related aberrations.
Fig. 3: Comparison between primary and relapse tumours reveals poor conservation of SNVs but high conservation of SVs.
Fig. 4: Breakpoint context reveals a possible role for R-loops in initiating ETMRs.
Fig. 5: ETMR cells are sensitive to combination therapy with PARP and TOP1 inhibitors.
Fig. 6: Hallmarks of ETMR.

Data availability

Raw and processed 450K/EPIC methylation values, and raw and processed expression data for all included ETMRs are deposited at the Gene Expression Omnibus (GEO) under accession number GSE122038. All NGS data are deposited at the European Genome-phenome Archive (EGA) under accession number EGAS00001003256. Source Data for Figs. 1a–c, 2c, 3b, c, 4d, g, 5a, b, d and Extended Data Figs. 1a, 2a–g, 4b, c, h, 5c, 6b, c, 8a–d, 9b, c, e–g, 10g are provided with the paper.

Code availability

All custom code used to generate the data in this study is available upon reasonable request.


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Download references


We thank the DKFZ sequencing core facility for technical support, assistance with data generation and data management, the DKFZ light microscopy facility for their assistance in generating microscopy images and BeiGene for providing pamiparib. This work was supported by the ICGC PedBrain Tumor Project, funded by the German Cancer Aid (109252) and by the German Federal Ministry of Education and Research: BMBF grants 01KU1201A (PedBrain Tumor) and 01KU1505A (ICGC-DE-MINING). Additional funding was awarded by the NIH (K22ES012264, 1R15ES019128 and 1R01CA152063), Voelcker Fund Young Investigator Award and CPRIT (RP150445) to A.J.R.B.; CPRIT (RP101491), NCI T32 postdoctoral training grant (T32CA148724), NCATS TL1 (TL1TR002647) and the AACR-AstraZeneca Stimulating Therapeutic Advances through Research Training grant to A.G.; CPRIT (RP140105) to J.C.R.; and NCI (P30CA054174) to the sequencing core facility. S.L. and M.K. are supported by the Solving Kid’s Cancer foundation and the Bibi Fund for Childhood Cancer Research. A.K. is supported by the Helmholtz Association Research Grant (Germany). M. Ryzhova is supported by an RSF Research Grant (18-45-06012). J.O.K. was funded by an ERC starting grant.

Author information

Authors and Affiliations



S.L. performed data analysis and interpretation. S.L., S.M.P. and M.K. wrote the manuscript with input from all co-authors. S.L., A.G., P. Landgraf, B.H., D.T.W.J., J.O.K., P. Lichter, A.H., A.J.R.B., S.M.P., A.K., S.W. and M.K. generated or contributed to the generation of sequencing data. S.L., S.N.G., T.R., S.M.W., A.G., I.B., J.K., M. Sill, V.H., D.A.Z. and S.P.-C. performed bioinformatic analyses. S.L., C.S., M.M., S.B., S.K., J.-M.H., N.M., B.S. and J.A.C. contributed to the design of experiments and conducted experiments. J.C.R., M. Ryzhova, A.J.R.B. and A.K. performed histopathological analysis on the samples. M. Ryzhova, J.A.C., T.M., B.H., O.W., J.E., F.S., D.S., D.W.E., B.A.O., A.D., C.H., D.F.-B., P.W., J.S., M. Remke, M.D.T., M.J.G.-d.-C., M.Ł., W.G., M.H., P.H., T.P., E.U.-C., F.B., V.R., S.A., J.M.-P., X.-N.L., U.S., M. Snuderl, M.A.K., F.G., N.J., A.v.D., K.v.H., A.H. and A.K. provided tumour samples and metadata. S.M.P., A.K. and M.K. managed the project and provided leadership.

Corresponding author

Correspondence to Marcel Kool.

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The authors declare no competing interests.

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Peer review information Nature thanks Jeffrey Chuang, Richard J. Gilbertson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Clinicopathological differences are not associated with molecular subgrouping.

a, t-SNE clustering analysis of DNA methylation profiles of 193 ETMRs. Samples were coloured according to their clinical, histological or molecular annotation. b, Schematic representation of location (position of the circle), histological diagnosis (outer ring) and C19MC status (inner ring) of ETMRs. Circle size denotes the relative number of primary tumours that have been diagnosed in each part of the brain; each wedge represents one tumour. Tumours could be assigned to multiple locations depending on the diagnosis. Tumours were excluded for which no information on the site of occurrence was available.

Source Data

Extended Data Fig. 2 miRNA expression correlates strongly between ETMRs with or without C19MC amplification.

a, Supervised clustering of the 416 differentially expressed mature miRNAs (two-sided negative binomial, Benjamini–Hochberg-adjusted P < 0.05) between ETMRs (n = 7) (excluding ETMRs without amplification) and other tissues (n = 38). b, Unsupervised clustering of mature miRNAs with a minimum expression of 32 in at least one sample and a variance higher than 10 between all samples (n = 294). Hierarchical clustering using average as distance measure was used to cluster the samples after values were z-score normalized. cg, Regression of the median expression of mature miRNAs derived from ETMRs (n = 7) against normal brain (n = 8), other entities (n = 10 for all entities) or ETMRs without C19MC amplification (n = 3). miRNAs that had a median expression below 32 RPM in either of the compared entities were excluded. miRNAs that were differentially expressed between ETMRs (with and without C19MC amplification) against other entities (two-sided negative binomial, Benjamini–Hochberg-adjusted P < 0.05) are highlighted. For each comparison, the Pearson correlation was calculated (P < 0.0005 for all comparisons).

Source Data

Extended Data Fig. 3 KEGG pathway enrichment in ETMRs.

a, b, Summary of KEGG pathway enrichment of ETMRs (n = 28) against healthy brain tissues (n = 38) (a) or 580 different brain tumours (b). Pathways are coloured by similarity based on NaviGO co-occurrence scores55 and manual assessment. Significantly upregulated genes were calculated using ANOVA (FDR-adjusted P < 0.01).

Extended Data Fig. 4 ETMRs consist of at least two distinct subtypes of cells.

a, Heat map showing z-score-normalized expression of 450 DNA repair genes and the corresponding pathways8 for 190 tumours of different entities including 28 ETMRs. Supervised clustering was used and samples were sorted by entity or C19MC amplification status. Entities include three ATRT subgroups, four MB subgroups, central nervous system ewing sarcoma family tumour with CIC alteration (CNS EFT-CIC), central nervous system neuroblastoma, with FOXR2 activation (CNS-NB FOXR2), central nervous system high-grade neuroepithelial tumour with MN1 alteration (HGNET-MN1), central nervous system high-grade neuroepithelial tumour with BCOR alteration (HGNET-BCOR), ETMRs with amplification of C19MC (red) and ETMRs without amplification of C19MC (blue). ETMR subsets were manually assessed based on DNA repair pathway expression. b, Debulking of mRNA expression using CIBERsort by using the median expression of single-cell RNA-sequencing data of the forebrain as gene signature10. The cumulative fraction of each cell type was calculated and samples were sorted according to the percentage of modelled neural stem cells. Samples were annotated based on the subsets derived from a. c, Box plots showing expression of stem cell markers (HMGA2, LIN28A), astrocyte markers (AQP4, GFAP) and genes involved in the DNA damage response (WEE1, CHEK2) in ETMRs with high DNA repair expression (n = 18) and low DNA repair expression (n = 10). P values were calculated using a two-sided Mann–Whitney U-test; ***P < 0.0005, **P < 0.005, *P < 0.05; NS, not significant. Boxes show the median, first and third quartile, and whiskers extend to 1.5× the interquartile range. d, Distribution of histology annotation of 18 ETMRs for which these data were available divided into two subsets. The number of EBL phenotypes was significantly enriched in the high DNA repair expression group using a two-sided Fisher’s exact test (P = 3.7 × 10−2). e, t-SNE clustering based on methylation profiles of a microdissected ETMR (ET174) (split in bulk, rosettes and neuropil) and 192 other ETMRs. f, Expression of LIN28A and AQP4 in rosette tissue and neuropil tissue of the same tumour. g, Copy-number profiles of microdissected neuropil and rosettes from the same tumour. h, Fold change in expression of six markers in two matched recurrences normalized to the primary tumour.

Source Data

Extended Data Fig. 5 Recurrent events in ETMRs without C19MC amplification.

a, Schematic representation of the translocation and amplification of a region on chromosome 11 with the host gene of the miR-17-92 miRNA cluster (also known as MIR17HG) shown in red on chromosome 13. Regions were reconstructed using mate pair sequencing. The actual amplified region is circular denoted by arrows on each end. b, Copy-number profile of a tumour containing the miR-17-92 cluster translocation and amplification. Copy numbers were derived from methylation array data with each dot representing a probe. Inset shows validation of both the chromosome 11 (YAP1; green) and chromosome 13 (MIR17HG; red) amplifications using FISH. c, Quantification of mature miRNAs in the miR-17-92 miRNA cluster (n = 20) confirms that the ETMR (blue) with the chromosome 11 and chromosome 13 amplification and translocation has higher expression of miR-17-92 cluster miRNAs. Each bar represents one tumour corresponding to the given entity. P values were calculated using a one-sided Mann–Whitney U-test; *P < 0.05. d, Example of a copy-number profile of a case showing clustered rearrangements around C19MC. This tumour did not have a C19MC amplification or DICER1 mutations. e, Copy-number profile of an ETMR without C19MC amplification or DICER1 mutation showing an overall instable genome with many regions containing clustered breakpoints.

Source Data

Extended Data Fig. 6 ETMRs recurrently show genomic instability.

a, Oncoplot showing the co-occurrence of all CNAs separated by C19MC amplification status. b, Overview of copy-number profiles of all ETMRs (n = 193). Bars (gain, balanced and loss) add up to 100% for each chromosome arm. c, Overview of copy-number profiles of all ETMRs with (n = 170) or without (n = 23) C19MC amplification. P values were calculated using two-sided Fisher’s exact tests and adjusted for multiple testing (Benjamini–Hochberg correction); ***P < 0.0005, **P < 0.005, *P < 0.05. d, Overview of CNAs in matched primary tumour and recurrence pairs for the most variable CNAs. Events (copy-number changes, clustered breakpoints or increases in ploidy) that were gained upon recurrence have a thicker outline. Percentages denote the percentage of matched samples acquiring a CNA or genome duplication. e, Example of a tumour for which polyploidy was validated using FISH (n = 28 tested samples), the chromosome 9 and 11 centromeres were used as probes. f, Examples showing clustered breakpoints on chromosome 19. Chromosome 19 is shown as a circular representation, translocations to other chromosomes were annotated as single positions. All SVs were detected using mate pair sequencing.

Source Data

Extended Data Fig. 7 Conservation of events for individual patients.

Summary of events occurring in seven matched primary tumours compared to recurrences (first, second or third relapse) and two matched relapses. For every sample conservation of SNVs is given as a graph with the allele frequencies (AF) of the primary tumour on the x axis and the recurrence on the y axis. In the last panel, two matched recurrences are shown with a recurrence on each axis. Boxes show events that are lost, conserved or gained. Each comparison has a table showing the total number of events in each quadrant (lost, primary AF > 10% and recurrence AF < 2%; stable, primary AF > 20% and recurrence AF > 20%; and gained, primary AF < 2% and recurrence AF > 10%). Conservation of SVs is given as a circular representation of the genome with the CNAs from the primary tumour in the outer rim and the recurrence in the inner rim. SVs were coloured by detection in either only the primary tumour (red), only in the relapse (grey) or in both (blue). Each combination also has a Venn diagram showing the total number of SVs that were detected in the primary tumour, the recurrence or both.

Extended Data Fig. 8 Mutations in primary tumours and relapses.

a, Box plots showing the total number of SNVs or indels in primary tumours (n = 20) compared to relapses (n = 12). Boxes show the median, first and third quartile, and whiskers extend to 1.5× the interquartile range. We detected, on average, 1,180 SNVs (range, 339–2,544) and 468 indels (range, 299–1,026) in primary tumours and 5,162 SNVs (range, 2,992–7,773) and 847 indels (range, 554–1,187) in relapsed tumours throughout the genome. In coding regions, there were on average 14 non-synonymous SNVs (range, 3–45) and 2 indels (range, 0–7) in primary tumours and 59 non-synonymous SNVs (range, 37–92) and 6 indels (range, 2–11) in relapsed tumours. b, Percentage of substitutions of either the combined primary tumours (n = 20) or combined relapses (n = 12) divided by substitution type and affected strand for SNVs residing in transcribed regions. Transcriptional asymmetry is defined as the difference between the amount of SNVs on the transcribed strand versus the untranscribed strand for each substitution type. Data are mean ± s.e.m., P values were calculated using two-sided Poisson tests; ***P < 0.0005, **P < 0.005, *P < 0.05. c, Substitution-type probability based on the 96 different trinucleotide contexts for a matched primary relapsed pair shown in d compared to a cisplatin signature16 and new paediatric cancer signature (P1)13. d, Cosine similarity between the cisplatin signature and other signatures (n = 36). P values were calculated using pairwise pearson correlation applied to the similarity matrix; ***P < 0.0005, **P < 0.005, *P < 0.05.

Source Data

Extended Data Fig. 9 ETMRs have dense and strongly conserved C > T and C > G mutations around breakpoints.

a, Rainfall plot showing an example of kataegis around C19MC. Every point represents a somatic SNV coloured by substitution type, the x axis represents the position in the genome and the position on the y axis represents the density of SNVs. b, Lollipop plot showing SNVs per 1 kb in a region of 10,000 bp surrounding breakpoints for all ETMRs. Pins represent the percentage of substitution types of all SNVs within 1 kb, while the height of the lollipops represents the substitutions per kb. c, Percentages of substitution types in regions 10 kb around breakpoints (left, n = 543 SNVs) and the rest of the genome (right, n = 84991 SNVs). P values were calculated using a one-sided Fisher’s exact test; ***P < 0.0005, **P < 0.005, *P < 0.05. d, Combined mutation density of four primary tumours coloured by conservation in the matched recurrence (blue is conserved, grey is not conserved) as shown by a rainfall plot (top), a density distribution (middle) and the breakpoint density (bottom). e, Allele frequencies of all primary (x axis) versus relapse (y axis) tumours. Boxes show conservation (lost, primary AF > 10% and recurrence AF < 2%; conserved, primary AF > 20% and recurrence AF > 20%; and gained, primary AF < 2% and recurrence AF > 10%) (n = 2,100 SNVs with allele frequency over 20% in the primary tumour). P value was calculated using a two-sided χ2 test. f, Percentage of substitution types for SNVs in each quadrant (lost, primary AF > 10% and recurrence AF < 2%; conserved, primary AF > 20% and recurrence AF > 20%; and gained primary AF < 2% and recurrence AF > 10%). g, Ratio of conserved SNVs compared with not conserved SNVs in regions around breakpoints with increasing sizes. Conservation is defined as SNVs with an allele frequency over 20% in the primary tumour and an allele frequency over 20% in the recurrence, SNVs with an allele frequency lower than 20% in the recurrence but higher than 20% in the primary tumour were defined as not conserved. P value between 10 kb around breakpoints and the rest of the genome using a two-sided χ2 test (n = 2,100, P = 5.4 ×10−11).

Source Data

Extended Data Fig. 10 Context of R-loops and DNA damage in ETMRs and after Dicer1 knockout.

a, Genome-wide density of R-loops in ETMRs, R-loops in Ewing sarcoma (EWS), RLFS and gene density. b, Representation of SVs genome-wide and their breakpoint context. Outer layers show the density of DRIP peaks (blue) or RLFS (red). The inner part shows all SVs from ETMRs sequenced using WGS, depicting SVs that fall in DRIP-seq peaks (blue) or RLFS (red). c, R-loop signal detected in genomic regions sorted by R-loop signal (including elements from non-B-DB62 and repeatmasker). R-loop signal was determined for 10,000 randomly selected elements for every type of genomic feature (n = 21). Violin plots depict kernel density estimates and represent the density distribution. d, Genome-wide association of breakpoints with genomic regions sorted by R-loop signal shown in c. Genome-wide associations were calculated as distance to nearest element compared to a set of 10,000 randomly generated breakpoints. Enrichments were calculated for Ewing sarcoma breakpoints66 and breakpoints from other entities22 (reference set). P values were calculated using a two-sided Mann–Whitney U-test and adjusted for multiple testing (Benjamini–Hochberg correction). e, Density of distances between genomic regions and breakpoints detected in ETMR, Ewing sarcoma, random breakpoints and reference breakpoints. f, Total percentage of breakpoints within 1 kb of genomic regions. g, Enrichment of SNVs (n = 85,534) in ETMR R-loops (n = 16,002 regions) and RLFS (n = 85,534 regions) compared to random regions of the same size. P values were calculated using a two-sided χ2 test; ***P < 0.0005, **P < 0.005, *P < 0.05. h, Genome-wide distribution of mouse RLFS and breakpoints occurring in Dicer1 knockout cells compared to wild-type. The outer rim shows the genome wide density of mouse RLFS, the inner rim the CNAs that were found between wild-type and knockout cells and the inner part shows the SVs that were detected between wild-type and knockout cells. Breakpoints falling within RLFS are highlighted in red. i, Copy-number profiles of an example of a translocation coupled to duplication in RLFS that were found in Dicer1 knockout compared to Dicer1 wild-type cells. Red arrows depict the location of the translocation and duplication.

Source Data

Supplementary information

Supplementary Information

Source data pertaining to Figure 4h. Uncropped dot blot of DNA-RNA hybrids extracted from WT and DICER1-KO mouse cells, ssDNA was used as loading control. Cropping applied in Figure 4h. is shown as intermittent boxes.

Reporting Summary

Supplementary Table 1

Information about ETMR samples included in the cohort.

Supplementary Table 2

Expression of mature miRNAs in ETMRs (n=10) and differential expression analysis of ETMRs (n=7) compared to other tissues (n=38). Pvalues were calculated using negative binomial testing and were Benjamini–Hochberg adjusted.

Supplementary Table 3

Normalized expression values of ETMRs included in the paper (n=28), expression values of different regions that were micro-dissected and the KEGG and GO-term enrichments of ETMRs (n=28) compared to normal brain (n=38) or other brain tumours (n=580).

Supplementary Table 4

Lists of genes used for analysis that used DNA repair genes and genes that were included for sequencing using targeted sequencing.

Supplementary Table 5

Identified exonic somatic non-synonymous SNVs in primary tumours and relapsed tumours using WGS.

Supplementary Table 6

Identified exonic non-synonymous SNVs using WES and targeted sequencing.

Supplementary Table 7

Copy number aberrations of the entire ETMR cohort and copy number changes between primary tumours and matched relapses.

Supplementary Table 8

Full list of identified somatic SVs in ETMRs.

Supplementary Table 9

Full list of somatic SNVs identified in primary tumours using WGS including non-coding regions, regions overlapping promotor regions and regions overlapping putative enhancers.

Supplementary Table 10

Abbreviations of tumour entities used in Figure 1.

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Lambo, S., Gröbner, S.N., Rausch, T. et al. The molecular landscape of ETMR at diagnosis and relapse. Nature 576, 274–280 (2019).

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