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Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma

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Abstract

Medulloblastoma is a highly malignant paediatric brain tumour currently treated with a combination of surgery, radiation and chemotherapy, posing a considerable burden of toxicity to the developing child. Genomics has illuminated the extensive intertumoral heterogeneity of medulloblastoma, identifying four distinct molecular subgroups. Group 3 and group 4 subgroup medulloblastomas account for most paediatric cases; yet, oncogenic drivers for these subtypes remain largely unidentified. Here we describe a series of prevalent, highly disparate genomic structural variants, restricted to groups 3 and 4, resulting in specific and mutually exclusive activation of the growth factor independent 1 family proto-oncogenes, GFI1 and GFI1B. Somatic structural variants juxtapose GFI1 or GFI1B coding sequences proximal to active enhancer elements, including super-enhancers, instigating oncogenic activity. Our results, supported by evidence from mouse models, identify GFI1 and GFI1B as prominent medulloblastoma oncogenes and implicate ‘enhancer hijacking’ as an efficient mechanism driving oncogene activation in a childhood cancer.

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Figure 1: Recurrent SVs activate the GFI1B proto-oncogene in medulloblastoma.
Figure 2: Summary of recurrent SVs identified in GFI1B-activated medulloblastomas.
Figure 3: Recurrent SVs juxtapose GFI1B proximal to active enhancers on 9q34.
Figure 4: Mutually exclusive activation of GFI1 and GFI1B in medulloblastoma.
Figure 5: GFI1 and GFI1B cooperate with MYC to promote medulloblastoma formation in mice.
Figure 6: Summary of inferred mechanisms underlying GFI1 and GFI1B activation in medulloblastoma.

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Data deposits

Short-read sequencing data have been deposited at the European Genome-Phenome Archive (EGA, http://www.ebi.ac.uk/ega/) hosted by the EBI, under accession number EGAS00001000215.

References

  1. Ostrom, Q. T. et al. CBTRUS statistical report: Primary brain and central nervous system tumors diagnosed in the United States in 2006–2010. Neuro-oncol. 15 (Suppl 2). 1–56 (2013)

    Article  Google Scholar 

  2. Jones, D. T. et al. Dissecting the genomic complexity underlying medulloblastoma. Nature 488, 100–105 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  3. Rausch, T. et al. Genome sequencing of pediatric medulloblastoma links catastrophic DNA rearrangements with TP53 mutations. Cell 148, 59–71 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Robinson, G. et al. Novel mutations target distinct subgroups of medulloblastoma. Nature 488, 43–48 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. Northcott, P. A. et al. Medulloblastomics: the end of the beginning. Nature Rev. Cancer 12, 818–834 (2012)

    Article  CAS  Google Scholar 

  6. Cho, Y. J. et al. Integrative genomic analysis of medulloblastoma identifies a molecular subgroup that drives poor clinical outcome. J. Clin. Oncol. 29, 1424–1430 (2011)

    Article  PubMed  Google Scholar 

  7. Northcott, P. A. et al. Medulloblastoma comprises four distinct molecular variants. J. Clin. Oncol. 29, 1408–1414 (2011)

    Article  PubMed  Google Scholar 

  8. Northcott, P. A. et al. Subgroup-specific structural variation across 1,000 medulloblastoma genomes. Nature 488, 49–56 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Santarius, T., Shipley, J., Brewer, D., Stratton, M. R. & Cooper, C. S. A census of amplified and overexpressed human cancer genes. Nature Rev. Cancer 10, 59–64 (2010)

    Article  CAS  Google Scholar 

  10. Kim, T. M. et al. Functional genomic analysis of chromosomal aberrations in a compendium of 8000 cancer genomes. Genome Res. 23, 217–227 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Bhatia, B. et al. Tuberous sclerosis complex suppression in cerebellar development and medulloblastoma: separate regulation of mammalian target of rapamycin activity and p27 Kip1 localization. Cancer Res. 69, 7224–7234 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  13. Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hock, H. et al. Gfi-1 restricts proliferation and preserves functional integrity of haematopoietic stem cells. Nature 431, 1002–1007 (2004)

    Article  ADS  CAS  PubMed  Google Scholar 

  15. Person, R. E. et al. Mutations in proto-oncogene GFI1 cause human neutropenia and target ELA2. Nature Genet. 34, 308–312 (2003)

    Article  CAS  PubMed  Google Scholar 

  16. Saleque, S., Cameron, S. & Orkin, S. H. The zinc-finger proto-oncogene Gfi-1b is essential for development of the erythroid and megakaryocytic lineages. Genes Dev. 16, 301–306 (2002)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Gilks, C. B., Bear, S. E., Grimes, H. L. & Tsichlis, P. N. Progression of interleukin-2 (IL-2)-dependent rat T cell lymphoma lines to IL-2-independent growth following activation of a gene (Gfi-1) encoding a novel zinc finger protein. Mol. Cell. Biol. 13, 1759–1768 (1993)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Scheijen, B., Jonkers, J., Acton, D. & Berns, A. Characterization of pal-1, a common proviral insertion site in murine leukemia virus-induced lymphomas of c-myc and Pim-1 transgenic mice. J. Virol. 71, 9–16 (1997)

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Gibson, P. et al. Subtypes of medulloblastoma have distinct developmental origins. Nature 468, 1095–1099 (2010)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  20. Goodrich, L. V., Milenkovic, L., Higgins, K. M. & Scott, M. P. Altered neural cell fates and medulloblastoma in mouse patched mutants. Science 277, 1109–1113 (1997)

    Article  CAS  PubMed  Google Scholar 

  21. Pei, Y. et al. An animal model of MYC-driven medulloblastoma. Cancer Cell 21, 155–167 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kawauchi, D. et al. A mouse model of the most aggressive subgroup of human medulloblastoma. Cancer Cell 21, 168–180 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zhukova, N. et al. Subgroup-specific prognostic implications of TP53 mutation in medulloblastoma. J. Clin. Oncol. 31, 2927–2935 (2013)

    Article  PubMed  PubMed Central  Google Scholar 

  24. Zornig, M., Schmidt, T., Karsunky, H., Grzeschiczek, A. & Moroy, T. Zinc finger protein GFI-1 cooperates with myc and pim-1 in T-cell lymphomagenesis by reducing the requirements for IL-2. Oncogene 12, 1789–1801 (1996)

    CAS  PubMed  Google Scholar 

  25. Schmidt, T. et al. Zinc finger protein GFI-1 has low oncogenic potential but cooperates strongly with pim and myc genes in T-cell lymphomagenesis. Oncogene 17, 2661–2667 (1998)

    Article  CAS  PubMed  Google Scholar 

  26. Plass, C. et al. Mutations in regulators of the epigenome and their connections to global chromatin patterns in cancer. Nature Rev. Genet. 14, 765–780 (2013)

    Article  CAS  PubMed  Google Scholar 

  27. Shen, H. & Laird, P. W. Interplay between the cancer genome and epigenome. Cell 153, 38–55 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013)

    Article  CAS  PubMed  Google Scholar 

  29. Nambiar, M., Kari, V. & Raghavan, S. C. Chromosomal translocations in cancer. Biochim. Biophys. Acta 1786, 139–152 (2008)

    CAS  PubMed  Google Scholar 

  30. Hovestadt, V. et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archival tumour material using high-density DNA methylation arrays. Acta Neuropathol. 125, 913–916 (2013)

    Article  PubMed  PubMed Central  Google Scholar 

  31. Northcott, P. A. et al. Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathol. 123, 615–626 (2012)

    Article  CAS  PubMed  Google Scholar 

  32. Hovestadt, V. et al. Decoding the regulatory landscape of medulloblastoma using DNA methylation sequencing. Nature http://dx.doi.org/10.1038/nature13268 (18 May 2014)

  33. Cairns, J. et al. BayesPeak–an R package for analysing ChIP-seq data. Bioinformatics 27, 713–714 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Richter, J. et al. Recurrent mutation of the ID3 gene in Burkitt lymphoma identified by integrated genome, exome and transcriptome sequencing. Nature Genet. 44, 1316–1320 (2012)

    Article  CAS  PubMed  Google Scholar 

  35. Lister, R. et al. Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells. Nature 471, 68–73 (2011)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  36. Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, 333–339 (2012)

    Article  CAS  Google Scholar 

  37. Abecasis, G. R. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012)

    Article  ADS  CAS  PubMed  Google Scholar 

  38. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571–1572 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kim, D. & Salzberg, S. L. TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol. 12, R72 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Yeung, K. Y., Fraley, C., Murua, A., Raftery, A. E. & Ruzzo, W. L. Model-based clustering and data transformations for gene expression data. Bioinformatics 17, 977–987 (2001)

    Article  CAS  PubMed  Google Scholar 

  43. 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)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  44. Su, A. I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl Acad. Sci. USA 101, 606–6067 (2004)

    ADS  Google Scholar 

  45. Boutros, P. C. LTR: Linear cross-platform integration of microarray data. Cancer Inform. 9, 197–208 (2010)

    Article  PubMed  PubMed Central  Google Scholar 

  46. Lee, A. et al. Isolation of neural stem cells from the postnatal cerebellum. Nature Neurosci. 8, 723–729 (2005)

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

For technical support and expertise we thank: the DKFZ Genomics and Proteomics Core Facility; B. Haase, D. Pavlinic and B. Baying (EMBL Genomics Core Facility); M. Knopf (NCT Heidelberg); the Sanford-Burnham Animal Facility and Cell Imaging, Tissue & Histopathology Shared Resource; and the UCSD Flow Cytometry Core Facility. We also thank Active Motif for the preparation of histone ChIP libraries. This work was principally supported by the PedBrain Tumor Project contributing to the International Cancer Genome Consortium, funded by the German Cancer Aid (109252) and by the German Federal Ministry of Education and Research (BMBF, grants 01KU1201A, MedSys 0315416C and NGFNplus 01GS0883). Additional support came from the German Cancer Research Center–Heidelberg Center for Personalized Oncology (DKFZ-HIPO), the EMBL International PhD Programme (T.Z.), Dutch Cancer Foundations KWF (2010-4713) and KIKA (M.Ko.), the US National Institutes of Health, National Center for Research Resources (P41 GM103504; G.D.B.), the CancerSys grant MYC-NET (German Federal Ministry of Education and Research, BMBF, 0316076A), the European Commission (Health-F2-2010-260791), and the Helmholtz Alliance PCCC (grant number HA-305). PAN is a Roman Herzog Postdoctoral Fellow funded by the Hertie Foundation and the DKFZ. R.J.W.-R. is the recipient of a Research Leadership Award from the California Institute for Regenerative Medicine (CIRM LA1-01747) and obtained additional support from the National Cancer Institute (5P30CA030199 and R01 CA159859), and the CureSearch for Children's Cancer Foundation.

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Authors and Affiliations

Authors

Contributions

P.A.N., C.L., T.Z., A.M.S., D.K., L.A.E., W.W., A.W., S.St., L.S., H.S.-C., L.L., F.K., J.F., B.R., S.Sc., N.D., S.Wo., T.R., C.C.B., P.v.S. and A.K. performed and/or coordinated experimental or technical work. P.A.N., T.Z., S.E., D.J.H.S., V.H., M.Z., S.Z., G.D.B., N.J., I.B., C.D.I., G.Z., J.E., R.Vo., J.K. and J.O.K. performed and/or coordinated data analysis. M.Re., F.M.G.C., S.V., M.Ry., E.T., P.H., E.S., A.D., P.S., J.S., K.Z., D.Su., M.U.S., M.E., H.L.G., G.W.R., A.G., M.M., K.v.H., S.R., T.P., W.S., R.J.G., A.K. and M.D.T. contributed data, provided reagents, or patient materials. P.A.N., C.L., T.Z., S.E., D.J.H.S., V.H., D.St., D.T.W.J., M.K., S.Z., H.-J.W., R.J.G., M.D.T., P.Li., J.O.K., R.J.W.-R. and S.M.P. prepared the initial manuscript and display items. P.A.N., G.D.B., S.Wi., B.B., C.L., M-L.Y., U.D.W., C.v.K., R.V., G.R., A.E.K., A.v.D., O.W., R.E., P.Li., J.O.K., R.J.W.-R. and S.M.P. provided project leadership. P.A.N., J.O.K., R.J.W.-R. and S.M.P. co-conceived and led the study. P.Li., J.O.K., R.J.W.-R. and S.M.P are co-senior authors of this study.

Corresponding authors

Correspondence to Peter Lichter, Jan O. Korbel, Robert J. Wechsler-Reya or Stefan M. Pfister.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Recurrent somatic copy-number aberrations target a common region on 9q34.

Affymetrix SNP6 copy-number output for 22 primary medulloblastomas from the published8 MAGIC series exhibiting focal somatic copy-number aberrations within the 9q34 region of interest defined by WGS in the current study. Of the affected samples, medulloblastoma subgroup information was available for 15 of 22 cases: SHH (n = 1*), group 3 (n = 11) and group 4 (n = 3). Close examination of the single non-group 3/group 4 medulloblastoma affected by a focal copy-number event in the region (MAGIC_MB1318, SHH) revealed that this sample exhibits a homozygous deletion (in the context of broad chr9q deletion) specifically overlapping TSC1 and is therefore unlikely to be related to the events which target GFI1B for transcriptional activation. Indicated coordinates are based on the hg18 reference genome (NCBI Build 36.1) that was used in the original MAGIC study.

Extended Data Figure 2 Non-functional DDX31–GFI1B fusion transcripts detected by RNA-seq.

a, A complex SV on 9q34 in ICGC_MB9 resulted in expression of DDX31 (exon 19) fused to GFI1B (intron 2, antisense orientation). Note the intronic reads in GFI1B after the fusion breakpoint. b, 9q34 inversions in ICGC_MB247 resulted in expression of DDX31 (exon 19) fused to GFI1B (exon 2, sense orientation). This fusion transcript included a frameshift, inferred to generate a C-terminal-truncated DDX31 protein and no GFI1B protein from this fused allele.

Extended Data Figure 3 Expression and correlation of 9q34 genes in medulloblastoma subgroups.

ac, Box-plots summarizing expression of BARHL1 (a), DDX31 (b) and GTF3C4 (c) according to medulloblastoma subgroup. Data set includes 375 medulloblastomas profiled on the Affymetrix U133plus2 array. d, Pearson correlation analysis showing correlated expression of DDX31 with BARHL1 and GTF3C4 in group 3 and group 4 medulloblastomas. DDX31 expression is positively correlated with both BARHL1 (r = 0.741) and GTF3C4 (r = 0.622). e, PRRC2B expression in medulloblastoma subgroups. Samples are from the same series summarized in ac. f, Distribution of H3K27ac ChIP-seq signal at predicted enhancers in group 3 medulloblastomas (data for MAGIC_MB360 are shown). Enhancer regions are plotted in increasing order based on their input-normalized H3K27ac signal. Super-enhancers are defined as the population of enhancers above the inflection point of the curve (horizontal dashed grey line). Positions of the predicted BARHL1/DDX31 and PRRC2B super-enhancers described in the text are highlighted.

Extended Data Figure 4 Frequency and distribution of GFI1/GFI1B activation in medulloblastoma subgroups.

a, Stacked bar graph indicates the proportion of GFI1/GFI1B-expressing cases in each of the four medulloblastoma subgroups, as determined by Affymetrix gene expression profiling of two independent cohorts (n = 727). b, Stacked bar graph indicates the proportion of GFI1/GFI1B-positive cases in each of the four medulloblastoma subgroups, as determined by immunohistochemistry performed with anti-GFI1 and anti-GFI1B antibodies on formalin-fixed paraffin-embedded sections derived from a medulloblastoma clinical trial cohort (HIT2000, NCT00303810; n = 156). cf, Representative positive and negative immunohistochemistry results for group 3 medulloblastomas stained with anti-GFI1 (c, d) and anti-GFI1B (e, f) antibodies, respectively.

Extended Data Figure 5 Demographic and clinical characteristics of GFI1/GFI1B-activated group 3 medulloblastoma.

a, b, Unsupervised hierarchical clustering of group 3 medulloblastoma samples profiled by Affymetrix gene expression array (a) or Illumina 450K DNA methylation array (b). c, Patient characteristics, including age, gender, histological subtype (histology) and metastatic status (M-stage) for group 3 medulloblastomas stratified according to GFI1 and GFI1B expression status. Both gene expression and immunohistochemistry cohorts are summarized. d, e, Overall survival of group 3 medulloblastomas stratified by GFI1 and GFI1B expression status for both our gene expression (d) and immunohistochemistry series (e).

Extended Data Figure 6 Summary of GFI1 SVs detected by WGS in group 3 medulloblastoma.

a, Schematics depicting the six different GFI1 translocations detected by large-insert paired-end sequencing of our GFI1-activated validation series. b, WGS coverage plots showing SVs affecting the GFI1 locus in GFI1-activated medulloblastomas sequenced in our series. c, Fluorescence in situ hybridization (FISH) analysis of MAGIC_MB1338 validating the unbalanced t(1:9) translocation (shown in a) predicted by WGS.

Extended Data Figure 7 Chromatin states proximal to SVs observed in GFI1-activated group 3 medulloblastomas.

a–d, ChIP-seq (H3K27ac and H3K9ac) and WGBS data respectively highlighting the active chromatin and methylation states present in the regions proximal to SV breakpoints identified in GFI1 translocation cases. e, Schematic summarizing the series of focal tandem duplications observed approximately 45 kb downstream of GFI1 in group 3 medulloblastomas (n = 3; ICGC_MB18 is shown as a representative case). Activating and repressive histone marks overlapping the region of interest are shown for a non-GFI1-activated group 3 medulloblastoma (MAGIC_MB360) and the tandem duplication case (ICGC_MB18).

Extended Data Figure 8 Association between GFI1/GFI1B activation and MYC in group 3 medulloblastoma.

a, MYC expression in group 3 medulloblastomas (n = 168) according to GFI1 and GFI1B activation status. b, Gene sets with significant enrichment in GFI1/GFI1B-associated genes from the MSigDB c2 gene set collection. The collection highlighted in red is the only result found that shows a significant enrichment in both GFI1 and GFI1B associated genes and a clear connection to a known pathway. c, Heat-map of the expression values for the 50 genes in the KIM_MYC_AMPLIFICATION_TARGETS_UP gene set with the most significant association with GFI1 or GFI1B expression (the complete gene set contains 187 profiled genes). Genes are ordered top to bottom from most to least significant. A set of 90 group 3 medulloblastomas included in the analysis is displayed. Sample-wise hierarchical clustering was performed only to enhance the visual organization of the heat map. d, Affymetrix SNP6 copy-number output for 82 primary group 3 medulloblastomas from the published MAGIC series, highlighting the incidence of MYC amplification in the context of GFI1/GFI1B-activation. MYC amplification was found at a comparable frequency in both GFI1-activated (n = 2 of 14, 14.3%) and non-GFI1/GFI1B-activated (n = 10 of 57, 17.5%) group 3 medulloblastomas. Indicated coordinates are based on the hg18 (NCBI Build 36.1) reference genome that was used in the original MAGIC study.

Extended Data Figure 9 Phenotypic characteristics of novel GFI1/GFI1B orthotopic mouse models.

a, b, Bioluminescent imaging of animals injected with either GFI1- (a) or GFI1B-expressing (b) neural stem cells at the indicated time points. No tumour signal was detectable in these animals. c, Haematoxylin and eosin staining of cerebellar sections derived from MYC + GFI1B tumour-bearing mice. d, Immunofluorescence imaging of cerebellar sections from MYC + GFI1B tumours stained with the indicated antibodies.

Supplementary information

Supplementary Table 1

This file contains the details on the sequencing cohorts included in the main paper. (XLSX 19 kb)

Supplementary Table 2

RNA-seq analysis did not disclose evidence for possible GFI1 fusion genes (data not shown), suggesting that the detected rearrangements contribute to GFI1 activation by alternative mechanisms. This table shows that observed translocation partners showed no apparent preference for intragenic or intergenic breakpoints. (XLSX 15 kb)

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Northcott, P., Lee, C., Zichner, T. et al. Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma. Nature 511, 428–434 (2014). https://doi.org/10.1038/nature13379

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