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Recurrent mutation of the ID3 gene in Burkitt lymphoma identified by integrated genome, exome and transcriptome sequencing

Abstract

Burkitt lymphoma is a mature aggressive B-cell lymphoma derived from germinal center B cells1. Its cytogenetic hallmark is the Burkitt translocation t(8;14)(q24;q32) and its variants, which juxtapose the MYC oncogene with one of the three immunoglobulin loci2. Consequently, MYC is deregulated, resulting in massive perturbation of gene expression3. Nevertheless, MYC deregulation alone seems not to be sufficient to drive Burkitt lymphomagenesis. By whole-genome, whole-exome and transcriptome sequencing of four prototypical Burkitt lymphomas with immunoglobulin gene (IG)-MYC translocation, we identified seven recurrently mutated genes. One of these genes, ID3, mapped to a region of focal homozygous loss in Burkitt lymphoma4. In an extended cohort, 36 of 53 molecularly defined Burkitt lymphomas (68%) carried potentially damaging mutations of ID3. These were strongly enriched at somatic hypermutation motifs. Only 6 of 47 other B-cell lymphomas with the IG-MYC translocation (13%) carried ID3 mutations. These findings suggest that cooperation between ID3 inactivation and IG-MYC translocation is a hallmark of Burkitt lymphomagenesis.

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Figure 1: Spectrum of mutations in Burkitt lymphoma.
Figure 2: Mutual relationship of ID3 mutations with CCND3 (exon 5), TP53 (exons 4–10) and MYC box mutations in mBL samples without IGH-BCL2 translocation.
Figure 3: Cell cycle analysis of ID3-GFP–transfected cell lines by fluorescence-activated cell sorting (FACS).

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Primary accessions

Gene Expression Omnibus

Referenced accessions

Ensembl

NCBI Reference Sequence

Protein Data Bank

References

  1. 1

    Swerdlow, S.H. et al. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues, Ch. 10, 179–268 (IARC Press, Lyon, France, 2008).

  2. 2

    Boerma, E.G., Siebert, R., Kluin, P.M. & Baudis, M. Translocations involving 8q24 in Burkitt lymphoma and other malignant lymphomas: a historical review of cytogenetics in the light of today's knowledge. Leukemia 23, 225–234 (2009).

    CAS  PubMed  Article  Google Scholar 

  3. 3

    Klapproth, K. & Wirth, T. Advances in the understanding of MYC-induced lymphomagenesis. Br. J. Haematol. 149, 484–497 (2010).

    CAS  PubMed  Article  Google Scholar 

  4. 4

    Scholtysik, R. et al. Detection of genomic aberrations in molecularly defined Burkitt′s lymphoma by array-based, high resolution, single nucleotide polymorphism analysis. Haematologica 95, 2047–2055 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5

    Hummel, M. et al. A biologic definition of Burkitt′s lymphoma from transcriptional and genomic profiling. N. Engl. J. Med. 354, 2419–2430 (2006).

    CAS  PubMed  Article  Google Scholar 

  6. 6

    Dave, S.S. et al. Molecular diagnosis of Burkitt′s lymphoma. N. Engl. J. Med. 354, 2431–2442 (2006).

    CAS  PubMed  Article  Google Scholar 

  7. 7

    Schmitz, R. et al. Recurrent oncogenic mutations in CCND3 in aggressive lymphomas. Blood (ASH Annual Meeting Abstracts) 118, 435 (2011).

    Google Scholar 

  8. 8

    Wilda, M. et al. Inactivation of the ARFMDM-2p53 pathway in sporadic Burkitt′s lymphoma in children. Leukemia 18, 584–588 (2004).

    CAS  PubMed  Article  Google Scholar 

  9. 9

    Johnston, J.M. & Carroll, W.L. c-myc hypermutation in Burkitt′s lymphoma. Leuk. Lymphoma 8, 431–439 (1992).

    CAS  PubMed  Article  Google Scholar 

  10. 10

    Love, C.L. et al. Whole genome and exome sequencing reveals the genetic landscape of Burkitt lymphoma. Blood (ASH Annual Meeting Abstracts) 118, 433 (2011).

    Google Scholar 

  11. 11

    Duan, S. et al. FBXO11 targets BCL6 for degradation and is inactivated in diffuse large B-cell lymphomas. Nature 481, 90–93 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12

    Wang, L. et al. SF3B1 and other novel cancer genes in chronic lymphocytic leukemia. N. Engl. J. Med. 365, 2497–2506 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13

    Klapper, W. et al. Molecular profiling of pediatric mature B-cell lymphoma treated in population-based prospective clinical trials. Blood 112, 1374–1381 (2008).

    CAS  PubMed  Article  Google Scholar 

  14. 14

    Klapper, W. et al. Patient age at diagnosis is associated with the molecular characteristics of diffuse large B-cell lymphoma. Blood 119, 1882–1887 (2012).

    CAS  PubMed  Article  Google Scholar 

  15. 15

    Casellas, R. et al. Restricting activation-induced cytidine deaminase tumorigenic activity in B lymphocytes. Immunology 126, 316–328 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16

    Salaverria, I. & Siebert, R. The gray zone between Burkitt′s lymphoma and diffuse large B-cell lymphoma from a genetics perspective. J. Clin. Oncol. 29, 1835–1843 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  17. 17

    Perk, J., Iavaraone, A. & Benezra, R. Id family of helix-loop-helix proteins in cancer. Nat. Rev. Cancer 5, 603–614 (2005).

    CAS  PubMed  Article  Google Scholar 

  18. 18

    Kee, B.L. E and ID proteins branch out. Nat. Rev. Immunol. 9, 175–184 (2009).

    CAS  PubMed  Article  Google Scholar 

  19. 19

    Seitz, V. et al. Deep sequencing of MYC DNA-binding sites in Burkitt lymphoma. PLoS ONE 6, e26837 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20

    Pan, L. et al. Impaired immune responses and B-cell proliferation in mice lacking the Id3 gene. Mol. Cell Biol. 19, 5969–5980 (1999).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21

    Li, J. et al. Mutation of inhibitory helix-loop-helix protein Id3 causes γδ T-cell lymphoma in mice. Blood 116, 5615–5621 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22

    Schmitz, R. et al. Burkitt lymphoma pathogenesis and therapeutic targets from structural and functional genomics. Nature 490, 116–120 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23

    Sander, S. et al. Synergy between PI3K signaling and MYC in Burkitt lymphomagenesis. Cancer Cell 22, 167–179 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24

    Dominguez-Sola, D. & Dalla-Favera, R. Burkitt lymphoma: much more than MYC. Cancer Cell 22, 141–142 (2012).

    CAS  PubMed  Article  Google Scholar 

  25. 25

    Medina, P.P. & Sanchez-Cespedes, M. Involvement of the chromatin-remodeling factor BRG1/SMARCA4 in human cancer. Epigenetics 3, 64–68 (2008).

    PubMed  Article  Google Scholar 

  26. 26

    Choi, Y.J. & Lee, S.G. The DEAD-box RNA helicase DDX3 interacts with DDX5, co-localizes with it in the cytoplasm during the G2/M phase of the cycle, and affects its shuttling during mRNP export. J. Cell Biochem. 113, 985–996 (2012).

    CAS  PubMed  Article  Google Scholar 

  27. 27

    Zenz, T. et al. Monoallelic TP53 inactivation is associated with poor prognosis in chronic lymphocytic leukemia: results from a detailed genetic characterization with long-term follow-up. Blood 112, 3322–3329 (2008).

    CAS  PubMed  Article  Google Scholar 

  28. 28

    van Dongen, J.J. et al. Design and standardization of PCR primers and protocols for detection of clonal immunoglobulin and T-cell receptor gene recombinations in suspect lymphoproliferations: report of the BIOMED-2 Concerted Action BMH4–CT98–3936. Leukemia 17, 2257–2317 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29

    Lefranc, M.P. et al. IMGT, the international ImMunoGeneTics database. Nucleic Acids Res. 27, 209–212 (1999).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31

    Lamprecht, B. et al. Derepression of an endogenous long terminal repeat activates the CSF1R proto-oncogene in human lymphoma. Nat. Med. 16, 571–579 (2010).

    CAS  PubMed  Article  Google Scholar 

  32. 32

    Bibikova, M. et al. High density DNA methylation array with single CpG site resolution. Genomics 98, 288–295 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33

    Hames, B.D. Gel Electrophoresis of Proteins, Vol. 3 (Oxford University Press, New York, 1998).

  34. 34

    Lüschen, S. et al. Sensitization to death receptor cytotoxicity by inhibition of fas-associated death domain protein (FADD)/caspase signaling. Requirement of cell cycle progression. J. Biol. Chem. 275, 24670–24678 (2000).

    PubMed  Article  Google Scholar 

  35. 35

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  37. 37

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  39. 39

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40

    Ye, K., Schulz, M.H., Long, Q., Apweiler, R. & Ning, Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41

    Robinson, J.T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42

    Wang, J. et al. CREST maps somatic structural variation in cancer genomes with base-pair resolution. Nat. Methods 8, 652–654 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43

    Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44

    Olshen, A.B. et al. Parent-specific copy number in paired tumor-normal studies using circular binary segmentation. Bioinformatics 27, 2038–2046 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45

    Boeva, V. et al. Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics 28, 423–425 (2012).

    CAS  Article  Google Scholar 

  46. 46

    Wang, K. et al. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 17, 1665–1674 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47

    Rice, P., Longden, I. & Bleasby, A. EMBOSS: The European Molecular Biology Open Software Suite. Trends Genet. 16, 276–277 (2000).

    CAS  PubMed  Article  Google Scholar 

  48. 48

    Ahmadpour, F. et al. Crystal structure of the minimalist Max-E47 protein chimera. PLoS ONE 7, e32136 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49

    Sali, A. & Blundell, T.L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50

    Long, A., Guanga, G.P. & Rose, R.B. Crystal structure of E47-NeuroD1/β2 bHLH domain–DNA complex: heterodimer selectivity and DNA recognition. Biochemistry 47, 218–229 (2008).

    Article  CAS  Google Scholar 

  51. 51

    Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. 52

    Hoffmann, S. et al. Fast mapping of short sequences with mismatches, insertions and deletions using index structures. PLoS Comput. Biol. 5, e1000502 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  53. 53

    Smyth, G.K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article3 (2004).

    PubMed  PubMed Central  Article  Google Scholar 

  54. 54

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. A Stat. Soc. 57, 289–300 (1995).

    Google Scholar 

  55. 55

    Goeman, J.J., van de Geer, S.A., de Kort, F. & van Houwelingen, H.C. A global test for groups of genes: testing association with a clinical outcome. Bioinformatics 20, 93–99 (2004).

    CAS  PubMed  Article  Google Scholar 

  56. 56

    Mansmann, U. & Meister, R. Testing differential gene expression in functional groups. Goeman′s global test versus an ANCOVA approach. Methods Inf. Med. 44, 449–453 (2005).

    CAS  PubMed  Article  Google Scholar 

  57. 57

    Läuter, J., Glimm, E. & Kropf, S. New tests for data with an inherent structure. Biometrical J. 38, 5–23 (1996).

    Article  Google Scholar 

  58. 58

    Huber, W. et al. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18, S96–S104 (2002).

    PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

This manuscript is dedicated to the memory of Karl Lennert, the founder of the Kiel classification of lymphoma, who died during the preparation of the manuscript. The authors thank O. Batic, C. Becher, C. Botz-von Drathen, A. Dietsch, J. Eils, M. Friskovec, K. Göbel, T. Grieb, S. Hengst, U. Jacobsen, T. Kaacksteen, H. Lammert, C. von der Lancken, H.-H. Müller, S. Radomski, J. Schieferstein, M. Schlapkohl, D. Schuster, S. Ölmez and L. Valles for their excellent technical support. We are grateful to G. Richter for excellent work in the coordination of the ICGC MMML-Seq Consortium and to the members of the MMML Consortium for providing extensive data of the sample analyzed within that project. We are also very grateful to all individuals who participate in this study. The project was funded by the Federal Ministry of Education and Research in Germany (BMBF) within the Program for Medical Genome Research (01KU1002A to 01KU1002J). The authors are responsible for the content of this publication. The MMML Consortium was funded by the Deutsche Krebshilfe from 2003 to 2011. Support of sequencing infrastructure by the Deutsche Forschungsgemeinschaft (DFG) and the KinderKrebsInitiative Buchholz/Holm-Seppensen is gratefully acknowledged. R.W. is the recipient of a Christoph-Schubert-Award from the KinderKrebsInitiative Buchholz/Holm-Seppensen.

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Contributions

B. Burkhardt, A.C., A.B., M. Rohde, J.L. and N.H. provided subject samples and clinical data. W.K., M.H. and P.M. coordinated and performed pathology review. D. Lenze, M. Szczepanowski, M.H. and W.K. stained and reviewed cryomaterial, prepared analytes and performed analyte quality control. D. Lenze and M.H. performed and interpreted immunoglobulin gene PCR. R.A.F.M. and H.G.D. characterized and provided cell lines. E.L., M. Schilhabel, V.H., S.P. and B.R. performed next-generation sequencing analyses, and A.R., P.R., P.L. and S.S. supervised next-generation sequencing analysis and interpreted data. C.L. coordinated transfer and data management of the sequences. M. Schlesner, B. Brors, S.H., S.H.B., P.F.S., D. Langenberger, V.H., J.O.K., T.R. and J.P. performed analysis of next-generation sequencing data, and B. Brors and R.E. conceived the statistical analysis. J.R., E.L., O.A., S.A.-K., M.P., S.L., R.B.R. and G.A. performed validation analyses. J.R. and R.W. were responsible for ID3 mutation and expression analyses. M.K., M. Rosolowski, M.L., H.T., C.P., R. Spang, K.M., R.K., R. Scholtysik, T.Z., D.K., L.T., D.H. and R. Siebert provided and analyzed data from the MMML cohort. M.K. and M. Rosolowski performed correlative and biometric analyses of the MMML cohort. J.R., M. Schlesner, M.K., S.H., R.E. and R. Siebert interpreted data and wrote the manuscript. R.E., M.H., W.K., P.R., A.R., B. Brors, O.A. and R. Siebert designed the study and coordinated the project. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Reiner Siebert.

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The author declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Note, Supplementary Tables 1–4, 6–8, 10, 11 and 15–21 and Supplementary Figures 1–18 (PDF 2842 kb)

Supplementary Table 5

Somatic structural variants (XLSX 14 kb)

Supplementary Table 9

Potentially protein changing somatic mutations in the four index patients (XLSX 411 kb)

Supplementary Table 12

U133A gene expression data of the potentially protein changing somatic mutations (XLSX 169 kb)

Supplementary Table 13

450K DNA methylation pattern of the potentially protein changing somatic mutations (XLSX 426 kb)

Supplementary Table 14

Results of ID3 and CCND3 mutation analysis of primary lymphoma (XLSX 17 kb)

Supplementary Table 22

Primer sequences (XLSX 14 kb)

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the ICGC MMML-Seq Project. Recurrent mutation of the ID3 gene in Burkitt lymphoma identified by integrated genome, exome and transcriptome sequencing. Nat Genet 44, 1316–1320 (2012). https://doi.org/10.1038/ng.2469

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