Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes

  • Nature Medicinevolume 24pages679690 (2018)
  • doi:10.1038/s41591-018-0016-8
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Diffuse large B cell lymphoma (DLBCL), the most common lymphoid malignancy in adults, is a clinically and genetically heterogeneous disease that is further classified into transcriptionally defined activated B cell (ABC) and germinal center B cell (GCB) subtypes. We carried out a comprehensive genetic analysis of 304 primary DLBCLs and identified low-frequency alterations, captured recurrent mutations, somatic copy number alterations, and structural variants, and defined coordinate signatures in patients with available outcome data. We integrated these genetic drivers using consensus clustering and identified five robust DLBCL subsets, including a previously unrecognized group of low-risk ABC-DLBCLs of extrafollicular/marginal zone origin; two distinct subsets of GCB-DLBCLs with different outcomes and targetable alterations; and an ABC/GCB-independent group with biallelic inactivation of TP53, CDKN2A loss, and associated genomic instability. The genetic features of the newly characterized subsets, their mutational signatures, and the temporal ordering of identified alterations provide new insights into DLBCL pathogenesis. The coordinate genetic signatures also predict outcome independent of the clinical International Prognostic Index and suggest new combination treatment strategies. More broadly, our results provide a roadmap for an actionable DLBCL classification.

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  1. 1.

    Basso, K. & Dalla-Favera, R. Germinal centres and B cell lymphomagenesis. Nat. Rev. Immunol. 15, 172–184 (2015).

  2. 2.

    Monti, S. et al. Integrative analysis reveals an outcome-associated and targetable pattern of p53 and cell cycle deregulation in diffuse large B cell lymphoma. Cancer Cell 22, 359–372 (2012).

  3. 3.

    Pasqualucci, L. et al. Analysis of the coding genome of diffuse large B-cell lymphoma. Nat. Genet. 43, 830–837 (2011).

  4. 4.

    Morin, R. D. et al. Frequent mutation of histone-modifying genes in non-Hodgkin lymphoma. Nature 476, 298–303 (2011).

  5. 5.

    Lohr, J. G. et al. Discovery and prioritization of somatic mutations in diffuse large B-cell lymphoma (DLBCL) by whole-exome sequencing. Proc. Natl. Acad. Sci. USA 109, 3879–3884 (2012).

  6. 6.

    Morin, R. D. et al. Mutational and structural analysis of diffuse large B-cell lymphoma using whole-genome sequencing. Blood 122, 1256–1265 (2013).

  7. 7.

    de Miranda, N. F. et al. Exome sequencing reveals novel mutation targets in diffuse large B-cell lymphomas derived from Chinese patients. Blood 124, 2544–2553 (2014).

  8. 8.

    Reddy, A. et al. Genetic and functional drivers of diffuse large B cell lymphoma. Cell 171, 481–494.e15 (2017).

  9. 9.

    Rosenwald, A. et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N. Engl. J. Med. 346, 1937–1947 (2002).

  10. 10.

    Monti, S. et al. Molecular profiling of diffuse large B-cell lymphoma identifies robust subtypes including one characterized by host inflammatory response. Blood 105, 1851–1861 (2005).

  11. 11.

    Ngo, V. N. et al. Oncogenically active MYD88 mutations in human lymphoma. Nature 470, 115–119 (2011).

  12. 12.

    Caro, P. et al. Metabolic signatures uncover distinct targets in molecular subsets of diffuse large B cell lymphoma. Cancer Cell 22, 547–560 (2012).

  13. 13.

    Davis, R. E. et al. Chronic active B-cell-receptor signalling in diffuse large B-cell lymphoma. Nature 463, 88–92 (2010).

  14. 14.

    Chen, L. et al. SYK inhibition modulates distinct PI3K/AKT- dependent survival pathways and cholesterol biosynthesis in diffuse large B cell lymphomas. Cancer Cell 23, 826–838 (2013).

  15. 15.

    Lenz, G. et al. Oncogenic CARD11 mutations in human diffuse large B cell lymphoma. Science 319, 1676–1679 (2008).

  16. 16.

    Muppidi, J. R. et al. Loss of signalling via Gα13 in germinal centre B-cell-derived lymphoma. Nature 516, 254–258 (2014).

  17. 17.

    Morin, R. D. et al. Somatic mutations altering EZH2 (Tyr641) in follicular and diffuse large B-cell lymphomas of germinal-center origin. Nat. Genet. 42, 181–185 (2010).

  18. 18.

    Pfeifer, M. et al. PTEN loss defines a PI3K/AKT pathway–dependent germinal center subtype of diffuse large B-cell lymphoma. Proc. Natl. Acad. Sci. USA 110, 12420–12425 (2013).

  19. 19.

    Lenz, G. et al. Stromal gene signatures in large-B-cell lymphomas. N. Engl. J. Med. 359, 2313–2323 (2008).

  20. 20.

    Dubois, S. et al. Biological and clinical relevance of associated genomic alterations in MYD88 L265P and non-L265P-mutated diffuse large B-cell lymphoma: analysis of 361 cases. Clin. Cancer Res. 23, 2232–2244 (2017).

  21. 21.

    Ennishi, D. et al. Genetic profiling of MYC and BCL2 in diffuse large B-cell lymphoma determines cell-of-origin-specific clinical impact. Blood 129, 2760–2770 (2017).

  22. 22.

    Pfreundschuh, M. et al. Six versus eight cycles of bi-weekly CHOP-14 with or without rituximab in elderly patients with aggressive CD20+ B-cell lymphomas: a randomised controlled trial (RICOVER-60). Lancet Oncol. 9, 105–116 (2008).

  23. 23.

    Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

  24. 24.

    Kamburov, A. et al. Comprehensive assessment of cancer missense mutation clustering in protein structures. Proc. Natl. Acad. Sci. USA 112, E5486–E5495 (2015).

  25. 25.

    Kasar, S. et al. Whole-genome sequencing reveals activation-induced cytidine deaminase signatures during indolent chronic lymphocytic leukaemia evolution. Nat. Commun. 6, 8866 (2015).

  26. 26.

    Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

  27. 27.

    Pasqualucci, L. et al. AID is required for germinal center-derived lymphomagenesis. Nat. Genet. 40, 108–112 (2008).

  28. 28.

    Chapuy, B. et al. Targetable genetic features of primary testicular and primary central nervous system lymphomas. Blood 127, 869–881 (2016).

  29. 29.

    Georgiou, K. et al. Genetic basis of PD-L1 overexpression in diffuse large B-cell lymphomas. Blood 127, 3026–3034 (2016).

  30. 30.

    Scott, D. W. et al. TBL1XR1/TP63: a novel recurrent gene fusion in B-cell non-Hodgkin lymphoma. Blood 119, 4949–4952 (2012).

  31. 31.

    Challa-Malladi, M. et al. Combined genetic inactivation of β2-Microglobulin and CD58 reveals frequent escape from immune recognition in diffuse large B cell lymphoma. Cancer Cell 20, 728–740 (2011).

  32. 32.

    Green, M. R. et al. Integrative analysis reveals selective 9p24.1 amplification, increased PD-1 ligand expression, and further induction via JAK2 in nodular sclerosing Hodgkin lymphoma and primary mediastinal large B-cell lymphoma. Blood 116, 3268–3277 (2010).

  33. 33.

    Steidl, C. et al. MHC class II transactivator CIITA is a recurrent gene fusion partner in lymphoid cancers. Nature 471, 377–381 (2011).

  34. 34.

    Brunet, J. P., Tamayo, P., Golub, T. R. & Mesirov, J. P. Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl. Acad. Sci. USA 101, 4164–4169 (2004).

  35. 35.

    Dierlamm, J. et al. Gain of chromosome region 18q21 including the MALT1 gene is associated with the activated B-cell-like gene expression subtype and increased BCL2 gene dosage and protein expression in diffuse large B-cell lymphoma. Haematologica 93, 688–696 (2008).

  36. 36.

    Lenz, G. et al. Molecular subtypes of diffuse large B-cell lymphoma arise by distinct genetic pathways. Proc. Natl. Acad. Sci. USA 105, 13520–13525 (2008).

  37. 37.

    Pham-Ledard, A. et al. High frequency and clinical prognostic value of MYD88 L265P mutation in primary cutaneous diffuse large B-cell lymphoma, leg-type. JAMA Dermatol. 150, 1173–1179 (2014).

  38. 38.

    Rovira, J. et al. MYD88 L265P mutations, but no other variants, identify a subpopulation of DLBCL patients of activated B-cell origin, extranodal involvement, and poor outcome. Clin. Cancer Res. 22, 2755–2764 (2016).

  39. 39.

    Rossi, D. et al. The coding genome of splenic marginal zone lymphoma: activation of NOTCH2 and other pathways regulating marginal zone development. J. Exp. Med. 209, 1537–1551 (2012).

  40. 40.

    Spina, V. et al. The genetics of nodal marginal zone lymphoma. Blood 128, 1362–1373 (2016).

  41. 41.

    Zhang, Q. et al. Inactivating mutations and overexpression of BCL10, a caspase recruitment domain-containing gene, in MALT lymphoma with t(1;14)(p22; q32). Nat. Genet. 22, 63–68 (1999).

  42. 42.

    Kiel, M. J. et al. Whole-genome sequencing identifies recurrent somatic NOTCH2 mutations in splenic marginal zone lymphoma. J. Exp. Med. 209, 1553–1565 (2012).

  43. 43.

    Flossbach, L. et al. BCL6 gene rearrangement and protein expression are associated with large cell presentation of extranodal marginal zone B-cell lymphoma of mucosa-associated lymphoid tissue. Int. J. Cancer 129, 70–77 (2011).

  44. 44.

    Zucca, E., Bertoni, F., Vannata, B. & Cavalli, F. Emerging role of infectious etiologies in the pathogenesis of marginal zone B-cell lymphomas. Clin. Cancer Res. 20, 5207–5216 (2014).

  45. 45.

    MacLennan, I. C. et al. Extrafollicular antibody responses. Immunol. Rev. 194, 8–18 (2003).

  46. 46.

    Erdmann, T. et al. Sensitivity to PI3K and AKT inhibitors is mediated by divergent molecular mechanisms in subtypes of DLBCL. Blood 130, 310–322 (2017).

  47. 47.

    Sun, Z. et al. PTEN C-terminal deletion causes genomic instability and tumor development. Cell Reports 6, 844–854 (2014).

  48. 48.

    Ortega-Molina, A. et al. The histone lysine methyltransferase KMT2D sustains a gene expression program that represses B cell lymphoma development. Nat. Med. 21, 1199–1208 (2015).

  49. 49.

    Boice, M. et al. Loss of the HVEM tumor suppressor in lymphoma and restoration by modified CAR-T cells. Cell 167, 405–418.e413 (2016).

  50. 50.

    Ying, C. Y. et al. MEF2B mutations lead to deregulated expression of the oncogene BCL6 in diffuse large B cell lymphoma. Nat. Immunol. 14, 1084–1092 (2013).

  51. 51.

    Zhang, J. et al. The CREBBP acetyltransferase is a haploinsufficient tumor suppressor in B-cell lymphoma. Cancer Discov. 7, 322–337 (2017).

  52. 52.

    Krysiak, K. et al. Recurrent somatic mutations affecting B-cell receptor signaling pathway genes in follicular lymphoma. Blood 129, 473–483 (2017).

  53. 53.

    Béguelin, W. et al. EZH2 is required for germinal center formation and somatic EZH2 mutations promote lymphoid transformation. Cancer Cell 23, 677–692 (2013).

  54. 54.

    Li, H. et al. Mutations in linker histone genes HIST1H1 B, C, D, and E; OCT2 (POU2F2); IRF8; and ARID1A underlying the pathogenesis of follicular lymphoma. Blood 123, 1487–1498 (2014).

  55. 55.

    Okosun, J. et al. Integrated genomic analysis identifies recurrent mutations and evolution patterns driving the initiation and progression of follicular lymphoma. Nat. Genet. 46, 176–181 (2014).

  56. 56.

    Yang, S. M., Kim, B. J., Norwood Toro, L. & Skoultchi, A. I. H1 linker histone promotes epigenetic silencing by regulating both DNA methylation and histone H3 methylation. Proc. Natl. Acad. Sci. USA 110, 1708–1713 (2013).

  57. 57.

    Xu-Monette, Z. Y. et al. Mutational profile and prognostic significance of TP53 in diffuse large B-cell lymphoma patients treated with R-CHOP: report from an International DLBCL Rituximab-CHOP Consortium Program Study. Blood 120, 3986–3996 (2012).

  58. 58.

    Sesques, P. & Johnson, N. A. Approach to the diagnosis and treatment of high-grade B-cell lymphomas with MYC and BCL2 and/or BCL6 rearrangements. Blood 129, 280–288 (2017).

  59. 59.

    Li, Y., Choi, P. S., Casey, S. C., Dill, D. L. & Felsher, D. W. MYC through miR-17-92 suppresses specific target genes to maintain survival, autonomous proliferation, and a neoplastic state. Cancer Cell 26, 262–272 (2014).

  60. 60.

    Landau, D. A. et al. Mutations driving CLL and their evolution in progression and relapse. Nature 526, 525–530 (2015).

  61. 61.

    Novak, A. J. et al. Whole-exome analysis reveals novel somatic genomic alterations associated with outcome in immunochemotherapy-treated diffuse large B-cell lymphoma. Blood Cancer J. 5, e346 (2015).

  62. 62.

    Chapman, M. A. et al. Initial genome sequencing and analysis of multiple myeloma. Nature 471, 467–472 (2011).

  63. 63.

    Fisher, S. et al. A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries. Genome Biol. 12, R1 (2011).

  64. 64.

    Gnirke, A. et al. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat. Biotechnol. 27, 182–189 (2009).

  65. 65.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

  66. 66.

    Lichtenstein, L., Wood, B., MacBeth, A., Birsoy, O. & Lennon, N. ReCapSeg: Validation of somatic copy number alterations for CLIA whole exome sequencing. Cancer Res. 76 Supplement, abstr. 3641 (2016).

  67. 67.

    Giannikou, K. et al. Whole exome sequencing identifies TSC1/TSC2 biallelic loss as the primary and sufficient driver event for renal angiomyolipoma development. PLoS Genet. 12, e1006242 (2016).

  68. 68.

    Burger, J. A. et al. Clonal evolution in patients with chronic lymphocytic leukaemia developing resistance to BTK inhibition. Nat. Commun. 7, 11589 (2016).

  69. 69.

    Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).

  70. 70.

    Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

  71. 71.

    Ramos, A. H. et al. Oncotator: cancer variant annotation tool. Hum. Mutat. 36, E2423–E2429 (2015).

  72. 72.

    Costello, M. et al. Discovery and characterization of artifactual mutations in deep coverage targeted capture sequencing data due to oxidative DNA damage during sample preparation. Nucleic Acids Res. 41, e67 (2013).

  73. 73.

    Giannakis, M. et al. Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma. Cell Reports 17, 1206 (2016).

  74. 74.

    Cancer Genome Atlas Research Network. Integrated genomic characterization of papillary thyroid carcinoma. Cell 159, 676–690 (2014).

  75. 75.

    DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

  76. 76.

    Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).

  77. 77.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

  78. 78.

    Abo, R. P. et al. BreaKmer: detection of structural variation in targeted massively parallel sequencing data using kmers. Nucleic Acids Res. 43, e19 (2015).

  79. 79.

    Layer, R. M., Chiang, C., Quinlan, A. R. & Hall, I. M. LUMPY: a probabilistic framework for structural variant discovery. Genome Biol. 15, R84 (2014).

  80. 80.

    Wala, J. A. et al. SvABA: genome-wide detection of structural variants and indels by local assembly. Genome Res. 28, 581–591 (2018).

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We thank all of the members of the Broad Institute's Biological Samples Genetic Analysis Genome Sequencing Platforms. In addition, we thank all of the patients and their physicians for trial participation and donating the samples. This work was supported by a Claudia Adams Barr Program in Basic Cancer Research (B.C.), a Medical Oncology Translational Grant Program (B.C.), two LLS Translational Research Awards (M.A.S.), and the Lymphoma Target Testing Center (M.A.S.). The computational work for this study was supported by grants U54HG003067, P01CA163222, R01CA18246, U24CA143845, U24CA210999, and R01CA155010 from the National Cancer Institute and the National Human Genome Research Institute, as well as Leukemia & Lymphoma Society grant 0812-14. The Mayo group was supported by a grant from the US National Institutes of Health (P50 CA97274). R.S., M.L., and L.T. received Funding from BMBF (Federal Ministry of Research, Germany; Kennzeichen FZK 031A428B and FZK 031A428H). The Ricover60 Trial was supported by a research grant from Deutsche Krebshilfe (M.P.).

Author information

Author notes

  1. These authors contributed equally: Bjoern Chapuy, Chip Stewart, Andrew Dunford.

  2. These authors jointly supervised this work: Gad Getz, Margaret A. Shipp.


  1. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    • Bjoern Chapuy
    • , Margaretha G. M. Roemer
    • , Caroline A. Coughlin
    •  & Margaret A. Shipp
  2. Harvard Medical School, Boston, MA, USA

    • Bjoern Chapuy
    • , Mike S. Lawrence
    • , Matthew L. Meyerson
    • , Todd R. Golub
    • , Rameen Beroukhim
    • , Scott J. Rodig
    • , Donna S. Neuberg
    • , Gad Getz
    •  & Margaret A. Shipp
  3. Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA

    • Chip Stewart
    • , Andrew J. Dunford
    • , Jaegil Kim
    • , Atanas Kamburov
    • , Mike S. Lawrence
    • , Jeremiah A. Wala
    • , Ignaty Leshchiner
    • , Ester Rheinbay
    • , Amaro Taylor-Weiner
    • , Julian M. Hess
    • , Chandra S. Pedamallu
    • , Dimitri Livitz
    • , Daniel Rosebrock
    • , Mara Rosenberg
    • , Adam A. Tracy
    • , Matthew L. Meyerson
    • , Todd R. Golub
    • , Rameen Beroukhim
    •  & Gad Getz
  4. Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA

    • Robert A. Redd
    •  & Donna S. Neuberg
  5. Department of Pathology, Massachusetts General Hospital, Boston, MA, USA

    • Mike S. Lawrence
    •  & Gad Getz
  6. Boston University School of Medicine, Section of Computational Biomedicine, Boston, MA, USA

    • Amy J. Li
    •  & Stefano Monti
  7. Institute for Medical Informatics, Statistics and Epidemiology, University Leipzig, Leipzig, Germany

    • Marita Ziepert
    •  & Markus Loeffler
  8. Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, and University of Tuebingen, Tuebingen, Germany

    • Annette M. Staiger
    •  & Heike Horn
  9. Department of Clinical Pathology, Robert-Bosch Krankenhaus, Stuttgart, Germany

    • Annette M. Staiger
    •  & German Ott
  10. Dana-Farber Cancer Institute, Center for Cancer Genome Discovery, Boston, MA, USA

    • Matthew D. Ducar
    • , Paul van Hummelen
    •  & Aaron R. Thorner
  11. Mayo Clinic, Rochester, MN, USA

    • Andrew L. Feldman
    • , Anne J. Novak
    • , James R. Cerhan
    •  & Thomas M. Habermann
  12. University of Iowa, Iowa City, IA, USA

    • Brian K. Link
  13. Department for Human Genetics, University Ulm, Ulm, Germany

    • Reiner Siebert
  14. Department of Pathology, University of Würzburg, Würzburg, Germany

    • Andreas Rosenwald
  15. Department of Hematology and Oncology, Georg-August University Göttingen, Göttingen, Germany

    • Gerald G. Wulf
    •  & Lorenz Trümper
  16. Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA

    • Scott J. Rodig
  17. Department of Medicine I, Saarland University, Homburg, Germany

    • Michael Pfreundschuh


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B.C., C.S., G.G., and M.A.S. conceived the project and provided leadership. B.C., C.S., A.D., J.K., A.K., R.R., M.L, A.J.L., G.G., and M.A.S analyzed the data. M.G.M.R., M.Z., A.M.S., J. W., M.D.D., I.L., E.R., A.T.-W, C.C., J.H., C.P., D.L., D.R., M.R., A.T., H.H., P.v.H., A.L.F., B.R.L., A.J.N., J.R.C., T.M.H., R.S., A.R., A.R.T., M.M., T.R.G., R.B., G.G.W., G.O., S.J.R., S.M., D.N., M.L., M.P., and L.T. contributed to the analysis and scientific discussions. B.C, C.S., A.D., G.G., and M.A.S. wrote the paper.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Gad Getz or Margaret A. Shipp.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–16 and Supplementary Note

  2. Reporting Summary

  3. Supplementary Table 1

    Sample summary

  4. Supplementary Table 2

    Patient characteristics

  5. Supplementary Table 3

    Significantly mutated genes

  6. Supplementary Table 4

    Mutational signature analyses

  7. Supplementary Table 5

    Chromosomal rearrangements

  8. Supplementary Table 6

    Significant CNAs and correlation to gene expression

  9. Supplementary Table 7

    Univariate and multivariate outcome associations

  10. Supplementary Table 8

    Gene sample matrix and features of consensus clusters

  11. Supplementary Table 9

    Clinical features and features across clusters

  12. Supplementary Table 10

    Ordering analyses

  13. Supplementary Table 11

    Outcome analyses of clusters