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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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Abstract

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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Acknowledgements

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.

Affiliations

  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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Contributions

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