Abstract

B cell receptor (BCR) signalling has emerged as a therapeutic target in B cell lymphomas, but inhibiting this pathway in diffuse large B cell lymphoma (DLBCL) has benefited only a subset of patients1. Gene expression profiling identified two major subtypes of DLBCL, known as germinal centre B cell-like and activated B cell-like (ABC)2,3, that show poor outcomes after immunochemotherapy in ABC. Autoantigens drive BCR-dependent activation of NF-κB in ABC DLBCL through a kinase signalling cascade of SYK, BTK and PKCβ to promote the assembly of the CARD11–BCL10–MALT1 adaptor complex, which recruits and activates IκB kinase4,5,6. Genome sequencing revealed gain-of-function mutations that target the CD79A and CD79B BCR subunits and the Toll-like receptor signalling adaptor MYD885,7, with MYD88(L265P) being the most prevalent isoform. In a clinical trial, the BTK inhibitor ibrutinib produced responses in 37% of cases of ABC1. The most striking response rate (80%) was observed in tumours with both CD79B and MYD88(L265P) mutations, but how these mutations cooperate to promote dependence on BCR signalling remains unclear. Here we used genome-wide CRISPR–Cas9 screening and functional proteomics to determine the molecular basis of exceptional clinical responses to ibrutinib. We discovered a new mode of oncogenic BCR signalling in ibrutinib-responsive cell lines and biopsies, coordinated by a multiprotein supercomplex formed by MYD88, TLR9 and the BCR (hereafter termed the My-T-BCR supercomplex). The My-T-BCR supercomplex co-localizes with mTOR on endolysosomes, where it drives pro-survival NF-κB and mTOR signalling. Inhibitors of BCR and mTOR signalling cooperatively decreased the formation and function of the My-T-BCR supercomplex, providing mechanistic insight into their synergistic toxicity for My-T-BCR+ DLBCL cells. My-T-BCR supercomplexes characterized ibrutinib-responsive malignancies and distinguished ibrutinib responders from non-responders. Our data provide a framework for the rational design of oncogenic signalling inhibitors in molecularly defined subsets of DLBCL.

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Acknowledgements

This research was supported by the Intramural Research Program of the NIH, CCR, NCI and by a NCI Strategic Partnering to Evaluate Cancer Signatures (SPECS II) grant (5U01CA157581-05), as well as by Deutsche Krebshilfe (#111399) and Deutsche Forschungsgemeinschaft (SFB1177). D.E.W. is a Damon Runyon Fellow (DRG-2208-14). S.R. is a H2020 Marie Sklodowska-Curie global fellow (#661066). We thank AstraZeneca for AZD2014.

Reviewer information

Nature thanks A. Alizadeh, I. Mellinghoff, A. Rothstein and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

    • Sandrine Roulland

    Present address: Aix-Marseille University, CNRS, INSERM, Centre d’Immunologie de Marseille-Luminy, Marseille, France

  1. These authors contributed equally: James D. Phelan, Ryan M. Young

  2. These authors jointly supervised this work: Thomas Oellerich, Louis M. Staudt

Affiliations

  1. Lymphoid Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    • James D. Phelan
    • , Ryan M. Young
    • , Daniel E. Webster
    • , Sandrine Roulland
    • , Monica Kasbekar
    • , Arthur L. Shaffer III
    • , James Q. Wang
    • , Roland Schmitz
    • , Masao Nakagawa
    • , Emmanuel Bachy
    • , Da Wei Huang
    • , Yandan Yang
    • , Hong Zhao
    • , Xin Yu
    • , Weihong Xu
    • , Wyndham H. Wilson
    • , Craig J. Thomas
    • , Thomas Oellerich
    •  & Louis M. Staudt
  2. Biometric Research Branch, Division of Cancer Diagnosis and Treatment, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    • George W. Wright
  3. Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Gaithersburg, MD, USA

    • Michele Ceribelli
    • , Lu Chen
    •  & Craig J. Thomas
  4. Department of Medicine II, Hematology/Oncology, Goethe University, Frankfurt, Germany

    • Yanlong Ji
    •  & Thomas Oellerich
  5. Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    • Maryknoll M. Palisoc
    • , Racquel R. Valadez
    • , Theresa Davies-Hill
    • , Elaine S. Jaffe
    •  & Stefania Pittaluga
  6. Departments of Pathology, City of Hope National Medical Center, Duarte, CA, USA

    • Wing C. Chan
  7. British Columbia Cancer Agency, Vancouver, British Columbia, Canada

    • Randy D. Gascoyne
  8. Hospital Clinic, University of Barcelona, Barcelona, Spain

    • Elias Campo
  9. Institute of Pathology, University of Würzburg, and Comprehensive Cancer Center Mainfranken, Würzburg, Germany

    • Andreas Rosenwald
  10. Department of Clinical Pathology, Robert-Bosch-Krankenhaus, and Dr. Margarete Fischer-Bosch Institute for Clinical Pharmacology, Stuttgart, Germany

    • German Ott
  11. University Health Network, Laboratory Medicine Program, Toronto General Hospital and University of Toronto, Toronto, Ontario, Canada

    • Jan Delabie
  12. Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ, USA

    • Lisa M. Rimsza
  13. Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    • Fausto J. Rodriguez
  14. Department of Oncology, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    • Fayez Estephan
    •  & Matthias Holdhoff
  15. Experimental Immunology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    • Michael J. Kruhlak
  16. Experimental Pathology Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    • Stephen M. Hewitt
  17. German Cancer Research Center and German Cancer Consortium, Heidelberg, Germany

    • Thomas Oellerich

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Contributions

J.D.P. and R.M.Y. designed and performed experiments, analysed data, made the figures and wrote the manuscript. D.E.W., M.J.K., S.P., S.R., M.K., A.L.S.III, M.C., J.Q.W., R.S., M.N. and E.B. designed and performed experiments and analysed data. G.W.W., D.W.H., L.C. and C.J.T. analysed data, Y.J., Y.Y., H.Z., X.Y., W.X., M.M.P., R.R.V. and T.D.-H. performed experiments, W.H.W., W.C.C., E.S.J., R.D.G., E.C., A.R., G.O., J.D., L.M.R., F.J.R., F.E., M.H. and S.M.H. provided annotated clinical samples. T.O. and L.M.S. designed experiments, analysed data, made the figures and wrote the manuscript. The authors state they have no competing interests.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Thomas Oellerich or Louis M. Staudt.

Extended data figures and tables

  1. Extended Data Fig. 1 CRISPR screen controls.

    a, Schematic of CRISPR–Cas9 screens in lymphoma cell lines. b, 991 negative control non-targeting (left) or 1,210 positive control, essential gene (right) targeting sgRNAs are displayed for each cell line with indicated metrics. Box and whisker plots display mean and interquartile data, outliers represent 10% of the total dataset. c, Cumulative CRISPR screen scores for indicated genes are displayed for lymphoma cell lines screened. Source Data

  2. Extended Data Fig. 2 Correlation of genome-wide and replication CRISPR screens.

    A subset of lymphoma cell lines were rescreened with replication libraries sgRNAs targeting each of the displayed 62 genes. Depletion scores of the genome-wide screen are shown on the x axis, and the z-score of the average log2 fold change of all sgRNAs targeting a given gene is shown on the y axis for the replication screen. Pearson correlations (n = 62) and linear regressions are displayed for each of the overlapping datasets. b, Cumulative CRISPR screen scores for TLR-pathway genes are displayed for ABC (blue) and GCB (orange) DLBCL cell lines. Source Data

  3. Extended Data Fig. 3 CD19 is required for GCB and Burkitt lymphoma survival.

    a, A panel of 67 lymphoma cell lines was transduced with an shRNA targeting CD19. Shown is the log ratio of the percentage of live, shRNA-containing (GFP+) cells at the last time point (tfinal, 10–12 days) versus the intial time point (tinitial, day 0). ABC lines are depicted in blue, GCB lines depicted in orange, and Burkitt lymphoma lines are depicted in dark red. Data are mean ± s.e.m. are displayed from independent biological replicates. See ‘statistics and reproducibility’ section. b, FACS gating strategy for live, GFP+ shRNA or sgRNA expressing cells with knockdown of CD19 or negative control genes. Source Data

  4. Extended Data Fig. 4 TLR9 overexpression and association with the BCR are features of ABC DLBCL.

    a, Gene expression values (log2 FPKM) values of TLR9 associated genes are shown by DLBCL subtype, ABC in blue (n = 294), GCB in orange (n = 164) and unclassified (Unc) in grey (n = 115). Gene expression data were correlated with DNA copy number and linear regression calculated for ABC samples. *P < 0.05, ***P < 0.001, linear regression (left); *P < 0.05, ***P < 0.0001, one-way ANOVA and Tukey’s post test (right). b, Amplification of the UNC93B1 and CNPY3 loci (black lines, below chromosome ideogram). Minimal shared amplified regions in ABC DLBCL biopsies are bracketed and genes displayed below. c, The essential TLR9 interactome in TMD8. TLR9–BioID2 interactome determined by SILAC-based mass spectrometry (y axis) plotted by the CRISPR screen score (CSS, x axis). Bait (TLR9) is labelled in blue. Essential interactors are labelled in red, those shared with HBL1 (Fig. 2c) are labelled in dark red. d, Venn diagram of the overlap of TLR9–BioID2 interactors identified by SILAC-based mass spectrometry in experiments performed in TMD8 and HBL1 ABC lines. The 47 overlapping proteins are listed. e, The enrichment of 47 overlapping TLR9–BioID2 proximal proteins is shown (top) relative to their CSS (bottom). Gene names labelled in red are enriched and toxic to both HBL1 and TMD8. See ‘statistics and reproducibility’ section. Source Data

  5. Extended Data Fig. 5 IgM interacts with intracellular TLR9 in ABC DLBCL lines.

    a, Whole-cell lysates of indicated DLBCL cell lines were immunoprecipitated with anti-IgM or isotype control antibodies before being immunoblotted with IgM or indicated TLR antibodies, representative blots; n = 3. b, ABC DLBCL cell lines HBL1 and TMD8 were incubated on ice with IgM or isotype control antibodies and lysed. Lysates were immunoprecipitated (plasma membrane) with IgM or isotype control. Unbound lysates (cytosolic) were then immunoprecipitated with IgM or isotype control antibodies before all immunoprecipitated lysates were immunoblotted with the indicated antibodies; representative blots, n = 2. c, Left, confocal images of PLA reaction between IgM and TLR9 in HBL1 and TMD8 cells transduced with control SC4, CD79A or TLR9 shRNAs. Cells were puromycin selected and shRNAs induced with dox for two days before being fixed and imaged. Right, quantification data from three separate experiments. Data are pooled biologically independent experiments of n > 100 cells scored per condition. Box plots represents median and 25–75% of data, whiskers display 10–90 percentile. **P < 0.001, ***P < 0.001, one-way ANOVA with Dunnett’s post test. d, An IgM–TLR9 PLA (red) was performed in a panel of ABC and GCB DLBCL cell lines and the presence of chronic active BCR signalling (‘+’ denotes present), MYD88 mutational status and IgH isotype (μ = IgM, γ = IgG) are displayed. Nuclei were stained with DAPI (blue) and membranes were visualized by WGA (green). e, The number of puncta per cell of IgM–TLR9 PLA is quantitated. Box and whisker plots display mean and interquartile data, whiskers display 10–90 percentile. Data are from three fields of cells quantified from one representative experiment of three biologically independent replicates. f, The data from Extended Data Fig. 5e segregated by ABC (blue, n = 9) and GCB (orange, n = 9) lines. Box plots represent median and 25–75% of data, whiskers display range. **P < 0.01, Mann–Whitney unpaired one-tailed t-test. g, IgG–TLR9 PLA (red) was performed in indicated DLBCL cell lines co-stained with DAPI (blue) and IgG-AlexaFluor488 (green). MYD88 mutational status, IgH isotype and presence of chronic active BCR signalling (‘+’ denotes present; ‘−’ denotes absent) are displayed. Representative data from two independent biological replicates. h, To define the cytoplasmic location of the BCR–TLR9 interaction, we counterstained ABC cells for LAMP1, a marker of late endolysosomes, in which TLR9 resides, and performed PLA between IgM–TLR9, IgM–LAMP1 and IgM–SYK. The PLA signal is in red, LAMP1 is counterstained in blue. Representative data from three independent biological experiments. i, To quantify the association between PLA signals and LAMP1 staining, we calculated the Pearson’s correlation coefficients across all pixels in each imaged cell (n = 25 cells per PLA pair). The highest correlation was between an IgM–LAMP1 PLA and LAMP1 staining (R = 0.471), whereas the correlation between an IgM–SYK PLA signal and LAMP1 was much lower (R = 0.153). The correlation between the IgM–TLR9 PLA signal and LAMP1 staining was intermediate (R = 0.310), indicating that a significant component of the IgM–TLR9 interaction is in LAMP1+ vesicles. Quantified data are from one of three independent biological experiments. j, Quantification of the IgM–TLR9 PLA signal after ectopic expression of either empty vector, TLR9, wild-type MYD88 or MYD88(L265P). Data pooled from 3 (HBL1) or 2 (TMD8) biologically independent replicates of n ≥ 100 cells scored per condition. Box plots represents median and 25–75% of data, whiskers display 10–90 percentile. *P < 0.05, **P < 0.01, ***P < 0.001; one-way ANOVA with Dunnett’s post test. See ‘statistics and reproducibility’. Source Data

  6. Extended Data Fig. 6 TLR9 knockdown phenocopies MYD88 knockdown.

    a, TLR9 shRNA is rescued by overexpression of TLR9. HBL1 cells were transduced with empty vector (EV) or wild-type TLR9 expressing dsRedExpress2 vectors and then with shRNA vectors marked by GFP targeting a control (SC4), MYD88 or TLR9. The percentage of double-positive cells was monitored by FACS and normalized to day 0. One of three representative biologically independent experiments is shown. b, Heat map of gene expression values showing the global phenocopy of MYD88-dependent genes after shRNA-mediated knockdown of TLR9 or MYD88 in HBL1 at indicated time points. c, Gene signatures enriched in downregulated genes from HBL1 or TMD8 after shRNA-mediated knockdown of TLR9. d, Normalized IκBα−luciferase reporter levels at indicated time points after knockdown of TLR9 with indicated shRNAs. Data are mean and s.e.m. of nine technical replicates from n = 3 independent biological experiments., *P < 0.05, ***P < 0.001, one-way ANOVA with Sidak’s multiple comparison test. Source Data

  7. Extended Data Fig. 7 The MYD88(L265P) interactome in ABC DLBCL.

    a, The essential MYD88(L265P) interactome in HBL1. MYD88(L265P) –BioID2 interactome from SILAC-based mass spectrometry (y axis) plotted by the CSS (x axis). Bait (MYD88(L265P)) is labelled in blue. Essential interactors are red, with those shared with either TMD8 or OCI-Ly10 labelled in dark red. b, Venn diagram of the overlap of MYD88(L265P)–BioID2 interactors in TMD8, OCI-Ly10 and HBL1 ABC lines. Proteins found in two or more experiments are listed. c, Lysates of TMD8, HBL1 and U2932 cells transduced with empty vector or MYD88(L265P)–BioID2, selected and treated with 50 μM biotin for 24 h. Lysates were prepared and immunoprecipitated with streptavidin before being immunoblotted with CARD11 and MYD88 antibodies. One representative blot is shown for each cell line from n = 3 biologically independent experiments (HBL1, TMD8) and n = 1 (U2932) d, Lysates of TMD8 cells transduced with empty vector, MYD88(L265P) or wild-type BioID2-fusion proteins, selected and treated with 50 μM biotin for 24 h. Lysates were prepared and immunoprecipitated with streptavidin before being immunoblotted with CARD11, MALT1 or MYD88 antibodies; representative blot; n = 3. e, Confocal image of a PLA of BCL10 with MYD88. Data pooled from 6 biologically independent replicates of n > 200 cells scored per condition. Box plots represent median and 25–75% of data, whiskers display 10–90 percentile; one-way ANOVA with Dunnett’s post test. f, BCL10–MYD88 and MALT1–MYD88 PLA in ABC (blue, n = 9) and GCB (orange, n = 9) lines. Box plots represent median and 25–75% of data, whiskers display range; Mann–Whitney unpaired, one-tailed t-test. g, BCL10–CARD11 PLA after shRNA knockdown of indicated genes in ABC (blue) and GCB (orange) lines. CD79B and MYD88 mutation status is displayed below each cell line. Data are from 3 fields of cells quantitated from 1 representative experiment of 3 (HBL1), 2 (BJAB, DOHH2) or 1 (OYB, RIVA) biologically independent replicates of n ≥ 90 cells scored per condition. Box plots represent median and 25–75% of data, whiskers display 10–90 percentile; one-way ANOVA with Dunnett’s post test. h, ABC lines expressing MYD88(L265P)–BioID2 were treated with DMSO or 10 nM ibrutinib for 24 h, and the numbers of biotin puncta were quantified from confocal images (representative experiment, n = 3). Fisher’s exact test, two-sided. i, SILAC-based mass spectrometry comparison of MYD88(L265P)–BioID2 interactome in TMD8 cells treated with DMSO (x axis) versus 10 nM ibrutinib (y axis). Proteins reduced upon ibrutinib treatment are shown in red, those similarly decreased in two separate cell lines (Fig. 4a) are labelled in dark red. Bait (MYD88) is labelled in blue. Venn diagram showing overlap of proteins decreased by more than 30% in OCI-Ly10 cells (Fig. 4a) is shown as an inset. *P < 0.05, **P < 0.01, ***P < 0.001; see ‘statistics and reproducibility’ section. Source Data

  8. Extended Data Fig. 8 IgM–TLR9 PLA identifies ABC samples with chronic active BCR signalling in tissue microarrays.

    a, IgM–TLR9 PLA was performed on a formalin-fixed, paraffin-embedded (FFPE) tissue microarray of lymphoma cell lines. PLA puncta were quantified and plotted as the absolute number of spots per cell from at least 2 images of 1 representative experiment from 3 independent tissue microarray replicates. Box plots represent median and 25–75% of data, whiskers display range. Cell lines are divided by putative lymphoma subtype for presentation. BL, Burkitt lymphoma; BPDC, blastic plasmacytoid dendritic cell neoplasm; HL, Hodgkin lymphoma; MZL, marginal zone lymphoma; PMBL, primary mediastinal B cell lymphoma; WM, Waldenström’s macroglobulinemia. b, Representative confocal fluorescent image from three independent biological samples of a germinal centre from a reactive lymph node. IgM–TLR9 PLA is shown in red; CD20 is in green; CD138 is in white; and DAPI is in blue. Source Data

  9. Extended Data Fig. 9 Waldenström’s macroglobulinaemia can utilize the My-T-BCR supercomplex.

    a, shRNA-mediated toxicity of indicated genes in two Waldenström’s macroglobulinaemia cell lines (RPCI-WM1 and MWCL-1). Control (SC4), CD79A, TLR9 or MYD88 shRNAs were expressed in tandem with GFP and the relative level of GFP was followed over time by FACS. Data are mean and s.e.m. of independent biological experiments; see ‘statistics and reproducibility’ section. b, Confocal images from one of two representative biologically independent experiments of the PLA reaction between IgM and TLR9 (red puncta). Cells were counterstained with DAPI (blue) and WGA (green). Scale bars, 10 μm. c, Normalized quantification (PLA score) of IgM–TLR9. Data were quantified from at least 28 cells per condition. Box plots represent median and 25–75% of data, whiskers display range. Source Data

Supplementary information

  1. Supplementary Figure 1

    This file contains the uncropped western blots

  2. Reporting Summary

  3. Supplementary Tables

    This file contains Supplementary Tables 1-18

  4. Video 1: Localization of the My-T-BCR complex was visualized by confocal microscopy.

    Cyan=Streptavidin, White=anti-phospho-IKKα/β, Red=anti-LAMP1, Green=anti-IgM-FAB. One of four independent biological replicates is shown

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DOI

https://doi.org/10.1038/s41586-018-0290-0

Further reading

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