Abstract
Diffuse large B cell lymphoma (DLBCL) is the most common B cell non-Hodgkin lymphoma and remains incurable in around 40% of patients. Efforts to sequence the coding genome identified several genes and pathways that are altered in this disease, including potential therapeutic targets1,2,3,4,5. However, the non-coding genome of DLBCL remains largely unexplored. Here we show that active super-enhancers are highly and specifically hypermutated in 92% of samples from individuals with DLBCL, display signatures of activation-induced cytidine deaminase activity, and are linked to genes that encode B cell developmental regulators and oncogenes. As evidence of oncogenic relevance, we show that the hypermutated super-enhancers linked to the BCL6, BCL2 and CXCR4 proto-oncogenes prevent the binding and transcriptional downregulation of the corresponding target gene by transcriptional repressors, including BLIMP1 (targeting BCL6) and the steroid receptor NR3C1 (targeting BCL2 and CXCR4). Genetic correction of selected mutations restored repressor DNA binding, downregulated target gene expression and led to the counter-selection of cells containing corrected alleles, indicating an oncogenic dependency on the super-enhancer mutations. This pervasive super-enhancer mutational mechanism reveals a major set of genetic lesions deregulating gene expression, which expands the involvement of known oncogenes in DLBCL pathogenesis and identifies new deregulated gene targets of therapeutic relevance.
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Data availability
Raw WGS data from 20 cases of primary DLBCL and 21 DLBCL cell lines were deposited in the dbGaP database under accession number phs000328.v3.p1. H3K27Ac ChIP–seq data from the 29 cell lines were deposited at the GEO database under accession number GSE182214. Other datasets used in this analysis were downloaded from the European Genome–Phenome Archive (EGAD00001004142, EGAD00001006087 and EGAD00001003783)10,34; dbGaP (phs000235.v14.p2 and phs000527.v3.p1); the NCBI (SRP020237); the GEO (GSE89688 (H3K27Ac data of normal GC B cells) and GSE69558 (H3K27Ac data of primary lymphoma cases)); and the ICGC Xena portal (https://xenabrowser.net/datapages/?hub=https://pcawg.xenahubs.net:443)83. Source data are provided with this paper.
Code availability
No custom codes were used in the study.
Change history
17 October 2022
A Correction to this paper has been published: https://doi.org/10.1038/s41586-022-05285-w
References
Pasqualucci, L. et al. Analysis of the coding genome of diffuse large B-cell lymphoma. Nat. Genet. 43, 830–837 (2011).
Morin, R. D. et al. Frequent mutation of histone-modifying genes in non-Hodgkin lymphoma. Nature 476, 298–303 (2011).
Reddy, A. et al. Genetic and functional drivers of diffuse large B cell lymphoma. Cell 171, 481–494 (2017).
Chapuy, B. et al. Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat. Med. 24, 679–690 (2018).
Schmitz, R. et al. Genetics and pathogenesis of diffuse large B-cell lymphoma. New Engl. J. Med. 378, 1396–1407 (2018).
Roschewski, M., Staudt, L. M. & Wilson, W. H. Diffuse large B-cell lymphoma-treatment approaches in the molecular era. Nat. Rev. Clin. Oncol. 11, 12–23 (2014).
Alizadeh, A. A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000).
Rosenwald, A. et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. New Engl. J. Med. 346, 1937–1947 (2002).
Pasqualucci, L. & Dalla-Favera, R. Genetics of diffuse large B-cell lymphoma. Blood 131, 2307–2319 (2018).
Arthur, S. E. et al. Genome-wide discovery of somatic regulatory variants in diffuse large B-cell lymphoma. Nat. Commun. 9, 4001 (2018).
Wilson, W. H. et al. Targeting B cell receptor signaling with ibrutinib in diffuse large B cell lymphoma. Nat. Med. 21, 922–926 (2015).
Wright, G. W. et al. A probabilistic classification tool for genetic subtypes of diffuse large B cell lymphoma with therapeutic implications. Cancer Cell 37, 551–568 e514 (2020).
Lacy, S. E. et al. Targeted sequencing in DLBCL, molecular subtypes, and outcomes: a Haematological Malignancy Research Network report. Blood 135, 1759–1771 (2020).
Mansour, M. R. et al. Oncogene regulation. An oncogenic super-enhancer formed through somatic mutation of a noncoding intergenic element. Science 346, 1373–1377 (2014).
Abraham, B. J. et al. Small genomic insertions form enhancers that misregulate oncogenes. Nat. Commun. 8, 14385 (2017).
Koues, O. I. et al. Enhancer sequence variants and transcription-factor deregulation synergize to construct pathogenic regulatory circuits in B-cell lymphoma. Immunity 42, 186–198 (2015).
Pasqualucci, L. et al. Hypermutation of multiple proto-oncogenes in B-cell diffuse large-cell lymphomas. Nature 412, 341–346 (2001).
Honjo, T., Muramatsu, M. & Fagarasan, S. AID: how does it aid antibody diversity? Immunity 20, 659–668 (2004).
Qian, J. et al. B cell super-enhancers and regulatory clusters recruit AID tumorigenic activity. Cell 159, 1524–1537 (2014).
Meng, F. L. et al. Convergent transcription at intragenic super-enhancers targets AID-initiated genomic instability. Cell 159, 1538–1548 (2014).
Hubschmann, D. et al. Mutational mechanisms shaping the coding and noncoding genome of germinal center derived B-cell lymphomas. Leukemia 35, 2002–2016 (2021).
Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).
Ryan, R. J. et al. Detection of enhancer-associated rearrangements reveals mechanisms of oncogene dysregulation in B-cell lymphoma. Cancer Discov. 5, 1058–1071 (2015).
Tippens, N. D. et al. Transcription imparts architecture, function and logic to enhancer units. Nat. Genet. 52, 1067–1075 (2020).
Pefanis, E. et al. RNA exosome-regulated long non-coding RNA transcription controls super-enhancer activity. Cell 161, 774–789 (2015).
Imielinski, M., Guo, G. & Meyerson, M. Insertions and deletions target lineage-defining genes in human cancers. Cell 168, 460–472 (2017).
Shinde, J. et al. Palimpsest: an R package for studying mutational and structural variant signatures along clonal evolution in cancer. Bioinformatics 34, 3380–3381 (2018).
Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).
Pham, P., Bransteitter, R., Petruska, J. & Goodman, M. F. Processive AID-catalysed cytosine deamination on single-stranded DNA simulates somatic hypermutation. Nature 424, 103–107 (2003).
Balinas-Gavira, C. et al. Frequent mutations in the amino-terminal domain of BCL7A impair its tumor suppressor role in DLBCL. Leukemia 34, 2722–2735 (2020).
Mottok, A. et al. Genomic alterations in CIITA are frequent in primary mediastinal large B cell lymphoma and are associated with diminished MHC class II expression. Cell Rep. 13, 1418–1431 (2015).
Kuhrt, D. & Wojchowski, D. M. Emerging EPO and EPO receptor regulators and signal transducers. Blood 125, 3536–3541 (2015).
Basso, K. & Dalla-Favera, R. Roles of BCL6 in normal and transformed germinal center B cells. Immunol. Rev. 247, 172–183 (2012).
Hilton, L. K. et al. The double-hit signature identifies double-hit diffuse large B-cell lymphoma with genetic events cryptic to FISH. Blood 134, 1528–1532 (2019).
Shapiro-Shelef, M. et al. Blimp-1 is required for the formation of immunoglobulin secreting plasma cells and pre-plasma memory B cells. Immunity 19, 607–620 (2003).
Mandelbaum, J. et al. BLIMP1 is a tumor suppressor gene frequently disrupted in activated B cell-like diffuse large B cell lymphoma. Cancer Cell 18, 568–579 (2010).
Parekh, S. et al. BCL6 programs lymphoma cells for survival and differentiation through distinct biochemical mechanisms. Blood 110, 2067–2074 (2007).
Shaffer, A. L. et al. Blimp-1 orchestrates plasma cell differentiation by extinguishing the mature B cell gene expression program. Immunity 17, 51–62 (2002).
Saito, M. et al. BCL6 suppression of BCL2 via Miz1 and its disruption in diffuse large B cell lymphoma. Proc. Natl Acad. Sci. USA 106, 11294–11299 (2009).
Liu, M. et al. Two levels of protection for the B cell genome during somatic hypermutation. Nature 451, 841–845 (2008).
Weikum, E. R., Knuesel, M. T., Ortlund, E. A. & Yamamoto, K. R. Glucocorticoid receptor control of transcription: precision and plasticity via allostery. Nat. Rev. Mol. Cell Biol. 18, 159–174 (2017).
Xiao, H. et al. Haploinsufficiency of NR3C1 drives glucocorticoid resistance in adult acute lymphoblastic leukemia cells by down-regulating the mitochondrial apoptosis axis, and is sensitive to Bcl-2 blockage. Cancer Cell Int. 19, 218 (2019).
Allen, C. D. et al. Germinal center dark and light zone organization is mediated by CXCR4 and CXCR5. Nat. Immunol. 5, 943–952 (2004).
Kaiser, L. M., Hunter, Z. R., Treon, S. P. & Buske, C. CXCR4 in Waldenstrom's macroglobulinema: chances and challenges. Leukemia 35, 333–345 (2021).
Pasqualucci, L. et al. BCL-6 mutations in normal germinal center B cells: evidence of somatic hypermutation acting outside Ig loci. Proc. Natl Acad. Sci. USA 95, 11816–11821 (1998).
Shen, H. M., Peters, A., Baron, B., Zhu, X. & Storb, U. Mutation of BCL-6 gene in normal B cells by the process of somatic hypermutation of Ig genes. Science 280, 1750–1752 (1998).
Shen, J. C. et al. A high-resolution landscape of mutations in the BCL6 super-enhancer in normal human B cells. Proc. Natl Acad. Sci. USA 116, 24779–24785 (2019).
Gamberi, B. et al. Microsatellite instability is rare in B-cell non-Hodgkin's lymphomas. Blood 89, 975–979 (1997).
de Miranda, N. F. et al. DNA repair genes are selectively mutated in diffuse large B cell lymphomas. J. Exp. Med. 210, 1729–1742 (2013).
Cattoretti, G. et al. Deregulated BCL6 expression recapitulates the pathogenesis of human diffuse large B cell lymphomas in mice. Cancer Cell 7, 445–455 (2005).
Batmanov, K., Wang, W., Bjoras, M., Delabie, J. & Wang, J. Integrative whole-genome sequence analysis reveals roles of regulatory mutations in BCL6 and BCL2 in follicular lymphoma. Sci. Rep. 7, 7040 (2017).
Morin, R. D. et al. Mutational and structural analysis of diffuse large B-cell lymphoma using whole-genome sequencing. Blood 122, 1256–1265 (2013).
Compagno, M. et al. Mutations of multiple genes cause deregulation of NF-κB in diffuse large B-cell lymphoma. Nature 459, 717–721 (2009).
Grande, B. M. et al. Genome-wide discovery of somatic coding and noncoding mutations in pediatric endemic and sporadic Burkitt lymphoma. Blood 133, 1313–1324 (2019).
Puente, X. S. et al. Non-coding recurrent mutations in chronic lymphocytic leukaemia. Nature 526, 519–524 (2015).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010)
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).
Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
Favero, F. et al. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann. Oncol. 26, 64–70 (2015).
Wala, J. A. et al. SvABA: genome-wide detection of structural variants and indels by local assembly. Genome Res. 28, 581–591 (2018).
Chen, X. et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 32, 1220–1222 (2016).
Quinlan, A. R. BEDTools: the Swiss-Army tool for genome feature analysis. Curr. Protoc. Bioinformatics 47, 11.12.1–11.12.34 (2014).
Zhang, J. et al. Disruption of KMT2D perturbs germinal center B cell development and promotes lymphomagenesis. Nat. Med. 21, 1190–1198 (2015).
Zhang, J. et al. The CREBBP acetyltransferase is a haploinsufficient tumor suppressor in B-cell lymphoma. Cancer Discov. 7, 322–337 (2017).
Giannopoulou, E. G. & Elemento, O. An integrated ChIP-seq analysis platform with customizable workflows. BMC Bioinformatics 12, 277 (2011).
Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).
Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Bunting, K. L. et al. Multi-tiered reorganization of the genome during B cell affinity maturation anchored by a germinal center-specific locus control region. Immunity 45, 497–512 (2016).
Bailey, T. L., Johnson, J., Grant, C. E. & Noble, W. S. The MEME Suite. Nucleic Acids Res. 43, W39–W49 (2015).
Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
Meyer, S. N. et al. Unique and shared epigenetic programs of the CREBBP and EP300 acetyltransferases in germinal center B cells reveal targetable dependencies in lymphoma. Immunity 51, 535–547 (2019).
Bereshchenko, O. R., Gu, W. & Dalla-Favera, R. Acetylation inactivates the transcriptional repressor BCL6. Nat. Genet. 32, 606–613 (2002).
Pasqualucci, L. et al. Mutations of the BCL6 proto-oncogene disrupt its negative autoregulation in diffuse large B-cell lymphoma. Blood 101, 2914–2923 (2003).
Unnikrishnan, A. et al. A quantitative proteomics approach identifies ETV6 and IKZF1 as new regulators of an ERG-driven transcriptional network. Nucleic Acids Res. 44, 10644–10661 (2016).
Shevchenko, A., Tomas, H., Havlis, J., Olsen, J. V. & Mann, M. In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nat. Protoc. 1, 2856–2860 (2006).
Meier, F. et al. Online parallel accumulation serial fragmentation (PASEF) with a novel trapped on mobility mass spectrometer. Mol. Cell. Proteom. 17, 2534–2545 (2018).
Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res.10, 1794–1805 (2011).
The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).
Acknowledgements
We thank A. Ciccia and T. S. Nambiar for their advice on the design of CRISPR–Cas9 experiments; K. R. Loeb and L. A. Loeb for sharing the BCL6 mutation data in memory B cells prior to publication; A. Singh for help with the multivariate analysis; and A. A. Ferrando, U. Klein and R. Küppers for reading the manuscript. This study was supported by NIH grants R35-CA210105 (to R.D.-F.), R01-CA172492 (to L.P.) and R01-CA233662 (to H.K.); an AstraZeneca Scholar Award (to R.D.-F. and L.P.); a Herbert Irving Comprehensive Cancer Center (HICCC) VELOCITY award (to L.P. and R.D.-F.); and a Translational Grant from the V Foundation (T2019-012) (to H.K.). The study was also funded in part through the NIH/NCI Cancer Center Support Grant P30-CA13696 (HICCC) and P30-CA072720 (Rutgers Cancer Institute of New Jersey), and used the resources of the HICCC CCTI Flow Cytometry Core Facility, the Molecular Pathology Shared Resource and the JP Sulzberger Genome Center at Columbia University Irving Medical Center, as well as the Biomedical Informatics Shared Resource at Rutgers Cancer Institute of New Jersey. E.B. was an AACR-AstraZeneca Lymphoma Research Fellow, and C.C. is supported by a Lymphoma Research Foundation fellowship. K.D. was supported by grant 1P01CA229100 from the National Cancer Institute (to D. W. Scott). L.P. is on leave from the University of Perugia Medical School, Perugia, Italy. The results published here are in whole or in part based on data generated by the Cancer Genome Characterization Initiative (CGCI) (phs000235), non-Hodgkin Lymphoma, developed by the NCI and available at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000235.v14.p2 and https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000527.v3.p1. Information about CGCI projects can be found online (https://ocg.cancer.gov/programs/cgci). We also acknowledge the members of the ICGC MALY-DE project (https://dcc.icgc.org) and the European Genome–Phenome Archive (https://ega-archive.org) for providing access to their datasets. All data were used according to the data use agreements.
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Authors and Affiliations
Contributions
R.D.-F. conceived the study. R.D.-F. and L.P. supervised the study. E.B. and L.P. designed experiments and analysed data. E.B. performed all of the laboratory experiments, with help from S.V. and C.C. H.K. supervised and implemented the statistical and bioinformatics analyses of the DLBCL cell lines, discovery cohort and pan-cancer cohort, which were analysed by R.K. and M.H. with help from J.W.L.; R.D.M. and L.K.H. supervised the bioinformatics analysis of WGS and RNA-seq data from the DLBCL extension cohort and BL cohort, with contributions from K.D. and J.C.H.W.; A.B.H. processed ChIP–seq data, which were analysed by K.B. and L.P.; R.K.S. performed mass spectrometry analysis. E.B., L.P. and R.D-.F. wrote the manuscript. All of the authors discussed the results and implications, and commented on the manuscript at all stages. L.P. and R.D.-F. acquired funding.
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R.D.-F. is a member of the scientific advisory board of NeoGenomics, and a consultant for Astra Zeneca. The work reported in this paper is not related to the current activities of these companies.
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Extended data figures and tables
Extended Data Fig. 1 Experimental strategy used for the identification of mutated E/SEs in DLBCL.
H3K27Ac ChIP–seq data were generated in duplicate from 29 cell lines and 2 independent pools of human GC B cells, and used for the identification of active E/SEs based on the ROSE algorithm. The resulting list of E/SEs was intersected with the list of SNVs identified by WGS analysis in the same cell lines (matching each cell line to its own E/SEs) or in a panel of 93 de novo DLBCLs with matched normal DNA (Discovery Panel), to identify recurrently mutated E/SEs. An independent panel of 150 primary cases with WGS data and 169 primary cases with targeted-sanger sequencing data (Extension Panel) was then used to confirm the recurrent targeting of specific mutational hotspots identified in the BCL6-iSE, the BCL2-SE, and the CXCR4-SE.
Extended Data Fig. 2 E/SEs identified by ROSE are enriched in H3K4me1 and recapitulate the E/SE landscape of primary large B cell lymphomas.
a. Heatmaps of the indicated histone mark signal at SEs, Es, and Promoter regions, defined as described in Methods. Shown are the 50 kb regions upstream and downstream of the H3K27Ac peak centre, set as 0. The colour scale indicates the normalized z score. Data are shown for GC B cells and include all “shared” E/SEs found significantly hypermutated in DLBCL (Fig. 1b, d) as well as promoter regions. b. ChIP–seq density profiles of H3K4me1, H3K4me3, H3K27Ac and H3K27me3 at SEs, Es, and Promoter regions. The plots indicate normalized mean ChIP–seq density, relative to the H3K27Ac peak summit, set as 0. For the SEs, data are provided overall (top left) and separately for intragenic SEs, intergenic SEs, and the set of recurrently hypermutated “shared” SEs presented in Supplementary Table 2 (HyperM SE)(bottom). c. Left panel: number of H3K27Ac peaks nominated as SEs in the GC B cell pool CB4 (n = 655) and also assigned to SEs (dark red) or classic Es (light red) in primary large B cell lymphoma cases. Data are provided separately for each of the 7 samples, and GC SEs not decorated by H3K27Ac in the primary samples are coloured in grey. The reverse analysis is shown in the right panel, as the relative percentage of DLBCL-specific active SEs also found in normal GC B cells (dark red if active SEs, light red if typical Es) or not decorated by H3K27Ac in normal GC B cells (grey shade, representing ‘de novo’ SEs).
Extended Data Fig. 3 Histone modification pattern of representative intragenic and TSS-distal SEs targeted by mutations in DLBCL.
a. ChIP–seq tracks of H3K4me3, H3K4me1, H3K27Ac, and H3K27me3 at representative intragenic SHM-targeted SEs in normal GC B cells vs. primary B cell lymphomas. Enrichment is visualized as reads per bins per million bps (BPM), and the genomic coordinates of the region shown (hg19) are provided at the bottom, with the annotated coding gene/s (RefSeq accession No: NM_001706 for BCL6, NM_000633 for BCL2, NM_003467 for CXCR4, and NM_016734 for PAX5). Green horizontal bars below the H3K27Ac tracks indicate regions identified as SEs by ROSE. The dotted square indicates the hypermutated region. In the bottom panel, distribution and number of mutations identified in cases of primary DLBCL. b. ChIP–seq tracks of H3K4me3, H3K4me1, H3K27Ac, and H3K27me3 at representative TSS-distal, SHM-targeted SEs in normal GC B cells. H3K27Ac is also shown for the DLBCL cell line LY1. Enrichment is visualized as reads per bins per million bps (BPM), and green horizontal bars below the H3K27Ac tracks indicate regions identified as SEs by ROSE. The cartoon on top provides a broader view of the genomic region expanded below (dotted lines), with annotated coding genes represented as solid boxes, intergenic regions represented as lines, and their promoter orientation indicated as an arrow (not in scale). The exact genomic coordinates (hg19) of the region magnified are provided at the bottom, and the distribution of somatic mutations found across the region in DLBCL (cell lines and primary cases) is plotted below the gene/s track.
Extended Data Fig. 4 Transcriptional activity at active E/SEs targeted by SHM.
a. Distribution of transcripts per million (TPM) values for hypermutated, not hypermutated, and inactive E/SEs identified in the 29 DLBCL cell lines. b. Distribution of TPM values for sense and antisense transcripts at the indicated chromatin domains, documenting divergent transcription (legend as in panel a). c. H3K27Ac ChIP–seq track (green) and RNA-seq tracks (red, antisense transcription; blue, sense transcription) at 4 representative loci targeted by SE-SHM. Green bar denotes the SEs identified by ROSE. BPM, bins per million (see methods). In panels a and b, pairwise comparisons between TPM values were significantly different across all categories and are thus not indicated (adjusted p < 0.05; Wilcoxon rank-sum test with BH correction).
Extended Data Fig. 5 Mutations are enriched at intragenic SEs encompassing H3K4me3+ promoters.
a. Overall mutation frequency (left panel) and percentage of hypermutated E/SEs (right panel; see Methods and Fig. 1b for definition) in 29 DLBCL cell lines. Data are provided separately for E/SEs encompassing promoters (i.e. overlapping with H3K4me3+) and devoid of promoters (i.e. not overlapping with H3K4me3+ regions and TSS-distal). Each dot represents one cell line, and mutation frequencies are expressed as fold changes vs. the background mutation frequency of the same cell line, set as 1 (dotted line). A horizontal red bar defines the mean across all 29 cell lines.b. Overall mutation frequency (left panel) and percentage of hypermutated active promoters (H3K27Ac+ H3K4me3+ regions) in 29 DLBCL cell lines. Data are provided separately for promoters embedded in SEs, promoters embedded in Es, and classical active promoters not embedded in E/SEs. Each dot represents one cell line, and the mutation frequencies are expressed as fold changes vs. the background mutation frequency of the same cell line, set as 1 (dotted line). A horizontal red bar defines the mean across all 29 cell lines. c. Overall mutation frequency (left panel) and percentage of hypermutated core E/SEs (right panel; see Methods and Fig. 1d for definition) in 93 primary DLBCL biopsies. Data are provided separately for E/SEs encompassing promoters and E/SEs devoid of promoters (defined as in a). Each dot represents one sample, and mutation frequencies are expressed as fold changes vs. the background mutation frequency of the same sample, set as 1 (dotted line). A horizontal red bar defines the mean across all 93 DLBCL samples. Note that these frequencies represent an underestimate of the actual mutation frequencies as, in the absence of sample-specific H3K27Ac data, the region interrogated represents the union of the core E/SEs found in all 29 cell lines. d. Overall mutation frequency (left panel) and percentage of hypermutated active promoters (H3K27Ac+ H3K4me3+ regions) in 93 cases of primary DLBCL. Data are provided separately for promoters embedded in SEs, promoters embedded in Es, and classical active promoters not embedded in E/SEs. Each dot represents one DLBCL biopsy, and mutation frequencies are expressed as fold changes vs. the background mutation frequency of the same sample, set as 1 (dotted line). A horizontal red bar defines the mean across all 93 DLBCL biopsies. All p-values were calculated by two-sided Wilcoxon rank-sum test after BH correction.
Extended Data Fig. 6 Mutation frequency and percentage of hypermutated E/SEs in cases of primary DLBCL.
a. Venn diagrams showing the overlap between E/SEs identified in normal GC B cells, GCB-DLBCL cell lines and ABC-DLBCL cell lines. The shadowed area marks the subset of E/SEs interrogated in each of the analyses shown in panels b and c (same row), and the corresponding number is given for each subset inside the diagram. The total number of E/SEs identified in GC B cells, GCB-DLBCL cell lines and ABC-DLBCL cell lines appears outside the Venn diagram, in brackets. b, c. Sample-based mutation frequency (b) and percentage (c) of hypermutated E/SEs in primary DLBCL specimens grouped based on phenotypic subtypes (UNC, unclassified; ND, not determined). The analysis of different regions (corresponding to those highlighted in the aligned Venn diagram) is displayed in different rows, and data for “shared” E/SEs (Fig. 1d, e) are shown for comparison on the top row, as these regions emerged as harbouring the highest mutation frequency and % of hypermutated cases in DLBCL. In the graphs, each dot denotes one primary DLBCL sample, and mutation frequencies are expressed as fold changes vs. background, calculated in the same sample as described in Methods. The grey dashed line in panel b represents the background mutation frequency, set at 1 for each sample (see Methods). All p-values were calculated by two-sided Wilcoxon rank-sum test after BH correction.
Extended Data Fig. 7 Mutation frequency and percentage of hypermutated E/SEs in BL and CLL.
a. Venn diagrams showing the overlap between E/SEs identified in normal GC B cells, GCB-DLBCL cell lines and ABC-DLBCL cell lines. The shadowed area marks the subset of E/SEs interrogated in each of the analyses shown in panels b and c (same row), and the corresponding number is given for each subset inside the diagram. The total number of E/SEs identified in GC B cells, GCB-DLBCL cell lines and ABC-DLBCL cell lines appears outside the Venn diagram, in brackets. b, c. Sample-based mutation frequency (b) and percentage (c) of hypermutated E/SEs in primary BL and CLL specimens grouped based on molecular subtypes (IGHV-unmutated: CLL-UM; IGHV-mutated: CLL-M). The analysis of different regions (corresponding to those highlighted in the aligned Venn diagram) is displayed in different rows. In the graphs, each dot denotes one primary biopsy, and mutation frequencies are expressed as fold changes vs. background, calculated in the same sample as described in Methods. The grey dashed line in panel b represents the background mutation frequency, set at 1 for each sample (see Methods). All p-values were calculated by two-sided Wilcoxon rank-sum test after BH correction. Note that 82% of hypermutated SEs in CLL map to the IG loci.
Extended Data Fig. 8 FishHook independently identifies E/SEs as recurrent mutational targets in DLBCL.
a. Significantly mutated regions (1 kb) identified by FishHook in WGS data from the 93 DLBCL Discovery cases. The dotted line denotes the q-value at 10−6. Asterisks indicate peaks overlapping with hypermutated SEs identified by the integrated ChIP–seq-based approach presented in Extended Data Fig. 1 (see also Methods). b. The percentage of cases of primary DLBCL hypermutated across the 3,775 SEs identified. c. Overlap of significantly mutated 1 kb-regions called by FishHook (n = 164) with the union list of active SEs identified by ChIP–seq; data are shown for all SEs (n = 3,775, diagram on the far-left side) or for SEs hypermutated in at least 2 cases (n = 159, third diagram from the left). In a separate analysis, contiguous 1 kb FishHook regions falling within the same SE were stitched into loci, and the overlap between these loci (n = 66) and the union list of active SEs is provided on the second and fourth diagrams.
Extended Data Fig. 9 SE-associated mutational signatures display features of AID activity.
a. Cosine similarity of the three de novo mutational signatures identified in SEs (Fig. 1f) to the COSMIC signatures database. Blue slices in the pie reflect the degree of similarity with the COSMIC SBS signature listed to the left. Signatures with the highest similarity are highlighted in red (SBS9, non-canonical AID [ncAID]; SBS40, ageing; SBS84, AID). b. Cumulative activity of the three signatures at SEs, Es and the rest of the genome (WG) across the 93 cases of DLBCL, overall (left panels) and separately for GCB- and ABC-DLBCL (right panels). c,d. Mutation frequency of WRCY and non-WRCY motifs in E/SEs, as compared to random regions of equivalent size selected from the “rest of the genome”; data are shown for primary DLBCL biopsies (c) and cell lines (d). All p-values were calculated by two-sided Wilcoxon rank-sum test after BH correction.
Extended Data Fig. 10 Percentage of cases carrying mutations in the BCL6-BLIMP1-BS, BCL2-NR3C1-BS, and CXCR4-NR3C1-BS across lymphoid and non-lymphoid cancers.
Percentage of cases carrying mutations in the BCL6-, BCL2-, and CXCR4- SE hotspots characterized in Figs. 4–6 across an independent panel of GC-derived lymphomas (DLBCL, FL, BL, and IGHV-mutated-CLL), non-GC derived lymphomas (IGHV-unmutated-CLL), and other cancer types from the ICGC pan-cancer project. In further support of the selective pressure for mutating these sequences, 15 bp domains located between 0.5 kb and 3 kb from the BS, within the same SE (BS-distal) were randomly selected with 100 permutations, and the percentage of mutated cases in these regions was calculated (grey bars; mean of 100 permutations). Particularly relevant is the lack of hotspot mutations in M-CLL (which have transited through the GC and therefore represent specific surrogates of normal GC alleles).
Extended Data Fig. 11 Mutations in the BCL6-B1BS are enriched in GCB- and ST2-DLBCL.
a. Immunoblot analysis of BCL6 protein expression in isogenic clones obtained from HLY1, LY18 and Karpas-422 after CRISPR-Cas9-mediated correction of specific mutations in the BCL6-iSE hotspot or after introduction of mutations in the control neutral region (n = 4 clones/sgRNA; related to Fig. 3c, d). α-Tubulin, loading control. Shown is one representative experiment out of two that gave similar results (for gel source data, see Supplementary Figure 1). On the right panel, quantification of BCL6 expression, as assessed by densitometry after normalization for loading control (two-tailed unpaired t-test). b. Sequencing traces of BCL6 PCR amplicons encompassing cell-line specific heterozygous SNPs segregating with the mutant allele, obtained from the 8 clones shown in a. A schematic showing the allelic distribution of the BCL6 SNP, relative to the somatic mutation corrected in each cell line, is provided on the top. Amplicons were generated from DNA (one representative clone shown) and cDNA (n = 4 clones/sgRNA). c,d. Immunoblot analysis of BLIMP1, BCL6 and HDAC1 expression in HLY1 (c) and LY18 (d) clones. In LY18, experiments were performed in basal conditions (UT) or upon CD40L stimulation. SUDHL4 is used as negative control for BLIMP1 and positive control for BCL6 expression. * non-specific band (for gel source data, see Supplementary Figure 1). e. Overlap between cases harbouring mutations in the BCL6-BLIMP1 binding site (B1BS), BCL6 translocations (Tx), and/or coding mutations in the PRDM1 gene. Data are from 391 cases of primary DLBCL analysed by WGS or Sanger sequencing. f. BCL6 expression levels in cases of primary DLBCL stratified based on the genetic lesions indicated in e (n = 181 cases with matched WGS and RNA-seq data). Significant differences were calculated by one-way ANOVA with Bonferroni correction. g. Relative distribution of cases harbouring the indicated genetic lesions in various DLBCL COO subtypes (two-tailed Fisher’s exact test). The total number of cases analysed within each subtype is provided on the x-axis label, and the number of mutated cases is shown on the top. h. Mutation harbouring the indicated genetic lesions in different LymphGen classes. The total number of cases analysed is provided on the x-axis label, and the number of mutated cases is shown on the top. A two-tailed Fisher’s exact test was used to determine whether cases carrying the indicated genetic alteration were significantly enriched in a specific LymphGen class versus all other classes combined. Of note, although mutations in BCL6-B1BS can be found at some frequencies in all COO and LymphGen subroups, they were preferentially enriched in the GCB- and ST2 subgroups.
Extended Data Fig. 12 Mutations in the BCL6-B1BS influence DLBCL class assignment.
a. Oncoplot of 223 cases of DLBCL classified into distinct genetic subtypes according to the LymphGen algorithm. Columns represent different cases of DLBCL and rows correspond to genetic alterations used by the LymphGen algorithm, with their overall percentage shown on the left. Colour coded keys below the plot indicate mutation type, presence/absence of BCL2 and BCL6 structural variants (SVs), presence/absence of mutations at the BCL2-NR3C1-, BCL6-BLIMP1-, and CXCR4-NR3C1-BS, and LymphGen class assignment, as obtained by running the algorithm without considering the BS mutations (LymphGen Original) or considering the BS mutations (LymphGen with BS mutations). b. Head-to-head comparison between LymphGen class assignments obtained as described in a. The results show 6 cases that changed class when including BS mutations, of which one was originally unclassified and became BN2 (purple), and 5 were originally classified and became “other” because they showed 0.5 < P(class) < 0.9 for multiple classes.
Extended Data Fig. 13 NR3C1 binding is abrogated by specific BCL2-SE mutations in the LY10 cell line.
a. NR3C1 ChIP–qPCR in wild-type (TMD8, top) and mutant (LY10, bottom) DLBCL cell lines, using primers encompassing the BCL2-SE hotspot, the known NR3C1 target NFKBIA, and a control non-target region (mean +/− SD; n = 3 technical replicates from one representative experiment out of 2 that gave analogous results; one-way ANOVA with Bonferroni correction). Data are expressed as % of input normalized on control IgG IP. b. Schematic of the BCL2 locus, with the hypermutated SE shown in red, and the primers used in the NR3C1-ChIP–PCR approximately positioned below the map. c. Gel electrophoresis of NR3C1 ChIP–PCR amplicons from the LY10 cell line, as compared to input and control IgG ChIP (data shown are from one representative experiment out of 2 independent experiments that gave analogous results). Band quantification was obtained by densitometry and the relative values are provided below the image, with input set as 1. d. Sequencing analysis of the PCR products shown in c. On the top panel, the reference BCL2 genomic sequence (NM_000624) and the sequence of the three BCL2 alleles (LY10 carries a trisomy 18) are aligned to the predicted NR3C1 binding motif. Sequencing traces of the ChIP–PCR amplicons document that only the wild-type “G” mutant allele is efficiently immunoprecipitated, as compared to the input, documenting abrogation of NR3C1 binding by the two mutations (Sanger sequencing performed with the reverse primer). e. Relative BCL2 expression changes in isogenic clones from the indicated cell lines, colour coded as in Fig 5f (n = 4 each except for LY10, where only 2 corrected clones were recovered, and control neutral clones, were 8 were used to exclude biological variability). For each cell line, the mean value of the unmanipulated clones is arbitrarily set as 1 (two-tailed unpaired t-test). f. Absolute BCL2 mRNA levels in the 3 cell lines used in CRISPR-Cas9 experiments, measured by RNA-seq (LY10, mutated; BJAB and SUDHL5, unmutated). g. Overlap between cases of DLBCL with mutations in the BCL2-NR3C1-BS, BCL2 translocations (Tx), and/or coding mutations in the NR3C1 gene. Data are from 328 cases analysed by WGS or Sanger sequencing. h. BCL2 expression levels in cases of primary DLBCL, stratified based on the presence of the indicated genetic lesions (n = 181 cases with available WGS and RNA-seq data). Data are expressed as TPM, and statistically significant differences were calculated by one-way ANOVA with Bonferroni correction. i. Relative distribution of cases harbouring the indicated genetic lesions in various DLBCL COO subtypes. P-values were calculated by two-tailed Fisher’s exact test to determine specific enrichment of a genetic lesion in each COO group versus the other groups combined (UNC < unclassified; ND, not determined). The total number of cases analysed within each subtype is provided in brackets, and the number of mutated cases is shown on the top. j. Relative distribution of cases harbouring the indicated genetic lesions in LymphGen genetic classes. A two-tailed Fisher’s exact test was used to calculate the enrichment of each genetic lesion in each LymphGen class versus the other classes combined. The total number of cases analysed within each class is provided in brackets, and the number of mutated cases is shown on the top.
Extended Data Fig. 14 NR3C1 binding to the CXCR4-SE is abrogated by somatic mutations.
a. NR3C1 ChIP–qPCR of the region encompassing the CXCR4-SE mutational hotspot or the NFKBIA control region and a negative control region in the WT SUDHL16 cell line (mean +/− SD; n = 4 technical replicates, from one representative experiment out of 2 independent experiments that gave analogous results, one-way ANOVA with Bonferroni correction). Data are expressed as fold enrichment vs. control IgG IP. b. NR3C1 ChIP–qPCR (left) and allelic quantification in the mutant (right) HLY1 cell line (mean +/− SD; n = 3 technical replicates, from one representative of 2 independent experiments, one-way ANOVA with Bonferroni correction and two-tailed Fisher’s exact test). c. Simplified schematic of the CXCR4 locus; the recurrently hypermutated SE is shown in red, and the primers used for NR3C1-ChIP are approximately positioned below the map. d. Gel electrophoresis of NR3C1 ChIP–PCR amplicons from the indicted cell lines, as compared to input and control IgG ChIP. Band quantification was obtained by densitometry and the relative values are provided below the image, with input set as 1 (data shown are representative of 2 independent experiments). e. Sequencing analysis of the PCR products shown in d. On the top panel, the reference CXCR4 genomic sequence (NM_003467) and the sequence of the mutated allele are aligned to the predicted NR3C1 binding motif (reverse strand). Sequencing traces of ChIP–PCR amplicons document reduced signal for the mutant allele, as compared to the input (arrow), indicating abrogation of NR3C1 binding by the mutations. f. Sequencing traces of DNA and cDNA amplicons obtained from the same clones shown in e. Arrows indicate the mutated position. g. Relative changes in CXCR4 expression between unedited and CXCR4-corrected isogenic clones (n = 8 each for HLY,1 and n = 6 each for BJAB), colour coded as in Fig. 6e. For each cell line, the mean value of unedited clones is set as 1 (two-tailed unpaired t-test). h. CXCR4 allelic expression in clones surviving correction of the CXCR4-SE mutation. Top: Schematic diagram of the wild-type (WT) and mutant (M) CXCR4 alleles in the HLY1 cell line, carrying the A413G nucleotide substitution. Additional SNVs segregating with the two alleles and used to track allele-specific expression are also indicated, in red. The nucleotide sequence of the two alleles with the predicted NR3C1 binding motif, is shown below the diagram and is aligned to the sequencing tracks (reverse strand) of representative DNA and cDNA amplicons obtained from isogenic HLY1 clones (control, clones edited in the neutral genomic region; corrected, clones corrected in the 413 position within the CXCR4-SE). Arrow points to the mutated/corrected nucleotide. i. Relative distribution of genetic lesions affecting the CXCR4:NR3C1 axis in DLBCL. Overlap between cases of DLBCL with mutations in the CXCR4-NR3C1 binding site (red) and/or in the NR3C1 coding exons (blue). Data are from 315 cases analysed by WGS or Sanger sequencing. j. CXCR4 expression levels in cases of primary DLBCL with WT vs. mutated CXCR4-SE sequences (n = 181 cases with available WGS and RNA-seq data). Data are expressed as TPM. k. Percentage of cases harbouring CXCR4-SE mutations in DLBCL COO subtypes. The total number of cases analysed within each subtype is provided in brackets, and the number of mutated cases is shown on the top. l. Percentage of cases harbouring CXCR4-SE mutations in different LymphGen genetic classes. P-values were calculated by two-tailed Fisher’s exact test for enrichment of a genetic lesion in a specific LymphGen class versus the other classes combined. The total number of cases analysed within each class is provided in brackets, and the number of mutated cases is shown on the top. Data indicate a significant enrichment in the BN2 subtype (two-tailed Fisher’s exact test).
Supplementary information
Supplementary Figure 1
Original source images for western blots and gels.
Supplementary Table 1
Mutation analysis of active E/SEs identified in DLBCL cell lines.
Supplementary Table 2
List of E/SE regions found hypermutated in DLBCL cell lines and primary cases.
Supplementary Table 3
Significantly mutated 1 kb regions identified by FishHook.
Supplementary Table 4
Genetic lesions targeting the BCL6–PRDM1, BCL2–NR3C1 and CXCR4–NR3C1 axis in the DLBCL discovery and extension panels.
Supplementary Table 5
Mutation analysis of the BCL6–BLIMP1-BS, BCL2–NR3C1-BS, and CXCR4–NR3C1-BS across lymphoid and non-lymphoid cancers.
Supplementary Table 6
List of oligonucleotides used in the study.
Source data
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Bal, E., Kumar, R., Hadigol, M. et al. Super-enhancer hypermutation alters oncogene expression in B cell lymphoma. Nature 607, 808–815 (2022). https://doi.org/10.1038/s41586-022-04906-8
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DOI: https://doi.org/10.1038/s41586-022-04906-8
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