Crebbp loss cooperates with Bcl2 overexpression to promote lymphoma in mice

Idoia García-Ramírez, Saber Tadros, Inés González-Herrero, Alberto Martín-Lorenzo, Guillermo Rodríguez-Hernández, Dalia Moore, Lucía Ruiz-Roca, Oscar Blanco, Diego Alonso-López, Javier De Las Rivas, Keenan Hartert, Romain Duval, David Klinkebiel, Martin Bast, Julie Vose, Matthew Lunning, Kai Fu, Timothy Greiner, Fernando Rodrigues-Lima, Rafael Jiménez, Francisco Javier García Criado, María Begoña García Cenador, Paul Brindle, Carolina Vicente-Dueñas, Ash Alizadeh, Isidro Sánchez-García and Michael R. Green

Key Points

  • Crebbp inactivation perturbs B-cell development, but cooperates with Bcl2 overexpression to promote lymphoma.

  • Transcriptional and epigenetic signatures of Crebbp loss implicate Myc in disease etiology.


CREBBP is targeted by inactivating mutations in follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL). Here, we provide evidence from transgenic mouse models that Crebbp deletion results in deficits in B-cell development and can cooperate with Bcl2 overexpression to promote B-cell lymphoma. Through transcriptional and epigenetic profiling of these B cells, we found that Crebbp inactivation was associated with broad transcriptional alterations, but no changes in the patterns of histone acetylation at the proximal regulatory regions of these genes. However, B cells with Crebbp inactivation showed high expression of Myc and patterns of altered histone acetylation that were localized to intragenic regions, enriched for Myc DNA binding motifs, and showed Myc binding. Through the analysis of CREBBP mutations from a large cohort of primary human FL and DLBCL, we show a significant difference in the spectrum of CREBBP mutations in these 2 diseases, with higher frequencies of nonsense/frameshift mutations in DLBCL compared with FL. Together, our data therefore provide important links between Crebbp inactivation and Bcl2 dependence and show a role for Crebbp inactivation in the induction of Myc expression. We suggest this may parallel the role of CREBBP frameshift/nonsense mutations in DLBCL that result in loss of the protein, but may contrast the role of missense mutations in the lysine acetyltransferase domain that are more frequently observed in FL and yield an inactive protein.


Diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) are the 2 most common forms of non-Hodgkin lymphoma. DLBCL can be subclassified into 2 subsets, 1 of which is characterized by molecular similarities to the germinal center B (GCB) -cell stage of differentiation (GCB-like DLBCL).1 FL also aligns with the GCB-cell stage of differentiation, but has a distinct histology and clinical course from GCB-like DLBCL owing to differences in the molecular etiology of these 2 diseases. However, FL and GCB-like DLBCL share some common genetic alterations, including frequent mutations of chromatin-modifying genes2-4 and activation of the BCL2 antiapoptotic oncogene as a result of the t(14;18)(q21;q32) translocation.5-7 In addition, FL can transform to a DLBCL-like histology through molecular alterations, including the gain of MYC expression.8-12

CREBBP is the second most frequently mutated chromatin-modifying gene in FL and DLBCL,3,13-16 following KMT2D, and coassociates with BCL2 alterations.3 The CREBBP gene encodes a lysine acetyltransferase (KAT) protein with a well-defined role in acetylating histone H3 on lysine 18 (H3K18Ac) at gene transcription start sites (TSSs) of active and poised genes, and prior studies have shown that these mutations result in a loss of H3K18Ac.17,18 CREBBP also has a role in acetylating histone H3 on lysine 27 (H3K27Ac) at gene enhancer regions.2,19,20 Importantly, these histone modifications can also be added by other redundant acetyltransferases, such as EP30021 and GCN5,22 and there is significant crosstalk between H3K18Ac, H3K27Ac, and other epigenetic modifications.2 We and others have shown that CREBBP mutations are early events in the clonal evolution of FL and are maintained in the tumor at progression and transformation.9,10,12,14,23 In addition, we showed that CREBBP point mutations in FL are associated with a marked downregulation of major histocompatibility complex (MHC) class II expression and may therefore drive immune evasion.14 Other studies have shown that CREBBP mutations in DLBCL may drive disease pathogenesis through the deregulation of BCL6 or TP53 function.17 Together these prior observations indicate that mutations of CREBBP play a role in FL and DLBCL, and the physiologic effects may be driven by deregulated acetylation of histone and/or non–histone proteins. However, it is currently unclear whether the functional consequences of CREBBP mutation are the same in these 2 diseases. Here, we investigate the role of CREBBP inactivation in B-cell development and lymphomagenesis using transgenic murine models. We provide insight into the molecular mechanisms of lymphomagenesis associated with CREBBP loss and show a distinction between CREBBP mutations that occur in FL compared with DLBCL.

Materials and methods

Transgenic mouse models

All animal work was conducted in accordance with national and international guidelines on animal care and was approved by the Bioethics Committee of University of Salamanca and by the Bioethics Subcommittee of Consejo Superior de Investigaciones Cientificas. The EµBcl224 (B6/CBA background), mb1-Cre (Cd79atm1(cre)Reth),25 and the heterozygous Crebbp floxed mice26 have been described previously. For simplification, mice with a single allele of Crebbp floxed will be denoted CbpWT/F and mice with both alleles of Crebbp floxed will be denoted CbpF/F. The CbpWT/F and CbpF/F strains were bred to mb1-Cre mice to generate CbpWT/Δ and CbpΔ/Δ strains, respectively. EµBcl2 mice were bred to CbpF/F mice to generate compound heterozygotes. F1 animals were crossed to obtain CbpF/F mice hemizygous for EµBcl2 (CbpF/FxEµBcl2). CbpF/FxEµBcl2 mice were bred to CbpWT/Δ mice possessing hemizygous mb1-Cre to obtain CbpWT/ΔxEµBcl2 or CbpΔ/ΔxEµBcl2, respectively. The strains and nomenclature are summarized in supplemental Figure 1, available on the Blood Web site. The mice were confirmed to efficiently delete the floxed allele of Crebbp in B cells (supplemental Figure 2). Upon signs of disease, mice were euthanized and subjected to standard necropsy procedures. As control, age-matched CbpWT/F and CbpF/F mice were used. All major organs were examined under the dissecting microscope. Tissue samples were taken from homogenous portions of the resected organ and fixed immediately after excision. Differences in Kaplan-Meier survival plots of transgenic and wild-type (WT) mice were analyzed using the log-rank (Mantel-Cox) test. The polymerase chain reaction (PCR) conditions for confirmation of Crebbp deletion and for immunoglobulin clonality assessment were previously described,26,27 and further detail is provided in the supplemental Methods.

Flow cytometry

Nucleated cells were obtained from total mouse bone marrow (flushing from the long bones), peripheral blood, thymus, lymph nodes, or spleen. To prepare cells for flow cytometry, contaminating red blood cells were lysed with red cell lysis buffer, and the remaining cells were then washed in phosphate-buffered saline (PBS) with 1% fetal calf serum. After staining, cells were washed once in PBS with 1% fetal calf serum containing 2 mg/mL propidium iodide (PI) to allow dead cells to be excluded from both analyses and sorting procedures. The samples and the data were acquired in an Accuri C6 Flow Cytometer and analyzed using Flowjo software. Germinal center formation was interrogated in the spleens of CbpWT/F, CbpF/F, CbpWT/FxEµBcl2, CbpF/FxEµBcl2, CbpWT/Δ, CbpΔ/Δ, CbpWT/ΔxEµBcl2, and CbpΔ/ΔxEµBcl2 mice 10 days after the injection of 1 to 2 × 108 sheep red blood cells into the peritoneum. For antibody clones, refer to supplemental Methods. Differences in population frequencies across strains were interrogated by a 1-way analysis of variance (ANOVA) with Tukey post hoc test.

Immunohistochemistry (IHC)

IHC was performed as previously described.27 Briefly, samples were sectioned, dewaxed, and heated in 10 mmol/L sodium citrate buffer for 30 minutes. Slides were incubated with primary antibodies, including anti-MYC (Clone-Y69; Master Diagnostica, prediluted), anti-Bcl6 (Clone-LN22; Leica Biosystems), and anti-Pax5 (Clone-1EW, Leica Biosystems). The anti-Bcl6 antibody was validated for staining of murine GCB cells (supplemental Figure 3). Samples were centrally reviewed by a panel of pathologists and diagnosed using uniform criteria based on clinical, histological, immunophenotypical, and molecular characteristics. For comparative studies, age-matched mice were used.

Gene expression profiling

Spleens from CbpF/FxEµBcl2, CbpWT/ΔxEµBcl2, and CbpΔ/ΔxEµBcl2 were disaggregated and viably cryopreserved for subsequent analysis. Each sample was thawed and washed twice with PBS, and B cells were isolated using negative selection with Dynabeads Mouse CD43 Untouched B cells (Life Technologies). Following isolation, B cells were counted using a hemocytometer; 100 000 cells were set aside for chromatin immunoprecipitation sequencing (ChIP-seq) analysis (below), and the remainder of cells was processed to isolate DNA and RNA using the Qiagen AllPrep Micro Kits. Purity was assessed by PCR analysis of the Crebbp transgene (supplemental Figure 2). Total RNA integrity was assessed using an Agilent TapeStation, profiled using Mouse 430 2.0 microarrays (Affymetrix) at the Stanford University Protein and Nucleic Acid Facility according to the manufacturer’s instructions and analyzed as described previously.27 For detailed methodology, refer to supplemental Methods. These data have been deposited in the Gene Expression Omnibus (GSE85490).


ChIP-seq was performed on 100 000 cells, in batches with 1 sample from each strain of mouse (CbpF/FxEµBcl2 and CbpΔ/ΔxEµBcl2) using IPstar reagents and instrument (Diagenode). All data comparisons are therefore made within batches. Data were aligned to the mouse genome (mm9) using BowTie2,28 deduplicated, and analyzed using the Fish the ChIPs package.29 Data were visualized using EaSeq.30 The nucleotide sequences of regions with significantly altered H3K18Ac in CbpΔ/ΔxEµBcl2 compared with CbpF/FxEµBcl2 B cells were downloaded using the University of California, Santa Cruz Genome Browser31 and interrogated for overrepresented DNA motifs and transcription factor binding sites using CisFinder.32 For further details, refer to supplemental Methods. These data have been deposited in the Gene Expression Omnibus (GSE85490).

Exome sequencing of murine tumors

DNA was extracted from lymphoma-involved spleens and from the spleens of age-matched control mice using Quick-gDNA MiniPrep kits (Zymo Research). DNA was sheared by sonication; libraries were constructed and 4-plexed, and the coding exome was captured using SeqCap Mouse Exome Capture (Nimblegen). Each multiplexed pool was sequenced on a lane of a HiSequation 2500 instrument (Illumina) in high-output mode. For additional details, refer to supplemental Methods.

Sequencing of primary human tumors

All human subjects research was approved by the respective institutional review boards. Targeted next-generation sequencing was performed for 380 genes to an average depth of 634X (minimum 263X, maximum 1396X) on DNA extracted from 126 FL and 140 DLBCL tumors (University of Nebraska Medical Center [UNMC] cohort). Sequencing data from 138 previously sequenced FL tumors14 were also reanalyzed with this pipeline to ensure consistency in variant calling. CREBBP mutation data from 46 FL and 134 DLBCL were obtained from a prior study and used as reported.33 Matched gene expression profiling data for 73 DLBCL cases were obtained from Gene Expression Omnibus (GSE12195). MYC translocation status was determined by fluorescence in situ hybridization (FISH) using break-apart probes in a subset of cases as part of routine clinical care at UNMC and was recorded from patient records. For additional information, refer to supplemental Methods.


Impaired B-cell development associated with Crebbp loss

To investigate the role of Crebbp on B-cell development and malignancy, we produced multiple transgenic mouse strains (supplemental Figure 1). Because CREBBP is targeted by monoallelic and biallelic inactivation in human tumors (supplemental Figure 4), we produced strains with 1 or both alleles of Crebbp gene flanked by LoxP sites (floxed) and crossed with the Mb1-Cre strain that expresses Cre-recombinase upon B-cell commitment to delete the floxed allele(s) in the B-lineage. Due to the presence of BCL2 translocations that coassociated with CREBBP mutations in FL and GCB-like DLBCL,3,14 we also used the EµBcl2 strain that overexpresses the Bcl2 oncogene in B cells under control of the immunoglobulin enhancer, so as to produce mice that both inactivate Crebbp and overexpress Bcl2 in B cells.

We interrogated B-cell development within 11- to 20-week-old mice (supplemental Table 1) and mice older than 5 months (supplemental Table 2) using flow cytometry. Consistent with an important role for Crebbp in B-cell development,26 those strains with inactivation of Crebbp exhibited deficits in the frequencies of B-cell subsets (Figure 1). Changes in B-cell frequencies included significantly reduced numbers of total B220+ B cells in both the bone marrow and the spleen, contributed to by a reduction in multiple B-cell subsets in CbpWT/Δ and CbpΔ/Δ compared with CbpWT/F mice, respectively (Figure 1). Interestingly, although overexpression of Bcl2 was not able to completely resolve this deficit, we observed a trend toward increased total B-cell numbers and the numbers of many of the underlying B-cell subsets in the bone marrow of CbpWT/ΔxEµBcl2 and CbpΔ/ΔxEµBcl2 compared with CbpWT/Δ and CbpΔ/Δ mice, respectively (Figure 1A-E). However, these differences were not statistically significant. Due to the reduced magnitude of the difference in the pre- or pro-B-cell compartment with Crebbp deletion compared with that observed at subsequent stages of differentiation, we suggest that Crebbp deletion may impact development from the pre- or pro-B-cell stage to immunoglobulin M (IgM)+ immature B cells. However, the stage at which differentiation is perturbed is not clearly identifiable in our data. Although similar decreases in B-cell frequencies were also observed in the spleen, the rescue of these frequencies by the EµBcl2 transgene was not observed, suggesting that this may be an effect on B-cell development in the bone marrow rather than on the survival of B cells in the periphery (Figure 1F-G). We also noted a modestly reduced ability of CbpWT/Δ and CbpΔ/Δ mice to produce GCB cells following vaccination with sheep red blood cells compared with CbpWT/F mice, but this was not statistically significant. GCB-cell development was significantly higher in all mice carrying the EµBcl2 transgene (P < .001) compared with strains without the transgene, and GCB-cell development was not significantly affected by Crebbp deletion in this background (Figure 2). We observed only minor changes in the expression of MHC class II with Crebbp inactivation (supplemental Figure 5), similar to those observed with short hairpin RNA–mediated knock-down of Crebbp,34 but far below the magnitude of those changes observed in primary FL with CREBBP mutation.14 These data show that Crebbp inactivation perturbs normal B-cell development, and this is partially but not significantly restored by overexpression of Bcl2.

Figure 1.

Crebbp deletion leads to impairments in B-cell development. (A) An example of flow cytometry density dot plots showing B220 and IgM staining of bone marrow cells from mice older than 5 months. Plots are gated on viable single leukocytes based upon PI staining and scatter characteristics. Gating shows pre- or pro-B cells (B220low, IgM), IgM+ B cells (B220+, IgM+), and B220high B cells that can be defined as recirculating B cells based upon expression of IgM and IgD. A notable change in the IgM+ and B220high B-cell populations can be observed across strains, with reduced numbers being associated with Crebbp deletion and being partially rescued by the EµBcl2 transgene. (B) A summary of total B-cell percentage in the bone marrow, measured as the percentage of B220+ cells among viable single cells, is shown for all strains. A 1-way ANOVA test showed a significant variance across strains in the dataset (P < .001). Post hoc testing (Tukey) revealed that this was driven by significantly higher numbers of total B cells in EµBcl2 mice compared with CbpWT/F (P = .011), CbpWT/Δ (P < .001), CbpΔ/Δ (P < .001), and CbpWT/Δ × EµBcl2 (P < .001) mice, but not between EµBcl2 and CbpΔ/Δ × EµBcl2 mice (P = .068). All other head-to-head comparisons were not significant (P > .05). (C) A summary of pre- or pro-B cells, measured as the percentage of B220low IgM cells as a proportion of all B220+ cells, is shown for all strains from mice older than 5 months. There was no significant variance in this population across strains (1-way ANOVA, P = .652). (D) A summary of immature B-cell frequencies, measured as the percentage of B220+ IgM+ IgD cells as a proportion of all B220+ cells, is shown for all strains. A 1-way ANOVA test showed a significant variance across the dataset (P = .034) that was driven by significantly higher frequencies in the CbpΔ/Δ × EµBcl2 strain compared with the CbpWT/Δ strain (Tukey, P = .018). No other head-to-head comparisons were statistically significant in post hoc testing (P > .05). (E) A summary of recirculating B-cell frequencies, measured as the percentage of B220+ IgM+ IgD+ cells as a proportion of all B220+ cells, is shown for all strains. One-way ANOVA showed a significant variance across the strains in the dataset (P < .001). Post hoc testing (Tukey) revealed that this was driven by a significantly higher frequency of mature B cells in EµBcl2 mice compared with CbpWT/F (P = .001), CbpWT/Δ (P < .001), CbpΔ/Δ (P < .001), CbpWT/Δ × EµBcl2 (P < .001), and CbpΔ/Δ × EµBcl2 (P = .004) mice. (F) An example of flow cytometry density dot plots showing IgD and IgM staining of splenocytes from mice older than 5 months. Plots are gated on viable single leukocytes based upon PI staining and scatter characteristics. A reduction in the frequency of immature (IgM+IgD), transitional (IgMhiIgD+), and mature (IgM+IgD+) cells can be seen with Crebbp deletion, and this is not restored by the addition of the EµBcl2 transgene. Note that the frequencies for these populations, shown in panels I and J of and supplemental Table 2, are based upon additional gating for B220+ cells that are not shown in this figure. (G) Box plots show the B220+ cells, as a percent of all viable single cells, across the 6 strains. There was a significant variability among strains (1-way ANOVA, P = .007) that was driven by significantly lower frequencies in CbpWT/Δ compared with CbpWT/F (Tukey, P = .036) and CbpWT/Δ × EµBcl2 (Tukey, P = .048) strains. No other head-to-head comparison was significant. (H) Box plots show follicular (B220+CD21+CD23+) B cells, as a percent of B220+ cells, across the 6 strains. There was significant variance across the strains, driven by significantly lower frequencies in CbpWT/Δ, CbpΔ/Δ, CbpWT/Δ × EµBcl2, and CbpΔ/Δ × EµBcl2 mice compared with both CbpWT/F and EµBcl2 (Tukey, P < .01 for all head-to-head comparisons). (I) The frequency of immature (B220+IgM+IgD) B cells, as a percentage of B220+ cells, are shown in box plots. There was significant variance across the strains (1-way ANOVA, P < .001) that included significantly lower frequencies in CbpWT/Δ (Tukey, P < .001) and CbpΔ/Δ (Tukey, P = .005) compared with CbpWT/F mice, and significantly lower frequencies in CbpWT/Δ × EµBcl2 (Tukey, P = .006), and CbpΔ/Δ × EµBcl2 (Tukey, P = .013) compared with EµBcl2 mice. (J) The frequencies of mature B cells (B220+IgM+IgD+), as a percentage of B220+ cells, are expressed in a box plot. There was significant variance across the strains (1-way ANOVA, P < .001) that was driven by significantly lower frequencies in CbpΔ/Δ, CbpWT/Δ × EµBcl2, and CbpΔ/Δ × EµBcl2 mice compared with both CbpWT/F and EµBcl2 mice (Tukey, P < .05 for all comparisons). There was no significant difference between CbpWT/Δ mice and either CbpWT/F (Tukey, P = .172) or EµBcl2 mice (Tukey, P = .082).

Figure 2.

GCB-cell formation following immunization. Mice from each strain were immunized with 1-2 × 108 sheep red blood cells into the peritoneum, and the spleens analyzed for germinal center formation 10 days later. (A) Example contour plots show GCB-cell frequencies across the 6 strains that were analyzed. Plots are gated on viable (PI-negative) single B cells (B220+), and GCB cells are defined as peanut agglutinin (PNA)-positive and Fas-positive cells. (B) Box plots show the trends of GCB-cell development across 8 CbpWT/F, 8 EµBcl2, 5 CbpWT/Δ, 7 CbpΔ/Δ, 6 CbpWT/Δ × EµBcl2, and 6 CbpΔ/Δ × EµBcl2 mice. One-way ANOVA showed a significant variability across the strains (P < .001), but post hoc analysis (Tukey) showed that this was driven by significantly higher frequencies of GCB cells in mice carrying the EµBcl2 transgene compared with mice without the EµBcl2 transgene (P < .001 to .040). There was no significant difference between CbpWT/F and either CbpWT/Δ or CbpΔ/Δ mice, nor between EµBcl2 and either CbpWT/Δ × EµBcl2 or CbpΔ/Δ × EµBcl2 mice (P > .05).

Combined Crebbp loss and Bcl2 overexpression leads to B-cell lymphoma development

We continued to monitor each strain of mice and noted no lymphoma development in either the CbpWT/F or EµBcl2 strains. However, mice from strains with Crebbp deletion (CbpWT/Δ, CbpΔ/Δ, CbpWT/ΔxEµBcl2, CbpΔ/ΔxEµBcl2) became ill with weight loss and lower activity. Notably, we observed only a low penetrance and long latency of disease in CbpWT/Δ mice, with increased penetrance and decreased latency in CbpΔ/Δ mice. However, lymphoma-specific survival in CbpWT/Δ and CbpΔ/Δ mice was not significantly different than the CbpWT/F control mice (log-rank, P = .460 and .069, respectively). The penetrance of lymphoma was further increased, and the latency was decreased in each of these strains of mice with the addition of the EµBcl2 transgene (Figure 3A; supplemental Table 3). As such, the CbpWT/ΔxEµBcl2 and CbpΔ/ΔxEµBcl2 mice had significantly worse lymphoma-specific survival compared with mice with the EµBcl2 transgene alone (log-rank, P = .048 and .025, respectively). Although there is no evidence to suggest that the mb1-cre promotes lymphoma, we did not follow mice with the mb1-cre transgene alone and therefore cannot exclude a possible contribution. The necropsy of ill mice revealed splenomegaly and enlarged lymph nodes (Figure 3B), with lymphocytic infiltration into the lungs, liver, and kidneys in some mice. The review of tumor histology by experienced hematopathologists (T.G. and K.F.) revealed that the majority of these tumors were most similar to FL grade 3 or DLBCL (Figure 3C). A subset of tumors from the CbpWT/Δ strain was similar to FL grade 1/2. All tumors were uniformly positive for both Pax5 and Bcl6, indicating that these tumors were of B-cell and GCB-cell origin, respectively (Figure 4A). We also noted that the majority of tumors possessed a clonal VDJ rearrangement with hallmarks of somatic hypermutation (Figure 4B), further supporting the GCB-cell origin of these tumors. As these lymphomas are clonal and have a long latency, we evaluated 2 CbpWT/Δ and 4 CbpΔ/ΔxEµBcl2 tumors by sequencing the coding exome at an average depth of 98X (min = 46X, max = 158X) to identify possible secondary genetic hits (supplemental Table 4). These tumors possessed an average of 148 coding somatic variants (min = 34, max = 275), but none targeted genes that have been previously implicated in human B-cell lymphoma (supplemental Table 5). These data therefore show that Crebbp inactivation can lead to the development of spontaneous GCB-cell lymphoma, and this is enhanced by Bcl2 overexpression.

Figure 3.

Crebbp deletion promotes B-cell lymphoma in tandem with Bcl2 overexpression. (A) A Kaplan-Meier plot shows the lymphoma-specific survival of 6 transgenic strains. It can be seen that deletion of 1 or both alleles of Crebbp leads to the development of lymphoma at a late time point in some mice, that the penetrance is increased and the latency is decreased with the addition of the EµBcl2 transgene. (B) An example of spleens and lymph nodes from CbpΔ/ΔxEµBcl2 mice that became ill, showing splenomegaly and lymphadenopathy that is indicative of lymphoma. (C) Hematoxylin and eosin staining from representative spleen and lymph node samples of age-matched tumor-free CbpWT/F and tumor-bearing CbpΔ/ΔxEuBcl2 mice. The B-cell lymphomas can be seen to be associated with a diffuse spread of centroblasts, with loss of normal architecture. Pathology review determined this specimen to be most similar to DLBCL histology.

Figure 4.

B-cell lymphomas in Crebbp transgenic mice are of GCB-cell origin. (A) An example of immunohistochemical staining for the spleen of an age-matched control mouse (CbpWT/F) and a lymphoma-involved spleen from a CbpΔ/ΔxEµBcl2 mouse. The control mouse shows normal benign follicles and an expected pattern of Pax5 and Bcl6 staining. The lymphoma-involved spleen shows diffuse histology with cells that are Pax5 and Bcl6 positive, supporting a GCB-cell origin. (B) Immunoglobulin rearrangements were assessed by PCR in DNA extracted from tumor-involved spleens and a spleen from age-matched control mice (CbpF/FxEµBcl2). Control mice showed a laddering pattern indicative of a polyclonal B-cell population, as shown in the left-most lane. In contrast, the majority of tumor samples from CbpWT/Δ, CbpΔ/Δ, CbpWT/ΔxEµBcl2, and CbpΔ/ΔxEµBcl2 mice showed a single dominant band that is indicative of a clonal B-cell population, as seen in these examples. Eight of these bands, highlighted in red boxes, were excised and cloned for sequencing. Analysis revealed the presence of somatic hypermutation in 7/8 tumors, with an average of 2% deviation (min = 0.8%, max = 4.2%) deviation from the germ-line V-gene sequence. This provides further evidence in support of the GCB-cell origin of these tumors, or that the B cells have previously transited through the germinal center. NA, not applicable; SHM, somatic hypermutation.

Indirect induction of Myc expression in B cells with Crebbp inactivation

To investigate the early molecular mechanisms of B-cell lymphoma pathogenesis associated with Crebbp inactivation, we isolated B cells from the spleens of adult (∼10 months of age) disease-free CbpF/FxEµBcl2 (n = 3), CbpWT/ΔxEµBcl2 (n = 3), and CbpΔ/ΔxEµBcl2 (n = 3) mice for transcriptional and epigenetic profiling. High B-cell purity was confirmed by PCR (supplemental Figure 4). Differential gene expression analysis identified 370 genes with significantly reduced expression and 335 genes with significantly increased expression in B cells from mice with 1 or both alleles of Crebbp deleted compared those with no Crebbp deletion (supplemental Table 6). As expected, this included reduced expression of Crebbp in the B cells from mice with Crebbp deletion. We also observed other features that may be associated with the pathogenesis of these tumors, such as reduced expression of Pten, increased expression of multiple probes mapping to immunoglobulin genes, and increased expression of Myc (Figure 5A). Increased surface immunoglobulin protein levels were not observed in association with their transcriptional upregulation. However, IHC staining for Myc showed strong staining in spleens from CbpΔ/ΔxEµBcl2 mice but no staining in spleens from CbpF/F mice (Figure 5B). As Myc is a strong oncogene, we posit that this molecular feature likely contributes to disease biology.

Figure 5.

Transcriptional changes associated with Crebbp deletion are not associated with gene-proximal H3K18Ac changes. (A) Gene expression profiling was performed on purified B cells from adult disease-free mice to determine early molecular alterations associated with disease etiology. Differential gene expression analysis revealed a signature of genes with significantly (false discovery rate < 0.25, fold change >1.2) reduced or increased expression associated with deletion of 1 or both alleles of Crebbp in the EµBcl2 background. This represents the intersection of head-to-head comparisons between mice with both alleles of Crebbp intact (CbpF/FxEµBcl2) compared with those with either 1 allele (CbpWT/ΔxEµBcl2) or 2 alleles (CbpΔ/ΔxEµBcl2) of Crebbp deleted and included increased expression of the Myc oncogene. The samples for which ChIP-seq was also performed are annotated at the bottom of the figure with roman numerals. (B) Increased expression of Myc was confirmed by immunohistochemical staining of spleens from CbpΔ/ΔxEuBcl2 mice compared with an age-matched control (CbpF/F). (C) Heat maps show the level of H3K18Ac from 10 kb upstream to 10 kb downstream from the TSS of genes with differential expression associated with Crebbp deletion. ChIP-seq was performed on the same samples that were interrogated by gene expression profiling, as annotated by corresponding roman numerals in panels A and C. The H3K18Ac level of each gene is aligned with the expression levels in panel A, and the average signal (line) ± the standard deviation (shaded region) is summarized for genes with reduced (blue) or increased (red) gene expression at the top of the heat map. There is a peak of H3K18Ac at the TSS of most genes with differential expression, showing our ability to detect H3K18Ac at TSSs. However, the changes in gene expression between strains were not associated with changes in H3K18Ac at the TSS ± 10 kb.

In addition to transcriptional profiling, B cells from 2 of the CbpF/FxEµBcl2 and 2 of the CbpΔ/ΔxEµBcl2 mice were also interrogated by ChIP-seq for the H3K18Ac mark. This mark was chosen because of prior associations between CREBBP mutations and reduced H3K18Ac.17,18 Notably, we did not observe any significant differences in the H3K18Ac level at the TSS or within 10 kb upstream or downstream of the genes with altered transcript abundance (Figure 5C). However, downregulated genes were significantly enriched for those with previously identified B-cell–specific enhancer regions35 (hypergeometric enrichment analysis false discovery rate = 0.085). These data therefore indicate that, although Crebbp deletion is associated with significant transcriptional alterations such as the induction of Myc expression that are likely to be important for disease biology, these are not due to the direct changes of H3K18Ac in gene-proximal regulatory regions and may be regulated by distant regulatory elements or secondary factors.

Crebbp inactivation is associated with an epigenetic signature related to Myc

Although changes in H3K18Ac were not observed at the TSSs of differentially expressed genes, we did detect significantly altered H3K18Ac in other genomic regions (Figure 6A; supplemental Table 7). Specifically, we identified 362 regions of significantly reduced acetylation (totaling 886 kb) and 1902 regions of significantly increased acetylation (totaling 3487 kb) in B cells from CbpΔ/ΔxEuBcl2 mice compared with those from CbpF/FxEuBcl2 mice. These were predominantly intragenic regions of the genome, with an average distance of 68 kb or 77 kb to the nearest TSS for regions of reduced or increased acetylation, respectively (Figure 6B). This localization is indicative of altered patterns of H3K18Ac at potential intragenic enhancer regions. To define the factors that may be mediating or regulating these changes in H3K18Ac, we performed an unbiased DNA motif enrichment analysis. Comparison of DNA sequences from regions with significantly altered H3K18Ac to regions of equal size immediately upstream and downstream identified 846 enriched motifs that corresponded to 581 homologous clusters (supplemental Table 8). By comparing overrepresented motif clusters with known transcription factor DNA binding sequence matrices, we identified transcription factors that may bind these regions. In line with the observed overexpression of Myc in these samples, 14 motif clusters contained significant homology to the Myc recognition site, and these motif clusters were found in 64% (1454/2264) of the regions with observed changes in H3K18Ac associated with Crebbp inactivation (Figure 6C). To provide further evidence that Myc binds these regions of deregulated H3K18Ac, we used public Myc ChIP-seq data from the Ch12 murine B-cell lymphoma and Mel murine erythroleukemia cell lines. The ChIP signal for Myc shows strong binding to the center of the peaks with increased acetylation in B cells from CbpΔ/ΔxEuBcl2 mice compared with CbpF/FxEuBcl2 mice (Figure 6D). The Myc signal was particularly notable in the B-cell–derived cell line, Ch12. Regions with decreased acetylation in B cells from CbpΔ/ΔxEuBcl2 mice compared with CbpF/FxEuBcl2 mice also showed Myc binding, but with reduced signal (supplemental Figure 6). As this indicated that Myc may play a role in disease etiology, we confirmed Myc expression in a larger panel of tumor-involved spleens and strong expression in all strains with 1 or both Crebbp allele(s) deleted compared with CbpWT/FxEµBcl2 mice (Figure 6E). These data therefore support a potential role for Myc in regulating the changes H3K18Ac that we observed in B cells with Crebbp inactivation.

Figure 6.

Regions of differential H3K18Ac are primarily intragenic and are bound by Myc. (A) Ratio heat maps of the level of H3K18Ac from CbpΔ/ΔxEuBcl2 B cells compared with CbpF/FxEuBcl2 B cells from 2 unique biological and technical replicates. These heat maps show the intersection of significant differences identified in each replicate, which includes a small number of regions with significantly reduced peaks of H3K18Ac and a large number of regions with significantly increased peaks of H3K18Ac. (B) Regions of reduced and increased acetylation primarily affect intragenic regions that are distant from the nearest TSS. The distance from the center of the peak of significantly reduced (above) or increased (below) regions of acetylation to the TSS of the nearest gene is shown using a heat plot. It can be seen that the majority of the peaks of differential H3K18Ac lie very far from the nearest TSS, suggesting that they may be distant regulatory elements. (C) An example of motifs that were most significantly enriched in regions of differential H3K18Ac and showed homology to MYC binding sites is displayed. This provided evidence that regions of altered H3K18Ac may be bound by MYC. (D) Public ChIP-seq data for Myc from the Ch12 murine B-cell lymphoma and Mel murine erythroleukemia cell line show a peak of strong Myc binding at the same location as the peak of increased H3K18Ac observed in CbpΔ/ΔxEuBcl2 B-cells. Regions are aligned and show the same physical location as panel A. The average signal (line) ± 1 or 2 standard deviations (shaded region) is summarized at the top of the heat map. The strong peak at the top and Myc binding signal that aligns with the center of the peaks of increased acetylation in our transgenic mice support the binding of Myc to these regions and implicate Myc in the observed epigenetic alterations. (E) Due to the potential importance of Myc in the disease etiology, we confirmed that Myc was also expressed in tumor samples from our transgenic mice. All tumors showed Myc staining, but this was absent from age-matched control spleens. Illustrative examples are shown, with only rare positive cells visible in the control spleen (CbpWT/FxEµBcl2), but broad positive staining in the tumor-involved spleens from mice with 1 or both alleles of Crebbp deleted. Cent., center; cMyc, Myc protooncogene.

Different patterns of CREBBP mutations in FL compared with DLBCL

Due to multiple contrasts between our murine model and human FL, we postulated that CREBBP mutations may have a different role in FL compared with DLBCL. To gain insight into this, we performed targeted sequencing of 126 FL and 140 DLBCL tumors and combined this with previously published data for 184 FL (total, n = 310) and 134 DLBCL (total, n = 274).17,23 We observed that a significantly higher proportion of CREBBP mutations in FL targeted the KAT domain compared with DLBCL (Figure 7A-B; supplemental Table 9). Specifically, arginine 1446 mutations account for 26% of CREBBP mutations in FL, but only 4% in DLBCL. In contrast, CREBBP mutations in DLBCL tumors tended to occur more frequently upstream of the KAT domain and had a significantly higher rate of missense/nonsense mutations compared with FL (Figure 7C). Gene expression microarray data were available for 73 DLBCL tumors with known CREBBP status (61 WT, 12 mutant). Although MYC translocation and DNA copy number status were unknown in these cases, tumors with CREBBP mutation displayed a higher expression of MYC compared with those with WT CREBBP (Figure 7D; 1-way Student t test, P = .026). In contrast, higher expression of MYC was not observed in association with CREBBP mutations in FL.14 In a separate set of 54 UNMC tumors, MYC translocation data were available from FISH. Of these, 9/54 were positive for MYC translocation; 10/55 possessed CREBBP mutations, and there was no overlap between these events (Figure 7E). However, this was not statistically significant due to the limited sample size (Fisher’s exact test, P = .183). Together these data show a significantly different spectrum of CREBBP mutations in DLBCL compared with FL and provide some evidence for CREBBP mutations being associated with increased MYC expression in primary DLBCL tumors.

Figure 7.

Significantly different distributions of CREBBP mutations in human FL compared with DLBCL. (A) CREBBP mutation data were obtained for a total of 310 FL and 274 DLBCL in which both diseases were interrogated in the same study using the same approach. Mutations in CREBBP were identified in 151 FL and 51 DLBCL, and the relative representation of the position of these mutations are expressed as a fraction of all CREBBP mutations in that disease, relative to the protein position of CREBBP isoform 1. The KAT domain is shaded in green and defined as amino acids 1342 to 1649. Larger peaks, indicative of a higher fraction of all CREBBP mutations, are seen upstream of the KAT domain in DLBCL compared with FL. In addition, a dominant hotspot can be seen at arginine 1446 in FL that is significantly reduced in DLBCL. In contrast, other hotspots at tyrosine 1482 and 1503 are present at relatively similar frequencies in both FL and DLBCL. (B) Pie graphs show that 78% (118/151) of CREBBP mutations fall within the KAT domain in FL, as compared with only 43% (22/51) in DLBCL (Fisher’s exact test, P < .001). (C) Pie graphs show that only 17% (25/151) of CREBBP mutations in FL create a frameshift of premature stop codon, while the remainder creates single-amino-acid substitutions or insertion/deletions. In contrast, missense/frameshift mutations are present at greater than twice this frequency, 39% (20/51), in DLBCL (Fisher’s exact test, P = .0016). (D) Gene expression microarray data from DLBCL tumors with previously determined CREBBP mutational status were used to evaluate MYC expression. There was a significantly higher expression of MYC in tumors with CREBBP mutation compared with those with no CREBBP mutation (1-tailed Student t test, P = .026). This is despite the unknown MYC translocation status that may alter the expression of MYC in a subset of cases. (E) MYC translocation status was available for 54 cases with known CREBBP mutation status. We observed no overlap in CREBBP mutation and MYC translocation, although this was not statistically significant.


We aimed to model inactivating mutations of CREBBP that are observed in human FL and DLBCL by specifically deleting Crebbp in B cells, in conjunction with Bcl2 overexpression. We used the Mb1-cre for deletion of Crebbp at an early stage of B-cell development because BCL2 translocation in human lymphoma is thought to be acquired as a byproduct of faulty VDJ recombination in pre- or pro-B cells.36 We observed that CREBBP mutations occur as an early secondary event in FL,14 which has been validated in subsequent studies,12 and we therefore speculate that this may also occur at an early stage of B-cell development. Inactivation of Crebbp was associated with marked reductions in the frequencies of multiple B-cell subsets in both young and adult mice, in line with prior observations that loss of Crebbp with a Cd19-cre resulted in ∼50% loss in peripheral B cells.26 Notably, combined inactivation of Crebbp and Ep300 resulted in almost complete loss of B cells.26 These observations and our own data therefore point not only to an important role for Crebbp in B-cell development but also toward functional redundancy with other histone acetyltransferases such as Ep300. We extended upon these observations by crossing strains that inactivate Crebbp in B cells with EµBcl2 mice that overexpress Bcl2 in B cells. Notably, the overexpression of Bcl2 partially rescued deficits in B-cell development.

Cooperation between Crebbp inactivation and Bcl2 overexpression was also demonstrated by the penetrance and latency of lymphoma development within our transgenic models. Strains with Crebbp inactivation alone developed lymphoma in a subset of mice, and this was more prevalent with biallelic inactivation compared with monoallelic inactivation, but neither were significantly different from the control mice. However, the penetrance of lymphoma was increased and the latency decreased by the addition of the EµBcl2 transgene; lymphoma development was significantly higher in mice with either monoallelic or biallelic inactivation of Crebbp in combination with EµBcl2 than in mice with the EµBcl2 transgene alone. These tumors were of GCB origin as evidenced by Bcl6 expression and the presence of immunoglobulin somatic hypermutation, in line with the origin of FL and GCB-like DLBCL that possess CREBBP mutations. These data suggest that GCB cells with Crebbp inactivation may rely on coacquisition of 1 or more mechanisms to inhibit apoptosis. Despite the long latency of lymphoma development in these mice, we did not observe secondary genetic mutations that may act as secondary drivers of lymphomagenesis. However, the clonal nature of these tumors suggests that there may be some, as yet unidentified, secondary event that is driving clonal outgrowth.

Having observed cooperation between Crebbp inactivation and Bcl2 overexpression in lymphoma development, we investigated the molecular consequences of Crebbp inactivation in the EµBcl2 background. Because of the long latency of lymphoma development, we did this at an earlier premalignant stage so as not to encounter the effects of secondary alterations. We therefore performed transcriptional and epigenetic profiling of nonmalignant B cells from strains that possessed the EµBcl2 transgene but either did not delete Crebbp or deleted 1 or both alleles of Crebbp, respectively. Transcriptional profiling revealed a transcriptional signature that was incrementally altered by deletion of 1 or both alleles of Crebbp, including significant overexpression of the Myc oncogene that we confirmed at the protein level in both premalignant and lymphoma-involved spleens from transgenic mice. However, we were surprised to observe that transcriptional changes were not associated with alterations of the Crebbp-catalyzed H3K18Ac mark at the TSS or proximal regions of these genes. This suggests that transcriptional deregulation may occur either through the modification of distant regulatory elements, through the altered activity of a nonhistone Crebbp target protein, or through both. This is consistent with prior observations that show that Crebbp-driven epigenetic changes show a high level of redundancy with those that are driven by Ep300, that epigenetic changes are context dependent, and that nonhistone targets may be central for the role of Crebbp in vivo.37-40 We found some evidence for an enrichment of genes with B-cell–specific enhancer elements35 among our differentially expressed genes, but confirmation of a role of distant regulatory elements needs to be performed with methods that capture the 3-dimensional conformation of the genome, such as Hi-C.41

We observed significant changes in H3K18Ac in B cells with biallelic inactivation of Crebbp, which primarily localized to intragenic regions. These regions of altered H3K18Ac had a significant overrepresentation of DNA sequences that possessed the Myc binding site. In addition, ChIP-seq data from the Ch12 murine B-cell lymphoma cell line, and to a lesser extent the Mel erythroleukemia cell line, showed strong binding of Myc to the center of these regions of altered H3K18Ac. These data therefore suggest that epigenetic alterations in these B cells may not be a direct result of Crebbp inactivation, which may be compensated for by other redundant acetylatransferases,26 but may instead result from the increased expression of Myc and its activity at intragenic enhancer regions. Importantly, the role for Myc at these “superenhancer” elements have been shown to be critical for the etiology of DLBCL.42

This murine model highlights multiple important contrasts to FL, which has a higher frequency of CREBBP mutations compared with DLBCL.3,17 First, the majority of the lymphomas in our transgenic mice appeared most similar to high-grade FL or DLBCL. In contrast, CREBBP mutations in human FL are more prevalent in low-grade tumors.14 Second, Crebbp deletion in our murine model was associated with the induction of Myc expression, which is rarely described in low-grade FL43 but is more common in DLBCL.44,45 These disparities led us to examine whether CREBBP mutation may have different roles in these 2 diseases. By interrogating CREBBP mutations from a large cohort of FL and DLBCL tumors, we identified a significantly different spectrum of CREBBP mutations in these 2 diseases. DLBCL tumors possessed more frequent frameshift/nonsense mutations that occur upstream of the KAT domain and likely result in a loss of the protein, a scenario more comparable to Crebbp deletion in our murine model. In contrast, FL tumors possess more frequent single-amino-acid substitutions with the KAT domain that are likely to yield a catalytically inactive protein. We suggest that these 2 mutational patterns likely have divergent functional consequences. In line with this, we found only a modest change in MHC class II expression on murine B cells with Crebbp deletion, whereas we previously observed marked (>10-fold) downregulation associated with point mutations of CREBBP in human FL.14 In addition, we observed no significant change in MYC expression associated with CREBBP mutation in human FL, but found some evidence for increased MYC expression associated with CREBBP mutation in human DLBCL. In further support of the role of CREBBP mutation in inducing MYC expression in DLBCL, we observed mutual exclusivity between CREBBP mutations and MYC translocations. This draws parallels between our observations in this murine model of CREBBP deletion and human DLBCL and provides evidence for a potential role of CREBBP mutations in promoting MYC expression in this disease. Importantly, CREBBP can control MYC expression through its inhibition of β-catenin via specific acetylation of lysine 49.46 Interference with this acetylation by knock-down of CREBBP or mutation of the lysine 49 residue of β-catenin has been shown to lead to specific transactivation of MYC, but not other β-catenin–regulated genes.46 This provides a potential functional link between CREBBP loss and MYC activation. However, the role of CREBBP mutation in MYC induction in human DLBCL, and the different function frameshift/nonsense compared with missense mutations of CREBBP, is an area that requires further research and validation in future studies. Specifically, in order to provide a firm association between CREBBP inactivation and MYC induction in human DLBCL, large cohorts of tumors will need to be interrogated by IHC for MYC in parallel with mutational analysis of CREBBP, FISH for t(8;14) translocations targeting MYC, and analysis of other genetic alterations that may affect MYC expression, such as DNA copy number gain47 or EZH2 mutation.48

While this paper was under review, 2 papers interrogating the role of Crebbp inactivation in murine B-cell lymphoma models were published.34,49 Jiang et al reported downregulation of MHC class II genes with short hairpin RNA–mediated knock-down of Crebbp, but this was not reported by Zhang et al, using B-cell–specific deletion of Crebbp with cre recombinase, and only modest changes were shown in our study. The role of Crebbp loss compared with Crebbp KAT-domain point mutation in the deregulation of MHC class II expression therefore requires further functional interrogation. In contrast to the focus on H3K18Ac in our study, both Jiang et al and Zhang et al interrogated H3K27Ac. Their similar analytical approaches, focused only on regions with reduced H3K27Ac that have also been implicated in GCB-cell function, led both studies to draw similar conclusions: that Crebbp loss is associated with reduced H3K27Ac at intragenic enhancers associated with genes that have a role in B-cell function and development. We also observed loss of H3K18Ac at intragenic regions, but the dominant epigenetic signature that we observed was 1 of H3K18Ac gain. Although these marks are both catalyzed by Crebbp, it is not clear how closely the function of H3K18Ac mirrors that of H3K27Ac in B-cell development, making comparison of these datasets challenging. Importantly, this study and the 2 prior reports34,49 all clearly support a role for Crebbp loss in promoting lymphoma in cooperation with Bcl2.

In conclusion, we have shown that Crebbp has an important role in B-cell development, and Crebbp loss cooperates with Bcl2 overexpression to promote GCB-cell lymphoma. These lymphomas have molecular profiles that are characterized by Myc overexpression and epigenetic alterations of intragenic regions that are bound by Myc. We highlighted important differences between CREBBP mutations in human DLBCL compared with FL and suggest that CREBBP frameshift/nonsense mutations may have a role in inducing MYC in DLBCL.


Contribution: Initial conception of the project was designed by A.A., I.S.-G., and M.R.G.; design of the study and development of methodology were performed by I.G.-R., I.G.-H., A.M.-L., G.R.-H., R.D., L.R.-R., O.B., D.A.-L., J.D.L.R., F.R.-L., R.J., M.B.G.C., F.J.G.C., P.B., C.V.D., and M.R.G.; data were acquired by I.G.-R., S.T., I.G.-H., A.M.-L., G.R.-H., R.D., D.M., L.R.-R., O.B., D.A.-L., J.D.L.R., R.J., M.B.G.C., F.J.G.C., C.V.-D., I.S.-G., and M.R.G.; K.F. and T.G. performed pathology review; management of patient samples was performed by T.G., M.B., J.V., M.L., and M.R.G.; I.G.-R., S.T., I.G.-H., A.M.-L., G.R.-H., L.R.-R., O.B., D.A.-L., J.D.L.R., R.J., D.K., M.B.G.C., F.J.G.C., C.V.-D., I.S.-G., and M.R.G. were responsible for analysis and interpretation of data (eg, statistical analysis, biostatistics, computational analysis); manuscript preparation was performed by I.G.-R., I.G.-H., A.M.-L., G.R.-H., L.R.-R., O.B., D.A.-L., J.D.L.R., R.J., M.B.G.C., F.J.G.C., C.V.-D., I.S.-G., and M.R.G.; administrative, technical, or material support (ie, reporting or organizing data, constructing databases) was compiled by I.G.-R., I.G.-H., M.B., T.G., L.R.-R., C.V.-D., and I.S.-G. The study was supervised by C.V.-D., I.S.-G., and M.R.G.

Conflict-of-interest disclosure: The authors declare no competing financial interests.

Correspondence: Michael R. Green, University of Nebraska Medical Center, 987660 Nebraska Medical Center, Omaha, NE 68198-7660; e-mail:{at}; Isidro Sánchez-García, IBMCC-CSIC/Universidad de Salamanca, Campus Unamuno s/n, 37007 Salamanca, Spain; e-mail: isg{at}; and Ash Alizadeh, Stanford University, 265 Campus Dr, Stanford, CA 94305-5458; e-mail: arasha{at}


The authors are indebted to all members of our groups for useful discussions and for their critical reading of the manuscript.

Research in the M.R.G. group is supported by grants from the Nebraska Department of Health and Human Services (LB506 2016-16) and the National Institutes of Health, National Cancer Institute (1R01CA201380). Research in the I.S.-G. group is partially supported by Fondo Europeo de Desarrollo Regional (FEDER) and by the Ministry of Economy and Competitiveness (SAF2012-32810, SAF2015-64420-R, and Red de Excelencia Consolider OncoBIO SAF2014-57791-REDC), Instituto de Salud Carlos III (PIE14/00066), Instituto de Salud Carlos III Plan de Ayudas Institute of Biomedical Research of Salamanca 2015 Proyectos Integrados (IBY15/00003), by Junta de Castilla y León (BIO/SA51/15, CSI001U14, UIC-017, and CSI001U16), Fundacion Inocente Inocente, by the German Carreras Foundation (Organisation der Deutsche José Carreras Leukämie-Stiftung eV R13/26), and by the Advanced Research on Interaction Mechanisms of electroMagnetic Exposures with Organisms for Risk Assessment project (European Union’s Seventh Framework Programme [FP7/2007-2013] under grant agreement no. 282891). The Nebraska Lymphoma Study Group tissue bank is supported by the Fred & Pamela Buffett Cancer Center’s National Cancer Institute Cancer Center Support Grant (P30CA036727). The I.S.-G. laboratory is a member of the EuroSyStem and the Developing Evidence to Inform Decisions about Effectiveness Network funded by the European Union under the FP7 program. Research in the C.V.-D. group is partially supported by a “Miguel Servet” Grant (CP14/00082, AES 2013-2016, FEDER) from the Instituto de Salud Carlos III (Ministerio de Economía y Competitividad). I.G.-R. was supported by BES-Ministerio de Economía y Competitividad (BES-2013-063789). G.R.-H. and L.R.-R. were supported by Fondo Social Europeo-Consejería de Educación de la Junta de Castilla y León. R.D. was supported by a fellowship from Région Ile de France (Appel hors DIM 2013). Research in the F.R.-L. group was supported by grants from Université Paris Diderot, CNRS, Institut National du Cancer (2012-1-PL-BIO), and the Plan Cancer-Environnement 2013. Ultrafast liquid chromatography analyses were done on the platform “Bioprofiler” (Unité de Biologie Fonctionnelle et Adaptative).


  • * I.S.-G. and M.R.G. are joint senior authors.

  • The data reported in this article have been deposited in the Gene Expression Omnibus database (accession numbers GSE85490 and GSE12195).

  • The online version of this article contains a data supplement.

  • The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

  • Submitted August 11, 2016.
  • Accepted March 5, 2017.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
  6. 6.
  7. 7.
  8. 8.
  9. 9.
  10. 10.
  11. 11.
  12. 12.
  13. 13.
  14. 14.
  15. 15.
  16. 16.
  17. 17.
  18. 18.
  19. 19.
  20. 20.
  21. 21.
  22. 22.
  23. 23.
  24. 24.
  25. 25.
  26. 26.
  27. 27.
  28. 28.
  29. 29.
  30. 30.
  31. 31.
  32. 32.
  33. 33.
  34. 34.
  35. 35.
  36. 36.
  37. 37.
  38. 38.
  39. 39.
  40. 40.
  41. 41.
  42. 42.
  43. 43.
  44. 44.
  45. 45.
  46. 46.
  47. 47.
  48. 48.
  49. 49.
View Abstract