Follicular lymphoma and diffuse large B-cell lymphoma (DLBCL) are the most common non-Hodgkin lymphomas distinguishable by unique mutations, chromosomal rearrangements, and gene expression patterns. Here, it is demonstrated that early B-cell progenitors express 2′,3′-cyclic-nucleotide 3′ phosphodiesterase (CNP) and that when targeted with Sleeping Beauty (SB) mutagenesis, Trp53R270H mutation or Pten loss gave rise to highly penetrant lymphoid diseases, predominantly follicular lymphoma and DLBCL. In efforts to identify the genetic drivers and signaling pathways that are functionally important in lymphomagenesis, SB transposon insertions were analyzed from splenomegaly specimens of SB-mutagenized mice (n = 23) and SB-mutagenized mice on a Trp53R270H background (n = 7) and identified 48 and 12 sites with statistically recurrent transposon insertion events, respectively. Comparison with human data sets revealed novel and known driver genes for B-cell development, disease, and signaling pathways: PI3K–AKT–mTOR, MAPK, NFκB, and B-cell receptor (BCR). Finally, functional data indicate that modulating Ras-responsive element-binding protein 1 (RREB1) expression in human DLBCL cell lines in vitro alters KRAS expression, signaling, and proliferation; thus, suggesting that this proto-oncogene is a common mechanism of RAS/MAPK hyperactivation in human DLBCL.

Implications:

A forward genetic screen identified new genetic drivers of human B-cell lymphoma and uncovered a RAS/MAPK–activating mechanism not previously appreciated in human lymphoid disease. Overall, these data support targeting the RAS/MAPK pathway as a viable therapeutic target in a subset of human patients with DLBCL.

B-cell malignancies comprise a large family of diseases ranging from highly curable Hodgkin lymphoma to the more diverse non-Hodgkin lymphoma subtypes including the indolent follicular lymphoma and the aggressive, genetically heterogeneous diffuse large B-cell lymphoma (DLBCL; ref. 1). Molecular profiling of B-cell malignancies has identified defining genetic features for many of the subtypes leading to new therapeutic targets and increased survival-rates for some diseases (1). DLBCL, which occur predominantly in older adults, diagnosis and treatment have greatly been impacted by the genetic profiling efforts. DLBCL is categorized into two unique molecular subtypes based on gene expression profiling: activated B-cell–like (ABC) and germinal center B-cell–like (GCB; ref. 1). Transcriptomic and genomic analyses identified recurrent genomic aberrations and signaling pathway alterations unique to each subtype and common to both (2, 3). Mutations in genes altering B-cell receptor (BCR) signaling and NFκB activation (e.g., CD79A, MALT1, and MYD88) are more common in ABC DLBCL, whereas mutations in genes altering histone modifications and B-cell homing (e.g., EZH2, CREBBP, and MLL2) are more common in GCB DLBC (4–6). Mutations in TP53, immunosurveillance genes (e.g., B2M, CD58), epigenetic modifiers (e.g., CREBBP), and MYC copy number alteration (CNA) gains occur in both subtypes (2). Whole-genome and -exome sequencing efforts have identified over 300 recurrently mutated genes in primary DLBCL samples (3, 5, 7, 8). However, there is still limited knowledge on functional impact of many of these mutations and genetic alterations on disease initiation and progression; genetically engineered mouse models (GEMM) provide a platform to begin evaluating these putative targets.

The Sleeping Beauty (SB) somatic cell mutagenesis system has successfully identified genetic drivers of various cancers including hepatic, intestinal, pancreatic, osteosarcoma, and T-cell (9–14). We previously reported the identification of novel genetic drivers of peripheral nerve–related cancers targeting SB mutagenesis to 2′,3′-cyclic-nucelotide 3′ phosphodiesterase (Cnp)-expressing cells in mice in the context of EGFR overexpression with Trp53R270H mutation (12). Mutagenesis alone or in the context of only Trp53R270H mutation was inefficient at developing peripheral nervous system tumors (12). We describe here how these animals developed highly penetrant (65%) lymphoid disease (follicular lymphoma and DLBCL). Analysis of SB-induced lymphomas identified 59 common insertion sites (CIS), of which several were associated with signaling pathways altered in human DLBCL formation: PI3K–AKT–mTOR, NFκB, and BCR signaling. We also identified several novel proto-oncogenes and tumor suppressor genes (TSG) for B-cell lymphoma, for example, Ras-responsive element binding protein 1 (Rreb1) and Ambra1, respectively. Furthermore, we described new roles for Rreb1, a MAPK pathway effector, in DLBCL maintenance and its impact on Kras expression, revealing an unknown mechanism for RAS activation in DLBCL.

Transgenic animals

Three transgenes were used to induce SB mutagenesis: Conditionally expressed SB (R26SB11LSL; ref. 15), Cnp promoter–driven cre recombinase (Cnp-Cre; ref. 16) and oncogenic transposon, concatemer (T2/Onc15). Cnp-Cre;R26SB11LSL;T2/Onc15 (SB-mutagenized) mice underwent insertional mutagenesis in Cnp+ cells. Genotyping PCR was performed on phenol-chloroform–extracted mouse tail DNA (10, 16, 17). Conditionally expressed Pten (Ptenf/f) and Trp53 (Trp53R270H) allele mice were utilized (17, 18). B6.129(Cg)-Gt(ROSA)26Sortm4(ACTB-tdTomato,-EGFP)Luo/J reporter mice (The Jackson Laboratory) were utilized for lineage tracing studies. All mice were bred and cared for under the guidelines of the University of Minnesota Animal Care and Use Committee.

V(D)J PCR

One-hundred nanograms of DNA from control and SB-mutagenized spleens underwent PCR to assess V(D)J clonality for VHJ558/JH3, VHQ52/JH3, VH7183/JH3, and DHL/JH3 recombination (19). PCR for Actb served as the loading control.

Flow cytometry

Single-cell suspensions from bone marrow (femur and tibia), spleen, and lymph nodes were stained with the following antibodies: α-IgM (Jackson ImmunoResearch), α-IgD (11–26), α-BP-1 (FG35.4), α-CD5 (53-7.3), α-CD19 (1D3), α-CD21/35 (7E9), α-CD23 (B3B4), α-CD24 (M1/69), α-CD25 (PC61.5), α-CD38 (90), α-CD43 (S7, BD Biosciences), α-CD45R (RA3-6B2) for Hardy fractionation (20). Antibodies were obtained from eBioscience unless otherwise indicated. SA-PerCP-Cy5.5 (eBioscience) was used to detect biotinylated antibodies. Cells were assayed on a LSRII flow cytometer (BD Biosciences); data were analyzed using FlowJo software (Treestar).

Transposon insertion site analysis

DNA-T2/Onc junctions were amplified by linker-mediated PCR (LM-PCR), purified using MinElute 96 UF Plates (Qiagen), and submitted for high-throughput HiSeq 2500 sequencing (Illumina) or 454 pyrosequencing (12). A total of 4 × 107 100-bp reads (Illumina) and 384,919 100-bp reads (454 pyrosequencing) were processed and analyzed using Transposon Annotation Poisson Distribution Association Network Connectivity Environment (TAPDANCE) software and gene-centric CIS analysis software (21, 22). Mouse build NCBI37/mm9 was used to map insertion cites and subsequent analyses.

IHC

The M.O.M. kit (Vector Laboratories Inc.) was used for blocking and antibody incubations. Primary antibodies: Ki67 (1:100; Leica Biosystems), RREB1 (1:100; Sigma-Aldrich), pErk (1:100; Cell Signaling Technology), pAkt (1:100; Cell Signaling Technology), Kras (1:100, Santa Cruz Biotechnology), and SB (1:100; R&D Systems). Corresponding biotinylated secondary antibodies (1:250; Vector Laboratories Inc.) were used followed by incubation with Vectastain ABC Kit (Vector Laboratories Inc.) and developed using peroxidase substrate kit DAB (Vector Laboratories Inc.). Slides were counterstained with hematoxylin, dehydrated, cleared with xylene, and mounted with permount (Thermo Fisher Scientific).

A tissue microarray containing classical Hodgkin lymphoma (cHL, n = 3), low-grade follicular lymphoma (LGFL, n = 10), and DLBCL (n = 34) was purchased from Cybri (CS20-00-002) and stained for RREB1 (above). IHC staining was quantified using the following criteria by: 0, negative; 1, faint; focal, equivocal, 2, positive in a minority of cells; and 3, positive in a majority of cells. Samples stained with same antibody conditions by the Human Protein Atlas were also assessed with the same criteria. cHL, n = 2; low-grade non-Hodgkin lymphoma, n = 7; high-grade non-Hodgkin lymphoma, n = 3.

Pathology

Board-certified pathologist Dr. Michael Linden (University of Minnesota, Saint Paul, MN) evaluated hematoxylin and eosin (H&E)-stained tissues for red and white pulp content, extramedullary hematopoiesis, megakaryocytes, erythroid precursors, immature granulocytes, lymphocyte size, number, morphology, plasmacytic differentiation, infiltration into extra-hematopoietic tissues, mitotic figures, and necrosis.

Comparative genomics

Whole-methylome, CNA, and transcriptomic data from 48 DLBCL human patient samples were acquired from The Cancer Genome Atlas (TCGA) database (23). Methylome data were listed as β values, CNA data were analyzed by GISTIC analysis, and transcriptomic data were listed as fragments per kilobase of transcript per million (FPKM) mapped reads. CISs were analyzed for enrichment into known pathway using Enrichr software (24).

Cell culture

We purchased CD19+ B cells (Sanguine Biosciences) and DLBCL human cell lines, Toledo, Farage, Pfeiffer, and DB (ATCC). BL2 was gifted from Reuben Harris and KM-H2, Daudi, and Ramos were gifted from Vivian Bardwell at the University of Minnesota (Minneapolis, MN). Cell lines were cultured in complete media (1× RPMI1640, 10% FBS, and 1× penicillin/streptomycin) and grown at 37°C in 5% CO2. Cell viability was assessed utilizing a Trypan blue exclusion assay on a hemocytometer every 24 hours for 5 days. No authentication or Mycoplasma tests were carried out. Cells from ATCC were passaged three times prior to experimental usage.

RREB1 shRNA knockdown

Cells were transduced with RREB1 shRNA lentiviruses (Open Biosystems) and flow sorted with the top 10% of GFP+ cells isolated and selected with 1 μg/mL puromycin.

RREB1 overexpression

RREB1 cDNA (Open Biosystems) was cloned into the Gateway Vector System (Life Technologies) and subcloned into a piggyBac (PB) transposon vector. Cells were transfected with 2 μg of RREB1 or Gfp PB transposon and 2 μg of PB7 transposase plasmid using the NEON transfection system (Life Technologies) followed by selection with 1 μg/mL puromycin. RREB1 expression was induced with an optimized doxycycline dosage.

qRT-PCR

qRT-PCR analysis was carried out as described previously (12). miRNA samples were isolated utilizing the miRNeasy Mini Kit (Qiagen) and assessed miR-143, miR-145, and U6 expression (Life Technologies).

Immunoblotting

Resolved lysates on polyvinylidene difluoride membranes were probed with antibodies against RREB1 (1:1,000, Sigma-Aldrich), KRAS4A, KRAS4B (1:1,000, Santa Cruz Biotechnology), PTEN, AKT, pAKT, p4EBP1 (1:1,000, Cell Signaling Technology), and GAPDH (1:10,000: Cell Signaling Technology). Corresponding HRP-conjugated secondary antibodies (1:2,000: Vector Laboratories) were utilized. Blots were developed via chemiluminescence and imaged on the LI-COR Odyssey.

Statistical analysis

Statistics were performed using GraphPad Software Prism Version 6.0d for the following analyses: survival with Kaplan–Meier survival curve with log-rank Mantel–Cox test; phenotypes analyzed using Fisher exact tests (FET) and χ2 tests. Nonparametric Mann–Whitney tests with standard error of the mean were carried out on spleen weights, qRT-PCR, densitometry, and cell proliferation assays. Correlation was done using Pearson correlation analysis.

SB mutagenesis in Cnp+ cells induced B-cell lymphoma

We previously utilized Cnp–Cre to model peripheral nervous system cancers in mice (12). Cnp, a phosphodiesterase, is highly-expressed in nervous system tissues (oligodendrocytes and Schwann cells) starting at E14.5 through adulthood with minimal expression in other tissues including spleen (lymphocytes), liver, heart, bone marrow stromal cells, and cultured mouse CD34+ bone marrow cells (25, 26). Utilizing IHC for SB expression and a PCR-based excision assay for SB activity, we determined SB was expressed and active in numerous tissues (brain, pancreas, liver, testes, skeletal muscle, lungs, spleen, heart, and kidneys) in our model (Supplementary Fig. S1A and S1B).

SB-mutagenized (Cnp-Cre;R26SB11LSL;T2/Onc15, n = 63) and control (Cnp-Cre;R26SB11LSL or Cnp-Cre;T2/Onc15, n = 88) mice were aged and assessed for phenotypic alterations. SB-mutagenized mice had significantly reduced survival compared with controls (log-rank Mantel–Cox, P < 0.0001) with median survival of 436 day versus 605 days in control animals (Fig. 1A). Similarly, SB-mutagenized mice carrying a Trp53R270H point mutation (Cnp-Cre;R26SB11LSL;T2/Onc15;Trp53R270H, n = 33) had significantly reduced survival (log-rank Mantel–Cox, P < 0.0001) with a median survival of 322 days versus 485 days compared with Trp53R270H (Cnp-Cre;R26SB11LSL;Trp53R270H or Cnp-Cre;T2/Onc15;Trp53R270H, n = 37) control animals (Fig. 1A). Examination of SB-mutagenized animals with or without Trp53R270H mutation revealed very rare peripheral nervous system tumors (12) but a highly penetrant lymphoid disease (65%, splenomegaly; Supplementary Table S1). The lymphoid disease was predominantly splenomegaly (62%) with some animals also presenting with an enlarged thymus (13.8%) and enlarged mesenteric lymph nodes (22.4%; Supplementary Fig. S1C and S1D). Solid tumors were observed in various tissues with the liver (22.4%) having the highest incidence followed by the brain (oligodendroglioma, astrocytoma; 3.4%) and fat pads (3.4%; Supplementary Fig. S1D and S1E). Overall, SB-mutagenized mice had a significant increase in the penetrance of lymphoid disease and solid tumor (75.9%) formation compared with control animals (29.5%; FET P < 0.0001; Fig. 1B). SB mutagenesis did not significantly alter the phenotype penetrance in Trp53R270H (79.4%) animals compared with Trp53R270H controls (72.4%).

Figure 1.

SB mutagenesis in Cnp+ cells induced B-cell lymphoma. A, Kaplan–Meier survival curve comparing SB-mutagenized (Cnp-Cre;R26SB11LSL;T2/Onc15, n = 63), control (Cnp-Cre;R26SB11LSL or Cnp-Cre;T2/Onc15, n = 88), SB-mutagenized mice carrying a Trp53270H point mutation (Cnp-Cre;R26SB11LSL;T2/Onc15;Trp53R270H, n = 33), and Trp53R270H (Cnp-Cre;R26SB11LSL;Trp53R270H or Cnp-Cre;T2/Onc15;Trp53R270H, n = 37) control animals. B, Pie charts depicting macroscopic phenotypes for each genotype. C, Histologic analysis of spleen samples. Image in the top left depicts size of experimental versus control spleens. Image in the bottom right is IHC for SB on a splenomegaly samples. The following images are H&E–stained sections of spleen, liver, lung and kidney. Each image depicts evidence of lymphoma (cells of uniform size and shape, stained darkly). D, Agarose gel images of PCR reactions for assaying V(D)J recombination of the B-cell receptor (IgH locus). C, C57Bl/6 spleen; normal, mice undergoing transposition with normal spleen weights (up to 0.2 g); splenomegaly, mice undergoing transposition with spleen wet weights >0.2 g. Recombination events assessed are: VHJ558/JH3, VHQ52/JH3, VH7183/JH3, and DHL/JH3. α-Actin served as a loading control. 100-bp DNA ladder is in the far left lane. Multiple bands in each lane indicate a polyclonal population as observed in normal spleens, whereas lack of bands or a single band indicate oligoclonal populations, which are commonly observed in lymphoid disease. E, Serial transplant of primary SB-mutagenized lymphoma samples into the flanks of recipient SCID/Beige mice. Spleens from primary SB-mutagenized mice (left) and allograft tumors (right) were isolated and analyzed by flow cytometry for T-cell (CD4, CD8), B-cell (CD19), and macrophage (Mac1, GR1) markers.

Figure 1.

SB mutagenesis in Cnp+ cells induced B-cell lymphoma. A, Kaplan–Meier survival curve comparing SB-mutagenized (Cnp-Cre;R26SB11LSL;T2/Onc15, n = 63), control (Cnp-Cre;R26SB11LSL or Cnp-Cre;T2/Onc15, n = 88), SB-mutagenized mice carrying a Trp53270H point mutation (Cnp-Cre;R26SB11LSL;T2/Onc15;Trp53R270H, n = 33), and Trp53R270H (Cnp-Cre;R26SB11LSL;Trp53R270H or Cnp-Cre;T2/Onc15;Trp53R270H, n = 37) control animals. B, Pie charts depicting macroscopic phenotypes for each genotype. C, Histologic analysis of spleen samples. Image in the top left depicts size of experimental versus control spleens. Image in the bottom right is IHC for SB on a splenomegaly samples. The following images are H&E–stained sections of spleen, liver, lung and kidney. Each image depicts evidence of lymphoma (cells of uniform size and shape, stained darkly). D, Agarose gel images of PCR reactions for assaying V(D)J recombination of the B-cell receptor (IgH locus). C, C57Bl/6 spleen; normal, mice undergoing transposition with normal spleen weights (up to 0.2 g); splenomegaly, mice undergoing transposition with spleen wet weights >0.2 g. Recombination events assessed are: VHJ558/JH3, VHQ52/JH3, VH7183/JH3, and DHL/JH3. α-Actin served as a loading control. 100-bp DNA ladder is in the far left lane. Multiple bands in each lane indicate a polyclonal population as observed in normal spleens, whereas lack of bands or a single band indicate oligoclonal populations, which are commonly observed in lymphoid disease. E, Serial transplant of primary SB-mutagenized lymphoma samples into the flanks of recipient SCID/Beige mice. Spleens from primary SB-mutagenized mice (left) and allograft tumors (right) were isolated and analyzed by flow cytometry for T-cell (CD4, CD8), B-cell (CD19), and macrophage (Mac1, GR1) markers.

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Histologic analysis of splenomegaly samples identified SB, highly expressed in splenic germinal centers with diffuse positive cells in surrounding marginal zone and red pulp (Fig. 1C). Pathologic analysis indicated SB-mutagenized splenomegaly samples were significantly (FET P = 0.0037) involved with lymphoma, specifically follicular lymphoma and DLBCL, compared with control splenomegaly samples (Fig. 1C; Supplementary Fig. S1F and S1G; Supplementary Table S2). Moreover, only SB-mutagenized mice (n = 5/17) had evidence of infiltrative DLBCL into surrounding tissues including liver, lungs, kidneys, skeletal muscle, and adrenal glands (Fig. 1C; Supplementary Fig. S1F and S1G).

To confirm tumor identity, we performed PCR-based clonality analysis, allograft experiments, and in vivo lineage tracing analysis. PCR-based clonality analysis of the BCR IgH locus from SB-mutagenized spleens identified the presence of monoclonal and oligoclonal populations in splenomegaly samples not present in the SB-mutagenized normal weight and C57BL/6 spleens (Fig. 1D). Allograft transplants of primary splenomegaly samples gave rise to CD19+ B-cell expansion in spleens of SCID/beige recipient mice (Fig. 1E). SB-expressing cells were immunophenotyped for T-cell, B-cell, and macrophage markers on bone marrow, thymus, lymph node, and spleen samples from control (Cnp-Cre n = 6; R26SB11LSL n = 5), SB induced without mutagenesis (Cnp-Cre;R26SB11LSL n = 5), and SB-mutagenized mice (n = 12). Because the conditional SB allele contains a GFP stop cassette unless exposed to Cre-recombinase, GFP−ve cells were used as a marker for SB expression. This analysis revealed SB expression across all four tissues with lymph nodes (74.2%, n = 10) and spleens (60.72%, n = 17), showing the highest percentage of recombination followed by thymus (40.3%, n = 14) and bone marrow (30%, n = 16; Supplementary Fig. S2A). Immunophenotyping of these tissues for lineage-specific markers indicated that SB expression occurred in myeloid, T cells, B cells, and the remaining supporting cells (stroma) of each tissue (Supplementary Fig. S2B–S2E). No significant changes in cellular distribution were observed in bone marrow and the peripheral lymph nodes. However, SB-mutagenized spleen and thymus samples had significant changes in cell-type distribution. Spleen samples had significantly (*, P < 0.05) reduced B-cell percentage with corresponding significant increase in myeloid and the supporting stromal cells (Supplementary Fig. S2C). Thymus samples had significantly (*, P < 0.05) reduced the T-cell percentage with corresponding increases in B cells, myeloid, and supporting stromal cells (Supplementary Fig. S2D). To correlate these changes in cellular distribution with SB activity, we analyzed the GFP fraction of the total live cells (Supplementary Fig. S2B–S2E). Relative to the Cnp-Cre;T2/Onc control animals, in which all cells are GFP, there is a bias for supporting stromal cells to undergo recombination in all tissue types and for B cells to recombine in the thymus.

Because the SB-mutagenized animals had an average age of 479 days, we could not rule out the effects of aging on the results. Therefore, we performed an additional lineage-tracing analysis on 60-day-old Cnp-Cre mice bred to conditional GFP reporter mice on bone marrow, lymph node, and spleen samples (Supplemental Figs. S3 and S4). Lineage-tracing analysis of the bone marrow demonstrated Cnp was predominantly expressed in B220+ B-cell precursors as only 13.9% of GFP+ve cells were B220 (Supplementary Fig. S3). Assessment of GFP expression during all stages of B-cell development indicated that Cnp-cre was active from the pre-/pro–B cells (24%) to the mature follicular and marginal zone B cells (87.5%; Supplementary Fig. S4). Collectively, these data demonstrate Cnp is expressed in many hematopoietic cells and that targeted mutagenesis of Cnp+ cells preferentially induces a B-cell lymphoma phenotype arising from early B-cell precursors.

Identification of B-cell tumor driver mutation genes

To identify genetic drivers of lymphomagenesis, T2/Onc insertions from 23 SB-derived splenomegaly samples were analyzed by Illumina sequencing to identify CISs utilizing two unique statistical methods: TAPDANCE CIS (tdCIS; ref. 22) and gene-centric CIS (gCIS; ref. 21). These analyses identified 18 tdCIS- and 43 gCIS-associated genes with 13 genes (27%) overlapping (Fig. 2A and B; Supplementary Table S3). The most common, significantly mutated genes were Bach2 (43%, P = 2.07 × 10−5), Ambra1 (35%, P = 3.08 × 10−4), Rreb1 (23%, P = 2.04 × 10−4), Arid1b (26%, q = 2.13 × 10−4), and Nfkb1 (26%, P = 3.90 × 10−4) of which Bach2 expression has been associated with DLBCL survival outcome and Nfkb1 is a known effector gene in human DLBCL (27, 28). ARID1B, the chromatin modifier in SWI/SNF complex, is known to be mutated in human follicular lymphoma (29). Ambra1 is a scaffold protein involved in autophagy and cell proliferation that functions as a TSG through regulation of Myc but has not been previously implicated in B-cell lymphoma (30). Rreb1 is a transcription factor involved in MAPK and PI3K signaling through KRAS and is implicated in thyroid and bladder cancer but not lymphomagenesis (31, 32). Genes previously implicated in human DLBCL and/or follicular lymphoma were also identified: Crebbp (17%, q = 1.13 × 10−7), Malt1 (22%, P = 3.90 × 10−4), and Pten (17%, P = 4.18 × 10−2; refs. 5, 7, 8, and 33). Three of the 23 splenomegaly samples analyzed did not contribute to CIS calling (Fig. 2B). To identify potential drivers of these individual samples, we assessed the top mutated genes for each splenomegaly sample (Supplementary Table S4). These three spleens had very low read counts for each of their putative gene drivers suggesting there may be additional factors driving lymphomagenesis: Wdtc1 (13), Pde2a (45), and Atp9b (245). In addition to the splenomegaly samples, we also sequenced five normal weight spleens in which the animals did not display any macroscopic or microscopic disease to identify potential early driver events (Fig. 2B). From these, we identified 3 CIS-associated genes: Nfkb1 (60%, q = 5.39 × 10−15), Kansl1 (60%, q = 2.40 × 10−11), and Sp4 (60%, P = 4.69 × 10−2). Nfkb1 is a CIS in the splenomegaly samples. Kansl1 is not a CIS but splenomegly samples did have T2/Onc insertions and Sp4 was exclusive to the nonsplenomegaly samples.

Figure 2.

CIS analysis for cooperating networks and pathways in DLBCL formation. A, Weighted word cloud of 48 CIS-associated genes from SB mutagenesis alone. Red indicates predicted proto-oncogenes. Blue indicates predicted TSGs. B, Heatmap depicting the clonal analysis of CISs across each tumor sample. Clonality (top row) was determined by results of (Fig. 1D): yellow, oligoclonal; green, clonal; white, no data. Contribution to CIS was determined by the percentage of each tumor that contributes to the total number of CIS: dark blue, high; light blue, none. CISs are listed on the left followed immediately by the percentage of tumors that contribute to the CIS calling. The number of reads for each insertion following illumina sequencing are shown: red, >10,000 reads; orange, 5,000–10,000 reads; green, 1,000–5,000 reads; blue, 100–1,000 reads; black, <100 reads. Empty squares indicate no contribution to the CIS calling. C, Weighted word cloud of 9 CIS-associated genes from SB mutagenesis on the Trp53R270H background. Red indicates predicted proto-oncogenes. Blue indicates predicted TSGs. D, Network depicts the most significant upstream regulators of the 48 CISs. Yellow, IPA upstream regulator; red, predicted proto-oncogene; blue, predicted TSG. E, Network depicts results from CIS cooccurrence analysis of the tdCIS genes. Red, predicted proto-oncogene, blue, predicted TSG. Statistical analysis was performed using GraphPad Prism (***, P = 0.0002).

Figure 2.

CIS analysis for cooperating networks and pathways in DLBCL formation. A, Weighted word cloud of 48 CIS-associated genes from SB mutagenesis alone. Red indicates predicted proto-oncogenes. Blue indicates predicted TSGs. B, Heatmap depicting the clonal analysis of CISs across each tumor sample. Clonality (top row) was determined by results of (Fig. 1D): yellow, oligoclonal; green, clonal; white, no data. Contribution to CIS was determined by the percentage of each tumor that contributes to the total number of CIS: dark blue, high; light blue, none. CISs are listed on the left followed immediately by the percentage of tumors that contribute to the CIS calling. The number of reads for each insertion following illumina sequencing are shown: red, >10,000 reads; orange, 5,000–10,000 reads; green, 1,000–5,000 reads; blue, 100–1,000 reads; black, <100 reads. Empty squares indicate no contribution to the CIS calling. C, Weighted word cloud of 9 CIS-associated genes from SB mutagenesis on the Trp53R270H background. Red indicates predicted proto-oncogenes. Blue indicates predicted TSGs. D, Network depicts the most significant upstream regulators of the 48 CISs. Yellow, IPA upstream regulator; red, predicted proto-oncogene; blue, predicted TSG. E, Network depicts results from CIS cooccurrence analysis of the tdCIS genes. Red, predicted proto-oncogene, blue, predicted TSG. Statistical analysis was performed using GraphPad Prism (***, P = 0.0002).

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Because TP53 mutations are prevalent in human DLBCL (34), we also sequenced seven SB-induced lymphomas in the Trp53R270H background by 454 pyrosequencing to identify cooperating mutations with Trp53 mutation to drive B-cell lymphoma. From these analyses, we identified 12 tdCIS genes (10 human homologs) with the most significantly mutated genes being Pik3r1 (42.9%), Kcnj12 (42.9%), and Pls1 (42.9%; Fig. 2C; Supplementary Table S3). Pik3r1, a modulator of PI3K signaling, has been previously implicated in human DLBCL (8). Kcnj12, a potassium channel, and Pls1, an actin-binding protein, have not previously implicated in DLBCL or follicular lymphoma. Collectively, Nfkb1 was the only CIS significantly mutated in both screens. Moreover, 10 of the 59 CIS-associated genes (A23004603Rik, chr4:94961700-94971700, Tnfsf8, Inpp4b, C80913, Chst15, Crybg3, Mfap3l, Tespa1, and Tnfrsf13b,) have not been previously identified in other SB screens (Candidate Cancer Gene Database, n = 69 studies, 12 tumor types; ref. 35), suggesting a specificity of these genes in B-cell lymphomagenesis and not cancer in general (Supplementary Table S5).

The position/orientation of the T2/Onc murine stem cell virus (MSCV) promoter, relative to the direction of gene transcription, can be used to predict whether T2/Onc is likely to drive or disrupt gene transcription. Transcriptional activation may occur if the majority of transposon insertions are orientated upstream of a gene or translational start site with MSCV promoters in the same direction as gene transcription; the gene would be a putative proto-oncogene (e.g., Bach2, Nfkb1, and Rreb1). Disruption of transcription may occur if the transposons land within a gene with no MSCV promoter orientation or insertion site bias within the locus; the gene would be a putative TSG (e.g., Ambra1 and Pten). Thirteen putative proto-oncogenes and 46 putative TSGs were identified from the 59 unique CISs (56 human homologs) from both screens (Supplementary Fig. S5A and S5B; Supplementary Table S6).

To determine whether T2/Onc insertions caused phenotypic alterations, we assessed the impact of the T2/Onc insertions on gene expression and disease-free survival. Of the 8 CIS genes assessed by qRT-PCR, only Rreb1 displayed significant (P = 1.24183 × 10−8) changes in mRNA expression compared with the wild-type spleens and SB-mutagenized splenomegaly samples that lacked T2/Onc insertions in the Rreb1 gene (Supplementary Fig. S5C). Eight CISs were significantly correlated with survival in SB-mutagenized mice (Supplementary Table S7; Supplementary Fig. S6). Hivep2, a MYC intron–binding transcription factor with known TSG role in glioma (36), was the most significantly correlated with reduced survival (P = 0.0004 log-rank, Mantel–Cox test, Supplementary Fig. S6). The chr4:94961700-94971700 CIS was also associated with significantly (P = 0.0018 log-rank, Mantel–Cox test, Supplementary Table S7, Supplementary Fig. S6) reduced survival. This region is a predicted enhancer element based on H3K4 mono-methylation marks (Supplementary Fig. S7; ref. 37). The closest genes to this region are Jun (∼242 kb) and Fggy (∼252 kb). Finally, Arid1b and Whsc1, known cancer-causing genes, were associated with a significant reduction in survival (Supplementary Table S7; Supplementary Fig. S6).

Pathways and upstream regulators of CISs

Enrichr (24) was used to identify significantly altered signaling pathways and cellular phenotypes in the 46 human homologs of the CISs from SB-mutagenized spleens (Supplementary Table S8). This identified several signaling pathways significantly enriched in the CIS list: B-cell receptor signaling [PPP2R5E, PTEN, CBLB, NFKB1, and MALT1: Benjamini–Hochberg (B-H) corrected P = 0.02] and NFκB (TNFRSF11A, NFKB1, and MALT1: B-H corrected P = 0.03) signaling pathways; pathways altered in human B-cell lymphomas. One of the top significantly enriched phenotypes was enlarged spleen (CREBBP, PTEN, TNFRSF11A, SMAP1, NFKB1, and RUNX1: B-H corrected P = 0.004) with many other genes implicated in B-cell proliferation (NFKB1, CREBBP, TNFRSF11A, BACH2, ARHGAP17, and RUNX1) and differentiation (BACH2 and MALT1).

Ingenuity Pathway Analysis (IPA, Qiagen) of upstream transcriptional regulators of the CIS genes identified GFI1 (P = 1.7 × 10−5), BCL6 (P = 1.11 × 10−3), and EP300 (P = 3.09 × 10−3) in the top five significantly enriched transcriptional regulators (Fig. 2D). CIS genes CREBBP (P = 1.85 × 10−2) and NFKB1 (P = 4.74 × 10−2) were also significantly enriched. EP300, CREBBP, and BCL6 are highly mutated in human DLBCL, whereas GFI1 has not been implicated (2). Enrichr analysis identified ZBTB7A (n = 12, B-H corrected P = 0.01) and SP1 (n = 15, B-H corrected P = 0.02) as significantly enriched transcriptional regulators, both of which have been implicated in several human cancers including B-cell lymphomas (38).

Cooccurring CISs

We performed cooccurrence analysis to identify CIS genes that were mutated together at a higher frequency than expected by chance (22). Cooccurrence analysis of the tdCIS from SB-mutagenized mice identified 15 pairs of cooccurring CISs (co-CIS; Fig. 2E; Supplementary Table S9). Twelve co-CISs involved at least one 1 that regulates transcription. Several genes were co-CISs with Bach2, the most mutated single CIS (43% of tumors), including the most significant pair of co-CISs with Hivep2 and Chr4:94961700. Moreover, Bach2/Hivep2 and Bach2/Chr4:94961700 co-CISs were associated with a significant reduction in disease-free survival (Supplementary Table S9). Bach2 alone was not significantly associated with survival, whereas Hivep2 and Chr4:94961700 did. Interestingly, the three tumors with insertions in Chr4:94961700 CIS also had insertions in Hivep2 and Bach2, suggesting a potential cooperative signaling network.

Comparative genomics of CISs in cancer, including lymphoma

To determine whether the CIS-associated genes were frequently mutated in human cancer, we queried the Catalogue of Somatic Mutations in Cancer (COSMIC) Cancer Gene Census, which annotates known cancer-causing genes (33). Eleven of the 54 CIS-associated genes with a human homolog are in Cancer Gene Census (P = 2.86 × 10−7, hypergeometric test). To assess the relevance of CIS genes to human lymphoma, we queried methylome, SNPs, RNA sequencing (RNA-seq) transcriptomic, and whole-genome/-exome sequencing data of human DLBCL samples from TCGA (n = 48 samples; Fig. 3; Supplementary Tables S10–S13). CREBBP was the most mutated gene in the list (n = 6/48 samples) followed by AMBRA1 (n = 3/48) and VANGL1 (n = 3/48; Supplementary Table S10). The SNP data identified CIS genes with a tendency toward CNA gains (n = 6) and CNA losses (n = 14) in more than 20% of the samples (Supplementary Table S11; Fig. 3A). Methylome data, which is predictive of gene expression, identified 38 CIS genes differentially methylated. Thirty-one CISs displayed hypomethylation, which is associated with gene expression (β value < 0.2) and 7 CIS genes displayed hypermethylation, which is associated with gene silencing (β value > 0.8; Supplementary Table S12; Fig. 3B). RNA-seq transcriptomic data identified 5 CIS genes overexpressed (z score > 2) in at least 10% of samples: CHST15, CNOT2, MFHAS1, TAF8, and TESPA1 (Supplementary Table S13; Fig. 3C). Combining these analyses, we predicted several CIS genes as strong genetic drivers of DLBCL. For example, CNOT2 was generally hypomethylated, overexpressed, and observed CNA gains, which is a predicted scenario for a proto-oncogene. CNOT2 was also predicted to be an oncogene based on CNA and expression profiling data from 392 DLBCL patient samples (39). HIVEP2, a predicted TSG in our screen, had a similar level of methylation and CNAs as 3 known TSGs (TP53, CDKN2A, and TNFAIP3), suggesting HIVEP2 is a TSG in human DLBCL. Furthermore, assessment of the 15 cooccurring CISs against the human DLBCL data sets identified two pairs of cooccurring CISs that are significantly coaltered: PTEN/MAP3K8 (Padj = 0.001) and BACH2/HIVEP2 (Padj = 0.004; Supplementary Table S9). Collectively, this gene-centric analysis confirmed our identification of known genetic drivers of human DLBCL and created a guide for making hypotheses about genes with unknown roles (e.g., KIAA0391 and KIAA1033) in DLBCL.

Figure 3.

CIS comparative analysis to human DLBCL samples. The graphs depict data from TCGA on CNA data via GISTIC scores (A), methylome data via β values (B), and RNA-seq expression by FPKM (C). Data were acquired from TCGA database. Each data point represents an individual sample n = 48 DLBCL patient samples. Methylome data is represented as the β value in which values above 0.8 indicate hypermethylation events, whereas values below 0.2 indicate hypomethylation events.

Figure 3.

CIS comparative analysis to human DLBCL samples. The graphs depict data from TCGA on CNA data via GISTIC scores (A), methylome data via β values (B), and RNA-seq expression by FPKM (C). Data were acquired from TCGA database. Each data point represents an individual sample n = 48 DLBCL patient samples. Methylome data is represented as the β value in which values above 0.8 indicate hypermethylation events, whereas values below 0.2 indicate hypomethylation events.

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Heterozygous loss of Pten is sufficient to drive B-cell lymphomagenesis

Targeting Cnp+ cells with SB mutagenesis and/or Trp53R270H mutations gave rise to a high prevalence of B-cell lymphoma and lineage tracing analysis identified early B-cell precursors to express Cnp. However, Cnp has not been previously identified as marker for cells that functionally develop B-cell lymphoma. To further explore the functional role of Cnp in B-cell lymphomagenesis and validate our SB screen, we crossed Cnp-Cre mice to animals harboring a conditional allele of Pten (40). PTEN is a known cancer-causing gene in numerous human cancers including B-cell lymphomas and is a putative CIS TSG from our screen.

One-hundred percent of Cnp-Cre;Ptenf/f mice (n = 8, median survival 102 days, log-rank Mantel–Cox P < 0.0001) succumbed to paralysis-related deaths with peripheral nerve hyperplasia and neurofibroma formation and enlarged cervical lymph nodes (Fig. 4A and B). Conversely, 100% of Cnp-Cre;Ptenf/+ mice (n = 16, median survival 323 days, log-rank Mantel–Cox P < 0.0001) succumbed to lymphoma-related deaths with enlarged cervical lymph nodes but no nervous system phenotype (Fig. 4A and B). Enlarged cervical lymph nodes possessed follicular lymphoma histologic features and were predominantly B220+CD19+CD21+CD35+ B cells by immunophenotyping (Fig. 4C and D; Supplementary Table S2). Western blot analysis for members of the PI3K/PTEN/AKT pathway indicated that Pten expression was maintained in the Cnp-Cre;Ptenf/+ animals with increased signaling through the downstream effector p4ebp1 (Fig. 4E and F). Collectively, these data suggest Cnp is expressed from cells that are prone to B-cell lymphoma formation and that Pten haploinsufficiency is sufficient to generate a follicular lymphoma–like phenotype.

Figure 4.

Pten haploinsufficiency is sufficient to driver B-cell lymphoma. A, Kaplan–Meier curve for Cnp-Cre;Ptenf/f mice (n = 8), Cnp-Cre;Ptenf/+ mice (n = 16), and control (Cnp-Cre;R26SB11LSL or Cnp-Cre;T2/Onc15, n = 88) mice. B, Images taken at time of necropsy of lymphoid tissues and peripheral nerves affected by Pten loss. Pie charts indicate percentage distribution of phenotypes for each genotype. C, Representative H&E images of lymph nodes from Cnp-Cre;Ptenf/+ mice displaying follicular lymphoma features. D, Flow cytometry analysis of B-cell content in peripheral lymph nodes of two Cnp-Cre;Ptenf/+ animals. E, Western blot analysis on lysates from lymph nodes of control animal (F2062) and Cnp-Cre;Ptenf/+ animals for Pten, pAkt, Akt, p4ebp1, and Gapdh. F, Densitometry quantification of the Western blot analysis in D.

Figure 4.

Pten haploinsufficiency is sufficient to driver B-cell lymphoma. A, Kaplan–Meier curve for Cnp-Cre;Ptenf/f mice (n = 8), Cnp-Cre;Ptenf/+ mice (n = 16), and control (Cnp-Cre;R26SB11LSL or Cnp-Cre;T2/Onc15, n = 88) mice. B, Images taken at time of necropsy of lymphoid tissues and peripheral nerves affected by Pten loss. Pie charts indicate percentage distribution of phenotypes for each genotype. C, Representative H&E images of lymph nodes from Cnp-Cre;Ptenf/+ mice displaying follicular lymphoma features. D, Flow cytometry analysis of B-cell content in peripheral lymph nodes of two Cnp-Cre;Ptenf/+ animals. E, Western blot analysis on lysates from lymph nodes of control animal (F2062) and Cnp-Cre;Ptenf/+ animals for Pten, pAkt, Akt, p4ebp1, and Gapdh. F, Densitometry quantification of the Western blot analysis in D.

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Rreb1 is a predicted proto-oncogene enhancing signaling through KRAS/MAPK

RREB1 is a transcription factor and proto-oncogene in solid tumor cancers (41). RREB1 operates in a feed-forward loop promoting KRAS expression and activation by inhibition of miR-143/145 expression to potentiate MAPK and PI3K signaling (31, 41). KRAS has been implicated as a driver in human DLBCL (5).

In this study, Rreb1 is a predicted proto-oncogene based on the orientation and position of T2/Onc insertions resulting in Rreb1 overexpression (Fig. 5A). mRNA fusion transcripts between the MSCV promoter/splice donor in T2/Onc and Rreb1 were identified in tumors with T2/Onc insertions in the Rreb1 locus and two tumors where LM-PCR did not identify T2/Onc insertions (Fig. 5B). Similar findings were observed in a SB osteosarcoma screen (42). Tumors containing T2/Onc-Rreb1 fusion transcripts demonstrated significantly increased Rreb1 mRNA expression (ANOVA multiple comparisons, P < 0.0001) and increased protein levels by IHC and Western blot analysis, suggesting Rreb1 functions as a proto-oncogene (Fig. 5C–E).

Figure 5.

T2/Onc insertions drive Rreb1 expression in SB-mutagenized splenomegaly. A, Schematic of T2/Onc insertions (arrows) within the Rreb1 locus. Direction of arrows indicates orientation of the MSCV 5′LTR relative to the direction of gene transcription (left-to-right). Asterisk (*) indicates which exons the primers amplify. B, Agarose gel image of PCR analysis of T2/Onc-Rreb1 mRNA fusion transcripts from SB-mutagenized spleen samples. C, Bar graph depicts qRT-PCR analysis of Rreb1, miR-143, and miR-145 expression in wild-type (n = 4) and splenomegaly samples without insertions (n = 3) and those with T2/Onc-Rreb1 fusions (n = 5). Bar height indicates mean and error bars show SEM. P values were calculated with the one-way ANOVA with multiple comparisons (Tukey). ***, P < 0.0001. D, Western blot analysis for Rreb1, Kras, and Gapdh expression in SB-mutagenized spleen samples. M194 is a normal weight wild-type control spleen. Asterisk (*) represents spleens containing T2/Onc-Rreb1 fusion transcripts. E, Bar graph depicts densitometry analysis of the Western blot analysis in D. Analysis performed using ImageJ software. F, IHC analysis of Rreb1, Kras, pErk, and pAkt expression in normal weight spleens from control and splenomegaly samples from SB-mutagenized spleens containing the T2/Onc-Rreb1 fusion transcripts. G, Quantification of the staining in F using criteria outlined in the Materials and Methods section to score TMA. Statistical analyses were performed using the GraphPad Prism Version 6.0d. Error bars represent the SEM (*, P < 0.05; **, P < 0.001).

Figure 5.

T2/Onc insertions drive Rreb1 expression in SB-mutagenized splenomegaly. A, Schematic of T2/Onc insertions (arrows) within the Rreb1 locus. Direction of arrows indicates orientation of the MSCV 5′LTR relative to the direction of gene transcription (left-to-right). Asterisk (*) indicates which exons the primers amplify. B, Agarose gel image of PCR analysis of T2/Onc-Rreb1 mRNA fusion transcripts from SB-mutagenized spleen samples. C, Bar graph depicts qRT-PCR analysis of Rreb1, miR-143, and miR-145 expression in wild-type (n = 4) and splenomegaly samples without insertions (n = 3) and those with T2/Onc-Rreb1 fusions (n = 5). Bar height indicates mean and error bars show SEM. P values were calculated with the one-way ANOVA with multiple comparisons (Tukey). ***, P < 0.0001. D, Western blot analysis for Rreb1, Kras, and Gapdh expression in SB-mutagenized spleen samples. M194 is a normal weight wild-type control spleen. Asterisk (*) represents spleens containing T2/Onc-Rreb1 fusion transcripts. E, Bar graph depicts densitometry analysis of the Western blot analysis in D. Analysis performed using ImageJ software. F, IHC analysis of Rreb1, Kras, pErk, and pAkt expression in normal weight spleens from control and splenomegaly samples from SB-mutagenized spleens containing the T2/Onc-Rreb1 fusion transcripts. G, Quantification of the staining in F using criteria outlined in the Materials and Methods section to score TMA. Statistical analyses were performed using the GraphPad Prism Version 6.0d. Error bars represent the SEM (*, P < 0.05; **, P < 0.001).

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To determine the functional impact of T2/Onc-Rreb1 fusion transcripts on Rreb1 signaling, we assessed miR-143/145 (qRT-PCR) and Kras expression (immunoblot and IHC) and its immediate downstream effectors, pErk and pAkt (IHC). qRT-PCR analysis demonstrated a significant reduction in mir143 (Mann–Whitney: P = 0.0004, P = 0.0039) and miR-145 (Mann–Whitney: P = 0.0056, P = 0.0268) in splenomegaly samples containing T2/Onc-Rreb1 fusions, compared with normal weight spleens and splenomegaly samples lacking the fusion, respectively (Fig. 5C). IHC analysis demonstrated increased staining for Rreb1, pAkt, and pErk in T2/Onc-Rreb1 tumors compared with normal weight spleens. Western blot analysis of the tumors demonstrated a significant increase in Rreb1 and an increase in Kras protein compared with controls (Fig. 5F and G). Collectively, these data demonstrated T2/Onc–driven Rreb1 expression functionally impacted miR-143, miR-145, and Kras expression and downstream signaling effectors, pErk and pAkt.

The proto-oncogene RREB1 is highly expressed in human DLBCL

TCGA data on RREB1 genomic and transcriptomic alterations indicate that patient survival was not significantly impacted by RREB1 alterations, but displayed a trend towards reduced survival (Fig. 6A). As RREB1 functions as a transcription factor, we performed RREB1 antibody staining on a human tissue microarray (TMA) comprised of cHL, LGFL, and DLBCLs to determine whether the genomic and transcriptomic alterations affect RREB1 protein levels (Fig. 6B). Fifty-three percent (N = 18/34) of DLBCL samples expressed RREB1 in the majority of cells (Fig. 6C). In general, RREB1 staining was more intense in LGFL and DLBCL samples compared with Hodgkin lymphoma samples.

Figure 6.

RREB1 expression in human DLBCL. A, Kaplan–Meier survival curve of 48 patients with DLBCL analyzed on the basis of the presence or absence of RREB1 mutations and CNAs. Log-rank Mantel–Cox analysis P = 0.3111. B, IHC analysis of RREB1 expression on TMA for human lymphoma samples. Images depict the four grades of staining observed in human DLBCL (details in Materials and Methods). C, Bar graph depicts the quantification of the staining from (A); cHL, classical Hodgkin lymphoma n = 5; LGFL, low-grade follicular lymphoma n = 13; DLBCL n = 41. Bar height represents the mean with SEM. D, Graph depicts RNA-seq FPKM values for KRAS plotted against RREB1 FPKM values for 48 DLBCL samples from TCGA database. Pearson coefficient correlation analysis. Statistical analyses were performed using the GraphPad Prism Version 6.0d.

Figure 6.

RREB1 expression in human DLBCL. A, Kaplan–Meier survival curve of 48 patients with DLBCL analyzed on the basis of the presence or absence of RREB1 mutations and CNAs. Log-rank Mantel–Cox analysis P = 0.3111. B, IHC analysis of RREB1 expression on TMA for human lymphoma samples. Images depict the four grades of staining observed in human DLBCL (details in Materials and Methods). C, Bar graph depicts the quantification of the staining from (A); cHL, classical Hodgkin lymphoma n = 5; LGFL, low-grade follicular lymphoma n = 13; DLBCL n = 41. Bar height represents the mean with SEM. D, Graph depicts RNA-seq FPKM values for KRAS plotted against RREB1 FPKM values for 48 DLBCL samples from TCGA database. Pearson coefficient correlation analysis. Statistical analyses were performed using the GraphPad Prism Version 6.0d.

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To determine whether RREB1 influences KRAS expression in DLBCL, TCGA RNA-seq data for RREB1 and KRAS expression on 48 DLBCL samples were analyzed (Fig. 6D). There was a significant positive correlation between KRAS and RREB1 mRNA expression (Pearson r = 0.6092, P < 0.0001; Supplementary Table S14). We performed the similar correlative analysis on 15 other cancers. Significant correlations with RREB1 and KRAS expression occurred in 10 of 15 cancers assessed including pancreatic (r = 0.4717) and colorectal cancer (r = 0.1448), where the RREB1/KRAS feedback loop has been described (31, 41). Collectively, these data suggest RREB1 is a putative proto-oncogene in DLBCL and potentially other cancers via a KRAS/RREB1 feed-forward loop.

Modulating RREB1 expression alters KRAS isoform usage and proliferation

In humans, alternative splicing gives rise to 10 unique RREB1 transcripts that encode for nine distinct protein products, whereas KRAS has four unique transcripts encoding four distinct protein products (Ensembl release 88; ref. 43). Quantitative PCR of human lymphoma cell lines indicated that the DLBCL cell lines expressed the least amount of RREB1 compared with the Hodgkin lymphoma line KM-H2 and the Burkitt lymphoma cell lines (Raji, Daudi, BL2, Ramos; Fig. 7A). To see whether these transcript levels reflected the protein levels, we performed immunoblotting on normal CD19+ B cells and human DLBCL, Burkitt lymphoma, and Hodgkin lymphoma cell lines for RREB1 and KRAS to better understand which isoforms are differentially expressed in normal and malignant B cells. Several RREB1 isoforms were expressed (Fig. 7B). The high molecular weight RREB1 isoforms were present in all cell lines but absent in CD19+-purified B cells (Fig. 7B). KRAS expression was also notably different between the cell lines (Fig. 7B). CD19+-purified B cells expressed two KRAS isoforms (KRAS-4A, 24 kDa; KRAS-4B, 21 kDa) at similar levels, common in many human tissues, whereas 3/4 DLBCL cell lines had more KRAS-4B than KRAS-4A protein (44).

Figure 7.

RREB1 expression influences Kras expression and human B-cell lymphoma proliferation. A, Bar graph depicts qRT-PCR analysis of RREB1 expression relative to ACTIN expression in the KM-H2 (Hodgkin lymphoma), Burkitt lymphoma cell lines (Raji, Daudi, BL2, and Ramos), and DLBCL cell lines (DB and Pfeiffer Farage). B, Western blot analysis of RREB1, KRAS, and GAPDH expression in a panel of human DLBCL cell lines (Toledo, Farage, DB, and Pfieffer) and KM-H2 (Hodgkin lymphoma) and Burkitt lymphoma cell lines (Ramos and Daudi). CD19 cells served as a normal control. C, Bar graph depicts qRT-PCR analysis of RREB1 expression relative to ACTIN expression in the DLBCL cell lines DB and Pfeiffer parental cell line and three derivatives exposed to three unique shRNAs targeting RREB1. DB control n = 3 (parental and noneffective shRNAs), DB knockdown n = 2 effective RREB1-shRNAs in triplicate. Pfeiffer control n = 2 (parental and noneffective shRNA), Pfeiffer knockdown n = 3 effective RREB1-shRNAs in triplicate. Student t test, ***, P < 0.0001. Bar height indicates mean and error bars show SEM. D, Western blot analysis for RREB1, KRAS, and GAPDH expression in the human DLBCL cell line DB targeted with shRNAs against RREB1. E, Graph depicts viable cell counts for modified DB cell lines over the course of 13 days. Control n = 3 (parental and noneffective shRNAs) in duplicate, knockdown n = 2 unique RREB1-shRNAs cell lines in duplicate. Bars indicate SE of the mean. Statistical analyses: one-way ANOVA multiple comparisons test 95% confidence interval. F, Graph depicts viable cell counts for Pfeiffer-modified cell lines over the course of 13 days. Control n = 1 in duplicate, knockdown n = 3 unique RREB1-shRNAs run in duplicate. Error bars indicate SEM. Statistical analyses: one-way ANOVA multiple comparisons test 95% confidence interval. Statistical analyses were performed using the GraphPad Prism Version 6.0d.

Figure 7.

RREB1 expression influences Kras expression and human B-cell lymphoma proliferation. A, Bar graph depicts qRT-PCR analysis of RREB1 expression relative to ACTIN expression in the KM-H2 (Hodgkin lymphoma), Burkitt lymphoma cell lines (Raji, Daudi, BL2, and Ramos), and DLBCL cell lines (DB and Pfeiffer Farage). B, Western blot analysis of RREB1, KRAS, and GAPDH expression in a panel of human DLBCL cell lines (Toledo, Farage, DB, and Pfieffer) and KM-H2 (Hodgkin lymphoma) and Burkitt lymphoma cell lines (Ramos and Daudi). CD19 cells served as a normal control. C, Bar graph depicts qRT-PCR analysis of RREB1 expression relative to ACTIN expression in the DLBCL cell lines DB and Pfeiffer parental cell line and three derivatives exposed to three unique shRNAs targeting RREB1. DB control n = 3 (parental and noneffective shRNAs), DB knockdown n = 2 effective RREB1-shRNAs in triplicate. Pfeiffer control n = 2 (parental and noneffective shRNA), Pfeiffer knockdown n = 3 effective RREB1-shRNAs in triplicate. Student t test, ***, P < 0.0001. Bar height indicates mean and error bars show SEM. D, Western blot analysis for RREB1, KRAS, and GAPDH expression in the human DLBCL cell line DB targeted with shRNAs against RREB1. E, Graph depicts viable cell counts for modified DB cell lines over the course of 13 days. Control n = 3 (parental and noneffective shRNAs) in duplicate, knockdown n = 2 unique RREB1-shRNAs cell lines in duplicate. Bars indicate SE of the mean. Statistical analyses: one-way ANOVA multiple comparisons test 95% confidence interval. F, Graph depicts viable cell counts for Pfeiffer-modified cell lines over the course of 13 days. Control n = 1 in duplicate, knockdown n = 3 unique RREB1-shRNAs run in duplicate. Error bars indicate SEM. Statistical analyses: one-way ANOVA multiple comparisons test 95% confidence interval. Statistical analyses were performed using the GraphPad Prism Version 6.0d.

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To interrogate the role of RREB1 as a proto-oncogene, we utilized shRNA knockdown and cDNA overexpression constructs in three human B-cell lymphoma cell lines (Pfeiffer, DB, and BL2). Pfeiffer and DB cells (DLBCL) were transduced with three unique shRNAs. Effective RREB1 shRNAs significantly reduced the mRNA expression by qRT-PCR in both Pfeiffer and DB cell lines (Fig. 7C) and decreased RREB1 protein by approximately 75% in DB cells (Fig. 7D). Effective shRNAs significantly decreased proliferation in Pfeiffer and DB cells compared with parental cells and noneffective shRNAs (Fig. 7E and F). RREB1 shRNA knockdown also reduced KRAS-4B and increased KRAS-4A expression in DB cells. Overexpression of full-length RREB1 cDNA in DB and BL2 cells altered the KRAS isoform expression but did not impact proliferation (Supplementary Fig. S8). Collectively, these functional data suggest RREB1 expression significantly impacts human DLBCL proliferation and influences the amount and isoform expression of KRAS.

In this study, we identified that early B-cell progenitors express Cnp and that when targeted with SB mutagenesis, Trp53R270H mutation or Pten loss gave rise to highly penetrant lymphoid diseases, predominantly follicular lymphoma and DLBCL. Analysis of SB-mutagenized splenomegaly samples on wild-type and Trp53R270H-mutant backgrounds identified 59 CIS-associated genes and several co-CISs, suggesting that specific, ordered cooperating genetic mutations are required for tumor development. CIS genes were also identified in known signaling pathways altered in human DLBCL including NFκB, PI3K–AKT–mTOR, and BCR signaling. Finally, we identified a new role for the putative proto-oncogene, Rreb1, and elucidated a mechanism by which KRAS signaling is altered in DLBCL by upregulation of RREB1 expression.

Comparative genomic analysis reliably identified known genetic drivers of DLBCL formation (e.g., CREBBP, and MALT1). We also identified potential new TSGs (e.g., HIVEP2, and PKN2) and proto-oncogenes (e.g., CNOT2, and RREB1) in DLBCL based on CNA, methylome, transcriptomic data, and consistent recurrent alterations in our SB screen. However, we identified genes with CNA gains (e.g., TNFRSF11A, and CNOT2) and overexpression in human DLBCLs predicted to be disrupted by SB-induced mutagenesis. This discrepancy may reflect differences in mouse and human DLBCL formation and/or the cell of origin targeted in the screen. Generally, we identified few annotated DLBCL oncogenes and TSGs in our CIS lists. However, assessment of the T2/Onc insertion profiles on genes that define a variety of human lymphoma subtypes indicated that insertions were present below the CIS threshold in several genes (ARID1A, BCL2, MLL3, and MYD88; Supplementary Table S15; Supplementary Fig. S10), which with a larger number of animals in the study would likely be significant. Importantly, many CIS genes contributed to signal transduction pathways that are routinely activated in human DLBCL (e.g., NFκB signaling, PI3K–AKT–mTOR; ref. 2). Further experimental evidence is required to determine the impact that the CIS genes may have on human DLBCL development.

RREB1 expression alone may not be predictive of oncogenic capacity but rather the specific isoform(s) expressed. From our analyses, we observed differences in RREB1 isoform expression between CD19+ B cells and seven lymphoma cell lines. Moreover in our SB model, these RREB1 isoform differences were associated with differences in Kras expression. It is likely there are differences in the kinetics of RREB1 isoforms binding to and suppressing the miR-143/145 miRNA cluster to perform oncogenic functions on Kras signaling. Therefore, to further determine the importance of RREB1 in DLBCL subtypes, and by extension, other cancers, a proteomic analysis may be warranted.

KRAS has two alternative versions of exon 4 generating two isoforms differing at the carboxy terminus: the canonical KRAS-4A, which can promote apoptosis and KRAS-4B, which has an anitapoptotic role (45, 46). The amino acid changes modify KRAS membrane localization altering KRAS-induced Raf-1 signaling (46). Both isoforms are coexpressed in many human tissues, but the 4A/4B ratio is altered in human colorectal cancer with reduced 4A and increased 4B (47). We observed a similar phenomenon in the DLBCL cell line DB, RREB1 cDNA overexpression increased Kras-4B expression, whereas shRNA knockdown increased Kras-4A and significantly reduced proliferation. Collectively, these data indicate RREB1 dosage alters KRAS isoform usage and significantly impacts DLBCL cellular proliferation. Further studies assessing the role of each RREB1 and KRAS isoform on oncogenic transformation are warranted.

Three signaling pathways were significantly enriched for with CISs: NFκB (n = 7/59), BCR (n = 7/59), and PI3K–AKT–mTOR (n = 11/59) signaling. Each pathway is involved in human DLBCL with current targeted therapeutic efforts underway (48). Pharmacologic inhibition of the PI3K–AKT–mTOR pathway in cells with activating mutations in PI3K/AKT/mTOR pathway genes reduced the proliferation and caused apoptosis (8, 49). Because of the importance of the PI3K–AKT–mTOR pathway in DLBCL maintenance, it is possible RREB1 overexpression is an alternative mechanism to enhance PI3K–AKT–mTOR pathway signaling via KRAS. Although neither RREB1- nor KRAS-activating mutations are common in human DLBCL, Lohr and colleagues have identified rare KRASG13D mutations in human DLBCL and RREB1 CNA gains do occur in human DLBCL with a subset of samples significantly overexpressing RREB1 transcripts that significantly correlate with increased KRAS expression (5). Currently, no RREB1 inhibitors exist, but there is a selective irreversible inhibitor targeting the KRASG12C mutation (50). Our SB mouse model of DLBCL provides a platform to further delve into the Rreb1/Kras connection to understand the extent the RAS/MAPK signaling pathway has in DLBCL formation and maintenance with potential exploitation in therapeutic testing, such as MEK inhibitors. It seems likely that RREB1 expression at high level is a common source of RAS/MAPK activation in DLBCL.

Overall, using a SB forward genetic screen, we identified 59 candidate driver genes promoting B-cell lymphomagenesis. Moreover, we determined a new role for the proto-oncogene RREB1 in DLBCL and KRAS isoform usage. Further functional testing of additional CIS genes may reveal new genetic pathways to target for treatment of DLBCL.

M.A. Farrar reports receiving a commercial research grant from Merck. D.A. Largaespada is the co-founder/co-owner of NeoClone Biotechnologies, Inc., Discovery Genomics, Inc., and B-MoGen Biotechnologies, Inc., is a consultant for Surrogen, Inc., and reports receiving funding from Genentech, Inc. B.S. Moriarity is the co-founder and the chief scientific officer for B-MoGen Biotechnologies. No potential conflicts of interest were disclosed by the other authors.

Conception and design: E.P. Rahrmann, D.A. Largaespada

Development of methodology: E.P. Rahrmann, D.A. Largaespada

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): E.P. Rahrmann, N.K. Wolf, G.M. Otto, L.H. Harris, L.B. Ramsey, J. Shu, T.K. Starr, B.S. Moriarity

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): E.P. Rahrmann, L.H. Harris, L.B. Ramsey, J. Shu, R.S. LaRue, M.A. Linden, S.K. Rathe, T.K. Starr, M.A. Farrar, D.A. Largaespada

Writing, review, and/or revision of the manuscript: E.P. Rahrmann, L.B. Ramsey, M.A. Linden, S.K. Rathe, T.K. Starr, M.A. Farrar, D.A. Largaespada

Other (central pathology review of microscopic data): M.A. Linden

The authors would like to thank the Biomedical Genomics Center at the University of Minnesota (Minneapolis, MN) for performing the Illumina deep sequencing. We also acknowledge the following shared resources of the Masonic Cancer Center at the University of Minnesota: The Mouse Genetics Laboratory, Biostatistics and Bioinformatics, Flow Cytometry Resource, and Comparative Pathology. We thank the Minnesota Supercomputing Institute for computational resources. We thank the Research Animal Resources at the University of Minnesota, specifically Alwan Aliye, for his technical support in mouse maintenance. This work received funding from the American Cancer Society Research Professor Award and NIH-NCI CA113636 (to D.A. Largaespada) the NIH-NINDS-P50 N5057531, and the Margaret Harvey Schering Trust. M.A. Farrar was funded by NIH R01 CA151845 and CA154998. T.K. Starr was supported by funding from the NIH NCI (5R00CA151672-04) and the Masonic Cancer Center NIH support grant (P30-CA77598).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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