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Oncogenes and Tumor Suppressors

Sleeping Beauty Screen Identifies RREB1 andOther Genetic Drivers in Human B-cell LymphomaEric P. Rahrmann1,2, Natalie K.Wolf1, George M. Otto2, Lynn Heltemes-Harris2,3,4,Laura B. Ramsey4, Jingmin Shu2, Rebecca S. LaRue2, Michael A. Linden2,5,Susan K. Rathe2, Timothy K. Starr2,6, Michael A. Farrar2,3,4, Branden S. Moriarity2,7,8, andDavid A. Largaespada1,2,7,8

Abstract

Follicular lymphoma and diffuse large B-cell lymphoma(DLBCL) are the most common non-Hodgkin lymphomasdistinguishable by unique mutations, chromosomal rear-rangements, and gene expression patterns. Here, it is dem-onstrated that early B-cell progenitors express 20,30-cyclic-nucleotide 30 phosphodiesterase (CNP) and that whentargeted with Sleeping Beauty (SB) mutagenesis, Trp53R270H

mutation or Pten loss gave rise to highly penetrant lymphoiddiseases, predominantly follicular lymphoma and DLBCL.In efforts to identify the genetic drivers and signaling path-ways that are functionally important in lymphomagenesis,SB transposon insertions were analyzed from splenomegalyspecimens of SB-mutagenized mice (n ¼ 23) and SB-muta-genized mice on a Trp53R270H background (n ¼ 7) andidentified 48 and 12 sites with statistically recurrent trans-poson insertion events, respectively. Comparison with

human data sets revealed novel and known driver genesfor B-cell development, disease, and signaling pathways:PI3K–AKT–mTOR, MAPK, NFkB, and B-cell receptor (BCR).Finally, functional data indicate that modulating Ras-responsive element-binding protein 1 (RREB1) expressionin 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 hyper-activation in human DLBCL.

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

IntroductionB-cell malignancies comprise a large family of diseases ranging

fromhighly curableHodgkin lymphoma to themore diverse non-Hodgkin lymphoma subtypes including the indolent follicularlymphoma and the aggressive, genetically heterogeneous diffuselarge B-cell lymphoma (DLBCL; ref. 1). Molecular profiling ofB-cell malignancies has identified defining genetic features formany of the subtypes leading to new therapeutic targets andincreased survival-rates for some diseases (1). DLBCL, which

occur predominantly in older adults, diagnosis and treatmenthave greatly been impacted by the genetic profiling efforts. DLBCLis categorized into two unique molecular subtypes based on geneexpression profiling: activated B-cell–like (ABC) and germinalcenter B-cell–like (GCB; ref. 1). Transcriptomic and genomicanalyses identified recurrent genomic aberrations and signalingpathway alterations unique to each subtype and common to both(2, 3). Mutations in genes altering B-cell receptor (BCR) signalingandNFkB activation (e.g.,CD79A,MALT1, andMYD88) aremorecommon in ABC DLBCL, whereas mutations in genes alteringhistone modifications and B-cell homing (e.g., EZH2, CREBBP,andMLL2) are more common in GCB DLBC (4–6). Mutations inTP53, immunosurveillance genes (e.g., B2M, CD58), epigeneticmodifiers (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 recurrentlymutated genes in primary DLBCL samples (3, 5, 7, 8). However,there is still limited knowledge on functional impact of many ofthese mutations and genetic alterations on disease initiation andprogression; genetically engineered mouse models (GEMM) pro-vide a platform to begin evaluating these putative targets.

The Sleeping Beauty (SB) somatic cell mutagenesis system hassuccessfully identified genetic drivers of various cancers includinghepatic, intestinal, pancreatic, osteosarcoma, and T-cell (9–14).We previously reported the identification of novel genetic driversof peripheral nerve–related cancers targeting SB mutagenesis to20,30-cyclic-nucelotide 30 phosphodiesterase (Cnp)-expressingcells in mice in the context of EGFR overexpression with

1Department of Genetics, Cell Biology, and Development, University ofMinnesota, Minneapolis, Minnesota. 2Masonic Cancer Center, University ofMinnesota, Minneapolis, Minnesota. 3Lab Medicine and Pathology, Universityof Minnesota, Minneapolis, Minnesota. 5Center for Immunology, University ofMinnesota, Minneapolis, Minnesota. 4Department of Laboratory Medicine andPathology, Division of Hematopathology, University of Minnesota, Minneapolis,Minnesota. 6Department of Ob-Gyn and Women's Health, University ofMinnesota, Minneapolis, Minnesota. 7Department of Pediatrics, University ofMinnesota, Minneapolis, Minnesota. 8Center for Genome Engineering, Universityof Minnesota, Minneapolis, Minnesota.

Note: Supplementary data for this article are available at Molecular CancerResearch Online (http://mcr.aacrjournals.org/).

Corresponding Author: Eric P. Rahrmann, University of Cambridge, RobinsonWay, Cambridge CB2 0RE, United Kingdom. Phone: 012-2373-0854; Fax:012-2376-9881; E-mail: [email protected]

doi: 10.1158/1541-7786.MCR-18-0582

�2018 American Association for Cancer Research.

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Trp53R270Hmutation (12). Mutagenesis alone or in the context ofonlyTrp53R270Hmutationwas inefficient at developing peripheralnervous system tumors (12). We describe here how these animalsdeveloped highly penetrant (65%) lymphoid disease (follicularlymphoma and DLBCL). Analysis of SB-induced lymphomasidentified 59 common insertion sites (CIS), of which several wereassociated with signaling pathways altered in human DLBCLformation: PI3K–AKT–mTOR, NFkB, and BCR signaling. We alsoidentified several novel proto-oncogenes and tumor suppressorgenes (TSG) for B-cell lymphoma, for example, Ras-responsiveelement binding protein 1 (Rreb1) and Ambra1, respectively.Furthermore, we described new roles for Rreb1, a MAPK pathwayeffector, inDLBCLmaintenance and its impact onKras expression,revealing an unknown mechanism for RAS activation in DLBCL.

Materials and MethodsTransgenic animals

Three transgenes were used to induce SB mutagenesis: Condi-tionally expressed SB (R26SB11LSL; ref. 15), Cnp promoter–drivencre recombinase (Cnp-Cre; ref. 16) and oncogenic transposon,concatemer (T2/Onc15). Cnp-Cre;R26SB11LSL;T2/Onc15 (SB-mutagenized) mice underwent insertional mutagenesis in Cnpþ

cells. Genotyping PCR was performed on phenol-chloroform–

extracted mouse tail DNA (10, 16, 17). Conditionally expressedPten (Ptenf/f) and Trp53 (Trp53R270H) allele mice were utilized(17, 18). B6.129(Cg)-Gt(ROSA)26Sortm4(ACTB-tdTomato,-EGFP)Luo/Jreporter mice (The Jackson Laboratory) were utilized for lineagetracing studies. Allmicewerebredand cared forunder the guidelinesof the University of Minnesota Animal Care and Use Committee.

V(D)J PCROne-hundred nanograms of DNA from control and SB-

mutagenized spleens underwent PCR to assess V(D)J clonalityfor VHJ558/JH3, VHQ52/JH3, VH7183/JH3, and DHL/JH3 recombi-nation (19). PCR for Actb served as the loading control.

Flow cytometrySingle-cell suspensions from bone marrow (femur and tibia),

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

Transposon insertion site analysisDNA-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 pyro-sequencing) were processed and analyzed using TransposonAnnotation Poisson Distribution Association Network Connec-tivity Environment (TAPDANCE) software and gene-centric CISanalysis software (21, 22). Mouse build NCBI37/mm9 was usedto map insertion cites and subsequent analyses.

IHCTheM.O.M. kit (Vector Laboratories Inc.) was used for blocking

and antibody incubations. Primary antibodies: Ki67 (1:100; LeicaBiosystems), RREB1 (1:100; Sigma-Aldrich), pErk (1:100; CellSignaling Technology), pAkt (1:100; Cell Signaling Technology),Kras (1:100, Santa Cruz Biotechnology), and SB (1:100; R&DSystems). Corresponding biotinylated secondary antibodies(1:250; Vector Laboratories Inc.) were used followed by incuba-tion with Vectastain ABC Kit (Vector Laboratories Inc.) anddeveloped using peroxidase substrate kit DAB (Vector Laborato-ries Inc.). Slides were counterstained with hematoxylin, dehy-drated, cleared with xylene, and mounted with permount(Thermo Fisher Scientific).

A tissue microarray containing classical Hodgkin lymphoma(cHL, n¼ 3), low-grade follicular lymphoma (LGFL, n¼ 10), andDLBCL (n ¼ 34) was purchased from Cybri (CS20-00-002) andstained for RREB1 (above). IHC staining was quantified using thefollowing criteria by: 0, negative; 1, faint; focal, equivocal, 2,positive in aminority of cells; and 3, positive in amajority of cells.Samples stained with same antibody conditions by the HumanProtein Atlas were also assessed with the same criteria. cHL, n¼ 2;low-grade non-Hodgkin lymphoma, n ¼ 7; high-grade non-Hodgkin lymphoma, n ¼ 3.

PathologyBoard-certified pathologist Dr. Michael Linden (University

of Minnesota, Saint Paul, MN) evaluated hematoxylin andeosin (H&E)-stained tissues for red and white pulp content,extramedullary hematopoiesis, megakaryocytes, erythroidprecursors, immature granulocytes, lymphocyte size, number,morphology, plasmacytic differentiation, infiltration intoextra-hematopoietic tissues, mitotic figures, and necrosis.

Comparative genomicsWhole-methylome, CNA, and transcriptomic data from 48

DLBCL human patient samples were acquired from The CancerGenome Atlas (TCGA) database (23). Methylome data werelisted as b values, CNA data were analyzed by GISTIC analysis,and transcriptomic data were listed as fragments per kilobase oftranscript per million (FPKM) mapped reads. CISs were ana-lyzed for enrichment into known pathway using Enrichr soft-ware (24).

Cell cultureWe purchased CD19þ B cells (Sanguine Biosciences) and

DLBCLhuman cell lines, Toledo, Farage, Pfeiffer, andDB (ATCC).BL2 was gifted from Reuben Harris and KM-H2, Daudi, andRamos were gifted from Vivian Bardwell at the University ofMinnesota (Minneapolis, MN). Cell lines were cultured in com-plete media (1� RPMI1640, 10% FBS, and 1� penicillin/strep-tomycin) and grown at 37�C in 5% CO2. Cell viability wasassessed utilizing a Trypan blue exclusion assay on a hemocy-tometer every 24 hours for 5 days. No authentication or Myco-plasma testswere carried out. Cells fromATCCwere passaged threetimes prior to experimental usage.

RREB1 shRNA knockdownCells were transduced with RREB1 shRNA lentiviruses (Open

Biosystems) and flow sorted with the top 10% of GFPþ cellsisolated and selected with 1 mg/mL puromycin.

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RREB1 overexpressionRREB1 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 mg of RREB1or Gfp PB transposon and 2 mg of PB7 transposase plasmid usingthe NEON transfection system (Life Technologies) followed byselection with 1 mg/mL puromycin. RREB1 expression wasinduced with an optimized doxycycline dosage.

qRT-PCRqRT-PCR analysis was carried out as described previously (12).

miRNA samples were isolated utilizing the miRNeasy Mini Kit(Qiagen) and assessedmiR-143, miR-145, andU6 expression (LifeTechnologies).

ImmunoblottingResolved lysates on polyvinylidene difluoridemembraneswere

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), andGAPDH (1:10,000: Cell Signaling Technology). CorrespondingHRP-conjugated secondary antibodies (1:2,000: Vector Labora-tories) were utilized. Blots were developed via chemilumines-cence and imaged on the LI-COR Odyssey.

Statistical analysisStatistics 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; phenotypesanalyzed using Fisher exact tests (FET) and x2 tests. Nonparamet-ric Mann–Whitney tests with standard error of the mean werecarried out on spleen weights, qRT-PCR, densitometry, and cellproliferation assays. Correlation was done using Pearson corre-lation analysis.

ResultsSB mutagenesis in Cnpþ cells induced B-cell lymphoma

We previously utilized Cnp–Cre to model peripheral nervoussystem cancers in mice (12). Cnp, a phosphodiesterase, is highly-expressed in nervous system tissues (oligodendrocytes andSchwann cells) starting at E14.5 through adulthoodwithminimalexpression in other tissues including spleen (lymphocytes), liver,heart, bone marrow stromal cells, and cultured mouse CD34þ

bonemarrow cells (25, 26). Utilizing IHC for SB expression and aPCR-based excision assay for SB activity, we determined SB wasexpressed and active in numerous tissues (brain, pancreas, liver,testes, skeletal muscle, lungs, spleen, heart, and kidneys) in ourmodel (Supplementary Fig. S1A and S1B).

SB-mutagenized (Cnp-Cre;R26SB11LSL;T2/Onc15, n ¼ 63) andcontrol (Cnp-Cre;R26SB11LSL or Cnp-Cre;T2/Onc15, n ¼ 88) micewere aged and assessed for phenotypic alterations. SB-mutagenized mice had significantly reduced survival comparedwith controls (log-rank Mantel–Cox, P < 0.0001) with mediansurvival of 436 day versus 605 days in control animals (Fig. 1A).Similarly, SB-mutagenized mice carrying a Trp53R270H pointmutation (Cnp-Cre;R26SB11LSL;T2/Onc15;Trp53R270H, n¼ 33) hadsignificantly reduced survival (log-rank Mantel–Cox, P < 0.0001)with amedian survival of 322days versus 485days comparedwithTrp53R270H (Cnp-Cre;R26SB11LSL;Trp53R270H orCnp-Cre;T2/Onc15;Trp53R270H, n ¼ 37) control animals (Fig. 1A). Examination of

SB-mutagenized animals with or without Trp53R270H mutationrevealed very rare peripheral nervous system tumors (12) but ahighly penetrant lymphoid disease (65%, splenomegaly; Supple-mentary Table S1). The lymphoid disease was predominantlysplenomegaly (62%) with some animals also presenting with anenlarged thymus (13.8%) and enlarged mesenteric lymph nodes(22.4%; Supplementary Fig. S1C and S1D). Solid tumors wereobserved in various tissues with the liver (22.4%) having thehighest 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 significantincrease in the penetrance of lymphoid disease and solid tumor(75.9%) formation compared with control animals (29.5%; FETP < 0.0001; Fig. 1B). SBmutagenesis did not significantly alter thephenotype penetrance in Trp53R270H (79.4%) animals comparedwith Trp53R270H controls (72.4%).

Histologic analysis of splenomegaly samples identified SB, high-ly expressed in splenic germinal centerswithdiffuse positive cells insurrounding marginal zone and red pulp (Fig. 1C). Pathologicanalysis indicated SB-mutagenized splenomegaly samples weresignificantly (FET P ¼ 0.0037) involved with lymphoma, specifi-cally follicular lymphoma and DLBCL, compared with controlsplenomegaly samples (Fig. 1C; Supplementary Fig. S1F andS1G; Supplementary Table S2). Moreover, only SB-mutagenizedmice (n¼ 5/17) had evidence of infiltrative DLBCL into surround-ing tissues including liver, lungs, kidneys, skeletal muscle, andadrenal glands (Fig. 1C; Supplementary Fig. S1F and S1G).

To confirm tumor identity, we performed PCR-based clonalityanalysis, allograft experiments, and in vivo lineage tracing analysis.PCR-based clonality analysis of the BCR IgH locus fromSB-mutagenized spleens identified the presence of monoclonaland oligoclonal populations in splenomegaly samples not pres-ent in the SB-mutagenized normal weight and C57BL/6 spleens(Fig. 1D). Allograft transplants of primary splenomegaly samplesgave rise to CD19þ B-cell expansion in spleens of SCID/beigerecipient mice (Fig. 1E). SB-expressing cells were immunophe-notyped for T-cell, B-cell, and macrophage markers on bonemarrow, thymus, lymph node, and spleen samples from control(Cnp-Cre n ¼ 6; R26SB11LSL n ¼ 5), SB induced without mutagen-esis (Cnp-Cre;R26SB11LSL n ¼ 5), and SB-mutagenized mice (n ¼12). Because the conditional SB allele contains aGFP stop cassetteunless exposed to Cre-recombinase, GFP�ve cells were used as amarker for SB expression. This analysis revealed SB expressionacross all four tissues with lymph nodes (74.2%, n ¼ 10) andspleens (60.72%, n ¼ 17), showing the highest percentage ofrecombination followed by thymus (40.3%, n ¼ 14) and bonemarrow (30%, n ¼ 16; Supplementary Fig. S2A). Immunophe-notyping of these tissues for lineage-specific markers indicatedthat SB expression occurred in myeloid, T cells, B cells, and theremaining supporting cells (stroma) of each tissue (Supplemen-tary Fig. S2B–S2E). No significant changes in cellular distributionwere observed in bone marrow and the peripheral lymph nodes.However, SB-mutagenized spleen and thymus samples had sig-nificant changes in cell-type distribution. Spleen samples hadsignificantly (�, P < 0.05) reduced B-cell percentage with corre-sponding significant increase in myeloid and the supportingstromal cells (Supplementary Fig. S2C). Thymus samples hadsignificantly (�, P < 0.05) reduced the T-cell percentage withcorresponding increases in B cells, myeloid, and supportingstromal cells (Supplementary Fig. S2D). To correlate thesechanges in cellular distribution with SB activity, we analyzed the

Driver Genes Identified in DLBCL Using an SB Screen

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Figure 1.

SB mutagenesis in Cnpþ cells induced B-cell lymphoma. A, Kaplan–Meier survival curve comparing SB-mutagenized (Cnp-Cre;R26SB11LSL;T2/Onc15, n ¼ 63),control (Cnp-Cre;R26SB11LSL or Cnp-Cre;T2/Onc15, n ¼ 88), SB-mutagenized mice carrying a Trp53270H point mutation (Cnp-Cre;R26SB11LSL;T2/Onc15;Trp53R270H,n ¼ 33), and Trp53R270H (Cnp-Cre;R26SB11LSL;Trp53R270H or Cnp-Cre;T2/Onc15;Trp53R270H, n ¼ 37) control animals. B, Pie charts depicting macroscopicphenotypes for each genotype.C,Histologic analysis of spleen samples. Image in the top left depicts size of experimental versus control spleens. Image in the bottomright 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 evidenceof 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 transpositionwith spleen wet weights >0.2 g. Recombination events assessed are: VHJ558/JH3, VHQ52/JH3, VH7183/JH3, and DHL/JH3. a-Actin served as a loading control. 100-bpDNA 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 asingle band indicate oligoclonal populations, which are commonly observed in lymphoid disease. E, Serial transplant of primary SB-mutagenized lymphomasamples into the flanks of recipient SCID/Beige mice. Spleens from primary SB-mutagenized mice (left) and allograft tumors (right) were isolated and analyzed byflow cytometry for T-cell (CD4, CD8), B-cell (CD19), and macrophage (Mac1, GR1) markers.

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GFP� fractionof the total live cells (Supplementary Fig. S2B–S2E).Relative to the Cnp-Cre;T2/Onc control animals, in which all cellsare GFP�, there is a bias for supporting stromal cells to undergorecombination in all tissue types and for B cells to recombine inthe thymus.

Because the SB-mutagenized animals had an average age of 479days, we could not rule out the effects of aging on the results.Therefore, we performed an additional lineage-tracing analysis on60-day-old Cnp-Cre mice bred to conditional GFP reporter miceon bone marrow, lymph node, and spleen samples (Supplemen-tal Figs. S3 and S4). Lineage-tracing analysis of the bone marrowdemonstrated Cnp was predominantly expressed in B220þ B-cellprecursors as only 13.9% of GFPþve cells were B220� (Supple-mentary Fig. S3). Assessment of GFP expression during all stagesof B-cell development indicated that Cnp-cre was active from thepre-/pro–B cells (24%) to themature follicular andmarginal zoneB cells (87.5%; Supplementary Fig. S4). Collectively, these datademonstrate Cnp is expressed in many hematopoietic cells andthat targeted mutagenesis of Cnpþ cells preferentially induces aB-cell lymphoma phenotype arising from early B-cell precursors.

Identification of B-cell tumor driver mutation genesTo identify genetic drivers of lymphomagenesis, T2/Onc inser-

tions from23 SB-derived splenomegaly sampleswere analyzed byIllumina sequencing to identify CISs utilizing two unique statis-tical methods: TAPDANCE CIS (tdCIS; ref. 22) and gene-centricCIS (gCIS; ref. 21). These analyses identified 18 tdCIS- and 43gCIS-associated genes with 13 genes (27%) overlapping (Fig. 2Aand B; Supplementary Table S3). Themost common, significantlymutated geneswereBach2 (43%, P¼ 2.07� 10�5),Ambra1 (35%,P ¼ 3.08 � 10�4), Rreb1 (23%, P ¼ 2.04 � 10�4), Arid1b (26%,q ¼ 2.13 � 10�4), and Nfkb1 (26%, P ¼ 3.90 � 10�4) of whichBach2 expression has been associated with DLBCL survival out-come 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 cellproliferation that functions as a TSG through regulation of Mycbut has not been previously implicated in B-cell lymphoma (30).Rreb1 is a transcription factor involved in MAPK and PI3K sig-naling through KRAS and is implicated in thyroid and bladdercancer but not lymphomagenesis (31, 32). Genes previouslyimplicated in human DLBCL and/or follicular lymphoma werealso identified: Crebbp (17%, q ¼ 1.13� 10�7),Malt1 (22%, P ¼3.90 � 10�4), and Pten (17%, P ¼ 4.18 � 10�2; refs. 5, 7, 8,and 33). Three of the 23 splenomegaly samples analyzed did notcontribute to CIS calling (Fig. 2B). To identify potential drivers ofthese individual samples, we assessed the top mutated genes foreach splenomegaly sample (Supplementary Table S4). Thesethree spleens had very low read counts for each of their putativegene drivers suggesting there may be additional factors drivinglymphomagenesis: Wdtc1 (13), Pde2a (45), and Atp9b (245). Inaddition to the splenomegaly samples, we also sequenced fivenormal weight spleens in which the animals did not display anymacroscopic or microscopic disease to identify potential earlydriver events (Fig. 2B). From these, we identified 3 CIS-associatedgenes: Nfkb1 (60%, q ¼ 5.39 � 10�15), Kansl1 (60%, q ¼ 2.40 �10�11), and Sp4 (60%, P ¼ 4.69 � 10�2). Nfkb1 is a CIS in thesplenomegaly samples. Kansl1 is not a CIS but splenomeglysamples did have T2/Onc insertions and Sp4 was exclusive to thenonsplenomegaly samples.

Because TP53mutations are prevalent in humanDLBCL (34),we also sequenced seven SB-induced lymphomas in theTrp53R270H background by 454 pyrosequencing to identifycooperating mutations with Trp53 mutation to drive B-celllymphoma. From these analyses, we identified 12 tdCIS genes(10 human homologs) with the most significantly mutatedgenes being Pik3r1 (42.9%), Kcnj12 (42.9%), and Pls1(42.9%; Fig. 2C; Supplementary Table S3). Pik3r1, a modulatorof PI3K signaling, has been previously implicated in humanDLBCL (8). Kcnj12, a potassium channel, and Pls1, an actin-binding protein, have not previously implicated in DLBCLor follicular lymphoma. Collectively, Nfkb1 was the onlyCIS significantly mutated in both screens. Moreover, 10 ofthe 59 CIS-associated genes (A23004603Rik, chr4:94961700-94971700, Tnfsf8, Inpp4b, C80913, Chst15, Crybg3, Mfap3l,Tespa1, and Tnfrsf13b,) have not been previously identified inother SB screens (Candidate Cancer Gene Database, n ¼ 69studies, 12 tumor types; ref. 35), suggesting a specificity of thesegenes 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 disruptgene transcription. Transcriptional activation may occur if themajority of transposon insertions are orientated upstream of agene or translational start site with MSCV promoters in the samedirection as gene transcription; the gene would be a putativeproto-oncogene (e.g., Bach2, Nfkb1, and Rreb1). Disruption oftranscriptionmayoccur if the transposons landwithin a genewithno MSCV promoter orientation or insertion site bias within thelocus; the gene would be a putative TSG (e.g., Ambra1 and Pten).Thirteen putative proto-oncogenes and 46 putative TSGs wereidentified from the 59 unique CISs (56 human homologs) fromboth screens (Supplementary Fig. S5A and S5B; SupplementaryTable S6).

To determine whether T2/Onc insertions caused phenotypicalterations, we assessed the impact of the T2/Onc insertions ongene expression and disease-free survival. Of the 8 CIS genesassessed by qRT-PCR, only Rreb1 displayed significant (P ¼1.24183 � 10�8) changes in mRNA expression compared withthewild-type spleens and SB-mutagenized splenomegaly samplesthat lacked T2/Onc insertions in the Rreb1 gene (SupplementaryFig. S5C). Eight CISs were significantly correlated with survival inSB-mutagenized mice (Supplementary Table S7; SupplementaryFig. S6). Hivep2, a MYC intron–binding transcription factor withknown TSG role in glioma (36), was the most significantlycorrelated with reduced survival (P ¼ 0.0004 log-rank, Mantel–Cox test, Supplementary Fig. S6). The chr4:94961700-94971700CIS was also associated with significantly (P ¼ 0.0018 log-rank,Mantel–Cox test, Supplementary Table S7, Supplementary Fig.S6) reduced survival. This region is a predicted enhancer elementbased on H3K4 mono-methylation marks (Supplementary Fig.S7; ref. 37). The closest genes to this region are Jun (�242 kb) andFggy (�252 kb). Finally,Arid1b andWhsc1, known cancer-causinggenes, were associated with a significant reduction in survival(Supplementary Table S7; Supplementary Fig. S6).

Pathways and upstream regulators of CISsEnrichr (24) was used to identify significantly altered signaling

pathways and cellular phenotypes in the 46 human homologs oftheCISs from SB-mutagenized spleens (Supplementary Table S8).






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 CISwas determined by the percentage ofeach 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 percentageof 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, predictedproto-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 ¼ 0.0002).

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This identified several signalingpathways significantly enriched inthe CIS list: B-cell receptor signaling [PPP2R5E, PTEN, CBLB,NFKB1, and MALT1: Benjamini–Hochberg (B-H) corrected P ¼0.02] andNFkB (TNFRSF11A, NFKB1, andMALT1: B-H correctedP ¼ 0.03) signaling pathways; pathways altered in human B-celllymphomas. One of the top significantly enriched phenotypeswas enlarged spleen (CREBBP, PTEN, TNFRSF11A, SMAP1,NFKB1, and RUNX1: B-H corrected P ¼ 0.004) with many othergenes implicated in B-cell proliferation (NFKB1, CREBBP,TNFRSF11A, BACH2, ARHGAP17, and RUNX1) and differentia-tion (BACH2 and MALT1).

Ingenuity Pathway Analysis (IPA, Qiagen) of upstream tran-scriptional regulators of the CIS genes identified GFI1 (P ¼ 1.7 �10�5), BCL6 (P¼ 1.11� 10�3), and EP300 (P ¼ 3.09� 10�3) inthe top five significantly enriched transcriptional regulators (Fig.2D).CIS genesCREBBP (P¼1.85�10�2) andNFKB1 (P¼4.74�10�2) were also significantly enriched. EP300, CREBBP, and BCL6are highly mutated in human DLBCL, whereas GFI1 has not beenimplicated (2). Enrichr analysis identified ZBTB7A (n ¼ 12, B-Hcorrected P ¼ 0.01) and SP1 (n ¼ 15, B-H corrected P ¼ 0.02) assignificantly enriched transcriptional regulators, both of whichhave been implicated in several human cancers including B-celllymphomas (38).

Cooccurring CISsWe performed cooccurrence analysis to identify CIS

genes that were mutated together at a higher frequency thanexpected by chance (22). Cooccurrence analysis of the tdCISfrom SB-mutagenized mice identified 15 pairs of cooccurringCISs (co-CIS; Fig. 2E; Supplementary Table S9). Twelve co-CISsinvolved at least one 1 that regulates transcription. Severalgenes were co-CISs with Bach2, the most mutated single CIS(43% of tumors), including the most significant pair of co-CISswith Hivep2 and Chr4:94961700. Moreover, Bach2/Hivep2 andBach2/Chr4:94961700 co-CISs were associated with a signifi-cant reduction in disease-free survival (Supplementary TableS9). Bach2 alone was not significantly associated with survival,whereas Hivep2 and Chr4:94961700 did. Interestingly, the threetumors with insertions in Chr4:94961700 CIS also had inser-tions in Hivep2 and Bach2, suggesting a potential cooperativesignaling network.

Comparative genomics of CISs in cancer, including lymphomaTo determinewhether the CIS-associated geneswere frequently

mutated in human cancer, we queried the Catalogue of SomaticMutations in Cancer (COSMIC) Cancer Gene Census, whichannotates known cancer-causing genes (33). Eleven of the 54CIS-associated genes with a human homolog are in Cancer GeneCensus (P ¼ 2.86 � 10�7, hypergeometric test). To assess therelevance of CIS genes to human lymphoma, we queried methy-lome, SNPs, RNA sequencing (RNA-seq) transcriptomic, andwhole-genome/-exome sequencing data of human DLBCL sam-ples from TCGA (n ¼ 48 samples; Fig. 3; Supplementary TablesS10–S13). CREBBP was the most mutated gene in the list (n ¼ 6/48 samples) followed by AMBRA1 (n ¼ 3/48) and VANGL1 (n ¼3/48; Supplementary Table S10). The SNP data identified CISgenes with a tendency toward CNA gains (n ¼ 6) and CNA losses(n¼ 14) inmore than 20% of the samples (Supplementary TableS11; Fig. 3A). Methylome data, which is predictive of geneexpression, identified 38 CIS genes differentially methylated.Thirty-one CISs displayed hypomethylation, which is associated

with gene expression (b value < 0.2) and 7 CIS genes displayedhypermethylation, which is associated with gene silencing(b value > 0.8; Supplementary Table S12; Fig. 3B). RNA-seqtranscriptomic 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). Combiningthese analyses, we predicted several CIS genes as strong geneticdrivers of DLBCL. For example, CNOT2 was generally hypo-methylated, overexpressed, and observed CNA gains, which is apredicted scenario for a proto-oncogene. CNOT2 was also pre-dicted to be an oncogene based on CNA and expression profilingdata from 392 DLBCL patient samples (39). HIVEP2, a predictedTSG in our screen, had a similar level of methylation and CNAs as3 known TSGs (TP53, CDKN2A, and TNFAIP3), suggestingHIVEP2 is a TSG in human DLBCL. Furthermore, assessment ofthe 15 cooccurring CISs against the human DLBCL data setsidentified two pairs of cooccurring CISs that are significantlycoaltered: PTEN/MAP3K8 (Padj ¼ 0.001) and BACH2/HIVEP2(Padj ¼ 0.004; Supplementary Table S9). Collectively, this gene-centric analysis confirmed our identification of known geneticdrivers of human DLBCL and created a guide for making hypoth-eses about genes with unknown roles (e.g., KIAA0391 andKIAA1033) in DLBCL.

Heterozygous loss of Pten is sufficient to drive B-celllymphomagenesis

Targeting Cnpþ cells with SB mutagenesis and/or Trp53R270H

mutations gave rise to a high prevalence of B-cell lymphomaand lineage tracing analysis identified early B-cell precursors toexpress Cnp. However, Cnp has not been previously identifiedas marker for cells that functionally develop B-cell lymphoma.To further explore the functional role of Cnp in B-cell lym-phomagenesis and validate our SB screen, we crossed Cnp-Cremice to animals harboring a conditional allele of Pten (40).PTEN is a known cancer-causing gene in numerous humancancers including B-cell lymphomas and is a putative CIS TSGfrom our screen.

One-hundred percent of Cnp-Cre;Ptenf/f mice (n ¼ 8, mediansurvival 102 days, log-rank Mantel–Cox P < 0.0001) succumbedto paralysis-related deaths with peripheral nerve hyperplasiaand neurofibroma formation and enlarged cervical lymph nodes(Fig. 4A and B). Conversely, 100% of Cnp-Cre;Ptenf/þ mice(n ¼ 16, median survival 323 days, log-rank Mantel–CoxP < 0.0001) succumbed to lymphoma-related deaths withenlarged cervical lymph nodes but no nervous system phenotype(Fig. 4A and B). Enlarged cervical lymph nodes possessed follic-ular lymphoma histologic features and were predominantlyB220þCD19þCD21þCD35þ B cells by immunophenotyping(Fig. 4C and D; Supplementary Table S2). Western blot analysisfor members of the PI3K/PTEN/AKT pathway indicated that Ptenexpression was maintained in the Cnp-Cre;Ptenf/þ animals withincreased signaling through the downstream effector p4ebp1(Fig. 4E and F). Collectively, these data suggest Cnp is expressedfrom cells that are prone to B-cell lymphoma formation andthat Pten haploinsufficiency is sufficient to generate a follicularlymphoma–like phenotype.

Rreb1 is a predicted proto-oncogene enhancing signalingthrough KRAS/MAPK

RREB1 is a transcription factor and proto-oncogene insolid tumor cancers (41). RREB1 operates in a feed-forward






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 b values (B),and RNA-seq expression by FPKM (C). Data were acquired from TCGA database. Each data point represents an individual sample n ¼ 48 DLBCL patient samples.Methylome data is represented as the b value inwhich values above 0.8 indicate hypermethylation events, whereas values below0.2 indicate hypomethylation events.

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Figure 4.

Pten haploinsufficiency is sufficient to driver B-cell lymphoma. A, Kaplan–Meier curve for Cnp-Cre;Ptenf/f mice (n ¼ 8), Cnp-Cre;Ptenf/þ mice (n ¼ 16), andcontrol (Cnp-Cre;R26SB11LSL or Cnp-Cre;T2/Onc15, n¼ 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/þ micedisplaying follicular lymphoma features. D, Flow cytometry analysis of B-cell content in peripheral lymph nodes of two Cnp-Cre;Ptenf/þ animals. E, Western blotanalysis on lysates from lymph nodes of control animal (F2062) and Cnp-Cre;Ptenf/þ animals for Pten, pAkt, Akt, p4ebp1, and Gapdh. F, Densitometryquantification of the Western blot analysis in D.






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loop promoting KRAS expression and activation by inhibitionof miR-143/145 expression to potentiate MAPK and PI3K signal-ing (31, 41). KRAS has been implicated as a driver in humanDLBCL (5).

In this study, Rreb1 is a predicted proto-oncogene based onthe orientation and position of T2/Onc insertions resulting inRreb1 overexpression (Fig. 5A). mRNA fusion transcriptsbetween the MSCV promoter/splice donor in T2/Onc andRreb1 were identified in tumors with T2/Onc insertions inthe Rreb1 locus and two tumors where LM-PCR did notidentify T2/Onc insertions (Fig. 5B). Similar findings wereobserved in a SB osteosarcoma screen (42). Tumors contain-ing T2/Onc-Rreb1 fusion transcripts demonstrated significantlyincreased Rreb1 mRNA expression (ANOVA multiple compar-isons, P < 0.0001) and increased protein levels by IHC andWestern blot analysis, suggesting Rreb1 functions as a proto-oncogene (Fig. 5C–E).

To determine the functional impact of T2/Onc-Rreb1 fusiontranscripts on Rreb1 signaling, we assessed miR-143/145(qRT-PCR) and Kras expression (immunoblot and IHC) andits immediate downstream effectors, pErk and pAkt (IHC).qRT-PCR analysis demonstrated a significant reduction inmir143 (Mann–Whitney: P ¼ 0.0004, P ¼ 0.0039) and miR-145 (Mann–Whitney: P¼ 0.0056, P¼ 0.0268) in splenomegalysamples containing T2/Onc-Rreb1 fusions, compared with nor-mal weight spleens and splenomegaly samples lacking thefusion, respectively (Fig. 5C). IHC analysis demonstratedincreased staining for Rreb1, pAkt, and pErk in T2/Onc-Rreb1tumors compared with normal weight spleens. Western blotanalysis of the tumors demonstrated a significant increase inRreb1 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 effec-tors, pErk and pAkt.

The proto-oncogene RREB1 is highly expressed in humanDLBCL

TCGA data on RREB1 genomic and transcriptomic altera-tions indicate that patient survival was not significantly impact-ed by RREB1 alterations, but displayed a trend towards reducedsurvival (Fig. 6A). As RREB1 functions as a transcription factor,we performed RREB1 antibody staining on a human tissuemicroarray (TMA) comprised of cHL, LGFL, and DLBCLs todetermine whether the genomic and transcriptomic alterationsaffect RREB1 protein levels (Fig. 6B). Fifty-three percent (N ¼18/34) of DLBCL samples expressed RREB1 in the majority ofcells (Fig. 6C). In general, RREB1 staining was more intense in

LGFL and DLBCL samples compared with Hodgkin lymphomasamples.

To determine whether RREB1 influences KRAS expression inDLBCL, TCGA RNA-seq data for RREB1 and KRAS expressionon 48 DLBCL samples were analyzed (Fig. 6D). There was asignificant positive correlation between KRAS and RREB1mRNA expression (Pearson r ¼ 0.6092, P < 0.0001; Supple-mentary Table S14). We performed the similar correlativeanalysis on 15 other cancers. Significant correlations withRREB1 and KRAS expression occurred in 10 of 15 cancersassessed including pancreatic (r ¼ 0.4717) and colorectalcancer (r ¼ 0.1448), where the RREB1/KRAS feedback loophas been described (31, 41). Collectively, these data suggestRREB1 is a putative proto-oncogene in DLBCL and potentiallyother cancers via a KRAS/RREB1 feed-forward loop.

Modulating RREB1 expression alters KRAS isoform usage andproliferation

In humans, alternative splicing gives rise to 10 unique RREB1transcripts that encode for nine distinct protein products, whereasKRAS has four unique transcripts encoding four distinct proteinproducts (Ensembl release 88; ref. 43). Quantitative PCR ofhuman lymphoma cell lines indicated that the DLBCL cell linesexpressed the least amount of RREB1 comparedwith theHodgkinlymphoma line KM-H2 and the Burkitt lymphoma cell lines (Raji,Daudi, BL2, Ramos; Fig. 7A). To seewhether these transcript levelsreflected the protein levels, we performed immunoblotting onnormal CD19þ B cells and human DLBCL, Burkitt lymphoma,and Hodgkin lymphoma cell lines for RREB1 and KRAS to betterunderstand which isoforms are differentially expressed in normaland malignant B cells. Several RREB1 isoforms were expressed(Fig. 7B). ThehighmolecularweightRREB1 isoformswere presentin all cell lines but absent in CD19þ-purified B cells (Fig. 7B).KRAS expression was also notably different between the cell lines(Fig. 7B). CD19þ-purified B cells expressed two KRAS isoforms(KRAS-4A, 24 kDa; KRAS-4B, 21 kDa) at similar levels, commonin many human tissues, whereas 3/4 DLBCL cell lines had moreKRAS-4B than KRAS-4A protein (44).

To interrogate the role of RREB1 as a proto-oncogene, weutilized shRNA knockdown and cDNA overexpression con-structs in three human B-cell lymphoma cell lines (Pfeiffer,DB, and BL2). Pfeiffer and DB cells (DLBCL) were transducedwith three unique shRNAs. Effective RREB1 shRNAs significant-ly reduced the mRNA expression by qRT-PCR in both Pfeifferand DB cell lines (Fig. 7C) and decreased RREB1 proteinby approximately 75% in DB cells (Fig. 7D). Effective shRNAssignificantly decreased proliferation in Pfeiffer and DB cellscompared with parental cells and noneffective shRNAs (Fig. 7E

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 arrowsindicates orientation of the MSCV 50LTR 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 analysisof Rreb1, miR-143, andmiR-145 expression in wild-type (n ¼ 4) and splenomegaly samples without insertions (n ¼ 3) and those with T2/Onc-Rreb1 fusions (n ¼ 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 performedusing ImageJ software. F, IHC analysis of Rreb1, Kras, pErk, and pAkt expression in normal weight spleens from control and splenomegaly samples fromSB-mutagenized spleens containing the T2/Onc-Rreb1 fusion transcripts. G, Quantification of the staining in F using criteria outlined in the Materials and Methodssection 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).






and F). RREB1 shRNA knockdown also reduced KRAS-4B andincreased KRAS-4A expression in DB cells. Overexpression offull-length RREB1 cDNA in DB and BL2 cells altered the KRASisoform expression but did not impact proliferation (Supple-mentary Fig. S8). Collectively, these functional data suggestRREB1 expression significantly impacts human DLBCL prolif-eration and influences the amount and isoform expressionof KRAS.

DiscussionIn 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 pene-trant lymphoid diseases, predominantly follicular lymphomaand DLBCL. Analysis of SB-mutagenized splenomegalysamples on wild-type and Trp53R270H-mutant backgroundsidentified 59 CIS-associated genes and several co-CISs, sug-gesting that specific, ordered cooperating genetic mutationsare required for tumor development. CIS genes were alsoidentified in known signaling pathways altered in humanDLBCL including NFkB, PI3K–AKT–mTOR, and BCR signal-ing. Finally, we identified a new role for the putative proto-oncogene, Rreb1, and elucidated a mechanism by whichKRAS signaling is altered in DLBCL by upregulation ofRREB1 expression.

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 RREB1mutations and CNAs. Log-rank Mantel–Cox analysis P ¼ 0.3111. B, IHC analysis of RREB1 expression on TMA for human lymphoma samples. Images depict thefour 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 n¼ 5; LGFL, low-grade follicular lymphoma n¼ 13; DLBCL n¼ 41. Bar height represents the mean with SEM. D,Graph depicts RNA-seqFPKM values for KRAS plotted against RREB1 FPKM values for 48 DLBCL samples from TCGA database. Pearson coefficient correlation analysis. Statisticalanalyses were performed using the GraphPad Prism Version 6.0d.

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Figure 7.

RREB1 expression influences Kras expression and human B-cell lymphoma proliferation. A, Bar graph depicts qRT-PCR analysis of RREB1 expression relative toACTIN 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 ofRREB1 expression relative to ACTIN expression in the DLBCL cell lines DB and Pfeiffer parental cell line and three derivatives exposed to three unique shRNAstargeting RREB1. DB control n ¼ 3 (parental and noneffective shRNAs), DB knockdown n ¼ 2 effective RREB1-shRNAs in triplicate. Pfeiffer control n ¼ 2 (parentaland noneffective shRNA), Pfeiffer knockdown n ¼ 3 effective RREB1-shRNAs in triplicate. Student t test, �� , P < 0.0001. Bar height indicates mean and error barsshow 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 n¼ 3 (parental and noneffective shRNAs) in duplicate, knockdownn ¼ 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 n¼ 1 in duplicate, knockdown n¼ 3 uniqueRREB1-shRNAs run in duplicate. Error bars indicate SEM. Statistical analyses: one-way ANOVA multiple comparisons test 95% confidence interval. Statisticalanalyses were performed using the GraphPad Prism Version 6.0d.






Comparative genomic analysis reliably identified knowngenetic drivers of DLBCL formation (e.g., CREBBP, andMALT1). We also identified potential new TSGs (e.g., HIVEP2,and PKN2) and proto-oncogenes (e.g., CNOT2, and RREB1) inDLBCL based on CNA, methylome, transcriptomic data, andconsistent recurrent alterations in our SB screen. However, weidentified genes with CNA gains (e.g., TNFRSF11A, andCNOT2) and overexpression in human DLBCLs predicted tobe disrupted by SB-induced mutagenesis. This discrepancy mayreflect differences in mouse and human DLBCL formationand/or the cell of origin targeted in the screen. Generally, weidentified few annotated DLBCL oncogenes and TSGs in ourCIS lists. However, assessment of the T2/Onc insertion profileson genes that define a variety of human lymphoma subtypesindicated that insertions were present below the CIS thresholdin several genes (ARID1A, BCL2, MLL3, and MYD88; Supple-mentary Table S15; Supplementary Fig. S10), which with alarger number of animals in the study would likely be signif-icant. Importantly, many CIS genes contributed to signal trans-duction pathways that are routinely activated in human DLBCL(e.g., NFkB signaling, PI3K–AKT–mTOR; ref. 2). Further exper-imental evidence is required to determine the impact that theCIS genes may have on human DLBCL development.

RREB1 expression alone may not be predictive of oncogeniccapacity but rather the specific isoform(s) expressed. From ouranalyses, we observed differences in RREB1 isoform expressionbetween CD19þ B cells and seven lymphoma cell lines. Moreoverin our SBmodel, these RREB1 isoform differences were associatedwith differences in Kras expression. It is likely there are differencesin the kinetics of RREB1 isoforms binding to and suppressing themiR-143/145 miRNA cluster to perform oncogenic functions onKras signaling. Therefore, to further determine the importance ofRREB1 in DLBCL subtypes, and by extension, other cancers, aproteomic analysis may be warranted.

KRAS has two alternative versions of exon 4 generating twoisoforms differing at the carboxy terminus: the canonical KRAS-4A, which can promote apoptosis and KRAS-4B, which has ananitapoptotic role (45, 46). The amino acid changesmodify KRASmembrane localization altering KRAS-induced Raf-1 signaling(46). Both isoforms are coexpressed in many human tissues, butthe 4A/4B ratio is altered in human colorectal cancer with reduced4A and increased 4B (47). We observed a similar phenomenon inthe DLBCL cell line DB, RREB1 cDNA overexpression increasedKras-4B expression, whereas shRNA knockdown increased Kras-4A and significantly reduced proliferation. Collectively, these dataindicate RREB1 dosage alters KRAS isoform usage and significant-ly impacts DLBCL cellular proliferation. Further studies assessingthe role of each RREB1 and KRAS isoform on oncogenic trans-formation are warranted.

Three signaling pathways were significantly enriched forwith CISs: NFkB (n ¼ 7/59), BCR (n ¼ 7/59), and PI3K–AKT–mTOR (n ¼ 11/59) signaling. Each pathway is involvedin human DLBCL with current targeted therapeutic effortsunderway (48). Pharmacologic inhibition of the PI3K–AKT–mTOR pathway in cells with activating mutations in PI3K/AKT/mTOR pathway genes reduced the proliferation andcaused apoptosis (8, 49). Because of the importance of thePI3K–AKT–mTOR pathway in DLBCL maintenance, it is pos-sible RREB1 overexpression is an alternative mechanism toenhance PI3K–AKT–mTOR pathway signaling via KRAS.Although neither RREB1- nor KRAS-activating mutations are

common in human DLBCL, Lohr and colleagues have identi-fied rare KRASG13D mutations in human DLBCL and RREB1CNA gains do occur in human DLBCL with a subset of samplessignificantly overexpressing RREB1 transcripts that significantlycorrelate with increased KRAS expression (5). Currently, noRREB1 inhibitors exist, but there is a selective irreversibleinhibitor targeting the KRASG12C mutation (50). Our SBmouse model of DLBCL provides a platform to further delveinto the Rreb1/Kras connection to understand the extent theRAS/MAPK signaling pathway has in DLBCL formation andmaintenance with potential exploitation in therapeutic testing,such as MEK inhibitors. It seems likely that RREB1 expressionat high level is a common source of RAS/MAPK activationin DLBCL.

Overall, using a SB forward genetic screen, we identified 59candidate driver genes promoting B-cell lymphomagenesis.More-over, we determined a new role for the proto-oncogene RREB1 inDLBCL and KRAS isoform usage. Further functional testing ofadditionalCIS genesmay reveal newgenetic pathways to target fortreatment of DLBCL.

Disclosure of Potential Conflicts of InterestM.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 forSurrogen, Inc., and reports receiving funding fromGenentech, Inc. B.S.Moriarity isthe co-founder and the chief scientific officer for B-MoGen Biotechnologies.No potential conflicts of interest were disclosed by the other authors.

Authors' ContributionsConception and design: E.P. Rahrmann, D.A. LargaespadaDevelopment of methodology: E.P. Rahrmann, D.A. LargaespadaAcquisition 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. MoriarityAnalysis 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. LargaespadaWriting, 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. LargaespadaOther (central pathology review of microscopic data): M.A. Linden

AcknowledgmentsThe authors would like to thank the Biomedical Genomics Center at the

University of Minnesota (Minneapolis, MN) for performing the Illuminadeep sequencing. We also acknowledge the following shared resources ofthe Masonic Cancer Center at the University of Minnesota: The MouseGenetics Laboratory, Biostatistics and Bioinformatics, Flow CytometryResource, and Comparative Pathology. We thank the Minnesota Super-computing Institute for computational resources. We thank the ResearchAnimal Resources at the University of Minnesota, specifically Alwan Aliye,for his technical support in mouse maintenance. This work receivedfunding from the American Cancer Society Research Professor Awardand NIH-NCI CA113636 (to D.A. Largaespada) the NIH-NINDS-P50N5057531, and the Margaret Harvey Schering Trust. M.A. Farrar was fundedby NIH R01 CA151845 and CA154998. T.K. Starr was supported by fundingfrom the NIH NCI (5R00CA151672-04) and the Masonic Cancer Center NIHsupport grant (P30-CA77598).

The costs of publication of this articlewere defrayed inpart by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received June 12, 2018; revised August 13, 2018; accepted October 15, 2018;published first October 24, 2018.

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Rahrmann et al.




Published OnlineFirst October 24, 2018.Mol Cancer Res Eric P. Rahrmann, Natalie K. Wolf, George M. Otto, et al. Drivers in Human B-cell Lymphoma

and Other GeneticRREB1 Screen Identifies Sleeping Beauty

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