SMARCB1 Deficiency Integrates Epigenetic Signals to ...€¦ · 02-04-2018 · Chromatin, Epigenetics, and RNA Regulation SMARCB1 Deﬁciency Integrates Epigenetic Signals to Oncogenic

Chromatin, Epigenetics, and RNA Regulation

SMARCB1 Deficiency Integrates EpigeneticSignals to Oncogenic Gene Expression ProgramMaintenance in Human Acute Myeloid LeukemiaShankha Subhra Chatterjee1, Mayukh Biswas1, Liberalis Debraj Boila1,Debasis Banerjee2, and Amitava Sengupta1

Abstract

SWI/SNF is an evolutionarily conservedmulti-subunit chroma-tin remodeling complex that regulates epigenetic architecture andcellular identity. Although SWI/SNF genes are altered in approx-imately 25% of human malignancies, evidences showing theirinvolvement in tumor cell–autonomous chromatin regulationand transcriptional plasticity are limiting. This study demonstratesthat human primary acute myeloid leukemia (AML) cells exhibitnear complete loss of SMARCB1 (BAF47 or SNF5/INI1) andSMARCD2 (BAF60B) associated with nucleation of SWI/SNFD.SMARCC1 (BAF155), an intact core component of SWI/SNFD,colocalized with H3K27Ac to target oncogenic loci in primaryAML cells. Interestingly, gene ontology (GO) term and pathwayanalysis suggested that SMARCC1 occupancy was enriched ongenes regulating Rac GTPase activation, cell trafficking, and AML-associated transcriptional dysregulation. Transcriptome profilingrevealed that expression of these genes is upregulated in primary

AMLblasts, and loss-of-function studies confirmed transcriptionalregulation of Rac GTPase guanine nucleotide exchange factors(GEF) by SMARCB1. Mechanistically, loss of SMARCB1 increasedrecruitment of SWI/SNFD andassociated histone acetyltransferases(HAT) to target loci, thereby promoting H3K27Ac and geneexpression. Together, SMARCB1 deficiency induced GEFs for RacGTPase activation and augmented AML cell migration and sur-vival. Collectively, these findings highlight tumor suppressor roleof SMARCB1 and illustrate SWI/SNFD function in maintaining anoncogenic gene expression program in AML.

Implications: Loss of SMARCB1 in AML associates with SWI/SNFD nucleation, which in turn promotes Rac GTPaseGEF expres-sion, Rac activation, migration, and survival of AML cells,highlighting SWI/SNFD downstream signaling as importantmolecular regulator in AML. Mol Cancer Res; 1–14. �2018 AACR.

IntroductionSWI/SNF (BAF) chromatin remodelers are evolutionarily con-

served, large (�2 MDa) multi-protein complexes, which utilizeenergy derived fromATP hydrolysis tomobilize nucleosomes (1).SWI/SNF core components include SMARCB1 (BAF47, SNF5 orINI1), SMARCC1/SMARCC2 (BAF155 and BAF170), and one ofthe mutually exclusive ATPase subunits, SMARCA4 (BRG1) andSMARCA2 (BRM). SWI/SNF complexes often include cell type–specific, lineage-restricted subunits, and play important roles inpluripotency and cellular reprogramming (1, 2). Cancer genomesequencing studies have identified SWI/SNF complexes as one ofthe most commonly mutated (�25%) chromatin modulators inhuman cancer (3, 4). However, mutational profiling alone may

not always inform transcriptional dependencies embedded intumorigenesis.

Emerging evidences indicate that SWI/SNF subunits criticallyregulate murine hematopoiesis. Recent studies have shown thatSMARCD2 mediates granulopoiesis through CEBPe-dependentmechanism(5, 6). Actl6a (BAF53a)plays essential role inhemato-poietic stem cell (HSC) function (7). Mutant allele of Arid1a(BAF250a) determines pool size of fetal liver HSC populations(8). In addition, SWI/SNF was also implicated in murine leuke-mia development. SMARCA4was shown to regulate proliferationof murine leukemic cells (9, 10). SMARCB1 plays tumor sup-pressor role in several cancers, and frequent deletion of SMARCB1is observed in chronic myeloid leukemia patients (11). Loss ofSmarcb1 in vivo leads to fully penetrant malignant rhabdoidtumors (12, 13).

Rac GTPases belong to small Rho GTPase family and areinvolved in regulation of a diverse array of cellular functionsincluding cell proliferation, survival, adhesion, migration, actinassembly, and transcriptional activation (14, 15). Similar to Rassuperfamily proteins, Rac GTPases cycle between inactive GDP-bound and active GTP-bound conformations, regulated by spe-cific guanine nucleotide exchange factors (GEF), to transducesignals to effector proteins (14). Recent studies have suggestedthat Rac GTPases play integral roles in myeloid leukemia cellhoming, engraftment, survival, and trafficking within the bonemarrow microenvironment (15–18). Attenuation of Rac GTPasesignaling in synergy with Bcl-2 inhibition has been shown as amodality for combination targeted therapy in MLL-AF9 leukemia

1Stem Cell & Leukemia Lab, Cancer Biology & Inflammatory Disorder Division,CSIR-Indian Institute of Chemical Biology, Translational Research Unit ofExcellence (TRUE), Salt Lake, Kolkata, West Bengal, India. 2Clinical Hematology,Park Clinic, Gorky Terrace, Kolkata, West Bengal, India.

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

S.S. Chatterjee and M. Biswas contributed equally to this article.

Corresponding Author: Amitava Sengupta, CSIR-Indian Institute of ChemicalBiology, Kolkata 700091, India. Phone: 9133-2473-0492; Fax: 9133-2473-5197;E-mail: [email protected]

doi: 10.1158/1541-7786.MCR-17-0493

�2018 American Association for Cancer Research.

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(19). Myeloid leukemia cells are characterized with elevated RacGTP level; however, molecular regulation of Rac activation inleukemia pathophysiology remains incompletely understood.

Here we identify that in human primary acute myeloidleukemia (AML) cells, SMARCB1 deficiency associates withnucleation of SWI/SNFD. SMARCC1, an intact core componentof SWI/SNFD, colocalized with H3K27Ac to target tumor onco-genic loci, including Rac GTPase GEFs, in AML cells. Loss ofSMARCB1 induced recruitment of SWI/SNFD and associatedhistone acetyltransferases (HAT) to target GEFs for Rac GTPaseactivation and promoted AML cell migration. Collectively,these findings highlight tumor suppressor role of SMARCB1and illustrate SWI/SNFD function in maintaining an oncogenicgene expression program in AML.

Materials and MethodsPatient cohort

Human AML (n ¼ 67) bone marrow aspirates (1–2 mL each)were obtained from Park Clinic, Kolkata from untreated, freshlydiagnosed patients after written, informed consent according toInstitutional Human Ethics Committee (HEC) approval andfollowing Indian Institute of Chemical Biology (CSIR-IICB) Insti-tutional Review Board (IRB) set guidelines. Sample collectionwaspart of routine diagnosis and the inclusion criterion for this studywas histopathologic confirmation of bone marrow aspirates orbiopsies, karyotyping, and immunophenotypic analyses (20).Bone marrow aspirates were also collected from age-matchednormal individuals (n ¼ 6) after informed consent, who turnedout to be pathologically negative for AML (20). Individual caseinformation is presented in Supplementary Tables S1 and S2.Umbilical cord blood samples (40 mL each) were obtained fromDeb Shishu Nursing Home (Howrah, West Bengal, India) fromterm pregnancies after written, informed consent according toCSIR-IICB HEC approval and following IRB set guidelines. Lowdensity (1.077 gm/cc) nuclear cells from AML bone marrow,normal bone marrow, or cord blood samples were isolated byFicoll (Sigma) separation and cryopreserved in liquid nitrogen.Normal blood specimens were obtained from age-matchedhealthy volunteers (n ¼ 3) after written, informed consent andnucleated cells were isolated using RBC lysis (BD Pharmingen).

Reverse transcription and quantitative PCRTotal RNA was isolated by using TRIzol (Life Technologies)

according tomanufacturer's recommendation. RNase-free DNasetreatment were carried out to remove any genomic DNA contam-ination using DNase I recombinant, RNase free kit (Roche). RNAamount was quantified and cDNA was prepared using TaqManReverse Transcription Reagents (Applied Biosystems). Geneexpression levels were determined byquantitative PCRperformedusing cDNAwith SYBR SelectMasterMix (Applied Biosystems) onthe 7500 Fast Real-Time PCR System (Applied Biosystems).GAPDH was used as a housekeeping gene. Relative expressionlevels were calculated using the 2�DDCt method (21–24). qRT-PCRprimer details are available in Supplementary Table S3.

Array Comparative Genomic Hybridization and analysisArray Comparative Genomic Hybridization (CGH) and anal-

ysis were carried out by Genotypic Technology Private Limited.GenomicDNAquantity andpuritywas assessed by theNanoDropND-2000 UV-Vis Spectrophotometer (NanoDrop Technologies)

and the integrity was assessed on a 0.8% Agarose Gel. GenomicDNA with OD260/OD280 >1.8 and OD260/OD230 � 1.3 wasused for microarray experiments. DNA was considered to be ofgoodqualitywhen a single clear bandwas seenwhen run against areference ladder. A total of 0.5mg ofDNA in10.1mLwas taken intoa microfuge tube and digestion master mix containing restrictionenzymes (Alu I, 5U and Rsa I, 5U) was added. The samples wereincubated at 37�C for 2 hours followed by heat inactivation ofenzymes at 65�C for 20 minutes. Samples were labeled usingAgilent sure tag DNA Labeling Kit (catalog no: 5190-3399).Control samples were labeled with Cy3 and test sample withCy5. The labeled samples were cleaned up using Amicon Ultracolumns 30-kDa size exclusion filter. DNA yield and incorpo-ration of labeled dye (specific activity) was measured usingNanoDrop spectrophotometer. One micrograms each of Cy3-and Cy5-labeled sample was added with human cot-1 DNA(catalog no. 5190-3393), Agilent aCGH/CoCBlocking agent (partnumber: 5188-6416), and hybridization buffer (part number:5188-6420). The labeled samples in above hybridization mixwere denatured at 95�C for 3minutes andwere incubated at 37�Cfor 30 minutes. The samples were then hybridized at 65�C for 24hours. After hybridization, the slides were washed using AgilentaCGH Wash Buffer1 (Agilent Technologies, part number 5188-5221) at room temperature for 5minutes andAgilent aCGHWashBuffer 2 (Agilent Technologies, part number 5188-5222) at 37�Cfor 1 minute. The slides were then washed with acetonitrile (partnumber: A2094) for 10 seconds. The microarray slides werescanned using Agilent Scanner (Agilent Technologies, part num-ber G2600D). Image analysis was performed using Agilent Fea-ture Extraction software, feature extracted raw data was analyzedusing Agilent Genomic Workbench 7.0 software. The data werenormalized using Lowess normalization. Significant regions hav-ing amplification and deletionswere identified among each of thesamples. Genomic view and chromosome view of the amplifica-tion and deletion region for each sample were generated. Graph-ical representation has been done using Human UCSC GenomeBrowser by loading the data in wiggle file format. Details areincluded as Supplementary array CGH Files.

Methylation-specific PCRGenomic DNA was isolated using the QIAamp DNA Blood

Mini Kit (Qiagen, catalog no. 51104). Isolated genomic DNAwasthen bisulfite converted, that is, unmethylated cytosines wereconverted to uracil using EpiTect Bisulfite Kit (Qiagen, catalogno. 59104) according to the manufacturer's protocol. Methyla-tion-specific primers for the gene of interest were made usingMethPrimer. Bisulfite converted DNAs were amplified usingmethylated DNA (M pair)-specific primers. Methylation-specificPCR at SMARCB1 promoter loci in primary AML blasts wascompared with normal BMNC. The fold change levels of themethylated DNA were calculated with respect to GAPDH (unre-lated control). Relative methylation levels were plotted afternormalizing it with GAPDH. qMSP primer details are availablein Supplementary Table S3.

Coimmunoprecipitation, histone acid extraction, andimmunoblotting

Nuclear extracts for immunoprecipitation experiments wereprepared using NE-PER Nuclear and Cytoplasmic ExtractionReagents (Thermo Fisher Scientific) and diluted in 1� RIPA (CellSignaling Technology) containing protease and phosphatase

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inhibitor cocktails. About 300 mg extracts were incubated with 2.0mg of respective antibodies against SMARCC1 (clone R-18, sc-9746, Santa Cruz Biotechnology), p300 (A300-358A, BethylLaboratories), CBP (clone D6C5, 7389S, Cell Signaling Technol-ogy), BRD4 (cloneE2A7X,13440S,Cell Signaling Technology), orrabbit IgG (P120-101; Bethyl Laboratories), and incubated over-night at 4�C with gentle rocking. Fifty microliters of protein A/Gagarose beads (Cell Signaling Technology) were added and incu-bated for 3–4 hours at room temperature. The beads were thenwashed 6 times with 1� RIPA supplemented with 300 mmol/LNaCl and resuspended in 1� SDS gel loading buffer. The proteinswere separated in SDS-PAGE and transferred to PVDF membrane(Millipore) and subsequently probed with respective antibodies.Detailed list of antibody is included in Supplementary Table S4.All antibodies were used at a dilution of 1:1,000 unless specifiedotherwise. Total cell lysate for immunoblotting was prepared byincubating cells in 1� RIPA for 15 minutes followed by briefsonication. Supernatants were collected following centrifugationat 16,000 � g for 15 minutes at 4�C. Protein concentration wasdetermined using Pierce BCA Protein assay kit (Thermo FisherScientific). SDS-PAGEwasused to separate proteins, transferred toPVDF membrane, and probed using respective antibodies. Den-sitometry analyses were performed using NIH Image J software.

Sucrose density gradient centrifugationA total of 700 mg–1.0 mg nuclear extracts isolated from pooled

(n¼ 5–7) primary AML BMNCswere prepared and diluted in 300mL of 1� RIPA. The extracts were overlaid on a 10 mL 20%–50%sucrose gradient (in 1� RIPA) in 13 � 89 mm polyallomer tubes(BeckmanCoulter). The tubeswere then centrifuged in a SW-41 Tiswing out rotor at 30,000 rpm for 12 hours at 4�C. A total of 500-mL fractions were collected and separated in SDS-PAGE, trans-ferred to a PVDF membrane, and subsequently probed withspecific antibodies.

ChIP-seq, ChIP-qPCR, and analysesChIP-seq experiments were carried out at Core Technologies

Research Initiative (CoTeRI), National Institute of BiomedicalGenomics (NIBMG, University of Kalyani, Kolkata, West Bengal,India). A total of 1 � 107 primary bone marrow nuclear cells(BMNC), isolated from three independent (biological replicates)AMLpatients (Supplementary Table S1 case nos. 82, 83, and 80 asAML 01, AML 02, and AML 03, respectively), per chromatinimmunoprecipitation (ChIP) set were crosslinked with formal-dehyde (Sigma) in culture media. After crosslinking, chromatinwas extracted and sonicated to fragment lengths between 150 and900 bp in chromatin extraction buffer containing 10mmol/L TrispH¼ 8.0, 1mmol/L EDTA pH¼ 8.0, 0.5mmol/L EGTA pH¼ 8.0.Chromatin was incubated with ChIP-grade antibodies toSMARCC1 [sc-9746 (R-18), Santa Cruz Biotechnology),H3K27Ac (ab4729, Abcam), H3K27Me3 (07-449, Millipore), orrabbit IgG (clone P120-101; Bethyl Laboratories) or mouse IgG(clone G3A1; 5415S; Cell Signaling Technology) or goat IgG (sc-2028, Santa Cruz Biotechnology) during overnight at 4�C withrotation. Six micrograms of antibody was used per immunopre-cipitation. All antibodies were used at 1:1,000 dilution. Detailedlist of antibody is included in Supplementary Table S4. ProteinA/G Agarose beads (Cell Signaling Technology) were then addedand incubated for 2 hours at 4�C. The beads were washed withchromatin extraction buffer and by increasing salt concentrationfor four times. The chromatin was eluted from the beads in

chromatin elution buffer at 65�Cwith gentle vortexing. The elutedchromatinwas treatedwithRNAse for 30minutes at 37�C.Reversecross-linkingwasperformedby treating the eluted chromatinwithProteinase K (Sigma) at 65�C for 2 hours. The DNA was finallyprecipitated by phenol–chloroform extraction; precipitated DNAwas dissolved in TE buffer, and subjected to ChIP-seq analyses.For ChIP-qPCR experiments, 4–5 � 106 normal CD34þ cells or293T cells and 2 mg of antibody, or ChIP DNA obtained fromprimary AML BMNCs were used. ChIP-qPCR primer details areavailable in Supplementary Table S5.

Size distribution of the ChIP-enriched DNA was checked usingHigh Sensitivity chips in 2100 Bioanalyzer (Agilent) for eachsample and quantitation was performed in Qubit Fluorometer(Invitrogen) by picogreen method. ChIP-seq library preparationwas performed using TruSeqChIP Sample Prep Kit (Illumina)according to the manufacturer's instructions. Ten nanograms ofinput ChIP-enriched DNA was used for ChIP-seq library prepa-ration. Final libraries were checked using High Sensitivity chips in2100 Bioanalyzer (Agilent). Average fragment size of final librar-ies was found to be 280 � 8 bp. Paired-end sequencing (2 � 100bp) of these libraries were performed in HiSeq-2500 (Illumina).Quality control analysis of the raw data using NGS-QC ToolKitwas done and HQ reads with filter criteria of bases having �20Phred score and reads with �70% were filtered. Paired-end reads(.fastq format) were alignedwith Bowtie software using –best and–m 2, that is, mismatches against reference genome Ensemblbuild GrCh37/hg19 (considering 2% input as the baseline) andsaved in SAM format, whichwas then converted to sorted BAMfileusing SAMTOOLS. PCR duplicates were removed using SAM-TOOLS rmdup. Peak callingwas performedusingMACS14modelbuilding with P value cutoff of 0.05. Annotation of the identifiedpeaks was performed with PeakAnalyzer.

Functional enrichment analysis (GeneOntology and Pathway)was done using The Database for Annotation, Visualization andIntegrated Discovery (DAVID) v6.8. Gene list were uploaded andconverted to its respective ENTREZ Gene ID. The converted genelist was submitted to DAVID and Functional Annotation Clus-tering was carried out which comprises of Gene Ontology andPathway analysis. R bioconductor package ChipSeeker was usedto generate heatmap, average profile distibutions, and pie charts.Unique gene names were used to plot the Venn diagram repre-sented by peaks in the respective samples either upstream ordownstream or overlap to the genetic region. Bigwig/bed fileswere imported into Integrated Genome Viewer (IGV) and snap-shots of particular genomic loci were captured. Formotif analysis,the MEME ChIP V4.10 was used to analyze the motif using thepeak sequences, with default parameters. and for transcriptionencoding motif, Jaspar database (Jaspar) was used using MEMEChIP tool. Details are included as Supplementary ChIP-seq Files.

RNA-seq and analysesRNA-seq experiments were performed by Bionivid Technology.

Total RNA was isolated from BMNCs from the identical AMLcohort (n ¼ 3) and age-matched normal (n ¼ 2) hematopoieticcells using TRIzol (Life Technologies) according tomanufacturer'sinstruction. DNase treatment was carried out to remove anygenomic DNA contamination using DNase I recombinant, RNasefree kit (Roche). RNA amount was quantified and the sequencinglibrary prepared using TruSeq RNASample PrepKit v2 (Illumina).Paired-end sequencing was performed on HiSeq 4000 usingTruSeq 3000 4000 SBS Kit v3 (Illumina).

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Raw data resulted in an average of 35.39� 106 reads in normalhematopoietic cells and 36.86 � 106 reads of 101-bp length inprimary AML cells. Quality control using NGSQC toolkit yieldedaround 34.28� 106 HQ reads in normal hematopoietic cells and36.05� 106HQ reads in primary AML cells. Around 88.2% of theHQ reads fromnormal and 85.12%HQ reads fromprimary AMLscould be mapped to the Homo sapiens (hg 38) genome referencesequence using TOPHAT, suggesting a good quality of RNAsequencing. Transcripts were given a score for their expressionby Cufflinks-based maximum likelihood method. A total of24,784 transcripts were identified using Cuffdiff validation to beexpressed in either normal or primary AML cells representing13,847 genes. Transcript type analysis revealed 95.5% of thetranscripts to be of "Full Length" or "Known Transcripts" and4.5% as "Potentially Novel Isoforms" Transcripts as per CufflinksClass Code distribution. This indicates a largely complete tran-scription machinery activity in both normal and AML cells.Significant Biology for Differentially Expressed Transcripts wasperformedwithGO-Elite_v.1.2.5 Software. A cutoff of P value lessthan 0.05 was considered for filtering the significantly enrichedGO pathways. In testing for differential expression, we considerlog2FC > 2 (upregulation) and log2FC 2 (downregulation). Forgene-set enrichment analysis (GSEA), differentially expressedgenes from individual comparisons were preranked on the basisof fold change such that maximally upregulated genes fall top-most in the list. This was used as an input to perform GSEA(GeneSpring). GSEA was performed on "H: Hallmark gene set"representing well defined biological states or processes availableon Molecular Signature Database. Details are included as Sup-plementary RNA-seq Files.

Gene enrichment and functional annotation analysisGene ontology (GO) analysis of the shared gene set (Supple-

mentary ChIP-seq Shared GENELIST) was carried out usingDAVID v6.8 (https://david.ncifcrf.gov/). The P value used in theanalysis is a modified one, termed as EASE score threshold(maximum probability). The threshold of EASE Score is a mod-ified Fisher exact P value used for gene enrichment analysis. Itranges from 0 to 1. Fisher exact P value ¼ 0 represents perfectenrichment. Usually P value is equal or smaller than 0.05 to beconsidered strongly enriched in the annotation categories.

PlasmidsshRNA-expressing lentiviral constructs targeting against

SMARCB1 (pLKO-shSMARCB1, 39587) and SMARCB1-overex-pression vector HA_INI1/BAF47 was a gift from Dr. OlivierDelattre (Institut Curie, Paris, France). shRNA-expressing lenti-viral construct targeting against SMARCB1 (pLKO.1-puro-CMV-TGFP, TRCN0000295966) was purchased from Sigma. Emptyvector SHC003 was purchased from Sigma. Lentiviral packagingconstructs PAX2 (Addgene; 12260) and pMD2.G (Addgene;12259) were purchased from Addgene.

HSPC isolation, lentivirus preparation, and transductionCD34þ HSPCs were isolated from freshly collected normal

BMNCs and cord blood nuclear cells or from cryopreservedspecimens using CD34Microbead positive selection kit (MiltenyiBiotec) followingmanufacturer's protocol (21, 23). For lentiviruspreparation, 293T cells weremaintained inDMEM supplementedwith 10% FBS, 100 U/mL penicillin, 100 mg/mL streptomycin,and2mmol/L L-glutamine (all fromGibco) at 37�Cwith 5%CO2.

Cells were seeded in T-225 flasks at 70% confluence and trans-fected with the target plasmidDNA, PAX2 (Addgene; 12260), andpMD2.G (Addgene; 12259) using calcium-phosphate transfectionmethod (23, 25). After overnight incubation, butyrate inductionwas given for 8 hours. Supernatant containing lentiviral particleswere collected after 36–40 hours of incubation at 37�C with 5%CO2, and ultracentrifuged at 25,000 rpm for 90 minutes at 4�Cusing (Sorvall, ThermoScientific). Virus pellet was resuspended inX-VIVO (Lonza) and aliquoted in several tubes and stored at�80�C. HL60 and U937 cell lines were stably transduced withlentiviral particles containing mock (sh-Control) or sh-SMARCB1plasmids in a U-bottom 96-well nontissue-culture–treated plate.A total of 1 � 105 cells were incubated overnight with virusparticles at a MoI of 5 and polybrene was added at a finalconcentration of 8 mg/mL to initiate lentiviral infection.

Cells, drug treatments and survival, and proliferation assaysHuman AML cell lines (obtained from Dr. Jose Cancelas,

Cincinnati Children'sHospital, Cincinnati,OH)weremaintainedin Iscove's modified Dulbecco's medium (IMDM) supplementedwith 10% FBS, 100 U/mL penicillin, 100 mg/mL streptomycin,and2mmol/L L-glutamine (all fromGibco) at 37�Cwith 5%CO2.293T cells (obtained from Dr. Jose Cancelas, Cincinnati Chil-dren's Hospital, Cincinnati, OH; refs. 15, 23) were maintained inDMEM supplemented with 10% FBS, 100 U/mL penicillin, 100mg/mL streptomycin, and 2mmol/L L-glutamine (all fromGibco)at 37�C with 5% CO2. Adherent cells were transfected at 70%confluency using the calciumphosphate transfectionmethod (16,22, 23, 25). Cell lines have been freshly authenticated using STRprofiling (Supplementary Cell Lines_STR Profiles).

JQ1 (cat SML0974) was purchased from Sigma. For calcu-lating IC50, parental or lentivirus transduced AML cell lineswere treated with varying doses of JQ1 from 0.1 to 50 mmol/L.Viable cell counts were taken after 48 hours of drug treatmentand the total number of GFPþ cells were analyzed by flowcytometry. Cell counts were normalized and plotted againstlogarithm of the inhibitor concentration using GraphPadPrism5 to measure the IC50. For proliferation assay, lentivirustransduced cell lines were grown in triplicate in regular mediafor 6 days. Trypan blue–negative cell numbers were determinedat respective time points and the total number of GFPþ cellswere analyzed by flow cytometry.

AML BMNCs were grown in regular media supplemented withcytokines in presenceof 500nmol/L 5-azacytidine (Sigma, catalogno. A1287) orDMSO(vehicle) for 72 hours.Mediawas calibratedwith fresh 5-azacytidine and cytokines after every 24 hours.Posttreatment, total RNA was isolated and gene expression levelswere determined by qRT-PCR.

PAK1 pulldown and Rac GTPase activation assayCells were lysed using 400 mL ofMLB buffer (1�) with repeated

pipetting, centrifuged at 14,000 � g for 5 minutes at 4�C andsupernatants were used for PAK1 pull down assay (Upstate,Millipore) as described earlier (15, 16, 18, 26, 27). To thesupernatant, 10 mL of Rac1-conjugated agarose beads were addedand incubated for 45 minutes at 4�C with gentle rocking. Thebeads were centrifuged at 14,000� g for 10 seconds at 4�C. Afterremoving the supernatants the beads were washed three timeswith MLB buffer (1�) and resuspended in 40 mL of proteinloading buffer, boiled for 5 minutes, separated in 12% polyacryl-amide gel, and transferred to PVDF membrane and probed using

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respective antibodies. Presence of active Rac GTP was determinedby using antibody against Rac GTP in the pulldown fraction andnormalized against total Rac present in the lysate. Densitometryanalyses were performed using NIH Image J.

Migration assayHL60 cells were transduced with lentiviral particles expressing

sh-SMARCB1 or sh-Control in a U bottom 96-well nontissueculture–treated plate. Cells were incubated overnight with virusparticles at a MoI of 5 and polybrene was added at a finalconcentration of 8 mg/mL. Forty-eight hours posttransduction,50,000 cells were seeded in duplicate in the top chamber of a 24-well Transwell (Corning Incorporated) in100mLof IMDM.After 4hours of incubation at 37�Cwith 5%CO2, the totalmigrated cellswere counted from bottom chamber containing 600 mL of IMDMsupplemented with 10% FBS, 100 U/mL penicillin, 100 mg/mLstreptomycin, and 100 ng/mL CXCL12 (PeproTech). Total num-ber of GFPþ cells was analyzed by flow cytometry.

Flow cytometric analysisAML cell lines were transduced with lentiviral particles expres-

sing sh-SMARCB1 or sh-Control and coexpressing GFP at a MoI of5. After 48 hours, cells were harvested at 500� g for 5minutes andwashed two times with cold PBS and resuspended in 500 mL ofPBS supplemented with 2% human serum and 7-AAD at a finalconcentration of 1 mg/mL. 7AAD�/GFPþ cells were analyzedin LSRFortessa (Becton Dickinson) using FACSDiva software(Becton Dickinson).

Correlation and survival analysis of TCGA AML cohortCross-cancer analysis of SMARCB1 and DOCK5 expression, as

well as SMARCB1 and DOCK5 expression heatmap cluster ofTCGA AML cohort, was derived using cBioPortal for CancerGenomics interface (28, 29). For correlation analysis, mRNAexpression (RNA Seq V2 RSEM) data were obtained from TCGAdatabase and plotted using GraphPad Prism5. For correlation aswell as Kaplan–Meier survival analysis, mRNA expression (RNASeq V2 RSEM) was used for clustering of samples; all samplesshowing expression level abovemean value for the particular genewere considered "hi," whereas all samples showing expressionlevel below mean value were considered "lo."

Statistical analysesStatistical analyses were performed using GraphPad Prism 5.

Statistics were calculated with Student's t test. Quantitative dataare expressed asmean� SEM. unless specified otherwise. For IC50

calculation, cell counts were normalized and plotted against

logarithm of the respective inhibitor concentration using Graph-Pad Prism5. Gene expression correlation plots were derived usingthe TCGA datasets of AML cohort available through cBioPortal.Densitometry analyses were performed using Image J software(NIH). For all statistical analyses, the level of significance was setat 0.05.

Database availabilityAll sequencing data have been submitted to the database with

accession numbers as follows. ChIP-seq: GSE108976. RNA-seq:SRA accession: SRP127783; BioProject ID: PRJNA428149.

ResultsHuman primary AML cells show loss of SMARCB1 expressionand SWI/SNFD nucleation

We set out to identify SWI/SNF contribution to human AMLpathogenesis. Gene expression analysis identified a significantloss of SMARCB1 (SNF5 or BAF47) in human primary AML bonemarrownuclear cells (BMNCs;P<0.0058, n¼ 67) comparedwithage-matched normal bonemarrow (NBM)CD34þhematopoieticstem/progenitor cells (HSPC; Fig. 1A; Supplementary Tables S1and S2). SMARCB1 expression was also downregulated in estab-lished AML lines (Fig. 1B). Array CGH analysis did not detectamplification, deletion, or copy number gain or loss, or any othergenetic alteration at the SMARCB1 locus in our AML cohort(Supplementary array CGH Files). However, compared withnormal hematopoietic cells, AML blasts showed a substantialincrease in repressive DNA methylation at CpG islands of theSMARCB1 promoter (Fig. 1C), accounting for SMARCB1 down-regulation observed in AML. In agreement with this result, inhi-bition of DNA methyl transferases in vitro restored SMARCB1levels in SMARCB1lo AML blasts (Fig. 1D).

Apart from SMARCB1, expression of SMARCD2 (BAF60B),SMARCE1 (BAF57), and ARID2 (BAF200) were also significantlyreduced in AML BMNCs compared with NBM CD34þ cells (Fig.1E and F). Consistent with themRNAdownregulation, SMARCB1and SMARCD2 protein levels were dramatically lost in primaryAML cells (Fig. 1G). Expression of SMARCC1 (BAF155), coresubunit, and SMARCA4 (BRG1), ATPase subunit of SWI/SNFremained intact in AML (Fig. 1G). Coimmunoprecipitationexperiments indicated association of endogenous SMARCC1with the remaining SWI/SNF complex in AML BMNCs as well asHL60 cells (Fig. 1H). Sucrose density gradient analysis furtherconfirmed presence of an endogenous, residual, nuclear SWI/SNFcomplex (hereafter called SWI/SNFD) in primary AML cells(Fig. 1I). Collectively, these data identify loss of SMARCB1 andSWI/SNFD nucleation in human AML.

Figure 1.HumanprimaryAMLcells show loss of SMARCB1 expression andSWI/SNFDnucleation.A,qRT-PCRexpression ofSMARCB1 inAML (n¼67) low-density bonemarrownuclear cells (BMNC) compared with age-matched normal bone marrow (BM; n ¼ 6) CD34þ HSPCs (considered as 1-fold). B, RT-qPCR expression of SMARCB1 inestablished AML cell lines compared to normal BM CD34þ cells (considered as 1-fold). Error bars represent means � SD. C, Methylation-specific PCR atSMARCB1 promoter loci in primary AML blasts compared with normal BMNC. The fold change levels of the methylated DNA were calculated with respect to GAPDH(unrelated control). Location of respective qMSP primers are shown in the schema. Error bars represent means� SD. D, qRT-PCR expression of SMARCB1 in primaryAML (n¼ 3) cells treated with 5-azacytidine or DMSO (considered as 1-fold). Untreated AML 17, AML 30, and AML 38 had significantly reduced (0.03, 0.21, and 0.13-fold, respectively) SMARCB1 expression. Error bars, means � SD. E, qRT-PCR expression analysis of SMARCD2, SMARCE1, and ARID2 in AML (n ¼ 67) BMNCscompared with normal bone marrow (n¼ 6) CD34þ HSPCs (considered as 1-fold). F, qRT-PCR expression analysis of remaining SWI/SNF subunits in AML (n¼ 67)BMNCs compared with normal bone marrow (n ¼ 6) CD34þ HSPCs (considered as 1-fold). G, Immunoblot analysis of primary AML BMNC and normal (N)hematopoietic cells.H,Coimmunoprecipitation of endogenous SMARCC1 or IgG in nuclear lysate of primaryAMLBMNCs (left) or HL60 cells (right). I, Sucrose densitygradient (20% to 50%) analysis of primary AML (pooled from n ¼ 7) BMNC-derived nuclear lysates and immunoblotted with respective SWI/SNF antibodies.qRT-PCR values were normalized to GAPDH. Statistics were calculated with Student t test; error bars, means � SEM (if not specified otherwise).Coimmunoprecipitation and immunoblots are representatives of 2–3 independent experiments with similar results.

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Figure 2.

SMARCC1 (SWI/SNFD) occupies target oncogenic loci in primary AML cells.A, ChIP-seq (P < 0.05) heatmaps showing occupancy of SMARCC1 or H3K27Ac peaks 2.5kb upstreamor downstream from transcription start site (TSS) in primaryAML (n¼ 3; AML01, AML02, andAML03as biological replicates) BMNCs.B,RepresentativeChIP-seq venn diagram analysis showing overlap of genes identified from SMARCC1, and H3K27Ac in primary AML (AML 01) BMNC. Number of cooccupied genes(13,158) is shown in the intersection. C, Pie chart representing ChIP-seq genomic distribution of SMARCC1 (left) and H3K27Ac (right) occupancy in primary AMLBMNCs. Data represent average of three biological replicates of AMLs. D, ChIP-seq profile plots showing SMARCC1, H3K27Ac ChIP-seq signal intensities 2.5 kbupstream or downstream from transcription start site (TSS; upper) or 2 kb upstream or downstream form TSS and transcription end site (TES; lower) in primary AML(AML02) BMNC.E,Venn diagramanalysis showing SMARCC1 andH3K27Ac ChIP-seq cooccupied genes that are shared (2660) among the three biological replicatesof AML BMNCs. F, ChIP-seq average genomic distribution of SMARCC1 and H3K27Ac on the shared (2,660) gene set in AML. PCR duplicates were removed usingSAMTOOLS rmdup. Peak calling was performed using MACS14 model building with P value cutoff of 0.05. Annotation of the identified peaks was performed withPeakAnalyzer. Unique gene nameswere used to plot the Venn diagram represented by peaks in the respective samples either upstream or downstreamor overlap tothe genetic region. Error bars, means � SEM.






SMARCC1 (SWI/SNFD) is involved in maintenance ofoncogenic gene expression program in primary AML cells

Mammalian SWI/SNF complex organizes nucleosome occu-pancy at target promoters and enhancers (30), thereby regulatinggene expression. Recent studies have shown SMARCB1 deficientSWI/SNF complex to be essential for rhabdoid tumor survival (31,32). To elucidate the function of SWI/SNFD in AML, we investi-gated genome-wide occupancy of endogenous SMARCC1,which would indicate SWI/SNFD binding, in primary AML cellsusing chromatin immunoprecipitation-sequencing (ChIP-seq).SMARCC1 ChIP-seq identified about 14,000 genes on averagein three independent (biological replicates) AML BMNCs (Fig. 2Aand B; Supplementary Fig. S1A–S1C). SMARCC1 localizedapproximately 10% at promoters, approximately 41% at genebody, and approximately 49% at transcription start site (TSS)-distal intergenic regions (Fig. 2C andD). In rhabdoid tumor, it hasbeen shown that SWI/SNF binding at TSS-distal enhancer loci,marked by H3K27Ac, is essential for its oncogenic role (31). AlsoSMARCB1 deficiency has been shown to regulate H3K27Ac levelat enhancers (32, 33). In general, H3K27Ac is enriched at sites ofactive transcription; therefore, SWI/SNF function is typicallyassociated with transcription activation. H3K27Ac ChIP-seq anal-ysis indicated SMARCC1 overlapped with H3K27Ac at 2,660genes (Supplementary ChIP-seq Files) that are shared among thethree biological replicates of AML. Detailed statistical analysis isincluded in Supplementary ChIP-seq Files as "SupplementaryChIP-seq_P values of Venn diagram genes." Enrichment ofSMARCC1 at these shared genes was approximately 10% atpromoters, approximately 55% at gene body, and approximately35% at TSS-distal intergenic regions (Fig. 2E and F).

To identify the role of SMARCB1 in directing SWI/SNFD func-tion, we compared the AML ChIP-seq data with primary, normalhematopoietic nuclear cells expressing SMARCB1-containingintact SWI/SNF complex (Supplementary Fig. S2A). ChIP-seqanalysis identified about 16,500 genes occupied by SMARCC1,out of which 13,987 also showed H3K27Ac (Supplementary Fig.S2B). The distribution pattern of SMARCC1 was also similar tothat in AML, with 4%, 37%, and 58% being the occupancy atpromoter, intron, and TSS-distal intergenic region respectively(Supplementary Fig. S2B). Hence unlike in rhabdoid tumor,SMARCB1 deficiency does not affect the overall occupancy ofSWI/SNF complex in AML, indicating that the regulation mayrather be gene or loci specific. Motif analysis of SMARCC1-binding sites in AML identified enrichment of several transcrip-tion factors, notably KLF4, HOXA13, and HOXD13 that areimplicated in AML (Supplementary Fig. S2C and SupplementaryChIP-seq Files). Motif analysis in normal hematopoietic cellsidentified enrichment of MEF2A, MEF2B, and MEF2D (Supple-mentary Fig. S2C and Supplementary ChIP-seq Files). Therefore,SWI/SNF is associatedwithdifferent sets of transcription factors inAML and normal hematopoietic cells. This suggests that althoughSMARCB1 loss does not alter overall chromatin affinity of SWI/SNF, itmay differentially determine recruitment to and regulationof altered gene sets.

Next, to identify gene sets regulated by SWI/SNFD, we per-formed functional annotation clustering of the SMARCC1 andH3K27Ac cooccupied genes. Gene Ontology (GO) terms andpathway analysis showed an enrichment of transcripts associatedwith Rac GTPase-dependent cell migration, hematopoietic self-renewal, and transcriptional regulation (Fig. 3A). Interestingly,among these gene sets, we noted SMARCC1 occupancy at several

Rac GTPase GEFs. To validate differential expression of gene setsidentified as SWI/SNFD targets, we next performed transcriptomeanalysis. RNA-sequencing of paired samples identified, amongthe SMARCC1 and H3K27Ac cooccupied genes, 280 were signif-icantly upregulated in AML, compared with normal (n ¼ 2)hematopoietic cells (Supplementary RNA-seq Files). Among theupregulated genes were VAV3 and the DOCK family of Rac GEFs.Importantly, although these Rac GTPase GEFs show SMARCC1binding also in controls, but the binding sites are significantlydistinct from that observed in AML (Fig. 3B; Supplementary Fig.S2D). In addition,H3K27Acdid not cooccupy SMARCC1bindingsites in normal hematopoietic cells (Fig. 3B; Supplementary Fig.S2D). Together, genes showing SWI/SNFD overlap with H3K27Acand resultant upregulation represent putative SMARCB1-depen-dent targets.

To further confirm differential SWI/SNFD binding due toSMARCB1 loss, ChIP-qPCR analysis was performed usingSMARCC1 and H2K27Ac antibodies in normal CD34þ cells andSMARCB1-deficient primary AML blasts (Fig. 3C; SupplementaryFig. S3A). SMARCC1 and H3K27Ac cooccupancy were signifi-cantly higher at the GEFs in AML blasts compared with theidentical loci in normal CD34þ cells (Fig. 3C; SupplementaryFig. S3A). Upregulation of the GEFs expression was additionallyvalidated byqRT-PCR analysis, which shows that they are elevatedin AML blasts compared to control (Supplementary Fig. S3B).Collectively, these results indicate differential locus-specific bind-ing of SWI/SNF at target GEFs in AML.

SMARCB1 levels correlate with DOCK expression and AMLpathophysiology

The Cancer Genome Atlas (TCGA) cross-cancer analysis revealsSMARCB1 median expression level to be minimum in AMLpatients, after mesothelioma (Fig. 4A). This as well as our AMLcohort indicates that SMARCB1 downregulation is a generalphenomenon observed in AML. Unlike SMARCB1, medianexpression of SMARCD2 was not downregulated, and was at parwith other cancers (Supplementary Fig. S3C). Therefore, wefocused our analysis on SMARCB1 for subsequent studies. Wewanted to evaluate whether SMARCB1 levels have any prognosticsignificance in AML. To this end we studied the survival trends ofAML patients corresponding to their expression of SMARCB1from TCGA database. In many AML subtypes, well-characterizedoncogenic translocations are enough to drive leukemogenesis. Toeliminate the effect of complex translocations, only patients withnormal karyotypewere considered. Patientswith lower SMARCB1levels showed correspondingly poorer nonsignificant overall (P¼0.561) as well as disease-free (P ¼ 0.230) survival.

DOCK family of Rac GTPase GEFs being one of the primarytargets of SWI/SNFD as identified from ChIP-seq and transcrip-tome analysis, we next wanted to check whether there wasany correlation between SMARCB1 and DOCK members inAML. TCGA cross-cancer analysis showed that DOCK5 hashighest expression in AML among multiple cancers (Fig. 4B).This is in stark contrast to SMARCB1 expression, which led usto hypothesize that SMARCB1 and DOCK5 expression mustbe inversely correlated. Indeed, TCGA database analysis con-firmed significant negative correlation between SMARCB1 andDOCK5 expression in AML (Fig. 4C). Patients with reducedSMARCB1 showed significantly upregulated DOCK5 levels(Fig. 4C). Like DOCK5, the other atypical Rac GTPase GEFs,DOCK2, DOCK8, and DOCK10 also showed reciprocal

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Figure 3.

SMARCC1 is involved in maintenance of oncogenic gene expression program in primary AML cells. A, Pathway (top) and Gene Ontology (GO) term (bottom)analysis of 2,660 gene set in AML. B, Representative ChIP-seq integrated genome browser view (IGV) snapshots showing occupancy of SMARCC1 and H3K27Acat VAV3 (top) and DOCK5 (bottom) loci in one of the AML BMNCs. C, ChIP-qPCR analyses showing occupancy of SMARCC1 and H3K27Ac on target genomicloci (Region 1) in normal, CD34þ HSPCs and primary AML blasts, ChIP-qPCR values were normalized to percent input. Location of the respective ChIP-qPCRprimers are shown in the schema. Statistics were calculated with Student t test; error bars, means � SD.






Figure 4.

SMARCB1 levels correlate with GEFsexpression and AML pathophysiology.A, SMARCB1 mRNA expression [RNA Seq V2(log)] from TCGA pan-cancer dataset. Boxindicates second lowest expression in AML.B, DOCK5 mRNA expression [RNA Seq V2(log)] from TCGA pan-cancer dataset. Boxindicates maximum expression in AML.C, SMARCB1 gene expression correlation plotswith GEFs in AML cohort (n ¼ 200) fromTCGA/cBioPortal dataset.

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correlation with SMARCB1 (Fig. 4C). Survival analysis furtherstrengthened the SMARCB1-DOCK interdependence, demon-strating that patients with low SMARCB1 and high DOCKexpression have poorer nonsignificant survival than patientswith high SMARCB1 and low DOCK levels.

SMARCB1 deficiency induces GEFs expression by promotingH3K27Ac at target loci

SWI/SNFD binding analysis indicated that SMARCB1 deficiencyis accompanied with elevated H3K27Ac at oncogenic loci in AMLcells. In agreement with this, lentivirus-mediated silencing ofSMARCB1 in normal CD34þ cells and in established AML celllines (Supplementary Fig. S3D–S3G), which still express residualSMARCB1, induced GEFs expression (Fig. 5A and B; Supplemen-tary Fig. S4A and S4B). Acetylation at H3K27, which controlstranscriptional state and facilitates gene expression are mediatedby HATs, and recently it has been shown that SWI/SNF coimmu-noprecipitates with HATs in rhabdoid tumors (33). Coimmuno-precipitation studies indicated interaction of SWI/SNFD andHATsin AML cells (Supplementary Fig. S4C). We investigated whetherloss of SMARCB1 can affect recruitment of SWI/SNFD andHATs attarget GEFs. Although SMARCB1 loss did not apparently affectSWI/SNF interaction with the HATs (Supplementary Fig. S4D andS4E), interestingly SMARCB1 deficiency resulted in an increasedoccupancy of SMARCC1,HATs, andH3K27Ac levels at targetGEFsloci (Fig. 5C; Supplementary Fig. S5A and S5B). Collectively, these

findings provide mechanistic evidence for SMARCB1 loss andSWI/SNFD-mediated transcriptional regulation of GEFs expres-sion in AML.

Loss of SMARCB1 induces Rac GTPase activationRac GEFs control the activation of Rac GTPase signaling;

therefore, we asked whether induction in GEFs expression inSMARCB1-deficient cells affect Rac activation. Consistent withthe role of SMARCB1 in preferential recruitment of SWI/SNF,HATand H3K27Ac, and expression of DOCK genes, loss of SMARCB1resulted in approximately 2-fold activation of RacGTPase (Fig. 6Aand B). This was accompanied with increase in cell migration,suggesting its tumor suppressor role (Fig. 6C). In our AMLdiscovery cohort, Rac GEFs emerged as important downstreamcandidates of SWI/SNFD. Mechanism of SWI/SNFD-mediatedoncogene induction is through recruitment ofHATs and increasedH3K27 acetylation. HATs as well as several SWI/SNF subunitscontain acetyl-lysine binding bromodomains, which is a target ofBET inhibitors (34). We therefore sought to determine whetherSMARCB1-deficiency would alter sensitivity of AML cells to BETinhibition. SMARCB1-deficient AML cells were significantly moresensitive to BET inhibition than controls (Supplementary Fig.S5C). Collectively, these results indicate that loss of SMARCB1 inAML cells results in increased occupancy of SWI/SNFD along withHATs to target GEFs (Fig. 6D), which induces GEFs expression,activation of Rac GTPase signaling and cell migration.

Figure 5.

SMARCB1 deficiency induces GEFs expression by promoting H3K27Ac at target loci. A, qRT-PCR expression of GEFs in normal CD34þ cells transduced with sh-SMARCB1 or sh-Control (considered as 1-fold). B, RT-qPCR expression of GEFs in AML cell lines expressing sh-SMARCB1 compared with sh-Control (consideredas 1-fold). C, ChIP-qPCR analysis showing occupancy of SMARCC1, H3K27Ac, p300, CBP, and BRD4 on target GEFs loci (Regions R1 and R2) in 293Tcells that were transiently transfected with sh-SMARCB1 or sh-Control. ChIP-qPCR values were normalized to IgG. Location of the respective ChIP-qPCRprimers are shown in the schema. qRT-PCR experiments are representatives of atleast two independent biological replicates with similar results.qRT-PCR values were normalized to GAPDH. Statistics were calculated with Student t test; error bars, means � SD. � , P < 0.05 was considered to bestatistically significant.






DiscussionIn this study, we present evidence that in human primary

AML cells there is loss of SMARCB1, which is associated withnucleation of SWI/SNFD. Leukemic SWI/SNFD retains core com-ponents SMARCC1 and SMARCA4. Recent reports suggest thatloss of Snf5, yeast homolog of mammalian SMARCB1, inducesformation of aberrant SWI/SNF complex (35), and SMARCB1-deficient malignant rhabdoid tumors depend on SMARCA4 fortransformation (36). SMARCB1haspreviously been implicated inrhabdoid tumor,withbiallelic inactivatingmutations sufficient todrive malignant transformation (13). A separate study has shownthat SMARCA4 regulates proliferation of murine leukemic cells(9). In addition, Smarcd2-deficient mice fail to generate function-ally mature myeloid cells (5, 6). Therefore, individual subunits ofSWI/SNF complex have been shown to coordinate diverse cellularfunctions regulating disease and development. Transcriptionalplasticity is an emerging aspect in tumorigenesis (37, 38). Herein,our study provides evidence and reinforces the importance ofleukemic SWI/SNFD with altered subunit stoichiometry configu-

ration toward maintenance of oncogenic gene expression pro-gram precisely in human primary AML cells. It strengthens theimportance of epigenetic perturbation of the SWI/SNF complex intumorigenesis. Essentially, our data indicate that in AML,SMARCB1 deficiency associates with altered SWI/SNFD, and leu-kemic cells depend on SWI/SNF core components for transcrip-tional dysregulation and survival.

Importantly, in our study among the SMARCC1 and H3K27Accooccupied genomic targets, we noted SMARCC1 occupancy atseveral Rac GTPase GEFs, which play important roles in cellsurvival, trafficking, and small GTPase signaling (17, 39, 40).SMARCB1 deficiency upregulated expression of theGEFs, andwasassociated with Rac GTPase activation and hypermigration ofAML cells. Earlier, we and others have shown that Rac GTPasescritically regulate leukemia cell engraftment and survival (15–18).VAV3, Rho/Rac GTPase GEF, was implicated in leukemogenesis(41, 42). DOCK2 is a noncanonical GEF for Rac GTPases, andDOCK2 inhibition in vivo attenuates AML cell survival (43, 44).SNF5 was implicated in regulation of RhoA-dependent

Figure 6.

Loss of SMARCB1 induces Rac GTPaseactivation. A, PAK1 pulldown assay inAML cell lines transduced with shRNA-expressing constructs againstSMARCB1 or control. Densitometryrepresents ratio of Rac GTP versustotal Rac normalized to sh-C.B, PAK1 pulldown assay of 293T cellstransiently transfected with twodifferent shRNA-expressingconstructs against SMARCB1 orcontrol. Densitometry represents ratioof Rac GTP versus total Racnormalized to sh-C. Data representone of two independent experimentswith similar results. C, Migrationtowards CXCL12 of GFPþ HL60 cellsexpressing sh-C or sh-SMARCB1. Datarepresent average of two independentexperiments with similar results.Statistics were calculated withStudent t test; error bars, means� SD.D, Schema representing loss ofSMARCB1-driven epigenetic signalintegration towards maintenance ofelevated Rac GTPase signaling in AMLcells. ��, P < 0.01 was considered to bestatistically significant.

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cytoskeleton organization and migration of malignant rhabdoidtumor cells (45). In our study, SMARCC1 occupancy was alsoenriched at KIT, ASXL1, SF3B1, TET2 target loci, and silencing ofSMARCB1 induced their expression. Many of these genes aresomatically mutated at relatively high frequency in myeloidmalignancies with poor prognosis (46, 47), suggesting thatSMARCC1/SWI/SNFD would help sustain expression of mutantoncoproteins in AML. Collectively, these findings account forSWI/SNFD involvement in maintenance of AML cell gene expres-sion program.

We demonstrate that a fraction of SMARCC1 and H3K27Accooccupied genomic targets in AML cells were enriched in TSS-distal intergenic regions (�30%). SWI/SNF was shown to playchromatin remodeling function at both promoters and enhancers(30, 48). Recent reports have demonstrated an interdependencyof SWI/SNF andHAT function (31). SMARCB1 levels were shownto regulate not only expression of p300, BRD4, andmediator, butalso control interaction of SWI/SNFwith p300 in rhabdoid tumor(31). Intact SWI/SNF function, overlapping with H3K27Ac isneeded for the maintenance of lineage-specific enhancers, regu-lating cell fate and differentiation (33). SMARCB1 deficiency,however, shifts SWI/SNF recruitment from enhancers to onco-genic super-enhancer regions (31). However, in contrast to someof these reports where experiments were performed in differentcell types, our results indicate that loss of SMARCB1 induces RacGEFs expression that is associated with elevated SMARCC1 andH3K27Ac occupancy at target loci. This is similar to an earlierstudy demonstrating that Snf5 localizes to Gli1-regulated pro-moters and that loss of Snf5 leads to activation of the Hedgehog–Gli pathway in malignant rhabdoid tumors (49). Essentially,these findings indicate that SWI/SNF function and epigeneticplasticity secondary to absence of specific SWI/SNF subunits arecell type and context-dependent phenomenon.

Genetic perturbations are usually attributed to genetic dele-tions and inactivating mutations. Apart from this DNA methyl-transferases play an important role in silencing expression of keytumor suppressor genes (50). Genotyping of our AML cohortindeed corroborates this, showing that SMARCB1 loss seen inAML is not due to genetic deletion, but rather increased DNAmethylation at CpG islands of proximal promoter region. Inaddition, we demonstrate that expression of SMARCB1 corre-sponds to prognosis in AML patients. SMARCB1 expression is thelowest in AML among multiple cancers; and also within AMLcohort, patients with low SMARCB1 levels to have comparativelyshorter overall as well as disease-free survival period comparedwith patients with higher SMARCB1 expression. That SWI/SNFD

mediated Rac GEF regulation is indeed important in AML path-ogenesis is demonstrated by reciprocal correlation betweenSMARCB1 and various members of the DOCK family in AML.

Low SMARCB1 corresponds to elevatedDOCK family expression.Moreover survival analysis considering SMARCB1 and DOCKlevels reflect the importance of this correlation, with AML patientshaving low SMARCB1 and high DOCK expression displayingpoorer disease-free survival compared with patients with highSMARCB1 and low DOCK expression.

To conclude this study,weelucidate that inhumanprimaryAMLcells SMARCC1, an intact core component of SWI/SNFD, coloca-lized with H3K27Ac to target oncogenic loci. Loss of SMARCB1inducedRacGTPaseGEFs expression, Rac activation andpromotedAML cell migration and survival. In summary, these findingsinform epigenetic signal integration downstream of SWI/SNFtoward oncogenic gene expression programmaintenance in AML.

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

Authors' ContributionsConception and design: A. SenguptaDevelopment of methodology: S.S. Chatterjee, M. BiswasAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): S.S. Chatterjee, M. Biswas, D. BanerjeeAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): S.S. Chatterjee, M. Biswas, L. Debraj Boila,A. SenguptaWriting, review, and/or revision of the manuscript: L. Debraj Boila,A. SenguptaAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): A. SenguptaStudy supervision: A. Sengupta

AcknowledgmentsThis study is supported by funding from DST (SB/SO/HS-053/2013; to A.

Sengupta),DBT (BT/PR13023/MED/31/311/2015; to A. Sengupta), DBTRama-lingaswami Fellowship (BT/RLF/RE-ENTRY/06/2010; to A. Sengupta), andCSIR, Govt. of India (NWP/BIODISCOVERY, BSC 0120; to A. Sengupta). S.S.Chatterjee, and M. Biswas acknowledge fellowships from CSIR and UGC,respectively.

The authors thankDr. PrasantaMukhopadhyay for providing umbilical cordblood samples. We acknowledge Dr. Olivier Delattre for sharing plasmids andAddgene for shipping DNA constructs. We also thank Drs. ArindamMaitra andSubrata Patra, CoTERI,National Institute of BiomedicalGenomics (NIBMG) forconducting ChIP-seq experiments; Dr. Madavan Vasudevan, Madhura andShemi Ramesh, Bionivid Technology for ChIP-seq analysis and RNA-sequenc-ing; Genotypic Technology, Bangalore for array CGHexperiments and IICB flowcytometry core for services.

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 September 8, 2017; revised January 11, 2018; accepted February 20,2018; published first February 26, 2018.

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Chatterjee et al.




Published OnlineFirst February 26, 2018.Mol Cancer Res Shankha Subhra Chatterjee, Mayukh Biswas, Liberalis Debraj Boila, et al. Human Acute Myeloid LeukemiaOncogenic Gene Expression Program Maintenance in SMARCB1 Deficiency Integrates Epigenetic Signals to

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SMARCB1 Deficiency Integrates Epigenetic Signals to ...€¦ · 02-04-2018 · Chromatin, Epigenetics, and RNA Regulation SMARCB1 Deﬁciency Integrates Epigenetic Signals to Oncogenic