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Therapeutics, Targets, and Chemical Biology

Targeting the Deregulated Spliceosome Core Machinery inCancer Cells Triggers mTOR Blockade and Autophagy

Virginie Quidville1,2,3, Samar Alsafadi1,2,3, Aïcha Goubar1,2,3, Fr�ed�eric Commo1,2,3, V�eronique Scott1,2,3,Catherine Pioche-Durieu2,3, Isabelle Girault1,2,3, Sonia Baconnais2,3, Eric Le Cam2,3, Vladimir Lazar2,3,Suzette Delaloge1,2, Mahasti Saghatchian1,2, Patricia Pautier2, Philippe Morice2,3, Philippe Dessen2,3,St�ephan Vagner1,2,3, and Fabrice Andr�e1,2,3

AbstractThe spliceosome is a large ribonucleoprotein complex that guides pre-mRNA splicing in eukaryotic cells. Here,

we determine whether the spliceosome could constitute an attractive therapeutic target in cancer. Analysis ofgene expression arrays from lung, breast, and ovarian cancers datasets revealed that several genes encodingcomponents of the core spliceosome composed of a heteroheptameric Sm complex were overexpressed inmalignant disease as compared with benign lesions and could also define a subset of highly aggressive breastcancers. siRNA-mediated depletion of SmE (SNRPE) or SmD1 (SNRPD1) led to amarked reduction of cell viabilityin breast, lung, and melanoma cancer cell lines, whereas it had little effect on the survival of the nonmalignantMCF-10A breast epithelial cells. SNRPE or SNRPD1 depletion did not lead to apoptotic cell death but autophagy,another form of cell death. Indeed, induction of autophagy was revealed by cytoplasmic accumulation ofautophagic vacuoles and by an increase in both LC3 (MAP1LC3A) protein conversion and the amount of acidicautophagic vacuoles. Knockdown of SNRPE dramatically decreased mTOR mRNA and protein levels and wasaccompanied by a deregulation of themTORpathway, which, in part, explains the SNRPE-dependent induction ofautophagy. These findings provide a rational to develop new therapeutic agents targeting spliceosome corecomponents in oncology. Cancer Res; 73(7); 2247–58. �2013 AACR.

IntroductionTargeted therapies have been shown to improve outcome in

breast cancers. Molecular targeted agents usually inhibit keyoncogenic pathways involved in cancer progression or resis-tance to conventional treatments.Most of these newmoleculartherapies target kinase pathways and are usually highly effec-tive in a specific molecular segment. In the recent years,accumulative evidence has been reported that beyond kinaseand DNA repair defects, many pathways are crucial for cancerprogression. For instance, metabolism, immune system, andepigenetics have been reported as targetable in oncology.Looking for novel pathways involved in cancer progression,we previously identified spliceosome assembly components as

the most enriched pathway in breast cancer samples, whencompared with benign lesions (1).

The aim of the present study was to determine whether thespliceosome could be a relevant therapeutic target in cancer.The spliceosome is a dynamic macromolecular ribonucleopro-tein (RNP) complex that catalyses the splicing of nuclear pre-mRNA (precursor mRNA) into mRNAs. Nuclear pre-mRNAsplicing is essential to remove internal noncoding regions ofpre-mRNA(introns) and to join the remaining segments (exons)into mRNA before export to the cytoplasm and translation. Thespliceosome does the 2 primary functions of splicing, that is,recognition of the intron/exon boundaries and catalysis of thecut-and-paste reactions that remove introns and join exons. Thespliceosomal machinery complex is formed from 5 RNP sub-units, termed uridine-rich (U-rich) small nuclear RNP (snRNP),transiently associated to a range of protein factors (2, 3). EachsnRNP (U1, U2, U4/U6, and U5) consists of a uridine-rich smallnuclear RNA (snRNA) complexedwith a set of 7 proteins knownas Sm or SNRP proteins. The Sm proteins (B/B', D1, D2, D3, E, Fand G) form a 7-member ring core structure that encompassesRNA. All Sm proteins share a conserved Sm domain, consistingof 2 sets of conserved sequences (Sm1 and Sm2) that areresponsible for the assembly of snRNAs in an ordered manner,to form the Sm core of the spliceosomal snRNPs (4, 5) and arethereby involved in pre-mRNA processing (6).

The splicing process has a crucial role in the control of theexpression of many genes (such as those involved in cell cycle,signal transduction, angiogenesis, apoptosis, and invasion;

Authors' Affiliations: 1Institut National de la Sant�e et de la RechercheM�edicale (INSERM)U981, Paris; 2Institut de canc�erologie GustaveRoussy,Villejuif; and 3Universit�e Paris-Sud XI, Kremlin-Bicêtre, France

Note: Supplementary data for this article are available at Cancer ResearchOnline (http://cancerres.aacrjournals.org/).

V.Quidville andS. Alsafadi contributed equally to thiswork andare joint firstauthors.

S. Vagner and F. Andr�e are co-senior authors.

Corresponding Author: Fabrice Andr�e, INSERM U981, Institut GustaveRoussy, 114 rue Edouard Vaillant, Villejuif 94805, France. Phone: 33-1-42114293; Fax: 33-1-42115217; E-mail: [email protected]

doi: 10.1158/0008-5472.CAN-12-2501

�2013 American Association for Cancer Research.

CancerResearch

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refs. 7–15). Mutations of splicing factors or alterations of theexpression of the components of the splicing machinery canaffect the splicing pattern of genes and contribute to thedevelopment of tumors (16–19). Deregulated alternative splic-ing can lead to the production of novel mRNA isoforms codingfor protein variants with key functions in cancer(7, 10, 13, 20, 21). A substantial fraction of alternative splicingevents in humans can also result inmRNA isoforms that harbora premature termination codon (PTC). These transcripts arepredicted to be degraded by the nonsense-mediated mRNAdecay (NMD) pathway which constitutes a mechanism of RNAsurveillance that detects and eliminates aberrantly splicedmRNAs harboring a PTC to prevent the synthesis of nonfunc-tional or deleterious truncated proteins (22, 23) and to regulatethe abundance of mRNA transcripts (24).

In the present study, we have evaluated whether compo-nents of the spliceosome machinery complex were overex-pressed in malignant tumors and whether blocking spliceo-some could lead to antitumor effects.

Materials and MethodsCell culture and siRNA transfection

All cell lines were supplied by American Type CultureCollection (ATCC, LGC Standards) between 2007 and 2011.EachATCCcell linewasfirst amplified to generate a cellmasterbank. All experiments were carried out from this cell bank. Allthe cell lines used were tested negative for mycoplasmacontamination using VenorGeM Advance PCR Kit (Biovalley).Human mammary carcinoma cell lines SKBr-3 (ATCC HTB-30), MDA-MB 468 (ATCC HTB-132), HCC1937 (ATCC CRL-2336), lung carcinoma A549 (ATCC CCL-185), and malignantmelanoma A375 (ATCC CRL-1619) were cultured as mono-layers at 37�C with 5% CO2 and maintained by regular passagein complete media consisting of RPMI-1640 GlutaMaxI orDulbeccos' Modified Eagle's Media (DMEM):F12 (Gibco BRL,Invitrogen Life Technology) supplemented with 10% heat-inactivated FBS (PAN Biotech, Dutscher), 1 mmol/L sodiumpyruvate (Invitrogen), and 10 m mmol/L HEPES (Invitrogen).MCF-7 (ATCC HTB-22) was maintained in completed DMEM/F12–GlutaMaxImedium supplemented with 0.01mg/mL insu-lin (Invitrogen). Nontumoral epithelial cell line MCF-10-2A(ATCC CRL-10781) was maintained in DMEM/F12–GlutaMaxImedium supplemented with 20 ng/mL EGF (Gibco, Invitro-gen), 100 ng/mL cholera toxin (Sigma-Aldrich), 0.01 mg/mLinsulin (Invitrogen), 500 ng/mL hydrocortisone (Sigma-Aldrich), and 5% heat-inactivated horse serum (Invitrogen).Forty-eight hours after plating, the cells were transfected withspecific SNRPE or D1 validated siRNA (see SupplementaryTable S1 for sequence information; Qiagen) or nontargetingcontrol siRNA (ON-TARGETplus Non-targeting Pool, Dharma-con) using HiPerfect transfection agent (Qiagen) according tothemanufacturer's instructions. Briefly, cells were plated in 12-well plates in 4 replicates and grown to approximately 50% to60% confluency. siRNA was diluted in OPTI-MEM minimalculture medium (Invitrogen) at a final concentration of 50nmol/L and thenHiperfect was added to the diluted siRNA andthe mixture was dropwise added to each well in a final volume

of 1 mL. For controls, cells were either exposed to Hiperfectreagent alone (mock) or transfected with a nontargetingcontrol siRNA. Transfection medium was replaced by freshculture medium every 48 hours until the end of experiment.After 96 hours of transfection, cells were harvested, countedusing the trypan blue exclusion method or crystal violetstaining, and processed for the various biochemical assays.siRNA transfection effect and siRNA concentration effect oncell number were estimated by 2-way ANOVA.

ForNMD inhibition experiments, cells were transfectedwithSNRPE siRNA or nontargeting control siRNA. Three days later,cells were treated for 8 hours with 100 mg/mL cycloheximide(Sigma-Aldrich) or with an equivalent amount of dimethylsulfoxide (DMSO) as a control. RNA was then isolated andmRNA expression was detected by real-time PCR (RT-PCR).Quantifications of the RT-PCR signals were calculated fromRT-PCR assays conducted on samples from 2 independenttransfections.

Immunoblot analysisCells were lysed in radioimmunoprecipitation assay (RIPA)

buffer, and proteins were quantified using a microBCA assay(Pierce, ThermoScientific). Equal amounts were separated onSDS-PAGE gels. Proteins were transferred to polyvinylidenefluoride (PVDF) membranes followed by immunoblot withantibodies specific for mTOR (Cell Signaling, Ozyme), 4E-BP1(Cell Signaling), phospho-4E-BP1 (Ser 65; Cell Signaling), anti-LC3B (Sigma-Aldrich), SNRPD1 (C-15) and SNRPE (L-17; SantaCruz Biotechnology). Immunolabeled proteins were detectedby a chemiluminescence detection system (Super Signal WestDura Signal, Pierce, ThermoScientific). Membranes were thenwashed in 0.1% TBS-Tween-20 and incubated with rabbit anti-GAPDH or mouse anti-actin (C4; Chemicon, Millipore) toquantify and normalize results.

Cell-cycle analysisCell-cycle distribution was measured in SKBr-3 cells trans-

fected with 50 nmol/L SNRPE-specific validated siRNA ornontargeting control siRNA. Cells were harvested at 72 hoursafter transfection, suspended in PBS, and fixed in 70% ethanol.Then, DNA content was evaluated after propidium iodidestaining. Fluorescence-activated cell-sorting analysis was car-ried out using a FACScan flow cytometer (Beckton Dickinson)and CellQuest software.

Apoptosis detectionThe PE Annexin V Apoptosis Detection Kit I (BD Bio-

sciences) was used to detect apoptosis by flow cytometry. At72 hours of siRNA transfection, cells were harvested, washed inPBS, and pelleted by centrifugation. They were resuspended at105 cells/100 mL in a binding buffer to which 5 mL of Annexin-Vand 5mL of 7-aminoactinomycin D (AAD)were added and thenincubated in the dark for 15 minutes at room temperature.Then, 400 mL of binding buffer was added and the cells wereimmediately processed with a FACScan flow cytometer. Thenumbers show the percentages of cells in each quadrant(bottom left: 7AAD�/PE�, intact cells; bottom right: 7-AADþ/PE�, early apoptotic cells; top left: 7-AAD�/PEþ,

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Cancer Res; 73(7) April 1, 2013 Cancer Research2248




necrotic cells; top right: 7-AADþ/PEþ, late apoptotic ornecrotic cells). Staurosporine (5 mmol/L)-treated SKBr-3 cellsserved as positive control of apoptosis induction.

Autophagy analysis by transmission electronmicroscopySKBr-3 cells were transfected with SNRPE-specific validated

siRNA or nontargeting control siRNA as described above.Forty-eight hours after transfection, cells were fixed with1.6% glutaraldehyde in 0.1 mol/L phosphate buffer (pH 7.4)at room temperature for 1 hour. Samples were rinsed in thesame buffer then postfixed with osmium tetroxyde and 1.5%potassium ferrocyanide in 0.1 mol/L cacodylate buffer (pH 7.4)for 1 hour at room temperature. Cells were rinsedwith distilledwater, dehydrated in increasing concentrations of ethanol, andembedded in epoxy resin. Embedded samples were thenconventionally processed for thin sectioning (70 nm), con-trasted by uranyl acetate and lead citrate, and observed withZeiss 902 electron microscopy in filtered zero loss mode usingCDD megaviewIII camera and SIS system (Olympus). Autop-hagic cells were counted on 60 cells series per sample. Theidentification of autophagy was based on the observation ofmacro-autophagic vacuoles, that is, multiple membranes vesi-cles engulfing a portion of the cytoplasm.

Detection and quantification of acidic vesicularorganelles with acridine orange staining using flowcytometryTo detect the presence of acidic vesicular organelles (AVO),

tumor cells staining with acridine orange (Sigma-Aldrich) wasconducted as described previously (25). Briefly, 96 hours aftersiRNA transfection, SKBr-3 cells were washed with PBS andstained with 1 mg/mL acridine orange in serum-free medium(Sigma Aldrich) for 20 minutes at 37�C. Acridine orange–stained cells were trypsinized, washed, and analyzed with flowcytometer. Green (510–530 nm, FL1-H channel) and red (>650nm, FL3-H channel) fluorescence emissions from 104 cellsilluminated with blue (488 nm) excitation light are measuredwith a FACScanto from BD Biosciences using DIVA software.Autophagy was quantified as a ratio between geomean fluo-rescence intensity of red versus green fluorescence (FL3/FL1).Treated cells with nutrient-free medium EBSS (Invitrogen) for24 hours before the addition of acridine orange served aspositive controls.

Gene expression microarrayTranscriptome analysis following SNRPE depletion was

conducted using exon array (ExonHit Therapeutics). SKBr-3cells were transfected with SNRPE-specific validated siRNA ornontargeting control siRNA as previously described. Total RNAwas extracted using RNA isolation RNeasy micro Kit (Qiagen)at 96 hours after transfection. The concentration of RNA wasmeasured using Nanodrop spectrophotometer. The quality ofRNA preparations was assessed using Lab-on-a-Chip Bioana-lyser 2000 technology (Agilent Technologies). Aliquots ofextracted total RNA from the samples were sent to ExonHitTherapeutics for array processing. ExonHit staff processed to ahuman Genome-Wide SpliceArray� microarray on the Affy-metrix platform using single color labeling. Processing includ-

ed sample amplification and labeling, hybridization, arrayscanning, and data normalization

Reverse transcription and RT-quantitative PCRTotal RNA was extracted using RNA isolation RNeasy micro

Kit (Qiagen) at 96 hours after transfection. mTOR mRNAexpression was analyzed by real-time quantitative PCR (RT-qPCR) in SKBr-3 cells transfected by SNRPE or control siRNA.One microgram of total RNA from each sample was reverse-transcribed by superscript II reverse transcriptase (Invitrogen)in the presence of random primers (Applied Biosystems, Lifetechnologies). Quantitative PCR (qPCR) was carried out on anequivalent amount of 12.5 ng total RNA per tube in a finalvolume of 25 mL. Oligonucleotide primers and TaqMan probeswere designed using the PrimerExpress computer software(Applied Biosystems) and purchased from MWG Biotech. Theprimers and probe sequences used for the qPCR formTOR andhousekeeping genes are detailed in Supplementary Table S2.The relative expression of mTOR mRNA was determined byusing the Ct value and the 2�DDCt method. The data arepresented as the fold change in gene expression normalizedusing housekeeping genes (TBBP, RPLPO).

Expression profiling of spliceosome-related genesTwo unpublished datasets of gene expression arrays were

used to determine a correlation between spliceosome expres-sion and clinical characteristics as well as the proliferationbiomarker MKI67. These two studies used Agilent 44K plat-form. The first study compared gene expression levels betweenovarian adenocarcinomas (n ¼ 21) and borderline (nonmalig-nant) lesions (n ¼ 21). The second one evaluated gene expres-sion in 148 breast cancer samples including 27 triple-negativebreast cancer (TNBC). In this latter study, the reference forgene expression was normal breast (pool of 3 samples). Inaddition to these 2 studies, we used publically available geneexpression data available from Landi and colleagues (26) andAndr�e and colleagues (1). Lung dataset from Landi and col-leagues included 107 non–small cell lung carcinomas (n¼ 58)and nontumor tissues (n¼ 49). Breast dataset from Andr�e andcolleagues included 165 samples (120 cancers and 45 benignlesions) obtained by fine-needle aspiration of breast lesion.Spliceosome complex components (from Biocarta database)were extracted from these datasets respectively. The log2 geneexpression was analyzed and when one gene is represented bymore than one probe set, the one with the highest variance wasused. To illustrate the gene expression distribution of thespliceosome complex components, boxplot graphs depict thisinformation according to sample types.

Statistical analysisEffects of siRNA transfections on cell lines proliferationwere

estimated by 2-way ANOVA. The effects of transfection con-ditions were first analyzed by considering all cell lines as a firstfactor and the transfection conditions as a second factor(nontransfected cells, mock, unspecific siRNA). Here, and foreach cell line, counting values were initially transformed intoproportions of the maximum value in each corresponding cellline and then linearized with a logit link. Next, the effects of the

Spliceosome as a Target for Anticancer Treatment

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2 specific siRNA transfections were analyzed independently.For each cell line, both nonspecific and specific siRNA countingvalues were initially transformed into proportions of thecorresponding mock counting value. After a logit transforma-tion, cell lines and transfection (specific siRNA vs. nonspecificsiRNA) were considered as the 2 factors in a 2-way ANOVA.

ResultsGenes involved in spliceosome machinery areoverexpressed in malignant disease and defineaggressive phenotypes

We evaluated the expression of 15 genes related to spliceo-somal assembly, according to the BioCarta database, includingU1snRNP-specific protein (SNRNP70 or U1-70K), constitutivesplicing factors (U2AF1,U2AF2), splicing regulator (SFRS2), andcore spliceosomal components (SNRP-A/A1, B/B2, C, D1/D2/D3, E, F, and G). Differential gene expression analysis betweenmalignant and normal tissues was first conducted on (i) amicroarray dataset from Landi and colleagues comparing geneexpression in 107 non–small cell lung carcinomas (n¼ 58) andnontumor tissues (n¼ 49) and (ii) an unpublished microarraydataset comparing 148 breast cancers and normal breastsamples. We found that genes related to spliceosome assemblycomponents are overexpressed in lung adenocarcinoma (Fig.1A) and breast cancer (Fig. 1B) as compared with nontumoraltissue. Next, we used an unpublished ovary microarray datasetto compare gene expression between 21 invasive ovariancancers and 21 borderline lesions. The borderline lesions aredefined as a subset of epithelial ovarian tumors of low malig-nant potential representing an intermediate stage betweenfully benign and fully malignant adenocarcinomas. Strikingly,spliceosome assembly components are overexpressed in ovar-ian cancer as compared with borderline tumors (Fig. 1C),suggesting that overexpression of spliceosomal machinerycomponents was related to malignancy. No major correlationwas found between the proliferation marker MKI67 and thespliceosome-related genes in the 3 datasets described (Sup-plementary Fig. S1).

We finally assessed whether overexpression of spliceosomeassembly components defines amore aggressive disease. To dothis, we looked at the correlation between the expression of the15 genes and clinical characteristics. For this analysis, we useda public dataset of 120 breast cancer samples and 45 benignbreast lesions (1). Interestingly, unsupervised clusteringdefined 2 populations, one of which overexpressed the spliceo-some assembly components (Fig. 2). This spliceosome positivecluster was enriched in patients presenting with a TNBC (lackexpression of estrogen receptor, progesterone receptor, andHER2) and Her2-overexpressing breast cancer (47% of thespliceosome-positive breast cancers). These findings sug-gested that overexpression of spliceosome assembly compo-nents were not only overexpressed in malignant diseases butalso could define a subset of highly aggressive cancers.

Knockdown of components of the spliceosome decreasesviability of cancer cells, but not of nonmalignant cells

To investigate the role of the spliceosome, we focused on thecore spliceosomal components SNRPE and SNRPD1 that were

consistently found to be among the top differentially expressedgenes in the 3 series of samples. We first used siRNA-mediateddepletion of SNRPE to knockdown SNRPE expression in theSKBr-3 and MDA-MB 468 breast cancer cell lines that werechosen because they are respectively HER2-amplified andtriple-negative. We also used the nontumoral MCF-10A breastcell line to compare the effects of depleting a spliceosomecomponent in cancer cells with nontumoral cells. As revealedby Western blot analysis, SNRPE protein level was efficientlyreduced by siRNA in the 3 cell lines (Fig. 3A). SNRPE knock-down induced a major decrease of cell viability as comparedwith mock in the 2 tumor cell lines [MDA-MB 468 (88.6% vs.102.1% in siControl cells) and SKBr-3 (58.1% vs. 104.9% insiControl cells)], whereas it had amuchweaker effect (24.2% vs.95.1% in siControl cells) in the nontumoral MCF-10A cells(Pinteraction ¼ 0.00022).

To ascertain that this effect is due to spliceosome targeting,we knocked down the expression of another core spliceosomalcomponent. We chose SNRPD1, another upregulated corespliceosomal protein inmalignant tumors (Fig. 3B). Here again,following SNRPD1 knockdown, we observed amajor reductionof cell viability in the 2 breast tumoral cell lines MDA-MB 468(78.4%) and SKBr-3 (57.7%) and a weaker effect (24.1%) in thenontumoral MCF-10A cells (Pinteraction ¼ 0.00224).

Of note expression of the genes encoding the other corespliceosomal components (SNRP-A/A1, B2, C, D2/D3, F, and G)was assessed by RT-qPCR in SNRPE- or SNRPD1- depletedcells. No or only weak changes were observed in the expressionof these genes (Supplementary Fig. S2), showing that thesiRNAs used in this study and targeting SNRPE or SNRPD1had no off-target effects on related components of thespliceosome.

Wenext evaluatewhether spliceosomedepletion impacts oncell proliferation of additional breast, melanoma, and lungtumoral cell lines. A significant decrease in cell viability wasobserved in all tested tumoral cell lines following SNRPE andSNRPD1 knockdown (Supplementary Fig. S3).

Altogether, these results revealed that all tumoral cell lineswere sensitive to spliceosome depletion, independently of theirproliferation rate (Supplementary Fig. S4). Moreover, a non-tumoral breast cell line (MCF-10A) was less sensitive to spli-ceosome depletion than tumoral cell lines, suggesting that theantiproliferative effect of spliceosome depletionwas specific tomalignant cells.

SNRPE or SNRPD1 targeting mediates cancer cell deaththrough autophagy

To further investigate the cellular mechanism that led to adecrease in cell viability, we assessed whether the spliceosomeregulated apoptosis or cell cycle. As revealed by fluorescence-activated cell-sorting (FACS) analysis, SNRPE knockdowninduced a marked increase in the sub-G1 population inSNRPE-depleted cells (19.32%) as compared with control cells(2.62%), indicative of cell death occurrence (Fig. 4A). Apoptoticcell death was analyzed by using double cell labeling withAnnexin V-PE and 7-AAD. SNRPE depletion did not lead to asignificant increase in the amount of Annexin V–positive cells(31.44% as compared with 39.7% for control cells; Fig. 4B) and

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Figure 1. Genes involved inspliceosome assembly areoverexpressed in malignant disease.Gene expression of spliceosomecomponents related genes (fromBiocarta database) using publicallyavailable gene expression datasourced from Landi and colleagues(25) non–small cell lung carcinomas(n ¼ 58) and nontumor tissues(n ¼ 49). A, unpublished breastdataset from Gustave RoussyCancer Institute of 148 samplesincluding 27 TNBCs and 121 ERþand/or HER2-overexpressing breastcancers B, unpublished ovariandataset from Gustave RoussyCancer Institute including21 invasiveovarian cancers and 21 borderlinetumors (C). In each panel, box plotsrepresent gene expression (log2values) between malignant andnormal tissue [lung (A) and breast (B)datasets] or borderline tumors[ovarian (C) dataset]. C

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therefore did not induce typical apoptosis. Similar effects wereobtained in SNRPD1-depletd cells (Supplementary Fig. S5).

We next analyzed autophagy, another form of cell death, byusing transmission electron microscopy (TEM) approach thatallowed detecting the presence of autophagic vacuoles in cellcytoplasm. Autophagic features such as intracellular vacuoli-zation and double-membrane structures referred as autopha-gosomes (early autophagic compartment) were observed inSNRPE-depleted cells (Fig. 5A). The amount of cells containingautophagosomes was 2-fold higher in SNRPE-depleted cells(9%) than in control cells (4%) at 48 hours posttransfection.The cytoplasmic accumulation of autophagosomes followingSNRPE depletion is consistent with an induction of the autop-hagic pathway.

We next evaluated the expression of the microtubule-asso-ciated protein 1 light chain 3 (LC3), commonly used as amarker of the autophagic process that directly correlates withthe extent of autophagosome formation (27, 28). LC3 proteinexists in 2 forms: a cytosolic 18-kDa LC3-I and a 16-kDa LC3-IIthat binds to autophagosomes membranes during autophagy.SNRPE or SNRPD1 depletion led to an increase in the LC3-IIform that indicated an autophagosome formation and aninduction of autophagy (Fig. 5B).

To further reinforce the data concerning autophagy, weevaluated the presence of late (autolysosome) markers ofautophagy using cell stainingwith acridine orange, a lysotropicdye that accumulates in acidic organelles formed duringautophagy. The quantitative analysis of acridine orange–stained cells showed that knockdown of SNRPE or SNRPD1protein expression in SKBr-3 cells induced a significant

increase of orange red fluorescence as compared with controlcells, indicating formation of acidic autophagolysosomalvacuoles following spliceosome depletion (Fig 5C).

Overall, these data indicated that targeting core spliceoso-mal component such as SNRPE or SNRPD1 could mediatecancer cell death through autophagy.

SNRPE knockdown leads to mTOR downregulationTo identify themolecularmechanisms leading to autophagy,

we analyzed gene expression profiles in SNRPE-depleted SKBr-3 cell line. Transcriptome analysis on exon microarraysrevealed that 51 genes were overexpressed and 41 genes weredownregulated (Supplementary Fig. S1) following SNRPEdepletion. Looking for genes involved in autophagy, we foundthat mTOR was among the most downregulated gene inSNRPE-depleted cells (Supplementary Fig. S6). Importantly,this decrease was robust as evidenced by the similar behaviorof all the 142 probes covering the mTOR mRNA (Fig. 6A). Thisdecrease (5-fold) inmTORmRNAabundance in SNRPE-deplet-ed cells was confirmed by RT-qPCR (Fig. 6B). Furthermore, adecrease inmTOR protein abundance was observed in SNRPE-depleted cells (Fig. 6C).

We next hypothesized that NMD could mediate the mTORloss induced by SNRPE-knockdown, as a recent study hasshown that knockdown of SNRPB, another core SNRP spliceo-somal protein, resulted in the inclusion of many PTC-contain-ing alternative exons (29). These aberrant PTC-containingmRNAs are subsequently degraded by the NMD machinery(29). To determine whether NMD was involved in the disap-pearance of the mTOR mRNA upon SNRPE depletion, we

Cluster Spliceosome negative

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Histologic diagnosis:Benign lesions

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Malignant lesions 54 (58%) 66 (92%)

Molecular class of breast cancerTriple-Negative BC

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HER2-overexpressing BC 6 (10% of BC) 19 (29% of BC)

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9 (15% of BC) 31 (47% of BC)

Figure 2. Expression ofspliceosome assemblycomponents defines aggressivedisease. Unsupervised clusteringof the 15 genes involved inspliceosome assembly (fromBioCarta database) and correlatedclusters to clinical characteristicsusing a public breast datasetincluding 120 breast cancersamples and 45 benign breastlesions (1). One clusteroverexpressed spliceosomeassembly components. Thisspliceosome-positive cluster wasenriched in aggressive breastdiseases including triple-negativeand HER2-overexpressing breastcancers (BC).

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disrupted NMD using cycloheximide in SNRPE-depleted cells.The translation inhibitor cycloheximide inhibits mRNA deg-radation by the NMD pathway, as this process requiresongoing translation. Following SNRPE depletion, NMD inhi-bition by cycloheximide resulted in an approximately 2.5-foldincrease in the relative abundance of mTOR mRNA, ascompared with 1.5-fold in control cells (Fig. 6D), suggestingthat a substantial fraction of the mTOR pre-mRNA wasaberrantly spliced into one or more mRNA isoforms thatwere degraded by NMD. Our results also showed that mRNAexpression for 2 control genes (GUSB and RPLPO) was not

increased upon cycloheximide treatment (Supplementary Fig.S7). These data indicated that the cycloheximide-dependentincrease in mTOR mRNA expression was specific to SNRPEknockdown.

To next evaluate whether SNRPE depletion could affectdownstream targets of the mTOR signaling pathway, weassessed the phosphorylation of 4E-BP1 that lies just down-stream of mTOR. SNRPE depletion induced a dephosphoryla-tion of 4E-BP1 (Fig. 6C) that indeed indicated a blockage ofmTOR signaling. Overall, these data indicated a global SNRPE-dependent deregulation of mTOR signaling.

Figure 3. Effect of siRNA-mediateddepletion of SNRPE or SNRPD1 oncell viability in malignant andnonmalignant breast cell lines.Breast tumor cell lines (MDA-MB468, SKBr-3) and nontumoral breastcell line MCF-10A were transfectedwith siRNAs targeting SNRPE andSNRPD1 or control siRNA asindicated. Depletion of SNRPE or D1was analyzed by Western blotting.Cell viability was assessed 96 hoursafter transfection by counting usingthe Trypan blue exclusion method orcrystal violet staining. The effect ofSNRPE or SNRPD1 depletion on cellviability versus mock was estimatedby2-wayANOVA.Results for SNRPEand SNRPD1 are shown in A and B,respectively. The levels of SNRPEand SNRPD1 were quantified bydensitometry using ImageJ softwareand expressed relative to actin orGAPDH signals.

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SN

RP

D1

siRNA siRNA siRNA

GAPDH

B

0.93 1.20 0.100.6 0.91 0.221.09 1.01 0.35SNRPD1 relative level






DiscussionIn the present study, we report that components of the Sm

core spliceosomal machinery are overexpressed in cancer,regulate mTOR, and impact on cell proliferation and autop-hagy (Fig. 6E).

Indeed, gene expression profiling using breast and lungdatasets revealed that genes related to the core spliceosomalcomponents are highly expressed in lung and breast cancer incontrast to nontumoral tissue. Moreover, gene expressionanalyses using 2 other datasets of ovarian and breast cancersindicated that overexpression of genes encoding spliceosomecomplex components could be associated with a more aggres-sive phenotype of cancers and could define a subset of highlymalignant cancers. Among these genes involved in spliceo-some complex, those encoding the core spliceosomal proteinsSNRPE and SNRPD1 were consistently found to be among thetop differentially expressed genes in the 4 series of samples.

Several studies have previously suggested that componentsof the splicing machinery are mutated or overexpressed in

malignant diseases. Indeed, mutations in genes encoding theU2AF35, ZRSR2, SRSF2, and SF3B1 splicing factors have beenfound in hematologic malignancies (16). Sampath and collea-gues reported that the splicing factor SPF45was overexpressedin carcinoma fromvarious origins including breast, lung, ovary,colon, and prostate (30). This group also reported that SPF45overexpression was associated with resistance to cyclophos-phamide (30). More recently, Niu and colleagues reported thatproteins involved in splicing machinery were overexpressed in8 samples of bladder cancer, as compared with normal tissue(31). The present study based on larger number of samplessuggests that genes encoding the spliceosome core machineryare overexpressed in solid tumors. Overexpression of spliceo-somal component is not only a hallmark of cancer but alsocould be a therapeutic target. Indeed, we have shown here thatdepletion of several components of the core spliceosome (i.e.,SNRPE or SNRPD1) has a selective action on the proliferationof transformed cell lines but not of an immortalized nontu-morigenic cell line (MCF-10A). In addition, SNRPE depletion

Untransfected SKBr-3 Control siRNA SNRPE siRNA

M1=G1: 58.50%

M2=S: 22.88%

M3=G2–M: 10.28%

M4=SubG1: 2.69%

DNA content

M1=G1: 46.15%

M2=S: 16.50%

M3=G2–M: 15.17%

M4=SubG1: 19.32%

DNA content

M1=G1: 58.88%

M2=S: 24.01%

M3=G2–M: 12.81%

M4=SubG1: 2.62%

DNA content

M4 M4

A

4.08% 11.69%

69.99% 15.25%

3.83% 73.33%

1.04% 21.80%

55.93%

18.79%

20.91%

4.37%

65.75%

17.46%

13.98%

2.82%

Untransfected

Control siRNA SNRPE siRNA

Staurosporine

7-A

AD

PE Annexin V

7-A

AD

PE Annexin V

7-A

AD

PE Annexin V

7-A

AD

PE Annexin V

B

Figure 4. Cell-cycle analysis andapoptosis detection followingSNRPE depletion. A, cell-cycleanalysis was used to monitorchanges in the DNA of SKBr-3cells harvested at 72 hoursposttransfection with 50 nmol/Lof SNRPE targeting siRNA ornontargeting control siRNA asindicated. B, apoptosis detectionwas quantitated using PE AnnexinV/7-AAD staining followed by flowcytometric analyses in cells at 72hours posttransfection with 50nmol/L SNRPE targeting siRNA ornontargeting control siRNA. Thenumbers show the percentages ofcells in each quadrant (bottom left,7AAD�/PE�, intact cells; bottomright: 7-AADþ/PE�, earlyapoptotic cells; top left, 7-AAD�/PEþ, necrotic cells; top right,7-AADþ/PEþ, late apoptotic ornecrotic cells). Staurosporine(5 mmol/L)-treated SKBr-3 cellsserved as positive control ofapoptosis induction.

Quidville et al.





leads to inhibition of mTOR expression. This effect may, in part,be the consequence of the production of aberrantly splicedmTOR mRNA isoforms degraded by the NMD pathway, as wehave foundhere thatNMDinhibitionbycycloheximide results inan increase in the relative abundance of mTORmRNA. Further-more, a recent study reports that regulationofmTORalternativesplicing leads to theproductionof a short transcriptdegradedbyNMD during adipogenesis (32). Strikingly, by regulating mTOR,SNRPE impacts on one of the most often deregulated signalingpathways in cancer.mTOR is a key component that coordinatelyregulates the balance between growth and autophagy inresponse to cellular physiologic conditions and environmental

stress (33).Consistentwith this observation,wehave shownherethat targeting SNRPE or SNRPD1 leads to cell death throughautophagy. Interestingly, a recent report using a short hairpinRNA screening procedure in Drosophila melanogaster identifiedthe spliceosome machinery as one of the main pathway regu-lating the mTORC1 signaling pathway (34). This observation,together with our study, highlights the importance of thespliceosome–mTOR axis and reinforces the interest of the coresplicecosomeas an interesting target in the control of eukaryoticcell growth and death.

These findings open up new avenues for drug development.Several drugs have already been developed to target the

Figure 5. Autophagy analysisfollowing SNRPE or SNRPD1depletion. A, SKBr-3 cells weretransfected with SNRPE siRNA ornontargeting control siRNA.Autophagic events characterized byTEM are mainly observed in SNRPE-depleted cells (c, d, e, f) as comparedwith control cells (a, b). Cells areshown at 2 magnifications, �3,000(a, c) and �20,000 (b, d). Twocharacteristic multimembranevesicles (autophagosomes) arepresented in e and f at highmagnification (�50,000). The doublemembrane is pointed out (whitearrow). B, immunoblot analysis ofLC3 protein conversion. SKBr-3 cellswere transfected with SNRPE,SNRPD1 siRNA, or nontargetingcontrol siRNA and then whole-cellprotein lysates were immunoblottedwith LC3B, SNRPE, SNRPD1, andb-actin antibodies. C, autophagywas quantified as a ratio betweengeomean fluorescence intensity ofred versus green fluorescence(FL3/FL1), showing an increase ofred/green (FL3/FL1) fluorescenceratio following depletion of SNRPE orSNRPD1 as compared with controlcells, as previously reported (25).Nutrient-free medium EBSS treatedcells served as positive controls.Depletion of SNRPE or D1 proteinlevel was analyzed by Westernblotting. Resultswere analyzedusingANOVA (GraphPad Prism 5.04software). Differences betweenmeans were tested by Mann–Whitney tests (��, P < 0.01 vs.control).

Control SNRPD1

siRNA

kDa

LC3B I

LC3B II

18

16

ββ-Actin 42

SNRPD1 15

Control SNRPEkDa

siRNA

SNRPE 11

β-Actin 42

LC3B I

LC3B II

1816

A

B

Control cells SNRPE-depleted cells

SNRPD1SNRPE

β-Actin β-Actin

Control SNRPE

siRNA

Control SNRPD1

siRNA

Acr

idin

e o

ran

ge

(FL

3/F

L1)

EBSS1

2

3

4

5

6

Un

tran

sfec

ted Mock Control SNRPE SNRPD1

siRNA

C






ASNRPE-depleted cellsControl cells

mT

OR

exo

n e

xpre

ssio

n (

log

2)

142 probes corresponding to mTOR gene on the array

mT

OR

mR

NA

lev

el

0

0.2

0.4

0.6

0.8

1

1.2

108

64

2

0 20 40 60 80 100 120 140

Untransfected Control SNRPE

siRNA

19 %

B

D

C

p-4E-BP1(ser65)

mTOR

Control SNRPE

β-Actin

4E-BP1p-4E-BP1

42

siRNA

289

15-20

15-20

SNRPE 11

kDa

0

0,5

1

1,5

2

2,5

3

siControl SNRPE siRNA

mR

NA

exp

ress

ion

rat

io

CH

X-t

reat

ed c

ell/u

ntr

eate

d c

ells

mTORAutophagy

snRNPs (U1, U2, U4, U5, U6)

Up in breast cancerUp in lung cancerUp in ovarian cancer

Survival Proliferation

siRNAs

E

E B

Sm Core Proteins

D1 B2

D2G

F D3

Figure 6. Knockdown of SNRPE alters mTOR expression and downstream signaling pathway. A, analysis of mTOR expression by exon arrays in SNRPE-depleted or control (nontargeting siRNA) SKBr-3 cells. mTOR expression was analyzed by exon arrays. mTOR expression is represented in a green–red heatcolor coding from low expression (light green) to high expression (red color) for transfected (~) and control (*) cells separately. The x-axis corresponds to therank of mTOR probes in the control cells sorted by their expression in an increasing order. B, mTOR mRNA expression was analyzed by RT-qPCRin SNRPE-depleted or control (nontargeting siRNA) SKBr-3 cells. The graph represents mTOR mRNA level (fold change) normalized using housekeepinggenes. C, expression of SNRPE and different mTOR downstream proteins was analyzed by Western blotting in SNRPE-depleted or control (nontargetingsiRNA) SKBr-3 cells. D, for NMD inhibition experiments, cells were transfected with specific SNRPE siRNA or nontargeting control siRNA.Three days later, cells were treated for 8 hours with 100 mg/mL cycloheximide (CHX) or with an equivalent amount of DMSO as a control. mTOR RNAexpressionwas detected byRT-PCR.Quantifications of theRT-PCRsignalswere calculated fromRT-PCR assays carried out on samples from2 independenttransfections. The graph represents the ratio of mTOR mRNA expression in cells treated with cycloheximide as compared with untreated cells andshows thatNMD inhibition resulted in anapproximately 2.5-fold increase in the relative abundanceofmTORmRNA inSNRPE-depletedcells as comparedwith1.5-fold increase in control cells. E, schematic representation of the sequence of processes leading from targeting SNRPE and SNRPD1 by specific siRNAsto the inhibition of mTOR survival pathway. The spliceosomal machinery complex is formed from 5 RNP subunits, termed uridine-rich (U-rich) smallsnRNPs. Each snRNP (U1, U2, U4, U5, and U6) consists of a uridine-rich snRNA complexed with a set of Sm proteins (B/B2, D1, D2, D3, E, F, and G) forminga ring core structure that encompasses RNA. The colored circles refer to the most significantly overexpressed Sm core proteins in the studied datasets(see Materials and Methods). Pink, breast cancer; blue, lung cancer; and green, ovarian cancer.

Quidville et al.





splicing process. Using a chemical library, Soret and colleagueshave reported that ellipticine inhibits several splicing events invitro through targeting splicing regulatory factors such as SRprotein (35). This drug has been used in the last 30 years aschemotherapy agent in breast cancer. Consistent studies haveshown that ellipticine was associated with a strong antitumoreffect in patients with breast cancer. More recently, 2 com-pounds spliceostatin A and the pladienolide derivatives, wereindependently reported to interact with SF3b, a subcomplex ofU2 snRNP, which is an essential component of the spliceosome(25, 26). The binding of pladienolide and spliceostatin A to SF3bleads to an inhibition of the spliceosomal functions resulting inimpaired splicing and thus altered gene expression patterns.Pladienolide derivative compounds exhibited potent anti-can-cer activity in several cancer cell lines and human tumorxenografts (36). Treatment of a drug screen panel of 39 cancercell lines with pladienolides resulted in major growth inhibi-tion with greatest activity against breast and lung cancer celllines (37). Spliceostatin A affects also proliferation of numerouscancer cell lines and inhibits tumor growth in various xenograftmodels (38). On the basis of the promising preclinical data withrespect to the potential anticancer activity of pladienolide,phase I dose-escalation trials with pladienolide D were initi-ated in Europe and United States. The previously mentioneddrugs target splicing regulators [i.e., SRSF/serine-arginine-rich(SR) splicing factors] or constitutive splicing factors (e.g., SF3b)but not the core spliceosomal machinery. Also, as they arederived from natural compounds, they do not specificallytarget splicing machinery. On the basis of the present study,there is a rationale to design chemical compounds that spe-cifically target SNRPE and SNRPD1. Other therapeutic strat-egies are possible to inhibit spliceosome activity. It has beenreported that spliceosome activity is regulated by kinases,including SRPK1 (39). This could provide a rationale to develop

kinase inhibitors that would eventually lead to the inhibition ofspliceosome activity.

Overall, our results suggest that core spliceosomemachinerycould be an attractive therapeutic target in malignant solidtumors. These findings provide a rationale to design new drugstargeting SNRPE, SNRPD1, and to develop spliceostatin A andthe pladienolide derivatives for patients with SNRPE/SNRPD1-overexpressing cancers.

Disclosure of Potential Conflicts of InterestThe sponsors did not participate in the design and conduct of the study;

collection, management, analysis, and interpretation of the data; and prepara-tion, review, or approval of the manuscript. Its contents are solely the respon-sibility of the authors. No potential conflicts of interest were disclosed

Authors' ContributionsConception and design: S. Vagner, F. AndreDevelopment of methodology: V. Quidville, S. Alsafadi, S. Vagner, F. AndreAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): V. Quidville, S. Alsafadi, C. Durieu, E. Le Cam, S.Delaloge, M. Saghatchian, P. Morice, S. Vagner, F. AndreAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): V. Quidville, S. Alsafadi, A. Goubar, F. Commo, C.Durieu, S. Baconnais, E. Le Cam, P. Dessen, S. Vagner, F. AndreWriting, review, and/or revision of themanuscript: V. Quidville, S. Alsafadi,F. Commo, C. Durieu, E. Le Cam, S. Delaloge, S. Vagner, F. AndreAdministrative, technical, or material support (i.e., reporting or orga-nizing data, constructing databases): S. Alsafadi, A. Goubar, V. Scott, I.Girault, V. Lazar, P. Morice, S. Vagner, F. AndreStudy supervision: V. Lazar, S. Vagner, F. AndrePreliminary biologic results before the conception of the study: P. Pautier

Grant SupportThis work was supported by the donation program of "Op�eration Parrain-

Chercheur" and grants from INCA and Dassault foundation.The costs of publication of this article were defrayed in part by the payment of

page charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received June 25, 2012; revised December 5, 2012; accepted January 11, 2013;published OnlineFirst January 28, 2013.

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2013;73:2247-2258. Published OnlineFirst January 28, 2013.Cancer Res Virginie Quidville, Samar Alsafadi, Aïcha Goubar, et al. Cells Triggers mTOR Blockade and AutophagyTargeting the Deregulated Spliceosome Core Machinery in Cancer

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