Regulationofleukemiccelldifferentiationand retinoid ... · 11/1/2005 · ATRA-induced leukemic...

Regulation of leukemic cell differentiation andretinoid-induced gene expression by statins

Antonella Sassano,1 Marco Lo Iacono,2

Giovanni Antico,1 Alison Jordan,1 Shahab Uddin,3

Raffaele A. Calogero,2 and Leonidas C. Platanias1

1Robert H. Lurie Comprehensive Cancer Center and Division ofHematology/Oncology, Northwestern University Medical Schooland Jesse Brown VA Medical Center, Chicago, Illinois;2Department of Clinical and Biological Sciences, University ofTurin, Turin, Italy; and 3Department of Human Cancer GenomicResearch, Research Center King Faisal Specialist Hospital andResearch Center, Riyadh, Saudi Arabia

AbstractThere is emerging evidence that, beyond their cholesterol-lowering properties, statins exhibit important antileukemiceffects in vitro and in vivo, but the precise mechanismsby which they generate such responses remain to bedetermined. We have previously shown that statinspromote differentiation of acute promyelocytic leukemiacells and enhance generation of all-trans retinoic acid(ATRA)–dependent antileukemic responses. We nowprovide evidence that statin-dependent leukemic celldifferentiation requires engagement and activation of thec-Jun NH2-terminal kinase kinase pathway. In addition, inexperiments, to define the molecular targets and media-tors of statin-induced differentiation, we found a remark-able effect of statins on ATRA-dependent genetranscription, evidenced by the selective induction of over400 genes by the combination of atorvastatin and ATRA.Altogether, our studies identify novel statin moleculartargets linked to differentiation, establish that statinsmodulate ATRA-dependent transcription, and suggest thatcombined use of statins with retinoids may provide a novelapproach to enhance antileukemic responses in acutepromyelocytic leukemia and possibly other leukemias.[Mol Cancer Ther 2009;8(3):OF1–11]

IntroductionStatins are agents with potent cholesterol-reducing prop-erties, resulting from their abilities to block HMG-CoAreductase activity and inhibit mevalonate synthesis (1–3).As their administration results in rapid lowering of plasmaLDL cholesterol levels, statins are used extensively in thetreatment of hypercholesterolemia and coronary arterydisease in humans (4–9). There is extensive evidence thatthe introduction of statins in the management andprevention of atherosclerosis had a dramatic effect in thecurrent practice of clinical medicine and has changed thenatural history of coronary artery disease in humans (4–9).Notably, extensive work over the years has established thatthese agents also exhibit important antiinflammatory,proapoptotic, and antineoplastic properties (10–14), raisingthe possibility that they may ultimately find a place in theprevention and/or treatment of patients with inflammatoryor malignant disorders. However, thus far, most of thework to define the antitumor potential of statins has beenfocused on clinical epidemiologic studies (reviewed in ref.14), whereas the therapeutic potential of these agents incombination with other chemotherapeutic and antineoplas-tic agents remains largely unexplored.An area where the ability of statins to promote antitumor

responses was recently directly examined is the treatment ofacute myelogenous leukemia (AML) in adults (15). Therehas been extensive previous work documenting that statinsexhibit antileukemic properties in vitro (16–19). Suchfindings are consistent with previously described observa-tions, establishing that synthesis and import contribute toprotective cholesterol increments inAML cells (20). They arealso consistent with the notion that a subset of AMLs relieson increased LDL accumulation during treatment withparticular drugs, indicating that combined use of statinswith chemotherapy in certain cases of acute leukemia couldbe proved beneficial. Remarkably, it was also recentlyshown that Ikaros , a transcription factor that directslymphoid lineage commitment and whose abnormal ex-pression is implicated in the development of leukemias (21),modulates cholesterol uptake (22), further underscoring therelevance of cholesterol regulation in leukemogenesis.Beyond the proapoptotic properties of statins, there is

evidence that these drugs induce differentiation of cells ofacute promyelocytic leukemia (APL) origin (23, 24). Inaddition, recent work from our laboratory has shown thatatorvastatin and fluvastatin enhance all-trans retinoic acid(ATRA)–induced differentiation and reverse ATRA resis-tance of APL-derived cell lines or primary cells (24). Thesedata have raised the possibility that statins can be used asdifferentiation enhancers, in combination with ATRA.However, the mechanisms by which they promote suchdifferentiation has been largely unknown. In the presentstudy, we provide direct evidence that engagement of the

Received 9/22/08; revised 12/19/08; accepted 12/29/08;published OnlineFirst 02/24/2009.

Grant support: NIH grants CA121192 and CA77816, a merit review grantfrom Department of Veterans Affairs, and Hairy Cell Leukemia ResearchFoundation.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely toindicate this fact.

Requests for reprints: Leonidas C. Platanias, Robert H. LurieComprehensive Cancer Center, 303 East Superior Street, Lurie 3-107,Chicago, IL 60611. Phone: 312-503-4267; Fax. 312-908-1372.E-mail: [email protected]

Copyright C 2009 American Association for Cancer Research.

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c-Jun NH2-terminal kinase (JNK) pathway is essential forstatin-dependent leukemic differentiation of cells of APLorigin, underscoring the importance of JNK in theinduction of statin responses. We also did experimentsaimed to compare the effects of statin treatment on theATRA-dependent induction of expression of differentiationgenes. In gene microarray studies, we found a dramaticatorvastatin-dependent enhancement of ATRA-induciblegene transcription in APL cells and were able to identify agroup of JNK-linked genes selectively induced by theATRA and atorvastatin combination. Altogether, these dataprovide insights on the molecular basis of the enhancingeffects of statins on ATRA-induced antileukemic responsesand support the hypothesis that combinations of statinswith ATRA may lead to novel approaches for the treatmentof APL and other leukemias.

Materials andMethodsCells Lines and ReagentsThe NB4 human APL cell line was grown in RPMI 1640

supplemented with 10% fetal bovine serum and antibiotics.The ATRA-resistant NB4.300/6 variant cell line (24, 25) wasprovided by Dr. Saverio Minucci (European Institute ofOncology) and was also grown in RPMI 1640 supple-mented with 10% fetal bovine serum and antibiotics.Atorvastatin was purchased from 21CEC PharmaceuticalsLtd. ATRA was purchased from Sigma. SP600125 wasobtained from Calbiochem. The cell-permeable JNK pep-tide inhibitor 1 was purchased from Axxora.

In vitro KinaseAssaysNB4 cells were preincubated for 60 min in the absence or

presence of the JNK inhibitor SP600125 (20 Amol/L) andthen treated with or without fluvastatin (10 Amol/L) orDMSO as solvent control for the indicated times. The cellswere subsequently lysed in phosphorylation lysis buffer(26, 27), and lysates were immunoprecipitated with anantibody against JNK1, purchased from Santa CruzBiotechnology, Inc., using protein G–sepharose. Theimmunoprecipitated complexes were subsequently washedthrice with phosphorylation lysis buffer containing 0.1%Triton X-100 and twice with kinase buffer (25 mmol/LHEPES, 25 mmol/L MgCl2, 25 mmol/L h-glycero-phosphate, 2 mmol/L DTT, 0.1 mmol/L Na3VO4, and20 Amol/L ATP) and resuspended in 30 AL of kinasebuffer containing 3 Ag of c-Jun fusion protein purchasedfrom Cell Signaling Technology, Inc., used as an exogenoussubstrate. [g-32P]ATP (10 ACi) was added to the mixture, andafter 30 min of incubation at room temperature, the reactionwas terminated by the addition of SDS sample buffer.Proteins were analyzed by SDS-PAGE, and the phosphory-lated form of c-Jun was detected by autoradiography.

Flow Cytometric AnalysisGranulocytic differentiation of APL cells was assessed by

flow cytometric analysis, as previously described (28).Briefly, NB4 cells were pretreated for 1 h with the indicatedinhibitors and then treated with atorvastatin or fluvastatinfor the indicated times, and cell differentiation was

determined by flow cytometric analysis after staining withthe anti-CD11b monoclonal antibody. The anti-CD11bmonoclonal antibody and a matched isotype control werepurchased from BD Biosciences.

MicroarrayAnalysisQuality and quantity of total RNA from samples was

determined using Agilent 2100 Bioanalyzer and a Nano-drop spectrophotometer. cRNA was synthesized usingIllumina RNA amplification kit (Ambion) starting from500 ng of total RNA and following the procedure suggestedby the manufacturer. Sentrix Human-6 Expression Bead-Chip hybridization, washing, and staining were also done,as suggested by the manufacturer. Arrays were scanned onIllumina BeadStation 500.

Microarray Data AnalysisBeadChip array data quality control was done using

Illumina BeadStudio software version 1.3.1.5. Probe aver-age intensity signal was calculated with BeadStudiowithout background correction. Raw data were analyzedwith Bioconductor (29). Average probe intensities were log2transformed and normalized by lowess method (30). Toremove low quality signals, two filters were appliedsequentially to the 47,293 reference sequences (RefSeq)available in the Sentrix Human-6 Expression BeadChip:gene-specific detection value should have been z0.6 in atleast 50% of the samples (25,845 of 47,293 RefSeq passedthis filter) and gene-specific interquantile range shouldhave been z0.2 (11,287 of 25,845 RefSeq passed this filter).Statistical analysis for the time-course experiments was

done using maSigPro Bioconductor library (31). MaSigProfollows a two-step regression strategy to find genes withsignificant temporal expression changes and significantdifferences between experimental groups. The methoddefines a general regression model for the data whereinthe experimental groups are identified by dummy variables(i.e., ATRA, atorvastatin, ATRA + atorvastatin). Theprocedure first adjusts this global model by the least-squared technique to identify differentially expressedgenes and selects significant genes applying false discoveryrate control procedures (V0.05). Secondly, backward step-wise regression is applied as a variable selection strategy tostudy differences between experimental groups and to findstatistically significant different profiles (P V 0.05). The finallist of significant differentially expressed genes was definedusing the R2 values of this second regression model. RefSeqprobes characterized by an R2 of z0.6 of the regressionmodel were selected for further analysis. RefSeq probesidentified by regression analysis were filtered to have atleast one of the experimental point characterized by anjlog2(fold change)j z 1. Clustering analysis was done usingTMEV v3.1 suite.4 Functional analysis was done usingIngenuity knowledge database.5

4 http://www.tigr.org5 http://www.ingenuity.com

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Quantitative ReverseTranscription ^ PCR (Taqman)Cells were treatedwithATRA (0.5 Amol/L) or atorvastatin

(2 Amol/L) or both agents for 8, 24, and 48 h, and RNA wasisolated using the RNeasy kit from Qiagen. Total cellularRNA (1 Ag) was reverse transcribed into cDNA using theOmniscript reverse transcription kit, and oligo(dT) primerwas purchased from Invitrogen. Real-time reverse tran-scription–PCR (RT-PCR) for CCL3, CCL4, IL1B, BTG-2 , andNCF2 genes was carried out by an ABI7900 sequencedetection system from Applied Biosystems (32), usingcommercially available FAM-labeled probes and primersfrom Applied Biosystems. Relative quantitation of mRNAlevels was plotted as fold increase compared withuntreated samples. Glyceraldehyde-3-phosphate dehydro-genase was used for normalization (33) DC t values (target

gene C t minus glyceraldehyde-3-phosphate dehydroge-nase C t) for each triplicate sample were averaged, andDDC t was calculated as previously described. mRNAamplification was determined by the formula 2-DDC t(32).

IPA 4.0 AnalysisIPA 4.0 searches for the presence of relations existing

between the differentially expressed genes and the genesannotated in the Ingenuity knowledge database wereanalyzed using the data mining tool IPA 4.0.5 For eachtreatment, each time point was subjected to IPA 4.0analysis, and subsequently, the results were subjected toa comparative analysis to identify functional classesassociated with differentially expressed genes in responseto different treatments. The data shown in Figs. 3B to D and6 were generated with an updated IPA 7.0 version.

Figure 1. Requirement of JNK for ator-vastatin-induced or fluvastatin-induceddifferentiation of APL cells. A, NB4 cellswere preincubated for 60min in the absenceor presence of SP600125 (20 Amol/L)and treated for 48 h in the absence orpresence of DMSO (control) or atorvastatin(2 Amol/L). The cells were subsequentlystained with a phycoerythrin-conjugatedanti-CD11b monoclonal antibody and ana-lyzed by flow cytometry. Columns, meanof four independent experiments; bars, SE.Paired two-tailed t test analysis: P =0.015for atorvastatin alone versus the combina-tion of SP600125 + atorvastatin andP = 0.03 for fluvastatin alone versus thecombination of SP600125+ fluvastatin. B,top, NB4 cells were preincubated for 60minin the absence or presence of the JNKinhibitor SP600125 (20 Amol/L) andthen treated with or without fluvastatin(10 Amol/L) or exposed to DMSO as solventcontrol for the indicated time. Cell lysateswere immunoprecipitated (IP ) with an anti-body against JNK1 or control nonimmunerabbit immunoglobulin (RIgG ). The immu-noprecipitates were then subjected toin vitro kinase assays using c-Jun asan exogenous substrate. Proteins wereanalyzed by SDS-PAGE, and the phosphor-ylated form of c-Jun was detected byautoradiography. Bottom, the same blotwas subsequently immunoblotted with ananti-JNK1 antibody. C, NB4 cells werepreincubated for 60 min in the absence orpresence of JNKI peptide (5 Amol/L) andtreated for 48 h in the absence or pre-sence of DMSO (control) or atorvastatin(2 Amol/L). The cells were subsequentlystained with a phycoerythrin-conjugatedanti-CD11b monoclonal antibody and ana-lyzed by flow cytometry. Columns, mean ofthree experiments; bars, SE. Paired two-tailed t test analysis: P = 0.0464 foratorvastatin versus JNKI + atorvastatin.

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ResultsOur previous studies (24) had shown that treatment of cellswith statins results in activation of the JNK kinase pathwayin leukemic cells and that such activation is required forstatin-induced apoptosis. To determine whether engage-ment of the JNK kinase plays a role in the induction ofdifferentiation of APL cells, experiments were donewherein JNK activity was blocked by either pharmacologicor molecular means, and induction of differentiation wassubsequently determined. Initially, the effects of a JNKpharmacologic inhibitor, SP600125, were determined.When NB4 cells were pretreated with SP600125, leukemiccell differentiation, in response to either atorvastin orfluvastatin, was blocked (Fig. 1A) whereas, as expected,SP600125 blocked statin induction of JNK kinase activitydetected by immune complex kinase assays (Fig. 1B). Todefinitively establish whether JNK activity is essential forstatin-dependent leukemic cell differentiation, the effects oftargeting JNK, using a peptide inhibitor (GRKKRRQRRR-PP-RPKRPTTLNLFPQVPRSQD-NH2) known to block JNKactivation and downstream engagement of c-jun (34–36),were determined. As in the case of pharmacologicinhibition, pretreatment of cells with the peptide inhibitorabrogated the induction of differentiation of leukemic cellsby atorvastatin (Fig. 1C), definitively establishing that suchstatin-regulated responses are JNK dependent.To determine whether the effects of statin treatment on

ATRA-induced leukemic cell differentiation reflect induc-tion of specific genes, the patterns of gene expressioninduced by ATRA, atorvastatin, or the atorvastatin + ATRAcombination were subsequently examined using DNAmicroarrays. We analyzed three prototypic situations(ATRA, atorvastatin, and ATRA + atorvastatin) usingIllumina Sentrix Human-6 Expression BeadChips over athree-point time course (8, 24, and 48 hours), and time-course points were replicated in three independent experi-ments done at different days. A total of 36 arrays werehybridized. After average probe intensity calculation, log2transformation, and normalization (29), probes character-ized by low-quality signal or invariant expression withinthe experimental conditions were discarded. A total of11,287 of the 47,293 RefSeq BeadChip probes were used toidentify significantly differentially expressed genes. Statis-tical analysis was done using a two-step regression strategy(30). One thousand nine hundred one RefSeqs were foundsignificantly differentially expressed in at least one condi-tion over the time-course treatments. After calculating thelog2(fold change) variation for all treatments with respect tothe corresponding control time point, 782 of 1,901 RefSeqprobes were characterized by a |log2(fold change)| z 1 forat least one of the time points. Principal componentanalysis (37) was done on the three prototypic situations(Fig. 2A). Principal component analysis was used toattribute the overall variability in the data to a reducedset of variables (principal components). To each principalcomponent, a certain fraction of the overall variability ofthe data was attributed, such that each determinedsuccessive component accounted for less of the variability

than the previous one. From such analysis, it became clearthat the transcription effects induced by atorvastatin alonewere clearly limited compared with those induced byATRA alone (Fig. 2A and B). Notably and most impor-tantly, the combined addition of ATRA and atorvastatinproduced a strong synergic effect. This was particularlyevident by examining the fold change distribution widthinduced by the various treatments (Fig. 2B). The averageeffects of treatment versus no treatment at the differenttime points were also grouped by hierarchical agglomer-ative clustering (Fig. 2C). Two main groups can beobserved in Fig. 2C: the set of RefSeqs that are mainlydown-regulated (green) and the set of RefSeqs that are up-regulated (red). All three prototypic situations followed thesame trend but with different levels of expression variation,with maximum effects seen in response to the combinationof ATRA + atorvastatin (Fig. 2C). Moreover, such expres-sion was time dependent, with maximal gene expressionoccurring at 48 hours (Fig. 2C).To understand the functional relevance of the different

groups of genes up-regulated or down-regulated by ATRA,atorvastatin, or the combination of ATRA + atorvastatin,the 782 differentially expressed RefSeqs were divided inthree groups (i.e., ATRA, atorvastatin, and ATRA+ ator-vastatin) on the basis of the presence of an absolute foldchange variation of at least 1 with respect to the mock cellin at least one of the points of the time-course treatments.Seven hundred five RefSeqs were found directly associatedto treatment with ATRA alone, whereas 250 RefSeqs werefound directly associated to treatment with atorvastatinalone. The combination of ATRA + atorvastatin could beassociated with the highest number of RefSeq at 782. Theoverlaps existing between various time points for eachtreatment, as well as the overlaps between all treatmentsfor RefSeq characterized by the presence of an absolutelog2(fold change) variation of at least 1 are summarized asVenn diagrams (Fig. 3A). As expected, the Venn diagramsclearly established that the transcriptional effects inducedby atorvastatin alone are limited (Fig. 3A, top right). Inaddition, they showed a strong transcriptional synergiceffect by the ATRA + atorvastatin combination (Fig. 3A,bottom left).The three treatment groups were then analyzed using the

data mining tool IPA 4.0.5 Using IPA 4.0, we searched forrelationships between differentially expressed genes iden-tified by us in these microarray studies and genesannotated in the Ingenuity knowledge base, the largestmanually gene annotation database based on functionalinformation available in published studies. A comparativeanalysis was done to identify the variation of expression offunctional classes associated to differentially expressedgenes with time. There were 15 top-ranked enrichedfunctional classes (Supplementary Table S1)6, and sevenof them were common to the three different treatments.

6 Supplementary material for this article is available at Molecular CancerTherapeutics Online (http://mct.aacrjournals.org/).






Among the genes belonging to the 15 enriched classes, 332Illumina IDs were associated to 254 Entrez Gene IDs.Loading those genes in IPA 4.0, 13 networks (Supplemen-tary Table S2)6 were obtained, and the three top-rankednetworks were all made of differentially expressed genes. Itshould be emphasized that networks were defined asgroups of two or more genes linked by a functionalassociation based on peer-reviewed published data where-as hub genes were those sharing a high number of relationswith other components of networks.In a subsequent analysis, using an updated IPA 7.0

version, the effects of the different combination treatmentsat 48 hours on changes for the three top-ranked networkswere assessed. The 48-hour time point was selected forthese studies, as it represents the time wherein maximumtranscriptional effects were seen. Clearly, marked syner-gistic effects were induced by the combination of ATRAwith atorvastatin (Fig. 3B–D). In cluster 1 (Fig. 3B),treatment of cells with atorvastatin alone regulated

expression of very few genes in the network. This isconsistent with the fact that this network is centered onIL1B. Up-regulation of IL1B expression can be efficientlyseen only within the combined ATRA + atorvastatintreatment (Fig. 3B). In cluster 2 (Fig. 3C), a differentexample of a centered gene (MYC) is shown, which isdown-regulated by ATRA alone, but whose expression isnot further modified by the addition of atorvastatin. Incluster 3 (Fig. 3D), a different pattern is shown, involvingthe EGR1 hub gene. In that case, the expression of EGR1 isdecreased by either ATRA or atorvastatin alone but isfurther down-regulated by the combination of ATRA +atorvastatin (Fig. 3D).Altogether, our data established that atorvastatin treat-

ment modulates ATRA-induced gene expression, raisingthe possibility that modulation of transcription may be a keymechanism by which statins enhance generation of antileu-kemic responses. This prompted us to classify the genes se-lectively induced by the ATRA + atorvastatin combination

Figure 2. Patterns of gene expression induced by atorvastatin, ATRA, or the combination of ATRA + atorvastatin. A, principal component analysis oftreatments (PCA ) was done using the average of the expression of three replications for time point of the three prototypic situations. B, distribution of foldchanges existing between treated versus untreated cells at various time points in the set of 782 RefSeqs identified as differentially significantly expressed.I, ATRA; II, atorvastatin; III, ATRA + atorvastatin. C, hierarchical clustering of 782 RefSeqs found differentially expressed by double-step regressionanalysis.






Figure 3. Induction of gene ex-pression by ATRA and/or atrovasta-tin in NB4 cells. A, Venn diagrams ofdifferentially expressed RefSeqscharacterized by an absolute log2(fold change) of at least 1. Overlapsof the genes expressed at differenttime points for ATRA alone (top left ),atrovastatin alone (top right ), andATRA + atorvastatin (bottom left ).Bottom right, differentially expressedRefSeqs characterized by an absolutelog2 (fold change) of at least 1 in atleast one of the different time pointsof treatment. B-D, relation foundwithin differentially expressed genesin at least one of the treatments andpresent in the enriched functionalclasses. Cluster 1, centered on IL1Bgene at time point 48 h (B); cluster 2,centered on MYC gene at time point48 h (C); cluster 3, centered onEGR1 gene at time point 48 h (D).






based on their functional relevance. Among the genesinduced by the ATRA + atorvastatin combination, therewere several genes involved in cell differentiation (Supple-mentary Table S3)6 and apoptosis (Supplementary Table S4).6

In those tables, only genes whose expression was selectivelymodified/regulated by the combination of ATRA andatorvastatin at 48 hours are shown. Genes, regulatedsynergistically by statins and ATRA and involved in

Figure 4. Synergistic effect of ATRA and atorvastatin on gene expression. NB4 cells were treated for 8, 24, and 48 h with ATRA (0.5 Amol/L),atorvastatin (2 Amol/L), or the combination of ATRA and atorvastatin, as indicated. Left, patterns of gene expression for CCL3 (A), CCL4 (B), IL1B (C),BTG2 (D), and NCF2 (E) were investigated using Illumina Sentrix Human-6 Expression BeadChips. Three independent microarray experiments were done,and two-step regression strategy was applied as statistical analysis. Right, expression of mRNA for CCL3 (A), CCL4 (B), IL1B (C), BTG2 (D), and NCF2 (E)were evaluated by quantitative real-time RT-PCR (Taqman). Glyceraldehyde-3-phosphate dehydrogenase was used for normalization. Data are expressedas fold increase over untreated samples and represent means F SE of three independent experiments.






apoptosis (Supplementary Fig. S1)6 or differentiation(Supplementary Fig. S2),6 were identified, suggesting theexistence of cellular cascades and/or networks that regulateapoptosis or differentiation induced by the combination.To further confirm the expression of key differentiation

genes regulated by the ATRA + atorvastatin combination,the induction of expression of CCL3, CCL4, IL1B, BTG2 , andNCF2 were assessed by real-time RT-PCR and comparedwith the patterns of expression seen in the microarraystudies. There was strong induction of the expression ofthese genes after 48 hours of treatment with the ATRA +atorvastatin combination (Fig. 4A–E, left), whereas similarresults were obtained when real-time RT-PCR studies weredone (Fig. 4A–E, right).

It is well established that resistance to ATRA develops inseveral cases of APL despite initial sensitivity to the agent(36). In previous work, we had shown that statins reverseATRA resistance and enhance leukemic differentiation ofATRA-resistant NB4 variants (24).As our data established that groups of genes associated

with differentiation and/or apoptosis are selectivelyinduced by the ATRA + atorvastatin combination, wesought to determine whether the expression of keydifferentiation genes by the combination also occurs inNB4.300/6 cells. NB4.300/6 cells were treated for either24 hours (Fig. 5, left) or 48 hours (Fig. 5, right) with eitherATRA, atorvastatin, or the combination of ATRA andatorvastatin, and the induction of expression of the CCL3,

Figure 5. Expression of genes as-sociated with differentiation in ATRA-resistant APL cells. NB4.306 cellswere treated for 24 h (left ) or48 h (right ) with ATRA (0.5 Amol/L),atorvastatin (2 Amol/L), or the combi-nation of ATRA and atorvastatin, asindicated. Expression of mRNAs forCCL3 (A), IL1B (B), BTG2 (C), andNCF2 (D) was evaluated by quantita-tive real-time RT-PCR (Taqman) usingglyceraldehyde-3-phosphate dehydro-genase for normalization. Data areexpressed as fold increase over un-treated samples. Columns, meansof two independent experiments;bars, SE.






IL1B, BTG2 , and NCF2 genes was examined. As expected,there was no induction of expression of these genes inresponse to treatment with ATRA alone at either 24 or48 hours of treatment, whereas induction of some geneswas seen in response to treatment with atorvastatin alone(Fig. 5). However, the combination of ATRA and atorvas-tatin resulted in a significant enhancement of the expres-sion of all genes in these cells (Fig. 5), consistent withreversal of ATRA-dependent refractoriness in the presenceof atorvastatin.As our data established that JNK plays an important role

in statin-dependent differentiation (current study) andapoptosis (24), we used IPA 4.0 analysis centered on JNK(mitogen-activated protein kinase 8) to identify networks ofgenes specifically induced by ATRA + atorvastatin in ourmicroarray assays with known functional linkages todifferentiation and/or apoptosis. For that purpose, the48-hour time point of combined treatment was used, as atthat time maximal regulation of transcriptional effects hadbeen observed. Several pathways and signaling elementslinked to differentiation or apoptosis were found to bedirectly or indirectly linked to JNK (Fig. 6), furthersuggesting the existence of a mechanism by whichinduction of JNK kinase activity by statins promotesdifferentiation and apoptosis of leukemic cells.

DiscussionAPL is a subtype of AML characterized by the presence ofthe t (15;17) chromosomal translocation, resulting in the

abnormal fusion of the retinoic acid receptor a gene withthe PML gene and the abnormal PML–retinoic acidreceptor a fusion protein (38–40). It is well establishedthat ATRA is a potent inducer of differentiation of APLcells in vitro and in vivo (reviewed in refs. 41–43), and afterits introduction in the management of the disease in the1980s, it has become a key component of standardcombination regimens for the treatment of this leukemia.However, it is now also well known that leukemic cellresistance to ATRA develops with time, and this has led tothe introduction and development of another agent withpotent anti-APL properties, arsenic trioxide, for themanagement of ATRA-resistant APL (reviewed in ref. 44).The development of retinoic acid resistance in a subset ofpatients with APL underscores the need for the develop-ment of novel therapeutic approaches to overcome suchresistance. Moreover, beyond their potential usefulness inAPL, development of similar novel approaches may proveto be of value in other AML subtypes that are normallyresistant to the effects of ATRA.We have recently shown that atorvastatin and fluvastatin

enhance the induction of differentiation and apoptosis ofAPL cells by ATRA (24). In addition, we have establishedthat these statins reverse the development of ATRAresistance in both NB4 variant APL cell lines and inprimary resistant APL cells (24). Such results have raisedthe possibility that combined use of ATRA with lategeneration statins could provide a novel approach toovercome leukemic cell resistance. Importantly, our previ-ous studies have also shown that statins enhance the

Figure 6. Key connection links ofthe JNK kinase pathway to statin-induced genes associated with dif-ferentiation. IPA 7.0 analysis wasdone, as described in Materials andMethods. Functional relations werederived by connecting JNK to variousgenes selectively regulated after48 h of treatment with the atorvas-tatin and ATRA combination.






suppressive effects of ATRA on bone marrow–derived orperipheral blood–derived primary leukemic progenitorsfrom patients with non-APL forms of AML, raising thepossibility that statins may sensitize other resistant AMLsubtypes to the antileukemic effects of ATRA in vivo (24).Although the clinical testing of such a hypothesis ispending, understanding the mechanisms by which statinspromote induction of ATRA-dependent antileukemiceffects is highly relevant and potentially important.Identifying such mechanisms could result in the identifi-cation of novel cellular elements that could be targeted in amore selective and specific manner for the treatment ofacute leukemias.The potent effects of new generation statins as inducers

of APL cell differentiation and their ability to reverse ATRAresistance prompted us to perform studies to define thecellular mechanisms involved in the generation of suchresponses. In initial studies, we examined the effects ofpharmacologic or molecular inhibition of the JNK kinasepathway in the induction of differentiation of the NB4 APLcell line that express the t(15;17) translocation. Thesestudies showed that JNK activity is essential for theatorvastatin-dependent or fluvastatin-dependent leukemiccell differentiation, as evidenced by the reversal of suchdifferentiation by either the pharmacologic JNK inhibitorSP600125 or by a peptide JNK inhibitor. Thus, activation ofJNK by statins is essential for the generation of bothproapoptotic responses (24) and leukemic cell differentia-tion in APL. It should be noted that JNK is the only majorpathway activated by statins in APL cells, and as we haveshown in previous work, the p38–mitogen-activatedprotein kinase pathway is not modulated by statinswhereas the extracellular signal-regulated kinase–mito-gen-activated protein kinase is inhibited (24).The selective engagement of JNK by statins and its

important role in leukemic cell differentiation prompted usto perform further work aimed to identify statin-dependentinduction of genes that participate in leukemic celldifferentiation or apoptosis and may be linked to JNK-regulated pathways. For that purpose, we did extensivemicroarray studies using Illumina BeadChips to define thegene expression profiles seen in NB4 cells in response tostatin treatment alone or the combination of statintreatment and ATRA. A number of genes that seem to beregulated by ATRA and atorvastatin and have relevance toapoptosis and differentiation were identified by using suchapproach. Although atorvastatin alone induced expressionof a minimal number of genes, it exhibited a remarkableeffect in promoting ATRA-inducible gene expression. Alarge number of genes associated with apoptosis ordifferentiation were found to be selectively induced bythe combination of atorvastatin and ATRA, a findingconsistent with the potent-enhancing effects of statins onATRA-induced differentiation and proapoptotic responsesin leukemic cells (24). Among the genes selectivelyexpressed by the combination were a number of chemo-kines, including macrophage inflammatory protein-1a(CCL3) and macrophage inflammatory protein-1h (CCL4),

which are well known to play important regulatory effectson hematopoietic progenitor growth and differentiation(45, 46). Similarly, IL1B , a key regulator of hematopoiesis(47), was a prominently induced gene by the ATRA +atorvastatin combination. Our microarray data analysisalso showed that the expression of a number of other geneswas drastically augmented in response to the treatmentwith ATRA + atorvastatin. Among those genes are BTG2and NCF2. BTG2 is involved in growth inhibition and cellcycle regulation (48), and its overexpression increasesretinoic acid receptor a transcriptional activity and celldifferentiation in response to ATRA in myeloid leukemiacells and CD34+ hematopoietic progenitors (49). NCF2expression has also been linked to myelopoiesis anddifferentiation (50), underscoring the functional relevanceof gene induction by the ATRA and statin combination.Importantly, in experiments wherein the expression of

gene targets was examined using real-time RT-PCR, wewere able to directly show up-regulation of CCL3, IL1B,BTG2 , and NCF2 in the variant cell line NB4.300/6, whichis completely refractory to the effects of ATRA (25), butundergoes differentiation and apoptosis in response totreatment by combinations of ATRA and statins (24). Thus,statins enhance ATRA-dependent expression of key genesinvolved in the differentiation and apoptosis in bothATRA-sensitive and ATRA-resistant APL cells. Althoughthe precise contributions of distinct elements in thegeneration of the antileukemic effects of ATRA remain tobe defined in future studies, our data establish a uniquemechanism by which statins reverse ATRA antileukemicresponses via regulation of the JNK pathway. Further workin that direction is warranted and may lead to thedefinition of new target proteins for the development ofnovel approaches for the treatment of ATRA-resistant APLand other AML subtypes.

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

Acknowledgments

We thank Dr. Saverio Minucci (European Institute of Oncology) for theNB4.300/6 cell line and Dr. Daniela Cantarella (Oncogenomics Center,Institute for Cancer Research and Treatment) for microarray dataacquisition.

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