the stat3/5 signaling biosignature in …...personalized medicine and imaging the stat3/5 signaling...

Personalized Medicine and Imaging

The Stat3/5 Signaling Biosignature inHematopoietic Stem/Progenitor Cells PredictsResponse and Outcome in MyelodysplasticSyndrome Patients Treated with AzacitidineParaskevi Miltiades1, Eleftheria Lamprianidou1, Theodoros P. Vassilakopoulos2,Sotirios G. Papageorgiou3, Athanasios G. Galanopoulos4, Christos K. Kontos5,PanagiotisG.Adamopoulos5, EvangeliaNakou1, SofiaVakalopoulou6,VassiliaGarypidou6,Maria Papaioannou7, Evdoxia Hatjiharissi8, Helen A. Papadaki9, Emmanuil Spanoudakis1,Vassiliki Pappa3, Andreas Scorilas5, Constantinos Tsatalas1, and Ioannis Kotsianidis1

on behalf of the Hellenic MDS Study Group

Abstract

Purpose: Azacitidine is the mainstay of high-risk myelodys-plastic syndromes (MDS) therapy, but molecular predictors ofresponse and the mechanisms of resistance to azacitidine remainlargely unidentified. Deregulation of signaling via Stat3 and Stat5in acute myeloid leukemia (AML) is associated with aggressivedisease. Numerous genes involved in cell signaling are aberrantlymethylated inMDS, yet the alterations and the effect of azacitidinetreatment on Stat3/5 signaling in high-risk MDS have not beenexplored.

Experimental Design:We assessed longitudinally constitutiveand ligand-induced phospho-Stat3/5 signaling responses bymul-tiparametric flow cytometry in 74 patients with MDS and lowblast count AML undergoing azacitidine therapy. PretreatmentStat3/5 signaling profiles in CD34þ cells were grouped by unsu-pervised clustering. The differentiation stage and the molecularproperties of the CD34þ G-CSF–inducible Stat3/5 double-posi-

tive subpopulation were performed by flow cytometry and quan-titative real-time PCR in isolated MDS progenitors.

Results: The pretreatment Stat3/5 signaling profiles inCD34þ cells correlated strongly with response and cytogeneticsand independently predicted event-free survival. We furtheridentified a CD34þ G-CSF–inducible Stat3/5 double-positivesubpopulation (DP subset) whose pretreatment levels wereinversely associated with treatment response and cytogenetics.The kinetics of the DP subset followed the response to azaci-tidine and the disease course, whereas its molecular character-istics and cellular hierarchy were consistent with a leukemiapropagating cell phenotype.

Conclusions:Our findings provide a novel link among Stat3/5signaling and MDS pathobiology and suggest that the Stat3/5signaling biosignature may serve as both a response biomarkerand treatment target. Clin Cancer Res; 22(8); 1958–68. �2015 AACR.

IntroductionThe introduction of azacitidine has radically transformed the

therapeutic approach and improved the outcome of patients withhigh-riskmyelodysplastic syndrome (MDS).Nevertheless, the exactmechanismof action remains tobe established,while bothprimaryand secondary resistance confer a grave prognosis, as there arecurrently no effective alternative therapies (1). In addition, there islack of a serviceable, widely accepted biomarker of response and/oroutcome that canoffer a timely and valid estimationof the expectedbenefit from azacitidine and help to tailor treatment (2, 3).

Stat3 and Stat5 regulate fundamental cellular processes andaberrant cell signaling via Stat3/5 is implicated in leukemogenesis(4–9). Constitutive upregulation of Stat3 and, less often, Stat5molecules hasbeen reported inacutemyeloid leukemia (AML),butits prognostic impact is contentious (10). This is because measure-ment of basal Stat3/5 levels in heterogeneous cell populations byusing conventional proteomic assays does not address the cytokineregulation of signaling cascades of malignant hematopoiesis andthus cannot portray the overall picture of signaling events at thesingle cell level (11). Single cell network profiling using

1Department of Hematology, Democritus University of Thrace, Alex-androupolis, Greece. 2Department of Hematology, Laikon GeneralHospital, National and Kapodistrian University of Athens, Athens,Greece. 3Second Department of Internal Medicine, Hematology Unit,Attikon University General Hospital, Athens, Greece. 4Department ofClinical Hematology, G. Gennimatas Hospital, Athens, Greece.5Department of Biochemistry and Molecular Biology, National andKapodistrian University of Athens, Athens, Greece 6Second Prope-deutic Department of Internal Medicine, Aristotle University of Thes-saloniki, Hippokration Hospital, Thessaloniki, Greece. 7Department ofHaematology, Aristotle University of Thessaloniki, AHEPA Hospital,Thessaloniki, Greece. 8Department of Hematology,Theageneion Hos-pital of Thessaloniki, Thessaloniki, Greece. 9Department of Hematol-ogy, University Hospital of Heraklion, Heraklion, Greece.

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

CorrespondingAuthor: Ioannis Kotsianidis, Department of Hematology, Demo-critus University of Thrace Medical School, Dragana, Alexandroupolis 68100,Greece. Phone: 3025-5103-0320; Fax: 3025-5103-0439; E-mail:[email protected]

doi: 10.1158/1078-0432.CCR-15-1288

�2015 American Association for Cancer Research.

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multiparametric phospho-specific flow cytometry can identifyphosho-Stat3/5 biosignatures at the hematopoietic stem/progen-itor cell (HSPC) level which reflect the biologic behavior of AMLand can distinguish patient subgroups with worse prognosis (11–13). In their seminal article, Irish and colleagues (12) have iden-tified a G-CSF–inducible Stat3/5 double-positive (DP) subpopu-lationofCD34þ cells inaggressiveAML,withouthowever testing itsprognostic value. An analogous DP subset has also been observedinPh�myeloproliferativeneoplasms (14), raising the possibility ofa shared signaling biosignature among myeloid neoplasms.

Both aberrant methylation and cell signaling deregulationcontribute to the pathogenesis of Myelodysplastic syndromes(MDS), while epigenetic defects of genes involved in cell signalingare frequently encountered in MDS patients, particularly in thelate stages of the disease (15–18). Moreover, in addition to thebidirectional interplay among the epigenetic machinery and cellsignaling (19), hypomethylating agents may indirectly affectsignal transduction (20–22). Despite the above, a comprehensiveview of Stat3/5 signaling alterations in late-stage MDS is missingand the effect of hypomethylating therapy on leukemic signalinghas not been addressed yet.

By using phospho-specificflow cytometry, we investigated phos-pho-Stat3/5 signaling profiles in the HSPCs from 74MDS and lowblast count AML patients during azacitidine therapy.We show thatthe pretreatment Stat3/5 biosignature in MDSHSPCs was stronglyassociated with response status and patient outcome. We furtheridentified a G-CSF–inducible phospho-Stat3/5 DP subpopulationin theCD34þ cell compartment (hereafter referred to asDP subset)whose pretreatment levels were inversely associated with response.The cellularhierarchyand themolecularpropertiesof theDPsubsetwere consistent with a leukemia stem cell phenotype, whereas itskinetics followed the disease course and response to treatment.

Patients, Materials, and MethodsPatients

Following Institutional Review Board approval, peripheralblood and bone marrowmononuclear cells from 74 patients and

10 donors with nonclonal myelopoiesis (i.e., lymphomas, solidtumors, and immune thrombocytopenia) were obtained beforetreatment initiation and at the indicated time points. Informedconsent was obtained in accordance with the Declaration ofHelsinki. All patients received azacitidine in a nonclinical trialsetting at an initial dose of 75 mg/m2 s.c. for 7 days on 28-daycycles. Dose reductions of 25% to 50% and/or treatment delayswere considered for severe myelotoxicity or myelosuppression-related complications. Granulocyte colony-stimulating factors(G-CSF)were used at the discretion of the treating doctor, whereasno erythropoiesis-stimulating agents were administered to anypatient. Response to therapy was evaluated using the Internation-al Working Group Response Criteria for MDS (23). Heavilytransfused patients were defined as those requiring � 4 RBCunits/8 weeks (24). Mononuclear cells were isolated after densitycentrifugation, frozen in liquid nitrogen, and processed within6 months after cryopreservation.

Antibodies, data acquisition, and analysisThe following antibodies were used: CD34 (clone 8G12),

pStat3 (Y705), pStat5 (Y694), CD2 (RPA-2.10), CD3 (HIT3a),CD4 (RPA-T4), CD8 (RPA-T8), CD19 (HIB19), CD20 (2H7),GPA (GA-R2), CD45(2D1), CD114 (LMM741), p53 (DO7) andKi-67 (B56) all from BD Biosciences; CD38 (LS198.4.3) fromBeckman Coulter; Bcl-2 (124) from Dako; CD123 (6H6), CD90(5E10) and CD45RA (HI100) from Biolegend. Fluorescenceminus one (FMO) was employed as negative control. Data wereacquired on a 5-color FC-500 (Coulter) and a 4-color FACSCa-libur (BD Biosciences) cytometers and analyses were performedwith Flowjo software (Treestar).

Single-cell phospho-specific flow cytometryThawed cells were washed once in RPMI to remove the

residual DMSO and allowed to rest for 1 hour in serum-freeRPMI medium at 37�C. Cells were then distributed at 2–5� 105

cells/well in 4 aliquots. Two aliquots remained unstimulatedand used as FMO control and untreated sample controls andthe others were stimulated for 15 minutes at 37�C with eitherhuman recombinant G-CSF or granulocyte macrophage stim-ulating factor (GM-CSF, Miltenyi Biotec GmbH) at a finalconcentration of 20 ng/mL for both cytokines. Stimulationwas halted by fixation with Cytofix Fixation Buffer (BD Bios-ciences) and cells were permeabilized with Perm Buffer III (BDBiosciences) and stained with phospho-Stat3 (clone Y705),phospho-Stat5 (clone Y694), and combinations of the afore-mentioned antibodies for 30 minutes at room temperature.Basal phosphorylation levels were expressed as the log2 ratio ofmean fluorescence intensity (MFI) of unstimulated pStat3 andpStat5 divided by the FMO control, namely log2[MFI (unsti-mulated)/MFI (FMO)] and potentiated levels as log2[MFI(stimulated)/MFI (unstimulated)].

Delineation of the differentiation stage and the leukemia stemcell characteristics of the CD34þ G-CSF–inducible pStat3þ/5þ

DP subsetTo map the cellular hierarchy of the CD34þ DP subset, cells

expressing mature lineage markers were depleted from mono-nuclear cells after staining with an antibody cocktail consistingof anti-CD2, CD3, CD4, CD8, CD19, CD20, and GPA (25) andanti-PE immunomagnetic MicroBeads (Miltenyi Biotec). Puri-fied Lin� cells were then subjected to positive selection of

Translational Relevance

Azacitidine is themain option for high-riskmyelodysplasticsyndrome (MDS) patients, but mechanisms of resistance arelargely unknown and there is paucity of a serviceable bio-marker of response. While abnormal Stat3/5 signaling inhematopoietic stem/progenitor cells has been implicated inacute myeloid leukemia pathobiology, the architecture andthe effect of azacitidine on Stat3/5 signaling in MDS have notyet been evaluated. Utilizing functional phenotyping by phos-pho-protein flow cytometry, we explored the alterations ofStat3/5 signaling in high-risk MDS patients treated with aza-citidine. We demonstrate that the Stat3/5 signaling biosigna-ture can predict response and outcome of azacitidine treat-ment. Moreover, we identified a prognostically relevantCD34þ G-CSF–inducible Stat3/5 double-positive subpopula-tion whose kinetics paralleled disease activity and were ame-nable to modulation by azacitidine in responding patients.

Our data identify Stat3/5 signaling aberrations as predictorsof resistance to azacitidine and set the scene for the therapeutictargeting of the Stat3/5 signaling network in MDS.

Stat3/5 Signaling Architecture in High-Risk MDS

www.aacrjournals.org Clin Cancer Res; 22(8) April 15, 2016 1959




CD34þ cells by means of CD34 microbeads (Miltenyi Biotec).Isolated Lin-CD34þ cells (purity always � 98%) were thenstained with pStat3, pStat5, CD38, CD90, CD123, andCD45RA according to the above phospho-flow protocol andanalyzed as previously described (25). For the characterizationof the molecular/LSC properties of the CD34þ DP subset,BMMNC were stained with pStat3, pStat5, and CD34 alongwith one of bcl-2, Ki-67, or p53.

Assessment of G-CSF receptor (CSF3R) protein and mRNAexpression

For the determination of CSF3R protein expression sampleswere analyzed by flow cytometry, by staining with CD114, CD34,CD45, and the corresponding isotype controls. Data are expressedas the ratio of the CD114MFI of CD34þ cells to theMFI of isotypecontrol. Immunomagnetically purified CD34þ cells (purity �98%) from the above samples were used for mRNA extraction(RNAqueous Micro kit, Ambion) and reverse transcription (RET-ROscript, Ambion). Quantification of the CFS3R mRNA wasperformed by Real-Time PCR using SYBR Green (Invitrogen) andthe following primers: human CSF3R forward, 50-CATCACAG-CCTCCTGCATCATC-30, human CSF3R reverse, 50-CTGAA-GCTCTGCTCCCAGTCTC-30, human GAPDH forward, 50-ACTC-CACGACGTACTCAGCG-30, human GAPDH reverse, 50-GGTC-GGAGTCAACGGATTTG-30. PCR conditions were as following:50�C for 2minutes, 95�C for 10minutes, 40 cycles of 95�C for 15seconds and 60�C for 1 minute, followed by melting curveanalysis from 65�C to 90�C. Reactions were carried out in dupli-cate in a PTC200 Peltier Thermal CyclerwithChromo4Real-TimePCR Detector. Data acquisition and analysis were performed byChromo4 Real-Time PCR Detector and Opticon Monitor 3.Relative CSF3R expression of CD34þ cells was calculated by2�DDCt method.

Mutations analysis of TET2 and TP53 coding regionsDNA was extracted from bone marrow mononuclear cells or

peripheral blood samples collected before the initiation ofazacitidine. Mutational analysis of the coding region of TET2and TP53 genes in 30 and 11 samples, respectively, was per-formed using next-generation sequencing (detailed methodprovided in the Supplementary Methods). For the analysis ofTET2, a 10% cutoff of allele fraction was used as suggestedpreviously (26).

Statistical analysisComparisons were performed by using c2, Mann–Whitney,

Kruskal–Wallis, Wilcoxon signed-rank, and Friedman tests, asappropriate, and survival analysis with Kaplan–Meier and log-rank test. Overall survival (OS) was defined as the time fromazacitidine initiation to death from any cause and event-freesurvival (EFS) as the time from azacitidine initiation to diseaseprogression, relapse, or death. Surviving patients were censoredat last follow-up. Multivariate survival analysis was based onCox proportional hazards model using a backward stepwiseselection procedure with entry and removal criteria of P ¼ 0.05and P ¼ 0.10, respectively. Multiple Experiment Viewer soft-ware (MeV, http://sourceforge.net/projects/mev-tm4/) wasemployed for unsupervised hierarchical cluster analysis withcomplete linkage algorithm and Euclidean distance as distancemetric (12).

ResultsThe pretreatment Stat3/5 signaling biosignature stronglycorrelates with clinical and biologic parameters

Patients' characteristics are listed analytically in Table 1. Two(2.7%) patients achieved marrow complete remission (mCR)with both erythroid and platelet hematologic improvement, 17attained CR (23%), 11 (14.9%) patients showed hematologicimprovements only in platelet count (HI), 17 (23%) remainedstable (stable disease, SD) and 27 (36.4%) failed (failure, F)azacitidine. The representativeness of our cohort was validatedby the successful application of the prognostic score proposed byItzykson and colleagues (24) in 72 evaluable patients. Threegroups with different OS (P ¼ 0.027) were identified (Supple-mentary Fig. S1). Unsupervised clustering of pretreatment signal-ing profiles in CD34þ cells of MDS patients identified 2 signalingclusters (SC), SC-1 and SC-2. The two clusters displayed similarlevels of constitutive Stat3/5 phosphorylation, whereas potenti-ated responses of Stat3/5 to G-CSF andGM-CSF stimulation wereweak in SC-1 and powerful in SC-2 (Fig. 1A). No differences wereobserved among the two clusters regarding age, sex, WHO sub-type, transfusion burden,WPSS, IPSS, IPSS-R, and TET2mutationstatus (Table 1). In contrast, patients in SC-1 achieved betterresponse to azacitidine (P ¼ 0.01), had worse cytogenetics bothby IPSS (P ¼ 0.01) and IPSS-R (P ¼ 0.02) and enjoyed longermedian EFS (12.5 vs. 7.8months, respectively, P¼ 0.01) than theones in SC-2 (Fig. 1 and Table 1), while median OS was alsoprolonged in patients of SC-1, without, however, reaching statis-tical significance (13.5 vs. 10.4 months, respectively; P ¼ 0.08).Multivariate analysis confirmed the independent prognostic pow-er of the pretreatment Stat3/5 signaling biosignature for EFS (P¼0.017), whereas heavy transfusion requirements was the otherindependent prognostic factor for both OS (P ¼ 0.004) and EFS(P¼ 0.029, Supplementary Tables S1 and S2). These findings arein line with prior observations in AML (12, 27), and stronglysuggest involvement of aberrant signaling via Stat3/5 in MDSpathobiology. We further performed unsupervised clusteringof patient and nonclonal samples (Supplementary Fig. S5). Allpatients with nonclonal myelopoiesis clustered with respondingpatients, further supporting the existence of an aberrant signalingbiosignature in nonresponders to azacitidine.

Identification of a prognostically relevant G-CSF–inducibleStat3/5 DP subpopulation of CD34þ cells

The complementary cytometric analysis of pretreatment Stat3/5 signaling profiles in our patients revealed an identical to theG-CSF–inducible DP subpopulation previously described in AML(12) and Ph� myeloproliferative neoplasms (14) The medianpretreatment levels of the DP subset were significantly lower inpatients who achieved CR or marrow CR (38.6% of total CD34þ

cells, range 0.13%–83.5%) compared to those with stable disease(75.4%, 1%–90%, P ¼ 0.008) and failure to azacitidine (74.7%,12.1%–90%, P ¼ 0.006), whereas patients with HI did not showany significant differences with the other groups (HI, 55.3%,0.5%–88.2%, Fig. 2A and B). Also, the levels of the DP subsetwere significantly lower in patients with poor-risk cytogenetics byIPSS (38.6%, 0.5%–75.4%) compared to those with intermediate(68.1%, 1%–85.5%, P ¼ 0.02) and good-risk (66.9%, 0.13%–

90%, P ¼ 0.005) karyotype. Similarly, poor-risk cytogenetics byIPSS-R were associated with lower percentage of the DP subset(38.4%, 1%–75.4%) compared with good-risk disease (70.8%,

Miltiades et al.

Clin Cancer Res; 22(8) April 15, 2016 Clinical Cancer Research1960




Table 1. Baseline patient characteristics and clinical information (n ¼ 74)

Total pts (n ¼ 74) Cluster 1 (n ¼ 37) Cluster 2 (n ¼ 37) P value

Age (median, range) 73.2 (48.6–83.7) 73.8 (52–83.5) 72.7 (48.6–83.7) P ¼ 0.28>65 58 (78.4%) 31 (83.8%) 27 (73%)<65 16 (21.6%) 6 (16.2%) 10 (27%)

Sex P ¼ 0.14Male 48 (64.9%) 27 (73%) 21 (56.8%)Female 26 (35.1%) 10 (27%) 16 (43.2%)

Baseline blood countsHemoglobin (g/dl) 8.7 (5.7–12.8) 8.8 (6.1–12.8) 8.7 (5.7–10.4) P ¼ 0.53ANC(�109/L) 1.55 (0.04–31) 2.26 (0.05–31) 0.92 (0.04–15.5) P ¼ 0.09Platelets (�109/L) 53.5 (9–383) 54 (9–383) 53 (9–300) P ¼ 0.375

Number of completed cycles P ¼ 0.046Median (range) 6 (1–36) 6 (2–33) 5 (1–36)

WHO classification P ¼ 0.1RCMD 3 (4%) 3 (8.1%) 0 (0%)RAEB-I 4 (5.4%) 1 (2.7%) 3 (8.1%)RAEB-II 31 (42%) 11 (29.8%) 20 (54.1%)CMML-II 13 (17.6%) 8 (21.6%) 5 (13.5%)AML-LBC 20 (27%) 13 (35.1%) 7 (18.9%)MDS/MPD 3 (4%) 1 (2.7%) 2 (5.4%)

IPSS P ¼ 0.79Intermediate-2 30 (40.5) 14 (37.8%) 16 (43.2%)High 34 (46%) 17 (45.9%) 17 (45.9%)N/A 10 (13.5%) 6 (16.2%) 4 (10.9%)

WPSS P ¼ 0.64High 23 (31.1%) 10 (27%) 13 (35.1%)Very high 14 (18.9%) 5 (13.5%) 9 (24.3%)N/A 37 (50%) 22 (59.5%) 15 (40.6%)

IPSS-R P ¼ 0.15Intermediate 5 (6.7%) 4 (10.8%) 1 (2.8%)High 25 (33.8%) 9 (24.3%) 16 (43.2%)Very high 34 (46%) 18 (48.7%) 16 (43.2%)N/A 10 (13.5%) 6 (16.2%) 4 (10.8%)

IPSS-R Cytogenetic risk P ¼ 0.024Good 35 (47.3%) 13 (35.1%) 22 (59.5%)Intermediate 19 (25.7%) 9 (24.3%) 10 (27%)Poor 11 (14.9%) 9 (24.3%) 2 (5.4%)Very poor 6 (8.1%) 5 (13.5%) 1 (2.7%)N/A 3 (4%) 1 (2.7%) 2 (5.4%)

IPSS Cytogenetic risk P ¼ 0.010Good 33 (45%) 13 (35.1%) 21 (56.8%)Intermediate 21 (28%) 9 (24.3%) 11 (29.7%)Poor 17 (23%) 14 (37.9%) 3 (8.1%)N/A 3 (4%) 1 (2.7%) 2 (5.4%)

PB blasts P ¼ 0.27Present 38 (51.4%) 17 (45.9%) 21 (56.8%)Absent 31 (41.9%) 18 (48.7%) 13 (35.1%)N/A 5 (6.7%) 2 (5.4%) 3 (8.1%)

GFM prognostic score 0.067Low 8 (11%) 5 (13%) 3 (8%)Intermediate 49 (67%) 20 (54%) 29 (78%)High 15 (20%) 11 (30%) 4 (11%)N/A 2 (3%) 1 (3%) 1 (3%)

Transfusions � 4 per month P ¼ 0.45Yes 23 (31.1%) 13 (35.1%) 10 (27%)No 51 (68.9%) 24 (64.9%) 27 (73%)

TET2 mutations (all)Yes 19/30 (63.3%) 5 (45%) 14 (74%) P ¼ 0.12No 11/30 (36.7%) 6 (55%) 5 (26%)

TET2 mutations (VAF � 10%)Yes 5/30 (16.7%) 1 (9%) 4 (21%) P ¼ 0.4No 25/30 (83.3%) 10 (91%) 15 (79%)

Best response P ¼ 0.017CR þ mCR 19 (25.7%) 15 (40.6%) 4 (10.8%)Hematologic improvement 11 (14.9%) 4 (10.8%) 7 (18.9%)Stable disease 17 (23%) 9 (24.3%) 8 (21.6%)Failure 27 (36.4%) 9 (24.3%) 18 (48.7%)

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0.13%–90%, P ¼ 0.03). Of note, consistent with the resultsobtained by the clustering of signaling profiles, nonclonalpatients had identical levels of the DP subpopulation withresponders to azacitidine (CR and HI), but significantly lowercompared with nonresponders (F and SD, SupplementaryFig. S5).

No differences in the levels of the DP subset were observedregarding age, gender, MDS subtype, TET2 mutation status, and

transfusion requirements (data not shown). We also tested therest GM- andG-CSF–inducible or not CD34þ subpopulations, forexample, single positive Stat3 or Stat5 as well as Stat3/5 DPsubsets, for correlations with clinical and biologic parameters,but we were unable to find any associations except significantlyhigher basal Stat3 levels in patients with poor-risk cytogeneticscompared with the good-risk ones by both IPSS (P ¼ 0.002) andIPSS-R (P ¼ 0.008, Supplementary Fig. S2).

Table 1. Baseline patient characteristics and clinical information (n ¼ 74) (Cont'd )

Total pts (n ¼ 74) Cluster 1 (n ¼ 37) Cluster 2 (n ¼ 37) P value

Median follow upMonths 47.7

Treatment after azacitidine failure total, n ¼ 8 P ¼ 0.4Intensive chemotherapy 7 4 3Allo-SCT 1 1 0

NOTE: Numbers in bold indicate P < 0.05.Abbreviations: CR, complete response; mCR, complete marrow response with incomplete blood count recovery; GFM, Groupe Francophone des Myelodysplasies;N/A, not applicable/not available; VAF, variant allele frequency.

basalpSTAT3/basalpSTAT5/G-CSFpSTAT3/G-CSFpSTAT5/

pSTAT5/GM-CSF

Signaling profiles

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t fre

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Parameters SC-2SC-1

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Response (P = 0.017) Cytogenetics IPSS-R (P = 0.024)GoodIntermediatePoorVery poorN/A

Cytogenetics IPSS (P = 0.010)

GoodIntermediatePoorN/A

B

Figure 1.Association of clinical parameters with pretreatment signaling biosignatures. A, heatmap of pretreatment signaling profiles. Basal and potentiated phosphorylationlevels are represented with a double gradient color scale (green-black-red) displaying underexpression relative to the mean as green, overexpressionas red, and spots where there is little differential expression as black. Unsupervised clustering of pretreatment basal and potentiated Stat3/5 levels of CD34þ

cells in 74 patients distinguished two signaling clusters (SC), SC-1 (left) and SC-2 (right). B, patients in SC-1 (n ¼ 37) showed significantly higher rates ofCR/mCR (P ¼ 0.017) and had worse karyotype according to both IPSS (P ¼ 0.010) and IPSS-R (P ¼ 0.024) compared to those in SC-2 (n ¼ 37), whereas therewere no differences among the two SCs regarding sex (P ¼ 0.14), WHO subtype (P ¼ 0.1), transfusion burden (P ¼ 0.45), WPSS (P ¼ 0.6), IPSS (P ¼ 0.8)and IPSSR (P ¼ 0.15, data shown in Table 1). Each box in the color chart represents a single patient. C, overall (OS) and event free (EFS) survival of patientsin SC-1 (n¼ 37) and SC-2 (n¼ 37). The former group enjoyed significantly longer EFS (P¼ 0.011), whereas OS was also prolonged in patients of SC-1, although notsignificantly (P ¼ 0.08).

Miltiades et al.





The kinetics of the DP subset follow the disease course andresponse to azacitidine

We next sought to investigate the effect of azacitidine anddisease course upon the kinetics of the DP subset. The lattersubpopulation was downregulated significantly on day15 of the

first cycle only in patients with CR. The pretreatment medianpercentage in these patients (38.6% of total CD34þ cells, range0.13%–84%) downregulated to 9.6% (0.3%–70.4%, P ¼ 0.01),whereas patients with HI (47.6%, 0.5%–82.2% changed to 32%,0.2%–72.2%, P ¼ 0.12), SD (75.4%, 1%–87.6% changed to

100 101 102 103 10100

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pSTA

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34+

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P = 0.008P = 0.006 P = 0.03

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FSDHI PoorIntGood Poor Verypoor

Good Int

IPSS-R CytogeneticsIPSS CytogeneticsResponse

Figure 2.Identification of a G-CSF-inducible Stat3/5 double-positive subpopulation of CD34þ cells, which is adversely associated with response to azacitidine. A,representative flow cytometric analysis of pretreatment samples of a patient who achieved CR (top, CR) and one who failed azacitidine (bottom, Failure).A CD34þ double-positive (DP) Stat3/5 subpopulation (DP subset) is strongly induced in the nonresponding patient after G-CSF stimulation. Plots are gatedon CD34þ cells. B, the median pretreatment levels of the DP subset were inversely associated with response and cytogenetic risk. Patients who achievedcomplete remission ormarrow CR (CRþmCR, n¼ 19) had significantly lower levels of the DP subset compared with thosewith stable disease (SD, n¼ 17, P¼0.008)and failure to azacitidine (F, n ¼ 27, P ¼ 0.006), whereas patients with hematologic improvement (HI, n ¼ 11) did not show any significant differences withthe other groups. Also, patients with poor-risk cytogenetics by IPSS (n¼ 17) had significantly lower levels of the DP subset compared with those with intermediate(n ¼ 21, P ¼ 0.02) and good-risk (n ¼ 33, P ¼ 0.005) karyotype. Likewise, poor-risk cytogenetics by IPSS-R (n ¼ 11) were associated with lower levels of theDP subset compared with good-risk disease (n ¼ 35, P ¼ 0.03). P values by Kruskal–Wallis test.






76.1%, 1%–83.7%, P ¼ 0.23), and failure (63%, 12.1%–87.2%changed to 58%, 23.8%-87%, P¼ 0.26), retained unaltered levelsof the DP subset (Fig. 3A). Of note, day 15 of the first cycle waschosen because the hypomethylating effect of azacitidine usuallypeaks 15 days after its administration (18), while clonal hema-topoiesis still dominates in bone marrow as shown by theidentical percentage of bone marrow CD34þ cells before (15%,range 4%–29%) and 15 days after first azacitidine administration(14.5%, 4%–26%, P ¼ 0.9) in our patients.

In 19 patients, the alterations of the DP subset were studiedlongitudinally during the disease course (Fig. 3B and C).Measurements were performed on days 0 and 15 of the firstcycle, at response evaluation after 6 cycles and at diseaseprogression or relapse. The levels of the DP subset remainedunaffected throughout the disease course in patients with SDand failure to azacitidine. In contrast, the DP subset in patients

achieving CR was significantly reduced on day 15 of the firstcycle and remained at low levels until disease relapse, whichwas accompanied by a marked expansion of the DP subset (P ¼0.007). Similar kinetics of the DP subset were observed inpatients with HI, without however, reaching statistical signif-icance (P ¼ 0.13).

Thus, it appears that the kinetics of the DP subset are followingthe disease course and response to azacitidine, indicating poten-tial involvement of the former subset in mechanisms underlyingdisease progression and resistance to azacitidine.

TheDP subpopulation is enriched in cells with a leukemia stemcell phenotype

Recent findings in both mice and humans challenge theleukemia stem cell (LSC)model and suggest that in approximate-ly 90% of CD34þ AML cases LSCs reside in the lymphoid-primed

C

pSTAT3

Progressionmonths60Day

7.49.5

1.338.630.3

3.319.229.424.6

30,3 30,3

15Day

pSTA

T5

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F

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% o

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cells

d15d0d15d0d15d0d15d0

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Failure

P = 0.26

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ProgressionResponsed15d0evaluation

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CRCRCR

Figure 3.The kinetics of the GCSF-inducibleStat3/5 DP subpopulation follow thedisease course and response toazacitidine. A, the DP subset wassignificantly downregulated on day 15 ofthe first azacitidine cycle only in patientswho achieved CR (n ¼ 13, P ¼ 0.01),whereas it remained unaltered inpatients with hematologic improvement(HI, n ¼ 8, P ¼ 0.12), stable disease(SD, n ¼ 9, P ¼ 0.23) and failure(F, n¼ 16, P¼ 0.26). B, kinetics of the DPsubpopulation in patients with CR(n ¼ 8), HI (n ¼ 5), SD (n ¼ 3), and F(n ¼ 3) were assessed longitudinallyduring azacitidine treatment.Measurements were performed on days0 and 15 of the first cycle, at responseevaluation after 6 cycles and when thedisease progressed or relapsed after aninitial response. In patients whoachieved CR the kinetics of the DPsubset paralleled disease severity andresponse to azacitidine (P ¼ 0.007 byFriedman test), while in patients with SDand F the DP subpopulation persisted athigh levels throughout the diseasecourse. The kinetics of the DP subset inpatients with HI also displayed a trend tofollow the disease course, which,however, did not reach statisticalsignificance (P¼ 0.13). C, representativeflow cytometric plots of serialmeasurements in a responder (CR, top)and a nonresponder (F, bottom) toazacitidine. In the responder, the DPsubset was downregulated on day 15 ofthe first cycle, remained at low levelsduring remission (6 months fromtreatment initiation) and expandedwhen the patient lost response toazacitidine and the disease progressed.In contrast, stable, high-level expressionof the DP subset was observed in thenonresponder throughout the diseasecourse. Plots are gated on G-CSFstimulated CD34þ cells.

Miltiades et al.





multipotent progenitor (LMPP)-like and granulocyte-macro-phage progenitor (GMP)-like compartments (25, 28). Interest-ingly, the same CD34þ cell compartments are clonally expandedin late-stage MDS (29). By combining surface with phospho-staining in purified Lin�CD34þ cells of 5 patients, we assessedthe cellular hierarchyof theDP subset (Fig. 4AandSupplementaryFig. S3). We observed that, in comparison with the other majorsignaling subset, namely the CD34þ G-CSF–unresponsive, Stat3/5 double negative (DN) cells of the same patient, the DP subsetwas enriched in LMPP-like (70.8%, range 46%–98.5%, vs. 43%,33%–69%, respectively) and GMP-like cells (79.5%, 53%–98%vs. 43%, 25%–76%), whereas it contained less multipotentprogenitor (MPP)-like (28%, 1.6%–44% vs. 52%, 28.7%-60%), common myeloid progenitor (CMP)-like (19%, 0.9%–

41% vs. 44%, 6.1%–57%), andmegakaryocyte-erythroid progen-itor (MEP)-like cells (1.5%, 0.1%–6.4% vs. 8.5%, 0.6%–14.5%,P ¼ 0.04 for all comparisons, Fig. 4B).

Bcl-2, p53, and Ki-67 are well established molecular indicatorsof resistance to apoptosis (30), oncogenesis (31), and cellular

proliferation (32), respectively, and were therefore used toaddress the molecular properties of the DP subset. After weconfirmed the compatibility of the phospho-flow protocol withthe intracellular staining for the above markers (not shown), wefound that the DP subset, compared with the DN one, exhibiteddecreased pretreatment levels of Ki-67 (52%, 22%–81.7% vs.66.5%, 33%–87%, respectively, P ¼ 0.01) and increased Bcl-2MFI (9.5, 6.9–25.5 vs. 6.1, 3.7–24.1, P < 0.001) and p53 MFI(3.67, 2–14.3 vs. 2.7, 1.6–8.7, P ¼ 0.03), indicating quiescenceand increased antiapoptotic and oncogenic properties, respec-tively (Fig. 5A and Supplementary Fig. S4; refs. 33, 34). Of note,the DP subset displayed significantly increased p53 levels com-pared with the DN one in both p53 mutated (n ¼ 3) andunmutated cases (n¼ 8, data not shown), whereas the expressionof the above molecules in both the DP and DN subsets remainedunaltered on d15 after azacitidine initiation (Fig. 5A), suggestingthat the above signaling subsets represent distinct cellular entitieswith stable characteristics.

CD

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103100 101 102 103

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101

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103

Figure 4.Delineation of the position of theGCSF-inducible Stat3/5DP subpopulation inthe hematopoietic hierarchy. A, assessment of the cellular hierarchy of Lin-CD34þ DP subset by phosphospecific flow cytometry. Representativecontour plots of HSPC compartments in the Lin-CD34þ G-CSF inducibleStat3/5 double positive (DP) and double negative (DN) subsets of patient#39. B, the DP subpopulation contained significantly higher levels ofgranulocyte-macrophage progenitor (GMP)-like and lymphoid-primedmultipotent progenitor (LMPP)-like cells and lower levels of multipotentprogenitor (MPP)-like, common myeloid progenitor (CMP)-like, andmegakaryocyte-erythroid progenitor (MEP)-like cells, compared with the G-CSF–unresponsive, Stat3/5 DN subset, whereas the HSC-like progenitors didnot differ among the two subsets. Five patient sampleswith predominance ofLMPP/GMP-like progenitors were analyzed. �P < 0.05 by Wilcoxon signed-rank test.

A

B

d0 d15

C

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114

MFI

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3R m

RN

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d0 d15

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3R m

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DPhigh DPneg

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114

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P = 0.95 P = 0.8

P = 0.56P = 0.27

Bcl

-2 M

FI

p53

MFI

%Κ

ι-67+

*

d0 d15

* **

** *

d0 d15 d0 d15

Figure 5.Molecular properties of the GCSF-inducible Stat3/5 DP subpopulation. A,expression of Ki-67 (n¼ 12), p53 (n¼ 11) and Bcl-2 (n¼ 13) on the CD34þ DPand DN subsets. The former subpopulation expresses significantly higherpretreatment (d0) levels of Bcl-2 and p53 and lower Ki-67. Identical findingsare observed 15 days after azacitidine initiation (d15), indicating that the DPand DN subsets represent separate cellular entities with distinct molecularcharacteristics. B, the interpatient variability and the azacitidine-inducedalterations of the DP subset are not due to quantitative changes of the G-CSFreceptor (CSF3R). Protein andmRNA levels of CSF3R in unstimulated CD34þ

cells were identical in patients with either very high (>87%, DPhigh, whiteboxes, n ¼ 4) or null (<4%, DPneg, gray boxes, n ¼ 4) expression of the DPsubset (left). C, likewise, in a separate group of four patients, CSF3Rexpression remained unaltered 15 days (d15) after first azacitidineadministration (d0), despite the significant downregulation of the DP subseton day 15 in each of these patients (not shown). �P < 0.05 and ��P < 0.001 byMann–Whitney and Wilcoxon signed-rank test as appropriate.






Taken together, the increased levels of LMPP andGMP-like cellsalong with the high expression of Bcl-2 and p53 and the lower Ki-67 levels imply that the DP subset is enriched in cells with a LSCphenotype.

The expression of CSF3R and its modulation by azacitidine arenot responsible for the interpatient variability and treatment-induced alterations of the DP subpopulation

Myeloid blasts display variable expression of CSF3R and aheterogeneous response to G-CSF stimulation (12, 27, 35). Sur-face CSF3R could not be assessed concomitantly in the DP andDN subsets of G-CSF–stimulated CD34þ blasts because it isdownregulated after G-CSF ligation (36). Therefore, to determinewhether CSF3R levels and/or their modulation by azacitidinecontribute to the interpatient variability and azacitidine-inducedmodifications of the DP subpopulation, we employed two dif-ferent approaches. In the first, we evaluated CSF3R expression on8 samples that showed either absence (<4%, DPneg) or very highexpression (�87%, DPhigh) of the DP subset, while in the secondwemeasured CSF3R levels on day 0 and day 15 of the first cycle ofazacitidine in 4 other patients who displayed significant down-regulation of theDP subset onday15.Using thefirst approach,wefound that patients with either high or null expression of the DPsubset showed identical pretreatmentmRNA and protein levels ofCSF3R (Fig. 5B). Likewise, CSFR3 protein and transcript expres-sion remained unaltered despite the significant downregulationof the DP subset from 66.1% (range, 23%–80%) on day 0 to45.5% (1%–59%, P ¼ 0.008) on day 15 in 4 patients (Fig. 5C),indicating that quantitative changes of CSFR3 are not implicatedin the generation of the G-CSF–inducible DP subset.

DiscussionAbnormal hematopoietic stem/progenitor cell (HSPC) signal-

ing via Stat3/5 is typically observed in leukemic hematopoiesis. Inboth adult and pediatric AML, functional phenotyping of Stat3/5signaling networks by phospho-protein flow cytometry providesimportant prognostic information and pathobiologic insights(12, 27, 37). Yet, despite the reciprocal interactions of DNAmethylation with Stat3/5 signaling (19, 29, 38), the high rate ofmutations (39) and aberrantmethylation of genes involved in cellsignaling (18) in high risk MDS, no current study addresses theStat3/5 signaling alterations at the single HSPC level in suchpatients. Only a recent study explored signaling abnormalitiesin MDS, but it was mainly focused on erythropoietin-inducedStat5 phosphorylation in erythroid progenitors in early diseasestages (6). In the current work, we demonstrate an abnormalStat3/5 signaling biosignature of HSPCs in high-risk MDS, whichis amenable to modulation by azacitidine and can predict treat-ment response and outcome.

Two signaling clusters were identified by hierarchical clus-tering of pretreatment basal and potentiated Stat3/5 and Stat5phosphorylation patterns. Patients in SC-1 displayed betterresponse to azacitidine and longer EFS, whereas OS was alsoprolonged, though not statistically significant. Intriguingly,although correlated favorably with prognosis, SC-1 was inverse-ly associated with cytogenetic risk and showed no correlationwith TET2 mutation status, emphasizing that signaling profilesare not merely a surrogate for the underlying molecular abnor-malities, but instead provide additional information. More-over, the characteristic similarity of SC-2 in our study with the

SC-P2 reported by Irish and colleagues in adult AML (12),which was also associated with disease resistance, highlights thebiologic relationship of AML with high-risk MDS and suggests acommon signaling biosignature of aggressive leukemic HSPCsin adult AML and high-risk MDS. In contrast, increased pStat3response to G-CSF ligation correlated with superior outcome inpediatric AML, potentially reflecting the biologic differencesamong adult and pediatric myeloid malignancies (27). Nota-bly, patients with nonclonal myelopoiesis and responders toazacitidine shared an identical Stat3/5 signaling biosignaturecharacterized by little or no potentiated G-CSF responses,further corroborating the role of Stat3/5 signaling aberrationsin azacitidine resistance.

Further analysis of Stat3/5 signaling profiles revealed a G-CSF–inducible Stat3/5 DP subpopulation of CD34þ cells whose levelscorrelated inversely with response to azacitidine. A phenotypi-cally identical, chemoresistant subpopulation has been previous-ly reported in adult AML and Ph� myeloproliferative neoplasms(12, 14), but no further investigation of the kinetics and themolecular properties of the DP subset was conducted. Weobserved a remarkable downregulation of the DP subset inresponding patients on day 15 after the first azacitidine admin-istration, whereas nonresponders exhibited no changes. Moreimportant, the kinetics of the DP subset in responders mirroredthose of the tumor burden and treatment response, as the formersubpopulation remained at low levels during CR, whereas aparallel development of resistance to azacitidine with an expan-sion of theDP subset occurred. Thesefindings strongly suggest, onone hand, that azacitidine can restore and partially control thepathologic Stat3/5 signaling in responding patients and on theother involvement of the DP subset in mechanisms underlyingdisease progression and azacitidine resistance. Of note, thoughthe distinction of clonal from normal hematopoiesis is oftenproblematic inMDS, the alterations of the DP subset pertained toclonal HSPCs as shown by two findings. First, the blast percentageon day 15 of the first cycle was identical to the pretreatment oneand second, three responding patients (2 with CR and one withHI) who downregulated significantly the DP subset after 6 cycleshad still abnormal karyotype indicating persistence of clonalhematopoiesis. In addition, it has been clearly demonstrated thateven patients in CR have residual MDS HSPCs in substantialnumbers (25, 29).

Although previous studies linked the DP subset to aggressivedisease, there is currently no detailed phenotypic and molecularcharacterization of the former subpopulation. First, we confirmedthe compatibility of the phospho-flow techniques with the intra-cellular measurement of Bcl-2, p53, and Ki-67 and the accurateassessment of HSPC subsets as has been previously shown(refs. 40, 41; Supplementary Fig. S3). We then observed a signif-icant enrichment of the DP subset in LMPP-like and GMP-likecells compared with the other dominant CD34þ signaling subset,the G-CSF–unresponsive Stat3/5-negative subpopulation. Also,Bcl-2 and p53 levels were significantly higher in the DP subset,whereas Ki-67 expression was lower compared with the DNsubset. Considering the negative role of Bcl-2 and p53 in thepathobiologyofMDS (33, 42, 43), thedownregulationofKi-67 inCD34þ cells when MDS progresses to overt leukemia (33) andrecent data regarding the hierarchy of LSCs (25), we surmise thatthe DP subset potentially possesses properties of leukemia-prop-agating cells, whichmay account for its association with poor riskdisease and resistance to azacitidine.

Miltiades et al.





Hypersensitivity of HSPCs to G-CSF may predispose to leuke-mic transformation and propagation via overexuberant Stat3/5activation, but there is considerable interpatient variability of G-CSF responsiveness in AML (12, 44, 45). We found that thedifferential response of Stat3/5 to G-CSF in our patients is notrelated to the protein and mRNA levels of CSF3R, suggesting thatothermechanisms such as qualitative defects of CFS3R, abnormalreceptor-associated proteins, dysfunctional downstream signal-ing elements, or abnormalities of positive or negative regulators ofsignaling via Stat3/5 are potentially responsible for the variabilityin G-CSF sensitivity (46). Of note, azacitidine downregulated theDP subset only in responding patients, implying that diversemolecular defects are responsible for the induction of the DPsubset in MDS patients, only a part of which is susceptible toepigenetic reprogramming by azacitidine.

In contrast to our results, Redell and colleagues observed apositive correlation of CSF3R expression with the magnitude ofpStat3 induction in pediatric AML samples. However, no associ-ation with simultaneous potentiation of Stat3 and Stat5 withCSF3R levels was reported, whereas several samples with highlevels of CSF3R failed to induce a Stat3/5 response. Moreover, thefact that CSF3R knockout mice can still mobilize effectivelyhematopoietic progenitors provide further support for aCSF3R-independent mechanism of induction of the DP subsetin MDS patients (44).

Collectively, we report for the first time a disturbed Stat3/5signaling architecture in high-risk MDS, which is amenable tomodulation by azacitidine therapy in responding patients and itsalterations parallel disease activity. Aside from furnishing criticalinsights in MDS biology, our findings have obvious translationalimplications. There is paucity of a serviceable biomarker ofoutcome in MDS patients treated with azacitidine, whereas themechanisms of resistance to azacitidine are largely unknown andthere is currently no effective treatment after azacitidine failure.The prognostic relevance of the Stat3/5 biosignature of MDSHSPCs in our study may help to identify which patients benefitmost from azacitidine, while its alterations during the diseasecourse can provide a tool for early detection of disease progres-sion. Also, small-molecule JAK inhibitors and siRNA-mediatedknockdown of Stat3/5 decreased the growth of CD34þ cells fromhigh-risk AMLpatients both in vitro and in vivo (47), whereas Stat3

inhibitors induced apoptosis and decreased colony formation inHSPCs from bothMDS (29) and AML (45) patients. Importantly,in all studies, the pharmacologic blockade of Stat3/5 selectivelytargeted clonal but spared normal HSPCs. Further corroboratingthese observations, our findings may serve as a guidepost for theongoing investigation of Stat3/5 inhibition as a therapeuticstrategy to overcome azacitidine resistance (4, 48).

Disclosure of Potential Conflicts of InterestT.P. Vassilakopoulos is a consultant/advisory board member for Genesis

Pharma. S.G. Papageorgiou reports receiving speakers bureau honoraria fromGenesis. I. Kotsianidis reports receiving speakers bureau honoraria and com-mercial research grants from Genesis Pharma. No potential conflicts of interestwere disclosed by the other authors.

Authors' ContributionsConception and design: I. KotsianidisDevelopment of methodology: P. Miltiades, E. Lamprianidou, S.G. Papageor-giou, E. Nakou, I. KotsianidisAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): T.P. Vassilakopoulos, S.G. Papageorgiou, A. Galano-poulos, S. Vakalopoulou, V. Garypidou, E. Hatjiharissi, H.A. Papadaki,E. Spanoudakis, C. Tsatalas, I. KotsianidisAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): P. Miltiades, E. Lamprianidou, T.P. Vassilakopoulos,C.K. Kontos, P.G. Adamopoulos, M. Papaioannou, E. Spanoudakis,I. KotsianidisWriting, review, and/or revision of the manuscript: P. Miltiades, E. Lampria-nidou, T.P. Vassilakopoulos, S.G. Papageorgiou, C.K. Kontos, V. Pappa,A. Scorilas, I. KotsianidisAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): P. Miltiades, E. Lamprianidou, I. KotsianidisStudy supervision: V. Pappa, I. KotsianidisOther (carried out part of the mutational analysis): C.K. Kontos,P.G. Adamopoulos

Grant SupportThis work was supported in part by an educational grant from Genesis

Pharma Hellas (to I. Kotsianidis).The costs of publication of this articlewere defrayed inpart 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 2, 2015; revisedOctober 8, 2015; acceptedNovember 26, 2015;published OnlineFirst December 23, 2015.

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Miltiades et al.




2016;22:1958-1968. Published OnlineFirst December 23, 2015.Clin Cancer Res Paraskevi Miltiades, Eleftheria Lamprianidou, Theodoros P. Vassilakopoulos, et al. Myelodysplastic Syndrome Patients Treated with AzacitidineStem/Progenitor Cells Predicts Response and Outcome in The Stat3/5 Signaling Biosignature in Hematopoietic

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