chloroquine eliminates cancer stem cells through deregulation of jak2 and dnmt1

STEM CELLS 2014;00:00‐00 www.StemCells.com ©AlphaMed Press 2014

CANCER STEM CELLS 1Methodist Cancer Center, Houston

Methodist Hospital, Houston, TX 77030.2Department of Nanomedicine, Hou‐ston Methodist Research Institute, Houston, TX 77030.3Department of Translational Imaging, Houston Methodist Research Insti‐tute, Houston TX 77030.4Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medical College, Houston, TX 77030. 5NCI Center for Modeling Cancer Develop‐ment, Houston Methodist Research Insti‐tute, Houston, TX 77030.6Department of Molecular Medicine/Institute of Biotech‐nology, The University of Texas Health Sci‐ence Center at San Antonio, San Antonio, TX, 782297Weill Cornell Medical School, NY, NY 10065, USA

*To whom correspondence should be

addressed: Jenny Chang, 6445 Main St., P21‐34, Houston, TX 77030, USA. Phone: 713‐441‐0681, Fax: 713‐793‐1642, E‐mail: [email protected]; †These authors contrib‐uted equally to this work.; Acknowledg‐ment: This work was supported by NIH/NCI grants R01 CA138197, U54 CA149196, Golf‐ers against Cancer, Breast Cancer Research Foundation, Causes for a Cure, Team Tiara, Emily W. Herrman Cancer Research Labora‐tory, and Komen for Cure KG 081694. We declare that none of the authors have any financial interest related to this work.

Received September 10, 2013; accept‐

ed for publication March 30, 2014; available online without subscription through the open access option.

©AlphaMed Press 1066‐5099/2014/$30.00/0 This article has been accepted for pub‐

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Please cite this article as doi: as doi: 10.1002/stem.1746

Chloroquine Eliminates Cancer Stem Cells through Deregulation of Jak2 and DNMT1 DONG SOON CHOI1†, ELVIN BLANCO2†, YOO‐SHIN KIM3, ANGEL A.

RODRIGUEZ1, HONG ZHAO4,5, TIM HUI‐MING HUANG6, CHEN,

CHUN‐LIANG6, GUANGXU JIN4, MELISSA D. LANDIS1, LACEY A. BUREY1, QIAN, WEI

1, SERGIO M. GRANADOS1, BHUVANESH

DAVE1, HELEN H. WONG1, MAURO FERRARI2, STEPHEN TC

WONG 4,5, JENNY C. CHANG1,6*

Key words. Breast Cancer Stem Cells • Autophagy • Chloroquine • DNMT1 •

Jak2. ABSTRACT

Triple negative breast cancer (TNBC) is known to contain a high per‐

centage of CD44+/CD24‐/low cancer stem cells (CSC), corresponding with a poor prognosis despite systemic chemotherapy. Chloroquine (CQ), an anti‐malarial drug, is a lysotropic reagent which inhibits autophagy. CQ was identified as a potential CSC inhibitor through in silico gene expression sig‐nature analysis of the CD44+/CD24‐/low CSC population. Autophagy plays a critical role in adaptation to stress conditions in cancer cells, and is related with drug resistance and CSC maintenance. Thus the objectives of this study were to examine the potential enhanced efficacy arising from addi‐tion of chloroquine (CQ) to standard chemotherapy (paclitaxel) in TNBC and to identify the mechanism by which CQ eliminates CSCs in TNBCs. Herein, we report that CQ sensitizes TNBC cells to paclitaxel through inhibi‐tion of autophagy and reduces the CD44+/CD24‐/low CSC population in both preclinical and clinical settings. Also, we are the first to report a mecha‐nism by which CQ regulates the CSCs in TNBC through inhibition of the Ja‐nus‐activated kinase 2 (Jak2) ‐ Signal transducer and activator of transcrip‐tion 3 (STAT3) signaling pathway by reducing the expression of Jak2 and DNA methyltransferase 1 (DNMT1). STEM CELLS 2014; 00:000–000

Chloroquine inhibits DNMT1 and Jak2 in CSC.

www.StemCells.com ©AlphaMed Press 2014

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INTRODUCTION

Triple negative breast cancer (TNBC) is defined by

the absence of expression of estrogen receptor, proges‐terone receptor, and human epidermal growth factor receptor 2 (Her2). It is an aggressive and lethal form of breast cancer with relative lack of targeted therapeutic options and poor prognosis [1, 2]. It constitutes roughly 15% of breast cancer cases and accounts for 25% of breast cancer mortality principally due to early relapse and frequent metastasis [1, 2]. In addition, TNBC has a higher percentage of CD44+/CD24‐/low cancer stem cells (CSC) compared to other types of breast cancer [1, 3]. We have previously reported an enrichment of CD44+/CD24‐/low CSCs following chemotherapy in wom‐en with locally advanced breast cancer, suggesting re‐sistance of CSCs to conventional therapy [4]. As chemo‐therapy is the only choice of treatment for TNBC pa‐tients, a viable strategy is needed to target CSCs in addi‐tion to conventional chemotherapy [5, 6].

Autophagy constitutes a salvage pathway for recy‐cling nutrients that has been implicated in various as‐pects of cancer, including cell survival under stress con‐ditions, drug resistance, and metastasis [7, 8]. Moreo‐ver, autophagy‐mediated metabolic coupling between cancer cells and neighboring stromal cells has been identified as a mechanism of cancer survival, growth, and resistance to therapy [9, 10]. Additionally, recent reports emphasize critical roles of autophagy in mainte‐nance of CSC self‐renewal in breast cancer [11, 12], leading to the exploration of potential anti‐autophagy strategies for eliminating CSCs in preclinical and clinical studies.

Chloroquine (CQ) is an anti‐malarial drug known to inhibit autophagy by disrupting lysosomal stability and function [7]. Previously, we identified a CD44+/CD24‐/low MS‐forming treatment‐resistant gene expression signa‐ture using biopsies obtained from women with primary breast cancer [4]. Through network analysis of the gene signature, CQ was repositioned as a putative anti‐cancer drug against CD44+/CD24‐/low CSCs. Interestingly, CQ has been identified as a cancer stem cell targeting agent for other aggressive cancers including breast cancer [11, 12], glioblastoma multiforme [13], and chronic myeloid leukemia [14]. However, the mechanism by which CQ affects the CD44+/CD24‐/low CSCs remains unclear.

We investigated the therapeutic potential of CQ in combination with paclitaxel (PTX) on the CD44+/CD24‐/low CSC population, and determined the value and fea‐sibility of incorporating CQ with chemotherapy for treatment of therapy‐resistant TNBC. We hypothesized that CQ affects the CSC self‐renewal through the inhibi‐tion of autophagy. Our findings suggest that CQ reduces the CD44+/CD24‐/low CSCs population in TNBC cells through autophagy and by down‐regulation of Janus‐activated kinase 2 (Jak2) signaling pathway with a con‐comitant inhibition of DNA methyltransferase 1 (DNMT1) expression.

MATERIALS AND METHODS

Materials and Cell culture. Triple negative breast

cancer cell lines (Hs578t, MDA‐MB‐231, HCC1937, and HCC38) were purchased from American Type Culture Collection (Manassas, VA, USA), with the exception of SUM159PT (Asterand, Detroit, MI). All cells were main‐tained in DMEM (Invitrogen, Grand Island, NY) and 10% FBS (Thermos Scientific Hyclone, Rockford, IL) in a hu‐midified 5% CO2 incubator at 37°C. SUM159PT cells were first maintained in F12 (Invitrogen) containing 10% FBS, insulin (5 μg/ml), and hydrocortisone (1 μg/ml), then adjusted to DMEM (high glucose and glutamine) with 10% FBS. All chemicals were purchased from Sigma unless otherwise specified. Chloroquine was first dis‐solved in DPBS (Invitrogen) at the concentration of 0.1 M (kept in ‐80°C) and diluted further in DPBS (CQ 1 mM). All CD marker antibodies and mouse IgG isotype antibodies were purchased from BD Biosciences, San Jose, California. Rabbit polyclonal anti‐p‐Jak2, rabbit monoclonal anti‐Jak2, rabbit polyclonal anti‐pSTAT3‐705, rabbit polyclonal anti‐pSTAT3‐727, mouse mono‐clonal STAT3, and mouse monoclonal anti‐Actin anti‐bodies were purchased from Cell Signaling Technology, Danvers, MA. Mouse monoclonal anti‐DNMT1, rabbit polyclonal anti‐SOCS1, and mouse monoclonal anti‐SOCS3 were purchased from Santa Cruz Biotechnology Inc., Dallas, TX. SYTOX® Blue Nucleic Acid Stain (SYTOX‐Blue) was purchased from Invitrogen for nuclear stain‐ing of dead cells.

In silico drug Repositioning for breast CSCs. Our previously published gene expression data of breast CSCs (CD44+/CD24‐/low and MS‐forming treatment‐resistant cells) was used for in silico drug repositioning analysis (GSE7513, SE7515 and GSE10281 [4]). The Can‐cer Signaling Bridges (CSBs)–based drug repositioning computational modeling method was applied to derive specific CSCs signaling pathways [15, 16].

Mammosphere Assay. Mammosphere (MS) assay was performed as previously described with minor modification [4, 17]. Modified methods are described in the Supplementary Materials and Methods.

Fluorescence‐activated cell sorting (FACS) analysis. Cell lines and clinical samples were stained with anti‐bodies against CD44‐APC and CD24‐FITC for FACS analy‐sis and cell sorting as previously described [17]. A sin‐gle‐arm, phase two clinical trial (NCT01446016) is cur‐rently active and enrolling patients at our institution. Patients with metastasis or locally advanced breast can‐cer previously treated with anthracyclines underwent treatment with a combination of taxane and chloroquine. Biopsies were then obtained at baseline and at day 42 after treatment. FACS analysis and sorting was performed at the Houston Methodist Hospital Re‐search Institute flow cytometry core using BD FACS Fortessa for FACS analysis of CSCs and BD FACS Aria II for cell sorting.



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Western blot and Immunoprecipitation Assays. Western blotting and immunoprecipitation experiments were performed with the listed primary and matching secondary antibodies as described previously [18]. De‐tailed procedures are described in the Supplementary Materials and Methods.

In vivo experiments. All animal procedures were approved by the Methodist Hospital Research Institute Animal Care and Use Review Office. Athymic nude Mice (Hsd:Athymic Nude‐Foxn1nu) (5 weeks old; 20‐23 g) were purchased from Harlan Laboratories, Inc., Hou‐ston, TX. Detailed methods are described in the Sup‐plementary Materials and Methods.

Immunofluorescence staining for the co‐localization of Jak2 and SOCS3. Cells were fixed and stained using antibodies listed in Supplementary Mate‐rials and Methods as described previously [18].

Real‐Time PCR for SOCS1 and SOCS3. Real‐Time PCR for SOCS1 and SOCS3 was performed as described pre‐viously [17] with minor modifications. Detailed methods are described in the Supplementary Materials and Methods.

SOCS3 promoter PCR for methylation analysis. For the PCR primer design, sequences of proximal SOCS3 promoter regions (‐5676 and +2633) was obtained from the NCBI reference sequence (NC_000017.10 GI:224589808) for Homo sapiens chromosome 17, GRCh37.p13 Primary Assembly. Primers were then de‐signed using primer3 [19] to result in about 200 to 250‐bp of PCR products. The sequences and the site of each primer are indicated in Supplementary Table S1.

Methyl‐CpG‐binding domain proteins‐enriched DNA sequencing (MBDCap‐Seq) assay and data analysis. Methylated DNA from control and chloroquine‐treated MDA‐MB‐231 cells was eluted using the MethylMiner Methylated DNA Enrichment Kit (Life Technologies) following the manufacturer’s instructions as described below. Genomic DNA was sonicated to ~300‐bp frag‐ments. Methylated DNA was captured by methyl‐CpG‐binding domain proteins and subsequently eluted in 1 M salt buffer for precipitation. Libraries were generated from eluted DNA (>10 ng) for single‐end 50‐bp sequenc‐ing following the protocols from Illumina (San Diego, CA). MBDCap‐seq libraries were sequenced using the Illumina HiSeq 2000 system protocols. Image analysis and base calling were performed with the standard Illumina pipeline. Using the ELAND algorithm, unique reads (up to 50 bp reads) were mapped to the human reference genome (hg19) with Bowtie version 0.12.7 [20] with reported parameters [21]. Further analysis of the MBDCap‐seq data was performed by the Houston Methodist Research Institute Genomics Core as de‐scribed in the Supplementary Materials and Methods.

Statistical Analysis. We used two‐tailed Student’s t‐test for comparison of two groups and one‐way ANOVA for multiple group comparison. Two‐way ANOVA was used for all animal experiments. Each value reported represents the mean of at least three replicate experi‐ments with standard deviations. The values in the ani‐

mal experiments represent the mean of 10 individual mice per group with standard error of the mean. Data were tested for normal distribution, and Student’s t‐test and ANOVA were used to determine statistical signifi‐cance. To account for multiple comparisons, Tukey’s multiple comparison tests for one‐way ANOVA and Boneferroni post tests for two‐way ANOVA were per‐formed with Graphpad Prism 5.0 (Graphpad Software Inc., La Jolla, CA, USA). In all cases, p values < 0.05 were considered statistically significant.

All other materials and methods are described in the Supplementary Materials and Methods.

RESULTS

In silico drug repositioning. Differentially expressed

genes (p<0.001, Student’s t test) from the CD44+/CD24‐/low and MS‐forming treatment‐resistant cells were used to identify CSC pathways (p<0.05, Fisher exact two‐tailed test). The enriched pathways included: NOTCH, VEGF, PTEN, sonic Hedgehog, Wnt/β‐catanin, JAK/STAT, P53, and PI3K/AKT signaling. The CSB‐analysis was then performed to extend the incomprehensive pathways and establish cross‐talks within pathways [15, 16]. The signaling networks included 140 gene nodes for the CD44+/CD24‐/low cells (Fig 1A and Supplementary Fig. S1A) and 153 gene nodes for the MS‐forming treat‐ment‐resistant cells (Supplementary Fig. S1B). After mapping all gene nodes to the drug database, a total of 21 drugs, including chloroquine, auranofin, and arsenic trioxide, were identified as candidate drugs which could target the CSC pathways. We chose to focus on chloroquine (CQ), which has been clinically used for several decades, displaying a safe toxicity profile, alone and in combination with paclitaxel.

CQ inhibits mammosphere formation and reduces CD44+/CD24‐/low populations in TNBC cell lines. To de‐termine whether CQ would have an effect on decreas‐ing mammosphere forming efficiency (MSFE), we per‐formed a dose response experiment for CQ in four dif‐ferent TNBC cell lines, Hs578t, MDA‐MB‐231, SUM159PT, and HCC1937 as shown in Fig. 1B. Even though sensitivity to CQ varied according to cell line, we found that CQ at 1 or 5 µM effectively decreased prima‐ry MSFE in Hs578t, MDA‐MB‐231, and HCC1937 TNBC cell lines (Fig. 1B), and also secondary MS formation in SUM159 and MDA‐MB‐231 cells (Fig. 1B) by specifically targeting the CD44+/CD24‐/low populations (Supplemen‐tary Fig. S2A). Hs578t and HCC1937 cells did not form secondary MS under the same culture conditions. Simi‐larly, we observed a significant dose‐dependent reduc‐tion in CD44+/CD24‐/low populations (15% to 50%) with CQ treatment alone or in combination with paclitaxel (PTX), correlating with the observed decrease in prima‐ry and secondary MSFE (Fig. 1C). Additionally, we found that CQ reduced breast CSCs identified by Aldehyde dehydrogenase1 (ALDH1) activity through ALDEFLUOR assay as described previously [22]. CQ alone showed significant reduction of ALDEFLUOR‐positive cells in



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MDA‐MB‐231 (50‐fold decrease) and SUM159PT (8‐fold decrease) (Supplementary Fig. S2B).

CQ‐PTX treatment reduced CD44+/CD24‐/low popula‐tion in patients. A clinical trial is currently underway to evaluate the efficacy of CQ in combination with PTX in women with treatment‐refractory advanced or meta‐static breast cancer. Consistent with in vitro results, the combination treatment of CQ and PTX reduced the CD44+/CD24‐/low population by 5‐ to 6‐fold in two pa‐tients after treatment cycles (Fig. 1D). However, a min‐imal reduction of the CSC population was observed in one patient. These results support the preclinical find‐ings and confirm the potential for improved patient response resulting from the combination of CQ and taxane therapy.

Inhibition of autophagy by CQ sensitizes TNBC cells to Paclitaxel. We next investigated whether the reduc‐tion of CSCs by CQ could be correlated with inhibition of autophagy, thus sensitizing TNBCs to chemotherapy. Firstly, inhibition of autophagy was confirmed by ob‐serving accumulation of autophagosomes in Hs578t cells treated with CQ (1 µM) alone and in combination with PTX (5 nM) using TEM. Autophagosomes were not detected in either control or PTX‐treated cells (Fig. 2A). Additionally, CQ induced puncta formation (green) and inhibited the formation of PTX‐induced autophagolysosomes (yellow) in MDA‐MB‐468 cells, expressing GFP‐tagged LC3B (Supplementary Fig. S3A). The inhibition of autophagy was further confirmed by detection of LC3B‐II and up‐regulated p62 in all cells treated with CQ alone or in combination with PTX (Fig. 2B). In PTX‐treated cells, a marginal increase in LC3B‐II along with a partial increase or decrease in p62 was observed (Fig. 2B), indicating autophagy induction. En‐hanced antitumor effects of the combination treatment over PTX alone were confirmed by increased cleaved caspase‐3 (Fig. 2B) and by enhanced apoptosis meas‐ured by Annexin V and/or Sytox‐Blue positive cell popu‐lations (Supplementary Fig. S3B). Additionally, CQ alone increased cleaved caspase‐3 in Hs578t and MDA‐MB‐231 cells (Fig 2B). Thus, these results suggest that CQ may be used in combination with chemotherapy in TNBC cells.

In vivo inhibition of tumor growth and lung metas‐tasis by CQ. We observed a significant 50% (p<0.0001) in vivo growth inhibition in orthotopic MDA‐MB‐231 G/L tumors by CQ treatment alone compared to controls (Fig. 2C). Additionally, the CQ treatment prevented spontaneous lung metastasis from 90% in controls to 20% in treatment mice, with significant reduction of tumor burden in lungs (p<0.003) (Fig. 2D). We next compared the impact of CQ‐PTX treatment against PTX alone in MDA‐MB‐231 G/L orthotopic tumor models. The combination treatment reduced tumor size by 50% compared to PTX alone (p<0.001) (Fig. 2E). Further‐more, we observed significantly slower tumor recur‐rence in CQ‐PTX treated mice compared to PTX alone treatment arm; 20% of the mice in the CQ‐PTX group showed complete regression of tumor during the

treatment cycle with no recurrence observed. Moreo‐ver, an additional 20% of the mice in the CQ‐PTX group showed consistent reduction in tumor size even after the last treatment, in contrast to continuous tumor growth observed in all mice in the PTX group (data not shown). The anti‐tumor effects of CQ‐PTX were also confirmed in the SUM159PT orthotopic xenograft mod‐el involving a four‐week treatment of Control (PBS) CQ (10mg/kg, daily, i.p.), PTX (15mg/kg, twice per week, i.p.), or in combination. Consistently, the CQ‐PTX com‐bination treatment arm was the only group to show significant inhibition of tumor growth while CQ alone or PTX alone showed no statistical difference in tumor volume compared to controls (Fig. 2F). These results may suggest that CQ enhances the anti‐tumor effects of PTX by decreasing the CSCs.

CQ reduces breast cancer stem cells in vivo. For cancer stem cell analysis, additional cohorts of mice bearing either MDA‐MB‐231 (n=7) or SUM159PT (n=5) orthotopic tumors were treated for two weeks with vehicle, CQ (10mg/kg, daily), PTX (15mg/kg, twice per week) or the combination, CQ‐PTX. We confirmed the enhanced anticancer effects of CQ‐PTX in both tumor cell lines compared to the control group or PTX alone (Fig. 3A and 3B). Additionally, we found that PTX signifi‐cantly increased the Aldeflour+ CSCs by three‐fold in MDA‐MB‐231 tumors (Fig. 3C) and the CD44+/CD24‐/low CSCs by two‐fold in SUM159PT models (Fig. 3D) com‐pared to controls. We did not observe any significant change in the CSC population by CQ alone, but CQ in combination with PTX reduced the PTX‐induced CSC population to control levels in both tumor cell lines (Fig. 3C and Fig 3D). We further investigated the tumorigenic potential of tumors by testing sphere forming ability. Interestingly, the PTX‐induced CSC increase correlated well with the increased MSFE in both the primary and the secondary MS of MDA‐MB‐231 and SUM159PT tu‐mors compared to the controls (Fig. 3E and 3F). The CQ‐PTX combination treatment significantly inhibited the PTX‐induced primary MSFEs of the two tumor cell lines comparable to control levels in the primary MS, and further reduced the MSFE more than four times lower than controls in the secondary MS for both MDA‐MB‐231 (Fig. 3E) and SUM159PT tumors (Fig. 3F). CQ did not alter the sphere forming ability compared to controls in the primary MS, but reduced the secondary MSFE by 4 fold in MDA‐MB‐231 tumors (Fig. 3E) and 2 fold in SUM159PT tumors (Fig. 3F). Finally, we confirmed the CSC targeting effects of CQ through a limiting dilution assay for MDA‐MB‐231 tumors using three dilutions; 75,000 (75k), 25,000 (25k), and 5,000 (5k) cells. CQ or CQ combination with PTX completely inhibited tumor formation for 6 weeks in all three dilutions of cells com‐pared to controls or PTX (Fig. 3G). As anticipated, the PTX‐mediated CSC increase also correlated well with higher tumor incidence rates at cell each dilution assay compared to controls; 100% vs 38% at 75k, 50% vs 13% at 25k, and 75% vs 38% at 5k dilutions (Fig. 3G). Also, by pairwise comparison, we confirmed that CQ significant‐



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ly reduced the CSC frequencies in tumors compared to controls or the PTX treatment group (Fig. 3G). Together, these results strongly support the CSC‐targeting effects of CQ in vivo.

CQ inhibits Jak2‐STAT3 signaling pathway in CSCs. As the Jak2/STAT3 signaling pathway is critical for maintenance of breast cancer stem cells [5], we investi‐gated the effects of CQ, PTX, and the combination on this signaling pathway. The phosphorylation of STAT3 (Tyr705) was compromised by CQ alone, PTX, or CQ‐PTX in Hs578t and SUM159PT cells, while CQ‐PTX was most effective at inhibiting phosphorylation (Fig. 4A). Analo‐gously, we observed significant reduction of pSTAT3 by CQ or CQ‐PTX compared to controls in MDA‐MB‐231 cells. However, PTX induced a substantially higher phosphorylation of STAT3 (Fig. 4A). The changes in STAT3 phosphorylation were correlated with the phos‐phorylation status of Jak2 in all three cell lines. Interest‐ingly, we observed significant reduction of Jak2 expres‐sion by CQ‐PTX in all three cell lines (Fig 4A). We next investigated the Jak2‐STAT3 signaling pathway in sorted CD44+/CD24‐/low CSC and non‐CSC populations of SUM159PT cells when treated with either CQ, PTX, or in combination, CQ‐PTX. We observed a reduction of Jak2 phosphorylation in CSCs by CQ, PTX, and CQ‐PTX, with the most significant inhibition achieved with CQ‐PTX compared to controls (Fig 4B). In non‐CSCs, only the combination treatment inhibited Jak2 phosphorylation. However, we found substantial reduction in Jak2 follow‐ing CQ‐PTX treatment only in the CSCs (Fig 4B). Addi‐tionally, CQ inhibited pSTAT3‐705, albeit, less signifi‐cantly than CQ‐PTX treatment, only in CSCs of SUM159PT, while PTX alone showed no effects (Fig.4B). In non‐CSCs, pSTAT3‐705 was up‐regulated by CQ, PTX, and CQ‐PTX. Consistently, the combination treatment also reduced the phosphorylation of STAT3 at S727 in CSCs (Fig.4B). Furthermore, CQ alone or in combination with PTX substantially inhibited the PI3K/Akt/mTOR pathway, an alternate pathway that can activate STAT3 in breast CSCs [23], through activation of PTEN (Sup‐plementary Fig. S4). These results suggest that CQ may affect CSCs by inhibiting activation of STAT3 and by re‐ducing Jak2 expression.

CQ‐PTX induces the expression of suppressor of cy‐

tokine signaling (SOCS) families in CSCs. Since SOCS1 and SOCS3 are known to induce Jak2 degradation upon its activation [24, 25], we investigated whether the SOCS family plays a role in CQ‐mediated Jak2/STAT3 deregulation. Gene expression analysis by RT‐PCR showed no alteration of Jak2 gene expression under any treatment (data not shown). In SUM159PT CSCs, a time‐dependent increase in SOCS1 and SOCS3, and reciprocal decrease in pJak2 and Jak2, was found following CQ‐PTX treatment compared to PTX alone at 48 hours (Fig. 4C). However, in an immunoprecipitation assay, SOCS3 was found associated with Jak2 and not SOCS1 in SUM159PT CSCs (Fig. 4D). Using immunofluorescence co‐

localization imaging, the increased interaction of Jak2 with SOCS3 was confirmed in SUM159PT CSCs treated with CQ‐PTX in comparison to PTX alone (Fig. 4E). Final‐ly, we were able to rescue Jak2 expression by silencing SOCS3 using siRNA in SUM159PT CSCs treated with CQ‐PTX (Fig. 4F). Moreover, silencing SOCS3 expression increased Jak2 protein level in normal culture condi‐tions, hinting at the Jak2 regulating nature of SOCS3 in SUM159PT CSCs (Supplementary Fig. S5). Taken togeth‐er, these results confirm that CQ‐PTX treatment result‐ed in the expression of SOCS1 and SOCS3 and enhanced interaction of SOCS3 with Jak2, causing reduction of Jak2 protein level in CSCs.

CQ suppressed the expression of DNA methyltransferase 1 in CSCs. The expression of SOCS1 and SOCS3 can be regulated by DNA methylation [26, 27]. To that end, we found that the CQ‐PTX combina‐tion treatment significantly reduced DNMT1 in of Hs578t, SUM159PT, and MDA‐MB‐231 bulk tumors compared to controls or PTX alone treatment (Fig. 5A). Likewise, we also observed significantly reduced DNMT1 by CQ or CQ‐PTX compared to controls and PTX alone respectively in CSCs and non‐CSCs of SUM159PT, while PTX increased DNMT1 expression in both popula‐tions of cells (Fig. 5B). The negative effects of CQ‐PTX on DNMT1 expression in CSCs of basal‐like TNBCs HCC1937 and HCC38 (Fig. 5B) was further confirmed. The changes in DNMT1 protein levels induced by CQ or CQ‐PTX sig‐nificantly correlated with changes in global DNA meth‐ylation. In Hs578t and MDA‐MB‐231 cells, CQ alone in‐duced hypomethylation by 50% (p<0.0001) and 8% (p<0.05), respectively (Fig. 5C). PTX also induced hypomethylation in Hs578t by 50% (p<0.0001), while no changes were observed in MDA‐MB‐231 cells. CQ‐PTX induced the most significant hypomethylation in both cell lines compared to controls or to PTX. In SUM159PT bulk tumor cells, no changes in methylation were ob‐served following CQ treatment, while PTX or CQ‐PTX induced substantial hypermethylation (Supplementary Fig. S6). However, CQ induced global hypomethylation in CSCs of SUM159PT by 50% (p<0.001) while PTX in‐duced hypermethylation (p<0.0001) compared to con‐trols (Fig. 5C). CQ‐PTX reduced global methylation by 10% relative to PTX treatment (p<0.05) (Fig. 5C). It is important to note that more than 85% in Hs578t and 97% of MDA‐MB‐231 cells were CD44+/CD24‐/low. Thus, we confirmed that the increase in SOCS1 and SOCS3 expressions was due to the down‐regulation of DNMT1 in SUM159PT CSCs (Fig. 5D). However, we found a 4‐fold increase in SOCS3 mRNA alone in CSCs treated with CQ‐PTX compared to PTX, while no difference in SOCS1 mRNA was detected (Fig. 5E). This result suggests that SOCS1 up‐regulation might be an indirect effect of DNA hypomethylation. Consequently, we observed CQ‐PTX induced hypomethylation in three different promoter regions of SOCS3 after CQ‐PTX treatment in SUM159PT CSCs compared to PTX (Fig. 5F). We also confirmed the effects of CQ‐PTX on DNMT1, pSTAT3, and Jak2 in vivo (Supplementary Fig. S7A and S7B). Taken together, our



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data suggests that CQ regulates the Jak2‐STAT3 path‐way to target CSCs through DNA methylation of SOCS3 in the presence of PTX.

Jak2‐STAT3 and DNMT1 synergistically regulate TNBC CSCs. Using siRNAs, we examined the impact of silencing Jak2, STAT3, and DNMT1, on TNBC CSCs. The silencing efficiency in Hs578t, MDA‐MB‐231, and SUM159PT cells was confirmed by detection of DNMT1, Jak2, and STAT3 using western blot assay (Fig. 6A). As shown in Figure 6B, silencing either of the genes result‐ed in reduction of the CD44+/CD24‐/low population by 5‐10% in Hs578t and MDA‐MB‐231 cells. The reduction of CSCs was more significant when two of the three genes were silenced simultaneously in Hs578t and MDA‐MB‐231 cells, resulting in an approximate 15% to 20% re‐duction of CSCs. However, the most significant reduc‐tion of CSCs was observed when all three genes were silenced simultaneously, resulting in roughly 25‐30% reduction of CSCs (Fig. 6B). Contrary to the aforemen‐tioned cell lines, SUM159PT cells showed a significant 50% reduction of CSCs following silencing of a single gene, with effects enhanced through silencing of Jak2 or STAT3 with DNMT1. However, in SUM159PT, the most effective CSC reduction was achieved when all three genes were silenced simultaneously. An MS assay was then performed after silencing each gene using specific siRNA in all three cell lines. Contrary to the FACS analy‐sis of the CD44+/CD24‐/low CSCs, the silencing of DNMT1, Jak2, or STAT3 altered MSFE more dramatically, with roughly a 30% to 70% reduction of MSFE observed in MDA‐MB‐231 and SUM159PT cells compared to con‐trols (Fig. 6C). In Hs578t cells, STAT3 silencing alone was effective at inhibiting MSFE by 70% (Fig. 6C). STAT3 si‐lencing was more effective at reducing MSFE than ei‐ther DNMT1 or Jak2 in all three cell lines. Interestingly, severely compromised MSFE was observed when any two of the three genes were silenced (Fig. 6C). Although there was additional reduction of MSFE by three‐gene silencing compared to two‐gene silencing, no signifi‐cance was found except in SUM159PT cells (Fig. 6C). These results confirm that DNA methylation plays a critical role in maintenance of breast CSCs concomitant‐ly with Jak2‐STAT3 signaling.

CQ rewrites DNA methylation in MDA‐MB‐231 Cells. Changes in DNA methylation by MBD‐enriched DNA from MDA‐MB‐231 cells was analyzed after 48 hour CQ treatment. Substantial differences were ob‐served in the number and make‐up of Model‐based analysis of ChIP‐seq (MACS) defined MDB‐enriched

peaks within the proximal promoter region ( ‐5000 to +200) of protein coding genes (Fig 7A). Upon more de‐tailed differentiation analysis of MACS defined MDB‐enriched peaks between the CQ and control treatments (MAnorm [28]), the proximal promoter regions of 359 genes uniquely methylated in the control treatment compared to CQ and 136 exclusively methylated in the CQ treatment were identified. To assess any biological significance of these genes with affected proximal regu‐latory regions, we conducted functional enrichment analysis with GeneCodis3 [29, 30]. Roughly one‐third of the genes with hypomethylated proximal promoters following CQ treatment were allocated into four func‐tional groups (p≤9.06e‐06); protein, nucleotide, ATP, and RNA binding functions (Figure 7B). The majority of the genes with hypermethylated proximal promoter regions in the CQ treatment group were predicted to have binding functions to zinc ion, protein, nucleotide, beta‐catenin, metal ion, and single‐stranded RNA (p≤7.83e‐05) (Fig. 7C). Enriched genes are listed in Sup‐plementary Table S2 and S3. Additionally, the uniquely methylated genes in controls were enriched only for one KEGG enriched pathway, protein processing in en‐doplasmic reticulum (p<0.0002), while genes for CQ were enriched for pathways in cancer (p=4.43e‐06) and the Wnt signaling pathway (p<0.0003) (Fig. 7D). Thus, these results suggest that CQ can regulate CSCs by af‐fecting multiple signaling pathways through DNA meth‐ylation via down‐regulation of DNMT1, and through inhibition of the PI3K/Akt/mTOR and Jak2‐STAT3 path‐ways (Fig. 7E).

DISCUSSION

Chloroquine, an autophagy inhibitor, was named as

a potential repositioned drug candidate for treatment against CSCs through in silico network analysis of gene signatures specific for drug resistant CD44+/CD24‐/low cells derived from patient biopsies. Based on our obser‐vation of CSC enrichment following chemotherapy [4, 31], autophagy was hypothesized as an underlying mechanism to maintain viable CSC populations in TNBC. This is further supported by previous studies, suggesting autophagy as a key regulator of breast CSCs [11, 12]. To this end, we demonstrated the anti‐CSC activity of CQ through the reduction of MSFE and the CD44+/CD24‐/low CSCs. This reduction of CSCs correlates well with the inhibition of PTX‐induced

autophagy and with increases in apoptosis. As CSCs have been implicated in metastasis and recur‐rence [22, 32‐34], we confirmed the anti‐CSC effects of CQ in vivo through inhibition of tumor growth, pre‐vention of spontaneous lung metastasis, and attenua‐tion of tumor recurrence. The enhanced anti‐tumor effects were accompanied with suppression of CSC enrichment following PTX treatment and significantly impaired tumor initiation ability in vivo. More im‐portantly, we found a significant reduction of

CD44+/CD24‐/low CSC populations in patients who un‐derwent clinical trials involving the combination ther‐apy of CQ with taxanes. Thus, our data strongly sup‐ports the anti‐CSC activity of CQ against CSCs in TNBC through autophagy inhibition.

The Jak2‐STAT3 pathway was compromised by CQ alone or in combination with PTX. A significant inhibi‐tion of the Jak2 phosphorylation by CQ alone was ob‐served in all cell lines examined. We suspect that CQ may induce endoplasmic reticulum (ER) stress which



7

mediate inhibition of Jak2 phopsphorylation through inhibition of autophagy, down‐regulation of the PI3K/Akt/mTOR pathway, and hypomethylation of ER stress related genes in MDA‐MB‐231 cells. Kimura et al. [35], and Um et al [36]. reported similar ER stress mediated inhibition of Jak2‐STAT3 pathway. However, the inhibitory effects of CQ on Jak2‐STAT3 were most profound following combination therapy, as demon‐strated by a decrease in phosphorylation and expres‐sion of Jak2 in all cell lines examined. Moreover, the inhibitory effect on Jak2 expression was CSC‐specific. These results are in agreement with previous reports on the essential role of the Jak2‐STAT3 signaling pathway for growth and maintenance of CD44+/CD24‐/low breast CSCs [5, 23]. Additionally, the decrease in Jak2 was accompanied with a reduction of DNMT1 expression that correlated well with the global DNA hypomethylation in CSCs. Similar to Jak2‐STAT3, DNMT1 is an important gene expression regulator in normal stem cells as well as CSCs [37, 38]. In leuke‐mia, haploinsufficiency of DNMT1 is known to impair leukemogenesis and self‐renewal of leukemia stem cells [39]. Furthermore, the epigenetic role of STAT3 has been described for inhibition of tumor suppressor genes through interaction with DNMT1 [40, 41]. Thus, our findings suggest that CQ regulates CSCs through epigenetic regulation in addition to the inhibition of autophagy.

SOCS1 and SOCS3 have been identified as versatile negative regulators of the Jak2‐STAT3 signaling path‐way [42‐44]. Along with down‐regulation of Jak2, the combination treatment induced expression of SOCS1 and SOCS3, as well as interaction of SOCS3 with Jak2 in CSCs. Additionally, SOCS1 and SOCS3 expression was inversely proportional to the expression of DNMT1, while the opposite was observed following PTX treatment alone. SOCS1 and SOCS3 are known to

interact with Jak2 and induce its degradation [24, 25, 42‐44]. Moreover, the expression of SOCS1 and SOCS3 are tightly regulated by DNA methylation 26, 27. Thus, we believe that CQ regulates the Jak2/STAT3 signaling pathway in CSCs through deregulation of DNA meth‐ylation mediated by loss of DNMT1 expression. In or‐der to determine whether Jak2, STAT3, or DNMT1 was critical for CSC maintenance, sequential gene silencing was performed for all the three genes. Our findings indicate that simultaneous silencing of Jak2, STAT3, and DNMT was most effective in reducing CD44+/CD24‐/low CSCs and significantly imapred the sphere forming ability. This study defines a possible mechanism of CQ for inhibition of CSCs through regu‐lation of the Jak2/STAT3 and DNA methylation through DNMT1.

In summary, this is the first study that identifies a CQ‐mediated decrease in CD44+/CD24–/low CSC due to inhibition of the Jak2‐STAT3 signaling pathway through expression of SOCS1 and SOCS3, which in turn deregulates Jak2 expression. Moreover, this is the first study to demonstrate that inhibition of the Jak2‐STAT3 pathway is associated with down‐regulation of DNMT1 and subsequent global DNA hypomethylation. More importantly, these pre‐clinical findings are reflected in a currently ongoing clinical trial involving CQ‐PTX treatment, where significant reduction in CD44+/CD24‐/low populations has been observed. Herein, we report that CQ reduces CSCs in TNBC by altering the Jak2‐STAT3 pathway and DNMT1 expression in addition to autophagy inhibition. Subse‐quent analysis of CQ‐mediated changes in epigenome and gene expression in combination with other epige‐netic inhibitors, such as HDAC inhibitors, may enable refinements in strategies targeting TNBC CSC subpop‐ulations



8

ACKNOWLEDGMENT

This work was supported by NIH/NCI grants R01

CA138197, U54 CA149196, Golfers against Cancer, Breast Cancer Research Foundation, Causes for a Cure, Team Tiara, Emily W. Herrman Cancer Research Labora‐tory, and Komen for Cure KG 081694. We declare that none of the authors have any financial interest related to this work.

AUTHOR CONTRIBUTIONS

D.S.C.: and E.B.: study conception, design, collection,

assembly, analysis, interpretation of data, and manu‐script writing.Y.‐S.K. collection and/or assembly, and

analysis of data and manuscript writing.; A.A.R.: provi‐sion of study material and patients; H.Z.: assembly and analysis of data and manuscript writing; T.H.‐M. H. and C.‐L.C.: collection of sequencing data; G.J.: assembly of data; M.L.: data analysis and interpretation of clinical data; L.A.B., Q., W., and H.H.W.: collection and assem‐bly of data; B.D.: data analysis; M.F. and S.T.C.W.: ad‐ministrative support and final approval of manuscript; J.C.: data analysis and interpretation of data, manu‐script writing, financial support, administrative support, final approval of manuscript.

DISCLOSURE OF POTENTIAL CONFLICT OF INTEREST:

The authors declare no potential conflicts of inter‐

est.

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Figure 1. Chloroquine targets breast CSCs of TNBC. (A). Twenty‐one FDA‐approved drug targets on 7 signaling nodes in the network signaling pathway for CD44+/CD24‐/low tumor cells. Circles denote gene nodes, yellow links are protein‐protein interactions, grey squares are drugs, and grey links are drug‐target interactions. The color of every gene node is determined by a statistical score, i.e. ‐log(P‐value), where P‐value is the student’s t test statistical value considering all of the probes’ value for the gene in the groups. The data scale is between 0 and 8. 0 is for no‐color and 8 for dark red circle. A node in a blue rectangle is enlarged for better view. The red oval circle indicates chloroquine. The en‐larged whole network is also presented in Supplementary Figure S1A. (B) CQ reduced primary MSFE of Hs578t, MDA‐MB‐231, and HCC1937 TNBCs and the secondary MSFE in MDA‐MB‐231 and SUM159PT cells in a dose‐dependent manner. (C) CQ reduced the CD44+/CD24‐/low CSC population alone or in combination with PTX. CQ1 and CQ5 indicates CQ at 1 µM or at 5 µM, respectively, while CQ1‐PTX or CQ5‐PTX indicates treatment with CQ at the corresponding doses in combination with PTX (5 nM). Groups treated with CQ alone were compared to control (B and C), and the combination treatment groups were compared to the PTX alone group (C). Con: control (DMSO), CQ: chloroquine, PTX: paclitaxel (in DMSO), and CQ‐PTX: combination of chloroquine and paclitaxel. One‐way ANOVA (Tukey) was used for statistical analysis. This figure represents the mean difference in suppression of MSFE or CD44+/CD24‐/low CSC population with standard deviation. Asterisk indicates p<0.05. (D) CQ and PTX combination treatment reduced CD44+/CD24‐/low CSCs in patients with drug resistant recurrent tumors. Pre indicates pre‐treatment and post indicates post‐treatment. The MS assay and FACS analysis for the CSCs were repeated at least three times with similar results. Representative results are shown.



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Figure 2. CQ inhibits PTX‐induced autophagy and sensitizes TNBC cell lines to PTX. (A) TEM images of autophagosomes in Hs578t cells after 48 hours treatment with Con (DMSO), CQ (1 µM), PTX (5 nM) or combination of CQ and PTX. Solid black arrows indicate autophagosomes. The TEM experiment was performed once and the repre‐sentative images are shown here. The black bars are 2 µm in length. (B) Western blotting results for p62, LC3B, and cleaved caspase 3 from lysates of Hs578t, MDA‐MB‐231, and SUM159PT cells after 48‐hour treatments as indicated. We confirmed similar effects of CQ in independently repeated experiments. CQ attenuated (C) growth of orthotopic MDA‐MB‐231 G/L tumors and (D) inhibited spontaneous lung metastasis of the tumors; Con: PBS and CQ: 10 mg/kg i.p. daily (n=10 per group). Total photon counts were normalized by setting the minimum at 3.50 x 105 and the maxi‐mum at 1.05 x 108 (p/sec/cm^2/sr) and transformed by log10 to analyze the effects of CQ on metastasis and tumor burden in lungs. Two‐tailed Student’s t‐test was used for the statistical comparison. (E) CQ‐PTX attenuated tumor recurrence (MDA‐MB‐231 G/L) compared to PTX aline. (F) The CQ‐PTX combination therapy reduced the growth of orthotopic SUM159PT tumors (n=10). All mice received the scheduled dose of each drug as indicated in the Materials and Methods. Two‐way ANOVA (Boneferroni post‐tests) was used to compare tumor volumes in all animal experi‐ments (C, E, and F). All control groups received PBS as the vehicle (*p<0.05, **p<0.01, ***p<0.001).



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Figure 3. CQ reduces the CSC population in vivo. (A) and (B) Effects of short‐term treatments on the growth of orthotopic MDA‐MB‐231 and SUM159PT tumors in athymic nude mice. Two‐Way ANOVA (Boneferroni post‐tests) was used for statistical analysis. CQ prevented PTX induced CSC enrichment as indicated by Aldefluore + population in MDA‐MB‐231 tumors (C) and the CD44+/CD24

‐/low population in SUM159PT tumors (D). CQ reduced the MSFE of MDA‐MB‐231 (E) and SUM159PT tumors (F). MS indicates mammosphere formation assay. One‐Way ANOVA (Tukey) was used for statistical analysis. (G) Limiting dilution assay for testing the tumor initiation ability in Scid‐beige mice bearing MDA‐MB‐231 tumors received with short term treatments (n=8 per group); 75k: 75,000, 25k: 25,000, and 5k: 5,000 tumor cells. (H) A pairwise comparison for CSC frequencies. P value indicates the significance of the difference on the CSC frequency of the compared two groups. For the statistical analysis, Extreme limiting dilution analysis [45] was used. Con (PBS), CQ (10mg/kg, ip daily), PTX (15mg/kg, twice per week, ip), CQ‐PTX, the combination. (*p<0.05, **p<0.01, ***p<0.001).



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Figure 4. CQ regulates the Jak2‐STAT3 pathway in CSCs. (A) Results of western blot assays for pSTAT3‐705, STAT3, pJak2, Jak2, and Actin on lysates of Hs578t, MDA‐MB‐231, and SUM159PT cells treated for 48 hours with a 4‐treatmet regimen; DMSO (Con) or CQ (1µ), PTX (5nM) in DMSO, or the combination. (B) Western blot analysis for the detection of pSTAT3‐705, pSTAT3‐727, STAT3, pJak2, Jak2, and Actin on lysates of sorted CD44+/CD24‐/low CSCs or non‐CSCs of SUM159PT cells. These cells were incubated in MC+ containing 2% FBS and treated with the 4‐treatmet regimen for 48 hours. (C) Western blot analysis of SOCS1 and SOCS3 expression in SUM159PT CSCs after treatment of PTX or CQ‐PTX for 24, 36, and 48 hours. (D) Immunoprecipitation of SOCS3 with Jak2 in SUM159PT CSCs treated with PTX (5 nM) or CQ (1 µM)‐PTX (5 nM). Western blot for SOCS3 and Jak2 are shown. Normal IgG showed no non‐specific bands at the expected molecular size (data not shown). (E) Confocal microscope images of SOCS3 co‐localization with Jak2 in SUM159PT CSCs treated with PTX or CQ‐PTX for 48 hours. Jak2 (red) and SOCS3 (green). Hoechst 33342 (100 ng/ml) stains for the nucleus. Co‐localization of SOCS3 and Jak2 is depicted in the separate black‐white cell images in which the white dots indicate the co‐localization. All images were taken using an Olympus FV1000 confocal microscope with a 100x objective lens and 2.5x optical zoom. The white bars indicated 10µm in length. (F) Western blot assay of pSTAT3‐705, STAT3, pJak2, Jak2, SOCS3, and Actin on lysates of SUM159PT CSCs in which SOCS3 was silenced prior to the 48 treatment with CQ‐PTX. siRNA‐Scr: siRNA with scrambled RNA sequences, siRNAi‐SOCS3: siRNA with SOCS3 targeting RNA sequences. These are representative results of independent duplicate or triplicate experiments.



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Figure 5. CQ induced DNA hypomethylation by reducing DNMT1 expression. (A) Western blot analysis of DNMT1 from extracts of Hs578t, MDA‐MB‐231, and SUM159PT cells, and (B) extracts of CSCs or non‐CSCs of SUM159PT and CSCs of HCC1937 and HCC38 cells. All cells were treated for 48 hours with the 4‐treatment regimen; DMSO (Con) or CQ (1 µM), PTX (5 nM) in DMSO, or the combination. (C) Measurement of global DNA methylation using genomic DNA ex‐tracted from Hs578t and MDA‐MB‐231 cells and DNA from SUM159PT CSCs. All cells were treated with the 4‐treatment regimen. One‐way ANOVA (Tukey) was used for statistical analysis. (D) Western blot assay for DNMT1, SOCS1 and SOCS3, and Actin after silencing DNMT1 in PTX treated SUM159PT CSCs for 30 hours. (E) Expression of SOCS1 or SOCS3 transcripts in SUM159PT CSCs treated with either PTX (5 nM) or CQ (1 µM) and PTX (5 nM) for 48 hours. Student’s t‐test was used for statistical analysis. (F) Images of DNA gel electrophoresis of SOCS3 promoter PCR to confirm CQ‐induced DNA hypomethylation. SUM159PT CSCs were treated with either PTX or CQ‐PTX for 48 hours before DNA extraction. Primer indicates the corresponding primer sets for PCR. Input indicates PCR reactions with the sonicated genomic DNA. N‐IgG is the commercially available mouse normal IgG used for MeIP as a negative control. 5‐mC Ab is the monoclonol antibody recognizing 5‐methycytosine of DNA for MeDIP. The black arrow indicates the expected PCR products. Images were cropped from the original found in Supplementary Figure S8. All experiments were repeated at least 2 or 3 times with similar results, and the representative results were presented. (*p<0.05, **p<0.02, ***p<0.0001)



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Figure 6. Both DNMT1 and the Jak2‐STAT3 pathway are critical for CSCs of TNBC. (A) Confirmation by western blot analysis in Hs578t, MDA‐MB‐231, and SUM159PT cells for DNMT1, Jak2, STAT3. GAPDH indicates the loading control. (B) FACS analysis of CD44+/CD24‐/low CSCs after silencing using the indicated single or combination of corresponding RNAi. (C) Impact of gene silencing on MSFE. D indicates silencing of DNMT1, J for Jak2, S for STAT3, D/J for the silenc‐ing of DNMT1 and Jak2, D/S for DNMT1 and STAT3, J/S for Jak2 and STAT3, and D/J/S for the silencing of DNMT1, Jak2, and STAT3. Scr indicates RNAi of scrambled sequences and serves as control. One‐Way ANOVA (Tukey) was used for statistical analysis. All experiments were repeated three times with similar results. The representative results are presented. (*p<0.05, **p<0.01, ***p<0.001).



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Figure 7. CQ induces hypomethylation and resets the DNA methylome. (A) A heatmap showing unique methylation status of the proximal promoter region (‐5000 to +200) of protein coding genes in MDA‐MB‐231 cells treated for 48 hours with PBS (Con) or chloroquine (CQ, 1 µM). The numbers on the left of the heatmap indicate the total number of genes with the uniquely methylated promoters. Methylation degree increases from blue to yellow (log10 trans‐formed tag counts). (B) and (C) Schematic diagrams illustrating results of the functional enrichment analysis with GeneCodis for the identified genes. The p values for the functional enrichment test were (B) p ≤ 9.06 x 10‐6 and (C) p ≤ 7.83 x 10‐5. (D) KEGG enriched pathway of genes that are hyper‐ or hypo‐methylated by CQ in MDA‐MB‐231 cells. (E) A schematic diagram illustrating how CQ can affect TNBC CSCs. Red indicates inhibition and blue activation. The red dotted line indicates possible inhibition and the blue dotted line represents activation through DNA methylation.



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chloroquine eliminates cancer stem cells through deregulation of jak2 and dnmt1

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