Decitabine Response in Breast Cancer Requires Efficient Drug Processing and Is Not Limited by Multidrug Resistance

Published OnlineFirst March 10, 2020; DOI: 10.1158/1535-7163.MCT-19-0745

MOLECULAR CANCER THERAPEUTICS | SMALL MOLECULE THERAPEUTICS

Decitabine Response in Breast Cancer Requires Efﬁcient Drug Processing and Is Not Limited by Multidrug Resistance Margaret L. Dahn1, Brianne M. Cruickshank1, Ainsleigh J. Jackson2, Cheryl Dean1, Ryan W. Holloway1, Steven R. Hall3, Krysta M. Coyle1, Hillary Maillet4, David M. Waisman1,5, Kerry B. Goralski3,6, Carman A. Giacomantonio1,7, Ian C.G. Weaver1,4,8,9, and Paola Marcato1,2

ABSTRACT ◥ Dysregulation of DNA methylation is an established feature of a clinical trial. Methylome analysis revealed that genome-wide, breast cancers. DNA demethylating therapies like decitabine are region-specific, tumor suppressor gene–specific methylation, and proposed for the treatment of triple-negative breast cancers (TNBC) decitabine-induced demethylation did not predict response to and indicators of response need to be identified. For this purpose, we decitabine. Gene set enrichment analysis of transcriptome data characterized the effects of decitabine in a panel of 10 breast cancer demonstrated that decitabine induced genes within apoptosis, cell cell lines and observed a range of sensitivity to decitabine that was cycle, stress, and immune pathways. Induced genes included those not subtype specific. Knockdown of potential key effectors dem- characterized by the viral mimicry response; however, knockdown onstrated the requirement of deoxycytidine kinase (DCK) for of key effectors of the pathway did not affect decitabine sensitivity decitabine response in breast cancer cells. In treatment-na€ve breast suggesting that breast cancer growth suppression by decitabine is tumors, DCK was higher in TNBCs, and DCK levels were sustained independent of viral mimicry. Finally, taxol-resistant breast cancer or increased post chemotherapy treatment. This suggests that cells expressing high levels of multidrug resistance transporter limited DCK levels will not be a barrier to response in patients ABCB1 remained sensitive to decitabine, suggesting that the drug with TNBC treated with decitabine as a second-line treatment or in could be used as second-line treatment for chemoresistant patients.

Introduction leukemias (AML)], where patients often share common epigenetic perturbations (2). This has generated interest in using demethylating DNA methylation is essential for gene regulation in normal cells (1). agents to treat solid tumors, including breast cancers (3). In cancer, DNA methylation is largely dysregulated with global Among breast cancers, triple-negative breast cancers (TNBC) have demethylation contributing to genomic instability and the hyper- poorer outcomes and represent the 15%–20% of tumors that lack methylation of CpG islands in the promoters of tumor suppressor hormone receptors and targeted therapies (4, 5). The possibility of genes causing their aberrant silencing (1). DNA methyltransferases treating TNBCs with DNMT inhibitor decitabine is currently being (DNMT) are required for both de novo methylation and maintenance investigated in clinical trials (i.e., NCT02957968 and NCT03295552). of existing DNA methylation; DNMT upregulation is associated with Therefore, assessing the factors that determine the response to DNMT both cancer and aberrant methylation. As such, DNMT inhibitors like inhibitors in breast cancer, specifically TNBC, is both timely and decitabine and azacytidine are commonly used to treat hematologic critical if decitabine is to be used successfully. To date, there are few disorders [myelodysplastic syndrome (MDS) and some acute myeloid studies which examine breast cancer cells exclusively and profile response to decitabine across many cell lines. A cytosine analog, decitabine is incorporated into DNA during 1 Department of Pathology, Dalhousie University, Halifax, Nova Scotia, Canada. synthesis, which imparts some specificity of the drug for rapidly 2Department of Microbiology and Immunology, Dalhousie University, Halifax, dividing cells. DNMTs bind DNA-integrated decitabine leading to Nova Scotia, Canada. 3Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada. 4Department of Psychology and Neuroscience, protein/DNA adduct formation, DNMT degradation, and subsequent Dalhousie University, Halifax, Nova Scotia, Canada. 5Department of Biochem- reduction of DNA methylation. This inhibits tumor growth by a istry and Molecular Biology, Dalhousie University, Halifax, Nova Scotia, Canada. number of potential mechanisms including demethylation and reex- 6College of Pharmacy, Dalhousie University, Halifax, Nova Scotia, Canada. pression of aberrantly silenced tumor suppressor genes (6), induction 7 Department of Surgery, Dalhousie University, Halifax, Nova Scotia, Canada. of the DNA damage response by protein/DNA adduct formation (7), 8Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada. cytotoxicity induced by global demethylation (8), and demethylation 9Brain Repair Centre, Halifax, Nova Scotia, Canada. of silenced tumor-associated antigens increasing antitumor immune Note: Supplementary data for this article are available at Molecular Cancer responses (9, 10). Recently, a novel mechanism has been proposed as a Therapeutics Online (http://mct.aacrjournals.org/). key determinant for the response of decitabine—demethylation of Corresponding Author: Paola Marcato, Dalhousie University, Room 11F11, Sir endogenous retroviral elements resulting in dsRNA/antiviral Charles Tupper Medical Building, 5850 College Street, Halifax, Nova Scotia B3H responses (11, 12). In addition, decitabine's predominant mode of 4R2, Canada. Phone: 1-902-494-4239; Fax: 1-902-494-2519; E-mail: [email protected] action is possibly dose dependent: lower doses cause reexpression of silenced genes with minimal DNA damage, while higher doses cause Mol Cancer Ther 2020;19:1–13 more pronounced DNA damage responses and apoptosis (12, 13). In doi: 10.1158/1535-7163.MCT-19-0745 various cancer models, a number of potential treatment response 2020 American Association for Cancer Research. biomarkers have been investigated including expression or mutation

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fi > of nucleoside transporters, decitabine metabolism genes deoxycytidine de ned as 50 cells. An IC50 value for each cell line was determined kinase (DCK), and DNA methylation regulating enzymes such as using percent colony-forming efficiency (with no treatment wells DNMTs and tet methylcytosine dioxygenase 2 (TET2), the methyla- representing 100% colony-forming efficiency) and the GraphPad tion of known tumor suppressor genes (e.g., CDH1, BRCA1, RASSF1, Prism equation: log(inhibitor) versus normalized response standard RUNX3 – ðxLogIC50Þ and ), and global methylation levels (14 20). It is unclear which slope analysis (Y ¼ 100=ð1 þ 10Þ Þ. mechanism of decitabine is most important for successful treatment of patients with breast cancer or if the effects of decitabine differ in the Tumor growth studies breast cancers of different subtypes (i.e., TNBCs vs. hormone- All animal studies detailed in this article have been conducted in expressing subtypes). accordance with the ethical standards and according to the Declaration in vitro Herein we performed transcriptome, methylome, growth of Helsinki and the Canadian Council on Animal Care standards. Eight- assays, gene knockdown studies, tumor xenograft assays, and patient week-old NOD/SCID female mice were injected with 2 106 MDA- dataset analyses to assess the proposed potential anticancer mechan- MB-468 or MDA-MB-231 cells, or 3.5 106 SUM159 cells admixed 1:1 isms of decitabine in breast cancer. Our analyses of a panel of 10 breast with Matrigel-HC (BD Biosciences) into the mammary fat pad. Once cancer cell lines revealed a range of sensitivity in breast cancer to palpable tumors formed, the mice were treated with 0.5 mg/kg deci- decitabine that was not based on hormone receptor status, genome- tabine or vehicle control (PBS) by intraperitoneal injection for 3/5 day wide methylation, demethylation of tumor suppressor genes, or cycles as described previously (13). Throughout the experiment, tumor induction of viral mimicry responses. Instead our analyses demon- volume was quantified (mm3,length width depth/2). strated the requirement for expression of the decitabine-processing enzyme DCK and the induction of pathways of genes enriched in Knockdown generation apoptosis, cell cycle, stress, and immune pathways. Finally, unlike the Lentiviral short hairpin RNA (shRNA) knockdown clones of commonly used breast cancer drug paclitaxel, decitabine efficacy in ABCB1, DCK, DDX58 (RIGI), IFIH1 (MDA5), SLC28A1,and breast cancer is not negatively impacted by increased expression of SLC29A1, were generated using the pGipZ vector (Dharmacon) ATP-binding cassette drug efflux transporter ABCB1, which is often packaged in HEK293T cells following standard protocols and seen as a mechanism of multi-drug resistance. listed in Supplementary Table S1. Clones were selected by adding 1.5 mg/mL puromycin and subsequently maintaining 0.25 mg/mL puromycin media. For all knockdown clones created, a GIPz vector Materials and Methods control clone (containing a scrambled nonspecificsequencein Cell culture place of a shRNA) was generated simultaneously. Verification of Cancer cell lines were obtained from ATCC, with the exception knockdown was done through qRT-PCR and Western blot analysis of SUM149 and SUM159 cells that were obtained from BioIVT (anti-DCK, Abcam, ab151966; anti-MDA5, Cell Signaling Tech- (previously Asterand). MDA-MB-231, MDA-MB-468, MCF7, SKBR3, nology, clone D74E4). T47D, and HEK293T cells were grown in DMEM (Invitrogen) supplemented with 10% FBS (Invitrogen) and 1 antibiotic antimy- Reverse transcriptase quantitative PCR cotic (AA; Invitrogen). MDA-MB-436 cells were grown in Leibovitz RNA was extracted from untreated and decitabine-treated cells Medium (L-15; Invitrogen) supplemented with 10% FBS, 1 AA, (1 mmol/L decitabine for 72 hours with media refreshed daily). Cells 10 mg/mL human insulin (Sigma), and 16 mg/mL L-glutathione were collected in TRIzol (Invitrogen) and RNA was purified using a (Invitrogen); MDA-MB-453 cells were cultured in L-15 medium PureLink RNA Kit (Invitrogen, Thermo Fisher Scientific) following supplemented with 10% FBS and 1 AA; Hs578T cells were cultured the manufacturer's instructions. Equal amounts of purified RNA in DMEM supplemented with 10% FBS, 1 AA, and 0.01 mg/mL were then reverse transcribed to cDNA using iScript (Bio-Rad) as human insulin. SUM149 and SUM159 cells were cultured in F-12 per the manufacturer's instructions. Diluted cDNA was used in Ham Nutrient Mix Medium supplemented with 5% FBS, 1 AA, qRT-PCR reactions with gene-specific primers (Supplementary 1 mmol/L 4-(2-Hydroxyethyl) piperazine-1-ethanesulfonic acid Table S2) and SsoAdvanced Universal SYBR Supermix (Bio-Rad) as (HEPES; Invitrogen), 0.01 mg/mL human insulin, and 0.05 mg/mL per the manufacturer's instructions with a CFX96 or CFX384 Touch hydrocortisone (Invitrogen). Cells were cultured in a humidified Real-Time PCR Detection System (Bio-Rad). Standard curves were 37 Cincubatorwith5%CO2, except for MDA-MB-436 and generated for each primer set and primer efficiencies were incorpo- MDA-MB-453, which were cultured without the addition of CO2. rated into the CFX Manager Software (Bio-Rad). Relative expression fi DDC for decitabine inducible genes was quanti ed using the t method Colony-forming assay of the CFX Manager Software (Bio-Rad), where gene-of-interest Cells were seeded at low density and allowed to adhere for quantification was normalized to reference genes RPL29 and TBP 24 hours in 24-well cell culture plates: MDA-MB-231 (30 cells/well), and then made relative to no-treatment control mRNA levels. For DC MDA-MB-468 (120 cells/well), MCF7 (60 cells/well), SKBR3 gene expression comparisons made between cell lines, the t expres- (60 cells/well), T47D (120 cells/well), MDA-MB-436 (200 cells/well), sion method was used to quantify gene expression. MDA-MB-453 (60 cells/well), Hs578T (60 cells/well), SUM149 (60 cells/well), and SUM159 (30 cells/well). Cells were treated with Patient dataset analysis 0.122 mmol/L–2 mmol/L decitabine (5-aza-20-deoxycytidine, Sigma- Breast cancer (METABRIC, Nature 2012 and Nat Commun 2016; Aldrich) for 72 hours with media refreshed every 24 hours. n ¼ 2,509) and breast invasive carcinoma [The Cancer Genome Alternatively, cells were treated with azacytidine (5-azacytidine, Atlas (TCGA), Cell 2015; n ¼ 817] clinical data (PAM50 subtype, Sigma-Aldrich). Cells were then grown in appropriate media (lack- hormone receptor status), and microarray z-score gene expression ing decitabine or azacytidine) for 7–10 days with media refreshed data were accessed via cBioPortal (21, 22). Microarray-based gene every other day. Colonies were then fixed in methanol for 10 min- expression of matched pre- and postneoadjuvant chemotherapy utes and stained using 0.05% crystal violet (Sigma); colonies were biopsies from patients with breast cancer was acquired from

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GSE28844 (23); histopathologic response was based on Miller and Results Payne grading system. Breast cancer cell lines have a broad range of sensitivity to decitabine, independent of subtype Human methylation 450K analysis To first explore the range of sensitivity to decitabine in breast Genomic DNA was extracted from untreated and decitabine- cancer, we treated a panel of 10 cell lines representing estrogen treated cells (1 mmol/L decitabine for 72 hours with media refreshed þ receptor–positive breast cancers (MCF7 and T47D), HER2 (SKBR3), daily) using the PureLink DNA Kit (Invitrogen). Methylation and TNBC (MDA-MB-231, MDA-MB-468, SUM149, SUM159, analyses using the human methylation 450K (HM450) bead MDA-MB-436, MDA-MB-453, and Hs578T). Given the current chip array (Illumina) was performed by The Centre for Applied clinical interest in treating TNBCs with decitabine (NCT02957968 Genomics at the Hospital for Sick Children (Toronto, Ontario, and NCT03295552), the panel has overrepresentation of TNBCs. In Canada), including bisulfite conversion, hybridization, background a subsequent colony-forming assay, the cell lines exhibited IC s subtraction, and normalization. Data are accessible from GSE78875 50 ranging from 1 to 74 nmol/L of decitabine (Fig. 1A). The in vitro (24) and summarized in Supplementary Data S1. Methylation sensitivity to decitabine appeared to be independent of hormone b-value of probes from promoter-associated regions for BRCA1, receptor status (Fig. 1A). CDH1, RASSF1,andRUNX3 were extracted. CpGs from RUNX3 With the focus of ongoing clinical application in TNBCs, we promoter–associated CpG island: cg19270505, cg11018723, also assessed the effect of decitabine on tumor growth of TNBC cg13629563, cg22737001, cg26421310, cg26672794, and cg02970551. MDA-MB-468, MDA-MB-231, and SUM159 cells. Using the low-dose treatment protocol established by Tsai and colleagues (13), NOD/ Gene array analysis SCID mice bearing palpable tumors of the TNBC cell lines were RNA purified from SUM159 cells that had been treated with treated intermittently with 0.5 mg/kg decitabine. This resulted in 1 mmol/L decitabine or vehicle for 72 hours (3n)wassenttoThe tumor growth suppression that mimicked the colony assay, with Centre for Applied Genomics at the Hospital for Sick Children MDA-MB-468 tumors being the most sensitive and SUM159 tumors (Toronto,Ontario,Canada)forsamplepreparation,amplification, being the most resistant to decitabine (Fig. 1B). hybridization to the Affymetrix HuGene 2.0 ST array, and data collection. The raw data for MDA-MB-231 and MDA-MB-468 is accessible from GSE103427 (25), and GSE from uploaded new data DCK is required for decitabine response in breast cancer (GSE133987). The Transcriptome Analysis Console Software cells and tumors (Thermo Fisher Scientific) was used to normalize the data and To assess the potential factors which may dictate breast cancer calculate fold changes in expression (Supplementary Data S2). sensitivity to decitabine, we considered the cell-specific factors Transcripts with confirmed gene annotations (not blank in the required for the cytosine analog's incorporation into DNA. Decitabine “Gene Symbol”) category that were 1.5-fold up- or downregulated is imported into cells by sodium/nucleoside cotransporters solute significantly (based on ANOVA P < 0.05) in at least one cell line carrier family 28 member 1 (SLC28A1) and SLC29A1 (18, 27). Higher were plotted on the basis of the average fold-change across all three expression of either transporter was not associated with increased cell lines (1,390 transcripts/1,284 genes; Supplementary Fig. S1). sensitivity (Supplementary Fig. S2). Furthermore, knockdown of Not all microarray gene expression “hits” contained HM450 anno- SLC28A1 in decitabine-sensitive MDA-MB-468 cells and -resistant tated CpG sites; unannotated transcripts were discarded. Only SUM159 cells did not alter the sensitivity of either cell line to decitabine genes with two or more different regions annotated were plotted. (Supplementary Fig. S3). Knockdown of SLC29A1 in intermediately All figures depict the mean methylation b-value of CpGs annotated sensitive MDA-MB-231 (Supplementary Fig. S4), which had high for a given region (e.g., TSS1500, TSS200 etc.). expression of the transporter (Supplementary Fig. S2), also did not alter the sensitivity of the cells to decitabine. Together, this data Gene set enrichment analysis suggests that assessing levels of the importers in patient tumors will Using the HM450 and gene array data, we identified genes that were not predict decitabine sensitivity or resistance in breast cancer. unmethylated in TSS1500 þ TSS200 þ Exon1 (≤0.5 b-value in all three Once imported, decitabine is sequentially phosphorylated by DCK, regions) and for which decitabine upregulated expression ≥1.5-fold cytidine/uridine monophosphate kinase 1 (CMPK1), and finally nucle- (Supplementary Data S3, Supplementary Table S3). Gene set enrich- oside diphosphate kinases 1 and 2 (NME1 and NME2; ref. 28). DCK is a ment analysis (GSEA) was performed using available online software rate-limiting step for the incorporation of decitabine in MDS and (http://software.broadinstitute.org/gsea/index.jsp) to compute over- AML (28, 29) and could possibly be a rate-limiting step for decitabine laps. The top 100 overlapping gene sets identified by GSEA for each cell response in breast cancer. Consistent with its requirement for decitabine line were identified. k, # genes in overlap; K, # genes in gene s; n, # genes processing, knockdown of DCK significantly decreased the sensitivity of in comparison; N, # genes in universe; enrichment ¼ k=n=K=N. Gene both the decitabine-sensitive MDA-MB-468 cells and decitabine- in vivo sets with similar functions were enriched across the three cell lines and resistant SUM159 cells (Fig. 2A). This was also observed ,where were color-coded for easier visualization. MDA-MB-468 tumors with DCK knockdown were comparably resistant to decitabine (Fig. 2B). The reduction in DCK levels also hampered Taxane-resistant breast cancer expression of decitabine-inducible genes (Supplementary Fig. S5) that MDA-MB-231 cells with acquired ABCB1-mediated taxane resis- were identified as upregulated by decitabine in the gene array (Supple- tance (taxol-res) and the matched taxane-sensitive cells (control) were mentary Data S2). It is noteworthy that MDA-MB-468 cells with DCK previously generated and characterized (26) and maintained in the same knockdown remained sensitive to the ribonucleoside analog and DNMT MDA-MB-231 growth conditions described above. Cells were treated inhibitor azacytidine (Fig. 2C), which does not require DCK for DNA with increasing doses of paclitaxel (Corporation Biolyse Pharma Cor- incorporation and activity (29). poration) or decitabine 10 mmol/L verapamil (verapamil hydrochlo- We assessed expression levels of DCK and the other nucleotide ride; Sigma-Aldrich) and a colony forming IC50 was calculated. kinases involved in processing deoxynucleotides/decitabine in the 10

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Figure 1.

Sensitivity of breast cancer cell lines to decitabine treatment. A, Colony-forming assay to determine in vitro decitabine IC50 for 10 breast cancer cell lines after 72-hour treatment. B, Xenograft determination of in vivo decitabine sensitivity. MDA-MB-468, MDA-MB-231, and SUM159 xenografts treated with 0.5 mg/kg decitabine over 3–4 weeks; hatch marks on x-axis indicate when mice were injected; error bars, SEM; one-way t test (, P < 0.01).

breast cancer cell lines and noted similar levels of DCK, CMPK1, patients who did not show a histopathologic response to treatment NME1, and NME2 across the cell lines (Fig. 2D). The lack of a (Fig. 2G). This suggests that these patients would be good candidates correlation between DCK expression and decitabine IC50 of the cell for decitabine treatment, at least with respect to the essential decitabine lines was likely due to the small range of differences in DCK levels processing enzyme DCK. between the cell lines (Fig. 2D). However, given the importance of DCK in mediating decitabine response in breast cancer as demon- Genome-wide and region-specific methylation of breast cancer strated by our knockdown experiments (Fig. 2A and B; Supplementary cells is not predictive of decitabine response Fig. S5), we assessed the expression of the DCK in two breast cancer Concurrent genome-wide hypomethylation and hypermethylation patient cohorts (METABRIC, Fig. 2E and TCGA Cell 2015, Fig. 2F). of promoter regions is observed in cancer cells (30); therefore, we DCK mRNA levels in the breast cancer patient tumors followed a wondered whether any correlations with genome-wide or region- normal distribution and was overall more abundant in the luminal B, specific methylation could predict response to decitabine in breast basal-like (which consists predominately of TNBCs), and TNBC cancer. For this purpose, we performed HM450 and gene expression subtypes. This suggests that for most patients (and in the patients analysis of decitabine-treated cells. The colony assay (Fig. 1A)isa with TNBC that are currently being treated by decitabine in clinical long-term 2-week plus assay and hence results in an enhanced trials), limited DCK expression will not be a barrier to successful decitabine sensitivity in the nmol/L range in IC50s. While informative decitabine therapy in these patients. Since the patients that will be for determining relative sensitivities, this assay is insufficient for treated with decitabine will likely have had anthracycline and/or harvesting cell samples for HM450 and RNA analysis, which requires taxane chemotherapy prior to enrollment in a clinical trial, it is >300,000 cells. Therefore, we determined a concentration of decitabine important to determine whether DCK levels are altered posttreatment that when applied to subconfluent monolayers of >300,000 cells with standard chemotherapy drugs. Microarray-based gene expression seeded in 6-well plates would result in approximate 50% growth from 56 matched pre- and post-chemotherapy breast tumor samples inhibition of cells after 72 hours (0.6–9 mmol/L; Supplementary showed that DCK expression was elevated after chemotherapy in Fig. S6). A dose of 1 mmol/L decitabine (which represents a median

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Figure 2. DCK is an important mediator of decitabine response and is present in patients with aggressive subtypes of breast cancer (BrCa). A, shRNA-mediated knockdown (KD) of DCK conferred in vitro resistance to decitabine in MDA-MB-468 and SUM159 cells. B, shRNA-mediated knockdown of DCK conferred in vivo resistance to decitabine to MDA-MB-468–treated tumors. C, DCK knockdown in MDA-MB-468 cells does not confer resistance to azacytidine. Error bars, SD; ANOVA with Dunnett post hoc test. D, qRT-PCR of nucleotide kinases (DCK, CMPK1, NME1, and NME2) across breast cancer cell lines, error bars, SD. E and F, Expression of DCK via microarray based on PAM50 subtype and triple-negative receptor status in METABRIC and TCGA (Cell, 2015) breast cancer patient cohorts. Straight lines note groups that are not signiﬁcantly different, ANOVA with (Tukey post hoc test); , P < 0.001; ns, not signiﬁcant. G, DCK expression via microarray in the GSE28844 dataset of patients with breast cancer pre- and post-anthracycline/taxane treatment; paired two-tailed t test (, P < 0.05; , P < 0.01; , P < 0.001).

IC50 in the cell line panel) was applied to subconfluent monolayers for overall similar among the cell lines (Fig. 3A and B). In agreement with 72 hours (Supplementary Fig. S6). Furthermore, this dose causes this, the levels of DNMT1A, DNMT3B, and TET 1, 2 and 3 (which act minimal apoptosis after 72 hours of treatment (day 4 post-seeding), in the demethylation of DNA; ref. 31), were also similar across the cell but in subsequent days (decitabine treatment has ceased but cell lines, consistent with the overall similar genome-wide methylation culture continued) causes dramatic cell death and growth inhibition (Supplementary Fig. S8). (Supplementary Fig. S7). This is further evidence of the long-term To determine whether promoter methylation was predictive of effects of decitabine, which are also captured by the ultra-sensitive decitabine sensitivity, CpGs identified within 1,500 bp or 200 bp of colony assay (Fig. 1A). the transcription start site (TSS1500 and TSS200, respectively), We analyzed HM450 data from 10 breast cancer cell lines (not within the 50 untranslated region (UTR), or within the first exon treated with decitabine) and found that total genome-wide DNA were evaluated with the HM450 assay. Overall, promoter DNA methylation was not associated with response to decitabine and was methylation was absent in most genes across all cell lines (Fig. 3B)

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Figure 3. Breast cancer sensitivity to decitabine is not associated with global methylation or promoter methylation. A, DNA methylation as determined via HM450 array among breast cancer cell lines as a frequency distribution of CpG sites with a given methylation b-value. B, Distribution of HM450 methylation b-values for promoter-associated regions TSS1500, TSS200, 50 UTR, and Exon 1 among the 10 breast cancer cell lines. C, The average methylation b-values for promoter-associated regions TSS1500, TSS200, 50 UTR, and Exon 1 among the 10 cell lines are plotted against the relative change in methylation of those promoter regions (methylation z-score) based on decitabine responsiveness. Sites located in top left zone of dotplot represent sites that are hypermethylated in (e.g., resistant) cell lines compared with the rest of the cell lines. D, The hypermethylated regions identiﬁed in C are represented by the methylation b-value in each cell line.

and little differential methylation between decitabine-response SUM149 cells are generally highly sensitive (Fig. 1B), but on a groups was observed (Fig. 3C). We noted that 434 genes had z- genome-wide scale had comparatively little demethylation score > 1 for at least one promoter-associated CpG, with the (Fig. 4A), while SUM159 are resistant, and yet comparatively lost majority of those hypermethylated promoter genes occurring in their methylation. Hence, although demethylation is associated with the decitabine-resistant MDA-MB-453 cell line (Fig. 3D). However, decitabine treatment in breast cancer cells, it is not correlative of IC50s there are only a few genes that are consistently methylated across determined by the colony assay (Figs. 1 and 4A). the three decitabine-resistant cell lines, and in patient tumors the The extent of genome-wide demethylation was reflective of gene- genes are consistently unmethylated across all patient samples specific demethylation of promoter regions and of known tumor (Supplementary Fig. S9). This suggests that the existence of specific suppressors RUNX3 (Fig. 4B), BRCA1, CDH1, and RASSF1 (Supple- CpG sites that can stratify breast cancers for decitabine response mentary Fig. S11), but not associated with response (Fig. 1). Intrigu- based on hypermethylation likely do not exist. We also performed a ingly, increased expression of RUNX3 (Fig. 4C) and CDH1 (Supple- principal component analysis of TSS200 and gene body CpGs in the mentary Fig. S11) in many cases was not paired with promoter 10 cell lines which shows that the cell lines do not separate demethylation (Fig. 4B; Supplementary Fig. S11), suggesting that according to sensitivity (Supplementary Fig. S10). induced expression of these well-characterized hypermethylated tumor suppressor genes (32–34) by decitabine is not necessarily due Genome-wide and gene-specific demethylation of tumor to promoter demethylation and may be a result of other effects of suppressor genes by decitabine in breast cancer cells is not decitabine treatment. In validation of the HM40 data, we performed correlated with decitabine response bisulfite pyrosequencing of 32 CpGs in the promoter region of RUNX3 Although baseline methylation did not reveal any putative biomar- (Supplementary Fig. S12). The sequencing data generally agrees with kers, we hypothesized that genomic demethylation in the presence of the HM450 data and demonstrated that the SUM159 promoter is decitabine may correlate with decitabine response. We analyzed hemimethylated and is overall minimally demethylated by decitabine HM450 data from six TNBC cell lines treated with 1 mmol/L decitabine treatment, while the more methylated MDA-MB-231 RUNX3 pro- for 72 hours. This revealed that decitabine induced a range of moter contains a cluster of CpG sites around the start codon that are demethylation in the cells; however, the extent of demethylation was demethylated upon decitabine treatment (CpG site 11–17; Supple- not reflective of the decitabine sensitivity in the cell lines. For example, mentary Fig. S12).

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Figure 4. Breast cancer sensitivity to decitabine (DAC) is not associated decitabine-induced demethylation or by induction of common hypermethylated tumor suppressor genes (e.g., RUNX3). A, DNA methylation as determined via HM450 array among breast cancer cell lines after treatment with 1 mmol/L decitabine for 72 hours. Methylation via HM450 of a promoter-associated CpG island in RUNX3 after treatment with 1 mmol/L decitabine for 72 hours (B)andRUNX3 mRNA levels via qRT-PCR after decitabine treatment (C); error bars, SD; one-way t test (, P < 0.01; , P < 0.001; ns, not signiﬁcant).

Decitabine upregulates gene expression via demethylation thesegenesets,butthisisnotthecaseasSUM159(decitabine of promoters and induces transcriptional programs for resistant) has the most extensive gene upregulation (Supplemen- stress, cell-cycle arrest, apoptosis, and immune response tary Fig. S1). Since our analyses of gene-specific demethylation of tumor To determine whether there is direct upregulation of gene suppressor failed to identify correlations with expression and expression via demethylation of promoter regions, HM450 methyl- decitabine sensitivity (Fig. 4; Supplementary Figs. S11 and S12) ation b-values from the genes that were up- or downregulated by we extended our gene expression analyses genome wide. Three cell decitabine in all three cell lines were examined (Supplementary lines spanning the range of decitabine sensitivity were assessed for Data S1). More often, the TSS1500, TSS200, 50 UTR, and Exon1 expression changes in the presence of decitabine by microarray. regions were methylated in upregulated genes compared with Extensive upregulation of gene expression was observed across downregulated genes in MDA-MB-231 (Fig. 5B), MDA-MB-468 all three cell lines while concurrently many genes were down- (Supplementary Fig. S13), and SUM159 cells (Supplementary regulated (Fig. 5A; Supplementary Fig. S1; Supplementary Data Fig. S14). As expected, there was general demethylation of these S2). Interestingly, although the magnitude of induction was var- regions in upregulated genes after decitabine treatment in MDA- iable, there was significant overlap in which transcripts were MB-231 (Fig. 5C), MDA-MB-468, and SUM159 cells (Supplemen- upregulated between the cell lines, with few differentially regulated tary Fig. S15). Intragenic (especially gene body) DNA methylation genes (Fig. 5A). Independent GSEA of the upregulated versus may serve as a positive regulator of transcription, where loss of downregulated transcripts showed that there is significant overlap methylation inhibits gene expression (38). This may explain why of the breast cancer decitabine up- or downregulated transcripts downregulated genes were more likely to have methylation of the with existing azanucleoside-mediated gene expression datasets 30 UTR and gene body compared with upregulated genes (Supple- (Supplementary Data S4). The gene set Kim_Response_to_TSA_ mentary Fig. S16). and_decitabine is from four glioma cell lines treated with combi- Genes that were confirmed to have hypermethylated promoter nation decitabine and trichostatin A (histone deacetylase inhibitor; regions only constituted 16%–28% of total decitabine-upregulated ref. 35), dataset Zhong_response_to_azactidine_and_TSA is from genes (Fig. 5B), leaving the majority of upregulated genes without four non–small cell lung cancer cell lines treated with a combi- a clear cause for upregulation (Supplementary Table S3). Direct gene nation of azacitidine and trichostatin A (36), and dataset Heller_ expression changes induced by demethylation are well-characterized HDAC_targets_silenced_by_methylation is from three multiple outcomes of decitabine treatment; however, the indirect gene expres- myeloma cell lines treated with a combination of azacitidine and sion changes that are not readily explained by methylation changes are trichostatin A (37). If these upregulated transcripts are hyper- not as well understood. These indirect changes may be due to the methylated tumor suppressor genes then we would expect the demethylation of transcription factors or the epigenetic resurrection of more decitabine-sensitive cell linestohavestrongerinductionof other upstream signaling pathways. In a GSEA of the decitabine-

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Figure 5. Decitabine induces expression of methylated genes, drug response pathways, and immune response pathways genes regardless of decitabine sensitivity. A, Gene expression microarray of cell lines treated with 1 mmol/L decitabine for 72 hours showing genes sig- niﬁcantly up- or downregulated (1.5-fold) in any cell line; GSEA showing genes from independent datasets of azanucleoside-treated cancer cells. B, HM450 methylation values of genes for which expression was affected by decitabine treatment in MDA-MB-231 cells signiﬁcantly up- (426 genes) or downregulated (168 genes; left). Proportion of genes with methylated TSS or exon 1 that are up- or downregulated in expression after decitabine treatment (right). C, HM450 methylation on genes for which decitabine affects expression in MDA-MB-231; change in methylation after 72 hours decitabine treatment (DAC-NT) versus predecitabine methylation (NT). D, Top 100 enriched gene sets in the unmethylated (TSS/Exon1) decitabine upregulated genes based on GSEA; gene included in a given pathway indicated by color.

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upregulated genes with unmethylated transcription start site (TSS) and enous retroviral elements (ERV), leading to a cascade of events Exon1 in each cell line, all three cell lines showed upregulation of genes that result in induction of the IFN response (Fig. 6A). We assessed associated with cell-cycle arrest, cell death, DNA damage, stress, the level of induction of previously described mediators of the immune response, and transcriptional machinery (Fig. 5D). decitabine-induced viral mimicry response in the breast cancer cell lines. We assessed expression of the ERVs in our cell lines Decitabine induces viral mimicry response in breast cancer cells, utilizing a panel of previously described and newly designed pri- but dsRNA sensors are not required for in vitro sensitivity mers (Supplementary Table S2). We were unable to detect expres- Among the most consistently upregulated pathways by decita- sion of many of these transcripts in the 10 breast cancer cell lines bine in the cell lines were immune-related pathways (Fig. 5D). (pooled cDNA sample of all cell lines no treatment and decitabine Consistent with this, IFN-inducible 20-50-oligoadenylate synthetase- treated; Supplementary Table S4). Decitabine-induced expression like (OASL) was upregulated in MDA-MB-231 cells upon decita- of ERVs MLT1C49 or MLT2B4 was observed in some of the cell bine treatment (Supplementary Data S2 and S4). Recent reports lines (e.g., SKRBR3 and MDA-MB-436) treated with 1 mmol/L have highlighted the requirement for the viral mimicry response decitabine for 72 hours; however, induction was inconsistent (IFN-induced OASL is part of this response) for decitabine sensi- and did not correlate with sensitivity (Fig. 6B). We further explor- tivity for in vitro and in vivo growth inhibition of colorectal and ed the induction of ERVs EnvE, ERVFRD1,andthe50 UTR ovarian cancer cells (11, 12). In those studies, demethylation by region of HERV-K in decitabine-sensitive MDA-MB-468 cells, decitabine leads to reexpression of epigenetically silenced endog- intermediate-sensitive MDA-MB-231 cells, and resistant SUM159

Figure 6. Decitabine induces expression of the dsRNA pathway but it is not essential for decitabine sensitivity. dsRNA “viral mimicry” pathway components (A)show increased gene expression via qRT-PCRinbreastcancercelllinesafter1mmol/L decitabine treatment for 72 hours; error bars, SD; one-sample t test (B). shRNA-mediated knockdown (KD) of MDA5 (IFIH1; C) or RIGI (DDX58; D) in MDA-MB-468 cells does not affect in vitro decitabine sensitivity in colony-forming assay. error bars, SD; ANOVA with Dunnett post hoc test (, P < 0.05; , P < 0.01; , P < 0.001).

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fl cells treated with their cell con uency IC50s of decitabine. This ( Fig. 7B). This may suggest that increased ABCB1 is a potential again resulted in limited induction of these ERVs, which did not mechanism of decitabine resistance in breast cancer; however, knock- correlate with decitabine sensitivities (Supplementary Fig. S17). Of down of ABCB1 in decitabine-sensitive MDA-MB-468 cells had note, these ERVs were also not induced in the MDA-MB-468 minimal impact on decitabine sensitivity (Fig. 7C). tumors that had been treated with decitabine (Fig. 1B; Supplemen- To further assess how multidrug resistance impacts decitabine tary Fig. S17). response, we used a variant of the MDA-MB-231 cell line with The general response to ERVs is better detected by assessing levels of acquired taxol- and anthracycline-resistance and ABCB1 overexpres- dsRNA recognition pattern receptors, melanoma differentiation– sion (26). We confirmed the resistant cell line had increased ABCB1 associated protein (MDA5, encoded by IFIH1), and retinoic acid- expression, was significantly more resistant to paclitaxel than the inducible gene I (RIG-I, encoded by DDX58) after decitabine treat- control cell line, and that paclitaxel resistance in the cells could be ment. RIG-I and/or MDA5 were induced by decitabine in most of the reduced by the addition of the ABCB1 inhibitor verapamil (Fig. 7D). cell lines (Fig. 6B). Downstream effectors such as mitochondrial Importantly, the taxol-resistant MDA-MB-231 cells were not resistant antiviral signaling protein (MAVS), transcriptional activator of IFN, to decitabine and ABCB1 inhibition did not sensitize cells to decita- interferon regulatory factor 7 (IRF7), anti-viral OASL, and IFN bine, implying that ABCB1 does not play a major role in decitabine response mediator IFN-stimulated gene 15 (ISG15) were also generally response (Fig. 7E). Therefore, increased expression of ABCB1 in the induced in the cell lines, but did not correlate with sensitivity (Fig. 6B). tumors of patients with breast cancer that will receive decitabine after We also utilized our gene expression microarray data to evaluate a having received chemotherapy will not be a significant barrier to larger panel of reported IFN-stimulated genes (Supplementary decitabine response. Table S5) in decitabine-treated MDA-MB-468, MDA-MB-231, and SUM159 cells (Supplementary Fig. S18). These analyses suggest two points; (i) that strong induction of IFN-stimulated genes is not Discussion associated with increased sensitivity to decitabine, and (ii) that dec- Our comprehensive analysis of decitabine response in breast cancer itabine-induced expression of IFN-stimulated genes is not due a direct cells screened many of the potential mechanisms of decitabine to demethylation of the promoter regions of these genes. Please note that determine whether essential factors and/or predictors of sensitivity the HM450 methylation data revealed that the most strongly induced could be identified that may impact the success of ongoing clinical genes (DDX58, HPSE, IFI6, IFIT1, IFIT3, OAS3, and ZC3HAV1) have trials in TNBCs. Together, this study revealed a range of responses to an unmethylated promoter region (TSS200/1500; Supplementary Data decitabine in breast cancer that could not be predicted on the basis of S1; Supplementary Fig. S19); therefore it is unlikely that the increased commonly proposed mechanisms (e.g., demethylation of tumor sup- expression is a result of decitabine directly demethylating these genes. pressor genes and viral mimicry response). Our transcriptome and Regardless, the induction of some of these genes is consistent with a methylome analyses also demonstrated that while decitabine induces viral mimicry response being induced through increased MDA5/RIG-I genome-wide expression changes and demethylation, these effects are in the breast cancer cells. not necessarily paired and do not correlate with sensitivity. Instead, it Via knockdown, MDA5 has been shown to be an essential became apparent that the many gene expression changes induced by mediator of the viral mimicry response induced by decitabine in decitabine are either an indirect result of its demethylation and/or a colorectal cancer (12). We therefore knocked down MDA5 in the consequence of cell death, DNA damage, and immune responses being most decitabine-sensitive MDA-MB-468 cells (Fig. 6C) to deter- induced. Notably, we describe a requirement for DCK, the decitabine mine, whether, like DCK knockdown (Fig. 2), this would render processing enzyme, for decitabine-induced growth suppression and the cells less sensitive to decitabine. However, in contrast to our gene expression changes. DCK is comparatively abundant in TNBC results with DCK (Fig. 2), knockdown of MDA5 did not make and is increased post-chemotherapy in nonresponding patients. This, MDA-MB-468 cells resistant to decitabine (Fig. 6C). Similarly, combined with our demonstration that multidrug-resistant/ABCB1- RIG-I knockdown did not make the cells more resistant to overexpressing breast cancer cells remain sensitive to decitabine, decitabine (Fig. 6D). Together these data suggest that while the suggests that decitabine may be useful as part of a second-line therapy viral mimicry response is generally induced in breast cancer cells, regimen for patients with TNBC. it does not appear to be the key determinant of decitabine sensit- The initial enthusiasm for hypomethylating agents (e.g., decitabine ivity in the cells in vitro. and azacytidine) to treat MDS has been tempered by years of clinical use, which indicate that fewer than half of patients maintain a response High levels of exporter ABCB1 does not limit decitabine to the therapy (41). This is also true for chronic monomyelocytic response in breast cancer cells leukemia (CMML, for which azacytidine and decitabine are the only A consideration for the patients with TNBC receiving decitabine in approved drugs), where overall response rates hover at 40% and clinical settings is potential presence of multidrug resistance acquired complete response at <20% (42). As an AML therapy, overall response during initial chemotherapy treatment. Increased expression of to decitabine is also approximately 40%, although there is some exporter multidrug resistance gene ABCB1 is a common resistance encouraging data to suggest a precision medicine approach could mechanism for many drugs (39, 40). Gene expression from 56 matched work for treating older patients with AML with decitabine. For breast tumor samples pre- and post-chemotherapy treatment showed example, DNMT3A mutations, TET2 mutations, and IDH1/2 muta- that ABCB1 expression was elevated post-chemotherapy in patients tions have all been associated with favorable outcomes in AML (and with at least partial response to treatment (Fig. 7A). This suggests that some MDS cases; refs. 43–45). However, similar stratification strat- the effect of ABCB1 expression in breast cancer needs to be assessed for egies are unlikely to work in breast cancer and other solid tumors; its impact on decitabine response. because mutations in epigenetic machinery genes are far less com- We assessed expression of ABCB1 across the 10 breast cancer cell mon (21, 22). Regardless, even for these blood malignancies/disorders lines, and noted increased expression in decitabine-resistant SUM159 there are many other clinical factors such as age, cytogenetics, prior and Hs578T cells, but also decitabine-sensitive MDA-MB-468 cells treatment regimen, global or gene-specific methylation, and gene

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Figure 7. ABCB1 causes resistance to taxanes but has minimal effects on decitabine sensitivity. A, ABCB1 expression via microarray in GSE28844 dataset of patients with breast cancer treated with anthracyclines and taxanes (paired two-tailed t test). B, Expression of ABCB1 across breast cancer cell lines by qRT-PCR. C, shRNA-mediated ABCB1 knockdown (KD) and in vitro decitabine sensitivity; ANOVA with Dunnett post hoc test. ABCB1 expression via qRT-PCR in taxol-resistant MDA-MB-231 cells; one-tailed t test. Colony forming IC50 assay in taxol-resistant MDA-MB-231 treated with ABCB1 inhibitor verapamil and paclitaxel (D) or decitabine (E). ANOVA with Tukey post hoc (, P < 0.05; , P < 0.01; , P < 0.001). expression patterns that influence response to decitabine (46). While cell lines and TCGA melanoma patient data, decitabine induced there is compelling evidence for each of these factors individually, most expression of PD-L1 in cells, and in hypomethylated treatment-na€ve recent studies cannot form a cohesive molecular profile of favorable patient tumors, there was strong expression of “viral mimicry” HERVs decitabine response for AML, MDS, or CMML. As such, our gene- and higher PD-L1 (51). However, decitabine treatment also increased specific and genome-wide transcription and methylation results in PD-L1 levels in patients with MDS, CMML, and AML (52). Impor- breast cancer are unsurprising and suggest that predicting decitabine tantly, patients with MDS with the most elevated PD-L1 post- response will be multi-factorial and decitabine likely be used in decitabine were also the most resistant to decitabine therapy. Together, combination with other drugs. these studies underlie the importance of determining how decitabine Potential rational combinations include using DNA hypomethylat- affects immunogenicity in the clinic versus in preclinical animal ing agents followed by immunotherapeutics such as checkpoint inhi- studies, and that these effects may also be cancer type specific. bitors. This strategy should increase the immunogenicity of tumors by On the basis of the mixed results of these well-established immu- inducing expression of hypermethylated cancer testis antigens (and nogenic pathways, we should be hopeful but cautious when interpret- HERVs), and concomitantly inhibit the downregulation of elicited ing any viral mimicry responses induced by decitabine. We see a clear antitumor cytotoxic T-cell responses by blocking PD-1–PD-L1 or induction of the viral mimicry response genes (e.g., OASL and ISG15) CTLA4–CD80/86 interactions. This is the intended strategy for an in breast cancer cells; however, this was not paired with a favorable ongoing clinical trial for TNBC (NCT02957968), where patients are decitabine response. The direct cytotoxicity of MDA5-mediated viral treated sequentially with decitabine plus checkpoint inhibitor pem- mimicry that was observed in colorectal cancer cells (12) was not brolizumab (anti-PD-1), followed by standard chemotherapy. replicated in our MDA5-knockdown breast cancer cells. Importantly, In these immunotherapeutic-based combination approaches, it is we did not assess the immunogenicity of decitabine treatment; hence, it also important to consider how decitabine affects PD-L1 expression, is possible that while induction of this pathway, while not directly and in this regard the data are somewhat contradictory. In murine solid cytotoxic in breast cancer cells, may be important for inducing tumor studies, decitabine reduced PD-L1 levels on tumor cells and antitumor effects in immunocompetent models and patients. þ subsequently improved infiltration of cytotoxic CD8 T cells and enhanced anti-PD-1 immunotherapy (47–50). In another study exam- Disclosure of Potential Conflicts of Interest ining DNA methylation and PD-L1 expression patterns in melanoma No potential conflicts of interest were disclosed.

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Authors’ Contributions was provided by grant funding to I.C.G. Weaver from the Natural Sciences and Conception and design: M.L. Dahn, B.M. Cruickshank, I.C.G. Weaver, P. Marcato Engineering Research Council of Canada (RGPIN-2013-436204). M.L. Dahn was Development of methodology: M.L. Dahn, I.C.G. Weaver, P. Marcato supported by CGS-D award from the CIHR, a Nova Scotia Health Research Acquisition of data (provided animals, acquired and managed patients, provided Foundation studentship, an NS graduate scholarship, and a Killam Laureate facilities, etc.): M.L. Dahn, B.M. Cruickshank, A.J. Jackson, C. Dean, R.W. Holloway, scholarship. B.M. Cruickshank was supported by Nova Scotia Research and S.R. Hall, K.M. Coyle, H. Maillet, D.M. Waisman, K.B. Goralski, I.C.G. Weaver, Innovation Graduate and Killam Laureate scholarships. K.M. Coyle was sup- P. Marcato ported by a CGS-D award from CIHR and by the DeWolfe Graduate Award Analysis and interpretation of data (e.g., statistical analysis, biostatistics, from the Dalhousie Medical Research Foundation, and a studentship from computational analysis): M.L. Dahn, B.M. Cruickshank, A.J. Jackson, C. Dean, the Beatrice Hunter Cancer Research Institute and the Canadian Imperial Bank S.R. Hall, P. Marcato of Commerce. The results published here are in part based upon data generated Writing, review, and/or revision of the manuscript: M.L. Dahn, S.R. Hall, by the TCGA Research Network: https://www.cancer.gov/tcga. The shRNA K.M. Coyle, D.M. Waisman, K.B. Goralski, P. Marcato knockdown clones were obtained by accessing Dalhousie University's Faculty of Administrative, technical, or material support (i.e., reporting or organizing data, Medicine Gene Analysis & Discovery Core Facility. constructing databases): M.L. Dahn, B.M. Cruickshank Study supervision: M.L. Dahn, C.A. Giacomantonio, P. Marcato The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance Acknowledgments with 18 U.S.C. Section 1734 solely to indicate this fact. Support was provided by grant funding to P. Marcato by the Cancer Research Society in partnership with the Institute of Cancer Research of the Canadian Received July 30, 2019; revised January 30, 2020; accepted March 5, 2020; Institutes of Health Research (CIHR; grant number 22185). In addition, support published ﬁrst March 10, 2020.

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AACRJournals.org Mol Cancer Ther; 19(5) May 2020 OF13

Decitabine Response in Breast Cancer Requires Efficient Drug Processing and Is Not Limited by Multidrug Resistance

Margaret L. Dahn, Brianne M. Cruickshank, Ainsleigh J. Jackson, et al.

Mol Cancer Ther Published OnlineFirst March 10, 2020.

Updated version Access the most recent version of this article at: doi:10.1158/1535-7163.MCT-19-0745

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