Epigenetic drugs, such as DNA methylation inhibitors (DNMTi) or histone deacetylase inhibitors (HDACi), are approved in monotherapy for cancer treatment. These drugs reprogram gene expression profiles, reactivate tumor suppressor genes (TSG) producing cancer cell differentiation and apoptosis. Epigenetic drugs have been shown to synergize with other epigenetic drugs or various anticancer drugs. To discover new molecular entities that enhance epigenetic therapy, we performed a high-throughput screening using FDA-approved libraries in combination with DNMTi or HDACi. As a screening model, we used YB5 system, a human colon cancer cell line, which contains an epigenetically silenced CMV-GFP locus, mimicking TSG silencing in cancer. CMV-GFP reactivation is triggered by DNMTi or HDACi and responds synergistically to DNMTi/HDACi combination, which phenocopies TSG reactivation upon epigenetic therapy. GFP fluorescence was used as a quantitative readout for epigenetic activity. We discovered that 45 FDA-approved drugs (4% of all drugs tested) in our FDA-approved libraries enhanced DNMTi and HDACi activity, mainly belonging to anticancer and antiarrhythmic drug classes. Transcriptome analysis revealed that combination of decitabine (DNMTi) with the antiarrhythmic proscillaridin A produced profound gene expression reprogramming, which was associated with downregulation of 153 epigenetic regulators, including two known oncogenes in colon cancer (SYMD3 and KDM8). Also, we identified about 85 FDA-approved drugs that antagonized DNMTi and HDACi activity through cytotoxic mechanisms, suggesting detrimental drug interactions for patients undergoing epigenetic therapy. Overall, our drug screening identified new combinations of epigenetic and FDA-approved drugs, which can be rapidly implemented into clinical trials. Mol Cancer Ther; 16(2); 397–407. ©2016 AACR.

Epigenetic alterations play major roles in establishing and maintaining aberrant gene expression profiles in cancer cells. Epigenetics is defined by molecular mechanisms, such as DNA methylation, histone modifications, nucleosome occupancy, and miRNA, that are involved in heritable gene expression patterns (1). Advances in epigenetics have revealed the importance and the diversity of epigenetic proteins encompassing more than 300 enzymes and regulators (2, 3). In cancer, the epigenome is characterized by hundreds of epigenetic abnormalities occurring at DNA methylation and chromatin levels. These epigenetic aberrations are caused by altered expression or mutations in epigenetic enzymes and regulators, which are implicated in unlimited cell proliferation, loss of cell differentiation, resistance to apoptosis, and metastasis (1, 4, 5). A well-known example of epigenetic reprogramming in cancer cells is the silencing of tumor suppressor genes (TSG) and the activation of oncogenes (5). TSG silencing involves promoter DNA hypermethylation and repressive chromatin marks catalyzed by DNA methyltransferases (DNMT) and by histone deacetylases (HDAC), respectively (5, 6).

Targeting epigenetic alterations in cancer cells is a new frontier in drug discovery, which is referred to as epigenetic therapy. The efficacy of epigenetic drugs, such as DNMT inhibitors (DNMTi) azacitidine and decitabine or HDAC inhibitors (HDACi) vorinostat, romidespin, and belinostat, has led to their approval as monotherapy, in hematologic malignancies (5). These drugs induce gene expression changes in cancer cells, which results in cancer cell differentiation, apoptosis, and recognition by the immune system (5). Epigenetic drugs, when used in monotherapy, produce complete responses in a subset of patients, and remissions are generally of short duration (7, 8). Strategies are being explored to improve the efficacy of epigenetic drugs by increasing epigenetic drug specificity and using combination strategies.

It is well known that epigenetic drugs can produce synergistic responses when used in combination with other epigenetic drugs, chemotherapy, immunotherapy, or targeted drugs. Early preclinical studies have focused on the combination of DNMTi and HDACi, which produced concomitant synergistic reactivation of TSG and anticancer effects (9). The goal of this approach is to relieve epigenetic silencing mechanisms associated with TSG silencing to produce a robust epigenetic reprogramming in cancer cells. Several combinations of DNMTi and HDACi have been identified in preclinical studies and are currently tested in clinical trials (10, 11). With the development of new epigenetic agents targeting histone methyltransferases, histone demethylases, or bromodomains, novel synergistic combinations are being explored with DNMTi and/or HDACi (12–15). The combination of epigenetic drugs with standard chemotherapy, targeted drugs, or immunotherapy is also a promising avenue. The rationale behind these combinatorial treatments is to use epigenetic drugs to lower apoptotic threshold, reverse drug resistance, or induce immunologic responses (16–19). For example, synergistic combinations were reported with chemotherapeutic drugs (retinoic acids, cisplatin, carboplatin, and clofarabine), with targeted drugs such as erlotinib, and with immunotherapy (IL2; refs. 20–26). The potential of epigenetic drugs to synergize with a variety of anticancer approaches may be related to the high number of molecular targets associated with epigenetic dysregulation in carcinogenesis (3).

In multifactorial diseases such as cancer, there is a strong rationale for the development of drug combinations over single-drug approaches, where combinational therapies are likely to be more effective than monotherapy (27). High-throughput screenings (HTS) based on phenotypic (cytotoxicity) or target-based assays are commonly used in drug discovery. Despite the availability of HTS approaches that can be used in a combinatorial setting, the majority of HTS studies involve single-drug screening, potentially missing the discovery of positive synergistic drug interactions. Innovative technologies suitable for combinatorial HTS are needed to accelerate drug development.

### Transcriptome and gene ontology pathway analysis

For transcriptome analysis by RNA-sequencing (RNA-Seq), YB5 cells were treated with decitabine at 100 nmol/L for 48 hours with or without 50 nmol/L proscillaridin A for an additional 48 hours. RNA was isolated using RNeasy Mini Kit (Qiagen). Strand-specific RNA libraries were generated from 1 μg of RNA using TruSeq stranded total RNA with Ribo-Zero Gold (Illumina). Sequencing was performed at Fox Chase Cancer Center genome facility (Temple University, Philadelphia, PA) using single-end reads (50 bp, average 50 million reads per sample) on the HiSeq2500 platform from Illumina. Sequenced reads were aligned to the hg19 genome assembly using TopHat2. Expression level and fold change of each treatment group were evaluated using edgeR (33). Differentially expressed genes were defined by at least 2-fold change compared with control and a q-value lower than 0.1. Gene ontology (GO) analyses were performed using Metascape (34). RNA-Seq data were deposited in the Gene Expression Omnibus (GEO) database with the accession number GSE89154.

### Statistical analysis

One-way ANOVA was used for statistical analysis, and P value was evaluated by the Tukey–Kramer multiple comparison test. Statistics and graphs were made using GraphPad Prism software.

### Combinatorial drug screening results

YB5 system was used to screen among 1,118 FDA-approved drugs to discover combinations that enhance GFP reactivation induced by epigenetic drugs decitabine and TSA. Three combinations were tested: (i) sequential combination with decitabine at 50 nmol/L for 72 hours, followed by 24-hour exposure with FDA-approved drugs at 50 μmol/L before flow cytometry analysis; (ii) simultaneous combination for 72 hours with decitabine (50 nmol/L) and FDA-approved drugs (10 μmol/L) followed by 24 hours without treatment prior to analysis; and (iii) sequential combination with TSA, in which YB5 cells were treated with FDA-approved drugs at 10 μmol/L for 72 hours, followed by 24-hour exposure to TSA at 200 nmol/L before flow cytometry analysis (Fig. 1A). Dose and schedule chosen for decitabine and TSA treatments were selected to allow the detection of synergistic interactions. Low doses of each epigenetic drug alone produced GFP reactivation in less than 20% of YB5 cells. Their sequential combination produced synergistic GFP reactivation in around 60% of the YB5 cells (Supplementary Fig. S2A). Sequential or simultaneous combinations with decitabine have been chosen to discover drugs that enhance its activity similarly to HDACi (9). Sequential combination with HDACi was performed to discover new drugs that produce synergistic interaction such as the one observed with decitabine (9). In this case, cells were treated with drug libraries followed by TSA. This sequence order was chosen as decitabine treatment followed by TSA produces synergistic responses (9). The opposite sequence order was not selected as HDACi followed by decitabine produced antagonistic effects due to HDACi-induced cell-cycle arrest, which blocked the activity of S-phase–specific drugs such as decitabine (35, 36). For each drug combination, a GFP ratio was calculated using the percentage of YB5 cells expressing GFP in the combinatorial treatment divided by the percentage of YB5 cells expressing GFP after decitabine or TSA in monotherapy. Positive hits (synergistic) or negative hits (antagonistic) were determined as drug combinations producing a GFP ratio above or below GFP ratio of 1 ± 3 SDs, respectively (Fig. 1B–D).

Sequential HTS with decitabine identified 31 drugs that increased GFP expression induced by decitabine (up to 4-fold) and 37 drugs that antagonized and even completely abolished its activity (Fig. 1B). Among top hits, epigenetic drugs azacitidine (DNMTi) and vorinostat (HDACi) had GFP ratios of 3.75 and 3.31, respectively, which validated our combinatorial HTS. We identified as positive hits all four antiarrhythmic drugs present in the libraries, belonging to the cardiac glycoside subfamily (ouabain, digoxin, digitoxin, and proscillaridin A) suggesting a class effect. In addition, the combination with arsenic trioxide produced a 3-fold increase in GFP ratio. Other drug combinations involving several antibacterials and antidiabetic drugs (phenformin and acetohexamide) induced decitabine activity with a GFP ratio up to 2.5-fold. Interestingly, out of the 31 positive combinations, 21 drugs (68%) did not induce GFP reactivation as single treatment, as described previously (31).

In simultaneous combinations with decitabine, 9 drugs increased decitabine effects (up to 2.5-fold), while 69 drugs reduced GFP expression (Fig. 1C). In this combination, all positive hits, except decitabine did not induce GFP reactivation in our single-drug screen (31). The range of GFP ratio, among positive hits, was between 2.2- and 2.57-fold.

In combination with TSA, GFP expression was induced up to 12-fold by 9 drugs, whereas 30 drugs decreased GFP expression induced by TSA (Fig. 1D). Nine hits were identified, including decitabine and azacitidine with GFP ratios of 13.27 and 6.69, further validating the screening system. Among the other 7 hits, 3 anticancer drugs enhanced TSA activity. These drugs encompassed methotrexate, pemetrexed, and sorafenib, which produced GFP ratios between 2.9 and 4.2. Interestingly, these anticancer drugs did not produce GFP reactivation alone in YB5 cells (31). Similarly to the HTS with decitabine, antiarrhythmic drugs, digitoxin and digoxin, enhanced TSA epigenetic activity.

Validation experiments on selected combinations confirmed our HTS results, for instance, the sequential combination with decitabine and cardiac glycosides (proscillaridin A and digitoxin) or the anticancer drug arsenic trioxide (Fig. 1E and F; Supplementary Fig. S2B–S2C). All FDA-approved drugs that enhanced GFP expression induced by decitabine or TSA are listed in Table 1. Altogether, these three HTSs revealed that about 45 FDA-approved drugs enhanced the activity of epigenetic drugs, such as DNMTi and HDACi. Some of these drugs represent interesting candidates for drug repositioning, particularly the class of antiarrhythmic drugs.

Table 1.

Positive hits identified in combination screen with decitabine (sequential and simultaneous treatment) or TSA (sequential treatment)

CombinationDrug nameDrug functionGFP ratio
Sequential with decitabine Ronnela Insecticide 4.02
Azacitidine Anticancer, DNA methylation inhibitor 3.75
Ouabain Antiarrhythmic, Na+/K+ channel blocker 3.42
Cycloheximide Antipsoriatic, protein synthesis inhibitor 3.39
Proscillaridin Antiarrhythmic, Na+/K+ channel blocker 3.37
Lanatoside C Antiarrhythmic, Na+/K+ channel blocker 3.33
Altrenogesta Progestin, estrus cycle suppression 3.33
Vorinostat Anticancer, histone deacetylase inhibitor 3.31
Butylated hydroxytoluenea Antioxidant 3.12
Digoxin Antiarrhythmic, Na+/K+ channel blocker 3.11
Bromperidola Antipsychotic 3.09
Arsenic trioxide Anticancer 3.08
Terbinafine hydrochloridea Antifungal 3.05
Oxybutynin chloridea Anticholinergic 3.00
Tacrine hydrochloridea Anticholinesterase, K+ channel blocker 2.98
Digitoxin Antiarrhythmic, Na+/K+ channel blocker 2.95
Amoxapinea Antidepressant 2.94
Phenformin hydrochloridea Antidiabetic 2.91
Diloxanide furoatea Amoebicide 2.79
Acetohexamide Antidiabetic 2.72
Oxantel pamoatea Anthelmintic 2.71
Ticlopidine hydrochloridea Antiplatelet 2.68
Rolitetracyclinea Antibacterial 2.67
Nialamidea Antidepressant 2.67
Rabeprazole sodiuma Gastric acid secretion inhibitor 2.64
Toltrazurila Coccidiostat 2.64
Lamotriginea Anticonvulsant 2.63
Pyridoxinea Vitamin B6 dietary supplement 2.62
Benzoxiquinea Anti-infective 2.60
Simultaneous with decitabine Trientine hydrochloridea Chelating agent 2.57
Decitabine Anticancer, DNA methylation inhibitor 2.51
Nitrofurazonea Anti-infective 2.49
Nitromidea Antibacterial 2.47
Triclosana Anti-infective 2.37
Etidronate disodiuma Bone resorption inhibitor 2.22
Netilmicin sulfatea Antibacterial 2.22
Arsanilic acida Antibacterial 2.22
Levobunolol hydrochloridea Antiglaucoma 2.21
Sequential with TSA Decitabine Anticancer, DNA methylation inhibitor 13.27
Azacitidine Anticancer, DNA methylation inhibitor 6.69
Digitoxin Antiarrhythmic, Na+/K+ channel blocker 5.76
Methotrexatea Anticancer 4.26
Pemetrexeda Anticancer 4.22
Bromocriptine mesylatea Antiparkinsonian 3.13
Thiram Antifungal 3.08
Sorafeniba Anticancer 2.97
Digoxin Antiarrhythmic, Na+/K+ channel blocker 2.90
CombinationDrug nameDrug functionGFP ratio
Sequential with decitabine Ronnela Insecticide 4.02
Azacitidine Anticancer, DNA methylation inhibitor 3.75
Ouabain Antiarrhythmic, Na+/K+ channel blocker 3.42
Cycloheximide Antipsoriatic, protein synthesis inhibitor 3.39
Proscillaridin Antiarrhythmic, Na+/K+ channel blocker 3.37
Lanatoside C Antiarrhythmic, Na+/K+ channel blocker 3.33
Altrenogesta Progestin, estrus cycle suppression 3.33
Vorinostat Anticancer, histone deacetylase inhibitor 3.31
Butylated hydroxytoluenea Antioxidant 3.12
Digoxin Antiarrhythmic, Na+/K+ channel blocker 3.11
Bromperidola Antipsychotic 3.09
Arsenic trioxide Anticancer 3.08
Terbinafine hydrochloridea Antifungal 3.05
Oxybutynin chloridea Anticholinergic 3.00
Tacrine hydrochloridea Anticholinesterase, K+ channel blocker 2.98
Digitoxin Antiarrhythmic, Na+/K+ channel blocker 2.95
Amoxapinea Antidepressant 2.94
Phenformin hydrochloridea Antidiabetic 2.91
Diloxanide furoatea Amoebicide 2.79
Acetohexamide Antidiabetic 2.72
Oxantel pamoatea Anthelmintic 2.71
Ticlopidine hydrochloridea Antiplatelet 2.68
Rolitetracyclinea Antibacterial 2.67
Nialamidea Antidepressant 2.67
Rabeprazole sodiuma Gastric acid secretion inhibitor 2.64
Toltrazurila Coccidiostat 2.64
Lamotriginea Anticonvulsant 2.63
Pyridoxinea Vitamin B6 dietary supplement 2.62
Benzoxiquinea Anti-infective 2.60
Simultaneous with decitabine Trientine hydrochloridea Chelating agent 2.57
Decitabine Anticancer, DNA methylation inhibitor 2.51
Nitrofurazonea Anti-infective 2.49
Nitromidea Antibacterial 2.47
Triclosana Anti-infective 2.37
Etidronate disodiuma Bone resorption inhibitor 2.22
Netilmicin sulfatea Antibacterial 2.22
Arsanilic acida Antibacterial 2.22
Levobunolol hydrochloridea Antiglaucoma 2.21
Sequential with TSA Decitabine Anticancer, DNA methylation inhibitor 13.27
Azacitidine Anticancer, DNA methylation inhibitor 6.69
Digitoxin Antiarrhythmic, Na+/K+ channel blocker 5.76
Methotrexatea Anticancer 4.26
Pemetrexeda Anticancer 4.22
Bromocriptine mesylatea Antiparkinsonian 3.13
Thiram Antifungal 3.08
Sorafeniba Anticancer 2.97
Digoxin Antiarrhythmic, Na+/K+ channel blocker 2.90

NOTE: Drug names, drug function, and GFP ratio are shown.

aDrugs without epigenetic activity in monotherapy, as published previously (31).

### Decitabine and proscillaridin A combination reactivated genes associated with calcium signaling and decreased epigenetic genes with oncogenic activity

To further explore the repositioning potential of antiarrhythmic drugs in combination with decitabine, we selected the cardiac glycoside proscillaridin A, which was the most active in our validation experiments (Fig. 1E). To characterize the epigenetic effects of the combination between decitabine and proscillaridin A, we measured drug-induced transcriptome changes by RNA-Seq. First, we defined the optimal experimental condition by treating YB5 cells with various doses and schedules of decitabine and proscillaridin A and measured GFP reactivation by flow cytometry. Maximal GFP reactivation was obtained after the sequential combination of decitabine at 100 nmol/L for 48 hours followed by proscillaridin A at 50 nmol/L for 48 hours (Supplementary Fig. S3). RNA-Seq was performed for drug combination, single-drug treatments, and untreated cells.

Venn diagram analysis on upregulated genes (fold change >2 and FDR <0.01) showed that proscillaridin A induced the reactivation of 5,622 genes, whereas decitabine alone reactivated 248 genes (Fig. 2A). The combination produced the reactivation of 5,891 genes, which were mainly caused by the effect of proscillaridin A treatment. Indeed, 87% of the genes (5,159 genes) were shared between the combination and the proscillaridin A treatment. Venn diagram analysis on downregulated genes (fold change <0.5 and FDR <0.01) showed a potent effect of proscillaridin A alone, which decreased the expression of 5,277 genes (Fig. 2B). Decitabine decreased only two genes in these conditions. The combinatorial treatment produced a downregulation of 5,187 genes, and the majority of them (95%, 4,928 genes) were downregulated by proscillaridin A alone. Therefore, our transcriptome analysis showed that proscillaridin induced a potent reprogramming, which was mainly driving gene expression changes in the combination treatment for either up- or downregulated genes.

Figure 2.

Reprogramming colon cancer cells by massively downregulating epigenetic genes. RNA-Seq was performed on YB5 cells that were either untreated or treated with decitabine (48 hours at 100 nmol/L), proscillaridin A (Pros.) 48 hours at 50 nmol/L), or their sequential combination (n = 3). A, Venn diagram analysis of the number of upregulated genes in treated cells versus untreated cells. B, Venn diagram analysis of the number of downregulated genes in treated cells versus untreated cells. Metascape analysis was performed on both up and downregulated gene datasets. C, Fold changes in the expression of genes exclusively upregulated by proscillaridin A and belonging to calcium pathway–associated genes by Metascape analysis. D, Fold changes in the expression of genes exclusively upregulated by the combination and belonging to calcium pathway–associated genes by Metascape analysis. E, Heatmap of genes specifically downregulated by the combination as compared with single-drug treatment, belonging to epigenetic pathways by Metascape analysis. Fold change ratios were calculated for downregulated genes shared between proscillaridin A and the combination (n = 4,928). Gene sets with ratio greater than 1 (n = 2,146) were analyzed by Metascape. The results show that 153 epigenetic genes were specifically downregulated (left). Most downregulated genes are shown (right), which included two known oncogenes in colon cancer, SMYD3 and KDM8.

Figure 2.

Reprogramming colon cancer cells by massively downregulating epigenetic genes. RNA-Seq was performed on YB5 cells that were either untreated or treated with decitabine (48 hours at 100 nmol/L), proscillaridin A (Pros.) 48 hours at 50 nmol/L), or their sequential combination (n = 3). A, Venn diagram analysis of the number of upregulated genes in treated cells versus untreated cells. B, Venn diagram analysis of the number of downregulated genes in treated cells versus untreated cells. Metascape analysis was performed on both up and downregulated gene datasets. C, Fold changes in the expression of genes exclusively upregulated by proscillaridin A and belonging to calcium pathway–associated genes by Metascape analysis. D, Fold changes in the expression of genes exclusively upregulated by the combination and belonging to calcium pathway–associated genes by Metascape analysis. E, Heatmap of genes specifically downregulated by the combination as compared with single-drug treatment, belonging to epigenetic pathways by Metascape analysis. Fold change ratios were calculated for downregulated genes shared between proscillaridin A and the combination (n = 4,928). Gene sets with ratio greater than 1 (n = 2,146) were analyzed by Metascape. The results show that 153 epigenetic genes were specifically downregulated (left). Most downregulated genes are shown (right), which included two known oncogenes in colon cancer, SMYD3 and KDM8.

Close modal

To determine the impact of gene expression changes, we performed GO analysis using Metascape (34). First, we focused our analysis on upregulated genes. Among 460 genes specifically upregulated by proscillaridin A (nonoverlapping to other treatments), Metascape analysis revealed that gene expression involved in metal ion transport was increased (Supplementary Fig. S4A). In this gene set, we identified a series of 22 upregulated genes (from 2- to 7-fold), which were associated with GO-Terms associated with calcium pathways (Fig. 2C; list of GO-Terms in Supplementary Table S1). The effects of proscillaridin A on calcium signaling were corroborated by our recent report, demonstrating that epigenetic changes can be triggered by drugs targeting calcium signaling (31). Among 693 genes upregulated (from 2- to 6-fold) specifically by the combination (nonoverlapping to other treatments), Metascape analysis also showed an increase in the expression in calcium ion transport genes. Specifically, 13 genes belonged to GO-Terms related to calcium pathways, further reinforcing the importance of calcium signaling in the epigenetic effects triggered by proscillaridin A alone or in combination with decitabine (Fig. 2D; Supplementary Fig. S4B, list of GO-Terms in Supplementary Table S1).

As most of the differentially expressed genes were shared between the treatment of proscillaridin A and the combination, we asked whether gene expression levels (i.e., fold changes) would differ between the combination and the treatment with proscillaridin A alone. We analyzed fold change ratios in gene expression between the combination and proscillaridin A. Among 5,043 genes, only two genes (ANKRD20A12P and TIMP3) had a more than 2-fold expression increase in the combination as compared with the single-drug treatment. Among upregulated genes shared between the three conditions (overlapping genes between decitabine, proscillaridin A, and combination), less than 0.1% doubled their expression levels in the combinatorial treatments, also confirming that the effects observed in the combination were mostly driven by the effects of proscillaridin A treatment.

We then focused our analysis on downregulated genes. Interestingly, the expression levels of almost half of these genes (2,163 genes) were more downregulated specifically in the combination (up to 3.7-fold) as compared with proscillaridin A alone. Metascape analysis demonstrated that 153 of those were associated with GO-Terms belonging to epigenetic pathways (list of GO-Terms in Supplementary Table S2). Most downregulated genes specifically in the combination as compared with proscillaridin A alone belonged to chromatin modifiers, such as INO80B, HDAC8, SMYD3, HMGN3, and KDM8 (Fig. 2E). Interestingly, SMYD3 and KDM8 were recently identified by others as potent oncogenes in colorectal cancer (37, 38). These data highlight the epigenetic component of the combination between decitabine and proscillaridin A that downregulates epigenetic pathways specifically involved in colon cancer development.

### Epigenetic activity as single drugs is not a prerequisite to enhance DNMTi or HDACi

We next asked whether minimal epigenetic activity is a requirement for any drug to enhance epigenetic drug activity. To answer this question, we compared GFP ratios of combination treatment with decitabine and TSA with the percentage of GFP-expressing cells measured in single-drug HTS (31). Cut-off values for GFP positivity in single-drug screening was established at 2.2% of YB5 cells expressing GFP as previously reported (shown by vertical gray dotted lines; Fig 3A–C; ref. 31). We found that the number of positive hits in the combinatorial HTS was equally distributed above and below the cutoff for GFP positivity in single-drug screening, suggesting that epigenetic activity is not required for drugs to enhance DNMTi or HDACi.

Figure 3.

Synergy between the combination of FDA-approved drugs with DNMTi or HDACi is obtained regardless of epigenetic activity of FDA-approved drugs in monotherapy. A–C, GFP ratios in each HTS condition were plotted against percentages of YB5 cells expressing GFP after single-drug treatments in sequential combination with decitabine (A), in simultaneous combination with decitabine (B), and sequential treatment with TSA (C). Horizontal dotted lines represent levels of three SDs above and below average GFP ratio in each screening condition. Vertical dotted lines represent threshold for GFP positivity calculated in the single-drug screening as reported previously (31). D–F, Percentage of GFP-positive cells was expressed by drug classes to compare the combination treatment with the single-drug treatment in sequential combination with decitabine (D), simultaneous combination with decitabine (E), and sequential combination with TSA (F). As a baseline level, vertical dotted lines represent percentage of GFP-positive cells obtained after decitabine (in D and E) and TSA (F) treatment.

Figure 3.

Synergy between the combination of FDA-approved drugs with DNMTi or HDACi is obtained regardless of epigenetic activity of FDA-approved drugs in monotherapy. A–C, GFP ratios in each HTS condition were plotted against percentages of YB5 cells expressing GFP after single-drug treatments in sequential combination with decitabine (A), in simultaneous combination with decitabine (B), and sequential treatment with TSA (C). Horizontal dotted lines represent levels of three SDs above and below average GFP ratio in each screening condition. Vertical dotted lines represent threshold for GFP positivity calculated in the single-drug screening as reported previously (31). D–F, Percentage of GFP-positive cells was expressed by drug classes to compare the combination treatment with the single-drug treatment in sequential combination with decitabine (D), simultaneous combination with decitabine (E), and sequential combination with TSA (F). As a baseline level, vertical dotted lines represent percentage of GFP-positive cells obtained after decitabine (in D and E) and TSA (F) treatment.

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In sequential combination with decitabine (31 hits identified), most drugs producing synergistic GFP reactivation were drugs lacking the ability to induce GFP reactivation in monotherapy (Fig. 3A). Only 10 drugs (32%) induced GFP activity as single agent, as reported previously (31). In simultaneous combination with decitabine, all hits (n = 9), except one (decitabine), did not produce GFP reactivation activity when used in monotherapy (Fig. 3B). In sequential combination with TSA, all 9 positive hits produced synergistic GFP ratio including 5 drugs, which produced GFP reactivation in monotherapy (Fig. 3C). Two of those, decitabine and azacitidine, were excluded from the graph as their synergistic interaction with HDACi is well characterized (29). Interestingly, pretreatment with 4 FDA-approved drugs (methotrexate, pemetrexed, bromocriptine mesylate, and sorafenib) enhanced TSA-induced GFP reactivation, but these drugs lacked epigenetic effects in monotherapy. Therefore, the data showed that synergistic responses obtained in our combinatorial HTS with decitabine or TSA are achievable by several types of FDA-approved drugs, where epigenetic activity in monotherapy is not required.

### Identification of antiarrhythmic and anticancer drugs that synergize with epigenetic therapies

We then asked whether we could identify specific drug classes that enhance epigenetic activities of DNMTi or HDACi. We grouped the drugs by medical classes and compared the percentage of GFP-positive cells in the combination treatments to the single-drug screening previously published (31). In these analyzes, we removed epigenetic drugs contained in the libraries. In the sequential HTS with decitabine, drug classes producing the most striking synergistic responses belonged to the antiarrhythmic class, mainly represented by cardiac glycosides and the anticancer drugs (Fig. 3D). Interestingly, other groups of drugs, such as antiparasitics, anti-infectives and antidiabetics, also produced potent GFP reactivation as compared with decitabine alone. Synergistic combination in simultaneous HTS with decitabine was achieved mainly by anti-infective drugs (Fig. 3E). In the combinatorial screen with TSA, anticancers and antiarrhythmics were drug classes producing synergistic GFP reactivation (Fig. 3F). In this case, the anticancer drug group encompassed two antifolate drugs (methotrexate and pemetrexed). As folate contributes to production of S-adenosylmethionine, the donor moiety for methylation reactions, it is likely that antifolate drugs may influence DNA methylation content, thereby enhancing HDACi activity. Overall, the data suggest that several drug classes, mainly antiarrhythmic and anticancer drugs, synergize with epigenetic therapy, suggesting new possible drug combinations that can be evaluated in clinical trials.

### FDA-approved drugs producing cytotoxicity antagonize DNMTi or HDACi epigenetic effects

We found that some FDA-approved drugs blocked or interfered the epigenetic effects of DNMTi or HDACi (Fig. 1B–D). This observation revealed potential antagonist drug interactions, which can be deleterious for cancer patients undergoing epigenetic therapy who are being prescribed several drugs simultaneously. Indeed, the HTS identified a series of 85 drugs that reduced or blocked GFP expression induced by decitabine or TSA (Table 2; Supplementary Table S3–5). The use of these drugs in the clinics may decrease the epigenetic effects of decitabine or TSA in cancer patients.

Table 2.

Antagonistic hits identified in combination HTS with decitabine (sequential and simultaneous) or TSA (sequential)

CombinationDrug nameDrug functionGFP ratio
Sequential with decitabine Fluphenazine hydrochloride Antipsychotic 0.00
Penfluridol Antipsychotic 0.00
Toremiphene citrate Anticancer 0.00
Hexylresorcinol Antiseptic 0.00
Gentian violet Antibacterial 0.00
Econazole nitrate Antifungal 0.01
Tamoxifen citrate Anticancer 0.01
Thioridazine hydrochloride Antipsychotic 0.01
Selamectin Anthelmintic 0.02
Sertraline hydrochloride Antidepressant 0.02
Perhexiline maleate Coronary vasodilator 0.02
Gramicidin A Antibacterial 0.02
Mefloquine Antimalarial 0.04
Dioxybenzone Sunscreen 0.04
Simultaneous with decitabine Methylbenzethonium chloride Anti-infective 0.00
Valrubicin Anticancer 0.00
Benzethonium chloride Antiseptic 0.00
Gentian violet Antibacterial 0.00
Dactinomycin Anticancer 0.00
Epirubicin hydrochloride Anticancer 0.01
Mitomycin Anticancer 0.01
Nitrogen mustard Anticancer 0.01
Nilotinib Anticancer 0.02
Gemcitabine hydrochloride Anticancer 0.03
Plicamycin Anticancer 0.03
Cytarabine Anticancer 0.03
Mitoxantrone hydrochloride Anticancer 0.03
Rapamycin Anticancer 0.03
Sequential with TSA Valrubicin Anticancer 0.00
Epirubicin hydrochloride Anticancer 0.00
Dactinomycin Anticancer 0.00
Plicamycin Anticancer 0.02
Mitoxantrone Anticancer 0.02
Teniposide Anticancer 0.09
Penicillin V potassium Antibacterial 0.11
Cycloheximide Antipsoriatic 0.15
Oxymetazoline hydrochloride Nasal decongestant 0.17
Bortezomib Anticancer 0.17
Fuchsin N Anthelmintic 0.18
Mechlorethamine Anticancer 0.19
Nitrogen mustard Anticancer 0.19
Mepenzolate bromide Anticholinergic 0.20
Benzethonium chloride Antiseptic 0.23
CombinationDrug nameDrug functionGFP ratio
Sequential with decitabine Fluphenazine hydrochloride Antipsychotic 0.00
Penfluridol Antipsychotic 0.00
Toremiphene citrate Anticancer 0.00
Hexylresorcinol Antiseptic 0.00
Gentian violet Antibacterial 0.00
Econazole nitrate Antifungal 0.01
Tamoxifen citrate Anticancer 0.01
Thioridazine hydrochloride Antipsychotic 0.01
Selamectin Anthelmintic 0.02
Sertraline hydrochloride Antidepressant 0.02
Perhexiline maleate Coronary vasodilator 0.02
Gramicidin A Antibacterial 0.02
Mefloquine Antimalarial 0.04
Dioxybenzone Sunscreen 0.04
Simultaneous with decitabine Methylbenzethonium chloride Anti-infective 0.00
Valrubicin Anticancer 0.00
Benzethonium chloride Antiseptic 0.00
Gentian violet Antibacterial 0.00
Dactinomycin Anticancer 0.00
Epirubicin hydrochloride Anticancer 0.01
Mitomycin Anticancer 0.01
Nitrogen mustard Anticancer 0.01
Nilotinib Anticancer 0.02
Gemcitabine hydrochloride Anticancer 0.03
Plicamycin Anticancer 0.03
Cytarabine Anticancer 0.03
Mitoxantrone hydrochloride Anticancer 0.03
Rapamycin Anticancer 0.03
Sequential with TSA Valrubicin Anticancer 0.00
Epirubicin hydrochloride Anticancer 0.00
Dactinomycin Anticancer 0.00
Plicamycin Anticancer 0.02
Mitoxantrone Anticancer 0.02
Teniposide Anticancer 0.09
Penicillin V potassium Antibacterial 0.11
Cycloheximide Antipsoriatic 0.15
Oxymetazoline hydrochloride Nasal decongestant 0.17
Bortezomib Anticancer 0.17
Fuchsin N Anthelmintic 0.18
Mechlorethamine Anticancer 0.19
Nitrogen mustard Anticancer 0.19
Mepenzolate bromide Anticholinergic 0.20
Benzethonium chloride Antiseptic 0.23

NOTE: Drug names, drug function, and GFP ratio are shown. Only top hits are shown in this table. A complete list of antagonistic drugs is shown in Supplementary Tables S3–S5.

We analyzed the drugs that decreased GFP expression induced with DNMTi or HDACi by three SDs. Antagonistic drugs were grouped according to their drug functions. In sequential combination with decitabine, we identified 7 drug classes that repressed GFP expression, while 3 classes were identified in simultaneous combination (Fig. 4A and B). In sequential combination with TSA, we found 4 drug classes that repressed GFP reactivation (Fig. 4C). In all three combinatorial drug screens, antibiotics and anticancer drugs were commonly identified as antagonistic drug classes (Fig. 4A–C, Table 2; Supplementary Tables S3–S5). Interestingly, in all HTSs, 3 drugs, nitrogen mustard, mechlorethamine, and dactinomycin, completely abolished GFP expression. These drugs belong to the cytotoxic chemotherapy drug class, which are known to trigger cell-cycle block and apoptosis. These results confirm the notion that drugs inducing cytotoxicity or blocking the cell cycle antagonize the effect of epigenetic drugs (5). The effect of cytotoxic drugs to GFP-expressing cells could be due to a decreased ability to maintain GFP activation or alternatively to an increased cell toxicity. As the percentage of GFP-positive cells is specifically decreased when YB5 cells were exposed to cytotoxic drug combinations, it is possible that the epigenetic treatments reversed some drug resistance mechanisms, causing a lower apoptosis threshold specifically among GFP-positive cell population. Such effects could be beneficial to cancer treatment by using epigenetic therapy to increase cancer cell sensitivity to chemotherapeutic agents without causing more side effects.

Figure 4.

Epigenetic combinatorial HTS reveals antagonist interactions with 85 FDA-approved drugs. Blockers of GFP induction induced by epigenetic drugs were defined as those drugs producing GFP ratios more than three SDs below GFP ratio average value in each HTS condition. A–C, Drugs producing antagonistic interaction were grouped according to their drug classes in sequential combination with decitabine (A), simultaneous treatment with decitabine (B), and sequential treatment with TSA (C). Percentage of dead cells (measured by PI staining) was shown for drug combinations with GFP ratios below (group name: in low levels), in between (group name: no variation), and above (group name: high levels) three SD intervals. As a control, percentage of dead cells was shown for the same drugs in the single HTS, as published previously (31). D–F, These analyzes were performed in HTS with sequential combination with decitabine (D), simultaneous combination with decitabine (E), and sequential combination with TSA (F). Statistical analysis was done by one-way ANOVA, followed by the Kruskal-Wallis test. *** indicates a P < 0.001. **** indicates a P < 0.0001.

Figure 4.

Epigenetic combinatorial HTS reveals antagonist interactions with 85 FDA-approved drugs. Blockers of GFP induction induced by epigenetic drugs were defined as those drugs producing GFP ratios more than three SDs below GFP ratio average value in each HTS condition. A–C, Drugs producing antagonistic interaction were grouped according to their drug classes in sequential combination with decitabine (A), simultaneous treatment with decitabine (B), and sequential treatment with TSA (C). Percentage of dead cells (measured by PI staining) was shown for drug combinations with GFP ratios below (group name: in low levels), in between (group name: no variation), and above (group name: high levels) three SD intervals. As a control, percentage of dead cells was shown for the same drugs in the single HTS, as published previously (31). D–F, These analyzes were performed in HTS with sequential combination with decitabine (D), simultaneous combination with decitabine (E), and sequential combination with TSA (F). Statistical analysis was done by one-way ANOVA, followed by the Kruskal-Wallis test. *** indicates a P < 0.001. **** indicates a P < 0.0001.

Close modal

To further analyze these results, we compared the percentage of dead cells (measured by PI staining) obtained after HTS either in monotherapy or in combination. Average percentage of dead cells was calculated for drug combinations producing GFP ratios below three SDs of the average GFP value (referred as low levels of GFP ratio), above three SDs of the average GFP value (referred as high levels of GFP ratio), and between three SD limits of the average GFP value (referred as no variation in GFP ratio) in monotherapy or combinatorial HTS (31). In sequential combination with decitabine, there was no significant difference in dead cell percentage between single and combination groups, suggesting that toxic drugs in monotherapy can produce synergistic GFP reactivation in combination with decitabine. Comparison among GFP ratio categories showed that drug combinations producing low GFP ratios exhibited a significantly high number of dead cells compared with other groups (P < 0.001; Fig. 4D). Similar results were obtained in the simultaneous combination screen with decitabine, where drug combinations in low GFP ratio category were significantly more toxic as compared with the groups (P < 0.05; Fig. 4E). However, the percentage of dead cells did not significantly change in the sequential treatment with TSA following FDA-approved drug treatments (Fig. 4F). Overall, these data validate in a large drug collection that cytotoxic drugs, such as anticancer drugs or antibiotics, can blunt the epigenetic effects of DNMTi or HDACi.

Drug discovery in oncology has focused on the development of single molecular entities with a well-defined mechanism of action. However, cancer treatment is highly dependent on drug combinations. Thus, combinatorial drug screening is an approach that can be used to discover new combinations that are more likely to bring clinical benefits for oncology patients. To accelerate drug discovery, screening among FDA-approved drug libraries, a process called drug repositioning, has been shown to be an effective strategy in oncology (27). Drug repositioning has several advantages compared with classical drug development, including cheaper, faster, and safer preclinical and clinical validation steps (27).

Here, we described combinatorial HTS using FDA-approved libraries in association with DNMTi and HDACi to discover new epigenetic drug combinations in colon cancer. We used YB5 cell line system, which responds quantitatively to epigenetic drug combinations by the induction of GFP as a surrogate epigenetically silenced TSG (29–31). The rationale to target epigenetic abnormalities in colon cancer is justified by the importance of DNA methylation alterations (characterized by cases harboring high levels of DNA methylation called CpG island methylator phenotype) and chromatin alterations in this malignancy (5, 39). Combinations between DNMTi decitabine and antiarrhythmics or anticancer drugs were the most potent combination in this screen. Particularly, proscillaridin A and arsenic trioxide produced synergistic GFP reactivation in sequential combination (31). These drug combinations demonstrate promising activity that should be investigated in clinical trials against colon cancer. Interestingly, the combination of decitabine with arsenic trioxide is currently the focus of several clinical trials (NCT00671697, NCT02188706, and NCT02190695) against acute myelogenous leukemia and myelodysplastic syndromes (40). We focused on the understanding of the epigenomic effects of the combination of DNMTi decitabine and proscillaridin A. This combination produced a potent epigenetic reprogramming in colon cancer cells, highlighted by hundreds of differentially expressed genes. Importantly, this combination is targeting calcium signaling, which we previously linked to epigenetic therapeutic effects (31). In addition, this combination specifically downregulated around 150 genes involved in epigenetic reprogramming, which may explain why thousands of genes are differentially regulated by the treatment. More precisely, this combination induced a pronounced downregulation of epigenetic modifiers with oncogenic properties in colon cancer, such as SMYD3 and KDM8 (37, 38).

A novel finding arising from these combinatorial HTS is that epigenetic synergy could be obtained in our model with drug combinations involving FDA-approved drugs lacking epigenetic activity. This finding paves the way for new epigenetic drug combinations and justifies our rationale for combinatorial HTS. Interestingly, we also noted that synergistic GFP reactivation was dependent on cell viability immediately after treatment. Indeed, drug combinations (at the selected dose level in these HTSs) that produced elevated cell kills did not allow GFP reactivation.

We conclude that combinatorial HTS is a promising strategy for drug discovery in oncology. Investigation of FDA-approved libraries in combination with epigenetic drugs allows us to propose new drug combinations for clinical trials. Here, we have focused on the combination of decitabine and proscillaridin A, which holds promising effects in downregulating oncogenic signals carried by specific epigenetic modifiers in colon cancer. More studies are needed to explore these epigenetic combinations reported in colon cancer and other malignancies as a source for new epigenetic drug combinations. Finally, these three HTSs also suggest potential detrimental drug interactions that should be carefully considered for patients treated with epigenetic drugs.

G.G. Malouf is a consultant/advisory board member for BMS, Novartis, and Pfizer. J.-P.J. Issa is a consultant/advisory board member for Astex and Teva. No potential conflicts of interest were disclosed by the other authors.

Conception and design: N.J.-M. Raynal, J.-P.J. Issa

Development of methodology: N.J.-M. Raynal

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): N.J.-M. Raynal, J.T. Lee, V. Gharibyan, G.G. Malouf

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): N.J.-M. Raynal, E.M. Da Costa, J.T. Lee, V. Gharibyan, H. Zhang, T. Sato, G.G. Malouf, J.-P.J. Issa

Writing, review, and/or revision of the manuscript: N.J.-M. Raynal, E.M. Da Costa, J.T. Lee, G.G. Malouf, J.-P.J. Issa

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): N.J.-M. Raynal, J.T. Lee, S. Ahmed, G.G. Malouf

Study supervision: N.J.-M. Raynal, J.-P.J. Issa

This work was supported by NIH grants P50 CA100632 and P01 CA049639 (to J.-P.J. Issa). J.-P.J. Issa is an American Cancer Society Clinical Research professor supported by a generous gift from the F. M. Kirby Foundation.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Baylin
SB
,
Jones
PA
.
A decade of exploring the cancer epigenome - biological and translational implications
.
Nat Rev Cancer
2011
;
11
:
726
34
.
2.
Jones
PA
,
Baylin
SB
.
The epigenomics of cancer
.
Cell
2007
;
128
:
683
92
.
3.
Huston
A
,
Arrowsmith
CH
,
Knapp
S
,
Schapira
M
.
Probing the epigenome
.
Nat Chem Biol
2015
;
11
:
542
5
.
4.
You
JS
,
Jones
PA
.
Cancer genetics and epigenetics: two sides of the same coin?
Cancer Cell
2012
;
22
:
9
20
.
5.
Taby
R
,
Issa
JP
.
Cancer epigenetics
.
CA Cancer J Clin
2010
;
60
:
376
92
.
6.
Kondo
Y
,
Shen
L
,
Cheng
AS
,
Ahmed
S
,
Boumber
Y
,
Charo
C
, et al
Gene silencing in cancer by histone H3 lysine 27 trimethylation independent of promoter DNA methylation
.
Nat Genet
2008
;
40
:
741
50
.
7.
Estey
EH
.
Epigenetics in clinical practice: the examples of azacitidine and decitabine in myelodysplasia and acute myeloid leukemia
.
Leukemia
2013
;
27
:
1803
12
.
8.
Falkenberg
KJ
,
Johnstone
RW
.
Histone deacetylases and their inhibitors in cancer, neurological diseases and immune disorders
.
Nat Rev Drug Discov
2014
;
13
:
673
91
.
9.
Cameron
EE
,
Bachman
KE
,
Myohanen
S
,
Herman
JG
,
Baylin
SB
.
Synergy of demethylation and histone deacetylase inhibition in the re-expression of genes silenced in cancer
.
Nat Genet
1999
;
21
:
103
7
.
10.
Kirschbaum
M
,
Gojo
I
,
Goldberg
SL
,
Bredeson
C
,
Kujawski
LA
,
Yang
A
, et al
A phase 1 clinical trial of vorinostat in combination with decitabine in patients with acute myeloid leukaemia or myelodysplastic syndrome
.
Br J Haematol
2014
;
167
:
185
93
.
11.
Juergens
RA
,
Wrangle
J
,
Vendetti
FP
,
Murphy
SC
,
Zhao
M
,
Coleman
B
, et al
Combination epigenetic therapy has efficacy in patients with refractory advanced non-small cell lung cancer
.
Cancer Discov
2011
;
1
:
598
607
.
12.
Momparler
RL
,
Idaghdour
Y
,
Marquez
VE
,
Momparler
LF
.
Synergistic antileukemic action of a combination of inhibitors of DNA methylation and histone methylation
.
Leuk Res
2012
;
36
:
1049
54
.
13.
J
,
Nilsson
LM
,
Muralidharan
SV
,
Green
LC
,
Li
Z
,
Gesner
EM
, et al
BET and HDAC inhibitors induce similar genes and biological effects and synergize to kill in Myc-induced murine lymphoma
.
2014
;
111
:
2721
30
.
14.
Fiskus
W
,
Sharma
S
,
Shah
B
,
Portier
BP
,
Devaraj
SG
,
Liu
K
, et al
Highly effective combination of LSD1 (KDM1A) antagonist and pan-histone deacetylase inhibitor against human AML cells
.
Leukemia
2014
;
28
:
2155
64
.
15.
Fiskus
W
,
Wang
Y
,
Sreekumar
A
,
Buckley
KM
,
Shi
H
,
Jillella
A
, et al
Combined epigenetic therapy with the histone methyltransferase EZH2 inhibitor 3-deazaneplanocin A and the histone deacetylase inhibitor panobinostat against human AML cells
.
Blood
2009
;
114
:
2733
43
.
16.
Owonikoko
TK
,
Ramalingam
SS
,
Kanterewicz
B
,
Balius
TE
,
Belani
CP
,
Hershberger
PA
.
Vorinostat increases carboplatin and paclitaxel activity in non-small-cell lung cancer cells
.
Int J Cancer
2010
;
126
:
743
55
.
17.
Bachmann
PS
,
Piazza
RG
,
Janes
ME
,
Wong
NC
,
Davies
C
,
Mogavero
A
, et al
Epigenetic silencing of BIM in glucocorticoid poor-responsive pediatric acute lymphoblastic leukemia, and its reversal by histone deacetylase inhibition
.
Blood
2010
;
116
:
3013
22
.
18.
Wrangle
J
,
Wang
W
,
Koch
A
,
Easwaran
H
,
HP
,
Vendetti
F
, et al
Alterations of immune response of non-small cell lung cancer with azacytidine
.
Oncotarget
2013
;
4
:
2067
79
.
19.
Li
H
,
Chiappinelli
KB
,
Guzzetta
AA
,
Easwaran
H
,
Yen
RW
,
Vatapalli
R
, et al
Immune regulation by low doses of the DNA methyltransferase inhibitor 5-azacitidine in common human epithelial cancers
.
Oncotarget
2014
;
5
:
587
98
.
20.
Qin
T
,
Si
J
,
Raynal
NJ
,
Wang
X
,
Gharibyan
V
,
Ahmed
S
, et al
Epigenetic synergy between decitabine and platinum derivatives
.
Clin Epigenetics
2015
;
7
:
97
.
21.
Lainey
E
,
Wolfromm
A
,
Marie
N
,
Enot
D
,
Scoazec
M
,
Bouteloup
C
, et al
Azacytidine and erlotinib exert synergistic effects against acute myeloid leukemia
.
Oncogene
2013
;
32
:
4331
42
.
22.
Gollob
JA
,
Sciambi
CJ
,
Peterson
BL
,
Richmond
T
,
Thoreson
M
,
Moran
K
, et al
Phase I trial of sequential low-dose 5-aza-2′-deoxycytidine plus high-dose intravenous bolus interleukin-2 in patients with melanoma or renal cell carcinoma
.
Clin Cancer Res
2006
;
12
:
4619
27
.
23.
Schwartsmann
G
,
Schunemann
H
,
Gorini
CN
,
Filho
AF
,
Garbino
C
,
Sabini
G
, et al
A phase I trial of cisplatin plus decitabine, a new DNA-hypomethylating agent, in patients with advanced solid tumors and a follow-up early phase II evaluation in patients with inoperable non-small cell lung cancer
.
Invest New Drugs
2000
;
18
:
83
91
.
24.
Glasspool
RM
,
Brown
R
,
Gore
ME
,
Rustin
GJ
,
McNeish
IA
,
Wilson
RH
, et al
A randomised, phase II trial of the DNA-hypomethylating agent 5-aza-2′-deoxycytidine (decitabine) in combination with carboplatin vs carboplatin alone in patients with recurrent, partially platinum-sensitive ovarian cancer
.
Br J Cancer
2014
;
110
:
1923
9
.
25.
TM
,
S
,
Ravandi
F
,
Jabbour
E
,
Garcia-Manero
G
,
Borthakur
G
, et al
Final results of a phase 2 trial of clofarabine and low-dose cytarabine alternating with decitabine in older patients with newly diagnosed acute myeloid leukemia
.
Cancer
2015
;
121
:
2375
82
.
26.
Momparler
RL
,
Dore
BT
,
Momparler
LF
.
Effect of 5-aza-2′-deoxycytidine and retinoic acid on differentiation and c-myc expression in HL-60 myeloid leukemic cells
.
Cancer Lett
1990
;
54
:
21
8
.
27.
Bertolini
F
,
Sukhatme
VP
,
Bouche
G
.
Drug repurposing in oncology–patient and health systems opportunities
.
Nat Rev Clin Oncol
2015
;
12
:
732
42
.
28.
Blatt
J
,
Corey
SJ
.
Drug repurposing in pediatrics and pediatric hematology oncology
.
Drug Discov Today
2013
;
18
:
4
10
.
29.
Si
J
,
Boumber
YA
,
Shu
J
,
Qin
T
,
Ahmed
S
,
He
R
, et al
Chromatin remodeling is required for gene reactivation after decitabine-mediated DNA hypomethylation
.
Cancer Res
2010
;
70
:
6968
77
.
30.
Raynal
NJ
,
Si
J
,
Taby
RF
,
Gharibyan
V
,
Ahmed
S
,
Jelinek
J
, et al
DNA methylation does not stably lock gene expression but instead serves as a molecular mark for gene silencing memory
.
Cancer Res
2012
;
72
:
1170
81
.
31.
Raynal
NJ
,
Lee
JT
,
Wang
Y
,
Beaudry
A
,
P
,
Garriga
J
, et al
Targeting calcium signaling induces epigenetic reactivation of tumor suppressor genes in cancer
.
Cancer Res
2016
;
76
:
1494
505
.
32.
Monneret
C
.
Histone deacetylase inhibitors
.
Eur J Med Chem
2005
;
40
:
1
13
.
33.
Robinson
MD
,
McCarthy
DJ
,
Smyth
GK
.
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data
.
Bioinformatics
2010
;
26
:
139
40
.
34.
Tripathi
S
,
Pohl
MO
,
Zhou
Y
,
Rodriguez-Frandsen
A
,
Wang
G
,
Stein
DA
, et al
Meta- and orthogonal integration of influenza "OMICs" data defines a role for UBR4 in virus budding
.
Cell Host Microbe
2015
;
18
:
723
35
.
35.
Griffiths
EA
,
Gore
SD
.
DNA methyltransferase and histone deacetylase inhibitors in the treatment of myelodysplastic syndromes
.
Semin Hematol
2008
;
45
:
23
30
.
36.
Gore
SD
,
Baylin
S
,
Sugar
E
,
Carraway
H
,
Miller
CB
,
Carducci
M
, et al
Combined DNA methyltransferase and histone deacetylase inhibition in the treatment of myeloid neoplasms
.
Cancer Res
2006
;
66
:
6361
9
.
37.
Zhang
R
,
Huang
Q
,
Li
Y
,
Song
Y
,
Li
Y
.
JMJD5 is a potential oncogene for colon carcinogenesis
.
Int J Clin Exp Pathol
2015
;
8
:
6482
9
.
38.
Sarris
ME
,
Moulos
P
,
Haroniti
A
,
Giakountis
A
,
Talianidis
I
.
Smyd3 is a transcriptional potentiator of multiple cancer-promoting genes and required for liver and colon cancer development
.
Cancer Cell
2016
;
29
:
354
66
.
39.
Toyota
M
,
Ahuja
N
,
Ohe-Toyota
M
,
Herman
JG
,
Baylin
SB
,
Issa
JP
.
CpG island methylator phenotype in colorectal cancer
.
1999
;
96
:
8681
6
.
40.
Welch
JS
,
Klco
JM
,
Gao
F
,
Procknow
E
,
Uy
GL
,
Stockerl-Goldstein
KE
, et al
Combination decitabine, arsenic trioxide, and ascorbic acid for the treatment of myelodysplastic syndrome and acute myeloid leukemia: a phase I study
.
Am J Hematol
2011
;
86
:
796
800
.