Introduction
Resistance to standard radio‐ and chemotherapy, as well as to novel targeted therapies, is a major clinical problem in cancer medicine and crucial for disease relapse and progression. Therefore, the clinical need to overcome therapy resistance, particularly for very aggressive tumor types, such as pancreatic cancer, is very high. There are various molecular mechanisms that lead to treatment resistance, and in a general view, many of those have been linked to a stemness‐associated survival phenotype (Holohan
et al,
2013). Thus, cancer stem cells are considered to be the most resistant fraction of tumor cells, which survive different types of treatment and give rise to tumor recurrence and finally progression toward a multiresistant, often metastatic disease (Clevers,
2011; Borst,
2012). Also, the activation of an epithelial–mesenchymal transition (EMT), considered a driving force toward cancer invasion and metastasis, was associated with treatment resistance (Thiery
et al,
2009; Floor
et al,
2011). This is of particular relevance, since EMT and stemness were linked at molecular level, explaining why resistant cancer (stem) cells often acquired an undifferentiated EMT phenotype (Polyak & Weinberg,
2009; Singh & Settleman,
2010; Puisieux
et al,
2014).
The EMT inducer ZEB1 is a transcriptional repressor of epithelial genes, such as E‐cadherin and the miR‐200 family of microRNAs. ZEB1 and miR‐200 members can repress expression of each other in a double‐negative feedback loop (Brabletz & Brabletz,
2010). Moreover, since miR‐200 as well as miR‐203, another microRNA repressed by ZEB1, can also suppress stemness traits, their downregulation by ZEB1 induces an EMT‐associated stemness phenotype (Yi
et al,
2008; Wellner
et al,
2009). Overexpression of ZEB1, as well as subsequent downregulation of miR‐200, has already been associated with a pro‐survival and drug‐resistant phenotype (Mongroo & Rustgi,
2010; Zhang
et al,
2015). Furthermore, artificial re‐expression of miR‐200 family members has been shown to lead to a partial re‐sensitization (Buck
et al,
2007; Arumugam
et al,
2009; Cochrane
et al,
2009; Li
et al,
2009; Singh
et al,
2009; Wellner
et al,
2009). How can this knowledge about the molecular links of EMT and drug resistance be translated to clinical application? A depletion of relevant factors, such as ZEB1, selectively in patients' cancer cells is practically impossible.
Here, we describe a systematic, stepwise approach to interfere with ZEB1 function and restore drug sensitivity by: (i) identifying additional relevant ZEB1 target genes, (ii) defining ZEB1‐dependent epigenetic modifications of these genes, (iii) screening for epigenetic drugs forcing their re‐expression, and (v) validating the most promising candidate drug for the restoration of treatment sensitivity. This strategy led to the detection of miR‐203 as another important ZEB1 target conferring treatment sensitivity and the identification of the class I HDAC inhibitor mocetinostat, which, in contrast to other HDAC inhibitors such as SAHA, interferes with ZEB1 expression and function and restores sensitivity to chemotherapy.
Discussion
Here, we describe a chain of molecular events important for cancer treatment resistance and the development of specific strategies to overcome it. Given the role of ZEB1 in mediating resistance to different types of cancer drugs, overexpression of one of its major target genes, miR‐203, can efficiently restore drug sensitivity. Using re‐expression of silenced miR‐203 as readout, we applied a screening strategy for epigenetic drugs and identified the HDAC class I‐specific inhibitor mocetinostat as a promising candidate. The efficiency of this strategy was validated particularly for pancreatic cancer, by demonstrating that mocetinostat reduced ZEB1 expression, upregulated expression of miR‐203 and other ZEB1 targets, and sensitized undifferentiated, ZEB1‐expressing cancer cells for chemotherapy.
The mechanisms underlying ZEB1‐associated treatment resistance are complex and likely also include unknown, target gene independent effects. However, our data indicate that besides miR‐200, the ZEB1 target miR‐203 is an important factor. miR‐203 has been described as a stemness‐inhibiting microRNA (Lena
et al,
2008; Yi
et al,
2008; Taube
et al,
2013), by suppressing factors such as the self‐renewal factor Bmi1 (Shimono
et al,
2009; Wellner
et al,
2009). Since stemness properties are considered to confer a survival phenotype, miR‐203 might exert a dual effect to restore drug sensitivity: by inhibiting stemness and by directly favoring apoptosis. Although shown to be directly pro‐apoptotic by targeting the anti‐apoptotic factors survivin (Wang
et al,
2012; Wei
et al,
2013) and BCL‐W (Bo
et al,
2011), its function concerning drug resistance is inconsistent. It has been shown to increase sensitivity of leukemic cells to ATO (He
et al,
2013b), lung cancer cells to gefitinib (Garofalo
et al,
2012), and colon cancer cells to paclitaxel (Li
et al,
2011), but also to suppress sensitivity of breast cancer cells to cisplatin (Ru
et al,
2011). Our data support a drug‐sensitizing effect of miR‐203. Inconsistent data are also found for the association of miR‐203 expression with clinical outcome: High expression indicated good prognosis in glioma (He
et al,
2013a) and prostate (Saini
et al,
2011), but surprisingly poor prognosis in colon (Bovell
et al,
2013) and pancreatic cancer (Greither
et al,
2010; Ikenaga
et al,
2010). Our data link high expression of miR‐203—and low expression of ZEB1—to reduced tumor recurrence and metachronous metastasis after curative surgery and adjuvant chemotherapy. A potential explanation for the conflicting data could be that miR‐203 acts as a double‐edged sword: It confers both growth advantage and treatment sensitivity by reducing the resistant and potentially low‐cycling cancer stem cell fraction. This hypothesis is in line with our data that overexpression of miR‐203 alone can increase proliferation (
Supplementary Fig S1C) and that it is upregulated in pancreatic cancers compared with normal pancreas (Fig
6A). Furthermore, our data comparing patient‐derived cancer cells indicate that high ZEB1/low miR‐203 expression might predict a favorable effect of mocetinostat in sensitizing cancer cells. Ongoing analyses on larger patient cohorts will show whether miR‐203 is a predictive marker to stratify pancreatic adenocarcinomas (PDACs) in treatment‐sensitive and treatment‐resistant subgroups. This might also include the classification into newly described PDAC subtypes (Collisson
et al,
2011).
Upregulation of miR‐203 turned out to be an efficient readout to screen for chemosensitizing substances, leading to the identification of the HDACi mocetinostat as a candidate. Notably, mocetinostat not only induced re‐expression of relevant ZEB1 targets, but also led to a reduction in ZEB1 itself, probably indirectly on protein level through translational suppression by re‐expressed miR‐200 members (Brabletz & Brabletz,
2010) and directly by affecting its transcription, since the ZEB1 promoter has a bivalent chromatin configuration, allowing for a very dynamic adaptation (Chaffer Christine
et al,
2013). As a positive consequence, additional unknown mechanisms concerning ZEB1‐mediated resistance are also targeted by mocetinostat. A drug‐sensitizing effect of mocetinostat has already been described (Sung
et al,
2011). Here, we demonstrated it to be specific compared to other HDACis, for example, SAHA, and described the selective downregulation of ZEB1 and upregulation of its target genes by mocetinostat as one potential mechanism, which strongly correlated with differences of mocetinostat and SAHA in sensitizing to gemcitabine. However, tests proofing a direct mechanistic link between the drug‐sensitizing effect of mocetinostat and the upregulation of miR‐203 or miR‐200 did not reach significance levels (
Supplementary Fig S3D). Therefore, we could not make an unambiguous statement concerning the crucial downstream effectors of mocetinostat. As HDAC inhibitor, it likely has many, still unknown molecular effects and target genes with potential relevance for its efficiency in restoring drug sensitivity and we do not want to claim that the upregulation of miR‐203/200 and inhibition of ZEB1 are its only or major effects in this context.
What could be the molecular basis for stronger effect of mocetinostat versus other HDACis, in particular SAHA, in downregulating ZEB1 and upregulating its target genes? ZEB1 has been shown to interact with HDACs1/2 (Wang
et al,
2009; Aghdassi
et al,
2012), and we detected strongest differences in the histone marks H3ac, H4ac, and H3K9ac, which are conferred by these enzymes, both after changes in ZEB1 expression and mocetinostat treatment. Compared to other HDACis such as SAHA, mocetinostat is highly specific for HDAC1 versus HDACs 2, 3, 11 and inert to HDACs 4, 5, 6, 7, 8 (Fournel
et al,
2008), which might explain a more selective and stronger effect. Also, the increase in the active mark H3K4me3 on ZEB1 target genes after mocetinostat treatment (Fig
3D and
Supplementary Fig S3B), which is attributable to the ability of class I HDACis to repress the JARID1 family of histone H3 lysine 4 demethylases (Huang
et al,
2011), can add to the effect of this HDACi. As described for the miR‐200c/miR‐141 gene cluster, DNA methylation and HDAC‐mediated histone marks depend on the EMT state (Davalos
et al,
2011; Lim
et al,
2013). We also found that ZEB1‐dependant changes in the EMT state induced the most prominent changes for HDAC‐dependent marks. In contrast, although ZEB1 is considered a transcriptional repressor, we detected no significant changes for repressive histone marks H3K9me2 and H3K27me3 conferred by its cofactor LSD1 and PRC2 complex factors, explaining lower efficacy of their inhibitors in restoring the expression of miR‐203. Our findings that even members of a restricted drug subgroup (here HDACis) show very different efficiency further underscore the relevance of the accurate screening strategy, as exemplified here with miR‐203 re‐expression as successful readout system.
In conclusion, our strategy aimed to use epigenetic drugs to restore chemosensitivity by inducing differentiation of resistant cancer cells, which are otherwise trapped in an EMT/stemness phenotype. Epigenetic drugs are of particular relevance, since they can effectively induce a long‐term antitumor memory response, even at low doses without an immediate cytotoxic effect (Baylin & Jones,
2011; Tsai
et al,
2012). Our data obtained from
in vivo experiments using cancer cells pre‐treated with mocetinostat (Fig
5D) support these findings. A clinical trial using a combination of low‐dose DNMT inhibitors and doxorubicin has already been successful for the treatment of diffuse large B‐cell lymphoma (Clozel
et al,
2013). Our results encourage the application of rational, mechanism‐based combinations of selected epigenetic drugs and standard chemotherapy also for the treatment of aggressive solid cancers, such as subtypes of pancreatic cancer.
Materials and Methods
Cell culture and drug treatment
All cell lines were purchased from ATCC and cultured according to the instructions. BxPC3 gemcitabine‐resistant cells were established previously (Wellner
et al,
2009). Accordingly, Tarceva‐resistant H358 cells were selected by culturing for several weeks in DMEM/10% FCS containing gradually increasing concentrations of Tarceva (1 μM to 5 μM) (OSI Pharmaceuticals). Generation and characterization of docetaxel‐resistant subclones of the prostate cancer cell DU‐145 were previously described (Puhr
et al,
2012). Mocetinostat/MGCD0103 (Chemietek), gemcitabine (Sigma), vorinostat/SAHA (Selleck), trichostatin A/TSA (Selleck), entinostat/MS‐275 (Selleck), trans‐2‐phenylcyclopropyl‐amine hydrochloride/TCP (Sigma), 3‐deazaneplanocin A/DZnep (Sigma), cytosine β‐D‐arabinose/cAra (Sigma), adenosine dialdehyde/ad dia (Sigma), 5′‐aza‐2′deoxycytidine/dAza (Sigma), or mock were added for 48 h, in MTT assays for 72 h. Cells were then harvested for specific assays. The heat map for the drug‐treated cells was created with the software GENE‐E (Broad Institute). Stable clones for shZEB1 and shcontrol knockdown were established as previously described (Spaderna
et al,
2008). For generation of clones stably overexpressing miR‐200c or miR‐203, lentiviral expression vectors were produced and cells were infected as previously described (Brabletz
et al,
2011), followed by selection under standard conditions in DMEM/10% FCS + 2 μg/ml puromycin. The absolute expression of overexpressed miRNAs was at comparable levels as determined by qRT–PCR.
Isolation of human patient‐derived pancreatic cancer cells and tissue specimen
Human tissues and tumor cells from human pancreatic cancer were obtained with patients' consent, as approved by the Ethics Commission of the University Freiburg Medical Center (no. 13/11,130538). Isolation of patient‐derived cancer cells was done as previously described (Smith
et al,
2008). In brief, patients' tumor explants were cut into small pieces (2 mm
3), which were further implanted subcutaneously into 7‐week‐old NMRI nu/nu female mice. After in‐mouse passaging, viable human pancreatic tumor cells were isolated, characterized as described, tested for mycoplasma contamination, and used for further assays. hPaca2 was isolated from a well‐differentiated pancreatic adenocarcinoma and hPaca1 from a moderately differentiated pancreatic adenocarcinoma. Formalin‐fixed, paraffin‐embedded samples of pancreatic carcinomas and associated clinical follow‐up data from patients, who underwent curative surgery (R0) and adjuvant chemotherapy with gemcitabine, were retrieved from the University Freiburg Medical Center. Tumor tissue was isolated by microdissection. RNA isolation and quantification of miR‐200c and miR‐203 expression in tumors were performed as previously described (Brabletz
et al, 2011).
Microarrays
The microarray data from this publication have been submitted to the ArrayExpress database (
https://www.ebi.ac.uk/arrayexpress/) and assigned the identifiers E‐MTAB‐3387 and E‐MTAB‐3391.
Specific inhibition of miRNAs using antagomirs
Antagomirs (Dharmacon) were designed as described (Krutzfeldt
et al,
2007). A total of 3 μM antagomirs were added to the normal cell culture medium right after seeding in 6‐well plates. Cells were harvested for specific assays 3–4 days later.
Cell viability assay (MTT)
All cell lines except hPaca1 were seeded with 3,000 cells per well in 96‐well format. hPaca1 was seeded in 12‐well plates with 50,000 cells per well. After 24 h, cells were treated as indicated. After 72 h, 5 mg/ml MTT (methylthiazolyldiphenyl‐tetrazolium bromide; Sigma) was added to the medium and incubated for 4 h. The medium was removed and precipitates were dissolved in 200 μl acidified isopropanol (0.04 N HCl). Absorption was measured at 570 nm with 650 nm as a reference wave length. Relative MTT activity was then calculated relative to activity 1 day after seeding (set to 0%). The activity of untreated cells 72 h after starting of drug treatment was set to 100%. A negative activity means that the drug reduced cell number below the number of cells at treatment start. Significance in differences was calculated with the two‐way ANOVA, Dunnett's multiple comparisons test (
P‐values in the graphs are: *
P = 0.01–0.05, **
P = 0.001–0.01, ***
P < 0.001, ****
P < 0.0001; for exact
P‐values see
Supplementary Table S4).
BrdU incorporation
A total of 3,000 cells/well for Panc1 and 1,500 cells/well for hPaca1 were seeded in 96‐well plates. BrdU was added for a 4‐h pulse and incorporation measured by ELISA at 450 nm (BrdU Cell Proliferation Assay Kit, Cell Signaling Technology) according to the manufacturer's instructions. To test for inhibition of proliferation by gemcitabine, 24 h after seeding, cells were treated with gemcitabine and, 72 h after beginning treatment, BrdU was added for 4 h.
Bisulfite sequencing
Genomic DNA was extracted using QIAamp DNA Mini Kit (Qiagen) and bisulfite‐treated with the EZ DNA methylation kit (Zymo Research) according to the manufacturer's instructions. Following PCR amplification of the bisulfite‐converted DNA, the product was cloned using the TOPO TA Cloning Kit (Invitrogen) and sequenced. DNA methylation status was analyzed with the CpG viewer software.
Cancer stem cell spheroid assay
For the detection of cancer stem cell potential, cell lines were processed in sphere‐forming assay as described previously (Wellner
et al,
2009). The number of colonies with a diameter > 75 μm for Panc1 and > 30 μm for hPaca1 was counted after 7 days.
Immunoblots
Western blots were performed as previously described (Spaderna
et al,
2008). A total of 40 μg of protein was separated by SDS–PAGE, transferred to nitrocellulose membrane, and immunoblotted using rabbit anti‐ZEB1 (1:5,000, HPA027524; Sigma), rabbit anti‐cleaved caspase‐3 (1:1,000, 9664; Cell Signaling), rabbit anti‐LC3BII (1:1,000, 3868; New England Biolabs), rabbit anti‐survivin (1:1,000, NB500‐201; Novus), mouse anti‐E‐cadherin (1:1,000, 610182; BD Biosciences), mouse anti‐vimentin (1:1,000, M0725; Dako), rabbit anti‐H3ac (1:2,000, 06‐599; Millipore), rabbit anti‐H4ac (1:1,000, 06‐598; Millipore), and mouse anti‐actin (1:5,000, A5441; Sigma) or mouse anti‐tubulin (1:5,000, T6199, Sigma) to control loading efficiency.
RNA isolation and quantitative RT–PCR
RNA was isolated using RNeasy Plus Mini Kit (Qiagen). For microRNA quantification, cDNA was synthesized with the miRCURY LNA cDNA synthesis kit (Exiqon) using 1,000 ng of RNA as template and diluted 1:60. For mRNA, cDNA was synthesized with the RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific) according to the manufacturer's instructions. Expression values were measured in triplicate on a Roche LightCycler 480 and normalized to β‐actin expression (mRNA) and to miR‐16 (microRNA). Results are computed as fold induction relative to controls.
Chromatin immunoprecipitation of histone modifications
Chromatin immunoprecipitation (ChIP) was performed as previously described (Wohrle
et al,
2007) with the following modifications: Samples were sonicated for 30 min with 30‐s intervals on power level ‘high’ with the Diagnode Bioruptor UCD200. The optical density at 260 nm was determined, and aliquots corresponding to 50 optical density units were used for each IP. The lysates were diluted with two sample volumes of IP dilution buffer before adding 5 μg of appropriate antibodies and 25 μl of Protein G magnetic beads (Active Motif). The antibodies rabbit anti‐acetyl histone H3 (06‐599), rabbit anti‐acetyl histone H4 (06‐598), rabbit anti‐trimethyl histone H3K4 (04‐745), anti‐acetyl histone H3K9 (17‐658), rabbit anti‐trimethyl histone H3K27 (07‐449), rabbit anti‐dimethyl histone H3K9 (17‐648), and isotype control immunoglobulin G (IgGs, PP64B) were purchased from Millipore. For qRT–PCR, 2.5 μl of the immunoprecipitated DNA and 2% of the reference material were used as templates.
Immunofluorescence
Cells were fixed with 4% formaldehyde or ice‐cold methanol and blocked with PBS/2% normal goat serum. Fixed cells were incubated with the primary antibodies mouse anti‐E‐cadherin (1:100, 610182; BD Biosciences), rabbit anti‐ZEB1 (1:200, HPA027524; Sigma), rabbit anti‐cleaved caspase‐3 (1:400, 9664; Cell Signaling), rabbit anti‐LC3BII (1:100, 3868; New England Biolabs), and mouse anti‐vimentin (1:500, M0725; Dako) as indicated in the text at 4°C overnight, followed by Alexa Fluor® 488‐conjugated goat anti‐mouse IgG (1:500, A‐11029; Life Technologies) and Cy3®‐conjugated goat anti‐rabbit IgG (H+L) (1:250, A10520; Life Technologies) or Alexa Fluor® 488‐conjugated goat anti‐rabbit IgG (1:500, A‐11034; Life Technologies) and Cy3®‐conjugated goat anti‐mouse IgG (H + L) (1:250, A10521; Life Technologies) for 1 h at room temperature and counterstained with DAPI (Molecular Probes).
Fluorescence‐activated cell sorting (FACS) analysis
For FACS analysis, cells were collected with 0.05% trypsin–EDTA solution, washed, and diluted to 1 million cells per ml in PBS/2% FCS containing the diluted primary antibodies anti CD24‐PE (555428; BD Bioscience) and anti CD44‐APC (561862; BD Bioscience) or anti CD133 (AC133, PE,130‐080‐801; Miltenyi Biotec) incubated in the dark for 20 min at room temperature, washed with PBS/2% FCS, resuspended in PBS/2% FCS at 0.5–1 million cells per ml, and analyzed using a BD LSR Fortessa and BD FACSDiva Software (Becton Dickinson). A total of 10,000 viable cells were counted. Dot plots and histograms were generated with FlowJo software.
Xenograft in vivo assays
All animal experiments were performed in accordance with Animal Welfare and approved by the local authorities (no. G‐12/44). Pre‐established fresh frozen fragments (3 mm3) of tumors derived from xenografted Panc1, hPaca1, or hPaca2 were implanted subcutaneously into 7‐week‐old NMRI nu/nu female mice. After 11 days (Panc1), 18 days (hPaca1), or 10 days (hPaca2), mice were randomized according to tumor volume into the different treatment groups and received single treatment or combinations of i.v. injections of gemcitabine‐ and p.o.‐administered mocetinostat or vehicle control at the indicated concentrations and time points. Five mice were used in each treatment group. Tumors were measured twice weekly, and relative tumor volume (RTV) was calculated by setting the volume of the individual tumor at the day of randomization to 100%. Mice were sacrificed at indicated time points and tumors were formalin‐fixed and paraffin‐embedded. For in vivo analyses of pre‐treated tumor cells, Panc1 cells were treated with 1 μM mocetinostat, 50 nM gemcitabine, a combination of both, or vehicle (DMSO) for 48 h, followed by another 7 days in culture without drug. A total of 2 million viable cells in a volume of 75 μl were injected subcutaneously into the flank of 5‐week‐old Foxn1 nude female mice (Harlan). Four mice were used in each treatment group. Tumors were measured twice weekly and absolute tumor volume was calculated. On day 9 after injections, tumors in all treatment groups were palpable. In order to better visualize and compare the tumor growth, the tumor volume at day 9 was set to 1 and the increasing slope of the tumor volume to day 37 was shown. At the end of the experiment, mice were sacrificed.
Immunohistochemistry
Immunohistochemistry on formalin‐fixed, paraffin‐embedded samples of mouse xenografts was performed as previously described (Brabletz
et al,
2004), using rabbit anti‐ZEB1 (1:200, HPA027524; Sigma) and mouse anti‐E‐cadherin (1:100, 610182; BD Biosciences) antibodies.
In situ Hybridization
Formalin‐fixed, paraffin‐embedded tissues were sectioned at 6 μm, deparaffinized, rehydrated, and treated with proteinase K (15 μg/ml, 20 min, 37°C). Slides were formaldehyde‐ and EDC‐fixed and then acetylated as described previously (Pena
et al,
2009). Sections were dehydrated and hybridized overnight at 50°C with 80 nM miRCURY LNA detection 5′‐DIG and 3′‐DIG‐labeled probe for miR‐203 and scrambled control (Exiqon). LNA U6 snRNA (Exiqon) was used as a positive control at 10 nM. After hybridization, further steps were performed using the instruction of v2.0 miRCURY LNA microRNA ISH Optimization Kit for FFPE (Exiqon).
Statistical analysis
Statistical evaluation was performed using GraphPad Prism 6. Each experiment was repeated three times or more, unless otherwise indicated. Data are presented as mean ± SEM. For qRT–PCR analysis, unpaired Student's t‐test was used to compare two groups of independent samples and a normal distribution was assumed. For the spheroid assay and the in vivo experiments, Mann–Whitney U‐tests were used. For MTT and BrdU assays, two‐way ANOVA with Dunnett's multiple comparisons test was used. For association of microRNA expression with clinical outcome, microRNA expression was normalized to the mean value of controls (lower expression group) and visualized by box plot. Statistical testing for observed differences was done by two‐sided Mann–Whitney U‐test for related samples with SPSS software version 22 (IBM SPSS Inc., Chicago IL) with the significance level set to P = 0.05. The mean value of the lower group in each figure was set to 1.
For in vivo experiments, sample size was calculated with GPower Software Version 3.1.7. For testing the hypothesis that tumor size with combination treatment is smaller than with monotherapy by one‐sided Student's t‐test with α = 0.05, β = 0.80, and effect size = 2, necessary sample sizes in each group were calculated as n = 4. To compensate for subsequent data loss due to mortality as a cause of treatment, n = 5 per group was chosen. For in vivo analysis of pre‐treated tumor cells, n = 4 was chosen per group due to decreased mortality risk. No animals or samples were excluded from any analysis. Animals were randomly assigned to groups for in vivo studies. Group allocation and outcome assessment were not done in a blinded manner, including for animal studies. In each group of data, estimated variation was taken into account and is indicated in each figure as SEM. For all graphs, *P = 0.01–0.05, **P = 0.001–0.01, and ***P < 0.001.
Determination of EC50, EC80, and drug combinations with calculation of the CI
Effective concentrations (EC) were determined as drug doses with half maximal (EC50) or 80% (EC80) of the maximal effect between the upper plateau (no drug) and lower plateau (maximal effect of the drug). For drug combinations, the highest drug dose used was below the EC50 of the drug alone in the respective cell line. The effect of a drug combination was determined according to Breitinger (Berenbaum,
1978; Breitinger,
2012). The combination index (CI) was calculated with a fixed dose for the first drug (here the chemotherapeutic) and the shift in the EC50 for the second drug (here mocetinostat, SAHA did not reached EC50 levels in the applied doses), by applying the following equation: CI = concentration of drug 1 in combination: EC50 drug 1 alone + calculated EC50 of drug 2 in combination: calculated EC50 of drug 2 alone (CI < 1 = synergistic; 1 = no effect; > 1 = antagonistic). MTT assays for drug combinations were run as described above.