Triple-negative breast cancer (TNBC) is an incurable disease with poor prognosis. At this moment, therapeutic options are limited to chemotherapy, and no targeted agent has reached the clinical setting. Bromodomain and extraterminal (BET) inhibitors are a new family of compounds that inhibit bromodomain-containing proteins affecting the expression of transcription factors, therefore modifying the expression of relevant oncogenic genes. In the present article, by using an in silico approach, we have identified the expression of upregulated transcription factors in TNBC compared with normal breast. Treatment with JQ1, a well-characterized BET inhibitor, modified some transcription factors, including DEP domain containing 1 (DEPDC), Forkhead box M1 (FOXM1), and Lim domain only 4 (LM04). In cell line models, administration of JQ1 or OTX015, another BET inhibitor, produced a significant antiproliferative effect and synergized with chemotherapies. Biochemical evaluation demonstrated an arrest at G1 as the main mechanism of action with a clear increase of p27. Addition of these compounds to chemotherapy induced apoptosis compared to each agent given alone. Evaluation of JQ1 in xenografted tumors in nude mice showed a profound antitumoral effect with a reduction of DEPDC, FOXM1, and LM04, in addition to an increase of p27. Globally, our data demonstrate the antitumor effect of this new family of compounds in TNBC, paving the way for its future clinical development. Mol Cancer Ther; 15(8); 1823–33. ©2016 AACR.
Several studies have focused on the identification and validation of genetic abnormalities associated with malignant transformation (1). Although many have been described in the recent years, only a limited number can be inhibited pharmacologically, and therefore only a small proportion of agents against them have reached the clinical setting. Indeed, the number of chemical compounds exceeds the number of targets identified, suggesting that greater efforts should be done to discover novel biologic mechanisms that can indirectly control some of the functions associated with oncogenic transformation (2). Targeting epigenetic components has therefore opened the opportunity to indirectly affect cancer vulnerabilities in some tumors (3). Epigenetic regulators have been considered key druggable players in some cancers and inhibition of their action produced clinical benefit (3). For instance, the histone deacetylase inhibitor vorinostat has shown activity in multiple myeloma and cutaneous T-cell lymphoma (4). In addition, the demethylating agent 5-azacytidine has been approved for the therapy of myelodisplastic syndromes (5).
A novel family of epigenomic mediators includes agents that act on bromodomains (6). These are protein domains that recognize residues of monoacetylated lysine, for instance those of the N-terminal tails of histones (6, 7). Proteins with bromodomains play a central role regulating the expression of several genes involved in malignant transformation (8). In the last years, studies have explored the antitumor effect of agents that inhibit the bromodomain and extraterminal (BET) family of bromodomain-containing proteins (9, 10). An example is the antitumor effect of these agents in several cancers such as neuroblastoma by reducing the expression of oncogenic transcription factors like c-myc, among others (10–14). These findings suggest that transcription factors are indirectly druggable, opening new avenues for targeting diseases where upregulation of these proteins may facilitate malignant transformation. However, these results also suggest the little specificity of these compounds particularly in diseases where many of these proteins can be involved in the malignant process.
Triple-negative breast cancer (TNBC) lacks the expression of hormone receptors and HER2 amplification and are mainly included in the basal-like gene profiling subgroup (15). TNBC is an incurable disease with poor prognosis, limited therapeutic options beyond treatment with chemotherapy; and rapid proliferation and metastatic potential (16–18). In this context, the identification of novel anticancer agents with prospective clinical activity is an important objective.
In the present work, by using gene expression analyses, we identified transcription factors that were altered in triple-negative tumors. Treatment with BET inhibitors showed great antitumor activity in vitro and in vivo inducing cells to arrest. BET inhibitors synergized with agents that act on mitosis and those that induce DNA damage leading to an increase in cell death. In conclusion, our data demonstrate the preclinical activity of BET inhibitors paving the way for its clinical development alone or in combination with chemotherapies in this therapeutically medical need.
Materials and Methods
Cell culture and drug compounds
MDA-MB-231, HS578T, and BT549 were grown in DMEM, and HCC3153 was grown in RPMI-containing 10% FBS, with 100 U/mL penicillin, 100 μg/mL streptomycin, and 2 mmol/L l-glutamine, respectively, in a 5% CO2 atmosphere at 37°C. All cell lines were provided by Drs. J. Losada and A. Balmain (originally from Dr. J.W. Gray's Laboratory, who in turn obtained them from the ATCC or from collection development in the laboratories of Drs. S. Ethier and A. Gazdar to avoid errors occurring when obtained through “second-hand” sources). In addition, cells were analyzed by STR at the molecular biology unit at the Salamanca University Hospital (Salamanca, Spain).
The cell culture medium and supplements were obtained from Sigma-Aldrich. Cell lines were treated with different drugs: bromodomain inhibitors (JQ1 and OTX015) and chemotherapeutic agents (vinorelbine, docetaxel, cisplatin, and carboplatin) were purchased from Selleckchem. Chemical structure of JQ1 and OTX015 is described (refs. 19, 20).
Whole-genome transcription profiling and gene-set enrichment analyses
We used a public dataset (GEO DataSet accession number: GDS2250) of mRNA level data from normal breast tissue and basal-like breast tumors to identify transcription factors that were upregulated with a 4-fold change. Affymetrix CEL files were downloaded and analyzed with dChip software (Dana Farber Cancer Institute, Boston, MA).
For whole-genome profiling, MDA-MB-231cells were grown in DMEM with 10% of FBS and added to a final concentration of 500,000 cells per plate. Next day, cells were treated with 500 nmol/L JQ1 for 12 and 24 hours. Total RNA was extracted from purified cell populations using RNeasy Mini Kit (Qiagen) according to the manufacturer's instructions. The RNA integrity was assessed using Agilent 2100 Bioanalyzer (Agilent). Labeling and hybridizations were performed according to protocols from Affymetrix. Briefly, 100 ng of total RNA was amplified and labeled using the WT Plus reagent Kit (Affymetrix) and then hybridized to Human Gene 2.0 ST Array (Affymetrix). Washing and scanning were performed using GeneChip System of Affymetrix (GeneChip Hybridization Oven 645, GeneChip Fluidics Station 450 and GeneChip Scanner 7G). Microarray accession number: GSE 79476.
Genes with different expression values from the control versus treated groups (12 and 24 hours) were obtained. The list of genes was analyzed using gene-set enrichment analyses DAVID Bioinformatics Resources 6.7 to identify pathways modified by drug treatment. We used an adjusted P < 0.05 to select the enriched gene sets.
Cell growth, drug combination analysis, colony-forming assay, and three-dimensional cultures
For cell growth analysis, cells were plated at 10,000 cells per well in 48-multiwell plates and cultured overnight in DMEM or RPMI supplemented with 10% FBS and 2 mmol/L l-glutamine. The following day, the medium was replaced with complete medium containing increasing amounts of JQ1 and OTX015 alone or JQ1 in combination with four chemotherapeutics to plot the dose–response histogram and for synergy studies, respectively. Cell proliferation was evaluated at 3 days by tetrazolium dye (MTT) colorimetric assay.
In parallel, to further characterize the antiproliferative effect of JQ1, breast cancer cells were treated with indicated JQ1 doses for 3 days. After drug treatment, cells were maintained in fresh complete medium without JQ1 for 2 to 4 additional days, and cellular growth was then determined using MTT assay.
To evaluate whether the combinations of JQ1 with vinorelbine, docetaxel, cisplatin, or carboplatin were synergistic, additive, or antagonic, we used the CalcuSyn Version 2.0 software (Biosoft) and combinational index (CI) was determined. Thus, values < 1 represent synergistic effect on cell proliferation of the 2 drugs, values equal to 1 indicate additive effect of the drugs, and values > 1 represent an antagonistic effect.
To perform the colony-forming assay, MDA-MB-231, HS578T, BT549, and HCC3153 cells were seeded at a density of 500,000 cells in 100-mm culture dishes. Next day, the cells were treated with JQ1 (100 nmol/L) or OTX015 (200 nmol/L).
For 3-dimensional cultures, MDA-MB-231 cells were grown in DMEM supplemented with 10% FBS and 2 mmol/L glutamine. Following passage, cells were trypsinized and resuspended in growth medium containing 2% Matrigel. Then, cells were seeded at a density of 25,000 cells/mL in a 48-multiwell plate containing an underlying approximately 1-mm thick bed of Matrigel and incubated at 37°C. After overnight incubation, cells were exposed to JQ1 (200 nmol/L) or chemotherapeutic agents (0.5 nmol/L docetaxel, 2.5 μmol/L cisplatin) alone or in combination for 3 days. The assay included the daily visualization of cells under a light microscope to determine the total number of colonies per plate as well as to monitor the phenotype.
Unless otherwise indicated, all results are presented as the mean ± SD of triplicates of a representative experiment that was repeated at least 3 times.
Cell-cycle and apoptosis detection assays
For cell-cycle analysis, MDA-MB-231 cells were cultured in 100-mm culture dishes, grown to a density of 500,000 cells, and treated, the next day, with vinorelbine (1nmol/L), docetaxel (0.5 nmol/L), cisplatin (2.5 μmol/L), and carboplatin (25 μmol/L) for 24 hours. MDA-MB-231 and HS578T were also treated with JQ1 (200 and 500 nmol/L) alone for 24 hours. Cell monolayers were then incubated in trypsin-EDTA, washed twice with cold PBS, fixed in ice-cold 70% ethanol for 30 minutes, and subsequently centrifuged at 5,000 rpm for 5 minutes. Cell pellets were washed in PBS + 2% BSA and treated with propidium iodide (PI)/RNAse staining solution (Immunostep S.L.) in the dark for 1 hour at 4°C, then and analyzed on FACSCanto II flow cytometer (BD Biosciences). The percentage of each cell-cycle phase was determined by plotting DNA content against cell number using the FACS Diva software.
For apoptosis experiments, MDA-MB231 and HS578T cells were seeded at a density of 300,000 cells per 100-mm dish and the next day treated with JQ1 (200 and 500 nmol/L) for 24 and 48 hours. For combinations, we used JQ1 (200 nmol/L), docetaxel (0.5 nmol/L), and cisplatin (2.5 μmol/L) for 48 hours in MDA-MB231. After drug exposure, adherent cells were trypsinized, pooled with the floating cells, and washed twice with cold-PBS. Then, cells were stained with 5 μL of Annexin V/DT-634 (Immunostep S.L.) and 3 μL of PI (10 mg/mL) in 1× binding buffer (10 mmol/L HEPES, pH 7.4, 140 mmol/L NaOH, 2.5 mmol/L CaCl2) for 1 hour at room temperature in the dark. The apoptotic cells were determined using a FACSCanto II flow cytometer (BD Biosciences). Both early apoptotic (Annexin V–positive and PI-negative) and late (Annexin V–positive and PI-positive) apoptotic cells were included in cell death determinations.
Furthermore, PARP levels in cells undergoing apoptosis after drug treatment were determined by flow cytometry. Then, cells were detached from dishes with trypsin and subjected to fixation and permeabilization procedures for combined surface and intracellular staining incubation with Annexin V/DT-634, and anti-PARP/FITC. Quantification of double-positive MDA-MB-231 cells was carried out using BD FACS Diva software.
Western blotting experiments and RNA interference experiments
MDA-MB-231and HS578T cells were plated at a density of 500,000 cells/100-mm dish, maintained overnight in DMEM + 10% FBS, and treated later with JQ1 at 500 nmol/L for 12, 24, and 48 hours. After treatment, cells were washed with cold PBS and lysed in cold lysis buffer [20 mmol/L Tris-HCl (pH 7.0), 140 mmol/L NaCl, 50 mmol/L EDTA, 10% glycerol, 1% Nonidet P-40, 1 μmol/L pepstatin, 1 μg/mL aprotinin, 1 μg/mL leupeptin,1 mmol/L phenylmethyl sulfonyl fluoride, 1 mmol/L sodium orthovanadate]. Then, insoluble material was removed by centrifugation. The protein concentration was determined using BCA (bicinchoninic acid) protein assay kit (Sigma Aldrich).
The lysates (50 μg) were resolved by 6% to 15% SDS-PAGE and protein transferred to polyvinylidene difluoride membranes (Millipore Corporation). The anti-GAPDH, anti-Wee1, anti-cyclin B, anti-CDK2, anti-CDK4, and anti-p21 antibodies were purchased from Santa Cruz Biotechnology. The anti-pCDK1 (Y15), anti-p27, anti-cyclin D1, anti-cyclin E, anti-pRbS807/811, and anti-pRbS780 antibodies were from Cell Signalling Technologies. The anti-cyclin A, anti-cyclin D3, and anti-BUBR1 antibodies were purchased from BD Biosciences. Protein-bound primary antibodies were detected using respective horseradish peroxidase–coupled secondary antibodies obtained from Santa Cruz Biotechnology.
To generate MDA-MB-231 cell lines expressing low levels of DEPDC, FOXM1, and LMO04, cells were grown to a final concentration of 500,000 cells per plate and then were transfected using JetPEI DNA transfection reagent (Polyplus-transfection SA) following the manufacturer's instructions with the lentiviral vectors PLK01 containing different short hairpin sequences for each gene (PLK01, DEPDC 4285, DEPDC 7210, FOXM1 5545, FOXM1 5546, LOM04 3238, LOM04 3239, LOM04 3240, LOM04 3241, and LOM04 3242). As control, we used the plasmid PLK01 containing a nontargeting sequence. Each transfection was repeated at least 4 times.
Quantitative reverse-transcription PCR
Total RNA was obtained from MCF10A breast noncancerous cell line, MDA-MB-231 untreated, and JQ1-treated at 24 and 48 hours. We used an RNeasy Mini kit (Qiagen), according to manufacturer's instructions. After extraction, concentration and purity were determined using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific) and, subsequently, 3 μg of total RNA was reversely transcribed using RevertAid H Minus First Strand cDNA synthesis kit (Thermo Fisher Scientific) in a thermocycler (Bio-Rad) under the following reaction conditions: 65°C for 5 minutes, 42°C for 60 minutes, and 70°C for 10 minutes. The cDNAs were then subjected to a real-time PCR analysis using Fast SYBR Green Master Mix in StepOnePlus Real-Time PCR system (Applied Biosystems) according to the manufacturer's instructions. Primer sequences used were as follows: SIPB1 forward 5′-GCATACCCCACGGAGAACT-3′ and reverse 5′-GGCTGTCCAACGGTAAGTCT-3′; SOX11 forward: 5′- GGAGTGATGTCACCCATATG-3′ and reverse 5′-GCAAGTTGCAAAGTCCTCG-3′; FOXC1 forward: 5′-GGCGAGCAGAGCTACTACC-3′ and reverse 5′-TGCGAGTACACGCTCATGG-3′; ZNF367 forward: 5′- CCTGCGACTCGGAGCAGAGCGAG-3′ and reverse 5′-GATCGGGGTCGTCCTGATGACC-3′; EN1 forward: 5′-CGCGGCGCCTGCTCACCAGCCG-3′ and reverse 5′-CGGGTGCCCAACGGGTGGACAGGG-3′; PITX1 forward: 5′-TCCACCAAGAGCTTCACCTT-3′ and reverse 5′-CGGTGAGGTTGTTGATGTTG-3′; TFAP2B forward: 5′- ACGTCCAGTCAGTTGAAGATG-3′ and reverse 5′-TATCCTCGAGTCATTTCCTGTGTTTCTC-3′; UHRF1 forward: 5′-CCAGCAGAGCAGCCTCATC-3′ and reverse 5′-TCCTTGAGTGACGCCAGGA-3′; ZIC1 forward: 5′-GGCCCGGAGCAGAGTAAT-3′ and reverse 5′- AGCCCTCAAACTCGCACTT-3′; NFE2L3 forward: 5′-AGAGAAAAGCACGAAGCTGTG-3′ and reverse 5′-CTCACTTTCTCTTTCCCTTTTGGG-3′; LM04 forward: 5′-CGGGAGATCGGTTTCACTAC-3; and reverse 5′-CCAGTGCCCTGCTAATTGTT-3′; DEPC forward: 5′-GCTACAAGTAAAGAGGGGATGG-3′ and reverse 5′-GGACAGAAAGGTAAGTCAGTGGG-3′; FOXM1 forward: 5′-TGCCCAGCAGTCTCTTACCT-3′ and reverse 5′-CTACCCACCTTCTGGCAGTC-3′. An initial step was performed at 95°C for 10 minutes, followed by 40 cycles of 95°C for 15 seconds and finished by 60°C for 1 minute. Each sample was analyzed in triplicates, and cycle threshold (Ct) values of transcripts were determined using StepOne Software v.2.1. The Ct values were calculated using GAPDH as reference. Untreated MDA-MB231 cells were used as control to calculate the Ct value and to determine the X-fold mRNA expression.
Mice were manipulated at the animal facility following legal guidelines. Female BALB/cAnNRi-Foxn1nu/Foxn1nu mice, 5 weeks old, were obtained from Janvier Labs. After 10-day quarantine, 2 × 106 to 5 × 106 MDA-MB-231 cells in 100 μL of DMEM with 20% Matrigel were injected into the fat pads of mice. When tumors reached a volume of 150 mm3, mice were randomly assigned into 2 groups (with equal average tumor volume): control (n = 5) and JQ1 (n = 5), and treatment was initiated. Animals were daily treated daily with JQ1 intraperitoneally (50 mg/kg). Tumors diameters were serially measured with callipers every 4 days, and tumor volumes were calculated using the following formula: V = (L × W2)/2, where V = volume (mm3), L = length (mm), and W = width (mm). The Student test was used to calculate statistical significance. Tumor samples were obtained after killing animals by CO2 inhalation and immediately frozen in liquid nitrogen.
RNA extraction and determination of LM04, DEPDC, and FOXM1 mRNA levels in untreated and JQ1-treated tumors by quantitative PCR were performed as described above. Tumor sample were minced, washed with cold PBS, and homogenized in cold lysis buffer (1.5 mL/100 mg of tumor). This homogenate was centrifuged at 10,000 × g for 20 minutes at 4°C, and the supernatants were transferred to new tubes. The protein expression of p27 was analyzed by Western blotting as describe above.
Results and Discussion
Expression of transcription factors in human TNBC are reduced by treatment with JQ1
As transcription factors are key regulators of oncogenic transformation, we used an in silico approach to identify those genes that encoded for transcription factors that were upregulated in basal-like tumors. Using a public dataset (ref. 21; dataset record: GDS2250), we did gene expression analyses to identify differences at a gene level between normal breast tissue and basal-like tumors and selected those genes that were up- or downregulated. We identified 426 genes in which 30 were transcription factors, and of these 30, 12 were upregulated (Fig. 1A). Supplementary Table S1 shows the list of genes that code for transcription factors with >4-fold change.
Next, we hypothesized that BET inhibitors could modify the expression of some of these genes. Using MDA-MB-231 as a model and the nontransformed cell line MCF-10A as a control, we evaluated whether treatment with the BET inhibitor JQ1 (6) was able to inhibit some of the identified upregulated genes. For this experiment, we chose the 500 nmol/L dose on the basis of results show in Fig. 2A. Most of these genes except for EN1 were more expressed in MDA-MB-231 compared with MCF-10A, what suggests overexpression of these transcription factors in situations of oncogenic transformation (Fig. 1B). After treatment with JQ1 at different time points, the expression of some of these genes were reduced (Fig. 1B). The most significant and maintained reduction were observed for the following genes: DEP domain containing 1 (DEPDC), Forkhead box M1 (FOXM1), and Lim domain only 4 (LM04). Although the expression of these genes has been associated with carcinogenesis in different tumor models, little has been described in breast cancer and particularly in TNBC (22–26).
BET inhibitors modify several gene set functions
Taken in consideration that BET inhibitors affected the expression of several transcription factors that subsequently can modify the expression of many different genes, involved in several oncogenic functions, we decided to performed gene-set enrichment analyses to get insights into the mechanism of action of these compounds. For this purpose, we treated cells with JQ1 and extracted RNA at 12 and 24 hours. Gene-set enrichment analyses identified several pathways and functions modified by the drug including transcription, biosynthetic process, immune response, and chromatin organization, among others (Fig. 1C). Supplementary Tables S2 and S3 describe the list of functions with the identified genes. We confirmed that LM04, DEPDC, and FOXM1 were reduced after treatment with JQ1 at 12 and 24 hours (Supplementary Table S4). These data suggest that BET inhibitors affect different functions that are controlled by several transcription factors.
BET inhibitors show antiproliferative effect in cell lines and synergize with chemotherapy
Taken in consideration that JQ1 was able to reduce the expression of upregulated transcription factors that had a significant oncogenic role, we decided to explore the activity of BET inhibitors on cell proliferation. We used a panel of 4 triple-negative cell lines including MDA-MB-231, HS578T, BT549, and HCC3153 and 2different BET inhibitors, JQ1 and OTX015, being the last drug currently in clinical development (27). Treatment with both agents reduced proliferation measured by MTT uptake in a dose- and time-dependent manner (Fig. 2A; Supplementary Fig. S1A). Studies using clonogenic cell survival assays also showed the effect of these drugs on the number of colonies formed at 10 days in MDA-MB-231, HS578T, BT549, and HCC3153 using doses below IC50 (Fig. 2B)
It has been suggested that their antiproliferative effect is maintained in time compared with other type of agents (28). To evaluate this effect, we treated MDA-MB-231 and HS578T with JQ1 for 3 days and evaluated their growth recovery 2 and 4 days later. As can be seen in Fig. 2C after the removal of the medium containing the drug, cells recovered their growth and the inhibitory effect did not last; finding that is not in line with previous published information (28). These data have implications for the clinical development of these kind of compounds, as demonstrated that the effect is not maintained for a long period of time.
As chemotherapy is the standard of care for the treatment of TNBC, we decided to combine BET inhibitors with chemotherapies that are routinely used in the clinical setting. For this purpose, we used the 2 more sensitive cell lines MDA-MB-231 and HS578T. As can be seen in Fig. 2D, JQ1 showed a synergistic interaction with agents that act on mitosis like docetaxel and vinorelbine and with DNA-damaging agents such as cisplatin or carboplatin.
To confirm the effect, studies where cells are growing in Matrigel were performed. Similarly, an increase effect was observed with the combination of docetaxel and cisplatin with JQ1 in MDA-MB-231 (Supplementary Fig. S1B).
We repeated the MMT experiments using doses below the IC50 to demonstrate the specific effect of the drug on proliferation (Supplementary Fig. S2). Finally, we did RNA interference against LM04, DEPDC, and FOXM1, showing that the transitory inhibition of their expression reduced proliferation at 72 hours (Supplementary Fig. S3).
Cell-cycle arrest by BET inhibitors
To explore the effect of BET inhibitors on cell cycle, we stained cells with PI. As can be seen in Fig. 3A, treatment with the BET inhibitor JQ1 induced arrest at the G1 phase in MDA-MB-231 and HS578T cells. Biochemical evaluation of the mechanism of action confirmed that JQ1 increased the expression of proteins that negatively regulate cell-cycle progression in this phase like p21 and p27. Elevated levels of cyclin D1 and D3 were also observed. We also identified a reduction of proteins involved in progression along G2 or M phases such as cyclin B3 or BURB1 (Fig. 3B). Next, we evaluated the effect on apoptosis by staining cells with Annexin V. Only in HS578T, a modest effect was identified at 48 hours (Fig. 3C). These results suggest that the main action of the drug on cell proliferation is through the induction of cell-cycle arrest rather than through stimulation of apoptosis.
Combination of BET inhibitors with chemotherapy induces apoptosis
As BET inhibitors alone were cytostatic and its combination with chemotherapy induced a synergistic effect, we decided to explore the mechanism of action of the combination. Previously, we observed that antimitotic and platinum-derived agents induced cell-cycle arrest at G2–M in MDA-MB-231 and HS578T cells (Supplementary Fig. S4A), so we hypothesized that the combination of agents that target on 2 different phases of the cell cycle could have a complementary effect. For this purpose, we treated cells with JQ1 and anti-mitotic and DNA-damaging agents. Combination of either classes increased the induction of apoptosis in MDA-MB-231 and HS578T (Fig. 3D), demonstrating that BET inhibitors in combination with chemotherapy have a cytotoxic effect. In addition, we observed an augmented expression of PARP in MDA-MB-231 when treated with both combinations compared with each agent given alone (Supplementary Fig. S4B and S4C).
In vivo effect of BET inhibitors and pharmacodynamic assessment
Finally, we explored the in vivo activity of BET inhibitors on xenografted tumors. For this purpose, we implanted MDA-MB-231 in nude mice and treated animal with JQ1 at 50 mg/kg intraperitoneally daily for 2 weeks. As can be seen in Fig. 4A, treatment with this compound produced a clear inhibition of the tumor growth and a clear reduction of tumor weight. No toxicities were observed in the treated group compared with the control group including loss of body weight (Fig. 4B).
To evaluate the mechanism of action in vivo, we treated animals at the mentioned dose and extracted tumors at 24 and 48 hours. The biochemical evaluation of the treated tumors demonstrated an increased expression of p27, as observed in cell lines, demonstrating its effect on G1 (Fig. 4C). Similarly, a reduced expression of LM04, DEPDC, and FOXM1 measured by qPCR was identified at different times (Fig. 4D). These findings confirmed the antitumor effect of this family of agents in vivo reproducing the data observed in cellular models. The function of these genes are described in Supplementary Table S5.
In the present article, we explore the antitumor activity of novel BET inhibitors in an unmet disease like TNBC. The approach used for the selection of these kind of compounds was based on an in silico evaluation of gene expression data where we identified the upregulation of some transcription factors.
Some of these transcription factors were modulated by treatment with BET inhibitors, when evaluated in cell lines. Similarly, gene expression analyses in MDA-MB-231 after treatment with JQ1 reduced the expression of some of these genes at 12 and 24 hours and showed modulation of relevant pathways.
Transcription factors have been considered as undruggable proteins for a long time. With the discovery of this family of compounds, novel avenues to explore these agents in specific tumor subpopulations have been opened for clinical evaluation. As per the number of genes modified by JQ1 in the gene expression analyses, BET inhibitors mediate their function by transcription factors that modulate the expression of many different genes. This unspecific mechanism of action challenges the identification of a biomarker or a companion test to select patients. In our work, we have described 3 relevant transcription factors modified by the drug in vitro and in vivo, all linked with malignant transformation. However, it is reasonable to consider that the inhibition of other transcription factors could be involved in the modulation of other genes.
The in vitro evaluation of 2 novel BET inhibitors showed antitumor efficacy that was confirmed latter using in vivo models. Of note, as JQ1 represents a tool compound, relatively high concentrations (500 nmol/L) was required for optimal effects. Therefore, a medical useful inhibitor would need to be optimized for further development. In any case, their range of activity was still at the nanomolar range. BET inhibitors acted on the G1 phase of the cell cycle as was observed by flow cytometry, data that are in line with previous reports in B-cell lymphomas (27, 28). The biochemical evaluation of their function showed a profound effect on proteins that regulate this phase, like an increase of p21, p27, and cyclin D, with a decrease of regulators more associated with latter phases of the cell-cycle cyclin B or BURB. The results from tumors xenografted in animals demonstrated in vivo an increase of p27.
An interesting finding was the synergistic interaction between BET inhibitors and chemotherapies used to treat triple-negative tumors—compounds affecting mitosis and producing DNA damage. Evaluation of cell cycle showed that both type of chemotherapies produced an arrest at G2–M. The combination of BET inhibitors that mainly stop cells at G1 with agents that both induced an arrest at G2–M suggests that acting of different phases of the cell cycle could be an attractive therapeutic approach. Indeed, BET inhibitors alone did not induce apoptosis in a relevant manner, but their combination with the mentioned agents led cells to a programmed death.
Finally, evaluation of JQ1 in animal models showed that the drug had the same antitumor effect on xenografted tumors confirming the data observed in cell lines. Administration of JQ1 was able to induce the expression p27. Similarly, a reduction of LM04, DEPDC, and FOXM1 was observed after 24 and 48 hours of treatment.
BET inhibitors are a family of compounds with drug candidates at different stages of drug development, from drugs in preclinical stage to others like OTOX15 that has finished its phase I program with signs of activity and no unexpected toxicities (27). For triple-negative breast tumors, BET inhibitors represent a promising therapeutic option that deserves to be evaluated in clinical studies.
In conclusion, our data demonstrate the antitumor efficacy of this family of compounds paving the way of its future clinical evaluation in this unmet disease.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Conception and design: J. Pérez-Peña, G. Serrano-Heras, J.C. Montero, V. Corrales-Sánchez, A. Ocaña
Development of methodology: J. Pérez-Peña, G. Serrano-Heras, V. Corrales-Sánchez, A. Pandiella, A. Ocaña
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J. Pérez-Peña, G. Serrano-Heras, V. Corrales-Sánchez, A. Pandiella, A. Ocaña
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): J. Pérez-Peña, G. Serrano-Heras, V. Corrales- Sánchez, A. Pandiella, A. Ocaña
Writing, review, and/or revision of the manuscript: J. Pérez-Peña, G. Serrano-Heras, V. Corrales-Sánchez, A. Pandiella, A. Ocaña
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): V. Corrales-Sánchez
Study supervision: G. Serrano-Heras, V. Corrales-Sánchez, A. Ocaña
I would like to thank Pilar F. and Santiago O. for their support.
The study was supported by Instituto de Salud Carlos III (PI13/01444), ACEPAIN; AMAC, Diputación de Albacete and CRIS Cancer Foundation (to A. Ocaña). Ministry of Economy and Competitiveness of Spain (BFU2012-39151 and RD12/0036/0003 to A. Pandiella), and the AECC (to A. Pandiella). J.C. Montero is a recipient of a Miguel Servet fellowship program. European FEDER program.
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.