Abstract
Background/Aim: Multiple myeloma (MM) is characterized by accumulation of a malignant clone of plasma cells in the bone marrow. Curative treatments are not yet available. Therefore, we undertook a drug repurposing approach to identify possible candidates from a chemical library of 1,230 FDA-approved drugs by virtual drug screening. As a target, we have chosen the non-receptor Bruton’s tyrosine kinase (BTK) which is one of the main regulators of the MM biomarker CD38. Materials and Methods: In silico virtual screening was performed by using PyRx. Flow cytometry was applied for cell cycle and apoptosis analysis. Furthermore, protein and gene expression was determined by western blotting and microarray hybridization. Lipid raft staining was observed by confocal microscopy. Results: The in silico identified lipid-lowering lomitapide presented with the strongest cytotoxicity among the top 10 drug candidates. This drug arrested the cell cycle in the G2/M phase and induced apoptosis in MM cells. Western blot analyses revealed that treatment with lomitapide induced cleavage of the apoptosis regulator PARP and reduced the expression of CD38, an integral part of lipid rafts. Using confocal microscopy, we further observed that lipid raft microdomain formation in MM cells was inhibited by lomitapide. In four MM cell lines (KMS-12-BM, NCI-H929, RPMI-8226, and MOLP-8) treated with lomitapide, microarray analyses showed not only that the expression of CD38 and BTK was down-regulated, but also that the tumor suppressor gene TP53 and the oncogene c-MYC were among the top deregulated genes. Further analysis of these data by Ingenuity pathway analysis (IPA) suggested that lomitapide interferes with the cross-talk of CD38 and BTK and apoptosis-regulating genes via TP53 and c-MYC. Conclusion: Lomitapide treatment led to disruption of lipid raft domains and induction of pro-apoptotic factors and might, therefore, be considered as a potential therapeutic agent in MM.
Multiple myeloma (MM) is characterized by the expansion of a malignant plasma cell clone in the bone marrow (BM) leading to elevated levels of monoclonal proteins in the serum and/or urine and associated organ dysfunctions, e.g., hematopoietic insufficiency, osteolytic bone disease, and kidney damage (1). At an advanced disease stage, MM cells can become independent of the BM microenvironment and grow in the peripheral blood and other extramedullary sites such as soft tissue and organs (2, 3). The malignant transformation originated from post-germinal center B cells via a multistep process involving the acquisition of genetic alterations and BM microenvironmental changes (4, 5). MM is the second most frequent blood cancer with an annual age-adjusted incidence of six cases per 100,000 persons in the USA and Europe (6). The global incidence is higher in developed countries, such as the United States, Western Europe, and Australia, which is attributed to the availability of better diagnostic techniques, as well as a greater clinical awareness of the disease (7). The incidence is higher in black people and lower in Asians than in white people (8). Although substantial therapeutic progress has been achieved in the past two decades, curative treatments are not yet available.
Lipid rafts are membrane microdomains enriched in saturated phospholipids, sphingolipids, cholesterol, and a variety of signaling and transport proteins (9). Lipid rafts play a vital role in triggering apoptosis, signal-transduction, immune response, and in many pathological situations cardiovascular and neurological diseases (9, 10). CD38 is a transmembrane glycoprotein associated with lipid rafts that is highly and uniformly expressed on MM cells but only at relatively low levels on normal lymphoid and myeloid cells (11, 12). CD38 is an adhesion molecule and ectoenzyme involved in the catabolism of nicotinamide adenine dinucleotide (NAD+) and nicotinamide adenine dinucleotide phosphate (NADP) (13). It is also considered as a receptor if ligated to CD31 or agonistic antibodies, triggering complex transmembrane signaling. This ligation potentially leads to activation of B and T lymphocytes and dendritic cells, the proliferation of T lymphocytes, and the production of pro-inflammatory and regulatory cytokines by monocytes and natural killers (14-18). The rearrangement of the cell surface with the formation of glycosphingolipid- and cholesterol-rich plasma membrane precedes the endocytosis of human CD38 molecule suggesting these raft microdomains mediate CD38 translocation at the intracellular level (19). Of note, the downstream signaling transduction of CD38 is regulated by activation of the non-receptor Bruton’s tyrosine kinase (BTK). The BTK is encoded by the X-linked immunodeficiency (xid) gene, and the B lymphocytes of immunodeficient xid mice reveal a missense mutation at a normally conserved residue in the unique region of BTK (20). There is a cross-talk between CD38 and BTK. B lymphocytes from unstimulated xid mice are unresponsive to CD38 stimulation both in terms of proliferative response and surface antigen modulation. Therefore, BTK is either an integral component or an indirect regulator of the CD38-induced signal transduction pathway (21). Furthermore, BTK is highly expressed in MM cells (22) and plays a role in signal transduction pathways for mature B-lymphocytes and plasma cells (23). Poor prognosis is a common trait of MM patients with elevated levels of BTK (22).
Lomitapide is a lipid-lowering drug prescribed for the treatment of homozygous familial hypercholesterolemia (HoFH), a disease that is characterized by severe hypercholesterolemia and very premature atherosclerotic cardiovascular disease (ASCVD) (24). Lomitapide inhibits the microsomal triglyceride transfer protein (MTTP), an enzyme responsible for assembly of very low-density lipoprotein (VLDL) and chylomicron via loading of triglyceride onto apolipoprotein B. Inhibition of MTTP results in a reduction of VLDL release and VLDL-mediated triglyceride secretion that leads to a reduction of LDL-cholesterol (LDL-C) and total cholesterol (TC) levels in plasma.
Drug repositioning is a process of finding novel uses of a specific drug outside its original medical indication. This concept is also known as drug redirecting, repurposing, rediscovery, or reprofiling (25). The drug repositioning approach surpasses the de novo drug discovery in regard to perceived safety and tolerability knowledge of the existing approved drugs. In the past, various approved drugs have been redirected for cancer treatment. Thalidomide was reapproved for MM in 2006. It was initially introduced as oral sedative and anti-emetic drug to treat morning sickness from the 1950s to the early 1960s. Due to its teratogenicity (congenital defects), it was withdrawn from the market in 1962 (26). Moreover, there is a long history in repurposing the anti-malarial artemisinin and its derivatives for cancer treatment from our own group (27, 28), and several clinical phase I/II trials with human as well as veterinary tumors have been accomplished (29-31).
In this study, we systematically implemented a drug repositioning approach to redirect a previously approved drug for the treatment of MM. As a starting point, we determined the CD38 regulator BTK as molecular target that is highly expressed in MM cells. Afterwards, we performed in silico virtual screening using a chemical library of 1230 FDA-approved drugs. Lomitapide was among the top compounds with high binding affinity towards BTK. Furthermore, the preliminary in vitro cytotoxicity testing showed that lomitapide was the most active compound towards MM cell lines. We further investigated the cytotoxic activity and mechanism of action of lomitapide in 9 MM cell lines. We identified lipid raft microdomains as target for lomitapide by which cell cycle arrest and apoptosis was triggered in MM cells. The lipid raft constituent CD38 was down-regulated by lomitapide. By microarray analyses, TP53 and c-MYC were identified as CD38 regulators.
Materials and Methods
Virtual drug screening in silico. In silico virtual screening by PyRx is a predictive computational drug discovery tool to screen libraries of compounds against potential target macromolecules. X-ray crystallography-based structures of BTK (PDB ID: 4RX5) was obtained from Protein Data Bank (32). The FDA-approved drug library was downloaded from the ZINC database (33) as one spatial data file format. The energy of these approved drugs was minimized and converted to docking pdbqt format using open babel in PyRx tool. A total of 1230 FDA-approved drugs were docked on BTK after centering the binding geometry (grid box) to cover the whole protein. The scoring function [binding energy (kcal/mol)] was calculated using the standard protocol of Lamarckian genetic algorithm (34).
Cell lines. The 9 MM cell lines listed in Table I are originated from the Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures (Braunschweig, Germany). The cells were propagated in RPMI 1640 medium supplemented with 10% FBS and 1% penicillin/streptomycin (Invitrogen/Thermo Fisher Scientific, Darmstadt, Germany) and incubated in humidified 5% CO2 atmosphere at 37°C.
Characteristics of human multiple myeloma cell lines.
Cell viability assay. The cell viability of selected FDA approved drugs was determined by resazurin assay. The resazurin dye was used to evaluate the biocompatibility of medical materials by measuring innate metabolic activity using a redox indicator. Viable cells with active metabolism can reduce resazurin to strongly-fluorescent dye resorufin (35). The protocol we are suing has been published (36, 37). The IC50 values have been calculated from dose response. Each assay was executed three times independently with six replicates each. All IC50 values are expressed as mean±standard deviation (SD).
Cell cycle analysis by flow cytometry. Flow cytometric analysis was performed to detect apoptotic cells with fractional DNA content. In this method, cellular DNA content is being determined by staining the cells with propidium iodide (PI) that functions as the DNA fluorochrome. PI binds DNA by intercalating between base pairs. It can be excited at 488 nm, and the cells can be distributed in four major phases of the cell cycle (sub-G1, G0/G1, S, G2/M). Quantitation of apoptotic cells was determined as the percentage of cells in the sub-G1 region (hypodiploidy) in cell cycle analysis.
MM cell line NCI-H929 (106 cells/well) was treated with varying concentration of lomitapide (Sigma-Aldrich, Taufkirchen, Germany) (0.5×IC50, IC50, and 2×IC50) for 24 h, 48 h and 72 h at 37°C. Then, the cells were harvested and washed two times with cold PBS (Thermo Fisher Scientific, Darmstadt, Germany). The cells were fixed using 80% cold ethanol and incubated for 3 h at –20°C. After washing the cells with PBS, 10 μg/ml RNase A (AppliChem LifeScience, Darmstadt, Germany) were added for 10 min for optimal DNA resolution. Afterwards, 50 μg/ml PI (Sigma-Aldrich) were added to the cells and the fluorescence was measured after 15 min with the Accuri™ C6 cytometer (BD Biosciences, Heidelberg, Germany) with 488 nm excitation. For each treatment period (24 h, 48 h and 72 h), the experiments were performed in triplicates. The results were analyzed using Excel (Microsoft Corp., Redmond, WA, USA) and FlowJo (Celeza, Olten, Switzerland).
Apoptosis assay by annexin V/PI staining. Annexin V is a calcium-dependent phospholipid that binds to phosphatidylserine (PS) that translocates from the intracellular compartment of the plasma membrane to the external leaflet upon initiation of apoptosis. Propidium iodide (PI) is excluded by living or early apoptotic cells with intact membranes and stains late apoptotic or necrotic cells with red fluorescence due to DNA intercalation. Therefore, cells with annexin V (−) and PI (−) are considered to be alive while cells with annexin V (+) and PI (−) are in early apoptosis. Necrotic cells or cells in late apoptosis are both annexin V and PI positive.
The MM cell line NCI-H929 (106 cells/well) was treated with varying concentrations of lomitapide (IC50, 2×IC50, and 4×IC50) for 72 h. Afterwards, cells were collected and washed with PBS (Thermo Fisher Scientific). Cells were stained with annexin V and PI binding buffer (Invitrogen/Thermo Fisher Scientific) according to the manufacturer’s protocol. Subsequently, 3×104 cells were gated and measured with the Accuri™ C6 cytometer (BD Biosciences). The annexin V-FITC signal was measured with 488 nm excitation and detected using a 530/30 nm band pass filter. The PI signal was analyzed with 561 nm excitation and detected using a 610/20 nm band pass filter. All parameters were plotted on a logarithmic scale. Cytographs were analyzed using the BD Accuri C6 software (BD Biosciences).
Analysis of protein expression via western blotting. Whole protein fractions were extracted from the KMS-12-BM cells using M-PER™ mammalian protein extraction buffer (Thermo Fisher Scientific) containing protease and phosphatase inhibitors. Afterwards, sodium dodecyl sulfate (SDS) polyacrylamide gel electrophoresis was carried out to separate the proteins and the lysates were then transferred to polyvinylidene fluoride membranes (Ruti®-PVDF) (Merck Millipore, Schwalbach, Germany) for western blotting. Five percent of bovine serum albumin was used to block the membranes and then the membranes were incubated with specific primary antibodies against CD38 (1:1,000) (Cell Signaling Technology Europe B.V., Frankfurt, Germany), PARP (1:1,000) (Cell Signaling Technology Europe), and β-actin (1:2,000) (Cell Signaling Technology Europe). The blots were probed with horseradish 2 h at room temperature. Finally, Luminata™ Classico Western HRP substrate (Merck Millipore) was added for 5 min in the dark. Alpha Innotech FluorChem Q system (Biozym, Oldendorf, Germany) was used for documentation and band analysis.
Transcriptomic profiling and signaling pathway analysis. Four MM cell lines (MOLP-8, RPMI-8226, NCI-H929, and KMS-12-BM) were treated with IC50 concentration of lomitapide. After 24 h, the total mRNA was extracted using InviTrap® spin universal RNA kit (STRATEC Molecular GmbH, Berlin, Germany) according to the manufacturer’s protocol. The quality of total RNA was validated by gel analysis on an Agilent 2100 Bioanalyzer (Agilent Technologies GmbH, Berlin, Germany). Only samples with RNA index values greater than 6.60 were selected for expression profiling. Microarray experiments were conducted in duplicates for treated and untreated samples by the Genomics and Proteomics Core Facility at the German Cancer Research Center (DKFZ, Heidelberg) using Illumina Human Sentrix12 Bead Chip arrays (Illumina Inc., San Diego, CA, USA). Biotin-labeled cRNA samples for hybridization Bead Chip arrays were prepared according to Illumina’s recommended sample labeling procedure based on the modified Eberwine protocol (38). Microarray scanning was done using a Beadstation array scanner, setting adjusted to a scaling factor of 1 and PMT settings at 430. Data was extracted for each bead individually, and outliers were removed if the MAD (median absolute deviation) was greater than 2.5. Data analysis was performed by using the quantile normalization algorithm without background subtraction, and differentially regulated genes were defined by calculating the standard deviation differences of a given probe in a one-by-one comparison of samples or groups. Chipster® data analysis platform (http://chipster.csc.fi/) was used for statistical analysis of the expression data. These steps include filtering of genes by standard deviation of deregulated genes and a subsequent calculation of significance using two group t-test (p<0.05). The statistically significant deregulated gene dataset was subjected to Ingenuity Pathway Analysis (IPA, Ingenuity Systems, Redwood City, CA, USA) to explore possible signaling pathways, the upstream regulators, and the molecular networks. Firstly, the data were analyzed by the core analysis tool of IPA to determine cellular networks and functions that might be affected by lomitapide treatment. Following this approach, the core analyses results were subjected to the “comparison analysis” tool of IPA, allowing the possibility to find possible common upstream regulators that might be responsible for lomitapide’s activity in all treated MM cell lines.
Lipid raft staining and confocal microscopy. NCI-H929 cells (1×106 cells/well) were treated with varying concentrations of lomitapide (0.25×IC50, 0.5×IC50, IC50 and 2×IC50) for 24 h. The cells were stained following the manufacturer’s protocol using Vybrant® lipid raft labeling kit (Thermo Fisher Scientific) Briefly, the living cells were labeled with cholera toxin subunit B (CT-B) conjugated with Alexa flour 594 (1 μg/ml) for 10 min at 4°C. Afterwards, CT-B– labeled lipid rafts on the cells were cross-linked with anti–CT-B antibody (1:200) for 15 min at 4°C. Next, the cells were washed three times with PBS and finally mounted on a μ-Slide VI0.4 microscopy chamber (ibidi GmbH, Martinsried, Germany). Fluorescence and differential interference contrast (DIC) microscopic images were acquired with a Spinning Disc Confocal Microscope (VisiScope 5-Elements, Visitron Systems GmbH, Puchheim, Germany) based on a Nikon Ti2-E and a CSU-W1 (Yokogawa) located at the Microscopy and Histology Core Facility of the Institute of Molecular Biology (Mainz, Germany). Following imaging settings were used: 405 nm and 594 nm laser excitation, filters (ET460/50m and 565/133 Bright Line HC), 60× water immersion objective (NA 1.2, CFI Plan Apo VC), sCMOS camera (BSI, Photometrics, Scientifica Ltd., Uckfield, East Sussex, UK), VisiView Software 4.5 (Visitron Systems).
Results
In silico virtual screening of FDA approved drugs. In order to have insight about possible candidates that target BTK, we performed in silico virtual screening of 1,230 FDA approved drugs using PyRx. As shown in Table II, the binding energies of the top 10 compounds varied from –15.1 kcal/mol to –12 kcal/mol. The top three drugs were antiviral agents whereas the rest were prescribed for various indications such as hypercholesterolemia, euvolemic hyponatremia, antidiarrheal, and antiemetic agents.
Binding energies of the top 10 FDA-approved drugs to BTK as identified by virtual drug screening using PyRx.
In vitro cell viability screening revealed lomitapide as most active drug. To corroborate the reliability of the in silico virtual screening results, we selected six compounds (saquinavir, lopinavir, ledipasvir, conivaptan, lomitapide, and loperamide) from the top 10 virtual screening list for in vitro cytotoxicity screening on two exemplarily selected MM cell lines (MOLP-8 and RPMI-8226). The drugs were screened at a fixed concentration (10 μM). The results revealed that only three compounds (lopinavir, lomitapide, and loperamide) have the ability to kill more than 80% of the two MM cells lines (Figure 1A). Interestingly, lomitapide was the most active drug on both MM cell lines. Therefore, it was considered for further analysis.
(A) Preliminary screening of selected FDA-approved drugs in MOLP-8 and RPMI-8226 multiple myeloma (MM) cell lines as determined by resazurin assay. (B) Dose response curves of 9 MM cell lines (MOLP-8, RPMI-8226, NCI-H929, KMS12-BM, KMS-11, OPM-2, L363, AMO-1 and JJN-3) for lomitapide as determined by the resazurin assay. Each assay was performed three times and each point in the curve represents the mean±standard deviation (SD).
Cell viability dose response curves of lomitapide-treated MM cell lines. We used the resazurin cytotoxicity assay to investigate the cytotoxic potential of lomitapide towards MM cells. Nine MM cell lines were treated with varying concentrations of lomitapide range from 10–5 to 102 μM. As shown in Figure 1B and Table III, the dose response curves were plotted and IC50 values were calculated.
IC50 values of multiple myeloma cell lines for lomitapide as determined by resazurin-based cytotoxicity assay.
Lomitapide induced apoptosis in MM cells. Following our efforts to elucidate the mechanism of action of lomitapide, we exemplarily assessed cell-cycle progression in NCI-H929 cells. The cells were treated with varying concentrations of lomitapide (0.5×IC50, IC50, and 2×IC50) for 24 h, 48 h and 72 h at 37°C. As shown in Figure 2A-C, the treated cells showed a typical DNA histogram that represented sub-G0/G1, G0/G1, S, and G2/M phases of cell cycle. The percentage of sub-G0/G1 significantly increased after cells were treated with lomitapide compared to the DMSO-treated control cells. The results revealed a dose- and time-dependent elevation of sub-G0/G1 population in the cells upon lomitapide treatment, suggesting that lomitapide triggered cellular apoptosis in MM cells.
Cell cycle distribution of NCI-H929 cells treated with varying concentrations of lomitapide (0.5×IC50, IC50, and 2×IC50) for (A) 24 h, (B) 48 h, and (C) 72 h incubation at 37°C. Data points are means of at least three independent experiments with standard error of the mean (SEM). Western blot analyses of KMS-12BM cells treated with varying concentrations of lomitapide (IC50, and 2×IC50) to detect (D) PARP-1 cleavage and (E) CD38 expression.
Furthermore, to verify the apoptosis-triggering effect in a second cell line, we assessed PARP cleavage in KMS-12-BM cells treated with lomitapide (IC50, and 2×IC50) for 24 h. Indeed, PARP was clearly cleaved in treated cells (Figure 2D). To further confirm the induction of apoptosis, we performed annexin V/PI staining. As shown in Figure 3, more than 80% of the cells appeared in late apoptosis upon treating NCI-H929 cells with lomitapide (annexin V+/PI+).
Apoptosis assay via annexin V/PI staining in NCI-H929 cells treated with varying concentrations of lomitapide (IC50, 2×IC50, and 4×IC50).
Lomatipide significantly down-regulated the expression of CD38. Since CD38 is considered as important player in MM, we evaluated its expression by western blotting. Five cell lines (MOLP-8, RPMI-8226, NCI-H929, KMS-12-BM, and L363) expressed CD38 while AMO-1, JJN-3, KMS-11, and OPM-2 cells did not. We selected KMS-12-BM cells and treated them with different concentrations of lomitapide (0.5×IC50, IC50, and 2×IC50) for 24 h at 37°C. The expression of CD38 was down-regulated in a dose-dependent manner (Figure 2E).
Lomitapide disrupted lipid raft formation. Based on two facts (1) lomitapide lowers cholesterol levels which is highly enriched in lipid rafts microdomains and (2) the disruption of lipid rafts triggers apoptosis via recruiting and clustering of the death receptors (39, 40), we hypothesized that lomitapide may induces apoptosis in MM cells via cholesterol depletion and subsequently rafts disruption. Therefore, we investigated the lipid rafts arrangements upon addition of lomitapide in varying concentrations to NCI-H929 cells. Remarkably, lomitapide disrupted the lipid rafts arrangements in a dose-dependent manner (Figure 4).
Detection of lipid raft microdomain arrangements in NCI-H929 cells treated with varying concentrations of lomitapide (0.25×IC50, 0.5×IC50, IC50, and 2×IC50) by confocal microscopy.
Gene expression profiling of lomitapide-treated MM cell lines highlighting TP53 and MYC as common molecular key players. Gene expression analyses were carried out to get a deeper insight into the molecular modes of action of lomitapide in MM cells. A total number of 884 genes were commonly deregulated in four CD38-positive MM cell lines investigated (MOLP-8, RPMI-8226, NCI-H929, and KMS-12-BM). The deregulated genes were involved in various cellular pathways such as DNA damage, cellular death, cell cycle, lipid metabolism, oxidative phosphorylation, and mitochondrial dysfunction.
A striking result emerging from the microarray data was that both TP53 and c-MYC were suggested by the “upstream regulator analysis” function of the IPA program as the top common upstream regulators of the four lomitapide-treated MM cell lines (Figure 5). Therefore, we searched which genes deregulated by lomitapide underlie TP53 regulation (Figure 6). The top up-regulated genes of activated TP53 were CCL3L3, ATF3, JUN, KLF6, ASS1, CCL5, CAV1, NFKB2, NFKBIA, PIM1, ATF4, DUSP1, TNFRSF10B, and ICAM1 whereas the top down-regulated genes were ID3, FABP5, ITGB7, MRPL12, ATPMC1, ADA, COQ3, NME1, MGST2, MRTO4, EIF4A1, and ALDOA. By using IPA, we did the same for c-MYC to identify the c-MYC-regulated genes upon lomitapide treatment (Figure 7). The top down-regulated genes of inhibited c-MYC were SLC3A2, JUN, KLF6, ASS1, CAV1, BCAT1, SHMT2, NFKBIA, SAT1, OAS1, ATF4, TNFRSF10B, FBXO32, and ICAM1 whereas the top down-regulated genes of inhibited c-MYC were ID3, FABP5, GAMT, RPS7, TNFRSF8, MRPL12, PGAM1, BZW2, RPL6, RANBP1, NME1, HNRNPA1, EIF4A1, PSMB8, RUVBL1, SRM, NOP56, PHB, HNRNPD, HSPD, PFKM, ALDOA, and LDHA. Interestingly, CD38 and its regulator BTK were both found to be down-regulated following lomitapide treatment. Using the IPA “pathway builder” tool, a schematic pathway elucidating the cross-talk between CD38, BTK, and apoptosis regulators genes is shown in Figure 8.
Upstream regulator analysis of upstream transcription regulators affected by lomitapide as determined by the IPA program. TP53 and c-MYC were identified as top common upstream regulators in four lomitapide-treated MM cell lines (KMS-12-BM, NCI-H929, RPMI-8226 and MOLP-8) compared to their untreated counterparts.
Network of deregulated genes that were differentially expressed upon TP53 activation after lomitapide treatment as determined by Ingenuity Pathway Analysis. Color code: green, downregulation; red, upregulation.
Network of deregulated genes that were differentially expressed upon c-MYC inhibition after lomitapide treatment as determined by Ingenuity Pathway Analysis. Color code: green, downregulation; red, upregulation.
Network of deregulated genes that were differentially expressed upon c-MYC inhibition after lomitapide treatment as determined by Ingenuity Pathway Analysis. Color code: green, downregulation; red, upregulation.
Discussion
The development of bioinformatics and biological computation tools have spurred researchers to dig into basic genomic, proteomic, and other “omics” data and exploit these data for the identification of drug targets and development of drugs for new disease indications. Recently, drug repositioning for cancer treatment emerged as a dynamic field of drug development due to the fast growth of bioinformatic knowledge and the availability of oncogenomic data. This concept could considerably lessen the risks of development and the costs, and shorten the lag between drug discovery and availability (41). In this study, our drug repositioning strategy was initially based on in silico virtual screening of FDA-approved drugs against a known CD38’s regulator BTK that plays a role as signal transducer in MM. The virtual screening outcomes were followed by in vitro cytotoxicity screening and finally elucidation of the mechanism of action of the selected compound towards MM cells. The hit rate of our in silico approach was plausible, since 50% of our selected drugs for in vitro screening were indeed found to be active against MM cells. Compared to literature data, this hit rate is remarkably high (42). Lomitapide achieved more than 95% killing activity towards RPMI-8226 and MOLP-8 cells at a fixed concentration (10 μM). This result impelled us to further illustrate the mode of action of lomitapide on MM cells.
Lomitapide is a lipid lowering agent notably reducing the cholesterol level by preventing the very low-density lipoprotein (VLDL) assembly. Since cholesterol and triglycerides are risk factors for the development of tumors (43), it is reasonable to propose that lomitapide may be repurposed for cancer prevention and treatment. Cholesterol is an integral part of the plasma membrane of mammalian cells that is assembled in the lipid rafts to maintain structural integrity and modulate fluidity of the cells. Additionally, the membrane lipid rafts recruit a large number of cancer-related signaling and adhesion molecules that have a role in membrane trafficking and downstream signal transduction (44). Moreover, cholesterol has been linked to survival and enhancing the proliferation of MM cells (45) and overexpression of LDL receptor in MM cells indicated that LDL-cholesterol is a vital player for their growth (46). Another class of lipid lowering drugs, the statins, exert their antineoplastic effects through triggering apoptosis or mediating growth suppressions of malignant cells (47, 48). The conspicuous observations emerging from cell cycle analysis and lipid raft disruption in the current study indicated that lomitapide triggered apoptosis in MM cells. This concurs well with previous findings reporting that the depletion of membrane cholesterol leads to the disruption of membrane lipid rafts, blockage of the adhesion and migration processes of cancer cells, and induction of apoptosis (49, 50).
To further corroborate our finding that lomitapide triggers apoptosis in MM cells, PARP-1 expression was determined. PARP-1 is a nuclear protein that has a wide range of functions as an enzyme in the repair mechanism of the DNA such as base excision repair, nucleotide excision repair and single strand base repair. During apoptosis, PARP-1 is enzymatically cleaved by the activation of caspases, a family of cysteine proteases (51).
The IgG
monoclonal antibody daratumumab is an immunomodulatory drug that targets CD38 and improved the outcomes of MM patients (52). Daratumumab kills MM cells by diverse mechanisms of action, including induction of apoptosis, complement-dependent cytotoxicity, antibody-dependent cell-mediated cytotoxicity, and antibody-dependent cellular phagocytosis (53), signal transduction (54), cell adhesion, and differentiation (55). Importantly, recent evidence suggested that CD38 enhanced proliferation and inhibited apoptosis of CD38-overexpressing cervical cancer cells by upregulation of MDM2, cyclin A1, CDK4, cyclin D1, NF-
B p65, and c-REL as well as downregulation of p53, p21, and p38 (56). Therefore, silencing CD38 is an attractive strategy to combat MM. This fits with our finding that downregulation of CD38 in response to lomitapide treatment triggered apoptosis in MM cells. Noteworthy, the CD38’s regulator, BTK, plays a decisive role in the development, differentiation, and proliferation of B-lineage cells, making it an attractive target for the treatment of B-cell malignancies (57). Intriguingly, BTK was down-regulated upon lomitapide treatment.
Interestingly, gene expression profiling revealed that lomitapide leads to the de-regulation of genes that are potential activators or repressors of TP53 and c-MYC. Riley et al. compiled a list of p53-regulated genes and analyzed their p53 response elements in DNA that bind the p53 protein and promote transcriptional control (58). Intriguingly, our gene expression profile showed four up-regulated genes (ATF3, CAV1, DUSP1 and TNFRSF10B) that can be transcriptionally activated by p53. Furthermore, several target genes for oncogenic c-MYC have been identified, and they might represent valuable therapeutic targets for inhibition tumor formation (59).
In our microarray analysis, 8 c-MYC target genes (SRM, NOP56, PHB, HNRNPD, HSPD, PFKM, NME1, and LDHA) were down-regulated in MM cells treated with lomitapide, indicating that lomitapide affects c-MYC downstream signaling. Notably, the cross-talk between p53 and c-MYC has been associated with the prognosis and progression of several hematologic malignancies (60).
C-MYC plays a critical role in evolving MGUS to MM, and its overexpression is a common trait for MM patients (61, 62). Cell cycle progression was associated with overexpression of c-MYC, whereas its downregulation induced cell cycle arrest and impaired mitogenic response (63). Wang et al. investigated 23 cell lines with short-hairpin-mediated depletion of c-MYC and noted that the arrest took place at the G0/G1 phase in normal cells and some tumor-derived cell lines, whereas in other tumor cell lines the arrest occurred at G2/M (64). Indeed, our results showed that lomitapide down-regulated c-MYC expression in treated MM cells, and as a consequence arrested the cell cycle in the G0/G1 phase, and cellular population accumulated in sub-G0/G1. Furthermore, c-MYC was also associated with apoptotic response. In 32Dcl3 myeloid progenitor cells, withdrawal of IL-3 resulted in c-MYC downregulation, cellular accumulation in the G0/G1 phase of the cell cycle, and rapid induction of apoptosis (65). The results in these myeloid progenitor cells are in accordance with our results in MM cells.
The guardian of the genome, p53, is a nuclear phosphoprotein that acts biochemically as a transcription factor and biologically as a powerful tumor suppressor (66). Under cellular stress conditions such as genotoxic damages, oncogene activation, and hypoxia, p53 responds by induction of cell cycle arrest and/or apoptosis (67). The alternate reading frame (ARF) is a master regulator for the cross-talk between p53 and c-MYC (68). ARF inhibited the p53 negative regulator, MDM2, function and at the same time negatively regulated the transcriptional activity of c-MYC (69). Both effects result in p53 overexpression and activation, thus facilitating p53-dependent cell cycle arrest and apoptosis. Additionally, p53 enhances apoptosis through transcriptional activation of the FAS/CD95 death receptor. The activated FAS death domain forms a cytoplasmic docking site for the adapter protein FADD, which recruits procaspase-8 to promote its autocatalytic activation, thereby initiating the apoptotic cascade (70, 71). Of note, lipid rafts constitute a linchpin that drives FAS/CD95 death signaling (39, 72). Remarkably, our gene expression profiling results revealed overexpression of FAS and ARF upon treatment of MM cells with lomitapide. In conclusion, we found that the induction of apoptosis via a lipid raft-associated CD38-BTK-p53-c-MYC axis may represent an important mechanism of action of lomitapide towards MM cells. This drug that was initially launched to treat homozygous familiar hypercholesterolemia may, therefore, be reconsidered for treatment of MM, which is still an incurable disease with short survival rates.
Acknowledgements
We thank the Microarray Unit of the Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ, Heidelberg), for providing excellent Expression Profiling services.
Footnotes
Authors’ Contributions
M.E.M.S. performed bioinformatics, flow cytometry, and confocal microscopy, and wrote the draft of the manuscript. J.C.B and S.B.M. performed western blotting. E.L. and M.C. provided the cell lines and edited the manuscript. T.E. supervised the project and wrote and edited the paper.
Funding
We are grateful to a donation of Mr. Marc Stobel, Frankfurt a.M., Germany. The study was intramurally funded. E.L. is supported by the Deutsche Krebshilfe (process number: 70112693).
Conflicts of Interest
The Authors declare that there are no conflicts of interest.
- Received April 13, 2022.
- Revision received May 9, 2022.
- Accepted June 6, 2022.
- Copyright© 2022, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).
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