Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2024 Oct 15.
Published in final edited form as: Eur J Pharmacol. 2023 Aug 29;957:176034. doi: 10.1016/j.ejphar.2023.176034

Synergism of small molecules targeting VDAC with sorafenib, regorafenib or lenvatinib on hepatocarcinoma cell proliferation and survival

C Ventura a,c,1, M Junco a,1, FX Santiago Valtierra a, M Gooz a, Y Zhiwei a, DM Townsend a, PM Woster a, EN Maldonado a,b,*
PMCID: PMC10586475  NIHMSID: NIHMS1934805  PMID: 37652292

Abstract

Voltage dependent anion channels (VDAC) in the outer mitochondrial membrane regulate the influx of metabolites that sustain mitochondrial metabolism and the efflux of ATP to the cytosol. Free tubulin and NADH close VDAC. The VDAC-binding small molecules X1 and SC18 modulate mitochondrial metabolism. X1 antagonizes the inhibitory effect of tubulin on VDAC. SC18 occupies an NADH-binding pocket in the inner wall of all VDAC isoforms. Here, we hypothesized that X1 and SC18 have a synergistic effect with sorafenib, regorafenib or lenvatinib to arrest proliferation and induce death in hepatocarcinoma cells. We used colony formation assays to determine cell proliferation, and a combination of calcein/propidium iodide, and trypan blue exclusion to assess cell death in the well differentiated Huh7 and the poorly differentiated SNU-449 cells. Synergism was assessed using the Chou-Talalay method. The inhibitory effect of X1, SC18, sorafenib, regorafenib and lenvatinib was concentration and time dependent. IC50s calculated from the inhibition of clonogenic capacity were lower than those determined from cell survival. At IC50s that inhibited cell proliferation, SC18 arrested cells in G0/G1. SC18 at 0.25–2 IC50s had a synergistic effect with sorafenib on clonogenic inhibition in Huh7 and SNU-449 cells, and with regorafenib or lenvatinib in SNU-449 cells. X1 or SC18 also had synergistic effects with sorafenib on promoting cell death at 0.5–2 IC50s for SC18 in Huh7 and SNU-449 cells. These results suggest that small molecules targeting VDAC represent a potential new class of drugs to treat liver cancer.

Keywords: VDAC, Synergism, Hepatocarcinoma, Sorafenib, Regorafenib, XI, SC18

1. Introduction

A dynamic balance between oxidative metabolism and aerobic glycolysis supports tumor growth (Alam et al., 2016; Cassim et al., 2020; Duraj et al., 2021; Jose et al., 2011; Rovini et al., 2022). Proliferating cells display the Warburg effect characterized by enhanced aerobic glycolysis and partial suppression of mitochondrial metabolism (Warburg, 1956; Warburg et al., 1927). Glycolysis generates 10–90% of total cellular ATP depending on the tumor type (Griguer et al., 2005; Nakashima et al., 1984). Oxidative metabolism is the result of mitochondrial oxidation of substrates with subsequent synthesis of ATP and metabolic intermediaries. Mitochondria in cancer cells are essential for rapid proliferation. In several tumors, an increase in mitochondrial metabolism reverses glycolysis (Chen et al., 2021; Dang, 2012; Eason and Sadanandam, 2016; Liu et al., 2023; Yun et al., 2012).

Most attempts to modulate cancer metabolism that focused on glycolysis inhibition, failed in the clinic (Heuser et al., 2023; Liu et al., 2012; Pajak et al., 2020; Rho et al., 2019; Xu et al., 2022; Yakisich et al., 2019). Inhibition of glutamine and fatty acid metabolisms are other mechanisms under investigation (Cluntun et al., 2017; Dhar and Lippard, 2009; Jin et al., 2020; Ma et al., 2018; Stacpoole, 2017; Sukjoi et al., 2021). Previously, we identified the compounds X1 and SC18, that target the voltage dependent anion channel (VDAC) located in the outer mitochondrial membrane (DeHart et al., 2018a, 2018b; Heslop et al., 2022a). From a functional standpoint, VDAC regulation is a unique mechanism “upstream” of the Krebs cycle and oxidative phosphorylation with amplifying effects on multiple intramitochondrial reactions that influence overall cell metabolism.

In mammalian cells, VDAC1 and 2 are the most abundant and VDAC3 is a minor isoform (Colombini, 2004; De Pinto et al., 2010). VDAC1 and 2, form a transmembrane β-barrel protein (Hiller et al., 2010; Ujwal et al., 2008). The open state of VDAC allows the flux of pyruvate, most respiratory substrates, ATP4−, ADP3−, AMP2−, HPO42−, and phosphocreatine. VDAC closing favors a non-selective flux of small cations (Colombini, 2016; Rostovtseva and Colombini, 1997). VDAC is endogenously regulated by several factors including NADH and α/β tubulin heterodimers (Azoulay-Zohar et al., 2004; Böhm et al., 2020; Maldonado et al., 2010, 2013; Rostovtseva et al., 2008; Tsujimoto and Shimizu, 2000). Phosphorylation by protein kinases A and C epsilon and GSK3β also modulate VDAC (Das et al., 2008; Sheldon et al., 2011).

Free tubulin and NADH close VDAC. X1 antagonizes the inhibitory effect of free tubulin on VDAC (DeHart et al., 2018b; Maldonado et al., 2013). X1 that opens VDAC, promotes oxidative stress leading to mitochondrial dysfunction, reversal of glycolysis and cell death in hepatocarcinoma cells (Heslop et al., 2020). β-NADH binds to an NADH-binding pocket of VDAC and closes the channel (Böhm et al., 2020). Recently, we identified the compound SC18 designed to occupy the NADH-binding pocket in the inner wall of VDAC (Heslop et al., 2022b). Both X1 and SC18 inhibit proliferation of human hepatocarcinoma (HCC) cells.

Hepatocellular carcinoma accounts for up to 90% of primary liver cancers (Llovet et al., 2021; Villanueva, 2019). Advanced stages of HCC are common (Erstad and Tanabe, 2017; Marrero et al., 2018). Approximately, 50–60% of patients with HCC are estimated to receive systemic therapies (Llovet et al., 2018). Sorafenib, a multikinase inhibitor still standard of care for inoperable HCC, is poorly effective (Llovet et al., 2008; Marisi et al., 2018). More recently, the tyrosine kinase inhibitors lenvatinib, regorafenib, and cabozantinib, have been approved for advanced HCC after progression on sorafenib (Bruix et al., 2017; Yamashita et al., 2020). Other treatments for HCC are vascular endothelial growth factor (VGEF) and immune checkpoints inhibitors (Cheng et al., 2019; Montironi et al., 2019; Yau et al., 2020).

Here, we hypothesized that X1 or SC18 synergize with sorafenib, regorafenib and lenvatinib to inhibit proliferation and promote death of HCC cells. We showed that both X1 and SC18 inhibited cell proliferation and promoted cell death in a dose and time dependent manner. The inhibitory effect on cell proliferation and viability was synergistic with sorafenib, regorafenib and lenvatinib. Overall, our findings open a new avenue to develop “metabolic” treatments for HCC and possibly other types of cancer.

2. Materials and methods

2.1. Materials

Lenvanitib (# 19375), regorafenib (# 18498) and sorafenib (# 10009644) were purchased from Cayman Chemical Company (MI, USA). X1 and SC18 were custom synthesized. Propidium iodide (#P21493), calcein (#C3100MP) and Hoesch 33342 (#H3570) were purchased from Invitrogen™, Thermo Fisher Scientific (Waltham, MA, USA). Mouse IgG1/FITC (#X0927) and Ki-67 Antigen/FITC (#F7268) were purchased from Agilent Dako (Glostrup, Denmark). RNase A (# R-050-100) was purchased from Gold Biotechnology (St. Louis, MO, USA). Crystal violet (#3329-75) was purchased from Difco Laboratories (Detroit, MI, USA). Paraformaldehyde (# 416785000) was purchased from Acros Organics (Waltham, MA, USA). Albumin from bovine serum (BSA) (# A7030) was purchased from Sigma (Munich, Germany). Triton (# BP151-100) was purchased from Fisher BioReagents (Waltham, MA, USA). Fetal bovine serum (FBS) was from Atlanta Biologicals (Bio-Techne, Minneapolis, Minnesota, USA). Penicillin, streptomycin, and RPMI-1640 containing 2.05 mM L-glutamine, were from Thermo Fisher Scientific (Waltham, MA, USA). Eagle’s Minimum Essential Medium (EMEM) was from the American Type Culture Collection (ATCC, Manassas, VA, USA). All other chemicals were analytical grade.

2.2. Cell culture

SNU-449 cells (# CRL-2234) were purchased from American Tissue Culture Collection (ATCC) (Manassas, VA). Huh7 human hepatocarcinoma cells were courtesy of Dr. Jack R. Wands, Brown University, Providence, RI. Huh7 cells were grown in Eagle’s minimum essential medium supplemented with 10% FBS, 100 units/ml penicillin and 100 μg/ml streptomycin. SNU-449 cells were grown in RPMI-1640 supplemented with 10% FBS, 100 units/ml penicillin and 100 μg/ml streptomycin. Both cell lines were maintained in 5% CO2/air at 37 °C. For all experiments, cells at passages 8 to 24, were cultured for 24 h and used at 70–80% confluency. As quality control, we assessed cell morphology and doubling times that remained unaltered among passages. Cell lines were authenticated by Short Tandem Repeat (ATCC).

2.3. Tumor spheroids

3D multicellular tumor spheroids (MCTs) were generated by culturing 2 × 104 Huh7 cells in 500 μl of EMEM/well in ultra-low attachment 24-well plates. Formation of MCTs was confirmed using brightfield imaging 2 d after plating. We treated MCTs for 6 d using the IC50s calculated for cell death in 2D cultures as follows: X1 (2.5 μM), SC18 (36.6 μM), sorafenib (5.9 μM), and 0.5 IC50s for the combinations of X1 + sorafenib and SC18 + sorafenib. Images of the spheroids were captured every 2 d with a FLoidTM Cell Imaging Station (Thermo Fisher Scientific). Area was measured at day 6 using FIJI 2.11.0 software. A minimum of 60 spheroids per condition were analyzed. MCTs were dissociated by trypsinization and gently pipetting. Cell viability was assayed by trypan Blue exclusion. The number of positive dead cells was represented as a percentage of the total number of cells counted in each condition. Three independent experiments were performed with 3 replicates per condition.

2.4. Clonogenic assay

Five hundred Huh7 or SNU-449 cells per well were plated in 6-well plates 24 h before each experiment. SNU-449 and Huh7 cells were treated with X1 (0.1, 0.3, 1, 3, 10 and 30 μM), SC18 (1, 3, 10, 25, 30, 50 and 100 μM), or sorafenib (0.03, 0.1, 0.3, 1, 3, 10 and 30 ymM). In additional experiments, SNU-449 cells were treated with regorafenib (0.1, 0.3, 1, 3, 10 and 30 μM), or lenvatinib (0.3, 1, 3, 10, 30 and 100 μM). After 10 d, cells were fixed with paraformaldehyde 4% in phosphate buffered saline (PBS) and stained with 0.05% crystal violet in 10% ethanol. Clonogenic capacity was evaluated by counting only colonies containing a minimum of 50 cells and expressed as a percentage of the total number of colonies in the presence of vehicle alone (Franken et al., 2006).

2.5. Cell cycle analysis

One hundred fifty thousand cells plated in 6-well plates were serum-starved for 24 h. Synchronized cells treated with SC18 (3, 10, 25 μM) or vehicle for 48 h, were trypsinized and fixed with ice-cold methanol. After centrifugation (200×g for 5 min), cells were resuspended in 0.2 mg/ml of DNase-free RNase A and stained with a propidium iodide (PI) solution (50 μg/ml PI in PBS). Cell cycle was analyzed by flow cytometry (CytoFLEX S Flow Cytometer, Beckman Coulter, US) using CytExpert 2.1 Software (Beckman Coulter, US). At least 20,000 per sample events were acquired.

2.6. Cell killing and cell viability assays

Cells were imaged every 2 d for 2 wks after treatment using a FLoidTM Cell Imaging Station. Cells were co-loaded with propidium iodide (PI, 30 μM) and calcein (2 μM) for 30 min. Excitation and emission were 586/15 nm and 646/68 nm for PI and 482/18 and 532/59 nm for calcein. FIJI 2.11.0 software was used to determine calcein or PI positive cells. A minimum of 4 randomly selected fields with 200–500 cells per field were imaged per sample from 3 independent experiments.

2.7. Immunofluorescence

SNU-449 cells plated in 10-well cell culture slides (15,000/well) (Greiner Bio-One, # 543079) were serum starved for 24 h. Synchronized cells were treated with SC18 (25 μM) or vehicle for 48 h. Cells were fixed using 4% paraformaldehyde in PBS for 10 min at room temperature and then permeabilized utilizing Triton 0.2% in PBS for 5 min. After blocking with 1% BSA for 30 min at room temperature, samples were incubated with anti-Ki67/FITC (1/100) or anti-mouse IgG1/FITC (1/100) antibodies in 1% BSA overnight at 4 °C. After washing 3 times with PBS, Hoechst (2 μg/ml) was added for 5 min at room temperature. Hoechst treated samples were washed again and maintained in PBS until imaging. Images were acquired using a Zeiss LSM 880 LSM (Thornwood, NY) with a 63 × 1.4 N.A. plan apochromat oil immersion lens. FITC was excited at 488 nm and emission was detected at 490–633 nm. Hoechst was excited at 405 nm and emission was detected at 410–501 nm. To analyze images, we created primary masks around each Hoechst 33342 stained nucleus using the FIJI 2.11.0 software and then determined the number of Ki67 positive cells. A minimum of 8 randomly selected fields with a minimum of 30 cells per field were imaged from 3 independent experiments.

2.8. Calculation of IC50

IC50s were calculated using clonogenic and cell killing assays. SNU-449 and Huh7 cells plated in 6-well tissue culture plates were exposed to SC18, X1, lenvatinib, regorafenib and sorafenib at increasing concentrations until maximum inhibitory effect was achieved. IC50s were calculated by least squares regression using GraphPad Prism 8.0.2 (GraphPad Software Inc. USA). [Inhibitor] vs. response – variable slope (four parameters) equation was used without constraining any parameter.

2.9. Chou-Talalay method

2.9.1. Dose-response curves and associated parameters

A dose-response curve was constructed for each drug utilizing the Compusyn software program (ComboSyn Inc., Paramus, NJ. USA). Half-maximal inhibitory concentration (IC50), and a coefficient (m) were calculated using the following formula:

fafu=(DDm)

D indicates drug dose, fa is the inhibited fraction by the drug dose D, fu is the unaffected fraction, Dm indicates the dose that causes 50% inhibition, and m is the coefficient signifying the shape of the dose-effect curve. m < 1, m > 1 and m = 1 reveal flat sigmoidal, sigmoidal, and hyperbolic dose-effect curves, respectively. The values of m, Dm and a linear correlation coefficient (r) for each single drug are the dose-effect parameters and required for the implementation of Chou-Talalay method.

2.9.2. Clonogenic assay for binary combinations based on constant-ratios

To perform in vitro drug interaction analysis, we used the clonogenic assay for different binary drug combinations. As recommended by Chou-Talalay (Chou, 2010), we used the IC50 constant-ratio drug combination design, where the contribution to the combination by each single drug ranged from double to 1/16 the IC50 of each drug.

2.9.3. Determination of the combination index (CI)

The Combination Index value was automatically calculated utilizing the Compusyn software program based on the Combination Index Equation as follows:

(CI)x=(D)1(Dx)1+(D)2(Dx)2=(D)1(Dm)1[fa(1−fa)1]1∕m1^+(D)2(Dm)2[fa(1−fa)1]1∕m2^

Where, (Dx)1 is the dose of the drug D1 alone that inhibits the clonogenic capacity of cell by x%, (Dx)2 is the dose of the drug D2 alone that inhibits the clonogenic capacity of cell by x%, (D)1 and (D)2 are the doses of the drugs D1 and D2 in combination that also inhibit the growth of cells by x%.

A CI value equal to 1, indicates an additive effect. A CI value smaller than 1, indicates synergistic interaction. CI values greater than 1, indicates antagonism.

2.9.4. Statistical analysis

Data were expressed as average ± SD. Data normality was assayed using Brown-Forsythe and Bartlett’s test. Student’s T test or One way ANOVA along with Dunnett’s Multiple Comparison post hoc test was used to compare differences between experimental groups. P values less than 0.05 were considered statistically significant. Data were analyzed by the GraphPad Prism 8.0.2 software (GraphPad Software Inc.).

3. Results

3.1. Clonogenic capacity

To determine the ability of single cells to grow into a colony, we used clonogenic assays in the well differentiated human hepatocarcinoma Huh7 cells and the poorly differentiated SNU-449 cells. We determined the inhibitory effects of X1 and SC18 on clonogenic capacity and used sorafenib as a comparison with an FDA-approved drug for HCC treatment. Clonogenic inhibition by X1, SC18 and sorafenib was concentration dependent both in SNU-449 and Huh7 cells (Fig. 1A-B). Regorafenib and lenvatinib, other FDA-approved drugs for treating HCC, also decreased clonogenicity in SNU-449 cells (Fig. 1 A). The lowest IC50s calculated corresponded to X1, followed by regorafenib, sorafenib, lenvatinib, and SC18 in SNU-449 cells. X1 was also the most potent inhibitor of clonogenicity in Huh7 cells (Fig. 1 C). Overall, Huh7 cells were more sensitive to the cytotoxic effects of X1, SC18 and sorafenib.

Fig. 1. X1, SC18, sorafenib, regorafenib and lenvatinib inhibit clonogenic capacity.

Fig. 1.

Open in a new tab

SNU-449 and Huh7 cells were treated for 10 d. Clonogenicity was evaluated as described in Methods. (A–B) Concentration-response curves and representative images; C) IC50 values calculated from curves in A and B. Sor: sorafenib; Len: lenvatinib; Reg: regorafenib; ND = Non determined. Each curve represents avg ± SD of six independent experiments.

3.2. Cell death

3.2.1. Quantitative analysis

Quantitative analysis of clonogenicity indicates not only the ability of single cancer cells to form a colony but it is also the result of how many cancer cells survive after a physical or pharmacological treatment (Franken et al., 2006). Thus, to investigate if X1, SC18 and sorafenib induced cell death in SNU-449 and Huh7 cells, we used an imaging method combining calcein and propidium iodide uptake. Results were confirmed by cell counting after trypan blue exclusion. Calcein allows visualization of the cytosol of live cells. Propidium iodide uptake occurs only if the plasma membrane is permeabilized after cell death. Thus, the combination allows to simultaneously quantify live and dead cells in 2D cultures. Cell death after treatment with X1, SC18 or sorafenib was concentration-dependent, being X1 more potent than SC18 in both cell lines (Fig. 2A-B). In general, IC50s for cell death (Fig. 2 C) were higher than those calculated from the inhibition of the clonogenic capacity (Fig. 1 C) in both cell lines. Similar to the effects on clonogenicity, X1 and SC18 but not sorafenib, killed Huh7 cells at lower concentrations indicating that the cell line influences not the type but the magnitude of the response.

Fig. 2. X1, SC18, and sorafenib promote cell death in a concentration dependent manner.

Fig. 2.

Open in a new tab

SNU-449 and Huh7 cells were treated with X1, SC18 or sorafenib for 1, 4 and 8 d, respectively. The duration of treatment corresponded to the time required to elicit a maximal response for each compound. Cell death was determined as described in Methods. (A–B) Representative images on left panels show calcein positive (green) and propidium iodide positive (red) cells. Scale bars: 125 μm (SNU-449) and 100 μm (Huh7); C) IC50 values calculated from curves in A and B. Each curve represents avg ± SD of six independent experiments. Sor: sorafenib.

3.2.2. Time course

The effect of X1, SC18 and sorafenib was also time dependent. X1 was the fastest acting compound, killing both SNU-449 and Huh7 cells as early as 60 min after treatment (not shown). Maximal effects on cell death after X1 were found 1 and 2 d after treatment in Huh7 and SNU-449 cells, respectively (Fig. 3A-B). By contrast, maximal cell killing for SC18 and sorafenib was observed after 6 d and 8 d, respectively (Fig. 3A-B).

Fig. 3. The cytotoxic effect of X1, SC18, and sorafenib is time dependent.

Fig. 3.

Open in a new tab

SNU-449 (A) and Huh7 (B) cells were treated with X1, SC18 or sorafenib for 12 d. Cell death was evaluated as described in Methods. Representative images on right panels show calcein positive (green) and propidium iodide positive (red) cells. Scale bars: 125 μm (SNU-449) and 100 μm (Huh7). Each curve represents avg ± SD of three independent experiments. **p < 0.01, ***p < 0.001 vs. vehicle (veh).

3.3. Cell cycle progression

At concentrations that inhibited clonogenicity in 50% of the cell population, X1 and sorafenib also promoted SNU-449 cell death by ~50% after 2 d and 8 d, respectively. However, at concentrations that inhibited clonogenic capacity by 50% (~25 μM), SC18 induced cell death was less than 10%, even after 12 d of treatment (Fig. 3 A). To determine if decreased clonogenic capacity and extended lag time to induce minimal cell death by SC18 were associated to alterations in progression through the cell cycle, we synchronized SNU-449 cells by serum starving 24 h before treatment with SC18 (0, 3, 10, and 25 μM) for 2 d. SC18 arrested cells in the G0/G1 phase, concurrent with a reduction in cells in G2/M phase (Fig. 4A-B). We also determined in the same synchronized cells, that SC18 at 25 μM decreased the expression of the proliferation marker Ki67 (Fig. 4 C).

Fig. 4. SC18 arrests cells in G0/G1 and decreases Ki67 positive cells.

Fig. 4.

Open in a new tab

Synchronized SNU-449 cells were treated with SC18 for 48 h. Cell cycle and Ki67 expression were evaluated as described in Methods. (A) Cell cycle distribution after SC18. Bar indicates avg ± SD; (B) Flow cytometry representative histograms; (C) Quantitative analysis of Ki67+ cells. Data from 8 randomly selected fields per experiment containing a minimum of 50 cells (left panel); Light green in the Hoechst-stained nuclei indicates Ki67 labeling (right panel). Data from four independent experiments. *p < 0.05, ***p < 0.001 vs. Vehicle (SC18 0 μM).

3.4. Synergism

3.4.1. Clonogenicity

To determine if SC18 had a synergistic effect with sorafenib, regorafenib and lenvatinib on the clonogenic capacity of SNU-449 and Huh7 cells, we used SC18 in combination with sorafenib at constant ratios for 10 d. We calculated synergism using the Chou-Talalay method (Chou, 2010). Our results demonstrated that SC18 was synergistic with sorafenib (CI < 1) on the clonogenic capacity at 0.25–2 IC50s of each compound in SNU-449 cells, and 0.5–2 IC50s in Huh7 cells (Fig. 5 A). We also showed that SC18 had a synergistic effect with regorafenib at 0.25–2 IC50 and lenvatinib at 0.5–2 (Fig. 5 B). We did not assay X1 and sorafenib on clonogenicity because X1 alone caused 50% of cell death 2 d after treatment at concentrations similar to the IC50 for inhibition of clonogenicity (Fig. 3 A). Thus, the killing effect of X1 would have masked the effects on the clonogenic capacity of the combination with sorafenib.

Fig. 5. SC18 synergizes with sorafenib, regorafenib and lenvatinib to inhibit clonogenicity.

Fig. 5.

Open in a new tab

(A) SNU-449 and Huh7 cells were treated with SC18 and sorafenib at constant ratios. Clonogenic capacity was evaluated as described in Methods. (B) SNU-449 cells were treated with SC18 in combination with lenvatinib and regorafenib. Coefficient of interaction (CI) was calculated using the Chou-Talalay method and expressed as function of the affected fraction (Fa). CI < 1 indicates synergism. Data from six independent experiments expressed as avg ± SD. *p < 0.05; **p < 0.01; ***p < 0.001. Sor: sorafenib; Len: lenvatinib; Reg: regorafenib.

3.4.2. Cell death

We also determined if synergism occurred on cell death induced by X1 or SC18 combined with sorafenib. The combination of SC18 and sorafenib was synergistic in both cell lines at 0.5–1 IC50s for each compound (Fig. 6 A-B). Noticeably, the synergistic effect was maximal (almost 100% cell death) 4 d after treatment at 0.5–1 IC50s. The onset of cell killing after the combination was much faster compared to the cell death induced by SC18 (6 d) or sorafenib (8 d) as sole treatments (Fig. 3). When we used the combination of X1 and sorafenib in SNU-449 cells, we observed maximal synergistic effects 4 d after treatment at concentrations between 0.5 and 1 IC50 for each, which was slower than X1 alone (2 d) and faster than sorafenib alone (8 d). The synergism between X1 and sorafenib on cell death could only be determined in SNU-449 cells because Huh7 cells were killed and degraded within the first 24 h after treatment. This phenomenon prevented the use of any reliable counting method to establish a CI.

Fig. 6. X1 and SC18 have a synergistic effect with sorafenib on cell death.

Fig. 6.

Open in a new tab

(A–B) SNU-449 and Huh7 cells were treated with X1 or SC18 in combination with sorafenib at constant ratios over 12 d. Cell death was evaluated as described in Methods. Coefficient of interaction (CI) was calculated using the Chou-Talalay method and expressed as function of the affected fraction (Fa). CI < 1 indicates synergism. Data from three independent experiments is expressed as avg ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Sor: sorafenib.

3.5. Tumor spheroids

2D cell cultures do not mimic the complexity of tumors that grow in a three-dimensional (3D) conformation (Baker and Chen, 2012; Kimlin et al., 2013; Weiswald et al., 2015). To determine if the effects on cell survival of X1, SC18 and sorafenib alone or in combination using 3D structures were comparable to 2D cultures, we generated three-dimensional multicellular tumor spheroids of Huh7 cells. MCTs are clusters of self-assembled groups of cells formed from single-cell suspensions maintained in ultra-low attachment plates. MCTs from Huh7 had different diameters and a regular surface. After treatment, cells in the periphery of the MCT formed blebs, the surface became irregular, and the diameter decreased (Fig. 7 A). X1, like in 2D cultures, caused cell disintegration characterized by the presence of abundant cell debris. Decreased area and cell viability were concurrent with the morphological changes in MCT. As shown in Fig. 7, SC18, X1 and Sor as sole treatments at concentrations of 0.5 IC50s reduced MCTs diameter (Fig. 7 B) and increased cell death (Fig. 7 C). Moreover, the combination of SC18 or X1 with Sor enhanced the inhibitory effects.

Fig. 7. X1, SC18 and sorafenib inhibit proliferation and promote cell death in tumor spheroids.

Fig. 7.

Open in a new tab

Multicellular tumor spheroids of Huh7 cells were treated with X1, SC18 or sorafenib alone or in combinations of 0.5 IC50s for 6 d. (A) Brightfield images of spheroids. Scale bar: 125 μm. (B) Changes in the area of spheroids. (C) Cell death calculated as described in Methods. Avg ± SD of three independent experiments. **p < 0.01, ***p < 0.001, ****p < 0.0001 vs. vehicle (veh); ###p < 0.001 vs. SC18; + p < 0.05 vs. X1; & p < 0.05, && p < 0.01 vs Sor. Sor: sorafenib.

4. Discussion

VDAC controls mitochondrial metabolism by regulating the flux of oxidizable substrates, ADP, Pi, and glycolytic ATP entering mitochondria, and mitochondrial ATP moving to the cytosol (Colombini, 2004). VDAC conductance in tumor cells is regulated by free tubulin and NADH, among other endogenous regulators (Böhm et al., 2020; Maldonado et al., 2010, 2013; Rostovtseva et al., 2008). Because of the global effects on mitochondrial metabolism, VDAC is an attractive pharmacological target (Fang and Maldonado, 2017; Heslop et al., 2021; Maldonado, 2017; Rovini et al., 2022). However, VDAC isoform specificity and lack of identifiable druggable sites, have been major obstacles for finding drugs to target the channel.

We have identified two small molecules that target VDAC by unrelated mechanisms. X1, identified in a cell-based screen, is an erastin-like compound that blocks the inhibitory effect of tubulin on VDAC conductance favoring the opening of the channel (DeHart et al., 2018b). Free tubulin in tumor cells closes VDAC 1 and 2, but not VDAC 3 (Maldonado et al., 2013). We have shown that X1 increases mitochondrial metabolism and ROS formation leading to JNK-mediated mitochondrial dysfunction, and reversal of glycolysis in HCC cells (DeHart et al., 2018a; Heslop et al., 2020). The combination of oxidative stress and reversal of the Warburg phenotype constitutes a double hit to induce cancer cell death (Fang and Maldonado, 2017). Although it has been long known that NADH regulates VDAC gating (Lee et al., 1996; Zizi et al., 1994), only recently, a binding site which is a specific pocket for NADH has been identified in VDAC1 (Böhm et al., 2020). The sequence of residues in the NADH-binding pocket in VDAC1 is fully conserved in VDAC2 and 3. Although it is very likely that the sequence and stereochemistry of the NADH-binding pocket in cancer cells be identical to primary cells, experimental data to support that claim is still missing. Following an in silico drug discovery strategy to find small molecules to occupy the NADH-binding pocket of all VDAC isoforms, we identified the compound SC18. SC18 may prevent VDAC closing by NADH and possibly other endogenous regulators (Heslop et al., 2022a).

Tumor bioenergetics is supported by a dynamic balance between enhanced glycolysis and partially inhibited mitochondrial metabolism (Maldonado, 2017; Rovini et al., 2022). In several tumor types, increased mitochondrial metabolism leads to decreased glycolysis (Chen et al., 2021; Dang, 2012; Eason and Sadanandam, 2016; Liu et al., 2023; Yun et al., 2012). We propose that VDAC opening mechanistically promotes metabolic imbalances that cause reversal of the Warburg phenotype, oxidative stress and arrest of cell proliferation and cell death in HCC, and possibly other types of cancer. Approximately half of patients with advanced stages of HCC receive systemic chemotherapies that are poorly effective. The multikinase inhibitor sorafenib, approved by the FDA as a first-line treatment of HCC in 2007, remained the standard of care for inoperable HCC for a decade (Llovet et al., 2008; Marisi et al., 2018). However, treatment with sorafenib only increases survival for a few months. Other drugs including the tyrosine kinase inhibitors lenvatinib, regorafenib, cabozantinib, and the monoclonal VEGFR2 antibody ramucirab, have been approved for advanced HCC after progression on sorafenib (Bruix et al., 2017; Cheng et al., 2019; Kudo et al., 2018, 2019; Montironi et al., 2019).

Here, we determined that the VDAC targeting small molecules X1 and SC18 had a synergistic effect with sorafenib, regorafenib and lenvatinib. To avoid cell line specific effects, we performed our experiments in the well-differentiated Huh7 and the poorly differentiated SNU-449 hepatocarcinoma cells. We studied the effects of SC18, X1, and the FDA-approved drugs to treat HCC, sorafenib, regorafenib, and lenvatinib as sole treatments on proliferation and cell survival. To determine the ability of a cell to form single colonies, we used a clonogenic assay and calculated an IC50 for each drug (Fig. 1). The quantitative analysis of clonogenic capacity determines the ability of a single cell to proliferate but also depends on how many cells survive a certain treatment (Franken et al., 2006). Thus, we performed parallel experiments to determine cell viability using a combination of calcein and propidium iodide uptake to simultaneously visualize live and dead cells. Because after X1 and to less extent SC18 and sorafenib, some dead cells were quickly detached from the plates, we confirmed the results using a trypan blue assay to count both attached and floating dead cells. The IC50s calculated from the clonogenic inhibition of X1, SC18 and sorafenib were lower compared to the IC50s calculated from the cell death assays. To assess if the inhibitory effects of the three drugs were also time-dependent, we treated both cell lines at increasing concentrations over a maximum of 12 d (Fig. 3). Overall, we observed a rapid increment of cell death when both cell lines were treated with X1 starting as early as 60 min after exposure to the compound. By contrast, SC18 and sorafenib increased cell death 6 d and 8 d after treatment.

At the IC50s for SC18 calculated from the clonogenic assays, more than 95% of Huh7 and SNU-449 cells survived 12 d after treatment. Because we did not observe increased cell death, we hypothesized that the reduction of clonogenic capacity could be associated to an interruption of the cell cycle. We showed that SC18 at 25 μM, arrested SNU-449 cells in G0/G1 phase. To confirm that cell proliferation was inhibited, we also determined the expression of Ki67, a proliferation marker. Expression of Ki67 decreased after SC18. Although investigation of the mechanisms underlying this effect are out of the scope of this work, they might indicate that alterations of the cell cycle are a direct consequence of metabolic changes induced by VDAC opening or through an off-target effect. The cell cycle arrest induced by SC18 could eventually increase the susceptibility to the cytotoxic effects of other chemotherapeutic agents.

A major challenge in HCC treatment is the discovery of new drugs that could be used in combination with preexisting FDA-approved treatments like sorafenib. We have previously demonstrated that both X1 and SC18 cause metabolic and bioenergetics imbalances induced by dysregulation of VDAC opening followed by mitochondrial dysfunction (DeHart et al., 2018a; Heslop et al., 2020, 2022a). By contrast, the mechanisms of action of sorafenib, regorafenib and lenvatinib that inhibit serine/threonine and tyrosine kinases, are not directly related to modulation of mitochondrial metabolism (Bruix et al., 2017; Kudo et al., 2018; Llovet et al., 2008). Thus, we expected that the combination of X1 and SC18 with kinase inhibitors, by targeting different mechanisms, could enhance tumor cell vulnerability. After establishing individual IC50s for SC18, sorafenib, regorafenib and lenvatinib, we assessed if there was a synergistic effect between our experimental drugs X1 and SC18 with conventional chemotherapy for HCC. We used the Chou-Talalay method that determines synergism between two drugs when the coefficient CI is < 1. We established that SC18 had synergistic effects with sorafenib, regorafenib and lenvatinib on the clonogenicity of SNU-449 and Huh7 cells. We also found synergism between SC18 and sorafenib on cell death in both cell lines. The synergistic effect of SC18 with sorafenib also shortened the total time required to achieve maximal effects by ~50% and 100% compared to SC18 and sorafenib alone. This phenomenon could eventually be very useful to reduce the side effects associated with prolonged chemotherapy. The effect of X1 was also synergistic with sorafenib on cell death. Because of the rapid killing induced by X1, synergism was assessed only using cell death assays. Synergism between X1 and sorafenib was observed in SNU-449 cells. The effect on Huh7 cells, that were more sensitive to X1 alone compared to SNU-449 cells was too fast causing cell detachment and cell degradation in less than 24 h. This fact prevented the utilization of imaging or counting methods to accurately assess cell survival to calculate an CI using the Chou-Talalay method.

Because 2D cell cultures lack the organization and architecture that influence growth like hypoxia, limitations to drug penetration, and cellcell interactions, we tested the response to X1, SC18, and sorafenib alone or in combinations using 3D MCTs. We generated MCTs from Huh7 single-cell suspensions that self-assemble when growing in ultra-low attachment plates. Decreases in cell viability were similar compared to 2D cultures 6 d after treatment. We also observed decreased diameter, morphological changes and disintegration of the spheroids in all experimental conditions compared to vehicles. The results confirming that the effects of X1, SC18 or sorafenib and the corresponding combinations, also occur in cells growing in 3D, increase the preclinical value of our work.

Overall, our findings indicate that X1 and SC18 inhibit cell proliferation and induce cell death as sole treatments. Remarkably, both compounds have a synergistic effect with chemotherapeutic agents currently used for treating HCC. Future experimentation will be required to determine if the effects observed in cell lines are also observed in animal models. Thus, X1 and SC18 may become the first of a series of specific VDAC-targeted modulators of mitochondrial metabolism with potential therapeutic value to treat liver cancer.

Funding and acknowledgments

This work was supported, in part, by the US National Institutes of Health (NIH/NCI) RO1 CA184456, South Carolina Translational Research Pilot Project UL1 TR001450-SCTR and Pilot Award in Redox and Oxidative Stress P30 GM140964-01 to E.N.M; the NIH S10 OD028663 and the Chan Zuckerberg Foundation Imaging Scientist Award to M.G. Authors declare no conflicts of interest.

Nonstandard abbreviations

ΔΨm

mitochondrial membrane potential

Oxphos

oxidative phosphorylation

Footnotes

Credit Author Statement

Ventura, C.: hands-on experiments, data processing, figure preparation, writing; Junco, M.: hands-on experiments, data processing, figure preparation, writing; Santiago Valtierra, F.X.: tumor spheroids experiments and data processing; Gooz, M.: imaging methodologies; Zhiwei, Y. and Townsend, D.: cell cycle experiments; Woster, P.W: drug discovery supervision; Maldonado, E.N.: team organization and supervision, experimental designs, data interpretation, writing and final editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.

References

  1. Alam MM, Lal S, FitzGerald KE, Zhang L, 2016. A holistic view of cancer bioenergetics: mitochondrial function and respiration play fundamental roles in the development and progression of diverse tumors. Clin. Transl. Med 5 10.1186/s40169-016-0082-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Azoulay-Zohar H, Israelson A, Abu-Hamad S, Shoshan-Barmatz V, 2004. In self-defence: hexokinase promotes voltage-dependent anion channel closure and prevents mitochondria-mediated apoptotic cell death. Biochem. J 377, 347–355. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Baker BM, Chen CS, 2012. Deconstructing the third dimension-how 3D culture microenvironments alter cellular cues. J. Cell Sci 125, 3015–3024. 10.1242/jcs.079509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bxxmhm R, Amodeo GF, Murlidaran S, Chavali S, Wagner G, Winterhalter M, Brannigan G, Hiller S, 2020. The structural basis for low conductance in the membrane protein VDAC upon β-NADH binding and voltage gating. Structure 28, 206–214.e4. 10.1016/j.str.2019.11.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bruix J, Qin S, Merle P, Granito A, Huang YH, Bodoky G, Pracht M, Yokosuka O, Rosmorduc O, Breder V, Gerolami R, Masi G, Ross PJ, Song T, Bronowicki JP, Ollivier-Hourmand I, Kudo M, Cheng AL, Llovet JM, Finn RS, LeBerre MA, Baumhauer A, Meinhardt G, Han G, 2017. Regorafenib for patients with hepatocellular carcinoma who progressed on sorafenib treatment (RESORCE): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 389, 56–66. 10.1016/S0140-6736(16)32453-9. [DOI] [PubMed] [Google Scholar]
  6. Cassim S, Vučetić M, Ždralević M, Pouyssegur J, 2020. Warburg and beyond: the power of mitochondrial metabolism to collaborate or replace fermentative glycolysis in cancer. Cancers 12. 10.3390/cancers12051119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chen CL, Lin CY, Kung HJ, 2021. Targeting mitochondrial oxphos and their regulatory signals in prostate cancers. Int. J. Mol. Sci 22 10.3390/ijms222413435. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cheng H, Sun G, Chen H, Li Yu, Han Z, Li Yangbing, Zhang P, Yang L, Li Yumin, 2019. Trends in the treatment of advanced hepatocellular carcinoma: immune checkpoint blockade immunotherapy and related combination therapies. Am. J. Cancer Res 9, 1536–1545. [PMC free article] [PubMed] [Google Scholar]
  9. Chou TC, 2010. Drug combination studies and their synergy quantification using the chou-talalay method. Cancer Res. 70, 440–446. 10.1158/0008-5472.CAN-09-1947. [DOI] [PubMed] [Google Scholar]
  10. Cluntun AA, Lukey MJ, Cerione RA, Locasale JW, 2017. Glutamine metabolism in cancer: understanding the heterogeneity. Trends Cancer 3, 169–180. 10.1016/j.trecan.2017.01.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Colombini M, 2016. The VDAC channel: molecular basis for selectivity. Biochim. Biophys. Acta Mol. Cell Res 1863, 2498–2502. 10.1016/j.bbamcr.2016.01.019. [DOI] [PubMed] [Google Scholar]
  12. Colombini M, 2004. VDAC: the channel at the interface between mitochondria and the cytosol. Mol. Cell. Biochem 256, 107–115. [DOI] [PubMed] [Google Scholar]
  13. Dang CV, 2012. Links between metabolism and cancer. Genes Dev. 26, 877–890. 10.1101/gad.189365.112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Das S, Wong R, Rajapakse N, Murphy E, Steenbergen C, 2008. Glycogen synthase kinase 3 inhibition slows mitochondrial adenine nucleotide transport and regulates voltage-dependent anion channel phosphorylation. Circ. Res 103, 983–991. 10.1161/CIRCRESAHA.108.178970. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. De Pinto V, Guarino F, Guarnera A, Messina A, Reina S, Tomasello FM, Palermo V, Mazzoni C, 2010. Characterization of human VDAC isoforms: a peculiar function for VDAC3? Biochim. Biophys. Acta Bioenerg 1797, 1268–1275. 10.1016/j.bbabio.2010.01.031. [DOI] [PubMed] [Google Scholar]
  16. DeHart DN, Fang D, Heslop KA, Li L, Lemasters JJ, Maldonado EN, 2018a. Opening of voltage dependent anion channels promotes reactive oxygen species generation, mitochondrial dysfunction and cell death in cancer cells. Biochem. Pharmacol 148, 155–162. 10.1016/j.bcp.2017.12.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. DeHart DN, Lemasters JJ, Maldonado EN, 2018b. Erastin-like anti-warburg agents prevent mitochondrial depolarization induced by free tubulin and decrease lactate formation in cancer cells. SLAS Discov 23, 23–33. 10.1177/2472555217731556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Dhar S, Lippard SJ, 2009. Mitaplatin, a potent fusion of cisplatin and the orphan drug dichloroacetate. Proc. Natl. Acad. Sci. USA 106, 22199–22204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Duraj T, García-romero N, Carrión-navarro J, Madurga R, de Mendivil AO, Pratacin R, Garcia-cañamaque L, Ayuso-sacido A, 2021. Beyond the warburg effect: oxidative and glycolytic phenotypes coexist within the metabolic heterogeneity of glioblastoma. Cells 10, 1–23. 10.3390/cells10020202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Eason K, Sadanandam A, 2016. Molecular or metabolic reprograming: what triggers tumor subtypes? Cancer Res. 10.1158/0008-5472.CAN-16-0141. [DOI] [PubMed] [Google Scholar]
  21. Erstad DJ, Tanabe KK, 2017. Hepatocellular carcinoma: early-stage management challenges. J. Hepatocell. Carcinoma 4, 81–92. 10.2147/jhc.S107370. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Fang D, Maldonado EN, 2017. VDAC regulation: a mitochondrial target to stop cell proliferation. Adv. Cancer Res 176, 139–148. 10.1016/j.physbeh.2017.03.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Franken NAP, Rodermond HM, Stap J, Haveman J, Van Bree C, 2006. Clonogenic assay of cells in vitro. Nat. Protoc 1, 2315–2319. 10.1038/nprot.2006.339. [DOI] [PubMed] [Google Scholar]
  24. Griguer CE, Oliva CR, Gillespie GY, 2005. Glucose metabolism heterogeneity in human and mouse malignant glioma cell lines. J. Neuro Oncol 74, 123–133. 10.1007/s11060-004-6404-6. [DOI] [PubMed] [Google Scholar]
  25. Heslop KA, Burger P, Kappler C, Solanki AK, Gooz M, Peterson YK, Mills C, Benton T, Duncan SA, Woster PM, Maldonado EN, 2022a. Small molecules targeting the NADH-binding pocket of VDAC modulate mitochondrial metabolism in hepatocarcinoma cells. Biomed. Pharmacother 150 10.1016/j.biopha.2022.112928. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Heslop KA, Milesi V, Maldonado EN, 2021. VDAC modulation of cancer metabolism: advances and therapeutic challenges. Front. Physiol 12 10.3389/fphys.2021.742839. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Heslop KA, Rovini A, Gooz M, Maldonado EN, 2022b. Fast determination of mitochondrial metabolism and respiratory complex activity in permeabilized and intact cells. Methods Mol. Biol 2497, 1–10. 10.1007/978-1-0716-2309-1_1. [DOI] [PubMed] [Google Scholar]
  28. Heslop KA, Rovini A, Hunt EG, Fang D, Morris ME, Christie CF, Gooz MB, DeHart DN, Dang Y, Lemasters JJ, Maldonado EN, 2020. JNK activation and translocation to mitochondria mediates mitochondrial dysfunction and cell death induced by VDAC opening and sorafenib in hepatocarcinoma cells. Biochem. Pharmacol 171 10.1016/j.bcp.2019.113728. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Heuser C, Renner K, Kreutz M, Gattinoni L, 2023. Targeting lactate metabolism for cancer immunotherapy - a matter of precision. Semin. Cancer Biol 88, 32–45. 10.1016/j.semcancer.2022.12.001. [DOI] [PubMed] [Google Scholar]
  30. Hiller S, Abramson J, Mannella C, Wagner G, Zeth K, 2010. The 3D structures of VDAC represent a native conformation. Trends Biochem. Sci 35, 514–521. 10.1016/j.tibs.2010.03.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Jin H, Wang S, Zaal EA, Wang C, Wu H, Bosma A, Jochems F, Isima N, Jin G, Lieftink C, Beijersbergen R, Berkers CR, Qin W, Bernards R, 2020. A powerful drug combination strategy targeting glutamine addiction for the treatment of human liver cancer. Elife 9, 1–20. 10.7554/eLife.56749. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Jose C, Bellance N, Rossignol R, 2011. Choosing between glycolysis and oxidative phosphorylation: a tumor’s dilemma? Biochim. Biophys. Acta Bioenerg 1807, 552–561. 10.1016/j.bbabio.2010.10.012. [DOI] [PubMed] [Google Scholar]
  33. Kimlin LC, Casagrande G, Virador VM, 2013. In vitro three-dimensional (3D) models in cancer research: an update. Mol. Carcinog 52, 167–182. 10.1002/mc.21844. [DOI] [PubMed] [Google Scholar]
  34. Kudo M, Finn RS, Qin S, Han KH, Ikeda K, Piscaglia F, Baron A, Park JW, Han G, Jassem J, Blanc JF, Vogel A, Komov D, Evans TRJ, Lopez C, Dutcus C, Guo M, Saito K, Kraljevic S, Tamai T, Ren M, Cheng AL, 2018. Lenvatinib versus sorafenib in first-line treatment of patients with unresectable hepatocellular carcinoma: a randomised phase 3 non-inferiority trial. Lancet 391, 1163–1173. 10.1016/S0140-6736(18)30207-1. [DOI] [PubMed] [Google Scholar]
  35. Kudo M, Ueshima K, Chan S, Minami T, Chishina H, Aoki T, Takita M, Hagiwara S, Minami Y, Ida H, Takenaka M, Sakurai T, Watanabe T, Morita M, Ogawa C, Wada Y, Ikeda M, Ishii H, Izumi N, Nishida N, 2019. Lenvatinib as an initial treatment in patients with intermediate-stage hepatocellular carcinoma beyond up-to-seven criteria and child–pugh a liver function: a proof-of-concept study. Cancers 11. 10.3390/cancers11081084. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lee AC, Xu X, Colombini M, 1996. The role of pyridine dinucleotides in regulating the permeability of the mitochondrial outer membrane. J. Biol. Chem 271, 26724–26731. 10.1074/jbc.271.43.26724. [DOI] [PubMed] [Google Scholar]
  37. Liu Y, Cao Y, Zhang W, Bergmeier S, Qian Y, Akbar H, Colvin R, Ding J, Tong L, Wu S, Hines J, Chen X, 2012. A small-molecule inhibitor of glucose transporter 1 downregulates glycolysis, induces cell-cycle arrest, and inhibits cancer cell growth in vitro and in vivo. Mol. Cancer Therapeut 11, 1672–1682. 10.1158/1535-7163.MCT-12-0131. [DOI] [PubMed] [Google Scholar]
  38. Liu Y, Sun Y, Guo Y, Shi X, Chen X, Feng W, Wu LL, Zhang J, Yu S, Wang Y, Shi Y, 2023. An overview: the diversified role of mitochondria in cancer metabolism. Int. J. Biol. Sci 19, 897–915. 10.7150/ijbs.81609. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Llovet JM, Kelley RK, Villanueva A, Singal AG, Pikarsky E, Roayaie S, Lencioni R, Koike K, Zucman-Rossi J, Finn RS, 2021. Hepatocellular carcinoma. Nat. Rev. Dis. Prim 7 (1), 6. 10.1038/s41572-020-00240-3. [DOI] [PubMed] [Google Scholar]
  40. Llovet JM, Montal R, Sia D, Finn RS, 2018. Molecular therapies and precision medicine for hepatocellular carcinoma. Nat. Rev. Clin. Oncol 15, 599–616. 10.1038/s41571-018-0073-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Llovet JM, Ricci S, Mazzaferro V, Hilgard P, Gane E, Blanc J-F, Cosme de Oliverira A, Santoro A, Raoul J-L, Forner A, Schwartz M, Porta C, Zeuzem S, Bolodni L, Greten TF, Galle PR, Seitz J-F, Borbath I, Haussinger D, Giannaris T, Shan M, Moscovici M, Voliotis D, Bruix J, 2008. Sorafenib in advanced hepatocellular carcinoma. N. Engl. J. Med [DOI] [PubMed] [Google Scholar]
  42. Ma Y, Temkin SM, Hawkridge AM, Guo C, Wang W, Wang XY, Fang X, 2018. Fatty acid oxidation: an emerging facet of metabolic transformation in cancer. Cancer Lett. 435, 92–100. 10.1016/j.canlet.2018.08.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Maldonado EN, 2017. VDAC-tubulin, an anti-Warburg pro-oxidant switch. Front. Oncol 7 10.3389/fonc.2017.00004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Maldonado EN, Patnaik J, Mullins MR, Lemasters JJ, 2010. Free tubulin modulates mitochondrial membrane potential in cancer cells. Cancer Res. 70, 10192–10201. 10.1158/0008-5472.CAN-10-2429. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Maldonado EN, Sheldon KL, Dehart DN, Patnaik J, Manevich Y, Townsend DM, Bezrukov SM, Rostovtseva TK, Lemasters JJ, 2013. Voltage-dependent anion channels modulate mitochondrial metabolism in cancer cells: regulation by free tubulin and erastin. J. Biol. Chem 288, 11920–11929. 10.1074/jbc.M112.433847. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Marisi G, Cucchetti A, Ulivi P, Canale M, Cabibbo G, Solaini L, Foschi FG, De Matteis S, Ercolani G, Valgiusti M, Frassineti GL, Scartozzi M, Gardini AC, 2018. Ten years of sorafenib in hepatocellular carcinoma: are there any predictive and/or prognostic markers? World J. Gastroenterol 24, 4152–4163. 10.3748/wjg.v24.i36.4152. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Marrero JA, Kulik LM, Sirlin CB, Zhu AX, Finn RS, Abecassis MM, Roberts LR, Heimbach JK, 2018. Diagnosis, staging, and management of hepatocellular carcinoma: 2018 practice guidance by the American association for the study of liver diseases. Hepatology 68, 723–750. 10.1002/hep.29913. [DOI] [PubMed] [Google Scholar]
  48. Montironi C, Montal R, Llovet JM, 2019. New drugs effective in the systemic treatment of hepatocellular carcinoma. Clin. Liver Dis 14, 56–61. 10.1002/cld.796. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Nakashima RA, Paggi MG, Pedersen PL, 1984. Contributions of glycolysis and oxidative phosphorylation to adenosine 5’-triphosphate production in AS-30D hepatoma cells. Cancer Res. 44, 5702–5706. [PubMed] [Google Scholar]
  50. Pajak B, Siwiak E, Sołtyka M, Priebe A, Zieliński R, Fokt I, Ziemniak M, Jaśkiewicz A, Borowski R, Domoradzki T, Priebe W, 2020. 2-Deoxy-D-Glucose and its analogs: from diagnostic to therapeutic agents. Int. J. Mol. Sci 21 10.3390/ijms21010234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Rho JM, Shao LR, Stafstrom CE, 2019. 2-deoxyglucose and beta-hydroxybutyrate: metabolic agents for seizure control. Front. Cell. Neurosci 13 10.3389/fncel.2019.00172. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Rostovtseva T, Colombini M, 1997. VDAC channels mediate and gate the flow of ATP: implications for the regulation of mitochondrial function. Biophys. J 72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Rostovtseva TK, Sheldon KL, Hassanzadeh E, Monge C, Saks V, Bezrukov SM, Sackett DL, 2008. Tubulin binding blocks mitochondrial voltage-dependent anion channel and regulates respiration. Proc. Natl. Acad. Sci. USA 105, 18746–18751. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Rovini A, Heslop KA, Maldonado EN, 2022. Mitochondria and tumor metabolic flexibility: mechanisms and therapeutic perspectives. In: Kenakin T (Ed.), Comprehensive Pharmacology. Elsevier, pp. 493–510. [Google Scholar]
  55. Sheldon KL, Maldonado EN, Lemasters JJ, Rostovtseva TK, Bezrukov SM, 2011. Phosphorylation of voltage-dependent anion channel by serine/threonine kinases governs its interaction with tubulin. PLoS One 6. 10.1371/journal.pone.0025539. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Stacpoole PW, 2017. Therapeutic targeting of the pyruvate dehydrogenase complex/pyruvate dehydrogenase kinase (PDC/PDK) Axis in cancer. J. Natl. Cancer Inst 109 10.1093/jnci/djx071. [DOI] [PubMed] [Google Scholar]
  57. Sukjoi W, Ngamkham J, Attwood PV, Jitrapakdee S, 2021. Targeting cancer metabolism and current anti-cancer drugs. In: Advances in Experimental Medicine and Biology. Springer, pp. 15–48. 10.1007/978-3-030-55035-6_2. [DOI] [PubMed] [Google Scholar]
  58. Tsujimoto Y, Shimizu S, 2000. VDAC regulation by the Bcl-2 family of proteins. Cell Death Differ. 7, 1174–1181. [DOI] [PubMed] [Google Scholar]
  59. Ujwal R, Cascio D, Colletier J-P, Faham S, Zhang J, Toro L, Ping P, Abramson J, 2008. The crystal structure of mouse VDAC1 at 2.3 Å resolution reveals mechanistic insights into metabolite gating. Proc. Natl. Acad. Sci. USA 105, 17742–17747. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Villanueva A, 2019. Hepatocellular carcinoma. N. Engl. J. Med 380, 1450–1462. 10.1056/NEJMra1713263. [DOI] [PubMed] [Google Scholar]
  61. Warburg O, 1956. On the origin of cancer cells. Science 123, 3191, 80. [DOI] [PubMed] [Google Scholar]
  62. Warburg O, Wind F, Negelein E, 1927. The metabolism of tumors in the body. J. Gen. Physiol 8, 519–530. 10.1097/00000441-193107000-00022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Weiswald LB, Bellet D, Dangles-Marie V, 2015. Spherical cancer models in tumor biology. Neoplasia 17, 1–15. 10.1016/j.neo.2014.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Xu JQ, Fu YL, Zhang J, Zhang KY, Ma J, Tang JY, Zhang ZW, Zhou ZY, 2022. Targeting glycolysis in non-small cell lung cancer: promises and challenges. Front. Pharmacol 13 10.3389/fphar.2022.1037341. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Yakisich JS, Azad N, Kaushik V, Iyer AKV, 2019. The biguanides metformin and buformin in combination with 2-Deoxy-glucose or WZB-117 inhibit the viability of highly resistant human lung cancer cells. Stem Cell. Int 2019 10.1155/2019/6254269. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Yamashita T, Kudo M, Ikeda K, Izumi N, Tateishi R, Ikeda M, Aikata H, Kawaguchi Y, Wada Y, Numata K, Inaba Y, Kuromatsu R, Kobayashi M, Okusaka T, Tamai T, Kitamura C, Saito K, Haruna K, Okita K, Kumada H, 2020. REFLECT—a phase 3 trial comparing efficacy and safety of lenvatinib to sorafenib for the treatment of unresectable hepatocellular carcinoma: an analysis of Japanese subset. J. Gastroenterol 55, 113–122. 10.1007/s00535-019-01642-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Yau T, Kang YK, Kim TY, El-Khoueiry AB, Santoro A, Sangro B, Melero I, Kudo M, Hou MM, Matilla A, Tovoli F, Knox JJ, Ruth He A, El-Rayes BF, Acosta-Rivera M, Lim HY, Neely J, Shen Y, Wisniewski T, Anderson J, Hsu C, 2020. Efficacy and safety of nivolumab plus ipilimumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib: the CheckMate 040 randomized clinical trial. JAMA Oncol. 6 10.1001/jamaoncol.2020.4564. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Yun J, Johnson JL, Hanigan CL, Locasale JW, 2012. Interactions between epigenetics and metabolism in cancers. Front. Oncol 2 10.3389/fonc.2012.00163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Zizi M, Forte M, Blachly-Dyson E, Colombini M, 1994. NADH regulates the gating of VDAC, the mitochondrial outer membrane channel. J. Biol. Chem 269, 1614–1616. 10.1016/s0021-9258(17)42070-9. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data will be made available on request.

RESOURCES