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. 2020 Oct 5;9:e56749. doi: 10.7554/eLife.56749

A powerful drug combination strategy targeting glutamine addiction for the treatment of human liver cancer

Haojie Jin 1,2,†, Siying Wang 1,†, Esther A Zaal 3,†, Cun Wang 1,2, Haiqiu Wu 4, Astrid Bosma 2, Fleur Jochems 2, Nikita Isima 2, Guangzhi Jin 5, Cor Lieftink 2, Roderick Beijersbergen 2, Celia R Berkers 3,6,✉, Wenxin Qin 1,✉, Rene Bernards 2,✉
Editors: Matthew G Vander Heiden7, Jonathan A Cooper8
PMCID: PMC7535927  PMID: 33016874

Abstract

The dependency of cancer cells on glutamine may be exploited therapeutically as a new strategy for treating cancers that lack druggable driver genes. Here we found that human liver cancer was dependent on extracellular glutamine. However, targeting glutamine addiction using the glutaminase inhibitor CB-839 as monotherapy had a very limited anticancer effect, even against the most glutamine addicted human liver cancer cells. Using a chemical library, we identified V-9302, a novel inhibitor of glutamine transporter ASCT2, as sensitizing glutamine dependent (GD) cells to CB-839 treatment. Mechanically, a combination of CB-839 and V-9302 depleted glutathione and induced reactive oxygen species (ROS), resulting in apoptosis of GD cells. Moreover, this combination also showed tumor inhibition in HCC xenograft mouse models in vivo. Our findings indicate that dual inhibition of glutamine metabolism by targeting both glutaminase and glutamine transporter ASCT2 represents a potential novel treatment strategy for glutamine addicted liver cancers.

Research organism: Human

Introduction

Liver cancer is one of the most common malignant tumors worldwide and ranks as the fourth leading cause of cancer death (Bray et al., 2018). Due to the rapid progress and delayed diagnosis, most liver cancer patients are diagnosed at an advanced stage, which leaves very few treatment options and poor prognosis. Currently approved agents, such as sorafenib and lenvatinib, provide only modest survival benefits to hepatocellular carcinoma (HCC) patients with low response rates (Llovet et al., 2008; Kudo et al., 2018). Therefore, there is an urgent need for novel therapies that target new critical dependencies of liver cancer.

Metabolic reprogramming contributes to tumor development and introduces metabolic liabilities that can be exploited to treat cancer. Despite significant efforts to develop drugs targeting metabolic pathways of cancer, clinical success has been very limited for decades. One particular potential vulnerability of cancer cells is the metabolic reprogramming toward aerobic glycolysis, known as the Warburg effect (Ward and Thompson, 2012). However, the clinical use of one compound known to block glycolysis, 2-deoxyglucose (2-DG), is limited by toxicity and insufficient inhibition of tumor growth at tolerable doses (Raez et al., 2013). Recent studies have highlighted a very prominent contribution of glutaminolysis to energy and macromolecule homeostasis in several types of hematological and solid tumors (Spinelli et al., 2017; Vander Heiden and DeBerardinis, 2017). Glutaminolysis converts glutamine to glutamate, which is catalyzed by mitochondrial glutaminase (GLS), and then further converted into tricarboxylic acid (TCA) cycle metabolites, generating ATP and NADPH. Glutaminolysis is also directly involved in the regulation of reactive oxygen species (ROS) homeostasis by providing not only precursors glutamate and cysteine for glutathione (GSH) synthesis but also promoting the production of NADPH via glutamate dehydrogenase (GLUD; Altman et al., 2016). Many tumor types, such as pancreatic cancer (Son et al., 2013), acute myeloid leukemia (AML; Jacque et al., 2015), breast cancer (Gross et al., 2014), and lung cancer (Wang et al., 2018), are particularly dependent on glutamine for proliferation and survival, as glutamine controls oxidative phosphorylation, nucleotide biosynthesis, and redox homeostasis in these cells, and removal of glutamine leads to apoptosis. Recently, it is reported that GLS1, one major isoform of GLS, regulates the stemness properties of HCC, and targeting GLS1 achieved some therapeutic effect against HCC cells (Li et al., 2019).

CB-839 (also known as telaglenastat) is a potent and noncompetitive allosteric GLS1 inhibitor. It exhibits significant anti-proliferative activity in several types of cancer cell lines and xenografts such as triple-negative breast cancer (Gross et al., 2014), lung adenocarcinoma (Galan-Cobo et al., 2019), chondrosarcoma (Peterse et al., 2018), and lymphoma cancer (Xiang et al., 2015). CB-839 is used increasingly in combination to treat cancer. CB-839 overcomes metabolic adaptation to the mTOR inhibitor MLN128 in xenografts and patient-derived xenografts (PDXs) of lung squamous cell carcinomas (Momcilovic et al., 2018). Moreover, the combination of CB-839 and CDK4/6 inhibitors has shown promising results in human esophageal squamous cell carcinoma (Qie et al., 2019). CB-839 is also under evaluation for the treatment of hematological malignancies and solid tumors in several phase I-II clinical trials (Song et al., 2018). However, it is still unclear whether CB-839 has therapeutic potential in liver cancer. Here we show that CB-839 monotherapy is insufficient for the treatment of HCC and identify a novel combination strategy that effectively targets glutamine addiction in liver cancer.

Results

Liver cancer is addicted to glutamine

To determine whether liver cancer is a glutamine (Gln)-dependent tumor type, we cultured a panel of liver cancer cell lines in a medium with 4 mM Gln or without Gln. Both long-term colony formation assay (Figure 1a) and short-term IncuCyte assay (Figure 1b) showed that Gln deprivation impaired the proliferation of most liver cancer cell lines (9 out of 11) in vitro. Accordingly, we defined these 9 cell lines, which need exogenous Gln for efficient proliferation, as Gln dependent (GD) cells, and the other two cell lines as Gln independent (GID) cells. To assess Gln dependency of liver cancer in patients, we analyzed Gln metabolism-related genes in The Cancer Genome Atlas (TCGA) named ‘PENG_GLUTAMINE_DEPRIVATION_DN’, whose expression is considered to be positively correlated with Gln metabolism. The heatmap shows the obvious upregulation of these genes in liver cancer tissues as compared to their corresponding non-cancerous liver tissues in TCGA cohort containing 50 paired HCC samples (Figure 1c). Gene set enrichment approach (GSEA) also showed positive enrichment of genes associated with Gln metabolism in both TCGA and Gene Expression Omnibus (GEO) data (Figure 1d and e). Moreover, SurvExpress survival analysis of 381 TCGA samples indicated that the dysregulation of Gln metabolism genes correlated with poor prognosis of human liver cancer patients (Figure 1f). Taken together, these results indicate that the liver cancers of poor prognosis have upregulated glutamine metabolism, which may indicate an increased requirement for Gln in HCC tumors.

Figure 1. Liver cancer is addicted to glutamine.

Figure 1.

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(a, b) A total of 11 liver cancer cell lines were cultured with 4 mM glutamine (Gln+) or glutamine deprivation (Gln-). Proliferation was assessed by colony formation assay (a) and IncuCyte assay (b), respectively. Liver cancer cell lines were divided into Gln dependent (GD) and Gln independent (GID) subtypes, respectively. (c) Differential expression profiles of Gln metabolism-related genes (Gene Set: PENG_GLUTAMINE_DEPRIVATION_DN) in 50 paired HCC samples in TCGA cohort. Heatmap illustrated the log2 fold-change values of Gln metabolism-related genes between cancerous tissues and their corresponding noncancerous liver tissues. Red color indicates gene upregulation; blue color indicates gene downregulation. Each column indicates a patient; each row indicates a gene. (d, e) Gene set enrichment analysis (GSEA) enrichment of cancerous tissues versus corresponding noncancerous tissues for gene set ‘PENG_GLUTAMINE_DEPRIVATION_DN’ in TCGA (d) and GSE14520, respectively (e). (f) Expression of Gln metabolism-related genes correlated with poor prognosis of liver cancer patients in TCGA. HR: hazard ratio; CI: confidence interval.

Figure 1—source data 1. Liver cancer is addicted to glutamine.

The glutaminase inhibitor CB-839 monotherapy achieves insufficient anti-tumor effect in liver cancer

The glutaminase isoenzyme GLS1 is a key enzyme in Gln metabolism. We first analyzed the expression levels of GLS1 in the GSE14520 cohort (n = 229), which provides data on gene expression for both paired human non-tumor and HCC tissues. The results indicate that mRNA levels of GLS1 are significantly increased in HCC tissues compared to non-tumor tissues (Figure 2a). Among the 229 paired samples, the GLS1 level is increased to more than double in about 55% of HCC patients (Figure 2b). Next, we analyzed the association between GLS1 level and prognosis of liver cancer patients. TCGA data indicated that the high mRNA level of GLS1 correlated with the poor prognosis of human liver cancer patients (Figure 2c). We also analyzed GLS1 expression using a tissue microarray (TMA) containing 377 HCC specimens by IHC analysis. HCC patients were classified into two groups: GLS1low group (n = 175) and GLS1high group (n = 202). The Kaplan-Meier analysis indicates that HCC patients with high protein expression of GLS1 exhibit worse overall survival (OS) and disease-free survival (DFS) as compared to patients with low protein expression of GLS1 (Figure 2d,e and f). To explore the therapeutic effect of GLS1 inhibitor CB-839 on liver cancer cells, we treated the panel of 11 liver cancer cell lines with increasing concentrations of CB-839 in long-term colony formation assays and short-term CellTiter-Blue cell viability assays. We found that CB-839 treatment only severely impaired the proliferation of three GD cell lines (Figure 2g and h). Other six GD cell lines and two GID cell lines only showed little response to CB-839 treatment in vitro (Figure 2g and h). We also analyzed the correlation between CB-839 sensitivity and expression level of its target and found no correlation between protein level of GLS1 and CB-839 sensitivity (Figure 2g, h and i). In addition, the protein level of GLS1 was not correlated to Gln addiction in vitro (Figures 1a, b and 2i), indicating that Gln addiction to liver cancer cells is not dependent on GLS1 level. These data suggest that targeting glutamine metabolism by GLS1 inhibitor CB-839 alone is insufficient for liver cancer therapy.

Figure 2. The glutaminase inhibitor CB-839 monotherapy shows an insufficient anti-tumor effect in liver cancer.

Figure 2.

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(a) mRNA levels of GLS1 in the cohort of GSE14520 (n = 229; probe for GLS1: 203159_at; N: nontumor tissues, T: tumor tissues). Data are represented as mean ± SEM. (b) Log2 fold change of GLS1 mRNA in 229 paired HCC samples in the cohort of GSE14520 (probe for GLS1: 203159_at; N: nontumor tissues, T: tumor tissues). (c) GLS1 expression and Kaplan-Meier OS analysis for patients with HCC in TCGA cohort (n = 364). (d–f) IHC staining analyses of GLS1 were performed in 377 patients with HCC. The patients were divided into two groups: GLS1low group (n = 175) and GLS1high group (n = 202). (d) Typical immunostaining images of GLS1 in GLS1low group and GLS1high group were shown. Scale bars = 100 μm. The Kaplan-Meier analysis for OS (e) and DFS (f) was performed according to GLS1 levels. (g, h) Liver cancer cell lines were treated with increasing concentrations of CB-839. Proliferation and viability were assessed by colony formation assay (g) and CellTiter Blue assay (h), respectively. (i) Lysates of liver cancer cell lines were western blotted for two splice variants of GLS1 (KGA and GAC). HSP90 served as a control. ***p<0.001, Student’s t test.

Figure 2—source data 1. The glutaminase inhibitor CB-839 monotherapy shows insufficient anti-tumor effect in liver cancer.

A compounds screen identifies that ASCT-2 inhibitor V-9302 sensitizes GD liver cancer cells to CB-839 treatment

The data shown above indicate that a significant number of liver cancer cell lines are glutamine dependent but fail to respond to CB-839 treatment. To study this in more detail, we investigated metabolite profiles of two GD liver cancer cell lines, SNU398 and HepG2. A total of 66 named metabolites were identified and mapped to seven major pathways. We found that CB-839 treatment significantly decreased a number of key downstream metabolites involved in Gln metabolism, such as glutamate (GLU), TCA cycle intermediate (α-KG), redox metabolite (glutathione, NADPH) in both cell lines (Figure 3a and b and Figure 3—figure supplement 1). These results indicate that CB-839 efficiently blocks Gln utilization and interferes with the dynamic changes of intermediates in Gln metabolism. Therefore, we hypothesized that CB-839 treatment already caused metabolic vulnerability, which could further be exploited for cancer therapy if co-treated with other anti-metabolic drugs. To prove this, we generated a chemical library consisting of 13 compounds inhibiting a variety of tumor metabolism targets, and tested their ability to enhance the anti-tumor effect of CB-839. Notably, we found that V-9302, a novel inhibitor of Gln transporter ASCT2 (Schulte et al., 2018), is the most potent agent in sensitizing both SNU398 and HepG2 GD liver cancer cells to CB-839 (Figure 3c and d). To study whether this combination has a broad anti-proliferative effect in liver cancer cells, we tested cell viability and proliferation in a panel of liver cancer cell lines after single drug or combination treatment with CB-839 and V-9302 in vitro. Indeed, the combination showed synergistic anti-proliferation effect in GD cell lines, but only showed limited anti-tumor effect in GID cell lines in vitro (Figure 4a,b and c and Figure 4—figure supplement 1). Moreover, similar results were observed in these cell lines when combining V-9302 with another GLS1 inhibitor BPTES (Figure 4—figure supplement 2). These findings suggest that the combination of GLS1 inhibitors and V-9302 could be a novel therapeutic approach for GD liver cancer cells.

Figure 3. A compounds screen identifies ASCT-2 inhibitor V-9302 sensitizing GD liver cancer cells to CB-839 treatment.

(a) Heatmap representation of 66 metabolites between treated and untreated groups. Intracellular metabolite levels measured by LC/MS-MS in SNU398 and HepG2 cells treated with DMSO or CB-839 (SNU398: 4 μM; HepG2: 8 μM) for 4 and 24 hr, respectively. These metabolites were mapped to seven major pathways including those of the glycolytic system, TCA cycle, urea cycle, redox reaction, purine and pyrimidine metabolism. Each column represented a metabolite. Deeper red color represents higher content; conversely, deeper green color represents lower content. (b) Graphic representation of glutamate (GLU), α-ketoglutarate (α-KG), glutathione (GSH), NADPH were shown in the LC/MS-MS screen in a. Data are represented as mean ± SEM, n = 3 independent experiments. (c) Schematic outline of the compounds screen on the basis of CB-839 treatment: Two GD cell lines, SNU398 and HepG2, were first treated with 4 and 8 μM CB-839, respectively. Then, 13 compounds inhibiting a variety of druggable tumor metabolism targets were tested at their IC50 concentrations for 4 d. (d) Heatmap represents the enhanced percentage of viability inhibition by 13 compounds in SNU398 and HepG2, respectively. Deeper red color represents higher enhance; conversely, deeper green color represents lower enhance. Statistical significance was assessed using a Student’s t test. *p<0.05, **p<0.01, ***p<0.001.

Figure 3—source data 1. Intracellular metabolite levels were measued by LC/MS-MS in SNU398 and HepG2 cells treated with DMSO or CB-839.
Figure 3—source data 2. Data related to Figure 3d.

Figure 3.

Figure 3—figure supplement 1. All metabolites shown in Figure 3a were labeled.

Figure 3—figure supplement 1.

Heatmap representation of 66 metabolites between treated and untreated groups. Intracellular metabolite levels measured by LC/MS-MS in SNU398 and HepG2 cells treated with DMSO or CB-839 (SNU398: 4 μM; HepG2: 8 μM) for 4 and 24 hr, respectively.
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Figure 4. Combination of CB-839 and V-9302 shows potential synergy in multiple GD liver cancer cells.

(a-c) Liver cancer cells (four GD cell lines and two GID cell lines) were treated with CB-839, V-9302, or the combination at the indicated concentration. CellTiter Blue viability assays (a), IncuCyte assays (b) and long-term colony formation assays (c) were performed, respectively. Data are represented as mean ± SEM. Statistical significance was assessed using a Student’s t test. *p<0.05, **p<0.01, ***p<0.001.

Figure 4—source data 1. Combination of CB-839 and V-9302 shows potential synergy in multiple GD liver cancer cells.

Figure 4.

Figure 4—figure supplement 1. The combination of CB-839 and V-9302 showed an anti-proliferation effect in GD cell lines in vitro.

Figure 4—figure supplement 1.

Liver cancer cell lines (SNU449, SK-Hep1, Huh6, Hep3B, and SNU387) were treated with CB-839, V-9302, or the combination at the indicated concentration. Long-term colony formation assays were performed.
Figure 4—figure supplement 2. The combination of BPTES and V-9302 showed an anti-proliferation effect in four GD cell lines, but not in two GID cell lines in vitro.

Figure 4—figure supplement 2.

Liver cancer cells (four GD and two GID cell lines) were treated with BPTES, V-9302, or the combination at the indicated concentration. Long-term colony formation assays were performed.
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Combination of CB-839 and V-9302 depletes glutathione and induces lethal ROS level in GD liver cancer cells

The alanine-serine-cysteine transporter, type-2 (ASCT2, encoded by gene SLC1A5), is a sodium-dependent solute carrier protein responsible for the import of neutral amino acids and is the primary transporter of glutamine in cancer cells. Several studies have attributed glutathione (GSH) synthesis and ROS stress with dysregulation of ASCT2 (Schulte et al., 2018; Yoo et al., 2020). To investigate whether the combination of CB-839 and V-9302 can disrupt the ROS balance in liver cancer, we first analyzed the GSH levels after single-drug treatment or combination in SNU398 and HepG2 cells, respectively. The results show that CB-839 or V-9302 alone significantly decreased the level of GSH, while the combination resulted in a further decrease in GSH in both cell lines (Figure 5a). GSH is an important antioxidant that acts as a free radical scavenger upon its reaction with ROS in cells (Bansal and Simon, 2018; Okazaki et al., 2017), raising the possibility that combination of CB-839 and V-9302 may interfere with ROS homeostasis of these liver cancer cells. Analysis of intracellular ROS levels showed that single agent CB-839 or V-9302 only modestly increased the ROS production. However, their combination dramatically increased the already-elevated ROS production, reaching a level that caused severe DNA damage, as evidenced by an increase in γ-H2AX (Figure 5b and c). To determine whether the combination inhibited cell viability and proliferation of liver cancer cells via the excessive ROS production, we treated these cells with the ROS scavenger N-acetyl-l-cysteine (NAC). The results show that NAC treatment rescued the cell viability and proliferation of SNU398 and HepG2 cells in the presence of both CB-839 and V-9302 (Figure 5d and e). Moreover, the strong synergistic induction of apoptosis can be rescued by NAC treatment in both SNU398 and HepG2 cells, as indicated by the IncuCyte caspase-3/7 apoptosis assay (Figure 5f). These results suggest a model explaining the synergistic effect between CB-839 and V-9302 (Figure 5g): GLS1 inhibition by CB-839 reduces mitochondrial Glu level, which decreases TCA-dependent NADPH production, elevating glutathione oxidation. Besides, CB-839 also decreases intracellular Glu that is essential for cystine import by xCT, a cystine/glutamate antiporter, thus cutting down the cysteine conversion that in turn serves as the rate-limiting precursor for GSH biosynthesis. CB-839 treated cells are vulnerable to other perturbations that further deplete glutamine anaplerosis, such as blockage of major Gln transporter ASCT-2 by V-9302. Collectively, these results indicate that the combination of CB-839 and V-9302 achieves a synergistic anti-tumor effect in liver cancer via disrupting the GSH/ROS balance.

Figure 5. Combination of CB-839 and V-9302 depletes GSH and induces lethal ROS levels in GD liver cancer cells.

Figure 5.

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(a) Intracellular GSH levels were measured by LC/MS-MS in SNU398 and HepG2 cells treated with indicated drugs for 48 hr, respectively. (b) ROS levels were measured using the CellROX Deep Red flow cytometry assay. (c) Western blot analysis was performed for γH2AX as a DNA damage marker. HSP90 served as a control. (d, e) Long-term colony formation assay and CellTiter-Blue viability assay show rescued proliferation and viability of SNU398 and HepG2 cells after ROS scavenger N-acetyl-cysteine (NAC) treatment. (f) Caspase-3/7 positive percentages of control, NAC, V-9302, CB-839, the combination, or combination plus NAC treated SNU398 and HepG2 cells in the presence of a caspase-3/7 activatable dye. (g) Schematic showing how the combination of CB-839 and V-9302 decreases GSH and induce apoptosis in liver cancer. All the data in this figure are represented as mean ± SEM. Statistical significance was assessed using a Student’s t test. *p<0.05, **p<0.01, ***p<0.001.

Figure 5—source data 1. Combination of CB-839 and V-9302 depletes GSH and induces lethal ROS level in GD liver cancer cells.

Combined treatment inhibits xenograft growth and induces apoptosis in vivo

To assess the effectiveness of the combination of CB-839 and V-9302 in vivo, SNU398 and MHCC97H cells were injected into nude mice to establish tumors. After tumors reached a size of about 100 mm3, animals were treated with vehicle, CB-839, V-9302, or the combination of both drugs. Results showed that the combination elicited a strong growth inhibition in both SNU398 and MHCC97H xenograft models, while single-drug treatment showed modest anti-tumor effects (Figure 6a,b,c and d). We also measured the body weight of mice during the treatment, and no body weight reduction was observed in all the treatment groups (Figure 6—figure supplement 1), indicating good tolerability for this novel drug combination. IHC analyses showed that treatment with the combination of CB-839 and V-9302 resulted in an obvious decrease in Ki67 positive cells (Figure 6e,f,g and j). In addition, the combination also significantly increased caspase-3 positive cells (Figure 6e,f,h and k) and γH2AX-positive cells (Figure 6e,f,i and l) in tumor tissues, supporting the induction of apoptosis and DNA damage in vivo. Taken together, our xenograft model experiments point out that combination of CB-839 and V-9302 is effective for liver cancer therapy in vivo.

Figure 6. Combined treatment inhibits xenograft growth and induces apoptosis in vivo.

SNU398 and MHCC97H cells were grown as tumor xenografts in BALB/c nude mice. Longitudinal tumor volume progression in SNU398 and MHCC97H tumor-bearing mice treated with vehicle (n = 6), CB-839 (150 mg/kg, oral gavage, twice per day; n = 6), V-9302 (30 mg/kg, intraperitoneal injection; n = 6), or combined therapies (n = 6) for 20 or 15 d, respectively. Growth curve and endpoint tumor volume of SNU398 (a, b) and MHCC97H (c, d) xenografts. (e, f) Representative images of HE, Ki67, cleaved caspase-3, and γH2AX in SNU398 (e) and MHCC97H (f) xenograft models. Scale bars = 50 μm. (g–i) Quantification of Ki67 positive cells (g), cleaved caspase-3 positive cells (h), and γH2AX positive cells (i) in SNU398 xenografts. (j–l) Quantification of Ki67 positive cells (j), cleaved caspase-3 positive cells (k), and γH2AX positive cells (l) in SNU398 xenografts. Data are represented as mean ± SEM. Statistical significance was assessed using a Student’s t test. *p<0.05, **p<0.01, ***p<0.001.

Figure 6—source data 1. Combined treatment inhibits xenograft growth and induces apoptosis in vivo.

Figure 6.

Figure 6—figure supplement 1. Combination of CB-839 and V-9302 showed no reduction of mice weight in vivo.

Figure 6—figure supplement 1.

SNU398 and MHCC97H tumor-bearing mice treated with vehicle (n = 6), CB-839 (150 mg/kg, oral gavage, twice per day; n = 6), V-9302 (30 mg/kg, intraperitoneal injection; n = 6), or combined therapies (n = 6) for 20 or 15 d, respectively. Graph shows mean ± SEM.
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Discussion

Increasing interest in the metabolic vulnerabilities of cancer has given rise to the development of multiple metabolism-targeted therapies targeting diverse aspects of nutrient transport and utilization. For example, de novo pyrimidine synthesis was identified as a metabolic vulnerability of triple-negative breast cancer (TNBC), suggesting that inhibition of pyrimidine synthesis could sensitize TNBC to chemotherapy (Brown et al., 2017). Metabolic vulnerabilities of cancer cells can also be exploited to address drug resistance. Cisplatin-resistant cancer cells were found to be strongly dependent on glutamine for nucleotide biosynthesis and therefore became exquisitely sensitive to treatment with antimetabolites that target nucleoside metabolism (Obrist et al., 2018). These findings highlight the possibility of precisely directing the metabolic therapeutics to certain cancer types through identification of their metabolic vulnerabilities.

Liver cancer remains one of the most difficult cancer types to treat due to a paucity of drugs that target critical dependencies and broad-spectrum kinase inhibitors like sorafenib and lenvatinib provide only a modest survival benefit to HCC patients (Bray et al., 2018; Lau, 2008). Exploiting metabolic vulnerabilities may represent a promising strategy for the treatment of liver cancer. Liver cancer has a metabolic dependency on glutamine. The HGF-MET axis was reported to inhibit pyruvate dehydrogenase complex (PDHC) activity but activate GLS to facilitate glutaminolysis in multiple liver cancer cells (Huang et al., 2019). Another study reported that the downregulation of Sirtuin four in liver cancer promoted HCC tumorigenesis via enhancing glutamine metabolism and regulating ADP/AMP levels (Wang et al., 2019). In addition, MYC-induced mouse liver tumors had increased GLS expression and decreased glutamate-ammonia ligase (GLUL) expression relative to surrounding tissues, exhibiting elevated Gln catabolism (Xiang et al., 2015). Consistently, our study showed that the vast majority of liver cancer cell lines was highly addicted to exogenous Gln in vitro, and the majority of Gln metabolism-related genes was upregulated in tumor tissues of HCC patients. A previous study showed that targeting GLS could attenuate the stemness properties of HCC (Li et al., 2019), indicating the possible application of GLS inhibitors in HCC. However, single-drug treatment of CB-839 only showed a very limited anti-tumor effect in most GD liver cancer cells. High-throughput screenings, such as genome-wide CRISPR screen, should be applied to discover the functionally important genes responsible for glutamine dependence or CB-839 sensitivity in liver cancer. The metabolome analysis revealed that CB-839 treatment already caused strong depletion of several key metabolites involved in Gln metabolism (such as GSH), which did not recover even after 24 hr of drug treatment, indicating a possible persistent metabolic vulnerability in the presence of CB-839. This metabolic vulnerability was further exacerbated by ASCT2 inhibitor V-9302, as a result of a further decrease in GSH levels and a lethal increase in the already-elevated levels of ROS. This synergistic effect may be explained by dual inhibition of Gln metabolism. On the one hand, GLS inhibition by CB-839 prevents glutamate production, a direct precursor of GSH synthesis, as well as cystine/glutamate exchange by xCT, leading to a decrease in cystine, another essential precursor for GSH synthesis. On the other hand, pharmacological blockade of the primary transporter of glutamine ASCT2 can decrease the uptake of extracellular glutamine, leading to the shortage of Gln supply and GSH synthesis (Schulte et al., 2018). However, there is still some debate about the target selectivity of V-9302 for ASCT2. Broer et al (Angelika et al., 2018) reported that V-9302 did not inhibit ASCT2 and glutamine uptake, but rather blocked sodium-neutral amino acid transporter 2 (SNAT2) and the large neutral amino acid transporter 1 (LAT1). Interestingly, it has been reported that, in addition to ASCT2, both SNAT2 and LAT1 also can mediate glutamine uptake in most cancer cells (Bröer and Bröer, 2017). Thus, the depression of glutamine uptake by V-9302 remains to be further investigated. Impressively, we find that the CB-839 induced metabolic vulnerability also can be further enhanced by other several anti-metabolic drugs such as OXPHOS inhibitor IACS-10759, PPP inhibitor DHEA, and PHGDH inhibitor NCT503 (Figure 3d). This observation suggests the possibility of developing other new combination strategies against liver cancer based on CB-839. However, it is worth noting that our in vitro study was performed over physiological glutamine concentrations, occurring at concentrations that may not be experienced in solid tumors.

The liver clears almost all of the portal vein ammonia, converting it into glutamine and urea, preventing entry into the systemic circulation. Chronic liver disease has impaired liver function and usually leads to dysfunction of ammonia metabolism. For example, there is a general correlation between higher levels of ammonia and more severe encephalopathy in cirrhosis (Olde Damink et al., 2002). As a mitochondrial enzyme, GLS plays important roles in liver ammonia metabolism (Botman et al., 2014). There are two distinct isoforms of GLS, GLS1 and GLS2, which possess discrete tissue distribution, structural properties, and molecular regulation. The GLS1 gene encodes two isoforms, kidney-type glutaminase (KGA, long transcript isoform) and the glutaminase C (GAC, short transcript isoform), which are expressed in kidneys and in a variety of other tissues including cancer cells (Katt et al., 2017). In our study, we found that the GLS1 expression level was significantly increased in HCC tissues. The GLS2 gene is strongly expressed and active in periportal hepatocytes, where they generate glutamate and release ammonia for urea synthesis (Katt et al., 2017; Curthoys and Watford, 1995). Interestingly enough, CB-839 selectively inhibits GLS1 but not GLS2 (Gross et al., 2014). Taken together, although many HCC patients co-exist liver disease and thus usually have impaired ammonia metabolism, GLS inhibition by CB-839 treatment is less likely to pose a problem for these patients.

The advantage of metabolism-targeted therapies is the ability to non-invasively assess the activity of the metabolic pathway in mouse models and even in the clinic. Recently, a voltage-sensitive, positron emission tomography (PET) radiotracer known as 18F-BnTP was developed to measure mitochondrial membrane potential in non-small-cell lung cancer. It was found that only mouse tumors with a high uptake of 18F-BnTP showed marked growth suppression when treated with oxidative-phosphorylation inhibitor IACS-010759 (Momcilovic et al., 2019). Another example is the widespread use of a radioisotope labeled glucose analog, [18F]fluoro-2-deoxyglucose (18F-FDG), in clinical use for diagnosing and staging cancer (Kelloff et al., 2005; Nair et al., 2012). 18F-FDG undergoes cellular uptake via the same pathway as glucose but becomes trapped intracellularly after phosphorylation by hexokinase, which can be visualized by PET imaging and used to detect glucose uptake in tumors. In our study, we find that the combination of CB-839 and V-9302 only shows synergy in liver tumors that are addicted to Gln. It suggests that measuring Gln uptake or Gln addiction is of potential importance for selecting patients that might benefit from this combination. One way to accomplish this is to test the Gln dependence of tumor cells in vitro, which might be consistent with their metabolic dependence in vivo. A more accurate and practical approach is developing a Gln-based PET imaging agent. The Gln tracers 5-11C-(2S)-glutamine (11C-Gln) and 18F-(2S,4R)4-fluoroglutamine (18F-(2S,4R)4-FGln) provide useful tools for probing and monitoring in vivo metabolism of Gln (Zhu et al., 2017). Tracer 11C-Gln was shown to visualize glutaminolytic tumors in vivo as a metabolic marker for probing glutamine-addicted tumor (Qu et al., 2012). Similarly, 18F-(2S,4R)4-FGln PET was reported to track cellular Gln pool size in breast cancers with differential GLS activity and applied as a response marker for CB-839 (Zhou et al., 2017). Therefore, the development of isotope-labeled Gln PET imaging may facilitate the translation of the drug combination strategy described here through the identification of those patients that are most likely to benefit.

Materials and methods

Key resources table.

Reagent type
(species)
or resource		 Designation Source or
reference		 Identifiers Additional
information
Cell line (Homo-sapiens) Hep3B ATCC Cat#:HB-8064
Cell line (Homo-sapiens) Huh7 JCRB Cat#:JCRB0403; RRID:CVCL_0336
Cell line (Homo-sapiens) HepG2 ATCC Cat# HB-8065; RRID:CVCL_0027
Cell line (Homo-sapiens) SNU398 ATCC Cat# CRL-2233; RRID:CVCL_0077
Cell line (Homo-sapiens) SNU449 ATCC Cat# CRL-2234; RRID:CVCL_0454
Cell line (Homo-sapiens) Huh6 RCB Cat# RCB1367; RRID:CVCL_4381
Cell line (Homo-sapiens) SK-Hep1 ATCC Cat# HTB-52; RRID:CVCL_0525
Cell line (Homo-sapiens) JHH1 JCRB Cat# NIHS0056; RRID:CVCL_2785
Cell line (Homo-sapiens) SNU387 ATCC Cat# CRL-2237; RRID:CVCL_0250
Cell line (Homo-sapiens) PLC/PRF5 ATCC Cat# CRL-802
Cell line (Homo-sapiens) MHCC97H RRID:CVCL_4972 Liver Cancer Institute of Zhongshan Hospital (Shanghai, China)
Chemical compound, drug CB-839 Selleck Chemicals S7655
Chemical compound, drug BPTES Selleck Chemicals S7753
Chemical compound, drug BAY-876 Selleck Chemicals S8452
Chemical compound, drug AZD3965 Selleck Chemicals S7339
Chemical compound, drug CPI613 Selleck Chemicals S2776
Chemical compound, drug Compound 3 k Selleck Chemical S8616
Chemical compound, drug NCT503 Selleck Chemical S8619
Chemical compound, drug AG221 Selleck Chemical S8205
Chemical compound, drug NLG-8189 Selleck Chemical S7756
Chemical compound, drug IACS-10759 Selleck Chemical S8731
Chemical compound, drug Dapagliflozin Selleck Chemical S1548
Chemical compound, drug 2-DG Selleck Chemical S4701
Chemical compound, drug DHEA Selleck Chemical S2604
Chemical compound, drug ND-646 MedChemExpress HY-101842
Chemical compound, drug V-9302 Probechem Biochemicals 1855871-76-9
Chemical compound, drug N-acetyl cysteine (NAC) Sigma-Aldrich 616-91-1
Antibody Anti-HSP90 (Mouse monoclonal) Santa Cruz Biotechnology sc-13119; RRID:AB_675659 (WB 1:2000)
Antibody Anti-GLS (Rabbit polyclonal) Proteintech Cat# 12855–1-AP, RRID:AB_2110381 (WB 1:1000)
Antibody Anti-γH2AX (Rabbit monoclonal) Cell Signaling Technology Cat# 9718; RRID:AB_2118009 (WB 1:1000)
(IHC 1:200)
Antibody Anti-Ki67
(Rabbit polyclonal)		 Abcam Cat# ab15580; RRID:AB_443209 (IHC 1:200)
Antibody Anti-Cleaved Caspase-3
(Rabbit polyclonal)		 Abcam ab2302 (IHC 1:200)
Commercial assay or kit CellROX Deep Red Flow Cytometry Assay Kit Life Technologies C10491
Software, algorithm javaGSEA desktop application http://software.broadinstitute.org/gsea
Software, algorithm Prism - Graphpad https://www.graphpad.com/scientific-software/prism/
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Cell lines

The human liver cancer cell lines Hep3B, Huh7, HepG2, SNU398, SNU449, Huh6, SK-Hep1, JHH1, SNU387, and PLC/PRF/5 were provided by Erasmus University (Rotterdam, Netherlands). MHCC97H was provided by the Liver Cancer Institute of Zhongshan Hospital (Shanghai, China). The majority of liver cancer cell lines were established from hepatocellular carcinoma (HCC). Among them, SK-Hep1 was established from an endothelial tumor in the liver and Huh6 is a hepatoblastoma cell line. HCC cells were cultured in DMEM with 10% FBS and penicillin/streptomycin (Gibco) at 37°C/5% CO2. All cell lines were tested negative for mycoplasma contamination. The cell lines were authenticated by applying short tandem-repeat (STR) DNA profiling.

Compounds and antibodies

CB-839 (S7655), BPTES (S7753), BAY-876 (S8452), AZD3965 (S7339), CPI613 (S2776), Compound 3 k (S8616), NCT503 (S8619), AG221 (S8205), NLG-8189 (S7756), IACS-10759 (S8731), Dapagliflozin (S1548), 2-DG (S4701) and DHEA (S2604) were purchased from Selleck Chemicals. ND-646 (HY-101842) was purchased from MedChemExpress. V-9302 (1855871-76-9) was purchased from Probechem Biochemicals. N-acetyl cysteine (NAC) was purchased from Sigma. Antibody against HSP90 (sc-13119) was purchased from Santa Cruz Biotechnology. Antibody against two different splice forms of GLS, KGA/GAC, (12855–1-AP) was purchased from Proteintech. Antibody against γH2AX (#9718) was purchased from Cell Signaling. Antibodies against Ki67 (ab15580) and Cleaved caspase-3 (ab2303) were from Abcam.

Protein lysate preparation and immunoblotting

Cells were washed with PBS and lysed with RIPA buffer supplemented with Complete Protease Inhibitor (Roche) and Phosphatase Inhibitor Cocktails II and III (Sigma). Protein quantification was performed with the BCA Protein Assay Kit (Pierce). All lysates were freshly prepared and processed with Novex NuPAGE Gel Electrophoresis Systems (Thermo Fisher Scientific) followed by western blotting.

Long-term colony formation assays

Cells were cultured and seeded onto 6-well plates at a density of 2–10 × 104 cells per well, depending on the growth rate, and were cultured in normal DMEM medium containing 4 mM glutamine (11995073, ThermoFisher), DMEM medium without glutamine (10313021, ThermoFisher), or the indicated drugs for 10–14 d (medium was changed twice a week). Cells were then fixed with 4% formaldehyde in PBS and stained with 0.1% crystal violet diluted in water.

Incucyte cell proliferation assay and apoptosis assay

Indicated cell lines were seeded onto 96-well plates at a density of 1000–8000 cells per well, depending on the growth rate and design of the experiments. About 12 hr after seeding, cells were cultured in medium with drugs of indicated concentrations using the HP D300 Digital Dispenser (HP) and imaged every 4 hr in Incucyte ZOOM (Essen Bioscience). Phase-contrast images were analyzed to detect cell proliferation based on cell confluence. For cell apoptosis, caspase-3/7 green apoptosis assay reagent was added to the culture medium and cell apoptosis was analyzed based on green fluorescent staining of apoptotic cells.

CellTiter blue viability assays

Cell lines were cultured and seeded into 96-well plates (2000–5000 cells per well). After about 12 hr after seeding, drugs with the indicated concentrations were added to liver cancer cells. Cell viability was measured with the CellTiter-Blue assay (Roche) after treatment with the drug for 72 hr. The relative viability of different cell lines in the presence of drug was normalized against control conditions (untreated cells) after subtraction of the background signal.

ROS detection

The cells were treated in the absence or presence of drugs for 48 hr. ROS level in cells was detected using CellROX Deep Red Flow Cytometry Assay Kit (C10491, Life Technologies) according to the manufacturer’s instructions.

Immunohistochemical staining and scoring

HCC specimens were obtained from patients who underwent curative surgery in Eastern Hepatobiliary Hospital of the Second Military Medical University in Shanghai, China. Patients were not subjected to any preoperative anticancer treatment. Ethical approval was obtained from the Eastern Hepatobiliary Hospital Research Ethics Committee and written informed consent was obtained from each patient. Immunohistochemistry (IHC) was performed according to our previous study (Jin et al., 2017). Briefly, formalin-fixed paraffin-embedded samples from HCC patients were probed with the GLS1 antibody (12855–1-AP, Proteintech). Formalin-fixed paraffin-embedded samples were also obtained from xenograft tumors and probed with antibodies against Ki-67 (sc-23900, Santa Cruz), against γH2AX (#9718) and cleaved caspase-3 (ab2303, Abcam). Following incubation with the primary antibodies, positive cells were visualized using DAB+ as a chromogen.

Semiquantitative scores were used to analyze the immunostaining of each HCC case in tissue microarray. Intensity score of staining was categorized into 0 (-), 1 (+), 2 (++), or 3 (+++), denoting negative, weak, moderate, or strong staining, respectively. Percentage score of immunostaining was categorized into 0 (0–5%), 1 (6–25%), 2 (26–50%), 3 (51–75%), or 4 (>76%) based on the percentage of positive cells. Three random microscope fields per tissue were calculated. The sum of intensity and percentage of staining was used as the final score of expression level and determined by the formula: final score = intensity score × percentage score. The final score of ≤4 was defined as a low expression of GLS1 and >4 as a high expression of GLS1.

Metabolomics

Cells were cultured in 6-well plates until 60% confluent. The medium was replaced 24 hr before harvesting. Then, cells were treated with DMSO and CB-839 (4 μM for SNU398 and 8 μM for HepG2) for 4 hr and 24 hr, respectively. After washing with ice-cold PBS, metabolites were extracted from cells in 0.5 mL lysis buffer containing methanol/acetonitrile/dH2O (2:2:1). Samples were spun at 16,000 × g for 15 min at 4°C. Supernatants were collected for LC-MS analysis.

LC-MS analysis was performed on an Exactive mass spectrometer (Thermo Fisher Scientific) coupled to a Dionex Ultimate 3000 autosampler and pump (Thermo Fisher Scientific). The MS operated in polarity-switching mode with spray voltages of 4.5 and −3.5 kV. Metabolites were separated using a SeQuant ZIC-pHILIC HPLC Columns (2.1 mm × 150 mm, 5 μm, guard column 2.1 mm × 20 mm, 5 μm; Merck) using a linear gradient of acetonitrile and eluent A [20 mM (NH4)2CO3, 0.1% NH4OH in ULC/MS grade water (Biosolve)]. The flow rate was set at 150 μL/min. Metabolites were identified and quantified using LCQUANTM Quantitative Software (Thermo Fisher Scientific) on the basis of exact mass within 5 ppm and further validated by concordance with retention times of standards. Metabolites were quantified using LCQUANTM Quantitative Software (Thermo Fisher Scientific). Peak intensities were normalized based on median peak intensity.

Compound screen

SNU398 and HepG2 cells were seeded in 96-well plates, respectively. A total of 13 compounds inhibiting a variety of druggable tumor metabolism targets were independently added into the plates at a certain concentration gradient, and cultured for 4 d. Then IC50 concentrations for each compound were analyzed. Then, SNU398 and HepG2 cells were firstly treated with 4 and 8 μM CB-839, respectively, and further treated with IC50 concentrations of each compound. For the compound has IC50 ≥100 μM, the concentration of 100 μM was used. For 2-DG, a routine concentration at the mM level was used. The synergistic viability inhibition was analyzed using the following formula: synergistic inhibition = inhibition of combination – inhibition of CB-839 × (1 + inhibition of candidate compound).

Xenografts model

All animals were manipulated according to protocols approved by the Shanghai Medical Experimental Animal Care Commission and the Shanghai Cancer Institute. SNU398 cells (8 × 106 cells per mouse) and MHCC97H (6 × 106 cells per mouse) were injected subcutaneously into the right posterior flanks of 6-week-old BALB/c nude mice (six mice per group), respectively. Tumor volume based on caliper measurements was calculated by the modified ellipsoidal formula: tumor volume = ½ length × width. When tumors reached a volume of approximately 50–100 mm3, mice were randomly assigned to 5 d/week treatment with vehicle, CB-839 (150 mg/kg, oral gavage, twice per day), V-9302 (30 mg/kg, intraperitoneal injection), or a drug combination in which each compound was administered at the same dose and scheduled as single agents.

Statistics

Statistical significance was calculated by Student’s t test with two tails. All data are expressed as mean ± SEM. Prism and Microsoft Excel were used to generate graphs and statistical analyses. *p value < 0.05, **p value < 0.01, ***p value < 0.001.

Acknowledgements

This work was supported by grants from the Dutch Cancer Society (KWF) through the Oncode Institute, the National Natural Science Foundation of China (81702838, 81920108025), National Science and Technology Key Project of China (2018ZX10302205), Shanghai Rising-Star Program (19QA1408200). Shanghai Municipal Commission of Health and Family Planning (2018YQ20).

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

Celia R Berkers, Email: C.R.Berkers@uu.nl.

Wenxin Qin, Email: wxqin@sjtu.edu.cn.

Rene Bernards, Email: r.bernards@nki.nl.

Matthew G Vander Heiden, Massachusetts Institute of Technology, United States.

Jonathan A Cooper, Fred Hutchinson Cancer Research Center, United States.

Funding Information

This paper was supported by the following grants:

  • Dutch Cancer Society KWF to Rene Bernards.

  • National Science and Technology Key Project of China 2018ZX10302205 to Haojie Jin.

  • National Natural Science Foundation of China 81702838 to Haojie Jin.

  • National Natural Science Foundation of China 81920108025 to Wenxin Qin.

  • Shanghai Rising-Star Program 19QA1408200 to Haojie Jin.

  • Shanghai Municipal Commission of Health and Family Planning 2018YQ20 to Haojie Jin.

Additional information

Competing interests

No competing interests declared.

Author contributions

Conceptualization, Data curation, Funding acquisition, Investigation, Writing - original draft, Project administration, Writing - review and editing.

Data curation, Validation, Investigation.

Data curation, Software, Investigation, Methodology.

Suggestion.

Data curation, Investigation.

Data curation, Investigation.

Investigation, Suggestion.

Resources, Data curation, Validation, Investigation.

Resources, Data curation, Software, Formal analysis, Methodology.

Data curation, Software, Formal analysis, Validation, Methodology.

Conceptualization, Supervision, Writing - review and editing, Suggestion.

Conceptualization, Supervision, Funding acquisition, Project administration, Writing - review and editing.

Conceptualization, Supervision, Funding acquisition, Project administration, Writing - review and editing.

Conceptualization, Supervision, Funding acquisition, Project administration, Writing - review and editing.

Ethics

Human subjects: Ethical approval was obtained from the Eastern Hepatobiliary Hospital Research Ethics Committee (EHBHKY2014-03-006), and written informed consent was obtained from each patient.

Animal experimentation: All animals were manipulated according to protocols approved by the Shanghai Medical Experimental Animal Care Commission and Shanghai Cancer Institute.

Additional files

Transparent reporting form

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files.

The following previously published datasets were used:

The Cancer Genome Atlas Research Network 2017. Liver Hepatocellular Carcinoma. The Cancer Genome Atlas. TCGA-LIHC

The Cancer Genome Atlas Research Network 2017. Cholangiocarcinoma. The Cancer Genome Atlas. TCGA-CHOL

Wang XW. 2010. Gene expression data of human hepatocellular carcinoma (HCC) NCBI Gene Expression Omnibus. GSE14520

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Decision letter

Editor: Matthew G Vander Heiden1
Reviewed by: Matthew G Vander Heiden2

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

The authors report that glutaminase inhibitors synergize with inhibitors of the neutral amino acid transporter ASCT2 as a potential treatment for hepatocellular carcinoma. They show that small molecule inhibition of ASCT2 synergizes with glutaminase inhibitors in cell lines that do not respond to glutaminase inhibitor alone. They also show that the combination drug treatment decreases glutathione levels and increases intracellular ROS, suggesting that glutathione depletion is a liability of glutamine starvation in liver cancer cells. The drug combination also slows tumor growth in xenograft models suggesting this drug combination warrants further study as a treatment for hepatocellular carcinoma.

Decision letter after peer review:

Thank you for submitting your article "A powerful drug combination strategy targeting glutamine addiction for the treatment of liver cancer" for consideration by eLife. Your article has been reviewed by two peer reviewers, including Matthew G Vander Heiden as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Jonathan Cooper as the Senior Editor.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.

Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in eLife, either of which would be linked to the original paper.

Summary:

The authors report that glutaminase inhibitors synergize with inhibitors of the neutral amino acid transporter ASCT2 as a potential treatment for hepatocellular carcinoma. They show that small molecule inhibition of ASCT2 synergizes with glutaminase inhibitors in cell lines that do not respond to glutaminase inhibitor alone. They also show that the combination drug treatment decreases glutathione levels and increases intracellular ROS, and treatment with the antioxidant N-acetylcysteine provides a partial rescue, suggesting that glutathione depletion is a liability of glutamine starvation in liver cancer cells. Finally, they observe the drug combination results in ~40% lower tumor volume and increased expression of markers of DNA damage and apoptosis in xenograft models.

Essential revisions:

Please see comments from both reviewers below. Please take all suggestions into consideration, but the following should be addressed with either data or discussion in a revised manuscript if possible:

1) Is any information available on how responder cells differ from non-responder cells? If clues can be gleaned from the Cancer Cell Line Encyclopedia and the Dependency Map (depmap.org) based on mutational status or gene expression please discuss this in the paper. Even in the absence of this, considering whether β-catenin mutations correlates with response should be considered per reviewer 2.

2) If possible please address how much V-9302 affects amino acid uptake from the media. If this is not possible, a discussion of the Broer et al. Front Pharmacol 2018 paper suggesting V-9302 is not selective for ASCT2 is warranted.

3) Please clarify how the 4 GD cell lines were chosen to test the drug combination chosen and provide data for other cells, including the GID lines, if available.

4) Please comment on expression of the ASCT1 splice variants, and consider whether this tracks with differential sensitivity (see reviewer 2 comment 1).

5) Please discuss potential issues of targeting glutamine metabolism in patients with HCC who also have impaired ammonia metabolism. If possible test ammonia levels in mice treated with the drug combination.

6) Please clarify how the 13 different "metabolic drugs" were selected to test for synergy with CB-839.

7) Please reconsider interpretation of Figure 5 per reviewer 2 point 5, as the effects on glutathione seems additive, while the effects on ROS and gH2AX are clearly synergistic. This indicates that there could be other reasons for the ROS, gH2AX effects.

8) Reviewer 2 noted that the NAC effect in Figure 5F is not dramatic, and not dose dependent in HEPG2 cells. If feasible, please repeat this experiment and test the effect of NAC alone to compare with the combination affect.

9) Please address additional points of clarification raised by the reviewers:

– All metabolites shown in Figure 3A should be more clearly labeled and/or presented in a supplementary figure.

– Make clear that KGA/GAC represent different splice forms of GLS.

– Discuss levels of glutamine in media relative to those in blood.

– Clarify if Figure 3B is a graphic representation of the findings of the MS screen (shown in Figure 3A), or results of an independent set of experiments for verification.

– Define that xCT = cystine-glutamate antiporter system for a broad audience.

Full reviewer comments for your reference:

Reviewer #1:

Jin et al. report that the glutaminase inhibitor CB-839 synergizes with the putative ASCT2 inhibitor V-9302 in models of hepatocellular carcinoma. They first confirm a previously observed dependency on glutamine in the media for most liver cancer cell lines to grow in culture, and note that this does not translate into sensitivity to the glutaminase inhibitor CB-839. Through a small molecule screen, they find that V-9302, thought to inhibit the neutral amino acid transporter ASCT2, synergizes with CB-839 in four of the seven glutamine-dependent models that did not respond strongly to CB-839. They show that the combination drug treatment decreases glutathione levels and increases intracellular ROS, and treatment with the antioxidant N-acetylcysteine provides a partial rescue, suggesting that glutathione depletion is a major liability of glutamine starvation in liver cancer cells. Finally, they observe the drug combination results in ~40% lower tumor volume and increased expression of markers of DNA damage and apoptosis in xenograft models.

The result that liver cancer cells respond heterogeneously to glutaminase inhibition is interesting, and fits with glutamine having other fates, including glutathione, that are independent of glutaminase. This is a nice point as many equate glutaminase and glutamine dependency, and the point could be better discussed. Some additional suggestions include:

1) A major questions raised by this work is what underlies such differential sensitivity to targeting of glutamine metabolism across the cells considered. While they understandably focus on the responders, is there any clue as to how they differ from the non-responders? Perhaps there are clues from the Cancer Cell Line Encyclopedia and the Dependency Map (depmap.org), or based on mutational status or gene expression?

2) Recent work (Broer et al. Front Pharmacol 2018) has suggested that V-9302 is not selective for ASCT2 and instead binds other amino acid transporters such as SNAT2 and LAT1, and that V-9302 could be acting by inhibiting uptake of other amino acids. Are data available as to how much V-9302 reduces glutamine uptake from media?

3) It would be useful as a resource for the field if all metabolites shown in Figure 3A were labeled and/or presented in a supplementary figure.

4) As a minor point, suggesting CB-839 is increasingly used to treat cancer in combination is potentially misleading. It is being tested in many trials, but is not yet proven to be effective consistently in any cancer.

5) How were the 4 GD cell lines to test the combination chosen? If data are available for other cells, including the GID lines, that information should be included.

6) The authors might consider that invoking TCA-cycle dependent NADPH production as an explanation for why ROS is increased with the drug combination is potentially inconsistent with the NAC rescue. NADPH is also required to keep NAC reduced in cells similarly to GSH.

Reviewer #2:

Following analysis of glutamine metabolism in HCC cell lines, and bioinformatics analysis of human HCC Haojie Jin et al. revealed that combined inhibition of mitochondrial glutaminase (GLS) and the glutamine transporter ASCT2, potently inhibits growth of many human HCC cell lines. Two different inhibitors of ASCT2 were similarly potent in combination with CB-839 – a GLS inhibitor. The combination increased depleted glutathione levels, increased ROS levels, and increased DNA damage marker levels and apoptosis. The latter were dependent on ROS as NAC treatment abolished the DDR and apoptosis. The same combination was tested in vivo, with seemingly good tolerability, and resulted in a twofold reduction in tumor volume, which was much better than each of the drugs alone.

Tumor metabolism is gaining interest lending hope that metabolic interventions will move into the treatment arena. Parenthetically, they are already there for many years as many of the classic chemotherapy drugs are "metabolic" molecules, interfering with nucleoside metabolism, lending hope that this is indeed a productive path to follow. HCC incidence is rapidly rising, and there are so far no treatments that really succeed in reducing mortality. Thus, the manuscript brings together two highly important topics and reveal a novel metabolic vulnerability of HCC.

1) Glutamine synthetase is overexpressed in HCC, and catalyzes the reverse reaction from GLS, so there should be sufficient levels of cytoplasmic glutamine. ASCT1 is expressed on both the cell membrane and mitochondrial membrane, using two different splice variants (https://doi.org/10.1016/j.cmet.2019.11.020), which can be discriminated by Western blot. I wonder whether the differential sensitivity of the different cell lines relates to the relative abundance of the two ASCT1 variants. This could strengthen the hypothesis that the drug combination targets mitochondrial glutamine metabolism.

2) GLS1 is a mitochondrial enzyme which is predominantly expressed at the periportal zone of the liver acinus. It plays key roles in ammonia metabolism. This could pose a problem for patients with HCC, in many of which ammonia metabolism is already impaired due to co-existing liver disease. The authors should at least discuss this issue, or possibly test ammonia levels in mice treated with the different drug combinations.

3) β–catenin is mutated in a significant number of human HCCs and directly regulates hepatocyte glutamine metabolism. Could the authors correlate β-catenin mutation in the cell line and human cohorts with CB-839 response and glutamine metabolism respectively?

4) The authors tested whether 13 different "metabolic drugs" synergized with CB-839. Can they elaborate on how these were selected?

5) In Figure 5 the effects on glutathione seems additive, while the effects on ROS and gH2AX are clearly synergistic. This indicates that there could be other reasons for the ROS, gH2AX effects. While NAC treatment indicates that ROS are causal in gH2AX and apoptosis, it doesn't directly implicate glutathione depletion as the cause for increased ROS. The interpretation of the data should be changed accordingly.

eLife. 2020 Oct 5;9:e56749. doi: 10.7554/eLife.56749.sa2

Author response

Essential revisions:

Please see comments from both reviewers below. Please take all suggestions into consideration, but the following should be addressed with either data or discussion in a revised manuscript if possible:

1) Is any information available on how responder cells differ from non-responder cells? If clues can be gleaned from the Cancer Cell Line Encyclopedia and the Dependency Map (depmap.org) based on mutational status or gene expression please discuss this in the paper. Even in the absence of this, considering whether β-catenin mutations correlates with response should be considered per reviewer 2.

Following the reviewers’ suggestion, we tried to analyze the data from Cancer Cell Line Encyclopedia and the Dependency Map. However, we found several cell lines used in our study, such as Hep3B and MHCC97H, are not included in these databases. Additionally, we also checked both the presence of mutations in β-catenin and the relative activity of β-catenin of liver cancer cell lines as described in previous publications [1,2]. As shown in Author response table 1 below, there is no obvious correlation between the mutation of β-catenin nor the activity β-catenin and glutamine dependency in our panel of cell lines.

To study the underling mechanism that might explain why different liver cancer cell lines have different dependence on glutamine, we have recently performed a genome-wide CRIPSPR resistance screen in a GD cell line SNU398 in the absence of glutamine. We have identified several candidate genes that can make GD cells resistant to deprivation of exogenous glutamine or the drug combination, but it is too early to include these tentative results (that currently also lack a mechanistic basis) in the current manuscript. According to the policy of eLife, we plan to report the data in a preprint on bioRxiv or medRxiv in the future, which would be linked to the original paper. Accordingly, we have discussed this approach in our revised manuscript (Discussion). Because the data shown in Author response table 1 are not informative, we decided to not include it in the manuscript.

Author response table 1

Mutation or relative activity of β-catenin in HCC cell lines. Cell lines underlined are glutamine independent (GID) cell lines.

2) If possible please address how much V-9302 affects amino acid uptake from the media. If this is not possible, a discussion of the Broer et al. Front Pharmacol 2018 paper suggesting V-9302 is not selective for ASCT2 is warranted.

We regret that we currently cannot provide data about how much V-9302 affects amino acid uptake from the media. However, according to the reviewer’s suggestion, we have discussed the Broer et al. Front Pharmacol 2018 paper in our revised manuscript as follows:

“However, there is still some debate about the target selectivity of V-9302 for ASCT2. Broer et al.[3] reported that V-9302 does not inhibit ASCT2 and glutamine uptake, but rather blocked sodium-neutral amino acid transporter 2 (SNAT2) and the large neutral amino acid transporter 1 (LAT1). […] Thus, the depression of glutamine uptake by V-9302 remains to be further investigated.”

Accordingly, we have added it into our revised manuscript (Discussion).

3) Please clarify how the 4 GD cell lines were chosen to test the drug combination chosen and provide data for other cells, including the GID lines, if available.

To test the synergistic anti-proliferation effect of CB-839 and V-9302, we tested 4 GD cell lines, which are not sensitive to single CB-839. Following the reviewer’s suggestion, we further tested the drug combination in other GD and GID cell lines by using colony formation assays. These data are shown in the revised manuscript (Figure 4—figure supplement 1) and mentioned the result in the main text (subsection “A compounds screen identifies that ASCT-2 inhibitor V-9302 sensitizes GD liver cancer cells to CB-839 treatment”).

In short, the combination only showed synergistic anti-proliferation effect in GD cell lines, but only showed very limited effect in two GID cell lines in vitro.

4) Please comment on expression of the ASCT1 splice variants, and consider whether this tracks with differential sensitivity (see reviewer 2 comment 1).

According to the reviewer’s suggestion, we analyzed the two different splice variants of ASCT2 by Western blot. As shown bin Author response image 1, we found no correlation between ASCT2 or mitochondrial ASCT2 and drug sensitivity in our panel of liver cancer cell lines. We decided to not include these data in the revised manuscript, as they do not provide important new insights. However, as mentioned in question 1, we have performed a genome-wide resistant screen with or without glutamine in a GD cell line and identified several candidate genes which may explain the varying sensitivity of the cell lines to the drug combination or exogenous glutamine. According to the policy of eLife, we plan to report the data in a preprint on bioRxiv or medRxiv in the future, which would be linked to the original paper.

Author response image 1. Western blot analysis of the two different splice variants of ASCT2 in liver cancer cell lines.

Author response image 1.

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5) Please discuss potential issues of targeting glutamine metabolism in patients with HCC who also have impaired ammonia metabolism. If possible test ammonia levels in mice treated with the drug combination.

Unfortunately, we don’t have fresh samples from in vivo experiments for ammonia analysis to answer the reviewer’s question. For as far as we know, ammonia measurements of tissues and plasma are problematic due to the volatile nature of ammonia, resulting potentially in false positive or false negative readings. Instead, we discussed this issue in our revised manuscript as follows:

“The liver clears almost all of the portal vein ammonia, converting it into glutamine and urea, preventing entry into the systemic circulation. […] CB-839 selectively inhibits GLS1 but not GLS2 [Gross et al., 2014]. Taken together, although many HCC patients have co-existing liver disease and thus usually have impaired ammonia metabolism, GLS inhibition by CB-839 treatment is less likely to pose a problem for these patients.”

We have added the discussion in the revised manuscript (Discussion).

6) Please clarify how the 13 different "metabolic drugs" were selected to test for synergy with CB-839.

To further exploit the metabolic vulnerability caused by CB-839, we selected metabolism-related drugs (13 compounds) which act in the major metabolic pathways including glycolysis, Krebs cycle, oxidative phosphorylation, pentose phosphate pathway, fatty acid β-oxidation, and gluconeogenesis. We have listed the detail targets of the 13 compounds in the Figure 3—source data 2.

7) Please reconsider interpretation of Figure 5 per reviewer 2 point 5, as the effects on glutathione seems additive, while the effects on ROS and gH2AX are clearly synergistic. This indicates that there could be other reasons for the ROS, gH2AX effects.

In our first submission, we analyzed the ROS level and γH2AX expression after 48 hours of drug treatment. However, we tested the glutathione levels after 4 and 24 hours of drug treatment, respectively. To further confirm the synergistic effect on glutathione levels, we treated the SNU398 cells and HepG2 cells with CB-839, V-9302, or their combination for 48 hours and measured the intracellular glutathione levels. These new data are now shown in Figure 5A of the revised manuscript. In short, we observe that the combination of CB-839 and V-9302 caused an obvious synergistic reduction in glutathione levels after 48 hours of drug treatment.

8) Reviewer 2 noted that the NAC effect in Figure 5F is not dramatic, and not dose dependent in HEPG2 cells. If feasible, please repeat this experiment and test the effect of NAC alone to compare with the combination affect.

According to the reviewers’ suggestion, we have repeated the experiment of HepG2 cells of Figure 5F. As shown in the revised Figure 5F, the two concentrations of NAC (1.25 mM and 2.5 mM) have no effect on apoptosis of HepG2 cells, while both concentrations can rescue combination-induced apoptosis. However, we still cannot observe clear dose dependent rescue, which suggests that the lower concentration of NAC (1.25 mM) is sufficient to achieve maximal effect in HepG2 cells.

9) Please address additional points of clarification raised by the reviewers:

– All metabolites shown in Figure 3A should be more clearly labeled and/or presented in a supplementary figure.

According to the reviewer’s suggestion, all metabolites shown in Figure 3A are now presented in Figure 3—figure supplement 1 and we also provide the related source file.

– Make clear that KGA/GAC represent different splice forms of GLS.

We have stated that KGA/GAC represent two different splice forms of GLS in the revised manuscript (Discussion and subsection “Compounds and antibodies”).

– Discuss levels of glutamine in media relative to those in blood.

As the reviewers mentioned, the normal concentration of glutamine in human plasma is usually around 0.6-0.9mM[1]. In tissues, such as the liver and the skeletal muscles, glutamine concentration is even higher than in plasma[4]. Considering the fact that glutamine is easily broken down in vitro, it is difficult to mimic the glutamine concentration of the tumor microenvironment in 2D culture models over time. To maintain the best viability of tumor cells in vitro, we performed all the experiments in normal DMEM medium with sufficient glutamine (4 mM). Similarly, other studies [Schulte et al., 2018; 5] also used normal culture medium with sufficient glutamine to test the drugs response on tumor cells in vitro. Accordingly, we have described it in the Discussion of our revised manuscript.

– Clarify if Figure 3B is a graphic representation of the findings of the MS screen (shown in Figure 3A), or results of an independent set of experiments for verification.

We have stated that Figure 3B is a graphic representation of the findings of the MS screen in our revised manuscript (Figure 3 legend).

– Define that xCT = cystine-glutamate antiporter system for a broad audience.

We have defined xCT with cystine-glutamate antiporter system in our revised manuscript (subsection “Combination of CB-839 and V-9302 depletes glutathione and induces lethal ROS level in GD liver cancer cells”).

In addition, we found SNU182 cells, which were used in Figure 1A and B of our first submission, had mycoplasma contamination. Therefore, we removed the related data of SNU182 cells in Figure 1A and B of our revised Figure 1. We are sorry for this mistake.

References:

1) Ding Z, Shi C, Jiang L, Tolstykh T, Cao H, Bangari DS, Ryan S, Levit M, Jin T, Mamaat K, Yu Q, Qu H, Hopke J, Cindhuchao M, Hoffmann D, Sun F, Helms MW, Jahn-Hofmann K, Scheidler S, Schweizer L, Fang DD, Pollard J, Winter C, Wiederschain D. Oncogenic dependency on β-catenin in liver cancer cell lines correlates with pathway activation. Oncotarget. 2017 Sep 28;8(70):114526-114539

2) Wang W, Xu L, Liu P, Jairam K, Yin Y, Chen K, Sprengers D, Peppelenbosch MP, Pan Q, Smits R. Blocking Wnt Secretion Reduces Growth of Hepatocellular Carcinoma Cell Lines Mostly Independent of β-Catenin Signaling. Neoplasia. 2016 Dec;18(12):711-723.

3) Angelika Bröer, Stephen Fairweather, Stefan Bröer. Disruption of Amino Acid Homeostasis by Novel ASCT2 Inhibitors Involves Multiple Targets. Front Pharmacol. 2018; 9: 785

4) Cruzat V, Macedo Rogero M, Noel Keane K, Curi R, Newsholme P. Glutamine: Metabolism and Immune Function, Supplementation and Clinical Translation. Nutrients. 2018 Oct 23;10(11):1564.

5) Le A, Lane AN, Hamaker M, Bose S, Gouw A, Barbi J, Tsukamoto T, Rojas CJ, Slusher BS, Zhang H, Zimmerman LJ, Liebler DC, Slebos RJ, Lorkiewicz PK, Higashi RM, Fan TW, Dang CV. Glucose-independent glutamine metabolism via TCA cycling for proliferation and survival in B cells. Cell Metab. 2012 Jan 4;15(1):110-21.

Associated Data

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

    Data Citations

    1. The Cancer Genome Atlas Research Network 2017. Liver Hepatocellular Carcinoma. The Cancer Genome Atlas. TCGA-LIHC
    2. The Cancer Genome Atlas Research Network 2017. Cholangiocarcinoma. The Cancer Genome Atlas. TCGA-CHOL
    3. Wang XW. 2010. Gene expression data of human hepatocellular carcinoma (HCC) NCBI Gene Expression Omnibus. GSE14520

    Supplementary Materials

    Figure 1—source data 1. Liver cancer is addicted to glutamine.
    elife-56749-fig1-data1.xls (88.5KB, xls)
    Figure 2—source data 1. The glutaminase inhibitor CB-839 monotherapy shows insufficient anti-tumor effect in liver cancer.
    elife-56749-fig2-data1.xls (85.5KB, xls)
    Figure 3—source data 1. Intracellular metabolite levels were measued by LC/MS-MS in SNU398 and HepG2 cells treated with DMSO or CB-839.
    elife-56749-fig3-data1.xls (31.5KB, xls)
    Figure 3—source data 2. Data related to Figure 3d.
    elife-56749-fig3-data2.docx (14.6KB, docx)
    Figure 4—source data 1. Combination of CB-839 and V-9302 shows potential synergy in multiple GD liver cancer cells.
    elife-56749-fig4-data1.xls (63KB, xls)
    Figure 5—source data 1. Combination of CB-839 and V-9302 depletes GSH and induces lethal ROS level in GD liver cancer cells.
    elife-56749-fig5-data1.xls (25KB, xls)
    Figure 6—source data 1. Combined treatment inhibits xenograft growth and induces apoptosis in vivo.
    elife-56749-fig6-data1.xls (46KB, xls)
    Transparent reporting form
    elife-56749-transrepform.docx (61.4KB, docx)

    Data Availability Statement

    All data generated or analysed during this study are included in the manuscript and supporting files.

    The following previously published datasets were used:

    The Cancer Genome Atlas Research Network 2017. Liver Hepatocellular Carcinoma. The Cancer Genome Atlas. TCGA-LIHC

    The Cancer Genome Atlas Research Network 2017. Cholangiocarcinoma. The Cancer Genome Atlas. TCGA-CHOL

    Wang XW. 2010. Gene expression data of human hepatocellular carcinoma (HCC) NCBI Gene Expression Omnibus. GSE14520

    Articles from eLife are provided here courtesy of eLife Sciences Publications, Ltd

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