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

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

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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:

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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.

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