Glutamine metabolism drives tumor aggressiveness but not chemoresistance in ovarian carcinoma cell lines

Introduction

Ovarian carcinomas, particularly high-grade serous carcinoma, are a leading cause of gynecological mortality, with a lethality rate of approximately 75% despite advances in surgery and chemotherapy (1). These tumors are characterized by rapid progression, strong peritoneal metastatic potential, and frequent chemoresistance, often linked to marked genomic instability, though recurrent molecular aberrations are rare except for TP53 mutations (1). The mechanisms underlying chemoresistance remain poorly elucidated, highlighting the urgent need for novel therapeutic strategies. Targeting the metabolic dependencies of tumor cells, particularly their reliance on glutamine, represents a promising approach (2).

Ovarian tumor cells exploit glycolysis, glutaminolysis, and β-oxidation to meet their energetic and anabolic demands (Figure 1) (2,3). Enhanced glycolysis generates metabolites critical for proliferation, such as ribose, glycerol, and serine (2). Reduced pyruvate kinase (PK) activity (4) and inhibition of pyruvate dehydrogenase (PDH) promote pyruvate conversion to lactate, even in the presence of oxygen (Warburg effect) (5,6), decoupling glycolysis from the Krebs cycle, which is primarily fueled by glutaminolysis (7). Glutaminolysis, involving the conversion of glutamine to glutamate by glutaminase and subsequently to α-ketoglutarate by glutamate dehydrogenase 1 (GDH1), provides precursors for macromolecule synthesis (lipids, proteins, nucleic acids) and adenosine triphosphate (ATP) production (2,8,9). Several mechanisms amplify glutamine utilization: overexpression of membrane transporters SLC1A5 and SLC7A5/3A2 driven by c-Myc (3,10), increased glutaminase expression via nuclear factor-κB (NF-κB) (11,12) or c-Myc-mediated suppression of microRNAs miR-23a/b (13), and GDH1 overexpression to compensate for reduced oxidative glucose catabolism (14). Intense glutaminolysis activates the PI3K/AKT/mTORC1 pathway (3), promoting proliferation and chemoresistance, particularly to cisplatin, in ovarian carcinomas (1,15-19). Clear cell carcinomas, often ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)-negative, may rely more heavily on glutaminolysis, potentially explaining their chemoresistance (20). However, glutamine deprivation induces proto-oncogene and metabolic gene expression, conferring a survival advantage to tumor cells via PI3K (21-23), partly through overexpression of transporters LAT1 and ASCT2 (24-27), which activate mTORC1 and protein synthesis (28,29). Supraphysiological glutamine supplementation, however, does not enhance tumor growth or radioresistance in vitro or in vivo (30,31).

Figure 1 Glutamine supports cancer cell survival, growth, and proliferation through both metabolic and signaling pathways. Adapted from DeBerardinis et al. (3). Following its uptake via the membrane transporter SLC1A5, glutamine is either exchanged for essential amino acids (via SLC7A5/3A2) or metabolized by GLS and GLUD into α-KG, fueling the TCA cycle. Derived metabolites contribute to the synthesis of nucleotides, non-essential amino acids, and glutathione. Glutamate may also act in a paracrine manner via mGluR1, activating ERK and PI3K/AKT/mTORC1 pathways, which promote tumor progression. NH₄+ produced by glutaminase is exported, contributing to microenvironmental acidification. α-KG, α-ketoglutarate; ATP, adenosine triphosphate; EAA, essential amino acids; GLS, glutaminase; GLUD, glutamate dehydrogenase; TCA, tricarboxylic acid.

Glutamine also plays a critical role in non-tumor cells, supporting immune function (lymphocytes) (32,33) and enterocyte integrity (34). Supplementation promotes Th1 lymphocyte polarization, enhancing antitumor immunity (35,36), and reduces infectious complications, mortality, and hospital stay duration in patients undergoing chemotherapy or radiotherapy (37,38). It exerts a cytoprotective effect on non-cancerous ovarian and intestinal cells, likely through glutathione synthesis, an intracellular antioxidant (39-43). Glutamine depletion, common in cancer patients, exacerbates malnutrition and iatrogenic effects (28), making supplementation appealing but requiring a thorough understanding of tumor and non-tumor metabolic dynamics.

This study aims to evaluate glutamine and glucose consumption in ovarian carcinoma cell lines ES-2, TOV-21G (clear cell), and OVCAR-3 (serous papillary) and correlate these parameters with tumor behavior (proliferation, invasiveness, chemoresistance). Tumor aggressiveness, characterized by rapid proliferation, increased invasiveness, and poor prognosis, is often driven by altered metabolic pathways, including glutamine metabolism, which this study investigates in ovarian carcinoma cell lines. It also investigates the impact of varying glutamine concentrations in the tumor microenvironment on chemosensitivity, to inform strategies for glutamine supplementation or glutaminolysis-targeted therapies. The novelty of this study lies in comparing distinct ovarian carcinoma subtypes and in demonstrating that glutamine availability does not alter chemosensitivity to standard chemotherapy, an observation not previously reported. Recent reports have underlined the importance of glutamine metabolism in cancer (18,19,44). However, systematic comparisons between clear cell and high-grade serous ovarian carcinoma lines, particularly regarding chemosensitivity to cisplatin and paclitaxel, remain scarce. Our work addresses this gap by directly comparing clear cell and high-grade serous ovarian carcinoma models, revealing that glutaminolysis drives aggressiveness but does not alter chemosensitivity to cisplatin or paclitaxel. We present this article in accordance with the MDAR reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1721/rc).

Methods

Cell lines and culture conditions

Three ovarian carcinoma cell lines, obtained from the American Type Culture Collection (ATCC^®), were used: ES-2 and TOV-21G (clear cell adenocarcinomas) and OVCAR-3 (high-grade serous papillary adenocarcinoma). Cells were cultured in RPMI-1640 medium (Gibco^®, Thermo Fisher Scientific, Illkirch, France) supplemented with 2 mM L-glutamine, 11 mM D-glucose, and 15% fetal bovine serum (FBS) at 37 ℃ in a 5% CO₂ atmosphere. Cultures were maintained at 80% confluence.

Primary cells derived from ascites of patients with ovarian carcinoma were obtained through collaboration with the oncology and gynecological surgery departments of our hospital and cultured according to a validated protocol (45). As a side note, although ES‑2 was originally classified as a clear cell adenocarcinoma cell line by ATCC^®, subsequent transcriptomic and immunohistochemical analyses have questioned this histological designation, suggesting instead that ES2 shares molecular features with high-grade serous papillary adenocarcinoma lines (46,47). To strengthen the histotype specificity of our observations, we therefore included TOV‑21G, a well-characterized and validated clear cell adenocarcinoma cell line. Experiments with established ovarian cancer cell lines were generally performed in three independent biological replicates from different passages, unless otherwise specified. Each biological replicate included technical triplicates.

Measurement of glucose and glutamine consumption

Culture preparation

For each cell line, 12-well plates (Falcon^® Multiwell^®, Le Pont-de-Claix, France) were seeded at a density of 2×10⁵ cells/well. After 24 h of incubation at 37 ℃ in 5% CO₂, the medium was replaced, and supernatant samples were collected at 30 min, 60 min, 4 h, 8 h, 24 h, and 48 h (n=2 wells per time point). A cell-free control was included in parallel. Nutrient consumption was assessed by measuring changes in their concentrations in the medium. Metabolite consumption or production was calculated from the difference between initial and final concentrations in the medium and expressed as µmol per mg of intracellular protein to account for differences in cell density.

Amino acid and protein quantification

Culture supernatants were depleted of proteins by adding 30% sulfosalicylic acid and centrifugation (13,000 rpm, 5 min). Amino acid concentrations were measured by ion-exchange chromatography using a JEOL AminoTac™ analyzer (Tokyo, Japan) (48). Intracellular protein content was determined to standardize nutrient flux measurements. After lysis of the cell monolayer with Triton™ X-100 (Sigma^®, Saint-Quentin-Fallavier, France) and centrifugation, supernatant proteins were quantified using the Lowry method (DC Protein Assay Kit^®, Bio-Rad^®, Paris, France) by measuring optical density at 750 nm.

Evaluation of cisplatin and paclitaxel cytotoxicity

Cell viability assay [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT)]

Cytotoxicity was assessed by determining the half-maximal inhibitory concentration (IC₅₀), defined as the drug concentration reducing cell viability by 50% compared to an untreated control. Cisplatin (1 mg/mL, Merck^®, 0.9% NaCl) and paclitaxel (6 mg/mL, Bristol-Myers Squibb^®, 0.9% NaCl), supplied by our hospital pharmacy, were freshly diluted in culture medium. Ninety-six-well plates (Falcon^® Microtest^®) were seeded at 2×10⁴ cells/well (49). After 24 h, the medium was replaced with medium containing increasing concentrations of cisplatin or paclitaxel. Following 24 h of incubation, cell viability was measured using the MTT assay (Sigma^®), based on the reduction of tetrazolium salt to formazan. Formazan crystals were solubilized in 200 µL of dimethyl sulfoxide (DMSO), and absorbance was measured at 560 nm (with background subtraction at 630 nm) using a microplate spectrophotometer (Bio-Rad^®). The percentage of cell survival was calculated as follows: cell viability (%) = [(Abs_{560 nm} − Abs_{630 nm}) treated cells/(Abs_{560 nm} − Abs_{630 nm}) untreated cells] × 100.

Effect of glutamine concentration on cytotoxicity

Cell lines were cultured in RPMI medium (11 mM D-glucose, 15% FBS) with four L-glutamine concentrations (50): 4 mM (hypermetabolic, C1), 1 mM (normometabolic, C2), 0.5 mM (hypometabolic, C3), and 2 mM (standard, S). The medium was prepared from glutamine-free RPMI supplemented with exogenous L-glutamine (Gibco^®). Glutamine is unstable, and its concentration in the medium decreases significantly after 48 h (51). The IC₅₀ was determined for each condition using the method described above.

Analysis of the PI3K/AKT/mTORC1 pathway

Expression of total and phosphorylated forms of AKT, S6K1, and 4E-BP1 (mTORC1 effectors) was analyzed by Western blot using an established protocol (52) with antibodies from Cell Signaling Technology^® (Beverly, MA, USA). Western blot analyses were normalized to housekeeping proteins [β-actin and glyceraldehyde-3-phosphate dehydrogenase (GAPDH)].

Glutaminase expression analysis

Expression of the glutaminase gene was assessed by reverse transcription-polymerase chain reaction (RT-PCR) in the three cell lines using a previously described method (53).

Measurement of doubling time

Doubling time was determined over 3 days. For each cell line, 2×10⁵ cells were seeded in 3.8 cm² wells, and cell counts were performed daily until day 3.

Statistical analysis

Multiple comparisons were analyzed using analysis of variance (ANOVA) followed by a Newman-Keuls post-hoc test. Results are expressed as mean ± standard error of the mean (SEM). A significance threshold of 5% (P<0.05) was adopted. Analyses were performed using Prism 5.0 software (GraphPad Software, Inc., San Diego, CA, USA).

Ethics statement

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was reviewed by the institutional ethics committee of Hôpital Cochin (AP-HP, Paris) in accordance with French legislation (No. 2012-300 of March 5, 2012, known as the “Loi Jardé”). As this retrospective study was based on de-identified human material collected during routine care, formal approval by the ethics committee (Comité de Protection des Personnes) was not required. Oral informed consent was obtained from all patients at the time of care, as per institutional policy.

Results

The experiments were designed to assess the hypothesis that enhanced glutamine metabolism, mediated by glutaminolysis and PI3K/AKT/mTORC1 signaling, drives tumor aggressiveness in ovarian carcinoma cell lines, particularly TOV-21G, while not significantly influencing chemoresistance, using comparative metabolic profiling and signaling analysis across TOV-21G, ES-2, and OVCAR-3 cell lines.

Glucose and glutamine consumption

Glucose and glutamine consumption were measured at 24 h and expressed as µmol/mg of intracellular protein (Figure 2).

Figure 2 Metabolic profiles of the different tumor cell populations at 24 h (mean ± SEM). Data are expressed as net metabolite consumption (Glc, Gln) or production (Lac, Glu) normalized to intracellular protein content. (A) Glc consumption. (B) Lac production. (C) Gln consumption. (D) Glu production. The number of independent experiments is indicated (n); for each, one time point represents the mean of two wells. *, P=0.04; **, P=0.03; ***, P=0.02; ns, not significant. Glc, glucose; Gln, glutamine; Glu, glutamate; Lac, lactate; SEM, standard error of the mean.

Glucose consumption and lactate production

The TOV-21G cell line exhibited significantly higher glucose consumption than ES-2 and OVCAR-3 (P=0.02) (Figure 2A). No significant difference was observed between ES-2 and OVCAR-3. Similarly, lactate production was significantly greater in TOV-21G compared to ES-2 and OVCAR-3 (P=0.03), with no notable difference between the latter two (Figure 2B).

Glutamine consumption and glutamate production

Glutamine consumption was significantly higher in TOV-21G compared to ES-2 and OVCAR-3 (13.3±1.3 vs. 6.5±0.3 and 7±0.5 U/mg protein IC, P<0.001) (Figure 2C). No significant difference was observed between ES-2 and OVCAR-3.

Glutamate production in the culture medium was comparable across the three cell lines, although there was a non-significant upward trend for TOV-21G (P=0.08) (Figure 2D).

Consumption by primary ascites cells

Primary cells derived from ascites of a high-grade serous papillary ovarian carcinoma, clinically resistant to platinum salts and ¹⁸F-FDG PET-positive, consumed less glucose and glutamine and produced less lactate than the three established cell lines (Figure 2).

Chemosensitivity to cisplatin and paclitaxel

Chemosensitivity under standard nutritional conditions

In standard medium (2 mM glutamine), the IC₅₀ for OVCAR-3 was significantly higher than those for ES-2 and TOV-21G for cisplatin (366.2±43.9 vs. 39.1±12.1 and 36.8±6.5 µM, P=0.03) and paclitaxel (270±30 vs. 61.3±1.3 and 38.4±8.4 µM, P=0.03) (Table 1). No significant difference was observed between ES-2 and TOV-21G.

Impact of glutamine concentration

Under hypermetabolic (C1: 4 mM), normometabolic (C2: 1 mM), and hypometabolic (C3: 0.5 mM) glutamine conditions, the IC₅₀ values for OVCAR-3 remained significantly higher than those for ES-2 and TOV-21G for both agents (P=0.045) (Figure 3). Varying glutamine concentrations did not significantly alter the IC₅₀ for any cell line or affect the cell density of untreated control wells (Table 1).

Figure 3 IC₅₀ of ES-2, TOV-21G, and OVCAR cell lines for cisplatin (A) and paclitaxel (B) in the presence of varying concentrations of Gln in the culture medium (mean ± SEM). Gln, glutamine; IC₅₀, half-maximal inhibitory concentration; SEM, standard error of the mean.

Analysis of the PI3K/AKT/mTORC1 pathway

Marked activation of phosphorylated AKT (Ser473) was observed in TOV-21G, less pronounced in OVCAR-3, and absent in ES-2 (Figure 4). Similarly, phosphorylation of mTORC1 effectors (S6K1 and 4E-BP1) was more intense in TOV-21G, moderate in OVCAR-3, and absent in ES-2.

Figure 4 Western blots of ES-2, TOV-21G, and OVCAR-3 cell lines showing, on one hand, the expression of phospho-AKT protein (A), and on the other, the expression of phosphorylated mTORC1 effectors: phospho-S6K1 (B) and phospho-4E-BP1 (C). Expression of housekeeping proteins GAPDH and β-actin is also shown. Molecular weights (kDa) of the detected proteins are as follows: total AKT (~62 kDa) and phospho-AKT (~60 kDa); total S6K1 (~70 kDa) and phospho-S6K1 (~68 kDa); total 4E-BP1 (~18 kDa) and phospho-4E-BP1 (~20 kDa); GAPDH (~37 kDa); β-actin (~42 kDa). Note that blots were stripped and reprobed; total protein is presented above the phosphorylated form to facilitate comparison. GAPDH, glyceraldehyde-3-phosphate dehydrogenase; phospho, phosphorylated.

Metabolic gene expression

Glutaminase messenger RNA (mRNA) expression was significantly higher in TOV-21G compared to ES-2 and OVCAR-3 (P=0.04) (Figure 5). Additionally, TOV-21G exhibited increased expression of GDH1 and c-Myc compared to ES-2 and OVCAR-3 (P=0.045) (data not shown).

Figure 5 RT-PCR of ES-2, TOV-21G, and OVCAR-3 cell lines showing GLS expression (mean ± SEM). 18S rRNA is used as a reference. *, P=0.04. GLS, glutaminase; mRNA, messenger RNA; rRNA, ribosomal RNA; RT-PCR, reverse transcription-polymerase chain reaction; SEM, standard error of the mean.

Doubling time

Tumor aggressiveness, defined by accelerated proliferation and enhanced invasiveness due to glutaminolysis and signaling activation, was assessed by comparing doubling times across cell lines.

The doubling time was significantly shorter for TOV-21G (11.5±1.5 h) compared to ES-2 (21±1 h) and OVCAR-3 (24.3±2.2 h) (P=0.04) (Figure 6). No significant difference was observed between ES-2 and OVCAR-3.

Figure 6 Doubling time of ES-2, TOV-21G, and OVCAR-3 cell lines (mean ± SEM). *, P=0.04. SEM, standard error of the mean.

Effect of glutamine substitution with glutamate

Substitution of glutamine with glutamate in the medium resulted in a more pronounced reduction in cell growth for TOV-21G (49% decrease) compared to ES-2 (33%) and OVCAR-3 (31%) (P<0.001) (data not shown).

Discussion

This study reveals marked metabolic heterogeneity among ovarian carcinoma cell lines, with TOV-21G (clear cell adenocarcinoma) exhibiting heightened glucose and glutamine uptake compared to ES-2 (clear cell) and OVCAR-3 (high-grade serous papillary). Elevated lactate production by TOV-21G, partly driven by glutaminolysis via nicotinamide adenine dinucleotide phosphate (NADP)-dependent malic enzyme (3,54), promotes angiogenesis, invasion, and immunosuppression (2). Secreted glutamate, although not significantly different across cell lines, may contribute to microenvironment acidification and, in addition, activate metabotropic glutamate receptor-1 (mGluR1) in a paracrine manner, thereby promoting tumor cell proliferation and survival through ERK and PI3K signaling pathways (3,55) (Figure 1). Metabolic imaging has shown that approximately 45.5% of ovarian clear cell carcinomas exhibit low ¹⁸F-FDG uptake on PET, whereas the majority (90%) of high-grade serous carcinomas display significant ¹⁸F-FDG accumulation (20). This likely reflects a lower reliance on aerobic glycolysis in clear cell tumors. Such low ¹⁸F-FDG uptake in clear cell carcinomas may point to a greater dependence on alternative pathways such as glutaminolysis. ¹⁸F-(2S,4R)-4-fluoroglutamine (¹⁸F-fluoroglutamine) PET could identify glutamine-dependent tumors, providing prognostic value (56). Primary ascites cells (platinum-resistant serous papillary) displayed lower glucose and glutamine consumption despite in vivo ¹⁸F-FDG positivity, highlighting metabolic heterogeneity (20). Our results are consistent with recent findings (57) showing that ovarian cancer cell lines and ascites-derived primary cells exhibit distinct metabolic signatures, underscoring the heterogeneity of glutamine metabolism in ovarian carcinoma. One limitation of our work is that primary ascites cells were derived from a single patient. Although this proof-of-concept approach provides useful translational insight, future studies on larger cohorts of patient-derived samples will be required to confirm and generalize these findings.

The increased glutaminolysis observed in TOV-21G cells is supported by significantly elevated glutaminase expression (P=0.04) and glutamine consumption (see the “Glutamine consumption and glutamate production” and “Metabolic gene expression” sections), likely driven by c-Myc overexpression (13). This phenotype is further characterized by increased GDH1 expression (P=0.045), promoting the conversion of glutamate to α-ketoglutarate and fueling the tricarboxylic acid (TCA) cycle. Despite this, TOV-21G cells exhibit higher absolute glutamate production compared to ES-2 and OVCAR-3 (Figure 2D), though not statistically significant (P=0.08). This apparent discrepancy may reflect an accelerated downstream metabolism in TOV-21G, where glutamate is rapidly processed by GDH1 or diverted toward glutathione synthesis in response to oxidative stress (54). mTORC1 activation (Figure 4) and c-Myc overexpression likely sustain this metabolic flux, preventing intracellular glutamate accumulation. These findings suggest a finely tuned glutaminolytic pathway in TOV-21G, characteristic of aggressive tumors. Further kinetic analyses of glutamate utilization—including GDH1 activity and glutathione flux—are warranted to validate this metabolic shunt and assess its therapeutic relevance.

Tumor aggressiveness, encompassing rapid cell proliferation, invasiveness, and poor survival outcomes, was evident in TOV-21G cells, driven by glutamine-dependent pathways and mTORC1 signaling. The glutaminolytic phenotype of TOV-21G is characterized by elevated expression of glutaminase and GDH1 (53), overexpression of c-Myc—driving both glutamine transporters (SLC1A5, SLC7A5/3A2) and glutaminase expression (13,58)—and pronounced activation of the PI3K/AKT/mTORC1 pathway, which upregulates GLUT1 and key glycolytic enzymes (59). These features, supported by activating mutations in PI3K and JAK1—absent in ES-2 and OVCAR-3 (60)—are correlated with a shorter doubling time (11.5±1.5 vs. 21±1 h for ES-2 and 24.3±2.2 h for OVCAR-3; P=0.04), suggesting increased tumor aggressiveness. Enhanced glutaminolysis in TOV-21G is also associated with greater invasiveness, mediated by JAK1-driven STAT3 phosphorylation (53) and PI3K/AKT/mTORC1/S6K1 activation (58,61,62) (Figure 7). High expression levels of glutaminolysis-related genes (glutaminase, GDH1), unlike glycolytic genes, are associated with shorter survival in ovarian carcinoma patients (53), reinforcing the potential prognostic utility of ¹⁸F-fluoroglutamine PET imaging (56).

Figure 7 JAK1/STAT and PI3K/AKT/mTORC1 pathways converge on c-Myc to enhance glutaminolysis and invasiveness in ovarian carcinoma. Adapted from Yang et al. (53) and Ni et al. (58). In TOV-21G cells, activating mutations in JAK1 and PI3K drive persistent STAT3 phosphorylation and mTORC1 signaling, respectively. mTORC1 promotes c-Myc translation via S6K1, leading to increased expression of glutaminase (GLS) and glutamine transporters (SLC1A5, SLC7A5/3A2). This feed-forward loop sustains glutaminolysis and supports tumor cell proliferation, survival, and invasion. Feedback inhibition is mediated by SOCS and CIS proteins.

The interplay between glycolysis and glutaminolysis in cancer cells is increasingly recognized as a critical determinant of tumor metabolism. Our data reveal enhanced glutaminolysis in TOV-21G cells, characterized by elevated glutamine consumption and glutaminase expression, which supports biosynthetic demands and mTORC1 activation (see the “Glutamine consumption and glutamate production” section). This is complemented by a modest increase in glucose uptake (see the “Glucose consumption and lactate production” section), suggesting a cooperative metabolic phenotype where glycolysis provides pyruvate and lactate, while glutaminolysis feeds the TCA cycle via α-ketoglutarate, as depicted in Figure 1. Studies indicate that c-Myc, overexpressed in TOV-21G (see the “Metabolic gene expression” section), upregulates both glucose transporters (e.g., GLUT1) and glutamine transporters (e.g., SLC1A5), linking these pathways to support rapid proliferation (13). Furthermore, the PI3K/AKT/mTORC1 axis, activated in our model (Figure 4), integrates glycolytic and glutaminolytic fluxes, enhancing tumor aggressiveness (63). Future investigations should quantify this synergy to elucidate its therapeutic implications in ovarian carcinoma. Tumor aggressiveness, defined by rapid proliferation (e.g., TOV-21G doubling time of 11.5±1.5 h), enhanced invasiveness via JAK1/STAT3 and PI3K/AKT/mTORC1 pathways, and association with poor survival, underscores the role of glutamine metabolism in driving these malignant traits in ovarian carcinoma (64).

Our results align with recent advances (18,19,44) but provide novel evidence that glutamine availability does not alter chemosensitivity to first-line agents in ovarian carcinoma, while simultaneously associating intense glutaminolysis with increased proliferation and invasiveness. This dual observation refines the understanding of glutamine metabolism as both a prognostic biomarker and a potential therapeutic target in ovarian cancer. Our findings add to the literature by showing that, despite intense glutaminolysis being linked to aggressiveness, glutamine availability itself does not modulate chemoresistance to cisplatin or paclitaxel, highlighting a unique aspect of ovarian carcinoma metabolism. Regarding chemosensitivity, OVCAR-3 exhibited significantly higher resistance to cisplatin (IC₅₀: 366.2±43.9 µM) and paclitaxel (IC₅₀: 270±30 µM) compared to ES-2 and TOV-21G (P=0.03), consistent with prior studies reporting 6–7-fold greater cisplatin resistance and 3.5–4-fold greater paclitaxel resistance (49). This resistance, independent of glutamine concentration (0.5–4 mM) (30,31), may involve glutathione metabolism for cisplatin or P-glycoprotein for paclitaxel (65). Although proliferation was not directly assessed using cell cycle or proliferation markers, the absence of changes in cell density in untreated control wells across the range of glutamine concentrations tested (0.5–4 mM) suggests that these quantitative variations in extracellular glutamine do not substantially affect basal cell growth under these conditions. The lack of glutamine’s impact on chemosensitivity or viability contrasts with Mathews et al. (50), who reported a 37% survival reduction at 0.5 mM glutamine under hypoglycemic conditions (3 mM glucose). The hyperglycemia in our study (11 mM), mimicking clinical conditions during chemotherapy with corticosteroids, likely mitigated this metabolic stress (50). Experiments under normoglycemia (6 mM) with deeper glutamine depletion (<0.5 mM) could provide further insight, pending clinical relevance. To avoid confounding effects, glucose concentration was held constant across all conditions, thereby isolating the impact of glutamine availability. Despite the known role of glutamine in glutathione synthesis and oxidative stress regulation, variations in glutamine concentration did not alter chemosensitivity to cisplatin or paclitaxel in our models. This suggests that, under hyperglycemic culture conditions, redox modulation by glutamine alone may be insufficient to influence drug response. Further studies under normoglycemic or stress-inducing conditions may provide additional insights.

The pronounced PI3K/AKT/mTORC1 activation in TOV-21G suggests potential sensitivity to mTORC1 inhibitors (e.g., everolimus), which could be tested in combination with cisplatin or paclitaxel to assess synergy. Analysis of glutathione, glutathione reductase, gamma-glutamyl transpeptidase 1 (GGT1), and DNA repair genes (ERCC1, BRCA1) may elucidate OVCAR-3’s chemoresistance (65). mGluR1 expression also warrants investigation (55). Only one ascites-derived sample was available for analysis, which restricts the strength of conclusions that can be drawn. Nevertheless, this preliminary observation underscores the need for expanded validation in multiple primary patient-derived cultures to confirm the translational relevance of glutamine metabolism in ovarian carcinoma. Metabolic heterogeneity necessitates studies on primary cells of diverse histologies to validate these findings in vivo. ¹⁸F-fluoroglutamine PET could guide therapeutic strategies by identifying aggressive tumors (18,66-68).

Although we did not evaluate the effect of pharmacological inhibitors in this study, our findings provide a rationale for testing glutaminase and mTORC1 inhibitors in ovarian carcinoma models, particularly in aggressive glutaminolytic phenotypes such as TOV-21G. Such approaches may further validate glutamine metabolism as a therapeutic vulnerability. Recent advances in cancer therapy have highlighted emerging drugs targeting glutamine metabolism, including glutaminase inhibitors (e.g., CB-839), ASCT2 inhibitors (e.g., V-9302), and cystine/glutamate transporter (xCT/SLC7A11) inhibitors, which aim to exploit cancer cells’ glutamine addiction (68). Our findings, demonstrating elevated glutaminolysis and PI3K/AKT/mTORC1 activation in TOV-21G cells, suggest potential sensitivity to these inhibitors, particularly given the role of mTORC1/S6K1 in driving glutamine metabolism. Combination strategies, such as CB-839 with V-9302 or xCT inhibitors, have shown synergistic effects by depleting glutathione and inducing oxidative stress, offering a promising avenue to enhance therapeutic efficacy in aggressive ovarian carcinomas (33). Future studies should explore these agents in our model to validate their impact on tumor aggressiveness and inform personalized treatment strategies.

Conclusions

This study underscores significant metabolic heterogeneity among ovarian carcinoma cell lines, with TOV-21G exhibiting a pronounced glycolytic and glutaminolytic phenotype, characterized by overexpression of glutaminase, c-Myc, and GDH1, alongside activation of the PI3K/AKT/mTORC1 pathway. These features correlate with a reduced doubling time, suggesting heightened tumor aggressiveness. Metabolic imaging using ¹⁸F-fluoroglutamine PET could identify this glutamine-dependent phenotype, offering diagnostic and prognostic value for predicting tumor aggressiveness and guiding personalized management, particularly for clear cell adenocarcinomas, which are often ¹⁸F-FDG PET-negative.

Varying glutamine bioavailability (0.5–4 mM) did not influence chemosensitivity to cisplatin or paclitaxel, nor did it significantly affect cell growth in the studied cell lines under the experimental conditions used. These findings suggest that, pending in vivo confirmation, glutamine supplementation could be considered in ovarian carcinoma patients undergoing chemotherapy. This approach, without exacerbating chemoresistance or tumor growth, may provide cytoprotective effects on healthy tissues and enhance antitumor immunity, thereby optimizing the therapeutic index.

Future studies on primary cells from diverse histologies are needed to validate these observations in a clinical context. Evaluating the expression of receptors such as mGluR1, involved in glutamatergic signaling, as well as DNA repair genes (ERCC1, BRCA1) or paclitaxel resistance genes (P-glycoprotein), could elucidate chemoresistance mechanisms, particularly in OVCAR-3. Additionally, trials combining cisplatin or paclitaxel with mTORC1 inhibitors (e.g., everolimus) could explore therapeutic synergies targeting glutaminolysis and the PI3K/AKT/mTORC1 pathway.

Acknowledgments

We would like to thank Christiane Chéreau, Servane Le Plénier, Sylvie Ricci, and Gabrielle Ventura (Paris, France) for their valuable support in experimental procedures and data collection.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1721/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1721/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1721/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1721/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was reviewed by the institutional ethics committee of Hôpital Cochin (AP-HP, Paris) in accordance with French legislation (No. 2012-300 of March 5, 2012, known as the “Loi Jardé”). As this retrospective study was based on de-identified human material collected during routine care, formal approval by the ethics committee (Comité de Protection des Personnes) was not required. Oral informed consent was obtained from all patients at the time of care, as per institutional policy.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Integrated genomic analyses of ovarian carcinoma. Nature 2011;474:609-15. [Crossref] [PubMed]
2. Icard P, Lincet H. La tumeur cancéreuse: un parasite métabolique? The cancer tumor: a metabolic parasite? Bulletin du Cancer 2013;100:427-33. [Crossref] [PubMed]
3. DeBerardinis RJ, Cheng T. Q’s next: the diverse functions of glutamine in metabolism, cell biology and cancer. Oncogene 2010;29:313-24. [Crossref] [PubMed]
4. Mazurek S. Pyruvate kinase M2: a key enzyme of the tumor metabolome and its medical relevance. Biomed Res 2012;23:133-41.
5. Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 2009;324:1029-33. [Crossref] [PubMed]
6. Icard P, Poulain L, Lincet H. Understanding the central role of citrate in the metabolism of cancer cells. Biochim Biophys Acta 2012;1825:111-6. [Crossref] [PubMed]
7. Nieman KM, Kenny HA, Penicka CV, et al. Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. Nat Med 2011;17:1498-503. [Crossref] [PubMed]
8. Teicher BA, Linehan WM, Helman LJ. Targeting cancer metabolism. Clin Cancer Res 2012;18:5537-45. [Crossref] [PubMed]
9. Zhao Y, Butler EB, Tan M. Targeting cellular metabolism to improve cancer therapeutics. Cell Death Dis 2013;4:e532. [Crossref] [PubMed]
10. Wise DR, DeBerardinis RJ, Mancuso A, et al. Myc regulates a transcriptional program that stimulates mitochondrial glutaminolysis and leads to glutamine addiction. Proc Natl Acad Sci U S A 2008;105:18782-7. [Crossref] [PubMed]
11. Kellenberger LD, Bruin JE, Greenaway J, et al. The role of dysregulated glucose metabolism in epithelial ovarian cancer. J Oncol 2010;2010:514310. [Crossref] [PubMed]
12. Yang G, Xiao X, Rosen DG, et al. The biphasic role of NF-kappaB in progression and chemoresistance of ovarian cancer. Clin Cancer Res 2011;17:2181-94. [Crossref] [PubMed]
13. Gao P, Tchernyshyov I, Chang TC, et al. c-Myc suppression of miR-23a/b enhances mitochondrial glutaminase expression and glutamine metabolism. Nature 2009;458:762-5. [Crossref] [PubMed]
14. Yang C, Sudderth J, Dang T, et al. Glioblastoma cells require glutamate dehydrogenase to survive impairments of glucose metabolism or Akt signaling. Cancer Res 2009;69:7986-93. [Crossref] [PubMed]
15. Lobo C, Ruiz-Bellido MA, Aledo JC, et al. Inhibition of glutaminase expression by antisense mRNA decreases growth and tumourigenicity of tumour cells. Biochem J 2000;348 Pt 2:257-61.
16. Durán RV, Hall MN. Glutaminolysis feeds mTORC1. Cell Cycle 2012;11:4107-8. [Crossref] [PubMed]
17. Mabuchi S, Kawase C, Altomare DA, et al. mTOR is a promising therapeutic target both in cisplatin-sensitive and cisplatin-resistant clear cell carcinoma of the ovary. Clin Cancer Res 2009;15:5404-13. [Crossref] [PubMed]
18. Jin J, Byun JK, Choi YK, et al. Targeting glutamine metabolism as a therapeutic strategy for cancer. Exp Mol Med 2023;55:706-15. [Crossref] [PubMed]
19. Fasoulakis Z, Koutras A, Ntounis T, et al. Ovarian Cancer and Glutamine Metabolism. Int J Mol Sci 2023;24:5041. [Crossref] [PubMed]
20. Tanizaki Y, Kobayashi A, Shiro M, et al. Diagnostic value of preoperative SUVmax on FDG-PET/CT for the detection of ovarian cancer. Int J Gynecol Cancer 2014;24:454-60. [Crossref] [PubMed]
21. Fafournoux P, Bruhat A, Jousse C. Amino acid regulation of gene expression. Biochem J 2000;351:1-12. [Crossref] [PubMed]
22. Kilberg MS, Pan YX, Chen H, et al. Nutritional control of gene expression: how mammalian cells respond to amino acid limitation. Annu Rev Nutr 2005;25:59-85. [Crossref] [PubMed]
23. Izuishi K, Kato K, Ogura T, et al. Remarkable tolerance of tumor cells to nutrient deprivation: possible new biochemical target for cancer therapy. Cancer Res 2000;60:6201-7.
24. Campbell WA, Sah DE, Medina MM, et al. TA1/LAT-1/CD98 light chain and system L activity, but not 4F2/CD98 heavy chain, respond to arginine availability in rat hepatic cells. Loss Of response in tumor cells. J Biol Chem 2000;275:5347-54. [Crossref] [PubMed]
25. Kobayashi H, Ishii Y, Takayama T. Expression of L-type amino acid transporter 1 (LAT1) in esophageal carcinoma. J Surg Oncol 2005;90:233-8. [Crossref] [PubMed]
26. Bode BP, Kaminski DL, Souba WW, et al. Glutamine transport in isolated human hepatocytes and transformed liver cells. Hepatology 1995;21:511-20.
27. Kim DK, Kim IJ, Hwang S, et al. System L-amino acid transporters are differently expressed in rat astrocyte and C6 glioma cells. Neurosci Res 2004;50:437-46. [Crossref] [PubMed]
28. Souba WW, Klimberg VS, Copeland EM 3rd. Glutamine nutrition in the management of radiation enteritis. JPEN J Parenter Enteral Nutr 1990;14:106S-8S. [Crossref] [PubMed]
29. Souba WW, Herskowitz K, Austgen TR, et al. Glutamine nutrition: theoretical considerations and therapeutic impact. JPEN J Parenter Enteral Nutr 1990;14:237S-43S. [Crossref] [PubMed]
30. Santoso JT, Lucci JA 3rd, Coleman RL, et al. Does glutamine supplementation increase radioresistance in squamous cell carcinoma of the cervix? Gynecol Oncol 1998;71:359-63. [Crossref] [PubMed]
31. Klimberg VS, Souba WW, Salloum RM, et al. Glutamine-enriched diets support muscle glutamine metabolism without stimulating tumor growth. J Surg Res 1990;48:319-23. [Crossref] [PubMed]
32. Newsholme EA, Crabtree B, Ardawi MS. Glutamine metabolism in lymphocytes: its biochemical, physiological and clinical importance. Q J Exp Physiol 1985;70:473-89. [Crossref] [PubMed]
33. Nan D, Yao W, Huang L, et al. Glutamine and cancer: metabolism, immune microenvironment, and therapeutic targets. Cell Commun Signal 2025;23:45. [Crossref] [PubMed]
34. Windmueller HG. Glutamine utilization by the small intestine. Adv Enzymol Relat Areas Mol Biol 1982;53:201-37. [Crossref] [PubMed]
35. Chang WK, Yang KD, Shaio MF. Effect of glutamine on Th1 and Th2 cytokine responses of human peripheral blood mononuclear cells. Clin Immunol 1999;93:294-301. [Crossref] [PubMed]
36. Horio Y, Osawa S, Takagaki K, et al. Glutamine supplementation increases Th1-cytokine responses in murine intestinal intraepithelial lymphocytes. Cytokine 2008;44:92-5. [Crossref] [PubMed]
37. Ziegler TR, Young LS, Benfell K, et al. Clinical and metabolic efficacy of glutamine-supplemented parenteral nutrition after bone marrow transplantation. A randomized, double-blind, controlled study. Ann Intern Med 1992;116:821-8. [Crossref] [PubMed]
38. Schloerb PR, Amare M. Total parenteral nutrition with glutamine in bone marrow transplantation and other clinical applications (a randomized, double-blind study). JPEN J Parenter Enteral Nutr 1993;17:407-13. [Crossref] [PubMed]
39. Winters R, Matthews R, Ercal N, et al. Glutamine protects Chinese hamster ovary cells from radiation killing. Life Sci 1994;55:713-20. [Crossref] [PubMed]
40. Klimberg VS, Souba WW, Dolson DJ, et al. Prophylactic glutamine protects the intestinal mucosa from radiation injury. Cancer 1990;66:62-8. [Crossref] [PubMed]
41. Klimberg VS, Salloum RM, Kasper M, et al. Oral glutamine accelerates healing of the small intestine and improves outcome after whole abdominal radiation. Arch Surg 1990;125:1040-5. [Crossref] [PubMed]
42. Amores-Sánchez MI, Medina MA. Glutamine, as a precursor of glutathione, and oxidative stress. Mol Genet Metab 1999;67:100-5. [Crossref] [PubMed]
43. Souba WW. Glutamine and cancer. Ann Surg 1993;218:715-28. [Crossref] [PubMed]
44. Monavarian M, Elhaw AT, Tang PW, et al. Emerging perspectives on growth factor metabolic relationships in the ovarian cancer ascites environment. Semin Cancer Biol 2022;86:709-19. [Crossref] [PubMed]
45. Shepherd TG, Thériault BL, Campbell EJ, et al. Primary culture of ovarian surface epithelial cells and ascites-derived ovarian cancer cells from patients. Nat Protoc 2006;1:2643-9. [Crossref] [PubMed]
46. Kwok AL, Wong OG, Wong ES, et al. Caution over use of ES2 as a model of ovarian clear cell carcinoma. J Clin Pathol 2014;67:921-2. [Crossref] [PubMed]
47. Devor EJ, Lapierre JR, Bender DP. ES 2 ovarian cancer cells present a genomic profile inconsistent with their reported history. Obstet Gynecol Res 2021;4:233-8.
48. Neveux N, David P, Cynober L. Measurement of amino acid concentration in biological fluids and tissues using ion-exchange chromatography. In: Cynober LA. editor. Metabolic & therapeutic aspects of amino acids in clinical nutrition. 2nd ed. Boca Raton: CRC Press; 2003:17-28.
49. Smith JA, Ngo H, Martin MC, et al. An evaluation of cytotoxicity of the taxane and platinum agents combination treatment in a panel of human ovarian carcinoma cell lines. Gynecol Oncol 2005;98:141-5. [Crossref] [PubMed]
50. Mathews EH, Stander BA, Joubert AM, et al. Tumor cell culture survival following glucose and glutamine deprivation at typical physiological concentrations. Nutrition 2014;30:218-27. [Crossref] [PubMed]
51. Heeneman S, Deutz NE, Buurman WA. The concentrations of glutamine and ammonia in commercially available cell culture media. J Immunol Methods 1993;166:85-91. [Crossref] [PubMed]
52. Willems L, Jacque N, Jacquel A, et al. Inhibiting glutamine uptake represents an attractive new strategy for treating acute myeloid leukemia. Blood 2013;122:3521-32. [Crossref] [PubMed]
53. Yang L, Moss T, Mangala LS, et al. Metabolic shifts toward glutamine regulate tumor growth, invasion and bioenergetics in ovarian cancer. Mol Syst Biol 2014;10:728. [Crossref] [PubMed]
54. DeBerardinis RJ, Mancuso A, Daikhin E, et al. Beyond aerobic glycolysis: transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. Proc Natl Acad Sci U S A 2007;104:19345-50. [Crossref] [PubMed]
55. Speyer CL, Smith JS, Banda M, et al. Metabotropic glutamate receptor-1: a potential therapeutic target for the treatment of breast cancer. Breast Cancer Res Treat 2012;132:565-73. [Crossref] [PubMed]
56. Lieberman BP, Ploessl K, Wang L, et al. PET imaging of glutaminolysis in tumors by 18F-(2S,4R)4-fluoroglutamine. J Nucl Med 2011;52:1947-55. [Crossref] [PubMed]
57. Ghini V, Sorbi F, Fambrini M, et al. NMR Metabolomics of Primary Ovarian Cancer Cells in Comparison to Established Cisplatin-Resistant and -Sensitive Cell Lines. Cells 2024;13:661. [Crossref] [PubMed]
58. Ni R, Li Z, Li L, et al. Rethinking glutamine metabolism and the regulation of glutamine addiction by oncogenes in cancer. Front Oncol 2023;13:1143798. [Crossref] [PubMed]
59. Elstrom RL, Bauer DE, Buzzai M, et al. Akt stimulates aerobic glycolysis in cancer cells. Cancer Res 2004;64:3892-9. [Crossref] [PubMed]
60. Cancer Cell Line Encyclopedia (CCLE). [Cited 2025 May 18]. Available online: http://www.broadinstitute.org/ccle/home
61. Migone TS, Rodig S, Cacalano NA, et al. Functional cooperation of the interleukin-2 receptor beta chain and Jak1 in phosphatidylinositol 3-kinase recruitment and phosphorylation. Mol Cell Biol 1998;18:6416-22. [Crossref] [PubMed]
62. Yuan L, Sheng X, Willson AK, et al. Glutamine promotes ovarian cancer cell proliferation through the mTOR/S6 pathway. Endocr Relat Cancer 2015;22:577-91. [Crossref] [PubMed]
63. Trejo-Solis C, Silva-Adaya D, Serrano-García N, et al. Role of Glycolytic and Glutamine Metabolism Reprogramming on the Proliferation, Invasion, and Apoptosis Resistance through Modulation of Signaling Pathways in Glioblastoma. Int J Mol Sci 2023;24:17633. [Crossref] [PubMed]
64. Prasad P, Roy SS. Glutamine regulates ovarian cancer cell migration and invasion through ETS1. Heliyon 2021;7:e07064. [Crossref] [PubMed]
65. Stordal B, Hamon M, McEneaney V, et al. Resistance to paclitaxel in a cisplatin-resistant ovarian cancer cell line is mediated by P-glycoprotein. PLoS One 2012;7:e40717. [Crossref] [PubMed]
66. Chopra A. 18F-(2S,4R)4-fluoroglutamine. In: Molecular Imaging and Contrast Agent Database (MICAD). Bethesda: National Center for Biotechnology Information (US); 2011.
67. Kao TW, Chuang YC, Lee HL, et al. Therapeutic Targeting of Glutaminolysis as a Novel Strategy to Combat Cancer Stem Cells. Int J Mol Sci 2022;23:15296. [Crossref] [PubMed]
68. Li S, Zeng H, Fan J, et al. Glutamine metabolism in breast cancer and possible therapeutic targets. Biochem Pharmacol 2023;210:115464. [Crossref] [PubMed]