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. 2023 Jan 19;26(2):106020.
doi: 10.1016/j.isci.2023.106020. eCollection 2023 Feb 17.

Exploiting metabolic vulnerabilities after anti-VEGF antibody therapy in ovarian cancer

Deanna Glassman  1 , Mark S Kim  1 , Meredith Spradlin  2   3 , Sunil Badal  2 , Mana Taki  1 , Pratip Bhattacharya  4 , Prasanta Dutta  4 , Charles V Kingsley  4 , Katherine I Foster  1 , Olamide Animasahun  5   6 , Jin Heon Jeon  5 , Abhinav Achreja  5 , Anusha Jayaraman  5 , Praveen Kumar  5 , Minal Nenwani  5 , Fulei Wuchu  5 , Emine Bayraktar  1 , Yutuan Wu  1 , Elaine Stur  1 , Lingegowda Mangala  1 , Sanghoon Lee  1   7 , Timothy A Yap  8   9 , Shannon N Westin  1 , Livia S Eberlin  2   3 , Deepak Nagrath  5 , Anil K Sood  1
Affiliations

Exploiting metabolic vulnerabilities after anti-VEGF antibody therapy in ovarian cancer

Deanna Glassman et al. iScience. .

Abstract

Despite modest clinical improvement with anti-vascular endothelial growth factor antibody (AVA) therapy in ovarian cancer, adaptive resistance is ubiquitous and additional options are limited. A dependence on glutamine metabolism, via the enzyme glutaminase (GLS), is a known mechanism of adaptive resistance and we aimed to investigate the utility of a GLS inhibitor (GLSi). Our in vitro findings demonstrated increased glutamine abundance and a significant cytotoxic effect in AVA-resistant tumors when GLSi was administered in combination with bevacizumab. In vivo, GLSi led to a reduction in tumor growth as monotherapy and when combined with AVA. Furthermore, GLSi initiated after the emergence of resistance to AVA therapy resulted in a decreased metabolic conversion of pyruvate to lactate as assessed by hyperpolarized magnetic resonance spectroscopy and demonstrated robust antitumor effects with a survival advantage. Given the increasing population of patients receiving AVA therapy, these findings justify further development of GLSi in AVA resistance.

Keywords: Cancer; Cellular physiology; Oncology.

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Conflict of interest statement

A.K.S. reports consulting for KIYATEC, Merck & Co., GSK, Onxeo, ImmunoGen, Iylon, and AstraZeneca; being a stockholder in Bio-Path Holdings; and receiving research funding from MTrap. T.A.Y. reports AstraZeneca, Almac, Aduro, Artios, Athena, Atrin, Axiom, Bayer, Bristol Myers Squibb, Calithera, Clovis, Cybrexa, EMD Serono, F-Star, GLG, Guidepoint, Ignyta, I-Mab, ImmuneSensor, Jansen, Merck, Pfizer, Repare, Roche, Schrodinger, Seattle Genetics, Varian, Zai Labs, and ZielBio. T.A.Y. is also the Medical Director of the Institute for Applied Cancer Science where the glutaminase inhibitor IACS-012031 was developed. S.N.W. reports AstraZeneca, Bayer, Clovis Oncology, Cotinga Pharmaceuticals, GSK, Mereo, Novartis, OncXerna, Roche/Genentech, Zentalis; and is a paid consultant for Agenus, AstraZeneca, Clovis Oncology, Eisai, EQRX, GSK, ImmunoGen, Lilly, Merck, Mereo, Novartis, Pfizer, Roche/Genentech, Vincerx, and Zentalis. L.S.E. is an inventor in patents owned by Purdue Research Foundation related to desorption electrospray ionization mass spectrometry imaging technology and receives royalties from its commercialization. L.S.E. has received research funding from Merck and Thermo Fisher Scientific.

Figures

None
Graphical abstract
Figure 1
Figure 1
Metabolic alterations in hypoxia and resulting from AVA therapy (A and B) RF24 and OVCAR8 cells were cultured in normoxia (N) or hypoxia (H) for 12 and 24 h. GLS and HIF-1α protein (A) and GLS mRNA (B) expression levels in both cell lines were measured. Error bars indicate SD, ∗∗∗p < 0.001. (C) Images of nude mice inoculated with 1 × 106 SKOV3ip1 cells and given bevacizumab (6.25 mg/kg twice weekly) until resistance emerged. (D) IHC staining of frozen mouse ovarian tumors for the hypoxia marker CA9 in control vs. bevacizumab-treated (AVA-treated) tumors. (E) Quantification of CA9 staining in control vs. AVA-treated tumors per high power field, error bars indicate SD ∗∗p < 0.01. (F) IHC staining of frozen mouse ovarian tumors for endothelial cell marker CD31 in control vs. AVA-sensitive or AVA-resistant tumors. (G) Quantification of vessel densities in control, AVA-sensitive, and AVA-resistant tumor samples. Error bars indicate SD. ∗∗∗p < 0.001 compared with the control group (Student’s t test). AVA, anti-VEGF antibody (bevacizumab); AVA-sens, AVA-sensitive; AVA-resis, AVA-resistant.
Figure 2
Figure 2
Adaptive resistance of ovarian cancer to AVA therapy induces alterations in glutamine metabolism (A) Plot of metabolic pathway impact analysis results after quantitative metabolic analysis by LC-MS of ovarian tumor from an orthotopic mouse model with SKOV3ip1 cells comparing control vs. AVA-resistant groups showing that the purine and pyrimidine metabolism differed markedly (FDR < 0.05). Data were generated using a MetaboAnalyst plot made with Prism software. (B) Simplified diagram of the purine and pyrimidine metabolic pathways showing the key metabolites involved in purine and pyrimidine nucleotide metabolism. The inserted scatterplots show significantly higher levels of downstream metabolites indicated in the dashed arrow lines (urate, xanthine, xanthosine, and 3-ureidopropionate) in the resistant group (R) than in the control group (C), p < 0.05. UMP, uridine monophosphate; OMP, orotidine 5′-monophosphate; CMP, cytidine monophosphate; PRPP, phosphoribosyl pyrophosphate; GMP, guanosine monophosphate; XMP, xanthosine monophosphate; IMP, inosine monophosphate; AMP, adenosine monophosphate. Key intermediates in amino acid metabolism, glycolysis, and purine synthesis are indicated in bold. (C) Intensity heatmap for metabolites selected by SAM as significantly altered among control, AVA-resistant, and AVA-sensitive tissues (FDR <5%) identified by DESI-MS imaging. Features were clustered using a Euclidean-distance formula according to the average signal intensity of the corresponding m/z value measured from tumor-specific regions. The color scale reflects Z score standard deviations from the mean relative abundance measured for each ion. For fatty acid (FA) species, X:Y indicates the number of carbons and double bonds, respectively. (D) DESI-MS images showing an increase in glutaminolysis in AVA-treated tumors compared to control tumors, with an increased relative abundances of glutamate relative to glutamine in AVA-resistant compared to AVA-sensitive tissues and a decreased abundance of glutamate and glutamine in all AVA-treated tumors.
Figure 3
Figure 3
Effect of HIF-1α on GLS expression and baseline GLS expression levels in ovarian cancer and endothelial cells (A) RF24 and OVCAR8 cells were transfected with HIF-1α or HIF-2α siRNA following exposure to 1% O2 for 24 h. Non-targeting siRNA (NS) was used for control. GLS mRNA relative expression level is shown with error bars indicating SD, ∗∗∗p < 0.001. (B) Western blot of GLS expression level in multiple untreated ovarian cancer cell lines. (C) Western blot of GLS expression in a series of endothelial cell lines cultured in both normoxic (N, 20% O2) or hypoxic (H, 1% O2) conditions; cell lines tested included a bevacizumab-resistant endothelial cell line (RF24-bev) and parental RF24 cells along with murine ovarian endothelial cells (MOEC) and human microvascular endothelial cells (HMEC).
Figure 4
Figure 4
Hypoxia enhances sensitivity to GLS inhibition in both cancer and endothelial cells in vitro (A) The viability of three ovarian cancer cell lines (SKOV3, OVCAR5, and OVCAR8) upon culture with GLSi alone for 24–48 h in normoxic conditions (p < 0.05). (B) The viability of the RF24 endothelial cell line after culturing with a positive control (VEGF), bevacizumab (AVA), GLSi monotherapy, or a combination of both therapies for 24 h in normoxic conditions (∗p < 0.05, ∗∗∗p < 0.001). (C) The viability of four endothelial cell lines (MOEC, HMEC, parental RF24, and RF24-bev) after culturing in increasing concentrations of GLSi in either normoxic (20% O2) or hypoxic (1% O2) conditions. (D) Bright-field microscopic images at 50X showing the vessel loop formation and the effect of bevacizumab (AVA) and GLSi monotherapy or in combination (AVA + GLSi) on the angiogenic capability of RF24-par cells in culture in normoxia (top row) versus hypoxia (bottom row). (E) Average branch counts of the vessel loops described in the tube formation assay in (D) detected at 6 h as measured in triplicate experiments; ∗∗p < 0.01, ∗∗∗p < 0.001. VEGF, vascular endothelial growth factor; AVA, anti-VEGF antibody; GLSi, glutaminase inhibitor.
Figure 5
Figure 5
Robust antitumor effect of GLSi combined with AVA in an SKOV3ip1 mouse model as assessed using gross necropsy and immunohistochemistry (A) In vivo schema of experimental protocol. (B) Nodule numbers and tumor weights of SKOV3ip1 mouse model after either no treatment (Control), bevacizumab (AVA), IACS-012031 (GLSi), or a combination of bevacizumab and GLSi (AVA + GLSi). Error bars, SD. ∗p < 0.05; ∗∗p < 0.01 (compared with the control group using the Student’s t test). (C) Blood vessel densities in ovarian tumor tissues harvested from mice in each treatment group as assessed by endothelial cell marker CD31. ∗p < 0.05; ∗∗∗p < 0.001. (D) Cell proliferation assay according to immunohistochemistry staining of mouse ovarian cancer tissues stained with anti-Ki67. ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001.
Figure 6
Figure 6
GLSi therapy has robust antitumor effects in combination with bevacizumab (AVA) (A) Representative DESI-MS ion images of pyruvate, lactate, glutamine, and glutamate in mouse tumors after treatment with a control vehicle, AVA, GLSi, or a combination of the two. H&E, hematoxylin and eosin. (B) Median normalized intensity of the relative abundance of glutamate metabolism and TCA cycle intermediates in control, AVA monotherapy, GLSi monotherapy, and combination therapy groups (Combo) mice in the SKOV3ip1 orthotopic ovarian cancer model. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001. (C) Median normalized intensity of the lactate-to-pyruvate ratio in the control and treatment groups. ∗p < 0.05; ns, not significant. (D) Median normalized intensity ratio of glutamate to glutamine in the control, AVA monotherapy, GLSi monotherapy, and combination therapy treatment groups. Statistical significance was determined by Tukey’s HSD test, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001.
Figure 7
Figure 7
Detection of GLSi therapy response by HP-MRS and evaluating changes in AVA sensitivity when GLSi is given after emergence of AVA resistance (A) Representative T2-weighted MRI (coronal slice) and real-time in vivo13C magnetic resonance spectroscopy images of AVA-resistant ovarian tumors in mice after intravenous hyperpolarized pyruvate injection for two treatment groups: vehicle control (left) and GLSi (right) with spectra collected from the MRI slabs on the ovarian tumors over 2 s. (B) The normalized lactate/pyruvate ratios for vehicle- and GLSi-treated mice. ∗∗∗p < 0.001. (C) Schema of the orthotopic SKOV3ip1 mouse model in which AVA resistance was established prior to the initiation of GLSi therapy. AVA, anti-VEGF antibody, (bevacizumab). (D) Body weights, tumor weights, and nodule numbers at the time of necropsy for mice treated with AVA combined with a vehicle vs. those given AVA combined with GLSi. Error bars, SD. ∗p < 0.05; ∗∗∗p < 0.001 (compared with the control group using the Student’s t test). ns, not significant. (E) Kaplan-Meier survival curve showing survival advantage with GLSi therapy after AVA resistance is established (p = 0.048, HR 2.07; 95% CI 1.03–4.15). AVA, anti-VEGF antibody, (bevacizumab); GLSi, glutaminase inhibitor.
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