Enzyme-mediated depletion of serum l-Met abrogates prostate cancer growth via multiple mechanisms without evidence of systemic toxicity

Abstract

Extensive studies in prostate cancer and other malignancies have revealed that l-methionine (l-Met) and its metabolites play a critical role in tumorigenesis. Preclinical and clinical studies have demonstrated that systemic restriction of serum l-Met, either via partial dietary restriction or with bacterial l-Met-degrading enzymes exerts potent antitumor effects. However, administration of bacterial l-Met-degrading enzymes has not proven practical for human therapy because of problems with immunogenicity. As the human genome does not encode l-Met-degrading enzymes, we engineered the human cystathionine-γ-lyase (hMGL-4.0) to catalyze the selective degradation of l-Met. At therapeutically relevant dosing, hMGL-4.0 reduces serum l-Met levels to >75% for >72 h and significantly inhibits the growth of multiple prostate cancer allografts/xenografts without weight loss or toxicity. We demonstrate that in vitro, hMGL-4.0 causes tumor cell death, associated with increased reactive oxygen species, S-adenosyl-methionine depletion, global hypomethylation, induction of autophagy, and robust poly(ADP-ribose) polymerase (PARP) cleavage indicative of DNA damage and apoptosis.

Keywords: hMGL; l-methionine depletion; prostate cancer.

Fig. 1.

Engineered human methioninase for cancer treatment. (A) Overview of the role of methionine in cell metabolism and the key metabolic and physiological effects arising from its depletion. (B) Michaelis–Menten parameters for engineered hMGL variants and for the parent enzymes, hCGL. (C) Serum l-Met concentration and pharmacokinetics of PEG/hMGL-4.0 following a single-dose intraperitoneal administration (50 mg/kg) in C57BL/6J mice. (D and E) Structural features of hMGL-4.0. (D) Comparison of the 50–56 loop in the “open” state in hMGL-4.0 (shown in red) and in the “closed” state in wild-type hCGL (shown in yellow). The secondary structures of CGL are shown in ribbon diagram with one monomer colored pink and the neighboring one light blue, with the exception the 50–56 loop colored in yellow for the wild-type and for hMGL-4.0 in red. PLP is shown in stick with carbon atom colored green. (E) The location of the mutated residues in hMGL-4.0. Residues that were engineered to create hMGL-4.0 that have been shown in space-filling mode. Coloring scheme for the secondary structures of CGL is identical with D. The PLP cofactor is shown in stick with carbon atoms colored yellow.

Fig. 2.

Efficacy of hMGL-4.0 administration in murine PCa models. (A) Quantitation of tumor volume in male FVB/N mice bearing allograft tumors of HMVP2 PCa spheroids following treatment with hMGL-4.0 or controls (PBS, n = 17; hMGL-4.0 [50 mg/kg], n = 18). (B) Relative concentrations of l-Met and 2-ketobutyric acid in serum determined by MS following termination of the HMVP2 allograft studies (n = 5 per group). (C) Quantitation of tumor volume in male nude mice bearing xenograft tumors of DU145 PCa cells following treatment with hMGL-4.0 or controls (PBS, n = 9; heat-deactivated hMGL-4.0 [50 mg/kg], n = 15; hMGL-4.0 [50 mg/kg], n = 18). (D) Quantitation of tumor volume in male nude mice bearing xenograft tumors of 22Rv1 PCa cells following treatment with PBS, n = 12; hMGL-4.0 (50 mg/kg), n = 12; hMGL-4.0 (20 mg/kg), n = 12. Throughout, data are expressed as mean ± SEM. (A, C, and D) Repeated-measures two-way ANOVA followed by Bonferroni’s multiple-comparison test; (B) two-tailed Student’s t test. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

Fig. 3.

Metabolomic analyses of HMVP2 PCa cells in treating with hMGL-4.0. (A–D) Relative concentrations of methionine and nonmethionine intracellular metabolites in HMVP2 PCa cells as a function of increasing concentration hMGL-4.0 after 24-h treatment (bars left of the dividing lines) and in HMVP2 allograft tumor tissues after treatment with control or hMGL-4.0 50 mg/kg (bars right of the dividing lines) as determined by MS (n = 3 cell culture replicates and n = 8 or 9 for control and hMGL-4.0 50-mg/kg treated tumor tissues). Select metabolites in (A) methionine pathway, (B) polyamine pathway, (C) cysteine pathway, and (D) relative ROS levels in HMVP2 PCa cells as a function of increasing concentration hMGL-4.0. Cellular ROS levels were measured by DCFDA fluorescence 4-h posttreatment (data are from four independent experiments; for each experiment n = 3 cell culture replicates at each dose). (E) Total, oxidized, and reduced GSH levels were measured at 24-h time point by the spectrophotometric method (n = 3 cell culture replicates at each dose). All data are expressed as mean ± SEM. One-way ANOVA followed by Bonferroni’s multiple comparison test (for in vitro cell culture) and Student t test (for in vivo tumor tissues). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

Fig. 4.

Cell cycle and signaling pathway analyses of HMVP2 PCa cells in treatment with hMGL-4.0. (A–E) HMVP2 cells were treated with indicated concentrations of hMGL-4.0 for 24 h. Metabolic stress markers and cell cycle regulatory proteins were measured by immunoblot. Immunoblots were performed at least three times with β-actin controls for each experiment. (D) Cell-cycle phase distribution was measured by guava-based flow cytometry at 24-h time point (n = 5 independent experiments); one-way ANOVA followed by Bonferroni’s multiple-comparison test. *P < 0.05, ****P < 0.0001.

Fig. 5.

Efficacy of hMGL-4.0 and curcumin. (A) Quantitation of tumor volume in male nude mice bearing xenograft tumors of 22Rv1 PCa cells following treatment with PBS, n = 12; hMGL-4.0 (50 mg/kg), n = 12; hMGL-4.0 (20 mg/kg), n = 12; 1% curcumin in diet, n = 10; or hMGL-4.0 (20 mg/kg) and curcumin in combination, n = 15. (B and C) Quantitation of (B) body weight (n = 7 to 8) and average (C) food consumption (n = 3 to 6) for each treatment group. Throughout, data are expressed as mean ± SEM. Repeated-measures two-way ANOVA (A and B) or one-way ANOVA (C) followed by Bonferroni’s multiple comparison test was used for statistical analyses. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.