Lactate trafficking inhibition restores sensitivity to proteasome inhibitors and orchestrates immuno-microenvironment in multiple myeloma

Abstract

Metabolic changes of malignant plasma cells (PCs) and adaptation to tumour microenvironment represent one of the hallmarks of multiple myeloma (MM). We previously showed that MM mesenchymal stromal cells are more glycolytic and produce more lactate than healthy counterpart. Hence, we aimed to explore the impact of high lactate concentration on metabolism of tumour PCs and its impact on the efficacy of proteasome inhibitors (PIs). Lactate concentration was performed by colorimetric assay on MM patient's sera. The metabolism of MM cell treated with lactate was assessed by seahorse and real time Polymerase Chain Reaction (PCR). Cytometry was used to evaluate mitochondrial reactive oxygen species (mROS), apoptosis and mitochondrial depolarization. Lactate concentration resulted increased in MM patient's sera. Therefore, PCs were treated with lactate and we observed an increase of oxidative phosphorylation-related genes, mROS and oxygen consumption rate. Lactate supplementation exhibited a significant reduction in cell proliferation and less responsive to PIs. These data were confirmed by pharmacological inhibition of monocarboxylate transporter 1 (MCT1) by AZD3965 which was able to overcame metabolic protective effect of lactate against PIs. Consistently, high levels of circulating lactate caused expansion of Treg and monocytic myeloid derived suppressor cells and such effect was significantly reduced by AZD3965. Overall, these findings showed that targeting lactate trafficking in TME inhibits metabolic rewiring of tumour PCs, lactate-dependent immune evasion and thus improving therapy efficacy.

FIGURE 1

Lactate concentration in patients' sera and lactate transporters in human myeloma cell lines (HMCLs) analysis. (A) Evaluation of the amount of circulating lactate in peripheral blood from patients with plasma cell disorders in respect with healthy controls. (B) Boxplot for the Ct values of MCT1, MCT4 and GPR81 from RT‐qPCR analysis of HMCLs (U266, NCI‐H929 and OPM2). (C) Western blot analysis of MCT1 and MCT4 proteins after 3, 6 and 24 h lactate exposure. β‐Actin protein was used as total protein loading reference. For analysis of Western blot, the optical density of the bands was measured using Scion Image software. (D, E) Quantification of apoptotic cells upon BTZ and CFZ exposure in multiple myeloma cell lines grown in medium control or supplemented with lactate for 72 h. All the data are presented as means ± SD of three independent experiments. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001

FIGURE 2

Combination of proteasome inhibitors (PIs) with AZD3965 increases PI‐induced cytotoxicity in vitro. (A) Immunofluorescence images showing co‐localization of MCT1 with CD138⁺ cells in bone marrow biopsy from a multiple myeloma (MM) patient at diagnosis and a refractory one. Scale bar: 50 μm. (B, C) The graphs show the mean values of the percentage of apoptotic cells (annexin‐V and propidium iodide positive) in NCI‐H929 cells cultured in medium supplemented with 20% MM serum after treatment with BTZ or (E, F) CFZ in combination with AZD3965 or 3‐OBA. (D, G) Mitochondrial membrane potential was assessed following DiOC2(3) staining. All the data are presented as means ± SD of three independent experiments. **p < 0.01; ****p < 0.0001

FIGURE 3

Multiple myeloma cells enhances mitochondrial metabolism after exposure to lactate. (A–C) Gene expression analysis of glycolytic‐ and OXPHOS‐related genes after 3 h lactate exposure. B2M gene was used as housekeeping gene. (D) Schematic principle of the MitoStress test performed. Individual parameters for acute response, proton leak, maximal respiration, non‐mitochondrial respiration and ATP production in U266 (E) and NCI‐H929 (F) cells after injection of lactate. (G–L) Statistical analysis in U266 and NCI‐H929 cell lines of the individual MitoStress test parameters. Measurement was done in three separate experiments with n = 5 replicates per condition. All the data are presented as means ± SD of three independent experiments. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001

FIGURE 4

Metabolic adaptation of myeloma plasma cells to high concentration of lactate. (A, B) Cell proliferation assessed in multiple myeloma cell lines grown in medium control or supplemented with lactate for 72 h. Graphs show the mean of cell number ± SD evaluated using EVE™ automated cell counter. (C) Evaluation of OCR in U266 and (G) NCI‐H929 after 72 h culture with medium control or supplemented with lactate. (D–F) Quantification of U266 and (H–L) NCI‐H929 cells MitoStress test parameters. Measurement was done in three separate experiments with n = 5 replicates per condition. All the data are presented as means ± SD of three independent experiments. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001

FIGURE 5

High lactate concentrations promote M‐MDSCs and T‐reg expansion in multiple myeloma (MM) microenvironment. (A) Evaluation of the percentage of M‐MDSC and (E) Treg in peripheral blood mononucleated cells (PBMCs) after exposure to lactate alone or in combination with AZD3965. (B) 3,5 DHBA does not induce expansion of M‐MDSCs and (F) Treg. (C) Analysis of M‐MDSCs and (G) Treg percentages after culturing PBMCs in h‐CM or h‐MM. (D) Evaluation of AZD3965 effects on circulating lactate‐induced M‐MDSCs and (H) Treg expansion. (I–L) M‐MDSCs and Treg analysis in PBMCs after co‐culture with HMCLs in presence or not of AZD3965. Data are presented as means ± SD of three independent experiments. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001

FIGURE 6

Lactate trafficking modulates tumour microenvironment in multiple myeloma