Progression of multiple myeloma is regulated by factors intrinsic to the clonal plasma cells (PC) and by the immune effector cells in the tumor microenvironment. In this study, we investigated the interaction between CD304 expression on myeloid-derived suppressor cells (MDSC) and galectin-1 from malignant PCs in the context of autologous stem cell transplantation (ASCT) for multiple myeloma. Using high-throughput screening, CD304 expression on circulating monocytic MDSCs (M-MDSC; CD14+HLA-DRlow/−) was compared before and after ASCT. There was a significantly higher M-MDSC expression of CD304 before ASCT and a clear correlation between circulating pre-ASCT M-MDSC frequency and serum galectin-1 concentration. Treatment of pre-ASCT M-MDSCs, but not post-ASCT M-MDSCs, with galectin-1 in vitro expanded the M-MDSC population and increased expression of CD304. High galectin-1 expression by malignant PCs was associated with poor clinical outcomes. M-MDSC development and expression of CD304 were differentially induced when healthy donor peripheral blood mononuclear cells were cultured with the human multiple myeloma cell lines RPMI-8226 and JJN3, which express high and low galectin-1, respectively. Inhibition of galectin-1 reduced M-MDSC proliferation induced by RPMI-8226 cells but not by JJN3 cells, and blockade of CD304 reduced M-MDSC migration induced by RPMI-8226 cells but not by JJN3 cells. In addition, blockade of CD304 reversed suppression of the in vitro cytotoxic effect of melphalan by pre-ASCT M-MDSCs. Our data demonstrate that multiple myeloma–derived galectin-1 could mediate the tumor-promoting effect of M-MDSCs through its interaction with CD304 on M-MDSCs and contribute to multiple myeloma progression after ASCT.
See related Spotlight on p. 488
Multiple myeloma is characterized by the accumulation of malignant plasma cells (PC) in the bone marrow (BM). Despite therapeutic advances in the last decade, multiple myeloma remains largely incurable because malignant PCs eventually develop resistance to therapies and the cancer progresses (1). Interaction of malignant PCs with different cell components of the tumor microenvironment has a significant role in the development of resistance to therapy and in the maintenance of residual disease. These interactions ultimately determine the clinical behavior of the disease, with a direct impact on overall prognosis (2).
For decades, increasing evidence has shown a variety of immune defects in multiple myeloma (3). Immune dysfunction is an important feature of multiple myeloma and can lead to a relapsing state and to infection. High-dose melphalan followed by autologous stem cell transplantation (ASCT) remains the standard treatment for young patients newly diagnosed with multiple myeloma and for select, fit elderly patients (4). Although use of immunomodulatory drugs and proteasome inhibitors after ASCT improves clinical outcomes (5, 6), the vast majority of patients eventually experience disease relapse and progression.
Myeloid-derived suppressor cells (MDSC) are a mixture of monocytic and granulocytic cells that constitute a significant component of the multiple myeloma tumor microenvironment (7). Evidence is emerging that these cells have an important role in multiple myeloma pathogenesis and clinical behavior. Several prior studies have shown that the number of MDSCs increases in both peripheral blood (PB) and BM of patients with multiple myeloma, with bidirectional interaction between MDSCs and malignant PCs within the multiple myeloma microenvironment (8, 9). Monocytic MDSCs (M-MDSC; CD14+HLA-DRlow/−) were first reported to be increased in frequency in the PB of patients with multiple myeloma at diagnosis compared with the PB of healthy donors (10). Later, the level of M-MDSCs in the PB was positively correlated with relapsed multiple myeloma and was negatively related to treatment response (11). We previously demonstrated that pre-ASCT M-MDSCs are correlated with poor clinical outcomes after ASCT because they reduce the cytotoxicity of melphalan. In contrast, the effect of MDSCs on melphalan was unaffected by post-ASCT MDSC phenotype (12).
In this study, high-throughput screening using isolated M-MDSCs before and after ASCT revealed that one of the most distinct markers, CD304 (also known as neuropilin-1), was highly expressed in M-MDSCs isolated pre-ASCT compared with those isolated post-ASCT. CD304 was shown to be upregulated in pre-ASCT MDSCs by galectin-1 derived from malignant PCs. Endogenous galectin-1 expression from malignant PCs correlated with poor prognosis. In addition, blocking the CD304–galectin-1 interaction suppressed multiple myeloma–induced migration of M-MDSCs and enhanced melphalan-induced cytotoxic effects on multiple myeloma cells in the presence of pre-ASCT MDSCs.
Materials and Methods
Patients and transplant procedures
A total of 80 consecutive patients with multiple myeloma who underwent ASCT as part of a frontline treatment at our institution between January 2014 and December 2016 were enrolled in this analysis. ASCT was performed after patients achieved a partial response (PR) or greater. Response categories were determined in accordance with the International Myeloma Working Group uniform response criteria. A PR was defined as 50% or greater reduction in serum monoclonal component plus a 24-hour urine monoclonal component of 90% or greater reduction or less than 200 mg (13). General ASCT procedures were performed as described previously (14). A schematic of the ASCT procedure and patient sample collection is shown in Supplementary Fig. S1A. Briefly, all patients were mobilized with cyclophosphamide (3 g/m2 total) for 2 days and then treated once daily with subcutaneous G-CSF (Lenograstim; JW Pharmaceutical) at 10 μg/kg/day. Conditioning consisted of 100 mg/m2 of melphalan for 2 days, except in patients with serum creatinine >2.0 mg/dL or in those on hemodialysis, who received 70 mg/m2 for 2 days. The G-CSF (5 μg/kg/day) was administered subcutaneously to all patients starting on the first day after transplantation until the absolute neutrophil count (ANC) reached >3.0 × 109/L. All patients received prophylactic ciprofloxacin and an antifungal agent (micafungin) starting 4 days before transplantation and were continued until ANC reached 1.0 × 109/L. Written informed consent was obtained from each patient before participation in this study. BM PCs from 49 patients with PC dyscrasia were also isolated using MACS CD138 Microbeads (Cat. #130-042-401) and cryopreserved at -80°C. This study was approved by the Institutional Review Board of The Catholic University of Korea and was conducted in accordance with the Declaration of Helsinki.
Blood sample collection and isolation of peripheral blood mononuclear cells
Blood samples for analysis of MDSC frequency were collected before and after ASCT in all 80 patients. Pre-ASCT sampling was performed 2 weeks before the conditioning chemotherapy. Post-ASCT sampling was performed 1 day after neutrophil engraftment, which was defined as the first of 3 consecutive days with ANC >0.5 × 109/L. The samples were stored at 4°C prior to analysis. The peripheral blood mononuclear cells (PBMC) were isolated from whole blood (30 mL) and were processed immediately by cell sorting prior to flow cytometric analysis (see below).
To purify M-MDSCs, PBMCs were mixed with anti–HLA-DR microbeads (Cat. #130-046-101). Then the cell suspension was loaded onto LD column (Cat. #130-042-901), which was placed in the magnetic field of a MACS separator (Cat. #130-090-976). The unlabeled cells run through, and this cell fraction was HLA-DR− cells. HLA-DR− cells were mixed with anti-CD14 microbeads (Cat. #130-050-201). Then the cell suspension was loaded onto LS column (Cat. #130-042-401). The magnetically labeled CD14+ cells were retained on the column. Magnetically retained CD14+ cells were eluted and used as M-MDSCs. To purify PCs, BM cells from 8 patients with monoclonal gammopathy of undetermined significance (MGUS), 7 patients with smoldering multiple myeloma (SMM), and 34 patients with symptomatic multiple myeloma at diagnosis were used. BM cells were mixed with anti-CD138 microbeads (Cat. #130-051-301). Then the cell suspension is loaded onto LS column. The magnetically labeled CD138+ cells were eluted and used as PCs. Microbeads, columns, and MACS separators were purchased from Miltenyi Biotec.
Cell-surface marker screening and flow cytometric analysis
To identify novel markers expressed in the M-MDSC subset, HLA-DR− sorted cells were stained with the CD14 antibody (Cat. #17-0149-42, eBioscience) for 30 minutes at 4°C in the dark. The cells were characterized using the LEGENDScreen human cell screening (PE) Kit (Cat. #700007, BioLegend), containing 360 specificity monoclonal antibodies, according to the manufacturer’s instructions. Cells were plated onto 96-well plates at a density of 3 × 105 cells per well and incubated for 30 minutes at 4°C in the dark. Immunophenotypic characterization was performed using an LSRII and analyzed by FlowJo software (BD Pharmingen). The following antibodies against human antigens were purchased from BD Pharmingen: Percp-Cy5.5–conjugated HLA-DR (Cat. #347364), allophycocyanin (APC)-conjugated CD14 (Cat. #555399), and Alexa Fluor 700–conjugated CD304 (Cat. #566038).
Transcriptome sequencing and bioinformatics analysis of sequencing data
The freshly isolated M-MDSCs from five patients' pre- and post-ASCT samples were suspended in TRIzol reagent (Cat. #15596018, Life Technologies), and the total RNA was immediately extracted according to the manufacturer’s instructions. The freshly extracted total RNA was stored at -80°C for subsequent testing. The sequencing library was prepared using the Illumina TruSeq RNA Sample Preparation Guide, which consisted of purification of poly-A–containing mRNA molecules, fragmentation of mRNA, reverse transcription, end repair, addition of a single “A” base, ligation of the adapters, and purification and enrichment with PCR. RNA sequencing (RNA-seq) was performed on a NextSeq500 Device (Illumina). The RNA-seq libraries were sequenced with paired-end 75-bp reads. The sequencing reads were mapped against hg19 using STAR (v2.5), which is an ultrafast universal RNA-seq aligner35. All parameters were set to the default values except allowed maximum mismatch, which was set to 5% for each read, and output of the BAM files, which was sorted by coordinates. The transcripts were assembled de novo using Cufflinks (v2.2.1)13. The novel GTF file was merged with the GTF file of hg19. With the merged GTF file, the fragments per kilobase of transcript per million (FPKM) of each transcript was estimated using the Cuffquant and Cuffnorm tools contained in the Cufflinks suite. Differential expression of each transcript and each gene was analyzed using Cuffdiff, another component of Cufflinks. Condition-specific genes were also identified with CummeRbund by estimating the Jensen–Shannon distance. The sequences have been submitted to ArrayExpress (accession E-MTAB-9767).
Total RNA from PCs and human myeloma cell lines (HMCL) was extracted using TRIzol. One microgram of total RNA was reverse transcribed into cDNA using an iScript cDNA synthesis kit (Cat. #1708891, Bio-Rad). Quantitative assessment of target mRNA levels was performed by real-time PCR using an iQ SYBR Green Supermix kit (Cat. #170-8884AP, Bio-Rad) with a CFX96 Real-Time PCR Detection System (Bio-Rad). The mRNA quantity was calculated using the 2–ΔΔCt method, and the level of GAPDH was used to normalize total RNA quantities. The sequences of forward and reverse primers are shown in Supplementary Table S1.
Cytokine measurements using ELISA
Whole blood was collected into BD serum separating tubes (SST) and centrifuged at 2,500 rpm for 10 minutes at 4°C. The supernatants, designated serum samples, were stored in aliquots at −80°C prior to analysis. Galectin-1 concentration was measured in patient sera and supernatants of cocultures by ELISA. The assays were performed according to the manufacturer’s protocol (Cat. #DY-1152-05, R&D Systems). The 96-well plates were coated with capture Abs and cultured overnight at room temperature, and the next day, the plates were blocked with 1% BSA in PBS. The standard or 5-fold diluted samples were added and incubated at room temperature for 2 hours. Following incubation, detection Abs were added to each well. Bound Abs were detected by streptavidin-horseradish peroxidase (HRP). The assay was developed with 3,3′,5,5′-tetramethylbenzidine (TMB) substrate. The ELISA plates were read at 450 nm using a microplate reader (VersaMAx, Molecular Devices).
HMCL culture conditions and reagents
The HMCLs RPMI-8226 (Cat. #CCL-155, ATCC), JJN3 (Cat. #ACC541, DSMZ), IM-9 (Cat. #CCL-159), OPM2 (Cat. #ACC50, DSMZ), and U266 (Cat. #TIB-196, ATCC) were cultured in RPMI1640 medium (Cat. #LM011-01, Welgene) supplemented with 10% FBS (Cat. #S001-01, Welgene) and 1% penicillin–streptomycin–glutamine solution (Cat. #10378016, Invitrogen). Cell lines were received in 2017, used at <18 passages, and confirmed to be Mycoplasma negative. The cell line authenticity was performed by surface marker analysis. Human recombinant galectin-1 was purchased from R&D Systems (Cat. #1151-GA), the galectin-1 inhibitor OTX008 was purchased from Axon Medchem (Cat. #2332), and the CD304 inhibitor EG00229 and melphalan were from Sigma (Cat. #SML1367).
In vitro cell proliferation assay
Measurement of cell proliferation was determined using the CellTiter 96 AQueous One Solution Cell Proliferation Assay (MTS) Kit (Cat. #G3580, Promega). The cells were seeded in 96-well tissue culture plates at 3 × 104 cells/well with increasing concentrations of OTX008 and EG00229 and incubated at 37°C for 24 hours. After 20 hours of drug exposure, 10 μL of MTS reagent was added into each well, and the cells were incubated at 37°C for 4 hours. Absorbance was detected at 490 nm with a microplate reader (VersaMAx, Molecular Devices).
Tumor-associated M-MDSC induction
Normal PBMCs were obtained from several volunteers. PBMCs (5 × 105 cells/mL) from healthy donors or HLA-DR− cells (1 × 105 cells/mL) from patients with multiple myeloma were cultured in T-25 culture flask in RPMI1640 medium containing 10% FBS and 1% penicillin–streptomycin–glutamine solution with or without HMCLs (1–5 × 104 cells/mL) for 2 to 5 days at 37°C. M-MDSCs were examined by flow cytometry (as described above).
Western blot analysis
For analysis, HMCLs were lysed in RIPA buffer (Cat. #P189900, Thermo Scientific) containing Protease Inhibitor Cocktail (Cat. #11697498001, Roche) for 20 minutes at 4°C. Protein concentration was assessed using Bio-Rad Protein Assay Kit (Cat. #5000002, Bio-Rad). Twenty micrograms of total extracts were separated by SDS-PAGE and subsequently transferred onto nitrocellulose membranes (Cat. #10401316, Whatman). The blots were incubated with antibody against galectin-1 (1:4,000, Cat. #ab108389, Abcam) using β-actin (Cat. #G043, ABM) as a loading control. The signals were detected using the ECL detection system (Clarity Western ECL Substrate, Cat. #1705061, Bio-Rad) and analyzed with a Fusion SL-4 3500 (Vilber Lourmat).
ELISA-based binding assay
Recombinant human CD304 (5 μg/mL, Cat. #768006, BioLegend) was coated on 96-well plates overnight at 4°C and blocked with 5% BSA in PBS. The 2-fold serially diluted recombinant-His tagged galectin-1 antibody (Cat. #ARG70146, Arigo Bio) was added and incubated at room temperature for 2 hours. Following incubation, 100 μL of primary anti-His mouse mAb (1:1,000, Cat. #11922416001, Sigma-Aldrich) was added to each well. Bound antibodies were detected by HRP-conjugated anti-mouse IgG antibody (1:10,000, Cat. #31430, Thermo Scientific). The assay was developed at room temperature with TMB (Cat. #50-76-00, KPL) substrate and monitored at 450 nm with a microplate reader (VersaMAx, Molecular Devices).
siRNA knockdown of galectin-1
RPMI-8226 cells were seeded into 6-well plates at 2 × 105 cells/well. siRNA was used to silence galectin-1 (Cat. #4390824, Thermo Scientific) according to the manufacturer’s protocol. siRNA (7.5 μL) and Opti-MEM (250 μL, Cat. #31985070, Thermo Scientific) were mixed. Lipofectamine 2000 (7.5 μL, Cat. #11668019, Thermo Scientific) was added to additional Opti-MEM (250 μL). These two solutions were then mixed and incubated for 5 minutes at room temperature, after which the reagent was added into each well and the cells were incubated for 1 to 2 days at 37°C. Control cells were treated with Stealth RNAi–negative control duplex (Cat. #4390843, Thermo Scientific).
Cell migration was detected using a transwell system. Briefly, RPMI-8226 or JJN3 cells (3 × 105 cells in 600 μL RPMI1640 medium containing 0.1% FBS) were seeded into the lower wells of 24-well transwell plates (3 μm pore size; Corning). M-MDSCs from pre-ASCT patients (3 × 104 cells/well) were resuspended in RPMI1640 medium containing 0.1% FBS and loaded on top of the filter membranes of the transwell insert (100 μL/well). After 24 hours of incubation at 37°C, the transwell insert was removed from the plate, and a cotton-tipped applicator was carefully used to remove the media and nonmigrating cells from the top of the membrane without damaging it. Next, the transwell insert and migrated cells were placed in 4% PFA for 15 minutes for cell fixation. After fixation, the cells were washed with PBS for 5 minutes, and the transwell insert was stained in 0.1% crystal violet solution for 30 minutes. After excess crystal violet was removed, the migrated cells were imaged under an inverted microscope (x200, Zeiss Axiolab 5, at least four random fields) and counted using ImageJ software (http://rsb.info.nih.gov). Five independent experiments were performed for each condition.
RPMI-8226 cells were cultured in RPMI1640 medium containing 10% FBS and 1% penicillin–streptomycin–glutamine solution with or without isolated M-MDSCs (multiple myeloma:MDSC ratio 1:1). The cocultured cells were then incubated with or without 10 μmol/L melphalan and 12.5 μmol/L EG00229. After incubation for 48 hours, the cells were harvested, stained with Annexin V–APC (Cat. #550474, BD Pharmingen) and propidium iodide (Cat. #556463, BD Pharmingen), and examined by flow cytometry (as described above).
Definitions and statistical analysis
Statistical comparisons between groups were performed using the two-tailed Student t test for continuous variables. Correlation analysis was performed using Spearman correlation coefficient. Overall survival (OS) from transplantation was defined as the time from ASCT to death from any cause, and surviving patients were censored at the last follow-up. Time to progression (TTP) was calculated as time from ASCT to disease progression, with censorship of deaths due to causes other than progression. Patients lost to follow-up were censored at the date of last contact. Survival curves were plotted according to the Kaplan and Meier method, and the log-rank test was used to assess potential prognostic factors. All P values were two-sided, and 5% was chosen as the level of statistical significance.
CD304 is strongly expressed on immunosuppressive HLA-DRlow/−CD14+ M-MDSCs pre-ASCT
We previously demonstrated that the level of circulating M-MDSCs before ASCT was significantly associated with higher Multiple Myeloma International Staging System stage and negatively related to transplant outcome (12). We hypothesized that cell-surface markers could be differently expressed between pre-ASCT and post-ASCT M-MDSCs because the former possess more potent immunosuppressive activity. PBMCs from patients with multiple myeloma were isolated before and after ASCT. The HLA-DRlow/− cells were subsequently stained with CD14 antibody (a previously known M-MDSC marker) and then seeded into plates containing 360 purified mAbs. Using high-throughput screening (15), we identified the top seven of the surface markers strongly expressed by pre-ASCT M-MDSCs. Among the seven markers that were more highly expressed on pre-ASCT M-MDSCs versus pre-ASCT M-MDSCs, CD304 was the most remarkably expressed surface antigen on the pre-ASCT M-MDSCs (Fig. 1A). Subsequently, we reanalyzed the whole transcriptional profiles of patients included in a previous study (12), and the analysis showed consistent dominant expression of CD304 in pre-ASCT M-MDSCs in comparison with post-ASCT M-MDSCs (Fig. 1B). These results were confirmed using qRT-PCR analysis of isolated M-MDSCs before and after ASCT. The mRNA expression of CD304 was much higher in pre-ASCT M-MDSCs than it was in post-ASCT M-MDSC phenotypes (Fig. 1C).
Strong relationship between pre-ASCT M-MDSCs and galectin-1 via CD304 expression
CD304 directly binds galectin-1, which is overexpressed in tumor-associated capillary endothelial cells in squamous cell carcinoma (16). In multiple myeloma cells, galectin-1 is expressed highly at both mRNA and protein levels and maintains interpatient and inter-HMCL variability (17, 18). First, we measured galectin-1 concentration in the sera of patients with multiple myeloma before and after ASCT. Galectin-1 level was significantly reduced after ASCT (Fig. 2A). A direct relationship between M-MDSCs and galectin-1 according to ASCT was examined using correlation analysis between the frequencies of circulating M-MDSCs and serum galectin-1 concentrations before and after ASCT, respectively (Fig. 2B). Galectin-1 concentration correlated with the frequency of circulating M-MDSCs only pre-ASCT. Moreover, supplementation with recombinant galectin-1 to the culture medium promoted proliferation of pre-ASCT M-MDSCs and their expression of CD304, whereas addition of galectin-1 did not have a significant effect on the post-ASCT M-MDSC phenotype (Fig. 2C), suggesting that galectin-1 enhanced M-MDSC proliferation through interaction with CD304. These data indicate that immunosuppressive pre-ASCT M-MDSCs more potently respond to galectin-1 with CD304 expression than do post-ASCT M-MDSCs.
Association of clinical activity with expression profile of LGALS1 in BM PCs
Progression from precursor MGUS to active multiple myeloma occurs through a “permissive” microenvironment (19). We analyzed the expression of LGALS1 by enriched PCs from 8 patients with MGUS, 7 patients with SMM, and 34 patients with symptomatic multiple myeloma at diagnosis. Higher expression of LGALS1 was associated with more aggressive forms of PC dyscrasias (Fig. 3A). Next, we tested whether an association could be established between LGALS1 expression level and patient clinical outcome after ASCT. Patients with the top 25% level of LGALS1 expression at the time of diagnosis had a shorter 3-year TTP and OS compared with those having a level of 75% or less (48.2% vs. 26.7%, P = 0 .062; 97.6% vs. 62.5%, P = 0.009; Fig. 3B).
Expression of galectin-1 and its role in HMCL proliferation
We assessed LGALS1 and CD304 mRNA expression in five HMCLs (Supplementary Fig. S1B and S1C, respectively). The HMCLs expressed galectin-1 but not CD304. Among the five HMCLs, RPMI-8226 had the highest level of galectin-1, whereas JJN3 expressed the lowest. Galectin-1 protein expression was higher in RPMI-8226 cells than it was in JJN3 cells (Fig. 4A). Next, we evaluated the in vitro effect of OTX008, a selective small-molecule inhibitor of galectin-1, on RPMI-8226 and JJN3 cells, which express the highest and lowest levels of galectin-1, respectively (Fig. 4B). OTX008 more sensitively inhibited proliferation of RPMI-8226 cells compared with JJN3 cells at micromolar concentrations, suggesting that its antiproliferative effect correlates with multiple myeloma galectin-1 expression. The result of antiproliferative effects of OTX008 on the five HMCLs is seen in Supplementary Fig. S2. In RPMI-8226 cells but not in JJN3 cells, in vitro OTX008 treatment was associated with downregulation of galectin-1 in a dose-dependent manner. These data were confirmed at the transcription and protein levels (Fig. 4C and 4D). Consistent with these data, galectin-1 concentration in the culture supernatant
s was significantly correlated with treated OTX008 dose in RPMI-8226 cells but was minimally correlated with that in JJN3 cells (Fig. 4E). Galectin-1 protein expression in JJN3 cells was not inhibited in the presence of OTX008 (0.1–10 μmol/L) up to 72 hours (Supplementary Fig. 3A). On the other hand, when a high concentration of OTX008 was added with an incubation time for 48 hours, galectin-1 expression in JJN3 cells was decreased (Supplementary Fig. S3B).
Effect of multiple myeloma–derived galectin-1 on induction of CD304 expression and proliferation of M-MDSCs
To determine the effect of galectin-1 from multiple myeloma cells on induction of M-MDSCs, we next assessed whether the two HCMLs expressing different levels of galectin-1 differently induced M-MDSCs. PBMCs were cultured with RPMI-8226 or JJN2 cells, and M-MDSC induction and CD304 expression were quantified using flow cytometric analysis. As shown in Fig. 5A, RPMI-8226 cells significantly induced more M-MDSCs from healthy donor PBMCs in vitro compared with JJN3 cells. Moreover, CD304 expression on M-MDSCs was significantly increased in the presence of RPMI-8226 cells compared with JJN3 cells. Galectin-1 concentration was also higher in RPMI-8226 coculture supernatants (Supplementary Fig. S4A).
We then compared the in vitro effect of OTX008 on proliferation and CD304 expression of isolated pre-ASCT M-MDSCs cocultured with either RPMI-8226 or JJN3 cells. Inhibition of galectin-1 suppressed M-MDSC proliferation and CD304 expression induced by RPMI-8226 cells but not by JJN3 cells (Fig. 5B). Galectin-1 was found at higher levels in coculture media of M-MDSCs and RPMI-8226 cells than it was from M-MDSCs and JJN3 cells. When OTX008 was applied, galectin-1 concentration in the coculture supernatant was reduced more for RPMI-8226 cocultures than it was for JJN3 cocultures (Supplementary Fig. S4B).
Next, we stably knocked down galectin-1 in RPMI-8226 cells using siRNA and then evaluated the proliferation and CD304 expression of cocultured M-MDSCs. LGALS1 and galectin-1 protein inhibition by the knockdown were confirmed using qRT-PCR and Western blot, as in Supplementary Fig. S4C. Flow cytometric analysis showed that M-MDSCs cultured with RPMI-8226 cells with stable knockdown of galectin-1 showed significantly decreased proliferation and reduced CD304 expression compared with M-MDSCs cultured with RPMI-8226 control cells in which galectin-1 expression was maintained (Fig. 5C).
In addition, we carried out an experiment to provide evidence that galectin-1 binds CD304. The experiment is illustrated schematically in Fig. 5D. It was demonstrated that galectin-1 directly binds to CD304 in a concentration-dependent manner, and the binding was inhibited in accordance to the dose of EG00229, a small-molecule inhibitor of CD304. Collectively, our results demonstrate galectin-1 secretion from multiple myeloma cells can induce development of M-MDSCs through CD304 expression.
In vitro M-MDSC migration induced by multiple myeloma cells is suppressed by CD304 inhibition
Next, the effect of EG00229 on the HMCL-induced migration of M-MDSCs was evaluated using a transwell coculture system (Fig. 6). The CD304 inhibitor significantly decreased M-MDSC migration induced by RPMI-8226 cells compared with migration induced by JJN3 cells. These results also suggest that multiple myeloma cell–derived galectin-1 interacts with CD304-expressing M-MDSCs. In addition, migration of M-MDSCs in the presence of each HMCL was significantly increased by supplementation with recombinant galectin-1 and decreased by CD304 inhibition in a similar way (Fig. 6).
CD304 inhibition can reverse attenuation of melphalan-induced cytotoxic effects by pre-ASCT M-MDSCs
To determine whether CD304 inhibition can recover melphalan-induced cytotoxicity attenuated by M-MDSCs, we examined the effect of EG00229 on multiple myeloma cell apoptosis induced by melphalan after coculture with either pre-ASCT or post-ASCT MDSCs (Fig. 7). Proliferation of HCMLs was not affected by EG00229 (Supplementary Fig. S5). Melphalan-induced cytotoxicity of RPMI-8226 cells was significantly attenuated by coculture with pre-ASCT M-MDSCs but not with post-ASCT MDSCs, as previously demonstrated (12). In vitro, addition of CD304 inhibitor reversed the protective effect of pre-ASCT M-MDSCs on RPMI-8226 cells, which was not the case with post-ASCT M-MDSCs. These results demonstrate that inhibition of CD304 signaling in multiple myeloma–promoting M-MDSCs resulted in stronger anti-multiple myeloma activity by melphalan.
MDSCs are a major component of the tumor microenvironment. The prevalence of a particular immune-suppressive mechanism depends on the type of MDSCs expanded in the tumor microenvironment, as well as on the stage of the tumor and the site of suppression (20). In addition to immune-suppressive mechanisms, MDSCs promote tumor progression by affecting the remodeling of the tumor microenvironment and tumor angiogenesis (21). Several prior studies show a close association between MDSC accumulation and clinical outcome in patients with cancer (22). A suppressive mechanism by MDSCs dominates, and this mechanism can change throughout cancer progression. Multiple myeloma is a PC malignancy characterized by tight adhesions between clonal cells and the BM microenvironment, which leads to increases in cancer cell survival, drug resistance, multiple myeloma–induced neoangiogenesis, immunosuppression, and osteolytic bone lesions (23). MDSCs and multiple myeloma cells appear to interact in a bidirectional manner whereby multiple myeloma cells are able to induce MDSCs and the MDSCs provide a safe haven within the BM microenvironment for multiple myeloma growth and progression (11). To therapeutically target the specific type of tumor-promoting MDSCs, their biological role and the molecular mechanisms governing the development of these cells should be better characterized. MDSCs can be functionally inactivated by targeting their suppressive machinery with a dramatic and durable antimyeloma clinical response (24). Our data show that galectin-1 can directly bind to CD304 on multiple myeloma–promoting M-MDSCs pre-ASCT but that post-ASCT M-MDSCs do not express CD304. The CD304–galectin-1 interaction can elicit migration of M-MDSCs by malignant PCs as well as by enhancing promyeloma activity of the M-MDSCs. These findings suggest that intervening in the interaction between galectin-1 from malignant PCs and CD304 on pre-ASCT M-MDSCs could provide a novel approach to augment the efficacy of high-dose melphalan and ASCT in multiple myeloma.
Prior studies indicate that inhibition of galectin-1 signaling may affect immune activation; therefore, this may be a new strategy for anticancer therapy (25). Galectin-1 is involved in regulation of homeostasis and differentiation of both T and B cells and can skew an immune reaction in an immunosuppressive or anti-inflammatory direction. Galectin-1 may particularly expand regulatory myeloid cells. It has been shown that galectin-1 promotes differentiation of IL27- and IL10-producing tolerogenic dendritic cells (26) and contributes to monocyte/macrophage polarization toward an alternatively activated (M2-like) phenotype (27). Macrophages are important supportive cells in multiple myeloma because of promotion of multiple myeloma cell growth and resistance to therapy (28). Infiltration by CD163+ macrophages in the BM is associated with poor outcome in multiple myeloma (29). Similarly, galectin-1 and galectin-9 support recruitment of MDSCs and increase their regulatory capacity (30, 31). Clinically, high expression of galectin-1 mRNA expression in purified CD138+ BM cells correlates with worse outcome for patients with multiple myeloma, indicating an association between multiple myeloma–producing galectin-1 and more aggressive biology in PC dyscrasias (18). Currently, however, the expression profile of galectin-1 and the gene encoding it (LGALS1) by multiple myeloma cells and the pathophysiologic role of galectin-1 in multiple myeloma–induced expansion of MDSCs are largely unknown. In this study, we found a positive association between serum galectin-1 level and circulating frequency of pre-ASCT M-MDSCs, but not post-ASCT M-MDSCs or any other MDSC subtype (Supplementary Fig. S6). These data suggest an interaction between multiple myeloma cells and cancer-promoting M-MDSCs through galectin-1 and CD304, respectively. In addition, we demonstrated that galectin-1 secreted by multiple myeloma cells can elicit a higher expression of CD304 on pre-ASCT M-MDSCs, which provides an immunosuppressive microenvironment for multiple myeloma progression.
Galectin-1 is produced by virtually all hematopoietic lines, where its synthesis is significantly enhanced upon malignant transformation. Galectin-1 is a hallmark biomarker for metastatic potential and poor prognosis in a variety of cancers (32). In multiple myeloma cells, galectin-1 is expressed at high levels, including both mRNA and protein levels, maintaining interpatient and inter-HMCL variability (17). In this study, different levels of expression of LGALS1 mRNA and galectin-1 protein were detected in freshly purified CD138+ PCs and in the HMCLs. We also found that high expression of LGALS1 in the BM PCs of patients correlated with active stage of multiple myeloma and with reduced OS after ASCT in patients with multiple myeloma. Our data are consistent with other reports (18, 33), which suggest galectin-1 is a prognostic factor that defines high-risk multiple myeloma and is a new potential therapeutic target in multiple myeloma. In contrast to PC galectin-1 levels, serum galectin-1 protein level prior to ASCT was significantly reduced after ASCT but was not associated with posttransplant outcome (Supplementary Fig. S7A). These findings indicate an important role for galectin-1 secreted from malignant PCs. Galectin-1 protein levels in the malignant PCs were not correlated with galectin-1 protein levels in the serum (Supplementary Fig. S7B). Prior results have shown that serum galectin-1 protein level is not significantly higher in patients with multiple myeloma compared with healthy controls and that serum galectin-1 protein level is not associated with OS, response to treatment, and clinical pathologic parameters (34).
CD304 is a multifunctional single-pass transmembrane protein that plays an important role in cancer development and antitumor immunity (35). In this study, we discovered that concomitant expression of CD304 on M-MDSCs and production of galectin-1 by multiple myeloma cells promotes M-MDSC proliferation and migration. This mechanism could diminish the anticancer effects of ASCT, which is the standard care for multiple myeloma. Using EG00229 to specifically inhibit CD304 on M-MDSCs, we demonstrated that CD304 inhibition enhanced melphalan-mediated antitumor responses and reduced M-MDSC migration by multiple myeloma cells. In future studies, we will identify the effects of targeting this interaction on both clinical responses and on relief of immunosuppression in preclinical and clinical models.
In summary, our results identify a role for the interaction of myeloma-derived galectin-1 with CD304 on M-MDSCs in recruiting M-MDSCs toward the tumor microenvironment, as well as in inhibiting anticancer effects of melphalan. High galectin-1 expression in malignant PCs correlated with poor outcomes after ASCT in multiple myeloma. As galectin-1 synthesis is significantly enhanced in advanced and refractory multiple myeloma, these data have new implications for the clinical role of M-MDSCs in multiple myeloma.
No disclosures were reported.
J.-Y. Lim: Data curation, software, formal analysis, methodology, writing–original draft, writing–review and editing. T.-W. Kim: Methodology. D.-B. Ryu: Methodology. S.-S. Park: Methodology. S.-E. Lee: Methodology, writing–original draft. B.-S. Kim: Methodology. C.-K. Min: Conceptualization, supervision, writing–original draft, project administration, writing–review and editing.
This study was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT; NRF-2019R1A5A2027588).
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