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. Author manuscript; available in PMC: 2024 Nov 2.
Published in final edited form as: Mol Cancer Ther. 2024 May 2;23(5):672–682. doi: 10.1158/1535-7163.MCT-23-0718

Momordicine-I suppresses head and neck cancer growth by reprogrammimg immunosuppressive effect of the tumor-infiltrating macrophages and B lymphocytes

Subhayan Sur 1,#, Pradeep Bhartiya 1, Robert Steele 1, Michelle Brennan 2, Richard J DiPaolo 3, Ratna B Ray 1,3,*
PMCID: PMC11065610  NIHMSID: NIHMS1966401  PMID: 38315993

Abstract

Head and neck cancer (HNC) is prevalent worldwide, and treatment options are limited. Momordicine-I (M-I), a natural component from bitter melon, shows antitumor activity against these cancers, but its mechanism of action, especially in the tumor microenvironment (TME), remains unclear. In this study, we establish that M-I reduces HNC tumor growth in two different immunocompetent mouse models using MOC2 and SCC VII cells. We demonstrate that the anticancer activity results from modulating several molecules in the monocyte/macrophage clusters in CD45+ populations in MOC2 tumors by single-cell-RNA sequencing.. Tumor-associated macrophages (TAMs) often pose a barrier to antitumor effects, but following M-I treatment, we observed a significant reduction in the expression of Sfln4, a myeloid cell differentiation factor, and Cxcl3, a neutrophil chemoattractant, in the monocyte/macrophage populations. We further find that the macrophages must be in close contact with the tumor cells to inhibit Sfln4 and Cxcl3, suggesting that these TAMs are impacted by M-I treatment. Coculturing macrophages with tumor cells shows inhibition of Agr1 expression following M-I treatment, which is indicative of switching from M2 to M1 phenotype. Furthermore, the total B-cell population in M-I-treated tumors is significantly lower, while spleen cells also show similar results when cocultured with MOC2 cells. M-I treatment also inhibits PD1, PD-L1, and FoxP3 expression in tumors. Collectively, these results uncover the potential mechanism of M-I by modulating immune cells, and this new insight can help develop M-I as a promising candidate to treat head and neck cancers, either alone or as adjuvant therapy.

INTRODUCTION

Head and neck cancer (HNC), especially oral cancer, originates from the mucosal epithelium (1). The American Cancer Society estimates >54,500 new cases and >11,500 deaths in 2023 (2). Tobacco and alcohol consumption, the use of areca nuts, and human papillomavirus (HPV) infection are major risk factors for head and neck cancers. Among them, HPV infection is predicted to decline in the future owing to successful vaccination campaigns and better prognosis (3). Despite these preventive measures and advancements in surgery and therapies, the survival rate of HNC remains a dismal ~50%, which worsens if the cancer spreads to other locations (4). Since conventional therapies show limited success due to resistance and adverse side effects, there is a dire need to identify new therapeutics from other sources, such as alternative medicines, and understand their mode of action at the molecular and cellular levels for safe and effective use to treat HNCs.

Herbal medicines or phytonutrients are emerging as powerful alternative medicines due to their natural preventive and therapeutic efficacy in various diseases, including cancers (5, 6). Phytochemicals are also emerging as promising alternative therapeutics. Several natural products have shown promising results in preclinical studies (7, 8), and some phytochemicals are reportedly able to target multiple molecules in signaling pathways for controlling cancer cell growth while having reduced or no toxicity and being inexpensive (5). Studies implicated that consumption of specific functional foods stimulate the immune cell activities (9). Some registered drugs and active ingredients, such as taxanes (paclitaxel), Anthra cyclines (doxorubicin), vinca alkaloids (vinblastine), are derived from natural sources and are currently undergoing clinical trials to determine their safety and efficacy (6).

We and others have shown potential anticancer effects of bitter melon (Momordica charantia) extracts (BME) and its active ingredients in cancer cell line-based models and preclinical models (5, 10–14). We recently identified Momordicine-I (M-I) [C30H48O4, PubChem CID:14807332] as a potent active component of BME (15). M-I is a cucurbitane-type triterpene. Our studies reveal that M-I is effective against HNC, similar to BME. M-I is non-toxic and has a stable pharmacokinetic profile in the mouse blood. However, the mechanism of M-I’s action, especially in the TME, is unclear. Since the interplay between cancer cells and TME contributes to tumor progression, angiogenesis, metastasis, drug resistance, and immune suppression, this study aims to identify the impact of M-I treatment in the TME and define how it reverses HNC progression.

The TME consists of epithelial cells, endothelial cells, fibroblasts, immune cells, signaling molecules, and extracellular matrix (16). Single-cell profiling has been extremely useful for understanding this heterogeneity, interactions between different populations of cells in the TME, and the consequences to cancer cells (17). High-throughput single-cell RNA sequencing (scRNA-seq) analysis has helped reveal the mechanism of action of new therapeutics at the molecular and cellular levels in breast, colon, esophageal, and lung cancer, however, only limited information is available on HNCs (17–20). In this study, we observed that M-I treatment significantly reduced head and neck tumor growth in two (MOC2 or SCCVII cells) syngeneic mouse models. We also observed inhibition of the PD1, PD-L1, and FoxP3 mRNAs in the tumors of M-I-treated animal models. It is apparent from these observations that these treatments affect the immune cell populations in the TME. Therefore, this study aims to use scRNA-seq technology to unravel the complex immunological events and patterns of gene expression within the TME to define how M-I exerts its antitumor efficacy in MOC2 tumors. The outcome of these pioneering studies unravels the potential anticancer mechanisms of this alternate medicine in HNC, which may aid in developing informed therapeutic strategies to improve its clinical benefits.

MATERIALS AND METHODS

Cell lines and M-I treatment

The mouse oral cancer cells, MOC2, was obtained from Kerafast, Inc., and were maintained in 2:1 IMDM-Hams Nutrient Mixture F10-F12 medium supplemented with insulin, hydrocortisone, EGF, FBS, and 1% penicillin/streptomycin at 37°C. Mouse HNC cell line SCCVII was kind gift from Dr. J. Martin Brown at Stanford University, and maintained in Waymouth’s medium containing 15% FBS and 1% of penicillin/streptomycin at 37°C as described previously (21). RAW 264.7 cells were obtained from ATCC and maintained in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% FBS and 1% penicillin/streptomycin at 37°C. Momordicine-I (M-I, >98% purity) was purchased from ChemFaces (Cat. No.: CFN92076), dissolved in DMSO, and added directly to the culture medium at indicated concentrations. The cell lines were periodically checked for Mycoplasma using a commercial detection kit (Lonza).

Cytotoxicity assay

MOC2 cells were seeded in a 96 well-plate (5000 cells/well) and treated with different concentrations of M-I for 48 h. Viable cells were counted using the trypan blue dye-exclusion method, using untreated and DMSO-treated cells as controls.

Tumorigenicity assay

MOC2 cells (1×106) containing 40% Matrigel were injected subcutaneously into the flank of C57BL/6 female mice (6 –7 weeks old). SCCVII cells (1×106) containing 40% Matrigel were injected subcutaneously into the flank of C3H/HeNTac female mice (6 –7 weeks old) as described previously (21). When palpable tumors developed (> 80–100 mm3), mice were randomly divided into two groups, with 10 mice in each group. The treatment group received 30 mg/kg/mouse of M-I (100 uL- M-I was dissolved in 5% DMSO/95% of a 30% w:v Captisol solution) intraperitoneally once daily, and the control group received the vehicle, as described previously (15). Body weight was monitored, tumor size was measured using a slide caliper, and tumor volume was calculated using the formula ½ L × W2. At the endpoint, mice were euthanized humanely, tumors were collected, parts of the MOC2 tumors were processed for scRNA-seq, and the remaining tumors were snap-frozen in liquid nitrogen for further analysis. All animal experiments were performed in accordance with NIH guidelines following a protocol approved (protocol # 2463) by the Institutional Animal Care and Use Committee (IACUC) of Saint Louis University.

Single-cell library preparation and RNA sequencing

Tumors (3 mice per group-pooled) were processed for single-cell isolation followed by cell sorting using FITC-conjugated CD45 antibody and propidium iodide (PI) staining, as described previously (22), with modifications. Tumor tissue was processed for single-cell isolation followed by cell sorting using FITC-conjugated CD45 antibody and propidium iodide (PI) staining. Briefly, mouse tumors were collected in serum-free RPMI medium, washed with 1x PBS, and minced thoroughly. The tumor tissues were incubated with 1U/ml Liberase –TL at 370C for 1 hr and filtered through a 100 μm strainer. Cells were collected from the supernatant, washed, and suspended in MACS buffer (PBS, pH 7.2, 2mM EDTA, and 0.5% BSA). Live cells were counted and stained with FITC-conjugated CD45 antibody (1:500) at 370C for 30 min. Cells were stained with 1 μg/ml PI and processed for cell sorting. CD45+/PI− cells were used for single-cell RNA sequencing (scRNA-seq). The 3′ scRNA-seq libraries were generated using Chromium Single Cell 3′ Reagent Kits v3.1 (10x Genomics) and analyzed using the Agilent Bioanalyzer in the Saint Louis University Genomics Core. Illumina sequencing of the prepared libraries was performed on a NovaSeq 6000 at Washington University. Raw data were processed using the CellRanger 6.0.2 pipeline (10x Genomics) to align reads to the mouse reference genome and generate feature-barcode matrices. Both ‘unsupervised’ (principal component analysis, PCA; uniform manifold approximation and projection (UMAP)) and ‘supervised’ (differential expression, hierarchical clustering, gene co-variance, gene ontology, and pathway) analyses were performed using the R Seurat package. Globally distinguished genes for each cluster and comparison identity class were identified by calculating the normalized gene expression for the average single cell. Significantly expressed genes with at least a 0.25 log2 fold change, and p-value < 0.05 were identified via the Wilcoxon rank-sum test with Bonferroni correction for multiple comparisons.

RNA isolation and quantitative real-time reverse transcription PCR (qRT-PCR)

Total RNA was isolated using TRIzol reagent (Invitrogen, CA, USA), and cDNA was synthesized using a random hexamer and Superscript III reverse transcriptase (ThermoFisher Scientific). Real-time PCR was performed to quantify gene expression using the TaqMan Universal PCR master mix and 6-carboxyfluorescein (FAM)-MGB probes for Arg1 (Mm00477552_m1), Nos2 (Mm00440502_m1) and Bcl11a (Mm00479358_m1). Gene expression analysis of Sfln4 (FP:5′ AAAGGATTTCATCTGGGAAG 3′; RP:5′ TCTCAGTTGGCTATCTTTCA 3′) and Cxcl3 (FP:5′ TACCAAGGGTTGATTTTGAG 3′; RP:5′ GCTCTTCAGTATCTTCTTGATG 3′) was carried out using an SYBR green-based detection system (Applied Biosystems). The mouse 18S rRNA gene was used as an endogenous control for normalizing target genes. The mean relative gene expression was analyzed using the 2-ΔΔCT formula (ΔΔCT = ΔCT of the sample-ΔCT of the untreated control).

In vitro macrophage assays

To perform in vitro experiments using MOC2 and RAW 264.7 cell lines and examine whether M-I influences macrophages alone or needs to be close to tumor cells, M-I was used to treat individual cell lines, a coculture, or a trans-well system. RNA was isolated, and the expression of Slfn4, Cxcl3, Chil3, Arg1, and NOS2 was quantitated by qRT-PCR using specific primers, as above.

In vitro B-cell assays

Spleen cells were isolated from C57BL/6 mice, cocultured with 8 × 104/well of MOC2 cells, and treated with M-I (15 μg/ml). After 48 hours, the supernatant was collected for FACS and RNA analyses. For flow cytometry, cells were stained with Live/dead dye-APC-EF780, CD45-FITC, and CD19-BV510. Total RNA was extracted from these cells for qRT-PCR quantification of Bcl11a, as described above.

Flow cytometry

Live cells (PI positive) were counted and stained with FITC conjugated CD45 antibody (1: 500, Invitrogen) at 37°C for 30 min, and processed for cell shorting using BD FACSAria II Cell Sorter. For in vitro B-cell assay, spleen cells are stained with Live Dead APC-Eflour 780 (1:1000- Invitrogen) and stained with CD19 antibody (BV510, 1:300- BD biosciences). Flow-cytometric analysis was performed on LSR II FACS analyzer (BD Biosciences) at the Saint Louis University Flow Cytometry Core Facility. Flow cytometry data were analyzed using FlowJo v.10 software (Tree Star Inc.).

Statistical analysis

All statistical calculations were performed using GraphPad Prism 9 software. In each experiment, biological and technical triplicates were used, compared with control and experimental, and the data are presented as the mean ± standard deviation. Data were analyzed using the paired Student’s t-test. Statistical significance was set at P <0.05.

Data availability statement:

Data generated in this study has been included in this manuscript. RNA-seq data generated in this study were deposited in GEO under accession number GSE254011. Additional data are available from the corresponding author upon reasonable request.

RESULTS

Momordicine-I treatment causes tumor growth inhibition and immunomodulation in head and neck cancer mouse models

First, we tested whether M-I has anti-proliferative activity similar to the BME using MOC2 mouse oral cancer cell line. MOC2 cells were originally established from a chemical carcinogen-induced head and neck cavity tumor of C57BL/6 mice (23), and the growth pattern, aggressiveness, and genetic characteristics of MOC2 tumors are very similar to human oral cancer (24). MOC2 cells were treated with different concentrations of M-I, and BME (as a positive control) for 48 hours. Compared to untreated control, M-I treatment reduced the viability of MOC2 cells in a dose-dependent manner with IC50 10.4 μg/ml (Fig. S1). Next, we implanted these MOC2 cells into the flanks of C57BL/6 mice. When the tumor size reached approximately 80 mm3, mice were randomly divided into two groups (n=10/group). The control group received vehicles and the experimental group received 30 mg/kg/mouse of M-I through IP once a day until the end of the experiment. As shown in Figure 1A and B, M-I treatment significantly reduced the tumor volume. To gauge the toxicity of M-I, we monitored the mice daily for body weight changes, food intake, and behavioral changes. We found no significant difference between treated and untreated groups of mice (Fig. 1C). Mice were sacrificed on day 20 and tumors were collected for biochemical analysis.

Fig. 1: Effect of M-I on head and neck cancer in a syngeneic mouse model.

Fig. 1:

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MOC2 cells (1 × 106) were injected subcutaneously into C57BL/6 mice, and tumor-bearing mice were randomly divided into two groups. The control group received DMSO, and the M-I group received 30 mg/kg/mouse M-I via intraperitoneal injection every day until the end of the experiment. A, Tumor volume post-M-I treatment. B, Representative images of tumors from control and M-I-treated groups. C, Bodyweight of control and treated mice. D, Relative mRNA expression of PD-L1, PD-1, and FoxP3 as analyzed by qRT-PCR. 18S rRNA was used as an internal control. E, SCCVII cells (1 × 106) were injected subcutaneously into C3H/HeNTac mice, and tumor-bearing mice were randomly divided into two groups (3 mice in control and 5 mice in experimental group). Mice were treated as described above. Representative images of tumors from control and M-I-treated groups (top panel). Tumor volume post-M-I treatment (bottom panel). Small bars indicate standard error (*P < 0.05, **P<0.01).

In the TME, immune and non-immune cell populations are associated with disease progression and therapy resistance (25). One of the most important mechanisms that impair antitumor immunity is the induction of immunosuppressive cell types. We previously found that BME had an immunomodulatory role in HNC (14, 21, 26). In fact, using 4-nitroquinoline 1-oxide (4-NQO) carcinogen-induced HNC model and treated with BME, we showed PD1 was significantly reduced. On the other hand, BME treatment decreases the infiltrating regulatory T (Treg) cells by inhibiting FoxP3+ populations in the SCCVII tumors. Therefore, we examined the effect of M-I on immunomodulation by sacrificing the mice after 20 days (from MOC2 tumor) and collecting the tumors for analysis. We found mRNA expression levels of immunomodulatory markers PD-L1 and PD-1 were significantly reduced in tumors following M-I treatment (Fig. 1D). Furthermore, Foxp3, a transcription factor expressed by immunosuppressive regulatory T cells (Tregs), was dramatically inhibited in M-I-treated tumors (Fig. 1D). To verify our observation in reduction of tumor growth by M-I, we included another mouse HNC syngeneic model with SCCVII cells. Tumor bearing mice were divided into two groups and treated as described above. Mice treated with M-I displayed a significant tumor reduction as compared to the control group (Fig. 1E). We did not observe changes in body weight between the two groups. These findings suggest that M-I treatment results in immunomodulation in the tumors, which may explain its antitumor activity against HNC.

Single-cell transcriptome profile in head and neck cancer after M-I treatment

Reduced expression of immunosuppressive transcripts in the MOC2-induced tumors following M-I treatment prompted us to analyze the entire landscape of immune cell characteristics impacted by M-I in these tumors. We performed single-cell RNA sequencing (scRNA-seq) analysis of tumors (pooled tumors from 3 individual mice) from each group by sorting the total cells using CD45 and PI staining (Fig. S2) and 16,500 live CD45+ were subjected to scRNA-seq analysis. After filtering and removing low-quality cells, we had 3,358 cells from the control group and 4,210 cells from the treatment group. The median unique molecular identifier (UMI) per cell was 5736 or 4951 (control or treated), with a median signal detection ability of 1622 or 1516 unique genes per cell (Fig. S3).

Analysis of the scRNA-seq dataset revealed fourteen distinct immune cell clusters in MOC2-induced tumors (Fig. 2A), and a dot plot shows that each cluster was present in both untreated and M-I-treated mice groups. We defined the identity of each cluster based on cluster-identification markers (Fig. 2B). We detected multiple types of immune cells, including macrophages, dendritic cells, mast cells, T cells, and B cells. T cells and dendritic cells were further clustered in the subclasses cDC1/cDC2 and Th2/Treg/Cd8+/Tgd, respectively. The top differentially expressed immune genes between control and M-I treated tumors are shown in the volcano plot (Fig. 2C). Gene ontology analysis of the treatment group in the scRNA-seq dataset showed significant decreases in the expression of immune-related genes (Fig. 2D). The enriched ontologies included mononuclear cell differentiation, B cell activation, lymphocyte proliferation, and humoral immune response. Genes that showed increased expression in the treatment group (Fig. 2D, red dots, right) were both immune and cell death related. The scRNA-seq data showed changes in the immune cell populations in mouse tumors following MI treatment, and the distribution of these immune cells is shown in the pie charts (Fig. 2E). These findings provide the first clues that M-I treatment likely results in altering immune-related pathways, which may explain its effect on inhibiting head and neck tumor growth.

Fig. 2: Single-cell transcriptome profiles of immune cell populations before and after M-I treatment in MOC2-induced tumors.

Fig. 2:

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CD45+ and PI− tumor cells from M-I or vehicle (control)-treated mice were sorted, and scRNA-seq analysis was performed. A, Seurat clustering of the scRNA-seq data from CD45+ and PI− cells in control and treated groups. B, Dot plot showing expression of cluster-identifying markers in different populations. C, Volcano plot showing log2(fold change) and −log10(P value) for differentially expressed genes (DEGs) in the immune populations. The horizontal dashed line is p =0.05 [−log10(0.05) = 1.3], the cut-off threshold. D, Bubble plot showing the annotation for gene ontology (GO) terms of DEGs; “DOWN” denotes reduced expression and “UP” denotes increased expression in the treatment group. The plot size is proportional to the number of genes contained in the corresponding GO term. E, Pie charts summarizing the average abundance (percentage of total alive single cells) of immune cell populations.

Following M-I treatment, we observed a significant decrease (P<0.0001) in B cell (5%) and CD8+ T cell (3%) populations within the TME. Conversely, there was a significant increase (P<0.0001) in the population of cDC2 cells (3%) (Fig. S4A–B). However, no significant difference was observed in the populations of NK cells, Tgd cells, Th2 cells, cDC1 cells, or mast cells. Treg populations displayed a significant difference between these two groups (Fig. S3). We further observed downregulation of Foxp3 expression in M-I-treated tumors. This suggests that M-I treatment may have a suppressive effect on tumor-promoting Treg activity within the TME. Thus, these results provide evidence that M-I treatment significantly impacts the immune components of the TME, potentially contributing to HNC suppression.

Macrophages from M-I-treated tumors have a distinct gene expression pattern

Unsupervised clustering identified five distinct clusters of tumor-associated macrophages (TAMs) (Fig. 3A). TAMs in tumors, in general, have the anti-inflammatory M2-polarized phenotype (27). Intriguingly, we found both M1 and M2 signature genes in both M-I-treated and untreated groups. A heatmap depicts the top marker genes for the unique macrophage clusters (Fig. 3B). The cluster labeled “Macrophage” expresses markers indicative of the M2 phenotype. The significantly modulated genes in the macrophage population from M-I-treated tumors are shown in a volcano plot (Fig. 3C), which includes several M1 and M2 markers. Ccl3, Ccl4, Ccl2, Cxcl12, colony-stimulating factor (CSF-1), IL-6 and IL-1β, and Vegfa are major determinants of monocyte infiltration in TME. We observed downregulation of Ccl3, Ccl4, Csf1, Il1b, and Vegfa in M-I-treated tumor macrophage populations, while a slight elevation of Ccl2 and no change in Cxcl12. Several interferon-regulatory genes are also differentially expressed. In the control mouse, TAMS had some characteristic M2 profiles, including upregulation of Mrc1(CD206), Arg1, and Chil3, whereas Nos2 is typically an M1 marker. In tumors, the expression of Chil3, Mrc1, and Arg1 are primarily restricted to the cluster labeled “Macrophage” or “M2” while the expression of Nos2 is concentrated in the other clusters labeled Mac/Mono1–4 (Fig. 3D). With M-I treatment, we observed changes in the M1/M2 gene expression patterns across the clusters. We observed downregulation of Mrc1(CD206) in the M2 macrophages, whereas Chil3 was upregulated in the majority of M-I-treated macrophage populations, including the clusters that display typical M1 phenotype. MMP8 has been implicated as one of the key players in the M2 phenotype of macrophages (28), and its expression is increased in M-I-treated macrophage populations.

Fig. 3: M-I treatment modulates macrophage populations.

Fig. 3:

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A, Unsupervised Seurat subclustering shows M1 and M2 type macrophage populations in the control vs. M-I treatment group. B, Heatmap of the top 25 genes from five macrophage clusters. C, Volcano plot illustrates expression of genes in macrophage/monocyte populations in treated vs. control tumors. The x-axis represents log2 -fold change, and the y-axis denotes (−log10) p-value showing statistical significance. The horizontal dashed line is p =0.05 [−log10(0.05) = 1.3], the cut-off threshold. D, UMAP and Violin plot showing expression of macrophage phenotype markers Nos2, Arg1, Chil3, and Mrc1, and expression of Slfn4, Cxcl3, and MMP8 genes from different populations in control vs. M-I-treated tumors.

Among the differentially expressed transcripts, Slfn4, Cxcl3, and Chil3 showed the highest expression in different populations (Fig. 3C). The myeloid cell differentiation factor Slfn4 belongs to the Schlafen family of proteins expressed in mice (29, 30). This gene is upregulated during macrophage activation and downregulated during their differentiation (31). Slfn4 is also upregulated in murine gastric metaplasia (29), suggesting an association with tumor promotion. We observed significant downregulation of Slfn4 following M-I treatment in most monocyte/macrophage populations (Fig. 3D). The chemokine C-X-C motif-chemokine ligand 3 (Cxcl3) is known for its neutrophil chemoattractant property, which results in excessive neutrophil responses, regulation of dendritic cells (32), and migration and proliferation of endothelial cells (33). In our study, we observed a significant decrease in Cxcl3 expression in macrophages/monocytes following M-I treatment (Fig. 3C and D). Chitinase-like 3 (Chil3) catalyzes the conversion of chitin to N-acety1-glucosamine. Chil3 has a wide range of functions, including activating the immune system, preventing metastasis, and promoting bacterial clearance (34). Our study revealed significant upregulation of Chil3 in monocytes/macrophages, dendritic cells, CD8+ T cells, and Treg cell populations following M-I treatment. Since we observed Sfln4 and Cxcl3 were significantly downregulated in most clusters of monocyte/macrophage populations, we verified and validated their expression using qRT-PCR analysis in the control and experimental mice groups. We found that the expression of both genes was significantly reduced in the M-I-treated MOC2 tumors compared to the untreated control (Fig. 4A and B). Taken together, our results highlight a mechanism of action of M-I treatment via altering the gene expression within TAMs but without significantly affecting their frequency in the TME of head and neck cancer.

Fig. 4: Effect of M-I on gene expression in tumor-associated macrophages.

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A and B, Relative mRNA expression of Cxcl3 (A) and Slfn4 (B) genes as analyzed by qRT-PCR in M-I-treated vs. control tumors. 18S rRNA was used as an internal control. C, RAW 264.7 cells were cocultured with MOC2 and treated with either M-I or vehicle (control) for 48 hours. mRNA expression of macrophage phenotype markers Arg1 and NOS2, as analyzed by qRT-PCR. D, mRNA expression of Cxcl3, Slfn4, and Chil3 genes. 18S rRNA was used as an internal control. Small bars indicate standard error (*P < 0.05; **P < 0.01; ***P< 0.001).

M-I treatment alters the phenotype of tumor-associated macrophages

Next, we examined whether M-I has a modulatory role in TAM’s functional phenotypes. TAMs are the major immune cell population in the head and neck TME, and their functional status determines cancer development, i.e., M1 and M2 type macrophages have opposing effects on cancer cells, either with activating or immunosuppressive functions, respectively (35). Single-cell studies of patient samples diagnosed with cancer reveal that TAMs are heterogeneous (18), and malignant tumors show a mixed expression of markers of M1 and M2, implying the phenotypes of TAMs in the TME is complex. To examine whether M-I influences the macrophage phenotypes, either independently or if the macrophages also need to be in close contact with the tumor cells, we performed in vitro experiments using MOC2 and RAW 264.7 cell lines. We treated the individual cell lines with M-I or vehicle, either as a separate culture or as a coculture or in a trans-well system. We examined the markers of M1 and M2 phenotypes using RNA isolated from control or M-I-treated RAW 264.7 cells. The arginase 1 (Arg1) expression was significantly inhibited in M-I-treated cocultured cells, whereas Nitric Oxide Synthase 2 (NOS2) expression was enhanced following M-I treatment, signifying the M2 phenotype is affected (Fig. 4C). We further found a significant downregulation of Sfln4 or Cxcl3 mRNA in M-I treated cocultured cells, whereas Chil3 is upregulated (Fig. 4D). No significant change was noticeable following M-I treatment when RAW 264.7 cells and MOC2 cells were cultured in Trans-well system. M-I also did not affect the expression of Sfln4, Cxcl3 or Chil3 in either MOC2 or RAW 264.7 cells following separate M-I treatments. We also observed upregulation of Chil3 and downregulation of Cxcl3 mRNA by M-I when co-cultures with bone marrow-derived macrophages from C57BL/6 mice and MOC2 cells. Treatment with M-I in RAW 264.7 cells for 48 hr did not display cell death. Together, these results corroborate our scRNA-seq results and indicate that M-I treatment alters the M2 phenotype of TAMs and that the TAMs need to be in close contact with cancer cells.

M-I treatment induces differential gene expression in B-cells and reduces their frequency

B cells were the most significantly reduced cell population in our scRNA-seq dataset. We detected 234 B cells (7.5% of the total cells) in the control population and only 86 B cells (2.2% of the total cells) in the M-I-treated population (Fig. 2E). The typical B cell markers CD79a, CD79b, CD19, and Ms4a1/CD20 were downregulated in the B cell population following M-I treatment but still present as cluster markers (Fig. 5A). The top differentially expressed genes based on p-value are displayed in a volcano graph plot (Fig. 5B), and some of these changes are further analyzed in a violin plot (Fig. 5C). In these analyses, Tagln2, Rabgap1l, Ccr7, S100a6, and Myadm are the top highly expressed genes in B-cells after M-I treatment, whereas Ifi27l2a, Slfn5, Bcl11a, Ccl6, and Trim30a genes are the most prominently decreased ones. Tumor-infiltrating B-cells include immunosuppressive fractions, called regulatory B cells (Bregs) (36), and removing Bregs suppresses colorectal cancer and hepatocellular carcinoma in animal models (37). Bregs play immunoregulatory roles by secreting tumor-promoting cytokines, e.g., Bregs produce IL-10 that impairs macrophages, dendritic cells, and CD8+T cells and enhances Treg activity. Although we detected reduced expression of Il10 in the B-cell population, the decrease was not statistically significant. However, Bcl11a expression was significantly reduced (Fig. 5C), which is known to be highly expressed in Bregs and plays a pivotal role in B-lymphocyte development (38, 39). To assess how M-I-treatment of cancer cells influences an immune effect in the B-cells, we isolated the B6 mouse spleen cells and cocultured with MOC2 cells with or without M-I for 48 hours. We employed flow cytometry to analyze live B cells, identified by their expression of CD45 and CD19 markers. M-I treatment significantly reduced the CD45+CD19+ population live cells when the spleen cells were cocultured with MOC2 cells (Fig. 6A), compared to vehicle treated cocultured cells. Moreover, the expression of Bcl11a was reduced about ten-fold in M-I-treated cocultured spleen cells (Fig. 6B), substantiating the scRNA-seq data analysis. Together these results indicate that the therapeutic effect of M-I treatment in HNC may occur likely via affecting Bregs activity, in addition to functionally altering the tumor-promoting TAMs.

Fig. 5: M-I treatment modulates the B-cell population.

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A, UMAP plots in B-cell populations using CD79a, CD79b, CD19, and Ms4a1/CD20 markers in the treated group vs. control group. B, Volcano plot showing log2(fold change) and −log10(P value) for DEGs from B-cell population illustrates expression of genes in treated tumors vs. control tumors. The horizontal dashed line is p =0.05 [−log10(0.05) = 1.3], the cut-off threshold. C, Violin plot displays significantly altered genes in B-cell clusters in M-I-treated vs. control tumors.

Fig. 6: In vitro characterization of spleen B cells cocultured with MOC2 cells in the presence of M-I.

Fig. 6:

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Spleen cells cocultured with MOC2 cells were treated with M-I for 48 hours. A, CD45+, and CD19+ populations from live cells were identified and analyzed by flow cytometry. A reduced number of CD19+ B cells are noted in M-I-treated cocultured cells. (B) mRNA expression of Bcl11a was analyzed by qRT-PCR from spleen cells cocultured with MOC2 cells, either untreated or M-I treated. 18S rRNA was used as an internal control. Small bars indicate standard error (*P < 0.05).

DISCUSSION

Our studies show that M-I treatment significantly reduces tumor growth in two syngeneic mouse models with little measurable toxicity. While natural alternate medicines have been shown to have effective remedial effects in many diseases, including cancers, with appreciable safety profiles, the mechanism of their actions remains obscure. We identified M-I as the active component, which shows comparable efficacy as the whole BME. Guided by these preliminary observations and reduced expression of PD-1, PD-L1, and FoxP3 transcripts, we analyzed CD45+ immune cell populations to better understand the impact of M-I treatment on MOC2 tumors and the composition of immune cells in the TME. Among the CD45+ populations, we find TAMs as the major component of the TME. Macrophages have two functional phenotypes, M1 (activated macrophages) and M2 (alternatively activated macrophages). M2 and a small fraction of M1 cells lack tumor-killing functions and help tumor cells escape immune cells (40). We found reduced expression of Arg1 and increased expression of NOS2 in TAM following M-I treatment, suggesting that M-I treatment impairs M2 TAMs. Although we found reduced expression of most of the M2 macrophage markers, such as CD163, Mrc1/CD206, in M-I treated tumors, some M2 markers like Ccl3 or Chil3 were increased in this group, supporting the notion the phenotypes of TAMs in the head and neck TME is complex. Interestingly, our in vitro coculture studies indicate that alteration of specific genes in TAMs occurs only when macrophages are in contact with tumor cells, a feature that may help avoid impacting normal cells when M-I is used as a therapeutic agent against HNC.

Macrophages are key contributors to PD-L1/CD274 expression in the TME of HNC (19). A recent study suggests that PD-L1+ macrophages are associated with TME of HNC(19). PD-L1 expression in macrophages was significantly reduced in the M-I-treated tumors, and differential expression of this transcript is observed in TAMs in our model after M-I treatment. TAMs also secrete cytokines/chemokines that promote tumor growth (41). CCL5, secreted from TAMs and DCs, is a target molecule in macrophage-targeted therapies. We observed significant downregulation of Ccl5 following M-I treatment in macrophage clusters.

A key finding of this study is that Cxcl3 and Sfln4 transcripts were consistently downregulated in all monocyte and macrophage cell subsets after M-I treatment. Cxcl3 is involved in human TME and affects both immune and non-immune cells. Cxcl3 expression was identified mostly in the monocyte/macrophage populations and downregulated following M-I treatment, which was later confirmed by quantitating Cxcl3 mRNA in the M-I-treated tumors. TCGA analysis reveals significantly higher levels of Cxcl3 mRNA in HNC tissues than in the normal tissues. Cxcl3 is a secreted growth factor that signals through its cognate receptor CXCR2, which is expressed in non-immune cells in various cancers (42). Sun et al., (43) recently showed that the Cxcl3-CXCR2 signaling pathway is involved in fibroblast-to-myofibroblast transition in a pancreatic cancer model. Interestingly, we did not observe any changes in Cxcl3 or CXCR2 expression following M-I treatment in the CD45− cells bulk RNA-seq analysis. We postulate that M-I inhibits Cxcl3 expression in macrophages by changing the M2 to M1 phenotype or by an unknown mechanism in HNC, which may interrupt the Cxcl3-CXCR2 signaling pathway. Sfln4 is a novel family of proteins implicated in lymphoid and myeloid cell development and differentiation (29). Slfn4 is expressed by infiltrating macrophages in the brain during neuroinflammation (44). Slfn4 is expressed only in mice and shares homology with the human SGLN12L, which shows high expression in patients with HNC when the TCGA database was analyzed using GEPIA2. In a murine system, Slfn4 was shown to be induced in the gastric mucosa following chronic Helicobacter pylori infection, exhibits myeloid-derived suppressor cell (MDSC) markers, and inhibits T-cell proliferation (29). Interestingly, we did not find any alterations in Sfln4 in T-cell populations in our HNC model following M-I treatment. We also observed a significant inhibition of Slfn4 expression in M-I treated SCCVII tumor. Therefore, it is possible that Slfn4 may be an important player in M-I mediated tumor regression. One unexpected and unique observation in the present study is the upregulation of Chil3 in a different subset of CD45+ cells in M-I-treated tumors. As Chil3 is known to activate the immune system, prevent metastasis, and promote bacterial clearance (34), further studies are needed to establish a functional role for Chil3 following M-I treatment in our model.

We made another interesting observation that the B-cell population was reduced following M-I treatment. An emerging body of evidence has recently recognized the role of tumor-infiltrating B cells in modulating the immune response to tumors and in complementing T cell-mediated antitumor immunity (45, 46). These tumor-infiltrating B cells are newly designated subsets of B cells, termed Bregs, that play a pivotal role in suppressing antitumor immune responses and promoting tumor progression by interacting with tumor cells. In fact, Bregs are also required to induce the regulatory activity of MDSCs, resulting in suppressing CD4+ and CD8+ T cells. In the B-cell cluster, we observed fewer number of cells and significant downregulation of Bcl11a in M-I treated tumors, which was verified with qRT-PCR in M-I treated tumors. However, the role of Bcl11a and how Bregs function in the HNC microenvironment remains unknown. Bcl11a expression is higher in triple-negative breast tumors, non-small-cell lung cancer and laryngeal squamous cell carcinoma (39). Together these data suggest that M-I treatment may reduce the putative Breg population and their pro-tumor activity. Further studies are needed to delineate the in-depth complexity of B cells within the head and neck TME in the context of M-I treatment. Taken together, our data shed new light on the immune mechanisms within the TME while providing clues on how M-I treatment exerts its antitumor therapeutic effect in HNC by immunomodulating TAMs and B cells. We did not observe a differential expression in memory B- or T-cell markers in our scRNA-Seq data analysis following M-I treatment.

In conclusion, our studies provide new insights into the heterogeneous immune cell populations, their functional phenotypes within the TME, and how M-I affects this immune landscape, conferring antitumor effects to HNC in syngeneic mouse models. M-I also displayed anti-proliferative effect on HNC cells in vitro. Therefore, it is conceivable that M-I may exert multiple mechanisms for reduction of tumor growth. M-I treatment causes a distinct transcriptomic signature in the TME, which involves immunomodulation that disrupts tumor-promoting M2 TAMs and B cell populations, thus restoring effective immunosurveillance over tumor cells. However, other signaling pathways in tumor and stromal cells may also be involved for regression of tumors following M-I treatment. The outcome of these pioneering studies unravels the potential anticancer mechanisms of this alternate medicine in head and neck cancer. These results may help develop M-I as a novel therapeutic agent to overcome immunosuppression and improve immunotherapy for head and neck cancer to improve its clinical benefits.

Supplementary Material

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Acknowledgments:

We thank Eric Ford for technical assistance and David DeBruin for helping with data analysis.

Funding:

This work was supported by grant R01 DE024942 from National Institutes of Health.

Abbreviations:

Arg2

Arginase 2

Chil3

chitinase-like 3

Cxcl3

C-X-C motif chemokine ligand 3

FITC

Fluorescein isothiocyanate

Foxp3

Forkhead Box P3

GSVA

Gene set variation analysis

M-I

Momordicine I

NK cell

Natural killer cell

NOS2

Nitric Oxide Synthase 2

PD-1

Programmed cell death 1

PD-L1

Programmed death-ligand 1

PI

Propidium iodide

scRNA-seq

single-cell RNA sequence

Slfn4

Schlafen 4

TAM

tumor-associated macrophage

TME

Tumor microenvironment

Footnotes

Conflicts of Interest: The authors declare that they have no conflict of interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1966401-supplement-1.pdf (147.9KB, pdf)
2
NIHMS1966401-supplement-2.pdf (211.5KB, pdf)
3
NIHMS1966401-supplement-3.pdf (271.2KB, pdf)
4
NIHMS1966401-supplement-4.pdf (359.9KB, pdf)

Data Availability Statement

Data generated in this study has been included in this manuscript. RNA-seq data generated in this study were deposited in GEO under accession number GSE254011. Additional data are available from the corresponding author upon reasonable request.

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