MicroRNA-92 expression in CD133⁺ melanoma stem cells regulates immunosuppression in the tumor microenvironment via integrin-dependent activation of TGF-β

Abstract

In addition to being refractory to treatment, melanoma cancer stem cells (CSC) are known to suppress host anti-tumor immunity, the underlying mechanisms of which need further elucidation. In this study, we established a novel role for microRNA-92 (miR-92) and its associated gene networks in immunosuppression. CSCs were isolated from the B16-F10 murine melanoma cell line based on expression of the putative CSC marker CD133 (Prominin-1). CD133⁺ cells were functionally distinct from CD133⁻ cells and showed increased proliferation in vitro and enhanced tumorigenesis in vivo. CD133⁺ CSCs also exhibited a greater capacity to recruit immunosuppressive cell types during tumor formation, including FoxP3+ Tregs, myeloid-derived suppressor cells (MDSC) and M2 macrophages. Using microarray technology, we identified several miRs that were significantly downregulated in CD133⁺ cells compared to CD133⁻ cells, including miR-92. Decreased expression of miR-92 in CSCs led to higher expression of target molecules integrin α_V and α₅ subunits, which in turn enhanced TGF-β activation as evidenced by increased phosphorylation of SMAD2. CD133⁺ cells transfected with miR-92a mimic and injected in vivo showed significantly decreased tumor burden, which was associated with reduced immunosuppressive phenotype intratumorally. Using the TCGA database of melanoma patients, we also noted a positive correlation between integrin α₅ and TGF-β1 expression levels and an inverse association between miR-92 expression and integrin alpha subunit expression. Collectively, the current study suggests that a miR-92-driven signaling axis involving integrin activation of TGF-β in CSCs promotes enhanced tumorigenesis through induction of intratumoral immunosuppression.

Introduction

Primary melanomas have been reported to harbor subpopulations of tumor cells with intrinsic self-renewal and proliferative capacity termed cancer stem cells (CSC) (1). The CSC theory may help explain the plastic, chemoresistant, and invasive natures of refractory melanomas. Several biomarkers have been utilized in the identification and isolation of melanoma CSCs including CD20 (1), aldehyde dehydrogenase (2), CD133 (3), and ABCB5 (4). The murine melanoma cell line B16-F10 was recently shown to contain a distinct subset of cells expressing CD133 that had long term tumorigenic potential and highly expressed the stem cell markers Oct4, Nanog, and Sox10 (5). In the present study, we utilized CD133⁺ B16-F10 cells to explore the intricate interactions between immune cells and CSCs to determine how specific subpopulations may drive immunosuppression in the tumor microenvironment (TME).

Immunosuppression can be mediated through several immune cell phenotypes including regulatory T cells (Treg), myeloid-derived suppressor cells (MDSC), and alternative macrophages (M2) (6). Many cancers, including melanoma, exploit these immune cell phenotypes to secrete cytokines and growth factors that create a permissive environment for cancers to proliferate and eventually metastasize. Transforming growth factor beta (TGF-β) is a pleiotropic cytokine with robust immunosuppressive activity including the ability to repress T cell activation and proliferation (7). Part of this immunosuppressive effect can be carried out by Tregs, which produce abundant TGF-β in order to modulate immune response to self and foreign antigens [extensively reviewed in (8)]. However, TGF-β is secreted in an inactive form and must undergo activation in order to stimulate downstream signaling cascades through binding of the TGF-β receptor (TGFBR) (9). One mechanism for converting latent TGF-β to its active form is through interactions with RGD-recognizing integrins (i.e. integrin α_v) which associates with latent TGF-β binding proteins (LTBP) and the TGF-β pro-domain to free TGF-β via mechanical shearing [9]. TGF-β activation and subsequent signaling through its receptor has been associated with immune evasion (10), epithelial to mesenchymal transition (EMT) (11), and tumor cell invasion (12); thus, targeting of TGF-β signaling in cancer remains a priority (13,14).

MicroRNAs (miR) are small (20–30 nucleotide) non-coding RNAs that generally function to suppress gene expression by targeting the 3’ UTR of mRNAs, several of which have been demonstrated to regulate cellular functions pertinent to oncogenesis and tumor progression (15). MicroRNA-92 (miR-92), a member of the miR-17–92 cluster, has been reported as both an oncomiR (16,17) as well as a tumor suppressor (18,19) depending on the cancer model. Importantly, miR-92 was shown to regulate expression of integrin α₅ in an ovarian cancer model (20). Remarkably, the role of miR-92 in melanoma has yet to be explored.

Our study reveals, for the first time, that miR-92 may regulate an integrin-mediated axis driving TGF-β-induced immunosuppression in the TME. Further, this axis may confer a selective survival advantage to CSCs present within the heterogeneous tumor population by modulating immunosuppression and exploiting immunosuppressive cell phenotypes such as Tregs and M2 macrophage populations present within the TME. These studies shed light on the biological function of CSCs in the context of immune surveillance and also provide a potential therapeutic target in refractory melanomas in which CSCs may contribute to patient relapse.

Materials and Methods

Cell culture and reagents

The B16-F10 cell line was obtained from American Type Culture Collection (Rockville, MD). All cell lines were grown in Dulbecco’s Modified Eagles Medium (DMEM) supplemented with 10% heat-inactivated Fetal Bovine Serum (FBS, Atlanta Biologicals), Penicillin (100 U/mL, Gibco), and Streptomycin (100 μg/mL, Gibco). Cells were incubated at 37°C at 5% CO2 and sub-cultured every 72 hours. Routine monitoring for mycoplasma contamination was performed using the MycoAlert Detection Kit (Lonza #LT07–218). Cells recovered from frozen aliquots were allowed one passage to reach exponential growth phase following recovery before being used in this study. Cells at passages greater than ten were not used in the experiments performed in this study. CD133-positive and –negative cells were isolated by fluorescence-activated cell sorting (FACS) and were grown in DMEM/F-12 serum-free media (SFM) containing 1 × N-2 Supplement (Gibco #17502–048) 10 ng/mL basic fibroblast growth factor (Peprotech #450–33), and 10 ng/mL epidermal growth factor (Peprotech #315–09) in low cluster 6-well plates (Corning #3471).

Fluorescence-assisted cell sorting (FACS), flow cytometry, and Spanning-tree Progression Analysis of Density-normalized Events (SPADE) analysis

B16-F10 cells were grown as non-adherent oncospheres in SFM as previously described (21). After 7–10 days of culture in low-cluster plates, oncospheres were dissociated into single cell suspensions and labeled using a PE-conjugated CD133 antibody (BioLegend #141204) in 100 μL of staining buffer (2% FBS/2mM EDTA in phosphate buffered saline (PBS)) at a dilution of 1:100. The appropriate isotype control (BioLegend #400508) was used to gate the CD133-positive and –negative populations. Cells were sorted using a BD FACS Aria II into 15 mL conical collection tubes containing ~10 mL of ice-cold PBS at 4 °C. Representative histograms demonstrating our gating strategy and post-sort purity have been provided (Supplemental Figure 1). After sorting, cells were centrifuged at 300 xg for 10 m, resuspended in an appropriate amount of PBS, and counted by trypan blue exclusion assay on a BioRad TC20 Automated Cell Counter before use in subsequent assays.

Primary tumors generated from subcutaneous (s.c.) injection of B16 cells or lungs from metastasis bearing mice intravenous (i.v.) were dissociated using a tumor dissociation kit (Miltyni #130–096-730) in order to dissociate whole tumor tissues into single cell suspensions following the manufacturers recommended protocol. The resulting cell suspensions were washed with and resuspended in PBS prior to initiating labeling with antibodies. Single cell labeling with fluorophore-conjugated primary antibodies against CD45 (BioLegend #103116), CD3 (BioLegend #100306), CD4 (BioLegend #100453), CD8 (BioLegend #100708), NK1.1 (BioLegend #108748), FOXP3 (BioLegend #126419), IL10 (BioLegend #505031), TGF-β (BioLegend #141410), IL17 (BD #564168), IFNγ (BD #563854), CD11b (BioLegend #101222), F4/80 (BioLegend #123110), CD11c (BioLegend #117334), GR1 (BioLegend #108457), Ly6C (BD #560595), Ly6G (BD #560603), and CD206 (BioLegend #141723) was performed for at least 30m on ice, washed with staining buffer, and subsequently analyzed on a BD FACSCelesta Flow Cytometer equipped with BD DIVA software in conjunction with FlowJo software. For intracellular labeling against transcription factors and cytokines (i.e. FOXP3), cells were fixed and permeablized using the True-Nuclear Transcription Factor Buffer Set kit (BioLegend #444201) following the manufacturer’s recommendations. Data were compensated using BD CompBeads (anti-mouse #552843, anti-rat/hamster #552845), labeled with single antibodies or isotype controls, and analyzed using FlowJo. SPADE V3.0 (22) was used to down-sample and cluster similarly labeled populations of cells following compensation and gating in FlowJo. Compensated FCS 3.0 files were exported and analyzed using the standalone version of SPADE 3.0 using the following parameters: Arcsinh transformation = 150, max allowable cells in pooled data = 200,000, outlier density= 1, fixed number of remaining cells = 100,000, clustering parameter = K-means, and the desire number of clusters = 50. Determinations for phenotyping each node/cluster was carried out based on single color controls and a representative figure is provided in Supplemental Figure 2.

MicroRNA microarray

Briefly, CD133⁺ and CD133⁻ populations were isolated via FACS from the B16-F10 murine melanoma as described above. Total RNA was extracted (Qiagen miRNeasy #74106) from B16-F10 cells sorted from three independent experiments. Each sample was individually analyzed for quantity (NanoDrop 2000, Thermo Scientific) and quality (BioAnalyzer 2100, Agilent). For miRNA microarray, aliquots from individual samples were pooled for each group (n = 3 per CD133^+/−). All samples used for downstream analysis had an RNA integrity number of at least 8. RNA profiling from samples was performed using the FlashTag Biotin HSR RNA Labeling Kit for GeneChip miRNA Arrays for the Affymetrix GeneChip miRNA 4.0 array platform. Labeled and hybridized chips were scanned on a GeneChip Scanner (Affymetrix) and microarray image data were analyzed using Affymetrix Power Tools. Data analysis and generation of representative figures (i.e. scatter plot) was performed using the Transcriptome Analysis Console (TAC, Affymetrix). MicroRNAs with a fold-change greater than 1.5 or less than -1.5 were considered for further validation and analysis. Predicted targets and alignment scores for specific miRNAs were generated using online software including TargetScan Mouse 6.2 and miRDB. Ingenuity Pathway Analysis (IPA, Qiagen) in combination with MetaCore pathway analysis tools (Thomson Reuters) were used to generate potential gene networks associated with significantly altered miRNAs and generate miR-gene interactome pathway maps.

Quantitative real-time polymerase chain reaction (qRT-PCR)

CD133⁺ and CD133⁻ B16-F10 cells were isolated by FACS, and total RNA was isolated using miRNeasy kit (Qiagen), following the manufacturers protocol. The expression of indicated mRNA and miRNA levels was determined by qRT-PCR. Total RNA was quantitated using a Nanodrop 2000 (Thermo Scientific). For miRNA expression analysis, cDNA was generated from total RNA using miScript II cDNA Synthesis Kit (Qiagen # 218161). Two step miRNA qRT-PCR were carried out using SsoAdvanced SYBR green Mix (BioRad #1725270) with mouse primers for SNORD96A (Qiagen #MS00033733), miR-669a-5p (Qiagen #MS0026222), miR-669l-5p (Qiagen #MS00043337), miR-466h-5p (Qiagen #MS00012201), and miR-92a-3p (Qiagen #MS00005971). Expression levels for miRNAs were normalized to SNORD96A. For mRNA expression analysis, cDNA was made from total RNA using miScript II cDNA synthesis kit. A two-step amplification with a 60° annealing temperature for qRT-PCR was carried out using SsoAdvanced SYBR green supermix from Bio-Rad with mouse primers for IL10, TGFβ1, TGFβ2, TGFβ3, Smad2, ITGB1, ITGB3, ITGA5, and ITGAV customized and order from IDT. All PCR experiments used a CFX96 Touch Real-Time PCR Detection System (Bio-Rad), and expression levels were normalized to β-actin mRNA levels. Fold changes were calculated using the 2^−ΔΔCT method. Specific primers sequences are provided in Supplemental Table 1.

Immunoblot and densitometry analysis

Cells were harvested and re-suspended in Radioimmunoprecipitation assay (RIPA, 150 mM NaCl, 1.0% IGEPAL CA-630, 0.5% sodium deoxycholate, 0.1% SDS, 50 mM Tris, pH 8.0) buffer (Sigma #20–188) containing a protease inhibitor cocktail (Sigma #P8340) and PhosStop phosphatase inhibitor (Roche # 04906845001). Protein concentrations of cell lysates were determined by a bicinchoninic acid assay (Thermo Scientific #23225) and 40–60 μg of total protein was loaded per lane on 10% Tris-Gly gels (BioRad #4561033), subjected to SDS-PAGE, and transferred to a nitrocellulose membrane using the iBlot system (Invitrogen). Lysates were probed with antibodies that recognize phosphorylated SMAD2 (Cell Signaling #8828S), total SMAD2 (Cell Signaling #5678S) β-Actin (Cell Signaling #4970S), Integrin β₁ (Cell Signaling #4749T), Integrin β₃ (Cell Signaling #4749T), Integrin α_v (Cell Signaling #4749T), and Integrin α₅ (Cell Signaling #4749T), and GAPDH (Cell Signaling #5174S). Densitometry and image analysis were performed using a ChemiDoc station equipped with ImageLab software (BioRad). Densitometry analysis of bands of interest from immunoblots was performed using ImageJ software.

Oncosphere formation assay

B16-F10 sorted populations were isolated based on CD133 positivity as previously described. Sorted cells were cultured in low-adherent 6-well plates (Corning) in SFM at a density of 1 × 10³ cells/mL. Cultures were grown for up to 10 days and amended with fresh SFM media twice per week. Oncospheres (> 100 μm) were counted and imaged using an EVOS light microscope (Life Technologies) and images were analyzed using Image-J software (NIH).

In vivo tumor growth models

Female C57Bl/6 (Jackson #000644) were used at 6–8 weeks of age. All mice were handled in accordance with the American Association for Laboratory Animal Science guidelines with the approval of the appropriate Institutional Animal Care and Use Committees at the University of South Carolina (protocol #2371). Mice were injected s.c. with 1 × 10⁵ B16-F10 cells in phosphate buffered saline (PBS; 100 μL). Tumor size was monitored thrice weekly until animals were sacrificed due to tumor burden. Tumor volume [V = L × W2 × (π/6)] was determined by measuring the greatest linear dimensions in length (L) and width (W).

For our experimental metastasis models, 2 × 10⁵ B16-F10 cells suspended in 100 μL phosphate buffered saline (PBS) were injected intravenously (i.v.) into 6–8 week old, female C57Bl/6 mice via the lateral tail vein. After approximately 14–16 days, mice were sacrificed. Upon sacrificing the mice, lungs were resected, imaged, dissociated, and labeled with antibodies for subsequent flow cytometry analysis.

In experiments involving in vivo growth of CD133+ Transfected cells, mice were injected with CD133+ cells transfected with miR-92a mock (HiPerfect reagent only) or mimic (as described below), and tumor volume was measured. On day 15, mice were sacrificed, tumors were dissociated, and labeled with antibody panels for various immune phenotypes using flow cytometry.

Transfection of miR-92a mimics and inhibitors

In brief, CD133+ cells (1.5×10⁵/well in 0.5 mL) post-sorting were cultured in 24 well plates at 37°C, 5% CO₂. The following day (24h post-seeding), transfection was performed following the manufacturers protocol. Seventy five ng of miR-92a mimic, miR-92a inhibitor, or miR-92a mimic + inhibitor (to a final concentration of 10 nM) were diluted in 100 μL of culture medium without serum. HiPerFect reagent (4.5 μL) (#301705, Qiagen) was added to the diluted miR-92a mimic, miR-92a inhibitor, or miR-92a mimic + inhibitor. The reagents were incubated for 10m at RT to allow for the formation of transfection complexes. The complexes were added to their respective wells and subsequently mixed by pipetting to ensure uniform dilution of the transfection complexes. The culture medium was changed after 12–15h. Following the change in medium, cells were incubated for 72h at 37°C, 5% CO₂. The cells were collected 72h post-transfection and used for microRNA assays or gene expression. Primer assays and gene expression was determined by Real-Time PCR and are described in the Material and Methods. Snord96a (#3150530, Qiagen) was used as an internal control for miR-92a expression and Actin (primer sequences provided previously) was used to normalize gene expression.

Co-culture and ELISA

Sorted tumor populations were cultured alone or with freshly isolated whole splenic cells at a 1:1 ratio (1×10⁶ total cells) in 100 μL of serum-free media for 24 h in cell culture treated 96-well plates (Corning #3595). The resulting supernatants were centrifuged at 400 xg to remove cells and debris, and frozen at -80° C until analysis. A free TGF-β precoated ELISA kit was used (BioLegend #437707) in order to determine the concentration of active TGF-β in each sample following the manufacturer’s recommended protocol. Splenic cells from each well were isolated by centrifugation at 400 xg for 10 m and labeled using the fluorophore-conjugated antibodies described previously. Following labeling, cells were washed and resuspended in 500 μL of staining buffer for flow cytometric analysis.

Analysis of TGCA samples

Expression data from cutaneous melanoma samples contained in the TCGA database were assessed and visualized using cBioPortal (23,24) and UCSC Xena (http://xena.ucsc.edu/). Queried genes included ITGAV, ITGA5, and TGFB1. Correlations based on the Pearson coefficient are represented in each scatter plot, and represent mRNA expression data for each of the genes provided. Mutational status for each queried gene is also provided above each mRNA expression heat map and explained in detail in the appropriate figure legend. Correlation analysis between miR-92a expression and the genes described above were also conducted using Stata 14 (StataCorp, 2015) from cutaneous melanoma data sets containing both microRNA and mRNA expression data available from the TGCA study. Simple linear regression analysis was performed to predict expression of integrin alpha subunits as a function of miR-92 expression using Stata. Figures reflecting these analyses are visualized by a scatter plot of the original data, the linear regression line, and the 95% confidence intervals of the regression line.

Statistical analysis

GraphPad Prism 5.0 software (GraphPad Prism Software, Inc.) was used for all statistical analyses. For all in vitro studies, two-group comparisons between control and test samples were done by two-tailed Student’s t-test and representative data from three independent experiments were presented. A one way ANOVA was performed on in vitro experiments containing more than one group, and significance was determined and denoted for each group accordingly. Subcutaneous and experimental metastasis in vivo data were analyzed for significance using two-way ANOVA and a two-tailed student’s t-tests, respectively. For all tests, statistical significance was assumed when p < 0.05. P-values were reported in each figure or in their respective figure legends.

Results

CD133⁺ B16-F10 cells are functionally distinct from CD133⁻ cells both in vitro and in vivo

To study the differential characteristics of CD133+ and CD133- B16-F10 cells, we injected 2×10⁵ B16-F10 cells from each phenotype s.c. into C57Bl/6 mice. We observed a 58% increase in mean tumor volume and a 52% increase in mean wet tumor weight in the CD133⁺ group compared to CD133⁻ group. Not only did CD133⁺ cells form palpable tumors quicker than CD133⁻ cells, they were also more tumorigenic (Figure 1A, 1B). CD133⁺ cells formed tumors in 6/6 mice, while CD133⁻ cells only formed tumors in 4/6 mice (Figure 1B). Using in vitro functional assays, we observed that CD133⁺ cells had a higher propensity to proliferate, form colonies, and generate anchorage-independent oncospheres when compared to CD133⁻ cells (Figure 1B–D). In colony-forming assays, CD133⁺ cells generated an average of 42.8 ± 8.4 colonies, while CD133⁻ cells only generated an average of 18.2 ± 3.0 colonies (Figure 1C). We also observed a significant increase in the ability to form anchorage-independent oncospheres in CD133⁺ populations compared to CD133⁻ cells (Figure 1D). Not only were CD133⁺ cells capable of generating significantly more floating spheres (106.8 ± 11.6) compared to CD133⁻ cells (41.8 ±10.7), but oncospheres from CD133⁺ cells were observed to be much larger in diameter (Figure 1D), suggesting that anchorage-independent survival and proliferation was enhanced in the CD133⁺ population.

Figure 1: CSCs are enriched in the CD133⁺ population and are functionally distinct from their CD133⁻ counterparts.

CD133⁺ cells form palpable tumors and display elevated growth kinetics in a syngeneic mouse model (A,B) (n = 6 per group). Tumor volumes represent mean tumor volume ± SEM. Mice (4/6) injected with CD133⁻ cells formed tumors while all (6/6) mice injected with CD133⁺ cells formed tumors. In vitro colony formation (C) and non-adherent oncosphere formation (D) was significantly increased in CD133-expressing populations. Images depict anchorage-dependent colony growth (C) and anchorage-independent oncosphere growth in SFM media (D) and are representative of data collected from three independent experiments. Statistical significance was determined at p < 0.05 and was denoted by an asterisk (*). P-values have been provided where appropriate. Dissociation, labeling, and analysis of representative tumor samples initiated by CD133⁺ and CD133⁻ B16-F10 melanoma cells demonstrated a significant shift in lymphocyte (E), MDSC (F), and macrophage (G) populations. Statistical analysis on samples generated from CD133⁺ and CD133⁻ initiated tumors identified several significant changes associated with each group (H). SPADE analysis further demonstrated the alterations in immune cell infiltration of the TME between CD133⁺ cells and CD133⁻ cells (I). Flow plots and SPADE analysis were generated from representative data collected from two independent in vivo experiments. Significance was determined by Student’s t test (p < 0.05) and is denoted by an asterisk. (*p < 0.05, **p < 0.01, ***p < 0.001).

Tumors initiated by CD133⁺ cells had a more immunosuppressed TME compared to CD133⁻ cells

Following syngeneic transplantation of CD133⁺ or CD133⁻ B16 tumor cells into C57Bl/6 mice, we allowed palpable tumors to grow to approximately 1 cm³ before resecting the tumors. Following resection, tumors were enzymatically dissociated and labeled to determine the infiltrating immune cell phenotypes in CD133⁺ and CD133⁻ initiated tumors. The data from a representative experiment are shown in Figure 1E–G and from multiple experiments are summarized in Figure 1H. CD133⁺ initiated tumors were observed to not only grow faster and larger than those initiated by CD133⁻ cells, but were also observed to have higher abundance of tumor-infiltrating Tregs, granulocytic MDSCs (gMDSC) (CD45⁺CD11b⁺GR1⁺Ly6G⁺), and M2 macrophages (CD45⁺CD11b⁺F4/80⁺CD206⁺) (Figure 1E–I). Infiltrating macrophages, identified as CD45⁺F4/80⁺CD11b⁺ were reduced within the TME of CD133⁺ generated tumors (0.53% of total CD45⁺ cells) compared to CD133⁻ tumors (1% of total CD45⁺ cells). No changes in pan-T cell (CD45⁺CD3⁺) or pan-MDSC (CD45⁺CD11b⁺GR1⁺) populations were observed. Concurrently, we observed a significant increase in T cells staining positive for TGF-β as well as IL-17a in tumors initiated by CD133⁺ cells (Figure 1E). TGF-β⁺ T cells increased from 13.6% in CD133⁻ tumors to 25.2% in tumors generated by CD133⁺ cells. We also observed that Helios⁺ Tregs were significantly increased in CD133⁺ tumor samples increasing from 4.9% in CD133⁻ tumor samples to nearly 10% in CD133⁺ tumors. Although we did not observe a difference in macrophage or MDSC populations, we did find that tumors initiated by CD133⁺ cells had significantly increased proportions of infiltrating gMDSC (10.1% of total MDSCs in CD133⁺ group versus 2.9% of total MDSCs in CD133⁻ group) and alternate macrophages (13.4% of total macrophages in CD133⁺ group versus 8.9% of total macrophages in CD133⁻ group). In order to highlight the changes associated with tumor initiation between CD133⁺ and CD133⁻ cells, we have included a representative SPADE tree from pooled tumor samples (Figure 1I). SPADE analysis shows a significant increase in immunosuppressive phenotypes including Tregs and cells staining positive for TGF-β or IL-10. Taken together, these data suggest that more suppressive immune cells infiltrated CD133⁺ tumors allowing for superior tumor growth when compared to CD133⁻ cells.

MicroRNA microarray identified miR-92 as a regulator of integrin expression

We used an Affymetrix microarray to screen expression profiles of several thousand microRNAs in CD133⁺ and CD133⁻ B16-F10 cells. Analysis of data showed that of the 3195 miRs screened, 2995 miRs were common to these two cell types while 144 miRs were downregulated and 56 upregulated in CD133⁺ cells when compared to CD133⁻ cells (Figure 2A). A comprehensive list of all miRNAs with greater than 2-fold change difference between samples is provided (Supplemental Table 2). The microarray also identified microRNAs of the miR-297–669 cluster to be downregulated in CD133⁺ cells including miR-669a-5p, miR-669l-5p, and miR-446h-5p, which was validated by qRT-PCR (Figure 2A). The data confirmed that these miRNAs were in fact downregulated in CD133⁺ cells with relative expression levels (normalized to CD133⁻) of 0.49, 0.51, 0.57, and 0.49 for miR-466h, miR-669a, miR-669l, and miR-92a, respectively (Figure 2A). Because the miR-297–669 cluster is not present in humans (but is conserved in rodents), further analysis of miRs in this cluster were not selected for further characterization. Assessment of miR-92 using Metacore (Figure 2B) and Ingenuity (Figure 2C) pathway analysis tools identified a network of genes associated with melanoma progression including CDC42, PTEN, and MAP2K (Figure 2C) and have been identified for clarity by blue circles. With the recent discovery that miR-92 could regulate integrin subunit expression [20], we used predictive sequence alignment software to explore potential integrins which may be targeted by miR-92. Integrin α_v and α₅ were highly predicted targets of miR-92 with weighted context scores of -0.28 and -0.34, and P_CT values of 0.92 and 0.90, respectively (Figure 2D). With this highly predictive targeting of integrin subunits coupled with the mechanistic relationship between integrin activation of TGF-β, we further explored the relationship between miR-92a and TGF-β in an in vivo model.

Figure 2: Microarray analysis of CD133⁺ B16-F10 cells compared to CD133⁻ cells.

Analysis of microarray data demonstrated significant disparities in miR expression between CSC and non-CSC compartments outlined in blue (CD133+ = orange, CD133- = green); all miRNA are shown in the above heatmap and sorted by fold-change (CD133-positive relative to CD133-negative in ascending order). Several of these miRNAs were validated using qRT-PCR (A). miR-92 was identified to target several cancer-associated gene networks using Metacore (B) and Ingenuity (C) pathway analysis tools. Using sequence alignment software, miR-92 is highly predicted to target mRNAs for integrin alpha subunits involved in activation of secreted TGF-β (D).

Integrin alpha-subunit expression and TGF-β signaling through SMAD2 are enhanced in CD133⁺ populations

The increase in TGF-β from dissociated primary tumors formed by CSCs, along with mechanical integrin-dependent TGF-β activation, lead us to initially look for microRNAs targeting integrins. Microarray analysis in combination with queried miR databases led to the identification of integrins as potential targets of miR-92a (Figure 2D), thus, we next utilized qRT-PCR and immunoblot analysis to determine whether or not integrin expression was significantly increased in CD133⁺ cells when compared to CD133⁻ cells (Figure 3). Normalized integrin mRNA expression was increased in CD133⁺ cells compared to CD133⁻ cells by 1.5, 2.5, 1.3, 1.6 fold for integrin β₁, β₃, α_v, and α₅ subunits, respectively (Figure 3A). Protein levels of integrin α subunits were also increased as assessed by immunoblot from two independent experiments in which B16 cells were isolated via FACS based on CD133 expression (Figure 3B). qRT-PCR analysis of several isoforms of TGF-β identified TGF-β1 as the primary isoform responsible for the disparities in protein expression. No significant difference was observed in TGF-β3 between the two groups (Figure 3A), and TGF-β2 was too lowly expressed to amplify using our parameters. All qRT-PCR experiments validating mRNA expression utilized β-Actin as a housekeeping gene. Although qRT-PCR analysis determined a significant difference in SMAD2 mRNA expression, we did not observe any difference in protein level expression in our western blot analysis; however, phosphorylated SMAD2, an indicator of TGF-β signal activation, was significantly induced in CD133⁺ tumor cells when compared to CD133⁻ cells (Figure 3B). Full images of the exposed membranes have been provided (Supplemental Figure 3).

Figure 3: RNA and protein expression analysis of integrin subunits and TGF-β associated signaling molecules.

Total RNA was isolated from CD133⁺ and CD133⁻ cells and assessed for integrin subunit expression (A, top) and TGF-β signaling through SMAD2 (A, bottom) by qRT-PCR. mRNA expression levels were normalized to the CD133⁻ cell phenotype. Protein level expression for integrin α_v and α₅ subunits as well as SMAD2 phosphorylation was assessed by western blot. Actin and GAPDH were used as reference proteins for western blots assessing integrin subunit expression and SMAD signaling, respectively. Band intensities were calculated using ImageJ software and relative densitometric intensity (normalized to Actin/GAPDH) are displayed below the appropriate band. PCR and immunblot analysis data were gathered from two independent experiments in which samples were run in triplicate (qRT-PCR) or in duplicate (immunoblot). Significance was determined by Student’s t test (p < 0.05) and is denoted by an asterisk. (*p < 0.05, **p < 0.01, ***p < 0.001).

CD133⁺ cells generate more TGF-β and induce Treg infiltration in an experimental metastasis model

When transplanted via tail vein injection, CD133⁺ cells created larger and more abundant lesions when compared to CD133⁻ cells (Figure 4A). CD133⁺ cells generated an average of 41.0 ± 4.5 metastatic nodules compared to 22.6 ± 3.6 nodules from mice receiving i.v. injection of CD133⁻ cells. When dissociated and labeled for infiltrating immune cells, both phenotypes were able to induce immune cell infiltration of the pulmonary tissues; however, when compared to CD133⁻ initiated metastases, metastatic outgrowths initiated by CD133⁺ cells demonstrated a significantly higher proportion of immunosuppressive cell phenotypes including gMDSCs, and TGF-β⁺ Tregs (Figure 4B and Figure 4C). In myeloid panels, we observed a significant increase in gMDSCs with a marginal decrease in mMDSCs; macrophage populations did not exhibit any significant change in lung tissues from either tumor cell type (Figure 4C). Spleens from each group showed a significant shift in mMDSCs as well as M1 macrophage populations (Supplemental Figure 4). In the lymphocyte panel, we observed a significant increase in total T cell (CD45⁺CD3⁺) and NK cells (CD45⁺NK1.1⁺) infiltration along with a concurrent decrease in CD8⁺ T cells in the lungs of CD133⁺ cell initiated mice when compared to mice in the CD133⁻ group (Figure 4D). A significant increase in the percentage of TGF-β⁺ Tregs (CD45⁺CD3⁺CD4⁺FOXP3⁺) as well as IL-17a⁺CD4⁺ T cells was observed along with a decrease in IFNγ⁺CD4⁺ T cell populations in CD133⁺ when compared to CD133⁻ samples (Figure 4D). The spleens of CD133⁺ transplanted mice also showed similar results with a significant decrease in IFNγ-producing cells as well as a concurrent increase in Treg populations (Supplemental Figure 5). SPADE analysis (Figure 4E) highlighted the differences in immune cell tumor infiltration between CD133⁺ initiated lesions (right) and CD133⁻ initiated lesions (left) in both myeloid (bottom) and lymphocyte (top) panels. Grayscale legends are provided for each panel in order to discriminate populations identified for each SPADE tree. These data suggested that tumors initiated by CD133⁺ tumor cells not only generated more immunosuppressive phenotypes within the TME, but also potentially resulted in less cytotoxic T cell response as well.

Figure 4 : Experimental metastasis model of murine melanoma using CD133-sorted cell populations.

CD133-sorted B16-F10 cells were injected i.v. into C57Bl/6 mice and allowed to colonize the pulmonary tissues (A). Pulmonary lesions were counted and measured upon resection of lung tissues and plotted as mean number of metastases ± SEM. CD133⁺ cells formed larger and more abundant macrometastases when compared to CD133⁻ cells (A). Lungs from tumor bearing mice were dissociated and labeled using myeloid (B, top) and lymphocyte (B, bottom) panels to identify shifts in subsets of T cells, macrophages, and MDSCs from the TME generated by each tumor phenotype. Representative flow plots from lung tissues analyzed by myeloid (C) and lymphocyte (D) panels demonstrated the significant shifts in immune cell phenotypes. Downstream analysis using SPADE software depicted changes in the TME between tissues colonized by CD133⁺ and CD133⁻ melanoma cells (E). Legend provided is based on these analyses (E, gray scale). The panels identified significant shifts in immune cell phenotypes in both lymphocyte (E, top) and myeloid (E, bottom) panels. Experimental metastasis models were repeated once (n = 5 per group). Statistical significance was determined by Student’s t-test and was denoted by an asterisk. P-values are provided where appropriate.

Free/active TGF-β was increased in co-cultures of CD133⁺ tumor cells and immune cells when compared to co-cultures using CD133⁻ cells

After isolating CD133⁺ and CD133⁻ cell populations, we used a co-culture model with splenic cells to determine if either phenotype was able to readily polarize splenocytes to the immunosuppressive phenotypes observed in our dissociated tumor samples. Concurrently, we measured the concentrations of active TGF-β in co-culture system as well as in splenocytes and tumors cells alone. Active TGF-β was significantly enhanced in co-cultures using CD133⁺ and splenic cells (4.9 ± 0.3 and 6.6 ± 1.6 pg/mL at 8 and 24 h, respectively) when compared to co-cultures using CD133⁻ cells (2.3 ± 0.5 and 3.9 ± 0.9 pg/mL at 8 and 24 h, respectively) (Figure 5A). Interestingly, we did not observe any differences in active TGF-β when CD133⁺ and CD133⁻ cells were cultured alone at either time point, although, samples from both tumor phenotypes contained significantly more free TGF-β than splenocytes cultured alone (Supplemental Figure 5). When cells isolated from our co-cultures were analyzed by flow cytometry, we observed a significant increase in CD11b⁺TGF-β⁺ cells in co-cultures of CD133⁺ cells when compared to CD133⁻ co-cultures (Figure 5B–D). No significant changes were observed in T cell populations (Figure 5C and 5D). SPADE analysis of flow cytometric data is provided to further represent the shift in TGF-β producing myeloid cells we observed in our samples.

Figure 5: ELISA for free/active TGF-β in a co-culture model demonstrated the enhanced ability of CSCs to convert secreted (inactive) TGF-β to its active form.

FACS-sorted CD133 –positive and –negative populations were co-cultured with splenocytes from C57Bl/6 mice for 8h (A,top) and 24h (A,bottom) at a 1:1 E:T ratio, and the resulting supernatants were analyzed for activated TGF-β. Flow cytometry and subsequent SPADE analysis was conducted on the resulting splenocytes to identify shifts in cell phenotypes after 24h co-culture (B-D). A legend for SPADE analysis has been provided (B). Flow cytometry panels resulting from co-cultures using both CD133⁺ (C) and CD133⁻ (D) are also provided. ELISA and flow cytometric analysis were repeated twice as independent experiments. Statistical significance was determined by Student’s t-test (p < 0.05) and is denoted by an asterisk. (*p < 0.05, **p < 0.01, ***p < 0.001).

miR-92a regulates integrin and TGF-β expression

To directly test if miR-92a targeted the integrins, B16-F10 cells sorted on positive CD133 expression were transfected with miR-92a mimic, inhibitor, and a combination of mimic and inhibitor for 48 hours. Gene expression was measured by qRT-PCR for miR-92a, ITGAV, ITGA5, TGFB1, and SMAD2 following the transfection and isolation of total RNA; all groups were normalized to CD133+ mock transfected cells as a reference. When transfected with a miR-92 mimic, we observed an 8.2-fold increase in miR-92a expression. Relative expression levels of ITGAV, ITGA5, TGFB1, and SMAD2 mRNA were 0.20, 0.17, 0.27, and 0.19, respectively (Figure 6A). Conversely, transfection with an inhibitor of miR-92a decreased miR-92a expression, while significantly increasing mRNA levels for ITGAV, ITGA5, TGFB1, and SMAD2 (2.7, 4.7, 2.8, and 2.4-fold increases, respectively). This phenotype was partially rescued when both mimic and inhibitor were transfected into CD133+ B16 cells returning to baseline mRNA levels of ITGA5, TGFB1, and SMAD2. These results indicated that miR-92a, was involved in the expression of genes that regulate TGF-β signaling and activation (Figure 6A).

Figure 6 : miR-92a regulated the integrin-TGF-β axis and inhibited tumor growth in vivo.

B16-F10 cells sorted on positive CD133 expression were transfected with miR-92a mimic, inhibitor, and a combination of mimic and inhibitor for 48 hours. Gene expression was measured by qRT-PCR for miR-92a, ITGAV, ITGA5, TGFB1, and SMAD2 following the transfection and isolation of total RNA; all groups were normalized to mock transfected cells as a reference (A). CD133+ cells isolated from the B16 cell line were transfected with miR-92 mimic or lipid. Transfected cells were injected s.c. into C57bl/6 mice and allowed to form tumors over 14 days (B). Tumor bearing mice were sacrificed upon endpoint and tumors were dissociated, labeled with panels of antibodies against phenotypic markers for lymphocytes and monocytes, and analyzed by flow cytometry (C-F) as previously described. Statistical analyses were performed using a Student’s t-test and one way ANOVA with significance determined at p < 0.05. Statistical significance is denoted in Panel A as follows: a = significant from 2,3,4; b = significant from 1,3,4; c = significant from 1,2,4; d = significant from 1,2,3; e = significant from 2,3. Statistical significance in Panel F is denoted as follows: *p < 0.05, **p < 0.01, ***p < 0.001.

Transfection of CD133+ cells with miR-92a mimic suppressed tumor initiation and growth through immune alterations in TME

To test if alterations in the expression of miR-92a in CD133+ cells would change tumor growth and immune response in vivo, CD133+ cells isolated from the B16 cell line were transfected with mock or miR-92 mimic. Transfected cells were next injected s.c. into C57Bl/6 mice and allowed to form tumors over 14 days (Figure 6B). Upon endpoint of the experiment, tumors initiated by cells transfected with miR-92 mimic showed a significantly decreased tumor burden with an average tumor volume of 114.4 mm3. This represents a 58% decrease in tumor volume from the mean tumor volume of the mock transfected CD133+ cells of 272.8 mm3. While all mice in both groups formed tumors, the significant reduction in tumor volume suggested that miR-92a expression had an inverse association with tumor growth. It was also observed that cells transfected with miR-92 mimic showed a significant delay in tumor formation compared to CD133+ mock transfected cells.

Tumor bearing mice were sacrificed upon endpoint and tumors were dissociated, labeled with panels of antibodies against phenotypic markers for lymphocytes and monocytes, and analyzed by flow cytometry (Figure 6C–F). In tumors initiated by miR-92/mimic, we observed a significant shift in tumor infiltrating cells with an overall increase in total T cells (4.9% vs 1.8%), but a decrease in pan-MDSC (3.0% vs 1.8%) and pan-macrophage (1.7% and 0.5%) populations when compared to tumors initiated by the mock transfected cells. When we analyzed subsets of T helper cells (CD4+), we found that immunosuppressive phenotypes of FOXP3⁺ Tregs, IL-10, and TGF-β producing CD4+ cells were significantly decreased following miR-92 mimic transfection (17.8% vs 4.3%; 34.5% vs 11.0%; 28.3% vs 5.7%, respectively). Anti-tumor pro-inflammatory phenotypes producing IFNγ were significantly increased from 0.5% to 2.3% when compared to tumor tissues from mock transfected group.

Analysis of miRNA and mRNA co-expression data identified associations between miR-92a and signaling pathways involved in TGF-β signaling and immune response

In order to associate our findings in preclinical murine models of melanoma to clinical data, we used the publicly available TCGA database in order to authenticate our results in human samples. We specifically identified samples taken from melanoma patients and observed a moderate positive correlation between integrin α₅ and TGF-β1 expression levels (Pearson coefficient = 0.66) (Figure 7A and Figure 7B). Further analysis identified positive correlations between integrin α₅ and LTBP1 (Pearson coefficient = 0.67) as well as NRP-1 (Pearson coefficient = 0.69) (Figure 7B). These associations between groups of genes involved in TGF-β activation and signaling in human samples validated our preclinical studies in murine models and indicate that CSCs may in fact use these signaling molecules and their connected signaling networks to evade immune-mediated tumor ablation. We next identified data sets consisting of both miRNA and mRNA expression in order to explore the relationship between miR-92a and the genes involved in TGF-β activation (i.e. integrins). Using these publicly available data, we observed a moderate inverse association between integrin alpha 5 and alpha V subunits, and miR-92a using Spearman (-0.33 and -0.38, respectively) and Pearson correlation (-0.30 and -0.37, respectively) coefficients. Linear regression analysis and the resulting graphics are provided to help visualize the relationship described (Figure 7); as miR-92a expression increased, we saw a subsequent reduction in integrin α_V and α₅ expression (Figure 7C). Along with the scatter plot of the original data, the regression line from simple linear regression analysis, and 95% confidence intervals of the regression line were also provided.

Figure 7: TCGA validates the association between integrin α5 and TGF-β and miR-92 in human clinical samples of melanoma.

Using cBioPortal and UCSC Xena, we probed datasets obtained from patients with skin cancer in order to identify correlations between mRNA level expression of integrin α5 and TGF-β1 (A). We identified several clinical specimens in which high expression of TGF-β was associated with elevated expression of integrin α5 (A) as highlighted by the rectangles (red = high expression, blue = low expression). Using the Pearson and Spearman coefficient, we determined a positive association between the two proteins (B). We also identified positive associations between integrin α5 and LTBP1 and NRP-1 (B). Further analyses of TCGA data sets with available miRNA expression data demonstrated an inverse association between miR-92 expression and integrin expression for alpha-5 and alpha-V subunits (C). A simple linear regression model was used to predict integrin expression as a function of miR-92a expression and provided 95% confidence intervals for our regression line. Spearman and Pearson correlation were performed using Stata 14 and coefficients have been provided (C, right).

Discussion

The main goal of the current study was to investigate if CD133 expression on CSCs would alter the expression of microRNA which would target genes involved in the regulation of immune response and consequently control tumor growth. Based on the expression of CD133, the current study identified a potential role for miR-92 in regulating immunosuppression by mediating integrin-dependent TGF-β activation. We showed that CSCs based on the CD133 biomarker are functionally distinct from the bulk tumor population and demonstrate superior tumorigenicity and growth in vitro and in vivo. These results are in line with previous studies describing CD133 as a biomarker for CSCs present in melanomas (3,25), and that these cell populations have intrinsic chemoresistant (26), angiogenic (27), and metastatic properties (28). CD133⁺ B16-F10 melanoma cells had enhanced tumor growth in subcutaneous tumor growth models and established larger and more abundant pulmonary lesions in our experimental metastasis model compared to CD133⁻ cells. Moreover, tumors and metastatic lesions initiated by CD133⁺ cells were significantly more immunosuppressed than CD133⁻ initiated tumors as assessed by intratumoral abundance of tumor-associated macrophages (TAM) and Tregs. Indeed, CSCs in several cancer models have been reported to exploit immune cells to create a tumor-tolerant niche during tumorigenesis and metastasis (29–31). Infiltration of the TME by these immune cell phenotypes resulted in significantly higher production of TGF-β, shifts in MDSC populations toward a granulocytic phenotype, and lower IFNγ producing cells in tumors initiated by CD133⁺ cells when compared to tumors initiated by CD133⁻ cells. Secretion and activation of TGF-β is a well-studied mechanism of immunosuppression and has been described in melanomas (32) as well as other models (33). Interestingly, blockade of TGF-β signaling in a B16-F10 melanoma model led to a T cell-mediated eradication of tumors (34). Other studies have found that TGF-β blockade was sufficient for significantly increasing antitumor immune responses (35), and helps promote response to immune checkpoint inhibitors in different mouse models of melanoma (36,37). In our model, we observed a significant increase in Tregs, a phenotype which can be induced by TGF-β (38,39), in CSC-initiated tumors when compared to tumors generated from non-CSCs. These data indicate that tumor growth and metastasis by B16-F10 cells may be driven by Treg-mediated immunosuppression.

Using microarray technology, we identified several miRs that were downregulated in the CD133⁺ CSC population which targeted TGF-β and its associated gene networks. Preliminary data indicated that several miRNAs from the miR-297–669 cluster were downregulated in CSCs compared to non-CSCs. Interestingly, it was recently reported that members of this cluster directly regulated TGF-β2 (40). Another miRNA identified by our microarray screen, miR-92a, is characterized as an oncomiR in various cancers (16,41,42) and is being employed as a potential serum biomarker for certain malignancies (43); however, mir-92 can also act as a tumor suppressor in other cancers (18,44,45). Downregulation of miR-92 in human breast cancers was associated with poor prognosis and correlated with stage and disease-free survival. Interestingly, the researchers also observed a significant increase in macrophage infiltration; however, the phenotype (M1/M2) of these tumor-infiltrating macrophages was not reported (46). Modulation of macrophage populations by B16-F10 cells has been linked to disease progression and metastasis (47), thus miR-92a may have far-reaching effects outside of regulating TGF-β signaling mechanisms. In fact, it was recently shown that miR-92 was acknowledged to alter miRNA profiles of induced pluripotent stem cells, an effect that was suggested to be p53 mediated (48). Additional studies have advocated a role for miR-92 in neuroblast self-renewal and maintenance (49). Interestingly, a recent report by Huber et. al. described several microRNAs that were found in melanoma exosomes and mediated MDSC expansion and differentiation from CD14⁺ monocytes (50). Additional evidence from a study of glioma exosomes, which reported that miR-92a can stimulate the proliferation and function of MDSCs (51), indicates that exosome-mediated transfer of miRNAs may, in part, function to induce immunosuppression within the TME. The functional effects of miR-92 seem to be somewhat ubiquitous as well as tissue and context-dependent; thus, more work exploring the functions of miR-92 in melanoma is justified.

CD133⁺ cells expressed higher integrin α₅ and α_v (RGD-recognizing subunits) on both a protein and mRNA level when compared to CD133⁻ cells. Integrin α₅ has been reported to be regulated by miR-92 in an ovarian cancer model (20); however, whether miR-92 regulates the α_v subunit has yet to be clarified. The integrin α_v subunit has been explicitly characterized to heterodimerize with integrin β subunits to form integrins α_vβ₃, α_vβ₅, α_vβ₆, and α_vβ₈, all of which have been reported to modulate TGF-β activation (52). Integrin α₅β₁ is the major receptor for fibronectin (53); fibronectin is required for TGF-β activation (54) and fibronectin matrix assembly (55), suggesting the unique possibility that integrin α₅ may also play a role in the liberation and activation of TGF-β. In endothelial cells, it was shown that fibronectin and its receptor (e.g. integrin α₅β₁) mediated SMAD phosphorylation following exogenous application of TGF-β1 and BMP-9 (56). Conversely, it was reported that TGF-β1 may regulate integrin α₅β₁ and integrin signal transduction (57). Further interactions between TGF-β1 and integrin α₅β₁ were reported in T cells where TGF-β activated cells were protected from apoptosis by an integrin-dependent mechanism (58). qRT-PCR analysis of the integrin β₁ and β₃ subunits reflected higher mRNA expression in CD133⁺ cells compared to CD133⁻. Interestingly, the integrin β₃ was shown to regulate senescence through induction of the TGF-β signaling pathway (59). Additionally, we observed an increase in TGF-β1 and activating phosphorylation of the downstream signaling molecule SMAD2. Our data demonstrate that CD133⁺ B16-F10 cells highly express components of integrin and TGF-β associated signaling cascades, which may in turn provide a selective survival advantage in the context of immunosuppression within the TME. Mechanistically, this axis may be regulated through miR-92 modulation of integrin-dependent TGF-β activation.

Co-cultures of splenic and tumor cells showed that while no significant change in active TGF-β concentrations was observed between CSCs and non-CSCs cultured alone, co-cultures of CD133⁺ tumor and splenic cells resulted in significantly higher free TGF-β when compared to co-cultures using CD133⁻ cells. Membrane-bound TGF-β was significantly increased in tumors resulting from CD133⁺ cells when compared to CD133⁻ cells in s.c. and i.v. models of melanoma; however, liberated TGF-β from each cell type remained unchanged in vitro. These data indicated that there are significant interactions between immune cells and tumor cells that collaborate to produce immunosuppression within the TME. Additionally, they indicate that TGF-β secretion and activation may involve multiple mechanisms involving several cell phenotypes that are not recapitulated in the culture of cancer cell lines. After 24 h, splenic cells from co-cultures were labeled and analyzed by flow cytometry to determine if any changes in myeloid cell or lymphocyte populations were stimulated by either tumor cell phenotype. While we did not see a significant change in regulatory T cell populations (as in our in vivo models), we did observe a significant increase in TGF-β⁺ myeloid cell populations in splenic cells co-cultured with CSCs compared to non-CSCs. It was recently described that B16-F10 tumors undergo significant changes in immune cell infiltration depending upon the stage of disease (60). These studies suggest the intriguing concept that palpable tumor formation and initial immunosuppression may be driven by immunosuppressive myeloid cell types (i.e. alternate macrophages), while late stage tumor growth and metastasis is controlled by Treg populations. In fact, an increased production of immature myeloid cells was observed in cancer patients (61). In these studies, immature myeloid cells inhibited antigen-stimulated T cell responses, which may help explain the significant decrease in CD8⁺ cytotoxic T lymphocytes in our CSC-induced metastasis models, and potentially clarify the disparities in tumor-associated immunophenotypes between our in vivo models and those characterized in our in vitro co-culture models.

Finally, we characterized miR-92 to functionally modulate expression of integrin subunits as well as mediators of TGF-β signaling, and correlate these in vitro data with clinical data derived from the TGCA database. When mimics and inhibitors of miR-92a where transfected into B16 CD133⁺ cells, the resulting expression profiles supported our original hypothesis that miR-92 controls TGF-β induced immunosuppression. Expression of ITGA5, ITGAV, TGFB1, and SMAD2 were all significantly affected when expression of miR-92a was altered. Mice receiving B16 cells with transfected miR-92a mimic showed significantly reduced tumor growth compared to mock transfected B16 cells.

We wanted to correlate our studies with clinical data from the TCGA database in which thousands of human tumor samples have been characterized. Exploring melanoma samples within the database, we identified a positive correlation between integrin α₅ and TGF-β1, LTBP1, and neuropilin-1 (NRP-1). LTBP1 targets latent TGF-β complexes to the ECM where it is subsequently activated (62). NRP-1 was recently shown to modulate TGF-β signaling in glioblastomas (63). Interestingly, NRP-1 is a biomarker to distinguish natural and inducible Tregs (64), enforcing the idea that immunosuppression in melanomas might be T cell-mediated. We also analyzed data sets containing both expression of mRNA and microRNAs. When association analyses were performed, we identified a moderate inverse correlation between integrin subunits and miR-92a as predicted by several databases which aligned the sequences of miR-92a and integrin α_v and α₅. We further performed a simple linear regression model to predict integrin mRNA expression using miR-92a expression as an independent variable based on the available miRNA and mRNA expression data. These data clearly showed a reduction in ITGAV and ITGA5 expression with increased expression of miR-92a.

Our study categorized miR-92 as a potential tumor suppressor in melanoma by modifying TGF-β-induced immunosuppression. Disparities in miRNA expression between stem cell-like populations and bulk tumor cells may confer the properties attributed to CSCs such as chemoresistance and metastatic outgrowth. While integrins are clearly involved in adhesion, we demonstrated that CSCs express significantly higher levels of RGD-recognizing alpha subunits than non-CSCs. Further, these disparities in expression may have resulted in altered TGF-β activation and subsequent immunosuppression. Given our initial data, further studies exploring the miR-92/integrin/TGF-β axis are warranted.

While our studies demonstrate a link between miR expression in tumor cells and their ability to grow in vivo and modulate immune cell phenotype in TME, it has some limitations and opportunities. Use of the B16-F10 cell line, although commonly used and easily accessible, may not mimic human disease as accurately as newer model systems including the Yale University Mouse Model (YUMM) (65). The YUMM systems have fully characterized driver mutations, are genomically stable, and syngeneic to the C57Bl/6 mouse strain providing a much more comprehensive understanding of the underlying genetic interactions in progressing disease states. Our microarray analysis was only performed using a sample size of one (i.e. one chip per experimental group) lending limited interpretability based on our significantly altered miRNAs. Nonetheless, we used microarray only as a screening tool and validated the expression of miRs of interest using RT-PCR. Finally, when reviewing TCGA data, only a small proportion of cases (limited to cutaneous melanomas) had both RNA and miRNA sequencing data available. Due to the small sample size, a more rigorous study involving a larger sample size would be needed to understand the translational impact.

Nonetheless, our study also forms the basis for further exploring the role of miRs in CSCs and may be of particular interest to researchers and clinicians exploring new therapeutics modalities targeting miRs thereby modulating the complex interactions between tumor and immune cells in the TME. Future studies elucidating miR expression in tumor cells and patient’s response to immunotherapy or characterizing the degree of immunosuppression in patient prognosis and treatment, would also be relevant clinically. In summary, our study found that melanoma CSCs modulate the TME by regulating a microRNA-gene network consisting of miR-92a, integrin α₅/α_v, and TGF-β.

Statement of Significance:

CD133⁺ cells play an active role in suppressing melanoma anti-tumor immunity by modulating miR-92 which increases influx of immunosuppressive cells and TGF-β1 expression.