Immunoregulatory Protein B7-H3 Reprograms Glucose Metabolism in Cancer Cells by ROS-Mediated Stabilization of HIF1α

B7-H3 is a glycoprotein that belongs to the B7 family of immunoregulatory proteins. B7-H3 is expressed in low levels in most normal tissues, but is overexpressed in a wide variety of cancers and is associated with tumor progression, metastasis, and poor patient outcome (1). B7-H3 overexpression in breast cancer is highly prevalent, and is associated with positive lymph node metastasis and advanced disease (2, 3). B7-H3 has recently been successfully used as a biomarker for ultrasound molecular imaging in breast cancer (4). Because of a large difference in expression between tumors and normal tissues, and its important function in cancer progression, B7-H3 has emerged as a promising new therapeutic target and biomarker for breast cancer, including triple-negative breast cancer (2, 3, 5, 6).

The predominant splicing variant of B7-H3 in humans is the 4Ig-B7-H3, which consists in exon duplication of the extracellular IgV-IgC domains followed by a transmembrane domain and a short cytoplasmic carboxy-tail with no conserved signaling motifs (7–10). In contrast, exon duplication does not occur in mice, which only express the 2Ig-B7-H3 isoform. In addition, a soluble B7-H3 splicing variant lacking the transmembrane domain has been found in peripheral blood mononuclear cells (11). Currently, there is a complex scenario for the immunomodulatory role of B7-H3 as both T-cell costimulatory and coinhibitory functions for human 4Ig-B7-H3 have been reported under different contexts (1). These conflicting functions are currently attributed to possible distinct B7-H3 receptors in target cells, but could also be explained by different functions of the various B7-H3 splicing isoforms, or differential glycosylation patterns of B7-H3. TLT-2 has been described as a coreceptor for murine B7-H3 in CD8(+) T-cell activation, with a costimulatory function of the murine B7-H3/TLT-2 pathway (12). However, this result has not been confirmed by other studies and the identity of the binding partner(s) for human 4Ig-B7-H3 remains elusive (13–15).

Despite its controversial immunologic role, the immune-independent functions of B7-H3 in cancer malignancy are almost unknown. We and others have shown that B7-H3 mediates breast cancer invasion, metastasis and drug resistance and that silencing B7-H3 delays tumor growth and sensitizes cancer cells to chemotherapeutic agents (16–21). Similar results have also been confirmed by other groups in pancreatic cancer (21, 22), colorectal cancer (20), hepatocellular carcinoma (23), and melanoma (19, 24). In addition, we and others have shown that B7-H3 overexpression in breast cancer, melanoma, and osteosarcoma cells increases their metastatic potential (16, 19, 25). B7-H3 has been shown to regulate matrix metalloproteinase MMP-2, Stat3, and IL8 secretion, and tissue inhibitors of metalloproteinases TIMP-1 and TIMP-2 (16, 19, 25, 26). These results clearly show that, in addition to immune-regulatory function, B7-H3 possess immune-independent functions that may also contribute to cancer progression.

Increased aerobic glycolysis in cancer cells, also known as the Warburg effect, is considered as one of the most fundamental metabolic alterations during malignant transformation (27). It has been well accepted that the dysregulated metabolic reprograming gives cancer cell advantage for proliferation and survival (28). Here we show for the first time that B7-H3 plays an important role in the regulation of cellular glucose metabolism. Given the important roles of B7-H3 in cancer tumorigenesis and progression, this study further demonstrates that B7-H3 may serve as an excellent therapeutic target for cancer treatment.

Cancer cell lines MDA-MB-231, MDA-MB-435, SKBR3, and MDA-MB-468 were purchased from ATCC 3 years ago. The cell lines have been tested and authenticated. On June 26, 2015, the cells were tested by analyses of 8 autosomal short tandem repeat loci and the gender identity locus amelogenin.

MDA-MB-231 TR33, MDA-MB-231 shB7-H3, MDA-MB-435 TR33, and MDA-MB-435 shB7-H3 cells were described earlier (17). SKBR3 and MDA-MB-468 cells were infected with retroviral particles expressing B7-H3 or the puromycin resistance gene only (vector control). Cells were selected and maintained with DMEM/F12 medium supplemented with 10% FBS and 2 μg/mL puromycin.

Control scrambled siRNA and a siRNA targeting human B7-H3 were purchased from Sigma-Aldrich. Transfection was performed using Lipofectamine 2000 (Life Technologies).

Hypoxic conditions described in the experiments were achieved by using a GasPak Pouch (BD Biosciences), which is capable of reducing the oxygen concentration to less than or equal to 1% (mean percentage: 0.7%); or by treating cells with 200 μmol/L cobalt (II) chloride (CoCl₂) for 24 hours.

Cells were seeded in 12-well plate overnight and then medium was aspirated and replaced with fresh complete medium. Hypoxia treatment was performed in the GasPack pouch. Alternatively, hypoxia-mimicking conditions were achieved by replacing medium with complete medium containing 200 μmol/L CoCl₂. After 24 hours, conditioned medium in each well was collected and the amount of lactate was then determined using the Lactate Assay Kit (Biovision).

The set up described above for the lactate production assay was also used for the measurement of glucose uptake, except that after 24 hours, the conditioned medium was collected and stored at −20°C until assayed. Glucose measurements were determined using the Amplex Red Glucose/Glucose Oxidase Assay Kit (Molecular Probes).

Cancer cells were evaluated using an Extracellular Flux Analyzer (XF24, Seahorse Bioscience). The day prior to the assays, cells were seeded in 24-well plates (40,000 cells/well). Mitochondrial metabolism was evaluated with the XF Cell Mito Stress Kit, and the glycolytic metabolism with the XF Glycolysis Stress kit.

For the Mito Cell Stress assay, prior to the assay, growth medium was replaced with serum-free XF Seahorse Assay Medium supplemented with 1 mmol/L sodium pyruvate and 25 mmol/L glucose, and then cells were incubated for 1 hour at 37°C in an incubator without CO₂. Cartridges equipped with oxygen- and pH-sensitive probes had been preincubated with calibration solution overnight at 37°C in an incubator without CO₂. At the time of measurement, cells were then placed in the XF24 Extracellular Flux Analyzer with cartridges of probes. Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were evaluated in a time course before and after injection of the following compounds: (i) oligomycin (1 μmol/L final concentration); (ii) FCCP [Carbonyl cyanide-4-(trifluoromethoxy)phenylhydrazone; 0.75 μmol/L final concentration]; (iii) antimycin A + rotenone (1.5 μmol/L each final concentration). The Glycolysis Stress assay was performed in a similar fashion, but using XF Seahorse Glycolysis Stress Base Medium supplemented with 2 mmol/L l-glutamine, and injection of the following compounds: (i) glucose (10 mmol/L final concentration); (ii) oligomycin (1 μmol/L final concentration); (iii) 2-deoxyglucose (2-DG; 100 mmol/L final concentration).

Intracellular reactive oxygen species (ROS) were measured using CellROX Deep Red and MitoSOX Red fluorogenic probes (Molecular Probes/Invitrogen). For hypoxic conditions, cells were placed in the BD GasPak pouch for 48 hours. Cells were harvested and placed in growth medium, loaded with 5 μmol/L CellROX Deep Red and MitoSOX Red for 15 minutes, and then washed once with PBS. Fluorescence intensity was measured by flow cytometry (BD LSR II, BD Biosciences).

Total RNA was isolated from cultured cells using TRIzol reagent (Life Technologies). DNase-treated total RNA were used in each cDNA synthesis reaction using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Quantitative PCR (qPCR) was performed with Fast Start Universal SYBR Green Master (Roche Life Science). Each qPCR reaction contained 5 μL of diluted cDNA (5× dilution), 6.5 μL nuclease-free distilled water, 12.5 μL SYBR Green Master Mix, and 0.5 μL of each primer (5 pmol/reaction). Sequences of primers: SOD2-fw (5′ CTGGACAAACCTCAGCCCTA 3′), SOD2-rv (5′ TGATGGCTTCCAGCAACTC 3′), Nrf2-fw (5′ AGTGGATCTGCCAACTACTC 3′), Nrf2-rv (5′ CATCTACAAACGGGAATGTCTG 3′). qPCR reactions were performed using the CFX96 Real-Time System with the C1000 Touch Thermal Cycler (Bio-Rad) using the following protocol: 10 minutes at 95°C, followed by 50 cycles of 15 seconds at 95°C and 1 minute at 60°C, and finally a 65°C to 95°C ramp to determine melting curve. The relative amounts of mRNA were calculated by using the comparative C_t method.

The mitochondrial fraction and mitochondrial proteins were prepared using Qproteome Mitochondria Isolation Kit (Qiagen). Cells were washed with PBS and resuspended with Lysis buffer from Kit and then incubated at 4°C for 10 minutes. Following a centrifugation at 1,000 × g at 4°C for 10 minutes, the supernatant was separated from the pellet. The supernatant was stored at −20°C for further investigation. The pellet was resuspended in ice-cold Disruption buffer and homogenized by using a 25G needle and a syringe. Following a centrifugation at 1,000 × g at 4°C for 10 minutes, the supernatant was transferred to a new tube. The supernatant was centrifuged at 6,000 × g at 4°C for 10 minutes. The pellet containing mitochondria was resuspended in protein lysis buffer and protein concentration was determined using the Bradford assay.

Cells were washed with PBS and resuspended in harvest buffer (10 mmol/L HEPES pH 7.9, 50 mmol/L NaCl, 0.5 mol/L Sucrose, 0.1 mmol/L EDTA, and 0.5% Triton X100). The supernatant was incubated on ice for 5 minutes. Following a centrifugation at 100 × g at 4°C for 10 minutes, the supernatant was separated from the pellet. The pellet was washed twice in Buffer A (10 mmol/L HEPES pH 7.9, 10 mmol/L KCl, 0.1 mmol/L EDTA, and 0.1 mmol/L EGTA). Following a centrifugation at 3,000 × g at 4°C for 10 minutes, the pellet was resuspended in Buffer C (10 mmol/L HEPES pH 7.9, 0.5 M NaCl, 0.1 mmol/L EDTA, 0.1 mmol/L EGTA, and 0.1% NP40). The nuclear pellet was collected by centrifugation at 13,000 × g at 4°C for 10 minutes and ressuspended in protein lysis buffer.

Western blotting was performed as we reported previously (17). Anti-human B7-H3 (AF1027) antibody was purchased from R&D Systems; anti-HIF1α (#610959) was from BD Biosciences; anti-human B7-H3 (sc-376769), anti-Nrf2 (sc-722), anti-SOD1 (sc-11407), anti-SOD2 (sc-30080), anti-Prx3 (sc-59661), anti-α-tubulin (sc-23948), and anti-VDAC (sc-58649) were from Santa Cruz Biotechnology; anti-lamin B1 (MA1-06103) and anti-mtHSP70 (MA3-028) were from Thermo Fisher Scientific; anti-β-actin (#3700), anti-survivin (#2808), anti-LDHA (#2012) were from Cell Signaling Technology; anti-PDK1 (ADI-KAP-PK112) was from Enzo Life Sciences.

Mouse experiments were done in accordance with protocols approved by the Institutional Animal Care and Use Committees of University of South Alabama. Six-week-old female athymic nude mice were used in the experiments. 1 × 10⁶ cancer cells suspended in 200 μL of PBS and Matrigel mixture (1:1) were injected into the mammary fat pads of each mouse. After development of palpable tumors, diameter of each tumor was measured every week using digital micrometer calipers, and the tumor volume was calculated using the formula: volume (mm³) = W² X L/2, where W and L are the minor and major diameters, respectively.

Five weeks after the cell injection, the mice were injected intravenously with 10 nmoles/100 μL of XenoLight RediJect 2-DeoxyGlucosone (DG)-750 (XenoLight, PerkinElmer). After 24 hours of dye injection, the mice were analyzed for glucose uptake by fluorescent imaging using an IVIS-200 camera system (Xenogen; ref.10). Ten minutes before in vivo imaging, mice were anesthetized with 3% isofurane using the XGI-8 Gas Anesthesia System (Caliper Life Sciences). The imaging results were analyzed using Living Image software. A region of interest was manually selected over relevant regions of signal intensity, and the intensity was recorded as the efficiency. Fluorescent intensity obtained from each mouse was plotted using Graph Pad Prism software version 5.0.

Statistical significance for experimental data was determined with unpaired Student t test (GraphPad Prism). Statistical differences of P < 0.05 were considered significant.

To study the possible role of B7-H3 in the regulation of glucose metabolism in cancer cells, we used two stable B7-H3 knockdown cell variants derived from the breast cancer cell line MDA-MB-231 and the melanoma cell line MDA-MB-435 that we described previously (17). Effective knockdown of B7-H3 expression was confirmed (Fig. 1A; TR33 denotes scramble vector control).

We measured lactate production and glucose uptake, two hallmarks of glycolysis, in control and B7-H3 knockdown cells. Both MDA-MB-231 shB7-H3 and MDA-MB-435 shB7-H3 cells showed a significantly lower lactate production (Fig. 1B) and glucose uptake (Fig. 1C) than their respective scramble control cells, both in normoxia and hypoxia conditions. As expected, all cells grown under hypoxia showed a higher rate of glycolysis compared with the cells grown in normoxia. We also obtained the similar results from cells treated with CoCl₂, which mimics hypoxia conditions by preventing HIF hydroxylation (Supplementary Fig. S1A and S1B). These results indicate that B7-H3 expression promotes glycolysis in these cancer cells.

To further characterize the role of B7-H3 in cancer cell glucose metabolism, we used the XF24 to measure in real-time the OCR and the ECAR as indicatives of oxidative respiration and glycolysis, respectively, in various breast cancer cell lines with stable B7-H3 knockdown or overexpression. We used the Mito Cell Stress and the Glycolysis Stress assays to determine various parameters of mitochondrial respiration and glycolytic capacity of the cells. We found that knockdown B7-H3 in MDA-MB-231 cells decreased basal ECAR (Fig. 2C and F) and increased basal OCR (Fig. 2A) when compared with control cells (TR33), which resulted in an increased basal OCR/ECAR ratio (Fig. 2G) that is indicative of an anti-Warburg effect. Both glycolysis and glycolytic reserve capacity are decreased in MDA-MB-231 shB7-H3 cells when compared with control cells (Fig. 2C and E). Conversely, overexpression of B7-H3 in SKBR3 breast cancer cells decreased basal OCR (Fig. 2B) and increased basal ECAR (Fig. 2D) when compared with empty vector (EV) control cells. Strikingly, overexpression of B7-H3 significantly increased both glycolysis and glycolytic reserve in SKBR3 cells (Fig. 2D and H). Similarly, B7-H3 overexpression in MDA-MB-468 breast cancer cells also increased basal ECAR (Fig. 2I) and decreased the basal OCR/ECAR ratio (Fig. 2J), indicating that B7-H3 overexpression promotes the Warburg effect in breast cancer cells.

We next tested whether B7-H3 knockdown cells respond differently to glycolysis inhibition compared with the control cells. We treated MDA-MB-231 shB7-H3 and MDA-MB-435 shB7-H3 cells and their control cells with a specific glycolysis inhibitor oxamate, a pyruvate analog that directly inhibits the conversion of pyruvate to lactate by lactate dehydrogenase, and then examined its effect on cell growth. The results showed that oxamate greatly inhibited the growth of all the cells in a dose-dependent manner (Supplementary Fig. S2A). However, compared with control cells, the growth of B7-H3 knockdown cells MDA-MB-231 shB7-H3 and MDA-MB-435 shB7-H3 was less inhibited, indicating B7-H3 high-expressing cells are more sensitive to glycolysis inhibition. These results were further confirmed by colony formation assay with B7-H3 high- and low-expressing cells (Supplementary Fig. S2B). To further test this, we compared the growth of these cells in medium with or without glucose (Supplementary Fig. S3A). Although glucose depletion slowed the growth of all tested cells when compared with complete media controls, the growth of both MDA-MB-231 shB7-H3 and MDA-MB-435 shB7-H3 cells was less inhibited by glucose depletion than that of their control counterparts. This indicates that the B7-H3 knockdown cells are less sensitive to glucose deprivation as compared with control cells. To further confirm whether B7-H3 increased growth rate required aerobic glycolysis, we grew cells in media containing galactose instead of glucose, thereby reducing glycolytic flux and forcing the cells to rely on mitochondrial oxidative phosphorylation. Under these conditions, the B7-H3 knockdown cells grew at the almost same rate as control vector cells (Supplementary Fig. S3B), demonstrating that the increased proliferation of B7-H3–expressing cells required enhanced glucose metabolism. Taken together, these results suggested B7-H3 plays an important role in reprogramming cancer cell metabolism in favor of glycolysis, and as a result, promoting cell growth.

HIF1α functions as a master regulator in the reprogramming of cancer metabolism in favor of glycolysis. To test whether B7-H3 promotes glycolysis in cancer cells involves HIF1α, we first investigated whether B7-H3 regulates HIF1α protein level. The Western blot analysis revealed HIF1α protein level was decreased in B7-H3 knockdown cells compared with their control cells in both normoxia and hypoxia conditions (Fig. 3A). We further confirmed these results in MDA-MB-231 by transiently knockdown B7-H3 using a siRNA that targets a different sequence than that in the shB7-H3 construct (Supplementary Fig. S4). Importantly, HIF1α level was rescued in MDA-MB-231 shB7-H3 cells transiently transfected with a B7-H3 expression vector (Fig. 3B). We next, evaluated the levels of two direct targets of HIF1α, lactate dehydrogenase A (LDHA), and pyruvate dehydrogenase kinase 1 (PDK1), and found that both are downregulated in B7-H3 knockdown cells under both normoxia and hypoxia conditions when compared with control cells (Fig. 3C). Conversely, overexpression of B7-H3 in SKBR3 and MDA-MB-468 cells results in increased levels of HIF1α, LDHA, and PDK1 under hypoxia conditions (Fig. 3D and E). Altogether, these results show that the B7-H3–induced metabolic shift in cancer cells is mediated by HIF1α and its targets in the glycolytic pathway.

We next investigated how B7-H3 regulates HIF1α. As we did not detect obvious changes in HIF1α mRNA levels between B7-H3 knockdown cells and control cells (Supplementary Fig. S5), we checked whether B7-H3 regulates HIF1α protein stability. After treatment with the proteasome inhibitor MG132, HIF1α protein was induced to the similar level in B7-H3 knockdown cells as in the control cells, indicating that B7-H3 upregulates HIF1α through protein stabilization (Fig. 4A).

ROS have been shown to inhibit the prolyl hydroxylases (PHD) and stabilize HIF1α, thus we tested whether stabilization of HIF1α by B7-H3 is ROS dependent. We measured cellular ROS levels under hypoxia condition by staining with CellROX Deep Red (Fig. 4B) and MitoSOX Red (Fig. 4C). Knockdown of B7-H3 in MDA-MB-231 and MDA-MB-435 cells results in lower levels of ROS when compared with control cells (Fig. 4B and C). Conversely, overexpression of B7-H3 in MDA-MB-468 cells results in higher ROS levels than in control cells (Fig. 4B and C). After treatment of NAC and PEG-catalase, ROS levels were reduced (Supplementary Fig. S6). These results show that B7-H3 regulates ROS levels in cancer cells.

Next, we tested whether suppressing ROS could block the stabilization of HIF1α by B7-H3. Indeed, treatment with the ROS scavengers N-acetylcysteine (NAC) and PEG-catalase under hypoxia reduced HIF1α protein (Fig. 4D and E). Accordingly, suppressing ROS also reduced LDHA and PDK1 expression levels in control cells to the similar levels as in B7-H3 knockdown cells (data not shown). Pentose phosphate pathway (PPP) is important in regeneration of NADPH from NADP⁺. NADPH is required for the scavenging of ROS. Therefore, the PPP plays a pivotal role in helping glycolytic cancer cells to suppress oxidative stress. We thus examined the activity of G6PD, a critical enzyme in PPP, in B7-H3 knockdown and overexpressing cells. The results showed that B7-H3 expression increased G6PD activity under hypoxia (Supplementary Fig. S7A), indicating that in addition to promoting glycolysis, B7-H3 expression also increases PPP to combat ROS. Finally, to test whether decreased ROS could underlie the reduced proliferation of B7-H3 knockdown cells, we cultured cells with NAC and measured growth rates. Strikingly, we found that B7-H3 knockdown cells grew at the almost same rate as control cells after treated with NAC (Supplementary Fig. S8). Thus, regulation of ROS by B7-H3 plays an important role in upregulation of HIF1α and cell growth in B7-H3–expressing cells.

To investigate how B7-H3 regulates ROS, we evaluated the levels of antioxidant enzymes in isolated mitochondria from cancer cells with different expression levels of B7-H3. Interestingly, we found that B7-H3 high expression was inversely correlated to the mitochondrial superoxide dismutase 2 (SOD2) and peroxiredoxin 3 (Prx3) in both MDA-MB-231 shB7-H3 knockdown and SKBR3 B7-H3 overexpression cells (Fig. 5A). In addition, we monitored the expression of superoxide dismutase 1 (SOD1), a cytoplasmic enzyme that can translocate to the mitochondrial intermembrane space, and found that, although cytoplasmic SOD1 was not altered either in SKBR3 B7-H3 or in MDA-MB-231 shB7-H3 cells, mitochondrial SOD1 was decreased in SKBR3 B7-H3 cells (Fig. 5A). Moreover, knockdown B7-H3 increased the enzyme activity of mitochondrial SOD, whereas the mitochondrial SOD activity was decreased in B7-H3 high overexpression cells (Supplementary Fig. S6B and S6C). These results indicated that B7-H3 regulates the mitochondrial antioxidant enzymes SOD1, SOD2, and Prx3. Because these antioxidant enzymes are target molecules of the transcriptional factor Nrf2, we examined the nuclear translocation of Nrf2, which is an indicator of Nrf2 activation, in MDA-MB-231 shB7-H3 and SKBR3 and MDA-MB-468 B7-H3–overexpressing cells. The results showed that B7-H3 suppresses Nrf2 translocation into the nucleus (Fig. 5B). Finally, we monitored mRNA levels of Nrf2 target gene SOD2 and found that it was also inversely correlated with B7-H3 expression in MDA-MB-231 shB7-H3 knockdown cells and SKBR3 B7-H3 overexpression cells when compared with their respective control cells (Fig. 5C and D). These results indicated that B7-H3 regulates cellular ROS level through Nrf2 and its targeting antioxidant enzymes.

To examine whether B7-H3 promotes glucose uptake and tumor growth in a mouse model, we used the overexpression model of B7-H3 in MDA-MB-468 human breast cancer cells to monitor tumor growth and glucose uptake in vivo. XenoLight RediJect 2-DeoxyGlucosone (DG)-750, a fluorescent probe that contains four molecules of 2-deoxyglucose (2-DG) per dye molecule was used to visualize the tumor size and the rate of glucose uptake by tumors. B7-H3–overexpressing MDA-MB-468 cells grew significantly larger tumors in comparison with control cells (Fig. 6A–C). Moreover, we observed a significant increase in glucose uptake in B7-H3–overexpressing tumors when compared with vector controls (Fig. 6D and E). To further confirm our results, we also examined the tumor growth and glucose uptake in vivo with MDA-MB-231 shB7-H3 xenograft mouse model. As expected, knockdown of B7-H3 resulted in significantly smaller tumors when compared with scramble controls (Fig. 6F–H) and decreased glucose uptake by tumors (Fig. 6I and J). Furthermore, downregulation of HIF1α, LDHA, and PDK1 in MDA-MB-231 shB7-H3 tumor in comparison with vector control tumor was confirmed by immunohistochemical analysis of xenograft tumors (Supplementary Fig. S9). Altogether, both of our two independent xenograft mouse models support our in vitro findings showing that B7-H3 plays a critical role in reprogramming glucose metabolism by enhancing aerobic glycolysis.

Although most functional studies focus on the immunologic role of B7-H3, our previous studies have revealed that B7-H3 has autocrine cellular functions in cancer cells that affect tumor progression (16, 17, 19). Here we demonstrated that B7-H3 regulates glucose metabolism through ROS-mediated HIF1α stabilization, which contributes to B7-H3 enhanced tumor growth (Fig. 7). We showed that knockdown of B7-H3 inhibits glycolysis by the decreased the stability and activity of HIF1α, as well as its downstream targets LDHA and PDK1. Conversely, overexpression of B7-H3 showed a reverse effect. Moreover, B7-H3 promotes HIF1α stability via increased ROS by suppressing the activity of transcription factor Nrf2 and Nrf2 target genes. Furthermore, in two independent xenograft mouse models, we showed that B7-H3 promotes breast cancer uptake of glucose and tumor growth in vivo. These novel findings provide a new insight for the role of B7-H3 in cellular metabolic reprogramming of cancer cells.

HIF1α plays a central role as integrator of pathways involved in glycolysis (29). Our results revealed that knockdown of B7-H3 downregulates HIF1α and its direct targets, LDHA and PDK1. Conversely, overexpression of B7-H3 increases HIF1α, LDHA, and PDK1. Both LDHA and PDK1 play a critical role in glycolysis. Thus, we demonstrate that HIF1α plays a central role in the regulation of glycolysis by B7-H3.

To study how B7-H3 regulates HIF1α stability, we found that ROS levels were significantly increased in B7-H3–overexpressing cells and conversely decreased in B7-H3 knockdown cells. Several studies have shown that ROS are both necessary and sufficient to stabilize and activate HIF1α (29). Here we showed that the ROS scavengers reduced HIF1α, as well as the cell proliferative rate, to almost the similar levels in B7-H3 expressing and knockdown cells. We revealed that B7-H3 expression reduced Nrf2 activity, resulting in downregulation of the Nrf2 antioxidant targets SOD1, SOD2, and Prx3 (30–32). However, the lack of knowledge of B7-H3 receptors and/or binding partners leaves the mechanism linking B7-H3 to Nrf2 and antioxidant enzymes to be still elucidated.

Our findings on the role of B7-H3 in cellular glucose metabolism discover a novel biologic function of the B7 family proteins, which could provide an opportunity for cancer therapeutic intervention. Currently, phase I clinical trials have been testing the safety of the anti-B7-H3 antibody 8H9 and MGA271 in several types of cancers (5, 33, 34). However, both antibodies have only been deployed as a tumor-targeting agents and it is unknown whether they can also affect the intrinsic functions of B7-H3. Our studies indicate that therapeutic agents designed for inhibiting the intrinsic tumor-promoting function of B7-H3 is promising for cancer treatment.

On the basis of the evidence that we present here, it is tempting to hypothesize that B7-H3 might also favor tumor growth by manipulating cancer immunosurveillance through alterations in the metabolism of cancer cells and immune cells. Numerous reports demonstrated that differentiation and activation of distinct subsets of immune cells are tightly modulated by specific metabolic programs (35–37). Murine B7-H3 has been reported suppressing Th1 effector responses (38, 39). In addition, Tregs upregulate B7-H3 expression in dendritic cells (DC) upon contact, subsequently inducing immunossupressive functions of DCs (40). Tumor infiltration with Tregs (Foxp3⁺ T cells) is associated with suppression of antitumor immunity and has been associated with worse prognosis in cancer patients (41, 42). Interestingly, a recent study of breast cancer patients revealed that B7-H3 high/Foxp3 high tumor staining was associated with significantly shorter recurrence-free survival than that of B7-H3 low/Foxp3 low (43). Future studies should investigate whether B7-H3-induced metabolic reprogramming also plays a role in regulation of cancer immunity to favor tumor growth and metastasis.

No potential conflicts of interest were disclosed.

Conception and design: S. Lim, L. Madeira da Silva, Z. Liu, T. Vu, G.A. Piazza, O. Fodstad, M. Tan

Development of methodology: Z. Liu, T. Vu

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): S. Lim, L. Madeira da Silva, R. Arora, J.B. Phillips, D.C. Schmitt, T. Vu, S. McClellan, Y. Lin, W. Lin, M. Tan

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): S. Lim, L. Madeira da Silva, R. Arora, J.B. Phillips, T. Vu, S. McClellan, M. Tan

Writing, review, and/or revision of the manuscript: S. Lim, L. Madeira da Silva, R. Arora, J.B. Phillips, D.C. Schmitt, W. Lin, O. Fodstad, M. Tan

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): Z. Liu, T. Vu, M. Tan

Study supervision: M. Tan

This work was supported from The Vincent F. Kilborn, Jr. Cancer Research Foundation (M. Tan), NIH grants R01CA149646 (M. Tan) and R01NS073132 (W. Lin), The Radiumhospitalets Legater Project 334003 (M. Tan and O. Fodstad) and National Natural Science Foundation of China Projects 81201559 (H. Liu) and 81328019 (M. Tan and M. Zhou).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.