Abstract
Cancer immunotherapy has reshaped the landscape of cancer treatment. However, its efficacy is still limited by tumor immunosuppression associated with the excessive production of lactate by cancer cells. Although extensive efforts have been made to reduce lactate concentrations through inhibition of lactate dehydrogenase, such inhibitors disrupt the metabolism of healthy cells, causing severe non-specific toxicity. We report herein a nanocapsule enzyme therapeutic based on lactate oxidase, which reduces lactate concentrations and releases immunostimulatory hydrogen peroxide, averting tumor immunosuppression and improving the efficacy of immune checkpoint blockade treatment. As demonstrated in a murine melanoma model and a humanized mouse model of triple-negative breast cancer, this enzyme therapeutic affords an effective tool towards more effective cancer immunotherapy.
One Sentence Summary:
Lactate oxidase nanocapsules drive lactate reduction and hydrogen peroxide generation to boost anti-tumor T cell responses.
INTRODUCTION
Compared with traditional cancer treatments (surgery, chemotherapy, and radiotherapy), cancer immunotherapy, which utilizes the host immune system (1) to attack cancer cells has brought substantial improvement for patients in terms of survival and quality of life (2). Immunotherapeutics, such as immune checkpoint blockade therapies (3), adoptive cell therapies (4), and cancer vaccines (5), have been integrated into standard of care regimens for cancers, including advanced melanoma (6), lymphoma (6), non-small cell lung cancer (7), and prostate cancer (8). However, these therapies fail to show positive responses in a meaningful number of patients, and tumor immunosuppression is believed to contribute to the resistance to immunotherapy (9).
The tumor microenvironment (TME) harbors malignant cells, immune cells, blood vessels, and extracellular matrix, which collectively affect the sensitivity of immunotherapy (10). Termed the Warburg effect, tumor cells rapidly undergo glycolysis (11) and produce excessive amount of lactate, resulting in a lactate-rich microenvironment (12). Effector T cells, such as cytotoxic T cells, primarily rely on glycolysis for energy production. However, elevated lactate concentrations in the TME disturb their glycolysis (13), leading to impaired proliferation, cytotoxicity, and reduced production of pro-inflammatory cytokines (14, 15). In contrast, tumor-resident regulatory T (Treg) cells possess metabolic flexibility, being able to use glucose and lactate as carbon sources (16). Treg cells effectively utilize lactate to fuel the tricarboxylic acid cycle and phosphoenolpyruvate synthesis, supporting their suppressive function and proliferation to promote immunosuppression within the TME (16). Furthermore, it has been shown that when lactate utilization is impaired, increased glucose uptake correlated with decreased suppressive function and long-term instability of Treg cells. Consequently, reducing lactate concentrations in the TME is critical for mitigating tumor immunosuppression and improving the functions of effector T cells during immunotherapy. In fact, elevated intratumoral concentrations of lactate are considered as an indicator of cancer progression and metastasis (17).
To date, extensive trials are being conducted to regulate lactate concentrations mainly through inhibiting lactate dehydrogenase (LDH) (18), the key enzyme that catalyzes the conversion between lactate and pyruvate in the presence of NAD+/NADH. LDH plays crucial roles in maintaining the energy balance between glycolysis and mitochondrial respiration, as well as in regulating the redox balance of NAD+/NADH. The use of LDH inhibitors, however, can disrupt the metabolism of healthy cells, causing severe non-specific toxicity. Therefore, alternative strategies are needed to reduce lactate concentrations without affecting healthy tissues.
In contrast to the immunosuppressive role of lactate, it has been documented that hydrogen peroxide (H2O2), which is often generated upon injuries, effectively recruited immune cells towards wound sites (19). Additionally, it has been found that H2O2 effectively activates NF-κB signaling (20, 21) and increases the secretion of pro-inflammatory cytokines such as interferon (IFN)-γ (22–24) in immune cells, which activates T cells and directs their migration towards and within tumors through stimulating CXC chemokine receptors and their ligands. Increasing tumoral H2O2 concentrations, in this context, could be an effective strategy to recruit and activate immune cells in the tumors. In light of these findings, we developed an enzyme therapeutic based on lactate oxidase (LOx) that effectively reduces lactate concentrations and releases H2O2 within tumors, alleviating tumor immunosuppression and enhancing the efficacy of immunotherapy.
RESULTS
Subhead 1: n(LOx) enhances the stability of lactate oxidase while retaining enzymatic activity.
Recombinant LOx from microorganisms possesses a short half-life and potential immunogenicity. To circumvent this limitation, we encapsulated LOx within a thin shell of polymer using our single-enzyme encapsulating platform (25–27). The encapsulation of LOx involves the enrichment of poly(ethylene glycol) methyl ether acrylate (PEGMA, monomer) and bisacrylamide (BIS, crosslinker) around LOx molecules via noncovalent interactions, and subsequent growth of a thin polymer shell around each LOx molecule by in situ polymerization (fig. S1). The encapsulation process leads to the formation of LOx nanocapsules, denoted hereinafter as n(LOx).
Compared with native LOx with an average size of about 10 nm, n(LOx) showed a spherical morphology with an average size of roughly 25 nm, as measured by transmission electron microscopy (Fig. 1A) and dynamic light scattering (fig. S2A). We further validated the encapsulation of LOx using protein gel electrophoresis, where n(LOx) remained in the stacking gel while native LOx migrated to the separating gel (Fig. 1B). Furthermore, the encapsulation changed the negative charge of native LOx (zeta potential, −7 mV) to a near-neutral zeta potential for n(LOx) (+2 mV) (fig. S2B). The n(LOx) retained more than 90% activity of the native LOx (fig. S2C); the Michaelis constant (Km) was slightly increased from 0.16 mM to 0.55 mM (Fig. 1C), which is not expected to substantially affect the enzyme kinetics given the high concentrations of lactate in tumors (10 to 30 mM) (28). Moreover, tumors secrete various proteases, such as matrix metalloproteinases, degrading free proteins within the TME (29). To assess the proteolytic stability of n(LOx), we measured the activity of native LOx and n(LOx) in the presence of a model protease, trypsin (10 μg/mL). Unlike native LOx, which completely lost the activity within 20 minutes, n(LOx) retained more than 80% activity even after a 2-hour incubation (fig. S2D). The enhanced enzyme stability is essential to assure its continuous function in tumors.
Fig. 1. n(LOx) retains the enzyme activity with enhanced stability.
(A) Representative transmission electron micrograph (TEM) of n(LOx). Scale bar, 50 nm. (B) Gel electrophoresis of native LOx and n(LOx). (C) Enzyme kinetics of native LOx and n(LOx) in generating H2O2. Km, Michaelis constant. Kcat, turn over number. (D) Lactate concentration in the culture media of MDA-MB-231 cells after incubating with native LOx or n(LOx) (n=3). (E) H2O2 generation in the culture media of MDA-MB-231, 4T1, or B16F10 cells after incubating with native LOx or n(LOx) (0.5 μg/mL) for 30 minutes (n=5). a.u., arbitrary units. H2DCFDA is used as an indicator of H2O2, which can be oxidized by H2O2 to produce highly fluorescent 2’,7’-dichlorofluorescein (DCF). The fluorescence intensity of oxidized H2DCFDA (DCF) was used to evaluate the generation of H2O2. (F) Fluorescence imaging of intracellular H2O2 in MDA-MB-231 tumor spheroids after incubating with native LOx or n(LOx). H2O2 and nuclei were stained by 6-chloromethyl (CM)-H2DCFDA (green) and DAPI (blue), respectively. Scale bar, 100 μm. (G) Biodistribution of native LOx and n(LOx) after intratumoral injection. 4T1-Luc cells were used for luminescent imaging. Native LOx and n(LOx) were labeled with DyLight 755 for fluorescence imaging. (H and I) Concentrations of lactate (H) and H2O2 (I) in tumors from mice treated as indicated (n=6 mice per group). H2O2 was measured with Amplex Red reagent. All data are presented as mean ± s.d. Statistical significance was calculated by one-way ANOVA with Tukey’s multiple comparison test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.
The ability of n(LOx) to reduce lactate concentrations was first examined using various cancer cell lines, including MDA-MB-231, 4T1, and B16F10, which abundantly produced lactate in the culture media (fig. S2, E and F). Specifically, adding native LOx or n(LOx) (10 μg/mL) to MDA-MB-231 cell cultures reduced the lactate concentration in the culture media from 12 mM to 6 mM in 1 hour (Fig. 1D). Meanwhile, in the presence of H2DCFDA (a fluorescent probe for H2O2), the cancer cells treated with native LOx or n(LOx) exhibited substantially higher fluorescent signals than phosphate-buffered saline (PBS)-treated controls, indicating increased concentrations of H2O2 (Fig. 1E and fig. S2, G and H). Consistent results were also obtained in tumor spheroids of MDA-MB-231 (Fig. 1F and fig. S3), confirming the ability of n(LOx) to reduce lactate and generate H2O2 in cancer cells.
The in vivo stability of n(LOx) was examined in a murine breast cancer model based on 4T1 cells. One week after engrafting luciferase-expressing 4T1 (4T1-Luc) cells into the mammary glands of BALB/c mice, native LOx or n(LOx) (0.5 mg/kg) labeled with Dylight 755 was injected into the tumors. Consistent with the enhanced stability of n(LOx) (fig. S2D), the fluorescent signals of n(LOx) were well retained within the tumor region 5 days after injection (Fig. 1G and fig. S4, A to C), whereas the fluorescent signals of native LOx rapidly became nearly undetectable at 24 hours (Fig. 1G). Further study suggested that n(LOx) exhibited a local half-life of 35 hours, which is longer than that of native LOx (about 6 hours), suggesting the favorable in vivo stability of n(LOx) (fig. S4D).
The ability of n(LOx) to reduce lactate concentrations and generate H2O2 was also examined in this breast cancer model. After intratumoral injection of native LOx or n(LOx), the average lactate concentration was reduced from 16 mM to 14 mM or 12 mM, respectively (Fig. 1H). Using Amplex Red Reagent as an H2O2 probe, the fluorescence intensity for the tumors treated with native LOx or n(LOx) was elevated by 2.5- and 4.6-fold, respectively, compared to the control group (Fig. 1I). It is also worth noting that, despite the reduced lactate concentrations, there was no change in tumor pyruvate values (fig. S4E). Overall, these results confirm the successful synthesis of n(LOx) that reduces lactate and generates H2O2 in tumors. n(LOx) also exhibited stability against the proteolysis in the TME, supporting its potential as a candidate cancer immunotherapy.
Subhead 2: n(LOx) suppresses the proliferation of Treg cells, activates cytokine-producing T cells, and promotes tumor infiltration of T cells.
The ability of n(LOx) to suppress the proliferation of Treg cells associated with elevated concentrations of lactate was examined using human peripheral blood mononuclear cells (PBMC). In the presence of 20 mM lactate, a lactate concentration similar to those in tumors, the relative proliferation rate of Treg (FOXP3+CD25hi) markedly increased by 22% (Fig. 2A), and the percentage of Treg cells in the CD4+ T cell compartment increased from 7.2% to 8% (Fig. 2B). In order to dissect the roles of lactate reduction and H2O2 generation both induced by n(LOx), catalase (CAT), an enzyme that effectively decomposes H2O2 into H2O and O2, was employed alongside n(LOx) as n(LOx)/CAT to only retain lactate reduction by removing H2O2 produced by n(LOx). Addition of n(LOx) and n(LOx)/CAT to the culture media restored Treg proliferation rates and the percentages of Treg cells of total CD4+ T cells to that of control values, suggesting that the reduced Treg population was mainly attributed to the reduced lactate concentrations, rather than H2O2 generation (Fig. 2, A and B, and fig. S5, A to C).
Fig. 2. n(LOx) suppresses Treg cell proliferation and promotes cytokine production by T cells.
(A and B) Relative cell proliferation (A) and percentages (B) of FOXP3+CD25hi Treg cells in CD4+ T cells after incubating with n(LOx) in the presence or absence of catalase (CAT) (n=6). (C and D) Percentages of IFN-γ-producing CD4+ (C) and CD8+(D) T cells after incubating with MDA-MB-231 cells treated with n(LOx) in the presence or absence of CAT (n=6). (E and F) Percentages of IL-2-producing CD4+ (E) and CD8+ (F) T cells after incubating with MDA-MB-231 cells treated with n(LOx) in the presence or absence of CAT (n=6). (G) Immunohistochemistry (IHC) staining of CD4, CD8, and IFN-γ in triple-negative breast cancer (4T1) tissues from the indicated treatment groups. Mice were intratumorally injected with PBS or n(LOx) at day 7, 10, 13, and 16 after tumor engraftment. Tumors were isolated for IHC 25 days after tumor engraftment. Scale bar, 50 μm. All data are presented as mean ± s.d. Statistical significance was calculated by one-way ANOVA with Tukey’s multiple comparison test. ***P < 0.001, ****P < 0.0001; ns, not significant.
The ability of n(LOx) to increase the population of cytokine-producing T cells was examined in PBMC/MDA-MB-231 co-cultures, where MDA-MB-231 cells were first treated with n(LOx) or n(LOx)/CAT for 24 hours followed by co-culturing with PBMC for 6 hours. The culture media with n(LOx) showed higher populations of both IFN-γ and interleukin-2 (IL-2) producing CD4+ and CD8+ T cells, whereas their populations in the n(LOx)/CAT group were similar to those of the controls (Fig. 2, C to F). Consistently, IFN-γ-producing total T cells (about 8.2%) and IL-2-producing total T cells (about 3.7%) were comparable in the n(LOx)/CAT and control groups, whereas the n(LOx) group showed markedly higher populations of IFN-γ-producing T cells (31.2%) and IL-2-producing T cells (13%) (fig. S5, D to G). This study indicates a critical role for H2O2 in activation of T cells for cytokine production. For confirmation, H2O2 was directly added to the co-cultures; as expected, we observed increased populations of the cytokine-producing T cells, although the efficacy was lower than n(LOx), which generates H2O2 continuously and maintains relatively high concentrations of H2O2 in the cultures (fig. S5, D to I).
The ability of n(LOx) to promote tumor infiltration and activation of T cells for cytokine production was further examined in the murine 4T1 breast cancer model. One week after 4T1 engraftment, native LOx or n(LOx) (0.5 mg/kg) was injected into the tumors at day 7, 10, 13, and 16. At day 25 after tumor engraftment, tumor tissues were collected. Both native LOx and n(LOx) increased IFN-γ secretion, as well as the population of CD4+ and CD8+ T cells in tumors (Fig. 2G and fig. S6). Improved tumor infiltration of CD11b+ pro-inflammatory macrophages, as well as natural killer cells, were also observed in tumors with the treatment of n(LOx) (fig. S6). Overall, these results confirm that n(LOx) effectively suppressed the proliferation of Treg cells under elevated concentrations of lactate, while promoting the tumor infiltration and activation of IFN-γ- and IL-2-producing T cells. In addition, the hepatic and renal functions (fig. S7, A to G) and hematoxylin and eosin (H&E) staining of the major organs (fig. S7H) of the mice treated with native LOx or n(LOx) showed no changes compared with controls, indicating favorable biocompatibility of n(LOx).
Subhead 3: n(LOx) boosts the efficacy of PD-L1 checkpoint blockades in a murine melanoma model.
The ability of n(LOx) to boost anti-tumor immunity and the efficacy of immune checkpoint blockade therapy was first studied in a murine melanoma model (B16F10). Mice were treated with native LOx or n(LOx) (0.5 mg/kg) by intratumoral injection after tumor engraftment (day 7, 9, and 11); anti-programmed cell death ligand 1 (aPD-L1, 100 μg/mouse) was injected intraperitoneally twice per week (Fig. 3A). Animals were carefully monitored and euthanized when the tumor volume reached 2000 mm3. Although aPD-L1 failed to control the tumor growth, n(LOx) markedly inhibited the tumor progression, and a combination of n(LOx) and aPD-L1 (n(LOx)/aPD-L1) further suppressed the tumor growth, resulting in dramatically reduced tumor volumes (< 100 mm3) after 30 days (Fig. 3B). Although native LOx also slowed down the tumor growth, its efficacy was lower than that of n(LOx), consistent with its short half-life and poor stability (fig. S8, A and B). Compared with aPD-L1, which slightly improved the median survival time (from 15 days to 17 days), n(LOx) extended the median survival time to 27 days, whereas mice treated with n(LOx)/aPD-L1 were alive after 30 days, suggesting n(LOx) boosted the effectiveness of aPD-L1 (Fig. 3C). The effectiveness of n(LOx) was further compared with CpG oligodeoxynucleotide, a commercial immunostimulator widely used in preclinical cancer models. We found that n(LOx) also outperformed CpG in suppressing tumor growth (fig. S9, A and B). To exclude the potential impact from the polymer shell of n(LOx), we prepared nanocapsules using a benign protein, bovine serum albumin (BSA). The treatment of BSA nanocapsules (n(BSA)) failed to control tumor growth (fig. S9C), confirming that the polymer shell did not contribute to the effectiveness of n(LOx). In addition, flow cytometry analysis indicate that n(LOx) mainly retained and functioned in the extracellular space of tumors (fig. S9D).
Fig. 3. n(LOx) synergizes with aPD-L1 therapy to suppress melanoma.
(A) Schematic illustration of the treatment schedule in the mouse model of B16F10 melanoma. (B) Tumor volume changes of mice treated with PBS, aPD-L1, n(LOx), or the combination therapy of n(LOx) and aPD-L1 (n(LOx)/aPD-L1). The error bars indicate s.e.m. (n=6). (C) Kaplan-Meier survival curve of mice in different groups (n=6 to 8 mice per group; Mantel-Cox test. **P < 0.01, ****P < 0.0001). (D to G) Percentages of CD45+ cells (D), CD11c+ dendritic cells (E), F4/80+CD11b+ macrophages (F), and CD3+ T cells (G) in tumors from different groups. (H and I) Percentages of CD69+ cells in CD4+ (H) and CD8+ (I) T cells in tumors from different groups. (J and K) Percentages of IFN-γ + cells in CD4+ (J) and CD8+ (K) T cells in tumors from different groups. In (D to K), the error bars indicate s.d.; n=6 mice per group. (L) Schematic illustration of the engraftment of secondary tumor after n(LOx) treatment. (M) Tumor volume changes of the secondary tumors from mice treated with PBS or n(LOx). The error bars indicate s.e.m. (n=6). (N) Fold change of the population of different immune cells in the secondary tumors from mice treated with n(LOx). M1 macrophages were identified as CD80hiCD206low macrophages, and M2 macrophages were identified as CD80lowCD206hi macrophages. Statistical significance in (D to K) was calculated by one-way ANOVA with Tukey’s multiple comparison test. Statistical significance in (M) was calculated by unpaired t-test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.
Compared with PBS and aPD-L1, n(LOx) and n(LOx)/aPD-L1 increased the populations of CD45+ tumor-infiltrating lymphocytes (TILs) from about 8% to 24.6% and 24.9% at day 14, respectively, and maintained the high populations until the end of the experiment at day 30 (Fig. 3D and fig. S10, A to E). Populations of dendritic cells, macrophages, and T cells, which are subtypes of TILs that collectively contribute to anti-tumor immunity, all increased after n(LOx) or n(LOx)/aPD-L1 treatment (Fig. 3, E to G). Furthermore, n(LOx) and n(LOx)/aPD-L1 treatment also boosted the populations of activated CD4+ T cells (CD69+/CD4+) from 37% (PBS) to 49% and 53% (Fig. 3H), as well as activated CD8+ T cells (CD69+/CD8+) from 44% (PBS) to 59% and 61%, respectively (Fig. 3I), accompanied by the increases in IFN-γ-producing CD4+ and CD8+ T cells (Fig. 3, J and K). The increased TILs and IFN-γ-producing T cells likely boosted the effectiveness of aPD-L1. In addition, depletion of CD4+ or CD8+ T cells compromised the efficacy of n(LOx) and resulted in faster tumor progression, confirming that the efficacy of n(LOx) mainly focuses on T cells (fig. S11, A to F).
Beyond boosting anti-tumor immunity and therapeutic efficacy of immune checkpoint blockade, n(LOx) may also induce abscopal effects. After intratumoral injection of n(LOx) in the right flank (0.5 mg/kg at day 7 and 9), a secondary tumor was engrafted in the left flank at day 10 (Fig. 3L). Compared to the PBS-treated control, the growth of the secondary tumors was suppressed in mice treated with n(LOx) (Fig. 3M and fig. S12A). Populations of TILs in the secondary tumors, including CD45+ TILs, CD3+ T cells, and CD11c+ dendritic cells, were increased (Fig. 3N and fig. S12, B to H). In addition, an increased population of M1 macrophages, but decreased populations of M2 macrophages and Treg cells, were observed in the secondary tumors (Fig. 3N and fig. S12, E to K). These results suggest that n(LOx) treatment in the primary tumors induced anti-tumor immunity in the secondary tumors.
To further understand how n(LOx) boosted anti-tumor immunity, single-cell RNA sequencing (scRNA-seq) analysis of the tumors was performed, including the cancer cells, fibroblasts, and T cells (fig. S13A). Gene ontology (GO) analysis indicates that n(LOx) markedly upregulated the genes related to T cell activation and leukocyte migration in the tumors (Fig. 4A). To gain insight into the gene networks in responding to the n(LOx) treatment, we aligned our data to the main genes associated with the crosstalk to T cells. As shown in Fig. 4B, n(LOx) upregulated gene pathways that positively regulate the activation of T cells and alpha-beta T cells specifically. In addition, pathways related to cell chemotaxis and cell killing were upregulated after n(LOx) treatment (fig. S13B). The genes associated with cell killing and activation of immune cells were also upregulated in the T cells (Fig. 4C). Further analysis of T cells reveals that n(LOx) also induced increases in expression of genes related to the cytokine response, such as Ifng and Irf1 (Fig. 4D), as well as a skewed T-helper 1 (Th1) type immune response (Fig. 4E). Such upregulation boosted the cytotoxic responses of Th1 cells and stimulated the proliferation of cytotoxic T cells for tumor suppression. Multiple chemokine genes, such as Cxcl1, Cxcl3, and Cxcl10, were also stimulated in different cell types, including the activated fibroblasts and monocytes (Fig. 4F). Upregulation of chemokine-production genes likely promoted the recruitment of T cells and their migration to the tumors. These findings suggest that n(LOx) not only promoted T cell activation and anti-tumor responses (fig. S12C) at the transcriptional level, but also promoted the recruitment of circulating T cells to tumors.
Fig. 4. n(LOx) treatment promotes T cell activation and anti-tumor responses in melanoma.
(A) scRNA-Seq gene ontologies (GO) for genes significantly impacted after n(LOx) treatment versus control (PBS) in B16F10 cancer cells. (B) Pathway analysis of upregulated genes relative to T cell activation in cancers after n(LOx) treatment. (C) scRNA-Seq GO terms for genes significantly impacted after n(LOx) treatment versus control (PBS) in T cells. (D) Heatmap of gene expression values in cancer cells and T cells relative to cytokine production after n(LOx) treatment. (E) Heatmap of gene expression values related to Th1 type immune responses after n(LOx) treatment. (F) Scaled expression of chemokine genes (Cxcl1, Cxcl3, and Cxcl10) in different cell types after n(LOx) treatment.
Subhead 4: n(LOx) promotes T cell immunity and boosts the efficacy of PD-L1 checkpoint blockade in a humanized mice model.
The efficacy of n(LOx)/aPD-L1 in human cancers was examined using a humanized mouse model of triple-negative breast cancer (TNBC) (table S1). The humanized mouse model was engrafted with allogeneic human PBMC in NOD scid gamma (NSG) mice to reconstitute a functional human T cell system with reduced rejection of xenografted human tumors. PD-L1+ human breast cancer cells (MDA-MB-231) were injected into the subcutaneous space of the mammary glands in NSG mice. PBMC were intravenously injected 10 days after tumor engraftment. Two weeks after PBMC injection (day 0), n(LOx) was injected intratumorally at day 0 and 2 at 0.5 mg/kg; starting at day 2, aPD-L1 (100 μg/mice) was intraperitoneally administered twice per week (Fig. 5A). Similar to the treatment of melanoma (30), intratumoral injection of drugs has been widely applied to patients with TNBC to avoid off-target side effects (31, 32).
Fig. 5. n(LOx) inhibits TNBC in a humanized mouse model.
(A) Schematic illustration of the treatment schedule in the humanized mouse model of MDA-MB-231 TNBC. (B) Luminescence imaging of H2O2 in the tumor from different groups. L-012 was used as an in vivo H2O2 probe. White circles indicate the tumors. (C) Tumor volume changes of mice treated with PBS, aPD-L1, n(LOx) and CAT (n(LOx)/CAT), the combination therapy of n(LOx) and CAT with aPD-L1 (n(LOx)/CAT/aPD-L1), n(LOx), or the combination therapy of n(LOx) and aPD-L1 (n(LOx)/aPD-L1). The error bars indicate s.e.m. (n=6). (D) Percentages of CD4+ T cells and CD8+ T cells in the tumors from mice receiving PBS, n(LOx), or n(LOx)/CAT. The error bars indicate s.d. (n=6). (E) Percentages of CD4+ T cells and CD8+ T cells in the tumors from mice receiving PBS, aPD-L1, n(LOx)/aPD-L1, or n(LOx)/CAT/aPD-L1. The error bars indicate s.d. (n=6). (F) Percentages of FOXP3+ Treg cells in CD4+ T cells in the tumor from mice treated with n(LOx). The error bars indicate s.d. (n=6). (G) Percentages of PD-1+ cells in CD8+ T cells in the tumors from different groups. The error bars indicate s.d. (n=6). (H) Immunofluorescence staining of CD3 (cyan) and IFN-γ (magenta) in tumor tissues. Scale bar, 20 μm. (I) Percentages of CD4+ and CD8+ T cells expressing TNF-α. The error bars indicate s.d. (n=6). (J) Percentages of IL-4+ cells in CD4+ T cells in the tumor from mice treated with n(LOx). The error bars indicate s.d. (n=6). (K) Percentages of CD4+ and CD8+ T cells with effector memory phenotypes. The error bars indicate s.d. (n=6). Statistical significance was calculated by one-way ANOVA with Tukey’s multiple comparison test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.
To dissect the role of lactate reduction and H2O2 generation, treatments of n(LOx)/CAT (lactate-concentration reduction only) and n(LOx) (lactate reduction and H2O2 generation) were examined. The abilities of n(LOx) to generate H2O2 and CAT to decompose H2O2 produced by n(LOx) in tumors were confirmed with in vivo imaging using a H2O2 probe (L-012). Tumors treated with n(LOx) showed intense luminescence signals, verifying that n(LOx) produced H2O2 in tumors. In comparison, no signal in the tumor site was detected in mice treated with n(LOx)/CAT due to the elimination of H2O2 by the CAT (Fig. 5B).
In terms of tumor progression, aPD-L1 alone failed to slow down the tumor growth; in contrast, n(LOx)/CAT, n(LOx), and n(LOx)/aPD-L1 suppressed tumor growth with increasing effectiveness (Fig. 5C). In addition, n(LOx)/CAT/aPD-L1 inhibited the tumor growth comparably with n(LOx) (Fig. 5C). This observation suggests that lactate reduction improved the efficacy of aPD-L1, and the improvement was further enhanced by H2O2 generation. As a result, mice treated with n(LOx)/aPD-L1 showed the best-controlled tumor volume (< 280 mm3) throughout the study (Fig. 5C and fig. S14A).
In terms of tumor infiltration of T cells, n(LOx) increased the population of CD4+ T cells from 3% to 7%, and the population of CD8+ T cells from 1% to 6%, respectively, in comparison with the PBS-treated control (Fig. 5D). In contrast, n(LOx)/CAT did not increase these populations of T cells. This suggests that H2O2 generation is the main driving force for T cell infiltration. Compared with PBS, aPD-L1 failed to increase the percentage of CD4+ and CD8+ T cells (Fig. 5E). In contrast, n(LOx)/aPD-L1 increased the percentage of CD4+ and CD8+ T cells to 11% and 9%, respectively, in comparison with n(LOx) (Fig. 5, D and E). Compared with n(LOx)/CAT, n(LOx)/CAT/aPD-L1 also improved T cell infiltration, demonstrating that lactate reduction also enhances aPD-L1 therapy, yet less effectively than the simultaneous lactate reduction and H2O2 generation.
Consistent with the enhanced tumor infiltration of T cells, n(LOx) and aPD-L1 also alleviated tumor immunosuppression associated with abundance of Treg cells and exhausted cytotoxic T cells with PD-1 overexpression (33). Compared with PBS, n(LOx)/CAT and n(LOx) reduced the population of FOXP3+ Treg cells from 15% to 8% and 5% (Fig. 5F), and the PD-1+ population in CD8+ T cells from 93% to 66% and 35% (Fig. 5G), respectively. Reduction in Treg cell population was also observed after the treatment of n(LOx)/aPD-1 (fig. S14B). Compared with PBS, aPD-L1 reduced the PD1+ population in CD8+ T cells from 93% to 64%; n(LOx)/CAT/aPD-L1 and n(LOx)/aPD-L1 further reduced the population to 52% and 17%, respectively (Fig. 5G).
Beyond the alleviated immunosuppression and enhanced tumor infiltration of T cells, n(LOx) and aPD-L1 also improved the effector functions of T cells in humanized mice. aPD-L1 alone helped to recruit T cells in tumors but failed to increase IFN-γ production (Fig. 5H and fig. S14, C and D). In comparison, n(LOx) effectively increased the production of IFN-γ (Fig. 5H), IL-2 (fig. S15A) and tumor necrosis factor-alpha (TNF-α, Fig. 5I), which were further improved by aPD-L1. In addition, n(LOx)/CAT and n(LOx), as well as their combination with aPD-L1, reduced the production of an immunosuppressive cytokine, interleukin 4 (IL-4) (Fig. 5J and fig. S15B). n(LOx) also increased the population of CD44highCD62Llow effector memory T cells (Fig. 5K), as well as CD44highCD62Lhigh central memory T cells (fig. S15C), corresponding to a decrease in the percentage of CD44lowCD62Lhigh naïve T cells (fig. S15D). Thus, these data show that combining aPD-L1 therapy with n(LOx) potently boosts T cell immunity and promotes control of tumors in humanized mice.
DISCUSSION
The mechanism of n(LOx)/aPD-L1 therapy involves a combination of reducing lactate concentrations, generating H2O2, and blocking the PD-1/PD-L1 pathway. As a signaling molecule that drives cancer progression, lactate favors the proliferation of Treg cells while suppressing the functions of effector T cells (16). In our study, removal of lactate from the TME reduced the population of Treg cells while increasing the production of proinflammatory cytokines by effector T cells. In addition, n(LOx) produced H2O2, a signaling molecule that drives the migration and activation of leukocytes, which increased the infiltration and activation of effector T cells, as well as Th1 responses.
It is commonly accepted that tumors produce elevated concentrations of H2O2 and reactive oxygen species (34, 35); however, the concentration of H2O2 in tumors remains difficult to quantify (36). The approximate production rate of H2O2 in vitro was estimated to be 0.5 nmol/104 cell/hour for activated phagocytes and 0.01 to 0.5 nmol/104 cell/hour for cancer cells in culture media (37). Estimated by lactate production rate, the rate of H2O2 generation by n(LOx) is 14.4 to 20.6 nmol/104 cell/hour in culture media from the cell lines in this work, which is tens to hundreds folds higher than those of cancer cells or activated phagocytes. Injecting n(LOx) into tumors created sufficient H2O2 gradients, which directed the migration and activation of T cells towards and within the tumors, which was confirmed by scRNA-seq. Moreover, considering the production of lactate within tumors may vary spatiotemporally, n(LOx) enables the generation of H2O2 dynamically in response to the heterogeneity and state of tumors (e.g., progression and metastasis), affording increased therapeutic efficacy.
Immune checkpoint blockade therapies work by blocking the suppressive immune checkpoint proteins from binding with their ligands (38). As shown, aPD-L1 alone resulted in poor efficacy due to tumor immunosuppression. Lactate reduction relieved this tumor immunosuppression and, at the same time, H2O2 generation promoted T cell infiltration. This allowed aPD-L1 treatment to boost T cell immunity. To date, cytokines (e.g., IFN-γ and IL-2) and toll-like receptor agonists (e.g., CpG) have been explored to promote the infiltration and activation of T cells in tumors. However, such strategies lack the ability to avert the immunosuppression associated with the over-production of lactate in tumors. In contrast, our enzyme-based strategy utilizes LOx to reduce immunosuppressive factors and simultaneously releases immunostimulatory factors.
Our study has limitations. We note that the therapeutic efficacy of n(LOx) was demonstrated through intratumoral injection, which is routinely used for the clinical treatments of breast cancers, melanoma, and lymphoma (39). For future research, it will be important to examine the efficacy of n(LOx) by systematic injection considering its benefits in activating anti-tumor T cell immunity. However, excessively elevated concentrations of H2O2 in the blood may result in oxidative injuries. Thus, controlling the H2O2 concentrations upon systematic injection of n(LOx) will be an essential issue, which may be addressed by co-administrating catalase (40). We also note that the currently available humanized models have the limitation of human leukocyte antigen (HLA)-mismatch between the tumor and the human immune cells, which may induce treatment-independent immune response to tumor cells (41). Therefore, the evaluation and conclusions on therapeutic efficacy in humanized mice with tumor xenograft need to be carefully interpreted and supported by results in immunocompetent mice. Nevertheless, among all humanized mouse models (42, 43), the PBMC humanized model is currently one of the most widely adopted models to study immune cell activation and anti-tumor responses of immune cells with immunotherapy (44–46). If advanced humanized mouse models become available in the future, we will conduct necessary research to further evaluate n(LOx).
In conclusion, we have developed a multifunctional LOx-based immune activator for cancer immunotherapy, which reduces lactate concentrations while releasing immunostimulatory H2O2 in tumors. This dual-action approach alleviated tumor immunosuppression, promoted the infiltration and activation of effector T cells, enhanced aPD-L1 efficacy, and boosted anti-tumor T cell immunity. We anticipate that this enzyme-based strategy could be extended to other cancer immunotherapies to improve therapeutic efficacy and reduce therapeutic resistance.
MATERIALS AND METHODS
Study design
The aim of this study was to use nanocapsules of lactate oxidase (n(LOx) as a multifunctional activator to boost T cell immunity and the efficacy of cancer immunotherapy. To accomplish this, we evaluated the effectiveness of n(LOx) in removing lactate produced by cancer cells and activating effector T cells. The efficacy of n(LOx) in recruiting circulating T cells and activating T cells in tumors was examined in a murine breast cancer model (4T1). The therapeutic efficacy of n(LOx) in combination with aPD-L1 therapy was evaluated in a murine melanoma model and a humanized model of triple-negative breast cancer. Tumor growth measurement, survival curve analysis, RNA sequencing, and immune cell population analysis were performed to examine the effect of n(LOx). All animal experiments were performed in accordance with the protocols approved by the UCLA Chancellor’s Animal Research Committee (ARC). For each experiment, age-matched animals were randomly assigned to different groups. The determination of sample sizes was empirically based on published work. Investigators were blinded throughout the animal experiments, including treatment administration, tumor size measurement, flow cytometry staining analysis, and histological analysis. All details on sample size, statistical analysis, and experimental design are provided in data files S1 and S2, as well as in the figures and legends.
Materials, cells, and animals
Lactate oxidase (LOx) was purchased from Toyobo Inc. Poly (ethylene glycol) methyl ether acrylate (PEGMA, average Mn = 480), N,N’-methylenebisacrylamide (BIS), N-(3-aminopropyl) methacrylamide (APm), ammonium persulfate (APS), N,N,N’,N’-tetramethylethylenediamine (TEMED), sodium L-lactate, catalase (CAT) from bovine liver, L-lactate dehydrogenase (LDH), horseradish peroxidase (HRP), 3,3’,5,5’-tetramethylbenzidine (TMB), and agarose were purchased from Sigma Aldrich. Q-Sepharose Fast Flow resin was purchased from GE Healthcare. Phosphate buffered saline (PBS/10x), 2’,7’-dichlorodihydrofluorescein diacetate (H2DCFDA), 6-chloromethyl (CM)-H2DCFDA, and Dylight 755 NHS ester were purchased from Fisher Scientific. Ultra-LEAF anti-mouse PD-L1 (B7-H1) antibody was purchased from BioLegend. Durvalumab (anti-human PD-L1 antibody) was purchased from UCLA Health. CellTiter-Blue cell viability assay kit was purchased from Promega Corporation. Amplex Red reagent (10-acetyl-3,7-dihydroxyphenoxazine) was purchased from Cayman Chemical. Dulbecco’s Modified Eagle Medium (DMEM), Eagle’s Minimum Essential Medium (EMEM), Roswell Park Memorial Institute Medium 1640 (RPMI-1640), fetal bovine serum (FBS), penicillin/streptomycin (P/S), and all the other cell culture reagents were obtained from Fisher Scientific unless otherwise specified. Matrigel membrane matrix was purchased from Corning. All the antibodies are listed in the table S2. 4T1, B16F10, MDA-MB-231, HeLa, and U87 cells were purchased from American Type Culture Collection (ATCC). HOK-16B cells were a gift from Prof. Cun-yu Wang’s lab at UCLA. Luciferase-expressing 4T1 (4T1-Luc) cells were prepared through lentiviral transfection at UCLA core facility. PBMC were purchased from the UCLA/CFAR Virology Core Laboratory. All animals were treated in accordance with the Guide for Care and Use of Laboratory Animals under the protocol approved by the UCLA Chancellor’s ARC. Female BALB/c mice and male C56BL/6J mice were purchased from Jackson Laboratory. Female NOD/SCID/IL-2Rγnull (NSG) mice were purchased from Radiation Oncology Animal Core at UCLA. All animals were maintained at a pathogen-free facility at UCLA.
Instruments
Transmission electron microscopy (TEM) images were acquired with a 120 kV T12 cryo-electron microscope (FEI). Dynamic light scattering data were performed on a Zetasizer Nano ZS instrument (Malvern). Fluorescence intensity of different reagents was measured with an Infinite M200 Pro microplate reader (Tecan). UV-VIS absorption was measured with a OneC Microvolume Nanodrop (Thermo Scientific). Fluorescence imaging of cells and tissues was imaged with a Leica SP8 fluorescent microscope or a Leica TCS-SP8-SMD confocal microscope. Tissue sections with immunohistochemistry (IHC) staining were imaged with an Aperio ScanScope AT microscope. Optical imaging of live animals or organ samples was acquired with an IVIS Lumina II Imaging System (PerkinElmer) and analyzed with Living Image (PerkinElmer). The flow cytometry analysis was performed on the Fortessa (BD Biosciences) or Attune NxT flow cytometer (Thermo Fisher Scientific).
Cell culture
4T1 and 4T1-Luc cells were cultured in RPMI-1640, supplemented with 10% FBS and 1% P/S. B16F10 and MDA-MB-231 cells were cultured in DMEM, supplemented with 10% FBS and 1% P/S. HeLa and U87 cells were cultured in IMDM, supplemented with 10% FBS and 1% P/S. PBMC were cultured in RPMI-1640, supplemented with 10% FBS and 1% P/S. HOK-16B cells were cultured in Gibco Defined Keratinocyte-SFM media supplemented with defined keratinocyte-SFM growth supplement (1 mL per 500 mL media).
Synthesis and characterization of LOx nanocapsules (n(LOx))
LOx was dissolved in 1x PBS at a concentration of about 5 mg/mL and dialyzed overnight at 4 °C to remove the residual inorganic salts. The concentration of LOx was measured by its absorption at 450 nm. Subsequently, PEGMA (16000:1, n/n, PEGMA:LOx), APm (800:1, n/n, APm:LOx), and BIS (1600:1, n/n, BIS:LOx) were added to the LOx solution. The in situ polymerization was initiated with APS (800:1, n/n, APS:LOx) and TEMED (2:1, w/w, TEMED:APS). The reaction mixture was kept at 4 °C for 2 hours and dialyzed in HEPES buffer (25 mM, pH 7.5) overnight to remove the unreacted molecules. n(LOx) was purified with an anion exchange column (Q-Sepharose) using HEPES buffer (25 mM, pH 7.5) as the elution buffer. The purified n(LOx) samples were concentrated with Amicon Centrifugal Filter Units (MWCO, 30kDa) and the final concentration of n(LOx) was determined by its absorption at 450 nm. TEM, SDS page, and DLS were used to confirm the synthesis of n(LOx).
Enzyme kinetics assay
The activity of LOx samples was measured by the generation of hydrogen peroxide (H2O2) during the oxidation of lactate. The concentration of H2O2 was assessed by the color change of TMB in the presence of H2O2 and HRP. Briefly, sodium L-lactate (4.5 mg/mL), TMB (1.0 mg/mL), and HRP (0.005 mg/mL) were mixed in PBS. LOx samples (0.001 mg/mL) were then added to the reaction mixture to initiate the reaction. The absorbance at 650 nm was recorded continuously with a UV-VIS spectrophotometer (Beckman), and the linear portion of the absorption curve was used to calculate the reaction rate.
To measure the Km and Vmax of LOx samples, various concentrations of sodium L-lactate (0, 0.04882813, 0.09765625, 0.1953125, 0.390625, 0.78125, 1.5625, 3.125, 6.25, 12.5, and 25 mM) were added to the reaction mixture of TMB (1.0 mg/mL) and HRP (0.005 mg/mL). Subsequently, LOx samples (0.001 mg/ml) were added, and the absorbance of the reaction mixture at 650 nm was monitored for 2 minutes to derive the kinetics with the Michaelis-Menten equation.
Measurement of lactate removal by LOx samples in the cell culture media
4T1, B16F10, MDA-MB-231, HeLa, and U87 cells were seeded in 96-well plates at 5,000 cells/well. The culture media of these cells were collected daily for 5 days. The concentration of lactate was determined using a standard assay with LDH, as described in the Supplementary Materials. To measure the lactate removal by LOx samples, MDA-MB-231 cells were seeded in 96-well plates at 5,000 cells/well. Three days after seeding the cells, LOx samples (10 μg/mL) were added to the media. Samples from the mixture were collected every 20 minutes for 1 hour and the concentration of lactate was determined.
Detection of H2O2
H2O2 generation in the cell culture media was detected by H2DCFDA, which can be converted to the highly fluorescent 2’,7’-dichlorofluorescein (DCF) upon oxidation by H2O2. 4T1, B16F10, MDA-MB-231, HeLa, and U87 cells were seeded at 5,000 cells/well and cultured for 3 days. H2DCFDA (5 μM) was added to the culture media and incubated with the cells for 30 minutes before adding LOx samples at different concentrations (0, 0.25, 0.5, 1, and 2 μg/mL). The fluorescent signals of DCF (Ex. = 485 nm, Em. = 535 nm) at 30 minutes were measured with a plate reader.
To investigate the intracellular H2O2 generation in 3D tumor spheroids (Supplementary Materials), MDA-MB-231 tumor spheroids were prepared as indicated above. LOx samples were added to the culture media at 0.5 μg/mL. Four hours after the incubation, CM-H2DCFDA (5 μM) was added to the culture media and incubated with the spheroids for 30 minutes. The spheroids were then washed with PBS and counterstained with DAPI. The 3D spheroids were observed under a fluorescent microscope.
PBMC studies
To analyze the population of Treg cells, PBMC were first stimulated using T cell activation/expansion kit (BioLegend). PBMC were then labeled with CFSE cell division tracker (BioLegend) and cultured in low glucose RPMI-1640 (300 mg/L) media. PBMC were seeded in a 6-well plate at 2×106 cells/well, and IL-2 (100 IU/mL) was added into the cell culture media. Lactic acid (20 mM) was added to the culture media before the addition of LOx samples at 0.05 μg/mL. To remove lactate without producing H2O2, CAT (5 μg/mL) was added simultaneously with LOx samples. The cells were incubated overnight the population of Treg cells was analyzed with flow cytometry. The flow cytometry gating strategies are summarized in fig. S16.
To analyze the IFN-γ and IL-2 production by T cells, MDA-MB-231 cells were seeded in a 6-well plate at 1×105 cells/well and cultured for 3 days before adding LOx samples (2 μg/mL). For the H2O2 group, H2O2 was added to MDA-MB-231 cells at a final concentration of 250 μM. The cells were incubated with LOx samples for 24 hours, followed by adding 2×106 PBMC in each well. PBMC were then incubated with the LOx-treated MDA-MB-231 cells for 6 hours. The T cell population and IFN-γ/IL-2 production were then analyzed with flow cytometry.
4T1 mouse model
4T1 cells (5×105 cells) in RPMI-1640/Matrigel (1:1, v/v) were subcutaneously injected into the second mammary gland of female BALB/c mice. Seven days after tumor injection, 18 mice were randomly divided into 3 groups (n=6) and treated with PBS, native LOx (0.5 mg/kg), or n(LOx) (0.5 mg/kg) intratumorally every 3 days at day 7, 10, 13, and 16 after tumor engraftment. At day 25 after tumor engraftment, tumor tissues were collected for histological analysis. Through the study, the body weight of all animals was monitored. For imaging purposes, 4T1-Luc cells (5×105 cells) in RPMI-1640/Matrigel (1:1, v/v) were used, and the details are described in the Supplementary Materials.
To detect the concentrations of lactate, pyruvate, and H2O2, tumor samples were immersed in cold PBS (100 mg tissue per 100 μL PBS) and homogenized with a Bead Mill 24 homogenizer. The homogenate was centrifuged at 20,000 g for 15 minutes, and the supernatant was collected. To measure the abundance of H2O2 in tumors, Amplex Red reagent was used as an indicator, and the supernatant was diluted by 10-fold. The reaction mixture was prepared in a 96-well plate with 20 μL supernatant and 80 μL Amplex Red solution (50 μM Amplex Red + 0.1 U/mL HRP). After 30 minute incubation, the fluorescent signals (Ex. = 530 nm, Em. = 590 nm) were analyzed with a plate reader. The concentrations of pyruvate in the supernatant were measured with a pyruvate assay kit (Cayman Chemical). To measure the liver and renal functions after the treatment of native LOx or n(LOx), about 200 μL serum samples were collected from each animal and sent to IDEXX Bioresearch for screening (test case # 50627).
B16F10 mouse model
B16F10 cells (5×105 cells) in DMEM/Matrigel (1:1, v/v) were subcutaneously injected into the right flank of male C57BL/6J mice. Seven days after tumor injection, mice were randomly divided into 5 groups (n=6 to 8 mice per group). Mice in the native LOx or n(LOx) group were intratumorally injected with native LOx (0.5 mg/kg) or n(LOx) (0.5 mg/kg), respectively, at days 7, 9, and 11. Mice in the aPD-L1 or n(LOx)/aPD-L1 group received intraperitoneal injections of aPD-L1 (100 μg/mouse) twice per week starting on day 11. Tumor growth was measured with a digital caliper, and the tumor volume (mm3) was calculated as (length x width2)/2. The animals were euthanized and tumor samples were collected when the tumor volume reached about 2000 mm3 or at day 30. Single-cell suspensions were then obtained from the tumor tissues and the tumor-infiltrating lymphocytes (TILs) were analyzed with flow cytometry. To analyze the TILs at an earlier time point (day 14), mice were randomly divided into 4 groups (n=6). Mice in the n(LOx) group were intratumorally injected with native LOx (0.5 mg/kg) or n(LOx) (0.5 mg/kg), respectively, at day 7, 9, and 11. Mice in the aPD-L1 or n(LOx)/aPD-L1 group received the intraperitoneal injection of aPD-L1 (100 μg/mouse) at day 11. All the mice were euthanized at day 14 and the TILs were analyzed with flow cytometry. To engraft the secondary tumors, B16F10 cells (5×105 cells) in DMEM/Matrigel (1:1, v/v) were subcutaneously injected into the right flank of male C57BL/6J mice. Mice in the n(LOx) group (n=6) were intratumorally injected with n(LOx) (0.5 mg/kg) at day 7 and 9. At day 10, B16F10 cells (5×105 cells) in DMEM/Matrigel (1:1, v/v) were subcutaneously injected into the left flank of male C57BL/6J mice. The animals were euthanized at day 23 and the TILs were analyzed with flow cytometry.
To deplete CD4+ and CD8+ T cells, mice (n=6) received intraperitoneal injections of 150 μg anti-mouse CD4 antibody (BioLegend, GK1.5) or anti-mouse CD8a antibody (BioLegend, 53–6.7) twice per week starting from one week before tumor engraftment. The depletion of T cells was verified with flow cytometry analysis of splenocytes. B16F10 cells (5×105 cells) in DMEM/Matrigel (1:1, v/v) were subcutaneously injected into the right flank of male C57BL/6J mice. <ice received intratumoral injections of n(LOx) (0.5 mg/kg) at day 7, 9, and 11 after tumor injection. The tumor volume was measured throughout the experiment.
Single-cell RNA sequencing and data analysis
C57BL/6J mice bearing B16F10 tumors received intratumoral injections of n(LOx) (0.5 mg/kg) at day 7 and 9 after tumor engraftment. At day 13, the mice were euthanized and tumor tissues were harvested. Single cell suspensions were obtained by passing the tumor tissues through a MACS SmartStrainer (30 μm). The red blood cells (RBC) were lysed with RBC lysis buffer (BioLegend) and dead cells were removed with the Dead Cell Removal Kit (Miltenyi Biotec). The cells were then counted and processed according to the protocol of the Chromium Next GEM Single Cell 5’ kit (10x Genomics, product 1000265). The library preparation were completed using the 10x library construction kit (product 1000215). After library preparation, the samples were pooled together and sequenced in one batch on a NovaSeq (Illumina) at UCLA Technology Center for Genomics & Bioinformatics.
The raw data were first preprocessed by 10x’s Cellranger pipeline. After the preprocessing, the single-cell 10x count tables were imported into Seurat in R for the following processing. The gene counts were log-transformed and principal component analysis with 30 dimensions was used to dimensionally reduce the gene count data. Graphically based clustering and manual annotation were applied to identify the different cell types. To identify the genes that were significantly differentially expressed between control and n(LOx) treatment, we used Wilcoxon rank-sum tests with the Benjamini–Hochberg adjusted P-values below 0.05 and absolute values of the log2 of the fold change between the average expression of the raw molecular counts above 0.25. The biological processes GO terms of the significantly differentially expressed genes were calculated through the R package ClusterProfiler with a false discovery rate (FDR) < 0.05.
MDA-MB-231 humanized mouse model
Currently available humanized mouse models include PBMC-humanized, SRC (scid-repopulating cell)-humanized, and BLT (bone marrow, liver, and thymus)-humanized mice, summarized in table S1. Among different humanized mouse models, the PBMC humanized model stands out as the most widely adopted choice for investigating immune cell activation and anti-tumor responses in the context of immunotherapy, primarily due to its ability to effectively reconstitute high frequencies of mature human T cells. Thus, the PBMC humanized model was selected to evaluate the therapeutic efficacy of n(LOx) and the combination treatment with human-specific anti-PD-L1 antibody. MDA-MB-231 cells (1.5×106 cells) in DMEM/Matrigel (1:1, v/v) were subcutaneously injected into the second mammary gland of female NSG mice. Ten days after tumor injection, PBMC (2×106 cells) were intravenously injected into the mice. Two weeks after PBMC injections (day 0), mice were randomly divided into 6 groups (n=7 to 8 mice per group). Mice in the n(LOx) group received intratumoral injections of n(LOx) (0.5 mg/kg) at day 0 and 2. Mice in the n(LOx)/CAT group were injected with n(LOx) (0.5 mg/kg) and CAT (1 mg/kg) to remove the H2O2. Mice receiving aPD-L1 were given durvalumab (100 μg/mouse) intraperitoneally twice per week starting on day 2. Tumor growth was measured with a digital caliper, and the tumor volume (mm3) was calculated as (length x width2)/2. The animals were euthanized, and tumor samples were collected when the tumor volume reached about 2000 mm3 or at day 24. Single cell suspensions were then obtained from the tumor tissues and T cells were analyzed by flow cytometry.
Statistical Analysis
All raw, individual-level data are presented in data file S1. Measurements in this study were obtained from distinct samples. GraphPad Prism 8 was used to perform statistical analysis. Graphs include means and error bars, with the latter representing s.d. or s.e.m. as indicated. Statistical significance was calculated by one-way ANOVA with Tukey’s multiple comparison test or unpaired t-test. The survival curve was analyzed by Mantel-Cox test.
Data and materials availability:
All data associated with this study are in the paper or supplementary materials. scRNA-seq data are deposited in GEO DataSets and are accessible through the accession codes GSM7246660, GSM7246661, GSM7246662, and GSM7246663.
Supplementary Material
Acknowledgements
The authors thank the technical support from the UCLA CFAR Virology Core (P30AI28697) and UCLA TCGB research center. The authors thank the support from UCLA AIDS Institute, the James B. Pendleton Charitable Trust, and the McCarthy Family Foundation.
Funding:
This work was supported by the UCLA ENN Center for Nanomedicine and Energy Conversion (to Y.L.), National Institutes of Health grant R01CA253215 and R56AI163386 (to J.W.), and National Center for Advancing Translational Science of the NIH under the UCLA CTSI grant UL1TR001881 (to J.W.).
Competing interests:
The authors of this manuscript have the following competing interests: I.C. has a financial interest in CSL Behring. J.W. has a financial interest in Vivibaba and the regents have licensed intellectual property invented by J.W. to Vivibaba. Y.L. has a financial interest in Vivibaba and the regents have licensed intellectual property invented by Y.L. to Vivibaba. No funding was provided by these companies to support this work. D.X., X.L., L.W., J.G., and Y.L. were at UCLA when the study was performed. D.X. is now a postdoctoral fellow at Department of Biochemistry, Stanford. X.L. is now a fellow at Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, China. L.W. is now an associate professor at Binzhou Medical University, China. J.G. is now a fellow in College of Materials Sciences and Engineering, Beijing University of Chemical Technology, China. Y.L. is now a fellow in Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, China.
Data and materials availability:
All data associated with this study are in the paper or supplementary materials. scRNA-seq data are deposited in GEO DataSets and are accessible through the accession codes GSM7246660, GSM7246661, GSM7246662, and GSM7246663.
References and Notes
- 1.Irvine DJ, Dane EL, Enhancing cancer immunotherapy with nanomedicine., Nat. Rev. Immunol. 20, 321–334 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Waldman AD, Fritz JM, Lenardo MJ, A guide to cancer immunotherapy: from T cell basic science to clinical practice., Nat. Rev. Immunol. 20, 651–668 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Garris CS, Arlauckas SP, Kohler RH, Trefny MP, Garren S, Piot C, Engblom C, Pfirschke C, Siwicki M, Gungabeesoon J, Freeman GJ, Warren SE, Ong S, Browning E, Twitty CG, Pierce RH, Le MH, Algazi AP, Daud AI, Pai SI, Zippelius A, Weissleder R, Pittet MJ, Successful Anti-PD-1 Cancer Immunotherapy Requires T Cell-Dendritic Cell Crosstalk Involving the Cytokines IFN-γ and IL-12., Immunity 49, 1148–1161.e7 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Cascone T, McKenzie JA, Mbofung RM, Punt S, Wang Z, Xu C, Williams LJ, Wang Z, Bristow CA, Carugo A, Peoples MD, Li L, Karpinets T, Huang L, Malu S, Creasy C, Leahey SE, Chen J, Chen Y, Pelicano H, Bernatchez C, Gopal YNV, Heffernan TP, Hu J, Wang J, Amaria RN, Garraway LA, Huang P, Yang P, Wistuba II, Woodman SE, Roszik J, Davis RE, Davies MA, Heymach JV, Hwu P, Peng W, Increased tumor glycolysis characterizes immune resistance to adoptive T cell therapy., Cell Metab. 27, 977–987.e4 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Goldman B, DeFrancesco L, The cancer vaccine roller coaster., Nat. Biotechnol. 27, 129–139 (2009). [DOI] [PubMed] [Google Scholar]
- 6.Geoerger B, Kang HJ, Yalon-Oren M, Marshall LV, Vezina C, Pappo A, Laetsch TW, Petrilli AS, Ebinger M, Toporski J, Glade-Bender J, Nicholls W, Fox E, DuBois SG, Macy ME, Cohn SL, Pathiraja K, Diede SJ, Ebbinghaus S, Pinto N, Pembrolizumab in paediatric patients with advanced melanoma or a PD-L1-positive, advanced, relapsed, or refractory solid tumour or lymphoma (KEYNOTE-051): interim analysis of an open-label, single-arm, phase 1–2 trial., Lancet Oncol. 21, 121–133 (2020). [DOI] [PubMed] [Google Scholar]
- 7.Qu J, Mei Q, Chen L, Zhou J, Chimeric antigen receptor (CAR)-T-cell therapy in non-small-cell lung cancer (NSCLC): current status and future perspectives., Cancer Immunol. Immunother. 70, 619–631 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Drake CG, Prostate cancer as a model for tumour immunotherapy., Nat. Rev. Immunol. 10, 580–593 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, Coussens LM, Gabrilovich DI, Ostrand-Rosenberg S, Hedrick CC, Vonderheide RH, Pittet MJ, Jain RK, Zou W, Howcroft TK, Woodhouse EC, Weinberg RA, Krummel MF, Understanding the tumor immune microenvironment (TIME) for effective therapy., Nat. Med. 24, 541–550 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Murciano-Goroff YR, Warner AB, Wolchok JD, The future of cancer immunotherapy: microenvironment-targeting combinations., Cell Res. 30, 507–519 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Vander Heiden MG, Cantley LC, Thompson CB, Understanding the Warburg effect: the metabolic requirements of cell proliferation., Science 324, 1029–1033 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Romero-Garcia S, Moreno-Altamirano MMB, Prado-Garcia H, Sánchez-García FJ, Lactate contribution to the tumor microenvironment: mechanisms, effects on immune cells and therapeutic relevance., Front. Immunol. 7, 52 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Quinn WJ, Jiao J, TeSlaa T, Stadanlick J, Wang Z, Wang L, Akimova T, Angelin A, Schäfer PM, Cully MD, Perry C, Kopinski PK, Guo L, Blair IA, Ghanem LR, Leibowitz MS, Hancock WW, Moon EK, Levine MH, Eruslanov EB, Wallace DC, Baur JA, Beier UH, Lactate limits T cell proliferation via the NAD(H) redox state., Cell Rep. 33, 108500 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Certo M, Tsai C-H, Pucino V, Ho P-C, Mauro C, Lactate modulation of immune responses in inflammatory versus tumour microenvironments., Nat. Rev. Immunol. 21, 151–161 (2021). [DOI] [PubMed] [Google Scholar]
- 15.Hirschhaeuser F, Sattler UGA, Mueller-Klieser W, Lactate: a metabolic key player in cancer., Cancer Res. 71, 6921–6925 (2011). [DOI] [PubMed] [Google Scholar]
- 16.Watson MJ, Vignali PDA, Mullett SJ, Overacre-Delgoffe AE, Peralta RM, Grebinoski S, Menk AV, Rittenhouse NL, DePeaux K, Whetstone RD, Vignali DAA, Hand TW, Poholek AC, Morrison BM, Rothstein JD, Wendell SG, Delgoffe GM, Metabolic support of tumour-infiltrating regulatory T cells by lactic acid., Nature 591, 645–651 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Pérez-Tomás R, Pérez-Guillén I, Lactate in the tumor microenvironment: an essential molecule in cancer progression and treatment., Cancers (Basel) 12 (2020), doi: 10.3390/cancers12113244. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Feng Y, Xiong Y, Qiao T, Li X, Jia L, Han Y, Lactate dehydrogenase A: A key player in carcinogenesis and potential target in cancer therapy., Cancer Med. 7, 6124–6136 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Niethammer P, Grabher C, Look AT, Mitchison TJ, A tissue-scale gradient of hydrogen peroxide mediates rapid wound detection in zebrafish., Nature 459, 996–999 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Schmidt KN, Amstad P, Cerutti P, Baeuerle PA, The roles of hydrogen peroxide and superoxide as messengers in the activation of transcription factor NF-kappa B., Chem. Biol. 2, 13–22 (1995). [DOI] [PubMed] [Google Scholar]
- 21.Lingappan K, NF-κB in Oxidative Stress., Curr. Opin. Toxicol. 7, 81–86 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Reth M, Hydrogen peroxide as second messenger in lymphocyte activation., Nat. Immunol. 3, 1129–1134 (2002). [DOI] [PubMed] [Google Scholar]
- 23.Los M, Dröge W, Stricker K, Baeuerle PA, Schulze-Osthoff K, Hydrogen peroxide as a potent activator of T lymphocyte functions., Eur. J. Immunol. 25, 159–165 (1995). [DOI] [PubMed] [Google Scholar]
- 24.Oh H, Ghosh S, NF-κB: roles and regulation in different CD4(+) T-cell subsets., Immunol. Rev. 252, 41–51 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Wen J, Wu D, Qin M, Liu C, Wang L, Xu D, Vinters HV, Liu Y, Kranz E, Guan X, Sun G, Sun X, Lee Y, Martinez-Maza O, Widney D, Lu Y, Chen ISY, Kamata M, Sustained delivery and molecular targeting of a therapeutic monoclonal antibody to metastases in the central nervous system of mice., Nat. Biomed. Eng. 3, 706–716 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Xu D, Wu D, Qin M, Nih LR, Liu C, Cao Z, Ren J, Chen X, He Z, Yu W, Guan J, Duan S, Liu F, Liu X, Li J, Harley D, Xu B, Hou L, Chen ISY, Wen J, Chen W, Pourtaheri S, Lu Y, Efficient delivery of nerve growth factors to the central nervous system for neural regeneration., Adv. Mater. 31, e1900727 (2019). [DOI] [PubMed] [Google Scholar]
- 27.Liu Y, Du J, Yan M, Lau MY, Hu J, Han H, Yang OO, Liang S, Wei W, Wang H, Li J, Zhu X, Shi L, Chen W, Ji C, Lu Y, Biomimetic enzyme nanocomplexes and their use as antidotes and preventive measures for alcohol intoxication., Nat. Nanotechnol. 8, 187–192 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.de la Cruz-López KG, Castro-Muñoz LJ, Reyes-Hernández DO, García-Carrancá A, Manzo-Merino J, Lactate in the regulation of tumor microenvironment and therapeutic approaches., Front. Oncol 9, 1143 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Quintero-Fabián S, Arreola R, Becerril-Villanueva E, Torres-Romero JC, Arana-Argáez V, Lara-Riegos J, Ramírez-Camacho MA, Alvarez-Sánchez ME, Role of matrix metalloproteinases in angiogenesis and cancer., Front. Oncol. 9, 1370 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Singh M, Overwijk WW, Intratumoral immunotherapy for melanoma., Cancer Immunol. Immunother. 64, 911–921 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Soliman H, Hogue D, Han H, Mooney B, Costa R, Lee MC, Niell B, Williams A, Chau A, Falcon S, Khakpour N, Weinfurtner RJ, Hoover S, Kiluk J, Rosa M, Khong H, Czerniecki B, A Phase I Trial of Talimogene Laherparepvec in Combination with Neoadjuvant Chemotherapy for the Treatment of Nonmetastatic Triple-Negative Breast Cancer., Clin. Cancer Res. 27, 1012–1018 (2021). [DOI] [PubMed] [Google Scholar]
- 32.Tchou J, Zhao Y, Levine BL, Zhang PJ, Davis MM, Melenhorst JJ, Kulikovskaya I, Brennan AL, Liu X, Lacey SF, Posey AD, Williams AD, So A, Conejo-Garcia JR, Plesa G, Young RM, McGettigan S, Campbell J, Pierce RH, Matro JM, DeMichele AM, Clark AS, Cooper LJ, Schuchter LM, Vonderheide RH, June CH, Safety and efficacy of intratumoral injections of chimeric antigen receptor (CAR) T cells in metastatic breast cancer., Cancer Immunol. Res. 5, 1152–1161 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Egelston CA, Avalos C, Tu TY, Simons DL, Jimenez G, Jung JY, Melstrom, Margolin K, Yim JH, Kruper L, Mortimer, Lee PP, Human breast tumor-infiltrating CD8+ T cells retain polyfunctionality despite PD-1 expression., Nat. Commun. 9, 4297 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Chen Q, Liang C, Sun X, Chen J, Yang Z, Zhao H, Feng L, Liu Z, H2O2-responsive liposomal nanoprobe for photoacoustic inflammation imaging and tumor theranostics via in vivo chromogenic assay., Proc Natl Acad Sci USA 114, 5343–5348 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Wang C, Wang J, Zhang X, Yu S, Wen D, Hu Q, Ye Y, Bomba H, Hu X, Liu Z, Dotti G, Gu Z, In situ formed reactive oxygen species-responsive scaffold with gemcitabine and checkpoint inhibitor for combination therapy., Sci. Transl. Med. 10 (2018), doi: 10.1126/scitranslmed.aan3682. [DOI] [PubMed] [Google Scholar]
- 36.Deng Z, Qian Y, Yu Y, Liu G, Hu J, Zhang G, Liu S, Engineering Intracellular Delivery Nanocarriers and Nanoreactors from Oxidation-Responsive Polymersomes via Synchronized Bilayer Cross-Linking and Permeabilizing Inside Live Cells., J. Am. Chem. Soc. 138, 10452–10466 (2016). [DOI] [PubMed] [Google Scholar]
- 37.Szatrowski TP, Nathan CF, Production of large amounts of hydrogen peroxide by human tumor cells., Cancer Res. 51, 794–798 (1991). [PubMed] [Google Scholar]
- 38.Topalian SL, Drake CG, Pardoll DM, Immune checkpoint blockade: a common denominator approach to cancer therapy., Cancer Cell 27, 450–461 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Cunningham CC, Chada S, Merritt JA, Tong A, Senzer N, Zhang Y, Mhashilkar A, Parker K, Vukelja S, Richards D, Hood J, Coffee K, Nemunaitis J, Clinical and local biological effects of an intratumoral injection of mda-7 (IL24; INGN 241) in patients with advanced carcinoma: a phase I study., Mol. Ther. 11, 149–159 (2005). [DOI] [PubMed] [Google Scholar]
- 40.Patgiri A, Skinner OS, Miyazaki Y, Schleifer G, Marutani E, Shah H, Sharma R, Goodman RP, To T-L, Robert Bao X, Ichinose F, Zapol WM, Mootha VK, An engineered enzyme that targets circulating lactate to alleviate intracellular NADH:NAD+ imbalance., Nat. Biotechnol. 38, 309–313 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Ajith A, Mulloy LL, Musa MA, Bravo-Egana V, Horuzsko DD, Gani I, Horuzsko A, Humanized mouse model as a novel approach in the assessment of human allogeneic responses in organ transplantation., Front. Immunol. 12, 687715 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Allen TM, Brehm MA, Bridges S, Ferguson S, Kumar P, Mirochnitchenko O, Palucka K, Pelanda R, Sanders-Beer B, Shultz LD, Su L, PrabhuDas M, Humanized immune system mouse models: progress, challenges and opportunities., Nat. Immunol. 20, 770–774 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Yin L, Wang X-J, Chen D-X, Liu X-N, Wang X-J, Humanized mouse model: a review on preclinical applications for cancer immunotherapy., Am. J. Cancer Res. 10, 4568–4584 (2020). [PMC free article] [PubMed] [Google Scholar]
- 44.Morillon YM, Sabzevari A, Schlom J, Greiner JW, The Development of Next-generation PBMC Humanized Mice for Preclinical Investigation of Cancer Immunotherapeutic Agents., Anticancer Res. 40, 5329–5341 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Chang D-K, Moniz RJ, Xu Z, Sun J, Signoretti S, Zhu Q, Marasco WA, Human anti-CAIX antibodies mediate immune cell inhibition of renal cell carcinoma in vitro and in a humanized mouse model in vivo., Mol. Cancer 14, 119 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Fisher TS, Kamperschroer C, Oliphant T, Love VA, Lira PD, Doyonnas R, Bergqvist S, Baxi SM, Rohner A, Shen AC, Huang C, Sokolowski SA, Sharp LL, Targeting of 4–1BB by monoclonal antibody PF-05082566 enhances T-cell function and promotes anti-tumor activity., Cancer Immunol. Immunother. 61, 1721–1733 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Sharma P, Semwal V, Gupta BD, Fiber optic surface plasmon resonance based lactate sensor using co-immobilization of lactate dehydrogenase and NAD+, Optical Fiber Technology 49, 22–27 (2019). [Google Scholar]
- 48.Badea MA, Balas M, Hermenean A, Ciceu A, Herman H, Ionita D, Dinischiotu A, Influence of Matrigel on Single- and Multiple-Spheroid Cultures in Breast Cancer Research., SLAS Discov. 24, 563–578 (2019). [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data associated with this study are in the paper or supplementary materials. scRNA-seq data are deposited in GEO DataSets and are accessible through the accession codes GSM7246660, GSM7246661, GSM7246662, and GSM7246663.





