PLT012, a humanized CD36-blocking antibody, is effective for unleashing anti-tumor immunity against liver cancer and liver metastasis

Abstract

Tumor cells develop various strategies to evade immune surveillance, one of which involves altering the metabolic state of the tumor microenvironment (TME). In response to metabolic stress in the TME, several tumor-infiltrating immune subsets upregulate CD36 to take up lipids. This leads to impaired anti-tumor immunity, as intratumoral regulatory T cells exhibit increased survival and suppressive activity, while CD8⁺ T cells become more susceptible to ferroptosis and exhaustion. Here, we develop a humanized anti-CD36 IgG4 antibody, PLT012, against the lipid-binding domain of CD36 with excellent safety and favorable pharmacokinetic features in mice and cynomolgus monkey. PLT012 alone or in combination with PD-L1 blockade or standard-of-care immunotherapy results in robust anti-tumor immunity in both immunotherapy-sensitive and -resistant hepatocellular carcinomas (HCCs). Notably, PLT012 also reprograms immune landscape of human HCC ex vivo. Our findings provide proof-of-concept evidence that PLT012 reprograms anti-tumor immunity in HCC, positioning it as a first-in-class immunotherapy targeting CD36.

Introduction

Metabolic transformations in cancer cells are controlled by oncogenic driver mutations and immunometabolic editing, (1,2) and are associated with tumor cell survival in the tumor microenvironment (TME). Metabolic reprogramming has been shown to modulate tumor growth, anti-apoptotic mechanisms, and immune evasion (3,4). Interestingly, lipid metabolism reprogramming is an emerging hallmark of cancer. The de novo lipogenesis, together with the recruitment of adipocytes and adipocyte-like fibroblasts in the TME, can perturb lipid composition and abundance (5–7), which in turn facilitates cancer progression and immune evasion (1,2). Lipid enrichment in combination with other metabolic challenges, including glucose deprivation and hypoxia, impose considerable metabolic stress on stromal cells within the TME. Tumor-infiltrating lymphocytes (TILs) failing to adapt to metabolic hurdles decrease their survival rates and lose their anti-tumor capacity (8–11). However, immunosuppressive cells, including regulatory T cells (Tregs), pro-tumorigenic tumor-associated macrophages (TAMs), and myeloid-derived suppressor cells seem to employ alternative metabolic adaptation strategies to survive and impair anti-tumor immunity in the TME (4,12). Therefore, finding a way to block the ability of immunosuppressive cells to escape metabolic stress and, at the same time, to improve the adaptation responses of tumoricidal immune cells is key to reprogramming the immune state in tumors and releasing host anti-tumor responses.

CD36, a fatty acid transporter and scavenger receptor, contains two hydrophobic clefts that effectively bind long-chain fatty acids and oxidized low-density lipoprotein (oxLDL) to promote lipid uptake and satisfy the metabolic demands of rapidly proliferating cells (13,14). In response to the lipid- and lactic acid-enriched TME, intratumoral Tregs have been shown to increase CD36 expression, which in turn boosts mitochondrial functions via a peroxisome proliferator activated receptor beta/delta (PPARβ/δ)-mediated mechanism (12). By increasing mitochondrial activity, intratumoral Tregs survive in the lactate-rich TME and maintain their immunosuppressive activity. Intriguingly, it has recently been shown that CD36 expression is upregulated in metastasis-associated macrophages isolated from metastatic liver tumors. This increase in CD36 expression allows macrophages to use lipids from tumor cells and stimulate tumor-promoting activities (15). In contrast, a high CD36 expression in tumor-infiltrating CD8⁺ T cells leads to the accumulation of oxidized lipids and subsequent T cell dysfunction and ferroptosis (10,11). Similarly, NK cells that show increased CD36 expression in lipid-enriched contexts have impaired in vitro tumoricidal activity (16); also, genetic ablation of CD36 in NK cells to prevent lipid uptake helps maintain NK effector functions in lipid-rich environments (17,18). These studies suggest that blocking CD36-mediated lipid uptake could be a promising approach to restore immune responses in tumors. Interfering with lipid uptake would simultaneously destroy intratumoral Tregs and macrophages and restore the survival and tumoricidal activities of tumor-infiltrating CD8⁺ T cells and NK cells.

Targeting CD36-mediated metabolic adaptations may elicit anti-tumor responses while preserving immune homeostasis, since CD36-mediated immune regulation is selectively employed by tumor-infiltrating immune cells rather than by their peripheral counterparts. Despite the fact that CD36 absence can be observed in 5-8% of Asian and African populations without any notable morbidity and that physiological parameters of CD36-deficient mice are within the norm (19,20), the broad expression profile of CD36 in the human body, including endothelial cells, cardiomyocytes, and red blood cells (21–23), poses a problem. Indeed, using antibodies against broadly expressed proteins may cause a strong antibody-dependent immune activation which may lead to severe complications. Furthermore, small molecules that inhibit CD36-mediated lipid uptake have shown unsatisfying pharmacokinetic and pharmacodynamic properties in vivo (24). It has also been reported that these molecules primarily affect lipid metabolism rather than CD36-mediated lipid transport (24). Thus, it remains a challenge to establish if we can use classic CD36-targeting approaches for cancer treatment in pre-clinical models and humans.

Here, we developed PLT012, a humanized IgG4 antibody targeting the lipid-binding pockets of CD36 with reactivity against multiple species and a superior safety profile in monkeys. We used a bioinformatic analysis to find human tumor types characterized by CD36-mediated immune regulation, and we showed that PLT012 antibody effectively increased anti-hepatocellular carcinoma (HCC) responses, including reduction of intratumoral Tregs, enhancement of CD8⁺ T cell infiltration, and improvement of cytotoxic functions in CD8⁺ T cells, in both hot and cold HCC types. Noteworthy, we also found that PLT012 can increase the abundance of progenitor-exhausted T cells, a critical cell subset responsible for the responsiveness to immune checkpoint blockade treatment in tumors. In line with this, we demonstrated that PLT012 together with the PD-L1 antibody or in combination with standard liver cancer therapies (anti-VEGF plus anti-PD-L1 agents) can elicit strong anti-tumor responses in mice with cold HCCs or in mice fed a high-fat diet which typically shows reduced responsiveness to anti-PD-L1 therapy. Additionally, we showed that PLT012 can be used to reduce colorectal liver metastases and to restore responsiveness to anti-PD-1 treatment in mice with hepatic metastasis from primary colon cancer. The mechanism of action of PLT012 was further validated in human HCC samples using an ex vivo culture platform. Our results show that PLT012 blocks CD36-mediated metabolic adaptation in Tregs and CD8⁺ TILs cells, thus leading to robust tumor growth inhibition and a shift in the nature of the TME: from immunosuppressive to immunosupportive.

Results

Generation and characterization of PLT012, a cross-reactive anti-CD36 antibody

The development of anti-human CD36 monoclonal antibodies (mAbs) that can block CD36-mediated lipid uptake, is largely hampered by : (a) the hydrophobic nature of the CD36 lipid-binding pockets, which can lead to the identification of candidates with weak and/or non-specific binding; and (b) the high frequency of false-negative results in standard antigen capture assays for candidate identification (25). To overcome these obstacles, we first screened synthetic human scFv phage display libraries to identify a primary hit antibody, followed by panning with light-chain shuffling libraries to enhance binding affinity against CD36. To further improve drug properties, two additional phage display libraries harboring CDR mutations were used for panning, resulting in an optimized CDR sequence for the lead antibody. Moreover, site-specific mutagenesis was performed on the CDR, guided by AI modeling and CamSol predictions, to improve retention time, which may be associated with non-specific hydrophobic interactions. Considering that CD36 can be expressed in many cell types, we engineered antibody candidates into an IgG4 form to reduce antibody-dependent immune responses (26), and optimized the Fc domain to minimize aggregation during antibody production. By using these approaches, we identified PLT012, a full-length human monoclonal IgG4 antibody, displaying reactivity against CD36 across various species, including rodents, monkeys, and humans. Of note, due to its cross-reactivity, we could examine PLT012 biochemical properties and anti-tumor functions without using murine surrogate antibodies. Next, we evaluated whether PLT012 could compete with D2712, a commercially available antibody against the murine CD36 lipid-binding pocket. Our result showed that PLT012 can compete with D2712 for binding to CD36 (Fig. 1A), indicating that PLT012 may recognize a similar epitope and inhibit CD36-mediated uptake of fatty acids and oxLDL. Indeed, we found that PLT012 strongly inhibited the binding (IC₅₀: 1.798nM) and uptake (IC₅₀: 1.357nM) of fluorochrome-labeled oxLDL in F293 cells that overexpress human CD36 (Fig. 1B). In addition, we confirmed that PLT012 treatment significantly repressed oxLDL uptake compared to control IgG in primary tumor-infiltrating immune cells, including myeloid cells, CD3⁺ T lymphocytes, and intratumoral Tregs isolated from MC38 tumor-bearing mice (Fig. 1C). Then, we examined whether PLT012 treatment could suppress tumor growth in syngeneic mouse melanoma and colorectal cancer models. In agreement with previous reports showing that CD36-deficient mice displayed reduced tumor growth (10–12), PLT012 promisingly exhibited anti-tumor responses in both Yumm1.7 melanoma and MC38 colorectal cancer models (Supplementary Fig. S1A and S1B). Altogether, our results show that PLT012, an anti-CD36 monoclonal antibody with reactivity against multiple species, can effectively inhibit CD36-mediated lipid uptake and induce effective anti-tumor responses.

Figure 1. Assessment of the binding epitopes of PLT012 on CD36 and the inhibition of fatty acid uptake for PLT012.

A, CD36-expressing F293 cells were incubated with PLT012 at concentrations ranging from 0.002 to 10 μg/mL, followed by FACS analysis using commercial anti-CD36 antibodies, D2712. B, Human CD36-expressing F293 cells were pre-incubated with PLT012 at concentrations ranging from 0.01 to 30 μg/mL, followed by oxLDL-Dil staining for oxLDL binding with an IC50 at 0.25 (Left) and uptake with an IC50 at 0.19 (Right). C, oxLDL-Dil uptake in the indicated cell populations from MC38 tumor was analyzed using flow cytometry. D, The structural analysis for in vitro-reconstituted protein complex comprising the Fab region of PLT012 and the extracellular domain (ECD) of CD36 using Cryo-electron microscopy (CryoEM). Two interaction domains on the CD36 ECD for PLT012 binding were identified, corresponding to amino acid residues 153 to 160 and 191 to 197, highlighted in red. E, Binding activity was assessed in F293 cells transiently expressing FLAG-tagged CD36 with specific point mutations in Domain 1 (mD1) and/or Domain 2 (mD2) in the presence of varying concentrations of PLT012 through flow cytometry. Data are representative outcomes from three independent experiments and are expressed as mean ± s.d. Statistical analysis was conducted using two-tailed, unpaired Student’s t-test.

Identification of PLT012 binding sites on CD36

PLT012 exhibited affinity for CD36 epitopes that are analogous to those recognized by the commercially available antibody against the murine CD36 and also possessed the ability to inhibit CD36-mediated lipid uptake; however, the specific regions involved in CD36-PLT012 interaction remain unknown. To characterize the binding properties of PLT012 and to examine whether the interaction interface is within the lipid-binding pocket of CD36, we used Cryo-Electron Microscopy (Cryo-EM). Our Cryo-EM analysis identified two interaction domains of PLT012 on the extracellular domain (ECD) of CD36: Domain 1, located within amino acids (aa) 153-160, and Domain 2, located in a region between aa 191-197 (Fig. 1D). These domains are positioned within the fatty acid- and oxLDL-binding regions of CD36, as confirmed by prior domain mapping studies (27). To investigate the impact of these domains in the PLT012-CD36 interaction, we first introduced point mutations to alter the charge and hydrophobicity of CD36 and then measured the binding affinity with PLT012. Flow cytometry-based binding assays showed that point mutations in Domain 1 reduced by almost 50% the binding of PLT012 to the ECD of CD36, while mutations in Domain 2 did not affect the interaction between the protein pair. Notably, the interaction between PLT012 and CD36 was almost completely abolished when both domains were mutated (Fig. 1E). These results suggest that PLT012 preferentially interacts with Domain 1 to limit lipid entry into the cell, while Domain 2 only provides supplementary support.

PLT012 targets immune infiltrates in hepatocellular carcinomas

CD36-mediated lipid uptake influences the immunosuppressive TME by modulating the survival and functions of intratumoral Tregs and CD8⁺ T cells (10–12). Considering this, we hypothesized that PLT012 could elicit robust anti-tumor responses in lipid-rich tumors. By using data from The Cancer Genome Atlas (TCGA), we performed an in silico analysis to examine which tumor types displayed both high expression of CD36 and an enrichment in genes relevant to fatty acid metabolism (Fig. 2A). The transcriptional profiles of the top nine tumor types retrieved from the in silico analysis were further analyzed with CIBERSORT – a deconvolution method that characterizes the cell composition of complex tissues from their gene expression profiles – to determine the relative abundance of CD8⁺ T cells and Tregs cell subsets (28). The results from the sequential analysis suggest that liver hepatocellular carcinoma (LIHC) may be a tumor type that can be sensitive to anti-CD36 antibody treatment (Fig. 2B). We did not include cholangiocarcinoma (CHOL) in our study because of the potential biases stemming from a limited sample size. To confirm whether TILs increase their CD36 expression in the context of liver cancer, we measured CD36 expression in Tregs and CD8⁺ T cells from peripheral blood mononuclear cells (PBMCs), spleens, and liver of HCC-bearing mice. To replicate the pathological progression of hepatocellular carcinomas, we induced liver cancer onset by overexpressing Myc and a constitutively active mutant of β-catenin in hepatocytes (MYC^OE/CTNNB1^N90 HCC) with a hydrodynamic injection system (Supplementary Fig. S2A). By staining single-cell suspensions with Alexa Fluor 647-conjugated PLT012, we found that both Tregs (CD45⁺CD3⁺CD4⁺FoxP3⁺) and CD8⁺ T cells (CD45⁺CD3⁺CD8⁺) isolated from HCC-bearing mice expressed higher levels of CD36 compared to their counterparts obtained from liver, spleen, or peripheral blood of normal healthy mice (Fig. 2C and D). Of note, we also observed that NK cells (CD45⁺CD3^-NK1.1⁺) in tumors have increased CD36 expression, which has been reported to impair NK cell cytotoxicity (16,17). These findings are consistent with previous studies reporting that the TME promotes CD36 expression in tumor-infiltrating immune cells (10,12). To further examine PLT012 distribution within tissues and to determine the kinetics of the treatment with PLT012, we intraperitoneally injected Alexa Fluor 647-conjugated PLT012 (10 mg per kg of body weight) into control or CTNNB1^N90/ MYC^OE HCC-bearing mice and collected samples from various organs at 4, 24, and 48 hours post-injection. We used in vivo bioluminescent imaging (IVIS) to quantify the fluorescence intensity in the samples and showed that PLT012 mainly accumulates in the liver, lung, intestine, epididymal white adipose tissue, and pancreas (Supplementary Fig. S2B and S2C). Healthy and tumor-bearing mice showed similar fluorescence intensity levels which may be attributed to the presence of adipocytes and endothelial cells, which express high levels of CD36 (5,21,29). Importantly, twenty-four hours post-injection, PLT012 signal was significantly stronger in livers from HCC-bearing mice compared to livers from normal mice, suggesting that the HCC microenvironment may facilitate the recruitment of CD36-expressing cells (Fig. 2E). To verify that PLT012 treatment can target tumor-infiltrating lymphocytes, we quantified the Alexa Fluor 647 signal in Tregs, CD8⁺ T cells, and NKs from livers of normal and HCC-bearing mice intraperitoneally treated with one dose of labeled PLT012. We observed that both Tregs and CD8⁺ T cells from HCC-bearing mice had a higher percentage of labeled cells compared with their counterparts from normal mice. Notably, these differences persisted for 48 hours following mAbs administration (Fig. 2F and 2G). In addition, NK cells from tumor-bearing mice exhibited higher levels of PLT012 signal at the 24-hour time point, but not at the 48-hour time point, compared to NK cells from normal livers (Fig. 2H). Taken together, these results reveal that HCC microenvironment could increase CD36 expression in infiltrating immune cells, including Tregs, CD8⁺ T cells, and NK cells, and that PLT012 may have the potential to restore anti-tumor immunity against HCC by targeting CD36-mediated immune regulations.

Figure 2. PLT012 displays enhanced distribution and targeting capabilities for hepatocellular carcinomas.

A and B, An in silico pan-cancer analysis of TCGA cohort revealed characteristics associated with fatty acid scores (A), as well as a correlation between tumoral populations of regulatory T cells (Tregs) and CD8+ T cells (B). C and D, Representative histogram plots (C) and quantitative results (D) for CD36 levels of indicated TILs derived from liver, blood, and spleen of normal (N) and HCC tumor-bearing (T) mice, stained by AF647-conjugated PLT012. E, In vivo distribution analysis of PLT012 in the liver from mice with or without MYC^OE/CTNNB1^N90 liver tumors were examined in IVIS 24 hours following intraperitoneal injection of one dose of AF647-conjugated PLT012 (10mg/kg, n=3/ group). Right, Representative liver fluorescence images from indicated groups. F-H, Quantitative results and Representative plots for in vivo binding of PLT012 in Tregs (F), CD8⁺ T cells (G), and NKs (H) of the liver tissues from normal and HCC tumors-bearing mice 24 and 48 hours after intraperitoneal administration of one dose of AF647-conjugated PLT012 (10mg/kg, n=4/ group). Data are representative results of two independent experiments (C-H). Data are means ± s.e.m. and analyzed by two-tailed, unpaired Student’s t-test (C-H).

PLT012 elicits strong anti-tumor responses in murine HCCs

We next examined the ability of PLT012 to stimulate anti-tumor responses in HCCs. To do so, we first assessed the impact of PLT012 treatment on T-cell activity and tumor growth in two murine HCC models: (a) the MYC^OE/p53^KO HCC model – driven by Myc overexpression and the loss of p53 function in hepatocytes – and (b) the CTNNB1^N90/MYC^OE HCC model – driven by Myc overexpression and characterized also by the overexpression of a mutated β-catenin (CTNNB1^N90) (Supplementary Fig. S2A). MYC^OE/ p53^KO HCC is known to have an inflamed tumor (hot tumor) microenvironment, characterized by high infiltration of CD103⁺ dendritic cells and CD8⁺ T cells, and exhibits a slower progression compared to CTNNB1^N90/MYC^OE HCCs (Supplementary Fig. S3A and S3B) (30). We found that PLT012 treatment strongly limited MYC^OE/ p53^KO HCC growth compared to control treatment and resulted in 32% of the HCC-bearing mice being tumor-free (Fig. 3A; Supplementary S3C). Measurements of tumor weight and ex vivo liver bioluminescence imaging (BLI) further corroborated these findings (Fig. 3B and C). Of note, PLT012 treatment also mitigated liver injury associated with tumor progression, as indicated by the reduced serum alanine transaminase (ALT) activity (Supplementary Fig. S3D). Furthermore, PLT012 treatment led to a reduction of Tregs and an increase of CD8⁺ T cells within the TME (Fig. 3D). Consequently, the CD8⁺ T/Tregs ratios in HCC from PLT012-treated mice were higher compared to the control group (Supplementary Fig. S3E). Since targeting CD36 has been shown to mitigate exhaustion and maintain the effector function of CD8⁺ T cells (10,11), we next examined whether PLT012 could alleviate T cell exhaustion by controlling the expression of exhaustion markers as well as granzyme B (GrB, a key molecule for cytolytic activity). Strikingly, PLT012 enhanced the abundance of CD8⁺ GrB⁺ T cells (Fig. 3E). Moreover, PLT012 treatment resulted in an increase in both progenitor-exhausted T cells (Prog Tex, characterized by CD44⁺ PD-1⁺ TCF1⁺ Tim3^-) and terminally exhausted T cells (Term Tex, characterized by CD44⁺ PD-1⁺ TCF1^- Tim3⁺) (Fig. 3F). Our results, together with the observation that Prog Tex abundance can influence the responsiveness to PD-1 blockade treatment (31–33), suggest that PLT012 is capable of restoring anti-tumor responses in HCC and might also improve responsiveness to PD-1 blockade.

Figure 3. PLT012 treatment promotes anti-tumor immunity in preclinical HCC mouse models.

A, MYC^OE/p53^KO HCC tumors received PLT012 treatment at a dosage of 10 mg/kg, given once every three days, with tumor progression monitored via bioluminescence imaging in IVIS System. Right, representative images for tumor growth in indicated groups. B and C, Tumor weight (B) and ex vivo BLI (C) of MYC^OE/p53^KO HCC tumors at the endpoint, as indicated in 3A. D, Proportion for CD8+ T cells (Left) and Tregs (Right) form indicated groups in MYC^OE and p53^KO HCC mouse model. E, CD8 T cell activity of indicated tumor samples assessed through determining the percentage of granzyme B (GrB)-positive T cell among CD45⁺ cells using FACS. F, Population for Prog Tex and Term Tex among CD45+ cells from tumors with indicated treatment. G, Tumor was generated by MYC^OE and CTNNB1^N90 plasmids and examined through BLI following treatment with either a control or PLT012 (10 mg/kg, administered once every three days for a total of 5 doses). Right, representative images for tumor growth in indicated groups. H and I, Tumor weight (H) and ex vivo BLI (I) of the liver tumors at the endpoint from 3G. J and K, Representative histology images (J) and quantitative results (K) for staining of CD8, granzyme B, FoxP3, and cleavage caspase 3 (cl. Caspase3) in indicated groups. Slides were counterstained with hematoxylin. Scale bars: 50 μm. L, Uniform Manifold Approximation and Projection (UMAP) visualization of T cells filtered in silico from whole liver CD45⁺ cells combining both PLT012 and Ctrl samples. Each T cell population detected was annotated using ProjecTILs alongside manual classification (See Fig. S3L). M, Ratio of PLT012 to Ctrl relative frequencies for annotated CD8⁺ Tex, CD8⁺ Tpex, and Tregs subsets, respectively (See Fig. S3M and Supplementary Table S1). N, Gene Set Enrichment Analysis (GSEA) normalized enrichment scores for T cell activation pathways, with significant enrichment (adjusted p-value < 0.05) based on differential gene expression in CD8⁺ Tex cells from PLT012 vs. Ctrl conditions (see Supplementary Table S2). Bar filling indicates the adjusted P-value associated with the enrichment analysis. Each symbol represents one individual. Data are cumulative results of three independent experiments, represented as means ± s.e.m. (A-K) and as a Box plot with Tukey whiskers indicating minimum to maximum range in panel K. The P-value is determined by two-tailed, unpaired Student’s t-test.

More than 35% of HCC patients harbor genetic mutations that lead to aberrant activation of Wnt/β-catenin signaling. These types of HCC have a cold TME with extremely low responsiveness to immune checkpoint inhibitors, in part due to a scarcity of TILs (30). To verify that CD36 blockade with PLT012 can overcome β-catenin-driven immune tolerance in HCC, we treated CTNNB1^N90/ MYC^OE HCC-bearing mice with PLT012. Our results showed that, like in the MYC^OE/ p53^KO HCC model, PLT012 significantly inhibited tumor growth and markedly decreased circulating ALT levels compared to the control treatment (Fig. 3G-I; Supplementary S3F). PLT012 treatment also increased the abundance of CD8⁺ T cells and decreased the proportion of Tregs in the tumors, thereby resulting in increased ratios of CD8⁺ T cells to Tregs within individual tumors (Supplementary Fig. S3G and S3H). In a similar way, PLT012 treatment of CTNNB1^N90/MYC^OE HCC mice also increased the abundance of GrB-expressing CD8⁺ T cells as well as the population of Prog Tex and Term Tex CD8⁺ T cells (Supplementary Fig. S3I and S3J). To confirm the compositional changes in the immune subsets, the activation of T cells, and tumor cell death occurrence in the tumors, we performed immunohistochemical (IHC) experiments to visualize FoxP3, CD8, GrB, and cleaved caspase-3. Our results confirm that PLT012 treatment can transform the TME from an immunosuppressive milieu to an immune-supportive environment characterized by high infiltration of CD8⁺ T cells into the tumor and a low abundance of intratumoral Tregs (Fig. 3J and 3K). We also observed a stronger signal for GrB and cleaved caspase-3 in tumors from PLT012-treated mice compared to controls, suggesting that CD8⁺ T cells exhibited enhanced cytotoxic activity that promotes tumor cell death. PLT012 significantly enhanced the antigen-specific T cell response, as assessed by OVA-specific tetramer staining (Supplementary Fig. S3K). In tetramer-positive CD8⁺ T cells, there were increased frequencies of antigen-specific Prog Tex and Term Tex in the PLT012 group, indicating that PLT012 boosts the adaptive immune system to control tumor progression (Supplementary Fig. S3K).

To explore the global immune response following treatment with PLT012, we isolated CD45⁺ tumor-infiltrating cells and subjected them to single-cell RNA sequencing (scRNA seq). We next studied the changes in T cell populations and classified the different cell types with scGate (34). We identified various CD8⁺ T cell subsets, Tregs, T helper 1 (Th1) cells, T follicular helper (Tfh) cells, and unconventional T cells based on their lineage-specific markers (Fig. 3L; Supplementary S3L). In line with the flow cytometry results, the scRNA-seq compositional analysis showed a reduction in Tregs and an increase in both total terminally and progenitor-exhausted T cells (Fig. 3M; Supplementary S3M). Noteworthy, PLT012 treatment induced robust changes in the transcriptome of terminally exhausted T cells compared to their counterpart from control group (Supplementary Fig. S3N; Supplementary Table S1). The analysis of differentially expressed genes showed that gene signatures associated with T cell activation and effector function were significantly enriched in terminally exhausted T cells from PLT012-treated tumors (Fig. 3N; Supplementary S3O; Supplementary Table S2), suggesting that PLT012 treatment can restore effector functions in exhausted T cells. Additionally, we observed that PLT012 treatment alters the expression of genes involved in lipid metabolism pathways in terminally exhausted T cells (Supplementary Fig. S3P; Supplementary Table S2). Furthermore, the depletion of CD8⁺ T cells significantly undermines the therapeutic effects of PLT012 (Supplementary Fig. S3Q), indicating that PLT012 treatment can induce CD8 T cell-mediated anti-HCC responses. Together, our results demonstrate that PLT012 treatment can induce robust anti-HCC immune responses in both hot and cold TME, increase CD8⁺ TIL cell number, restore CD8⁺ TIL effector functions, as well as reduce the intratumoral Treg populations.

PLT012 enhances the efficacy of the standard-of-care immune therapy for HCCs

Considering that Prog Tex cells are responsible for the anti-tumor responses induced by PD-1/PD-L1 blocking therapy (31–33) and that PLT012 increases the amount of Prog Tex populations in HCCs, we hypothesized that PLT012 may enhance the therapeutic efficacy of the PD-1/PD-L1 blocking therapy. To verify this, we treated CTNNB1^N90/ MYC^OE HCC-bearing mice with either anti-PD-L1 mAb alone, PLT012 alone, or a combination of PLT012 and anti-PD-L1 mAbs. As expected, anti-PD-L1 mAbs induced mild anti-tumor responses in cold HCCs; however, the combination of PLT012 and anti-PD-L1 mAbs in a single treatment exhibited more profound anti-tumor effects than monotherapy (Fig. 4A-C). Interestingly, the combinatorial therapy further boosted the ability of PLT012 to mediate the reprogramming of the immune state in tumors, including reducing Treg percentages, increasing the total number of CD8⁺ T cells, Term Tex cells, and Prog Tex cells, and concurrently elevating the amount of GrB-expressing CD8⁺ T cells (Fig. 4D-H). Next, we wondered if the incorporation of PLT012 into the standard-of-care (SoC) regimen for the treatment of human HCC, which uses anti-VEGF and anti-PD-L1 mAbs (35), could induce superior outcomes and therapeutic benefits. We found that including PLT012 in the SoC regimen resulted in a significant improvement in tumor suppression (Fig. 4I-K). Notably, more than 70% of the treated mice showed an overall positive response to the triple therapy (PLT012+anti-PD-L1+anti-VEGF), while the therapeutic effects following SoC therapy alone were only observed in 27% of the subjects (Fig. 4L). Importantly, while no subjects achieved a tumor-free status with the control treatment alone, anti-VEGF alone, or SoC alone, the combination of SoC and PLT012 led to a tumor-free status in 45% of the CTNNB1^N90/ MYC^OE HCC-bearing mice. In conclusion, targeting CD36 with PLT012 presents a promising strategy to be used in combination with immune checkpoint inhibitors to reprogram the tumor immune microenvironment in the management of liver cancers.

Figure 4. PLT012 administration improves therapeutic efficacy of immune checkpoint inhibitors in the treatment of HCC.

A, Tumor growth under the indicated treatment regimen, assessed via BLI in IVIS, n=14-15/group. B and C, Ex vivo BLI (B) and corresponding tumor weights (C) of MYC^OE/CTNNB1^N90 HCC tumors across the designated groups. D-H, FACS analysis for Tregs (D), CD8⁺ T cells (E), granzyme B (GrB⁺)-expressing CD8⁺ T cells (F), progenitor-exhausted T cells (Prog Tex; G), and terminal-exhausted T cells (Term Tex; H) in indicated groups. I-K, Tumor growth (I), ex vivo liver BLI (J), and tumor mass (K) of MYC^OE/CTNNB1^N90 HCC tumor model following indicated treatments, n=11/ group. L, Response rate from 4I, presented as Complete Response (CR), Partial Response (PR), Progressive Disease (PD), and Stable Disease (SD). Data are the cumulative results from at least two independent experiments, with each symbol representing one individual. Data are means ± s.e.m. and analyzed by One-way ANOVA.

PLT012 restrains HCC progression under high dietary lipid uptake conditions

Excessive dietary fat intake and a deregulated lipid metabolism in livers have been reported to modulate fatty acid biosynthesis in HCC and promote aggressiveness in human and murine HCC (36–38). HCC subtypes characterized by increased fatty acid biosynthesis are also associated with more aggressive phenotypes and reduced survival rates (39); to date, an effective treatment to restrict HCC progression under high dietary fat intake is still missing because of the complications due to auto-reactive T cells and to changes in other immune parameters (40–42). Since, in Tregs and CD8⁺ T cells, CD36 expression can be induced by a lipid-rich environment (10,12), we reasoned that feeding HCC-bearing mice a high-fat diet might promote CD36 expression in these cell types and enhance the immunosuppressive state in tumors. To test this hypothesis, twenty days post hydrodynamic injection, CTNNB1^N90/MYC^OE HCC-bearing mice were fed either a chow diet (CD) or a Western diet (WD) rich in sugars and fats (Fig. 5A). The WD increased mice body weight, accelerated the progression of liver cancer, and promoted an immune-suppressive TME, as evidenced by the increase in intratumoral Tregs and the reduction in CD8⁺ T cells (Supplementary Fig. S4A-F). Notably, by using both PLT012 and HM36 – a commercially available anti-CD36 antibody – we found that feeding mice a WD promoted CD36 expression in intratumoral Tregs and tumor-infiltrating CD8⁺ T cells, including both progenitor and terminally exhausted CD8⁺ T cells (Supplementary Fig. S4G and S4H), indicating that an increase in lipid uptake may reinforce CD36-mediated immune responses in the TME. Next, we tested the anti-tumor responses of PLT12 and anti-PD-L1 mAbs in these experimental settings. In alignment with a previous report, the administration of the anti-PD-L1 antibody only limitedly curtailed tumor growth (43); however, PLT012 consistently showed a great ability to reduce tumor growth when the mice were maintained on a CD (Fig. 5B-D). Notably, PLT012 treatment remained effective in restricting tumor growth even in mice fed a WD. Differently from what was observed with the anti-PD-L1 mAb treatment, PLT012 treatment resulted in less intratumoral Treg in HCC from both CD- and WD-fed groups (Fig. 5E). Furthermore, PLT012 treatment strongly promoted the infiltration of CD8⁺ T cells, including progenitor and terminally exhausted CD8⁺ T cells, and increased the amount of GrB⁺ populations (Fig. 5F-I), which led to a more robust anti-tumor immune response in liver cancers with an aberrant dietary lipid uptake. Collectively, these results support the hypothesis that targeting CD36 with PLT012 represents a promising strategy to restore immune responses in HCCs with or without excessive metabolic challenges due to dietary lipid uptake.

Figure 5. Impacts of PLT012 on HCC in a lipid-dense milieu.

A, Experiment design and procedure, n=12-13/group. B-D, Tumor growth (B), tumor mass (C), and ex vivo tumor BLI (D) of MYC^OE/CTNNB1^N90 HCC tumors were assessed in indicated groups. E-I, Proportions of Tregs (E), CD8+ T cells (F), granzyme B⁺ CD8 T cells (G), Prog Tex (H), and Term Tex (I) within the CD45⁺ cell population were quantified using FACS. Data are the cumulative results from three independent experiments, with each symbol representing one individual. Data are means ± s.e.m. and analyzed by One-way ANOVA. (Created with BioRender.com)

PLT012 treatment restricts liver metastasis and restores systemic anti-tumor immunity

The immune tolerance of the liver, in particular the development of Tregs, significantly impairs the immunosurveillance mechanisms (44–46) rendering the liver a frequent site for metastases from various malignancies. The presence of liver metastases in cancer patients is associated with a reduced response to immunotherapy, which represents a critical unmet medical need that requires additional attention (47). Taking advantage of the unique attributes of the POG570 cohort (48), which includes a comprehensive transcriptome dataset of biopsy samples from various metastatic sites in patients with advanced malignancies, we initially assessed the metabolic properties of the TME (Supplementary Fig. S5A). Following the exclusion of metastatic samples with inadequate sample sizes, our analysis revealed that liver metastases across multiple cancer types exhibited a TME characterized by high CD36 expression, increased fatty acid metabolism scores, Treg abundance and reduced infiltration of CD8⁺ T cells when compared to other metastatic locations (Fig. 6A). To explore which primary tumor types exhibited high fatty acid scores and high CD36 expression in liver colonization, we applied the same analytical methodology to samples derived from liver metastasis of various cancer type in the same cohort. Our analysis showed that liver metastasis from breast cancer (BRCA), colon adenocarcinoma (COAD), pancreatic cancer (PAAD), and stomach cancer (STAD) exhibited significant lipid metabolism alterations with respect to other metastatic diseases or other metastatic sites within the same cancer type (Fig. 6B). Notably, tissue microenvironment is not the sole determinant of fatty acid metabolism activation. For instance, liver metastases from non-small cell lung carcinoma (NSCLC) did not display significant fatty acid metabolism-related changes in comparison to other metastatic sites of NSCLC, suggesting that both intrinsic factors and the surrounding environment collectively influence metabolic features (Fig. 6B; Supplementary S5B). Therefore, we further assessed the correlation among CD36 expression, fatty acid (FA) score, and exhaustion score to identify tumor types with liver metastasis which could be used for CD36 blockade treatment with monoclonal antibodies (Supplementary Fig. S5C). Our data showed that, in liver metastases originating from COAD, CD36 expression strongly correlates with the FA score and the exhaustion score. This suggests that the growth of liver metastases may be significantly influenced by CD36-mediated immune regulation, highlighting the potential therapeutic implications of targeting CD36 in this specific context.

Figure 6. PLT012 treatment suppresses primary and metastasis in a mouse model of colon cancer.

A and B, An in silico analysis of POG570 cohort for expression level of CD36 and fatty acid metabolism score, as well as relative percentages for Treg and CD8 T cells across various metastatic sites (A) and aggressive tumor types (B). C, Schematic representation of PLT012 treatment protocol utilized in the MC38 colon cancer metastasis model. D and E, Growth curve of MC38 liver metastases (D) and subcutaneous tumors (E) in mice treated with vehicle control (Ctrl), anti-PD-1, PLT012, or combination (combo). F and G, Analysis of the percentages of CD8 T cells and Tregs percentages within CD45⁺ cells in liver metastasis (F) and primary tumors (G). H and I, Representative plots (H) and quantitative results (I) for population of M1 and M2 macrophages among CD45⁺ cells from the indicated groups. Data are the cumulative results from two independent experiments. Each symbol represents one individual. Data are means ± s.e.m. and analyzed by One-way ANOVA.

A previously published work reports that, in colon cancer liver metastasis, antigen-specific CD8⁺ T cells undergo apoptosis following interaction with CD11b⁺ F4/80⁺ monocyte-derived macrophages. Consequently, liver metastases eliminate tumor-specific T cells within the body, leading to acquired resistance to immune checkpoint blockade (47). Indeed, by using a tumor engraftment model that allows establishing subcutaneous and liver metastases within the same mouse, we found that PD-1 blockade partially loses its ability to limit subcutaneous MC38 colorectal cancer growth when liver metastases were present (Supplementary Fig. S6A). Since we found that most liver metastases of human cancers have elevated CD36 expression and display several tumor-immune microenvironmental features controlled by CD36, we reasoned that PLT012 could also target liver metastases and re-instate systemic antitumor immunity. To verify this, we established a preclinical model of liver metastasis by inoculating syngeneic MC38 colorectal cancer cells both subcutaneously and intrahepatically. We treated mice engrafted with MC38 cells with either control vehicle, anti-PD-1 mAb alone, PLT012 alone or anti-PD-1+PLT012 (Fig. 6C). PLT012 exhibited superior antitumor effects compared to control treatment or anti-PD-1 monotherapy on both liver metastasis (Fig. 6D) and local tumors (Fig. 6E) as assessed by BLI and endpoint tumor weight measurements (Supplementary Fig. S6B-E). Most importantly, the combination of PLT012 with the anti-PD-1 therapy significantly improved tumor suppression in primary tumors and the abundance of CD8⁺ TILs in both primary and metastatic tumors compared to monotherapy (Fig. 6D-G; Supplementary S6F). In addition, PLT012 treatment alone and in combination with anti-PD-1 effectively reduced intratumoral Treg abundance in both primary and metastatic tumors (Fig. 6F and 6G). Next, we examined whether PLT012 treatment could influence tumor-associated macrophage (TAM) phenotypes in subcutaneous tumors and liver metastases (Supplementary Fig. S6G). Our findings show that PLT012 alone is able to promote an increase in the M1-like macrophage population (CD45⁺Ly6G^-CD11b⁺F4/80⁺Tim4^-Clec4f⁺MHCII⁺CD206^-) and a decrease in the M2-like population (CD45⁺Ly6G^-CD11b⁺F4/80⁺Tim4^-Clec4f⁺MHCII^- CD206⁺) in liver metastases (Fig. 6H and I). Similarly, PLT012 alone induced a remarkable increase in M1-like TAMs (CD45⁺Ly6G^-CD11b⁺F4/80⁺MHCII⁺CD206^-) but a reduction in M2-like TAMs (CD45⁺Ly6G^-CD11b⁺F4/80⁺MHCII^-CD206⁺) in primary tumors (Supplementary Fig. S6H and S6I). Noteworthy, the combined treatment with PLT012 and anti-PD-1 further amplified these differences in subcutaneous tumors (Supplementary Fig. S6H and S6I). Surface CD206 staining confirmed that PLT012 treatment, as well as the combined therapy treatment, reduced CD206⁺ population in tumors, whereas treatment with anti-PD-1 alone failed to produce the same beneficial outcome (Supplementary Fig. S6J and S6K). Taken together, our results indicate that PLT012 can reprogram the tumor immune microenvironment, thus restricting colon cancer liver metastasis and improving sensitivity to PD-1 blocking therapy in mice with liver metastasis.

PLT012 is well-tolerated in non-human primates

To get approval for new treatments in human patients, it is key to assess and prove the safety of the molecule under study. To this aim, we first confirmed CD36 expression in red blood cells (RBCs) and platelets from mice, monkeys, and humans. Our results showed that among the three models, monkeys and humans have the most comparable CD36 expression patterns, characterized by low and high CD36 expression levels in RBCs and platelets, respectively (Fig. 7A). Also, CD36 expression levels in peripheral immune cell populations, including T cells, B cells, and monocytes, were comparable in monkeys and humans (Fig. 7B). Therefore, cynomolgus monkeys were chosen for the safety testing of PLT012 in a repeated-dose study (Fig. 7C). The administration of PLT012 was well tolerated in monkeys receiving three different dose levels (10mg/kg, 60mg/kg, and 200mg/kg), with no treatment-related mortality, clinical signs, or alterations in body weight, food intake, hematological parameters, or coagulation metrics. Furthermore, ALT and aspartate aminotransferase (AST) levels were comparable between the three dosing groups and control groups, apart from a transient increase in the 200 mg/kg group at 24 hours post-injection, indicating that PLT012 treatment did not affect liver functions throughout the study (Fig. 7D). Moreover, PLT012 treatment did not affect RBC and platelet counts, regardless of the different CD36 expression patterns in monkey RBCs and platelets (Fig. 7E). Immunophenotyping with fluorescence-activated cell sorting (FACS) further indicated a good tolerance to PLT012 treatment characterized by a stable immune profile, including CD4⁺ T cells, CD8⁺ T cells, NK cells, and monocytes, maintained throughout the study (Fig. 7F). Consistently, a previously published phenotypic assay also indicated limited effects on hematopoiesis and similar T-cell phenotypes in Cd36-KO and wild-type mice (49). Altogether, these results demonstrate that PLT012 is exceptionally safe with no observed adverse effects, even at the highest dose (200 mg/kg).

Figure 7. The safety and efficacy study of PLT012 was evaluated involving repeated doses in non-human primates and human HCC patients’ ex vivo tissue fragments.

A and B, FACS analysis of CD36 expression levels in red blood cells (RBCs) and platelets (A), as well as in CD4, CD8, B cells, and monocytes from PBMC samples of humans, cynomolgus monkeys, and mice (B). C, Diagram depicting the procedure for the dose range finding (DRF) study of PLT012, involving 5 repeated doses at concentrations of 0, 10, 60, and 200 mg/kg. D and E, Serum concentrations of ALT and AST (D) and number of circulating RBC and platelets (E) were measured over the study period at the indicated doses. F, Proportions of CD4, CD8, NK cells, and monocytes within CD45⁺ cells were assessed on Day 1 and Day 29 in cynomolgus monkeys receiving PLT012 treatment. G, Proportion of CD36 positive CD8 T cells and Tregs identified in human HCC tissue fragments through FACS, following the indicated treatment (n=11). H, FACS analysis was conducted to evaluate the pharmacodynamic immune profiles of indicated TILs in ex vivo human HCC tissue fragments after a two-day culture period with either control or PLT012 antibodies (n=11). I, Response rates based on pharmacodynamic markers into three classifications: SR (Strong Response, defined as a fold change greater than 2), MR (Moderate Response, defined as a fold change between 1 and 2), and NR (No Response). Data are presented as mean ± sd in panels A-F, with a sample size of n = 4 animals, comprising both male and female subjects, for each treatment group. (Created with BioRender.com)

PLT012 triggers desirable immune responses in human HCC tumors

The pharmacodynamic effects, anti-tumor activity, and safety profile observed in the pre-clinical studies emphasize the potential of using PLT012 in cancer therapy. Interestingly, a newly established ex vivo culture model – developed from patient-derived tumor fragments – has been shown to maintain stromal compartments, including immune infiltrates and tumor micro-structures, and can be used to follow the immunological responses of human tumor tissues in response to ex vivo PD-1 blockade (50). To translate our pre-clinical findings into a clinically relevant context, we tested the efficacy of PLT012 in modulating the abundance and functions of intratumoral CD8⁺ T and Tregs cells in ex vivo cultures obtained from HCC tumor samples. We obtained HCC samples from 11 patients at different stages of disease progression (Supplementary Table S3), generated ex vivo cultures and treated them with control IgG or PLT012 for two days. We used flow cytometry to identify intratumoral CD8⁺ T cells and Tregs in the samples (Supplementary Fig. S7). We first assessed CD36 expression in intratumoral Tregs and CD8⁺ T cells before treatment; then, we characterized the immune profile following treatment with either control IgG or PLT012. Despite the high variability in the percentage of CD36-positive intratumoral Tregs and CD8⁺ T cells (range 10% to 80%) (Fig. 7G), nearly all cultures positively responded to PLT012 treatment with an increase in the proportion of CD8⁺ T cells, a reduction in the proportion of Tregs, an expansion of the CD8⁺ GrB⁺ T population, and an increase of the CD8/Treg ratio (Fig. 7H). Importantly, assessments of Treg-related pharmacodynamic markers (i.e., Treg percentages and CD8/Treg ratios) indicated an 81.8% response rate following PLT012 treatment. Furthermore, PLT012-induced increase in CD8⁺ T cell percentage and GrB expression occurred in 45.4% of the patients (Fig. 7I). Of note, no correlation between the expression of CD36 in Tregs and CD8⁺ T cells and the level of response to PLT012 treatment was found, suggesting a feed-forward effect of PLT012 in promoting an immune-supportive microenvironment. These findings point out that PLT012 effectively modulates the TME in HCCs by targeting CD36. The efficacy of PLT012 in modulating ex vivo the immune profiles of cells from HCC patient samples and its good safety profile in non-human primates make it a promising therapeutic agent to improve HCC patients' outcomes.

Discussion

Here, we demonstrate that modulation of CD36-mediated lipid signaling with the PLT012 antibody leads to better therapeutic outcomes in liver cancer and colon cancer patients with metastasis in the liver. PLT012 has the potential to enhance the tumoricidal efficacy of immune cells and to modify the TME in a way that promotes a shift towards a more immunosupportive milieu characterized by an increased M1/M2 macrophage ratio and a higher CD8⁺ T cell to Treg ratio. Moreover, PLT012 can provide superior anti-HCC immune responses in mice under a high-fat diet and elicit anti-liver metastasis responses in murine models. Also, PLT012 can be used to restore full responsiveness to PD-1 blocking agents in primary colon cancer in mice with liver metastasis. Importantly, PLT012 targets the fatty-acid binding region of CD36 with high affinity and, thanks to its isotype, exhibits limited antibody-dependent cellular cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC). The rationale design renders PLT012 a superior molecule for tumor suppression with a negligible impact on normal immune homeostasis and vascular balance. Notably, our findings can facilitate the translation of fundamental research into clinical oncology, as evidenced by the results obtained in human HCC tumor ex vivo cultures and cynomolgus monkeys for product safety and pharmacokinetic studies. In summary, our work introduces a novel perspective in tumor immunology, providing valuable insights for the development of metabolism-targeting therapies that could significantly influence the management of liver cancer and other metastatic conditions affecting the liver.

Treatment with checkpoint inhibitors has resulted in improved survival rates in patients with liver cancer when compared to Nexavar® (sorafenib) (51). The combination of Tecentriq® (atezolizumab) and Avastin®(bevacizumab) has revealed an objective response rate (ORR) of approximately 30%, with a median overall survival of 19.2 months. Additionally, another SoC therapy combining Imjudo® (tremelimumab) and Imfinzi® (durvalumab) demonstrated an ORR of 20%, with a median overall survival of 16.43 months, which is comparatively lower (43). Nonetheless, the mortality rate in HCC patients still remains too high. Given the unmet medical needs and the growing market demand for treatments that can address a wider range of patients, it is crucial to evaluate new therapies against Atezolizumab plus Bevacizumab. Additionally, further research defining HCC subtypes based on immune profiles helps predict optimal combinations and innovative agents that promise better outcomes. Our findings reveal that the use of PLT012 with either anti-PD-L1 or in SoC therapy has remarkable therapeutic effects and leads to 30%-40% of HCC-bearing mice becoming tumor-free, which demonstrates the importance of exploring CD36-targeting as an option for the treatment of HCC and metastatic HCC. Of note, in addition to its pro-angiogenic effects, VEGF-A enhances the infiltration and activation of myeloid-derived suppressor cells (MDSCs) and induces M2 macrophage polarization (52–54), ultimately contributing to the development of an immunosuppressive TME. Furthermore, CD36-mediated fatty acid uptake has been reported to activate oxidative metabolism in both MDSCs and M2 macrophages (55,56). Given the distinct mechanisms, combining bevacizumab with PLT012 shows promise for overcoming VEGF-A–dependent immunosuppression and disrupting the metabolic advantages, which could enhance T cell infiltration and activation within the tumor microenvironment. Additionally, it has been reported that CD36 promotes ferroptosis and increases exhaustion severity in TILs, suggesting that CD36 antagonizes the effect of PD-1/PD-L1 blockade which promotes T cell reinvigoration and enhances TILs anti-tumor immunity (57). Moreover, genetic ablation of CD36 leads to increased production of interferon-γ and tumor necrosis factor in intratumoral CD8⁺ T cells and higher frequency of progenitor-exhausted T cells (10). However, our results show that PLT012 does not prevent the formation of terminally exhausted T cells, but PLT012 treatment promotes effector functions in those terminally exhausted T cells. Of note, PLT012 treatment promotes the abundance of progenitor-exhausted T cells. These findings suggest that the expression of CD36 may contribute to the functional and survival decline of both terminally exhausted T cells and progenitor-exhausted T cells. Given that T cells engage various metabolic pathways to fine tune their differentiation and functional status (58–61), our results suggest that CD36-mediated lipid uptake may modulate effector function in terminally exhausted T cells and stemness in progenitor exhausted T cells. However, it remains unclear whether the inhibition of CD36 differentially regulates T cell differentiation programs such as memory formation and immune rejuvenation by influencing lipid compositions. Studying how CD36-driven changes in lipid composition influence transcriptional and functional changes may provide critical information for this yet unexplored area.

In addition to facilitating fatty acid uptake, CD36 has been shown to interact with protein-related ligands such as thrombospondin-1 (TSP-1) via the CLESH (CD36, LIMP-2, Emp sequence homologous) domain thereby regulating angiogenesis and platelet aggregation (62,63). Since PLT012 is designed to specifically target the lipid-binding pocket of CD36; we did not observe changes in platelet number and aggregation in any of our pre-clinical model. This emphasizes the importance of targeting the lipid-binding pocket of CD36 to minimize side effects.

Interestingly, it is well documented that tumor cells with higher metastatic potential, including oral squamous cell carcinomas (OSCC), melanoma, and breast cancer, have increased CD36 expression (64–66). In gastric cancer cells, CD36-mediated fatty acids uptake leads to the phosphorylation of AKT that inhibits the degradation of glycogen synthase kinase 3β (GSK-3)/β-catenin, thereby facilitating metastasis formation (67). In addition, CD36-mediated lipid uptake can initiate epithelial-mesenchymal transition (EMT) by modulating Wnt and TGF-β signaling pathways (68). In the context of OSCC, shRNA-mediated knockdown of CD36 or the application of an antibody targeting the lipid-binding pocket of CD36 has been shown to suppress metastasis formation without causing significant adverse effects in pre-clinical models (65). These findings point out that lipid metabolism regulation by CD36 is important for immune surveillance regulation within the TME and metastatic colonization promotion at distant sites. For this reason, it is worth investigating whether PLT012-mediated antimetastatic effects are restricted to liver metastasis or can be exploited to restrict metastasis formation in other organs.

Furthermore, since the inhibition of CD36-mediated fatty acid uptake may alter metastatic behaviors of cancer cells and increase the susceptibility of drug-resistant cells to tyrosine kinase inhibitors (69,70), CD36 can be considered a viable target for the prevention and treatment of cancer metastasis.

Materials and Methods

Assays for affinity binding of antibody, oxLDL binding, and oxLDL uptake

For the evaluation of PLT012 binding affinity, human CD36-expressing F293 cell lines were incubated with PLT012 at concentrations ranging from 0.002 to 10 μg/mL at 4 °C for 30 minutes, followed by staining with commercial PE-conjugated anti-CD36 antibodies (0.4 μg/mL), clones D-2712 (BD Biosciences Cat# 552544, RRID:AB_2072646), at 4 °C for 20 minutes. For the assessment of oxLDL binding in tumor-infiltrating lymphocytes (TILs), CD45+ TILs were isolated from MC38 tumors two weeks post-inoculation using the mouse Tumor Dissociation Kit and CD45 microbeads (Miltenyi Biotec), in accordance with manufacturers' protocols. To evaluate the inhibitory effect of PLT012 on oxLDL binding and uptake, human CD36-expressing F293 cells or indicated TILs were initially pre-incubated with either control IgG or anti-CD36 antibodies (5 mg/mL) at 4 °C for 30 minutes. Subsequently, the cells were treated with oxLDL-Dil (5 mg/mL, Kalen Biomedical #770232-9) in RPMI medium containing 1% fatty acid-free BSA at 4°C for 2 hours to measure oxLDL binding or at 37°C for 5-15 minutes to measure oxLDL uptake, respectively. Data collection was done using an Attune NxT Flow Cytometer (Thermo Fisher Scientific). The uptake of oxLDL was analyzed by flow cytometry and expressed as a percentage (%) of positive cells based on a histogram gate placed on negative control cells.

CryoEM structural analysis of the CD36(ECD)-PLT012(Fab) complex

The CD36 extracellular domain complexed with the Fab domain of a specific protein was reconstituted and then loaded into a Chameleon system (SPT Labtech) by spraying a nanoliter onto a self-wicking 1.2/1.3 grid (SPT Labtech). The single particle cryo-EM data was collected using an automated process on a Titan Krios G4 TEM operating at 300 kV (Thermofisher Scientific). Raw EER movies were imported to cryoSPARCv4.3 (71), drift-corrected and dose-weighted with Patch Motion Correction, and estimated for contrast transfer function (CTF) with Patch CTF Estimation. After initial processing and selection, a total of 21,008 images were used for further analysis. Particles were picked and further processed to obtain a 3D reconstruction. The resulting 3D reconstruction was used to generate templates for particle picking, and a total of 16,529,240 particles were extracted. Following 3 rounds of 2D classification and refinement, the final 3D class with 84,937 particles reached a resolution of 3.75 Å. To account for flexibility in the Fab region, a Flexible 3D refinement was performed, resulting in a 3.45 Å reconstruction (B-factor -127).

To compensate for artifacts and improve the interpretability of the model building, the quality of 3D reconstruction was enhanced with EMReady (72). The initial model was obtained from AlphaFold (73), fitting into the cryo-EM density using a rigid body fit in Chimera (74). Atomic model of the Fab region was manually inspected and adjusted in Coot (75), refined with Servalcat (76) and the quality was assessed with MolProbity (77). Atomic model of CD36 region was fit into the cryo-EM density using rigid body fit in Chimera and manually inspected and real-space refined with Phenix (78). Raw cryo-EM images were deposited to the Electron Microscopy Public Image Archive (EMPIAR) with accession code EMPIAR-X. The cryo-EM map of CD36-Fab was deposited to the Electron Microscopy Data Bank (EMDB) with accession code EMD-X. Built atomic model of Fab region was deposited to the Protein Data Bank (PDB) with accession code PDB-X.

Cell lines

YUMM1.7 melanoma cell line (RRID:CVCL_JK16) and MC38 cell line (RRID:CVCL_B288) were kindly provided by Marcus Bosenberg (79) and Pedro Romero, respectively. YUMM1.7 and MC38 cells were cultured in DMEM with 10% fetal bovine serum and 1% penicillin-streptomycin and used for experiments when they were in the exponential growth phase. Cell lines were regularly authenticated by STR profiling. All the cell lines were verified to be free from Mycoplasma contamination.

In vivo experiment

In subcutaneous engraftment tumor models, Yumm1.7-gp33 (8 × 10⁵ cells) and MC38-OVA cells (5 × 10⁵ cells) were administered via subcutaneous injection into 6-week-old C57BL/6 mice. Tumor dimensions were assessed every 2-3 days post-engraftment, with tumor volume calculated using the formula: volume = (length × width²)/2. MC38 cells were injected subcutaneously (1 × 10⁶) and intrahepatically (5 × 10⁵) for experimental liver metastasis models as previously described (47).

HCC mouse models are generated by hydrodynamic delivery of endotoxin-free plasmid DNA through the lateral tail vein within 5-7 seconds, as previously described (80). The pT3-Myc_LucOVA plasmid is created by replacing the OS sequence in the pT3-EF1A-MYC-IRES-LucOS plasmid (RRID:Addgene_129776) with the OVA sequence. In the MYC^OE/p53^KO HCC model, a total of 12 μg of pT3-Myc_LucOVA plasmid, 13.2 μg of p53 gRNA plasmid (RRID:Addgene_59910), and 4 μg of SB100x (RRID:Addgene_34879) were injected into the mice. For the CTNNB1^N90/MYC^OE HCC model, each mouse received 12 μg of pT3-b-catenin (RRID:Addgene_31785), 12 μg of pT3-Myc_LucOVA plasmid, and 8 μg of SB100x. Tumor growth was monitored weekly through bioluminescence imaging starting from week 2. Mice exhibiting persistent tumor growth were randomly assigned to specific treatment groups to ensure objectivity. Tumor samples were collected 35-38 days post-hydrodynamic injection for subsequent weight measurement and immune profiling via flow cytometry. The bioluminescence activity of the mice was evaluated using an IVIS imaging system following luciferin administration (150 mg/Kg). All procedures were approved by the University of Lausanne’s and the National Defense Medical Center’s Institutional Animal Care and Use protocols.

Flow cytometric analysis

The entire liver with tumor lesions was finely minced and digested in RPMI medium with 2% FBS, 1% penicillin-streptomycin, DNase I (1 μg ml^-1; Sigma-Aldrich), and collagenase (0.5 mg ml^-1; Sigma-Aldrich) at 37°C for 40 minutes. The resulting mixture was filtered through a 70-μm cell strainer, and the filtered cells were treated with RBC Lysing Buffer (BioLegend), followed by washing with FACS buffer (phosphate-buffered saline with heat-inactivated 2% FBS and 0.1% sodium azide). Tumor-infiltrating leukocytes were then enriched using Percoll density gradient centrifugation (800g for 20 min) at room temperature. Single-cell suspensions were incubated with anti-CD16/32 Fc receptor-blocker (BioLegend Cat# 101320, RRID:AB_1574975) on ice for 10 min before staining. Viable cells were identified by staining with BD Horizon Fixable Viability Stain 450 (BD Biosciences Cat# 562247, RRID:AB_2869405) at room temperature for 15 min. Cells were processed for surface marker staining for 1 hour at 4 °C and then intracellular staining. Intracellular staining was performed according to the instruction of Transcription Factor Fixation/Permeabilization Buffer Set (BioLegend). Briefly, cells were fixed and permeabilized in True-Nuclear Fix solution for 20 min and then stained by indicated antibody for 1 hour. Samples were analyzed on Cytek Northern Lights flow cytometers, and data were analyzed with FlowJo v10 (RRID:SCR_008520). The following antibodies were used for flow cytometry: anti-CD45 (BioLegend Cat# 103136, RRID:AB_2562612), anti-CD19 (BioLegend Cat# 115530, RRID:AB_830707), anti-CD3ε (BD Biosciences Cat# 564378, RRID:AB_2738779), anti-CD4 (BD Biosciences Cat# 566939, RRID:AB_2869957), anti-FoxP3 (BD Biosciences Cat# 560401, RRID:AB_1645201), anti-CD25 (BioLegend Cat# 102048, RRID:AB_2564124), anti-CD8a (BD Biosciences Cat# 747134, RRID:AB_2871881), anti-CD44 (BD Biosciences Cat# 566506, RRID:AB_2744396), anti-granzyme B (BD Biosciences Cat# 563388, RRID:AB_2738174), anti-Tim 3 (BD Biosciences Cat# 747622, RRID:AB_2744188), anti-PD-1 (BioLegend Cat# 109110, RRID:AB_572017), anti-CD11b (BioLegend Cat# 101217, RRID:AB_389305), anti-CD11c (N418), anti-CD206 (BioLegend Cat# 141732, RRID:AB_2565932), anti-CD103 (BioLegend Cat# 156915, RRID:AB_2904296), anti-MHC class II I-Ab/I-E (BioLegend Cat# 107639, RRID:AB_2565894), anti-F4/80 (BD Biosciences Cat# 746070, RRID:AB_2743450), anti-Ly6C (BD Biosciences Cat# 566987, RRID:AB_2869991), anti-Ly6G (BD Biosciences Cat# 740554, RRID:AB_2740255), anti-Tim 4 (BioLegend Cat# 130010, RRID:AB_2565719), and anti-NK1.1 (BioLegend Cat# 108724, RRID:AB_830871). The OVA tetramer staining was performed following the provided instructions (MBL International Cat# TS-5001-2C, RRID:AB_3090188).

Immunohistochemistry analysis

Paraffin sections at a thickness of 4 μm were deparaffinized and stained with antibodies against CD8α (Abcam Cat# ab217344, RRID:AB_2890649), FoxP3 (Cell Signaling Technology Cat# 12653, RRID:AB_2797979), cleavage caspase 3 (Cell Signaling Technology Cat# 9661, RRID:AB_2341188), and granzyme B (Cell Signaling Technology Cat# 44153, RRID:AB_2857976), followed by PolyTek Polymerized Imaging System (ScyTek Laboratories Inc). Chromogenic staining was determined in the Zeiss Axioscan 7 slide scanner. Six random regions were selected to quantify positive levels for each tumor tissue section, and the value for each image was quantified using ImageJ (RRID:SCR_003070).

In silico analysis on TCGA and POG570 cohorts

Data from the Cancer Genome Atlas (TCGA) cohort was acquired from UCSC Xena (http://xena.ucsc.edu/). The gene expression quantification was conducted using the UCSC Xena Toil RNA-seq pipeline. Furthermore, information from the POG570 cohort was obtained from cBioPortal. The RPKM gene expression values were transformed to TPM using the method suggested by Wagner et al. (81).

The Fatty Acid Metabolism signature was obtained from the Molecular Signatures Database (MSigDB; HALLMARK_FATTY_ACID_METABOLISM), and the T Cell Exhaustion signature was sourced from Bao X et al. (82). The enrichment levels of these signatures in each sample were quantified using the "GSVA" R package (version 1.48.3) and the single-sample gene set enrichment analysis (ssGSEA) method.

Tumor Infiltrating Lymphocytes Fraction Analysis: CIBERSORT, a deconvolution algorithm, was used to estimate cell type proportions within bulk cancer samples based on gene expression data. We estimated the proportions of 22 types of infiltrating immune cells using the CIBERSORT method (83,84). For the TCGA cohort, the immune infiltration proportions were obtained from TIMER2.0 (http://timer.cistrome.org/). For the POG570 cohort, the "immunedeconv" R package (version 2.1.0) was used with CIBERSORT (absolute mode), and the CIBERSORT source code was downloaded under a licensed agreement. We extracted the proportions of T regulatory (Treg) cells and CD8 T cells with the default parameters.

Cancer types with a mean of Fatty Acid (FA) enrichment score (ES) greater than zero and a CD36 expression level higher than the median of the mean CD36 expression among 33 cancer types, minus one standard deviation (SD), were selected. A higher gene set enrichment score indicates a higher activity level of the associated biological process or pathway.

scRNA-seq analysis

The cell multiplexing (CellPlex reagents, 10 × Genomics) and single-cell library preparation were conducted using Single Cell 3' v3.1 kit (10 × Genomics) according to the manufacturer's protocol. The libraries were sequenced on the Illumina NovaSeq 6000 platform with NovaSeq 6000 S4 Reagent Kit (300 cycles). The scRNA-seq data was processed, and the matrix data containing gene counts for each cell per sample was generated using Cell Ranger (V3.0.2, https://support.10xgenomics.com/).

CellBender (85) was used to minimize ambient RNA content. Subsequent downstream analyses and data normalization were performed using the Seurat v5 pipeline (86). For quality control, cells were filtered based on specific criteria: cells with fewer than 400 or more than 10,000 detected genes, fewer than 1,000 or more than 40,000 counts, a ribosomal gene proportion below 0.04 or above 0.50, a mitochondrial gene proportion above 0.2, log10 genes per UMI ratio below 0.65, or a hemoglobin-related gene proportion exceeding 0.03 were excluded. After this step, 9,422 cells from condition PLT012 and 12,322 cells from the Ctrl condition were retained for further analysis.

Cell types were annotated using scGate (34), and doublets were removed using DoubletFinder (87), assuming a doublet rate of 10%. The doublet-free dataset was re-annotated with scGate, and cell type classifications were further refined through unsupervised clustering and marker definition. This process allowed to remove additional doublets missed by DoubletFinder and to better define clusters of unconventional T cells, such as γδT and iNKT cells. Subsequent analyses were performed on 2,227 T cells from condition PLT012 and 4,385 T cells from the Ctrl condition.

Conventional T cells (CD4+ and CD8+ T cells) were further subclassified using ProjecTILs (88), with a murine reference atlas for tumor-infiltrating conventional T cells, including CD8+ Tex, CD8+Tpex, and Treg (https://doi.org/10.6084/m9.figshare.12478571.v3). Differential gene expression was performed for each T cell subtype between PLT012 and Ctrl using `FindMarkers` function from Seurat, with default parameters (Wilcoxon Rank Sum test). Differential expression analysis was performed only when at least 10 cells were present in at least one condition.

Gene Set Enrichment Analysis (GSEA) focused on T cell activation, effector functions, and cellular metabolism pathways. These pathways were derived by filtering the mouse MSigDB database (89). The custom combined list of pathways was used for GSEA analysis using ClusterProfiler (90), using 2000 permutations. The enrichment analysis was based on genes ranked by -log10 of the adjusted p-value plus the absolute value of log2 fold change and its sign, obtained from the differential expression analysis between conditions.

Cynomolgus monkey toxicity study

Cynomolgus monkeys (Macaca fascicularis) received 5 once-weekly IV doses of PLT012 at concentrations of 0, 10, 60, or 200 mg/kg on Day 1, 8, 15, 22 and 29. Each group was comprised of two males and two females, with one animal of each sex exhibiting low expression and one exhibiting high expression of CD36 in RBC. The animals were monitored for clinical signs, including injection site reactions, body weight, and ophthalmoscopy. Clinical pathology (hematology, serum chemistry, and coagulation) was evaluated before the study commenced and on days 2, 8, 16, and 30 (before the scheduled necropsy). Immunotoxicology (immunophenotyping) was conducted on blood samples collected before the study began and 4, 24, and 168 hours after the 1st and 4th administrations. Upon completion of the 30-day study, all cynomolgus monkeys underwent necropsy with comprehensive macroscopic postmortem examination and organ weight recording. A complete list of tissues was obtained for histopathological examination.

The experiments involving cynomolgus monkeys were carried out at Wuxi AppTec (Suzhou, China). The protocol, along with any amendments or procedures concerning the care or use of animals in this study, was reviewed and approved by the WuXi AppTec Institutional Animal Care and Use Committee (IACUC) before the study commenced.

Human sample ex vivo culture platform

Human HCC samples were collected from Centre Hospitalier Universitaire Vaudois (CHUV, Switzerland), Clinic Favoriten (Vienna), Chang Gung Memorial Hospital (Taiwan), and New Taipei City Tucheng Hospital (Taiwan) in accordance with the approval of the institutional review board (IRB) at each institution. The detailed culture procedures were described previously (50,91). In short, cryopreserved vials containing minced tumor fragments were thawed, embedded in Matrigel-containing matrix (Matrix High Concentration, Phenol Red-Free, 4 mg/ml final concentration; BD Biosciences), and cultured for two days with either either 10 μg/mL of control human anti-β-Gal-hIgG4 (S228P; InvivoGen) or PLT012. Tumor-associated cell suspensions were then collected from pooled tumor fragments. These suspensions underwent FACS analysis of the immune profile using Aurora spectral flow cytometry (Cytek) with the following antibodies: anti-CD3 APC-F750 (BioLegend Cat# 981006, RRID:AB_2894549), anti-CD4 NovaFB585 (Thermo Fisher Scientific Cat# H001T03B04-A, RRID:AB_3097881), anti-CD8 PerCP (BioLegend Cat# 980916, RRID:AB_2890877), anti-CD14 PerCP-eF710 (Thermo Fisher Scientific Cat# 46-0149-42, RRID:AB_10671405), anti-CD19 PerCP-eF710 (Thermo Fisher Scientific Cat# 46-0199-42, RRID:AB_2866432), anti-CD36 BUV805 (BD Biosciences Cat# 748645, RRID:AB_2873052), anti-CD45RA BV785 (BioLegend Cat# 304140, RRID:AB_2563816), anti-CCR7 PE-Cy7 (BioLegend Cat# 353226, RRID:AB_11126145), anti-FOXP3 PE-Cy5 (Thermo Fisher Scientific Cat# 15-4777-42, RRID:AB_2811750), anti-GzmB BV510 (BD Biosciences Cat# 563388, RRID:AB_2738174), and ZombieNIR (BioLegend Cat#423106).

Quantification and statistical analysis

Statistical analyses for multiple groups were performed using one-way ANOVA, and two-tailed, unpaired Student t-tests were conducted for two-group comparisons. The data are presented as means ± s.e.m., and each point represents a biological replicate. The Box and Whisker plot displayed the distribution of immune cells, with the Whisker indicating minimum to maximum values.

Statement of Significance.

Despite the success of cancer immunotherapies, like immune checkpoint inhibitors, many patients still fail to demonstrate significant responses due to metabolic constraints in tumors. PLT012 rejuvenates anti-tumor immunity by targeting metabolic pathways to reprogram the immune landscape of liver cancer and liver metastasis, with potential to impact future HCC immunotherapy.

Code availability

No customized code has been used to produce analytic results for this study.

Data availability

Data generated in this study are publicly available in Gene Expression Omnibus (RRID: SCR_005012) at GSE291964. Raw cryo-EM images were deposited to the Electron Microscopy Public Image Archive (EMPIAR) with accession code EMPIAR-12599. The cryo-EM map of CD36-Fab was deposited to the Electron Microscopy Data Bank (EMDB) with accession code EMD-52203.