Tumor cells develop various strategies to evade immune surveillance, one of which involves altering the metabolic state of the tumor microenvironment. In response to metabolic stress in the tumor microenvironment, several tumor-infiltrating immune subsets upregulate CD36 to take up lipids. This leads to impaired antitumor immunity, as intratumoral regulatory T cells exhibit increased survival and suppressive activity, whereas CD8+ T cells become more susceptible to ferroptosis and exhaustion. In this study, 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 monkeys. PLT012 alone or in combination with PD-L1 blockade or standard-of-care immunotherapy results in robust antitumor immunity in both immunotherapy-sensitive and -resistant hepatocellular carcinomas (HCC). Notably, PLT012 also reprograms the immune landscape of human HCC ex vivo. Our findings provide proof-of-concept evidence that PLT012 reprograms antitumor immunity in HCC, positioning it as a first-in-class immunotherapy targeting CD36. Significance: Despite the success of cancer immunotherapies, like immune checkpoint inhibitors, many patients still fail to demonstrate significant responses because of metabolic constraints in tumors. PLT012 rejuvenates antitumor immunity by targeting metabolic pathways to reprogram the immune landscape of liver cancer and liver metastasis, with potential to influence future HCC immunotherapy.

Background: Tumor-infiltrating leukocytes (TILs) are immune cells surrounding tumor cells, and several studies have shown that TILs are potential survival predictors in different cancers. However, few studies have dissected the differences between hepatitis B- and hepatitis C-related hepatocellular carcinoma (HBV−HCC and HCV−HCC). Therefore, we aimed to determine whether the abundance and composition of TILs are potential predictors for survival outcomes in HCC and which TILs are the most significant predictors. Methods: Two bioinformatics algorithms, ESTIMATE and CIBERSORT, were utilized to analyze the gene expression profiles from 6 datasets, from which the abundance of corresponding TILs was inferred. The ESTIMATE algorithm examined the overall abundance of TILs, whereas the CIBERSORT algorithm reported the relative abundance of 22 different TILs. Both HBV−HCC and HCV−HCC were analyzed. Results: The results indicated that the total abundance of TILs was higher in non-tumor tissue regardless of the HCC type. Alternatively, the specific TILs associated with overall survival (OS) and recurrence-free survival (RFS) varied between subtypes. For example, in HBV−HCC, plasma cells (hazard ratio [HR] = 1.05; 95% CI 1.00–1.10; p = 0.034) and activated dendritic cells (HR = 1.08; 95% CI 1.01–1.17; p = 0.03) were significantly associated with OS, whereas in HCV−HCC, monocytes (HR = 1.21) were significantly associated with OS. Furthermore, for RFS, CD8+ T cells (HR = 0.98) and M0 macrophages (HR = 1.02) were potential biomarkers in HBV−HCC, whereas neutrophils (HR = 1.01) were an independent predictor in HCV−HCC. Lastly, in both HBV−HCC and HCV−HCC, CD8+ T cells (HR = 0.97) and activated dendritic cells (HR = 1.09) had a significant association with OS, while γ delta T cells (HR = 1.04), monocytes (HR = 1.05), M0 macrophages (HR = 1.04), M1 macrophages (HR = 1.02), and activated dendritic cells (HR = 1.15) were highly associated with RFS. Conclusions: These findings demonstrated that TILs are potential survival predictors in HCC and different kinds of TILs are observed according to the virus type. Therefore, further investigations are warranted to elucidate the role of TILs in HCC, which may improve immunotherapy outcomes.

In vivo distribution assay for PLT012 treatment in HCC mouse model.

Characterization for TILs population and gene signatures in liver cancer mouse models following PLT012 treatment.

Table S1 from PLT012, a Humanized CD36-Blocking Antibody, Is Effective for Unleashing Antitumor Immunity Against Liver Cancer and Liver Metastasis

An in silico analysis on POG570 to evaluate the potential of PLT012 in targeting metastatic diseases.