Minkyung Song

The tumour microenvironment imposes severe metabolic constraints that reshape anti-tumour immunity across the cancer-immunity cycle. Rather than serving merely as passive byproducts of tumour growth, tumour-derived metabolites and nutrient imbalances act as potent metabolic checkpoints−stage-specific barriers that disrupt the functional progression of dendritic cells (DCs) and T cells from antigen presentation to effective tumour clearance. In this review, we propose a framework that overlays the cancer-immunity cycle with major metabolic checkpoints, including glucose and amino acid competition, acidosis and lipid overload, to clarify how distinct metabolic stresses create immune bottlenecks at different stages of the anti-tumour response. We then discuss how distinct tumour metabolic phenotypes, characterized by high glycolysis, amino acid dependency or lipid dysregulation, generate local environmental stresses that differentially reprogram DC function and T cell fitness. Particular emphasis is placed on the DC–T cell axis as a critical site where multiple metabolic defects converge, destabilizing antigen presentation, co-stimulation and immunological synapse function. We further survey emerging therapeutic strategies aimed at restoring the DC–T cell axis and effective anti-tumour immunity, ranging from small-molecule metabolic inhibitors to metabolically engineered adoptive cell therapies designed to function in hostile microenvironments. Finally, we highlight emerging technologies such as single-cell and spatial multi-omics, real-time metabolic imaging and microphysiological systems that can resolve the spatiotemporal heterogeneity of tumour immunometabolism and support more precise immunometabolic interventions.

Table S3 contains all statistically significant DEGs from RNAseq data

Table S2 contains all the information related to mass spectrometry

Table S5 contains all the primers used in this study

Table S1 describes all the human ascites samples analyzed

Table S4 describes the organoids used in this study

Figure S1. (a) Effect of dietary iron levels on ovarian cancer progression. Figure S2. Flow cytometry gating strategy for the analysis of peritoneal cells from mice bearing ID8Defb29/Vegfa tumors that were treated with deferiprone as single agent or in combination with cisplatin. Figure S3. (a-f) Wild-type female mice were implanted with ID8-Defb29/Vegfa tumors and treated as described in Fig 2 d. Figure S4. (a) Representative images of NKp46 staining on the entire omentum. Figure S5. (a) MTT assays for ID8-Defb29/Vegfa, PPNM, and MP cells treated with different concentrations of deferiprone for 12, 24, and 48 hours. Figure S6. (a) Human ovarian cancer cell lines were treated with deferiprone for 48 hours and expression of the indicated genes was analyzed by qRT-PCR. Colors indicate fold change (FC) upon deferiprone treatment compared with vehicle control. Figure S7. (a-b) Mitochondrial iron was traced using mito-ferrogreen in iron-overloaded ID8-Defb29/Vegfa cells that were exposed to deferiprone (100 µM) or vehicle control. Figure S8. (a) Schematic representation of treatment regimens under IFNAR1 blockade related to Fig. 6 a-c.