Spatial profiling of the metabolism-immune axis in ovarian cancer - Wang, Zhi-Bin; Long, Ming-Hui; Yu, Ping; Wang, Ya-Li; Yang, Zheng; Huang, Ma-Sha (Frontiers in Pharmacology, 2026)
Ovarian cancer remains a lethal disease marked by profound therapeutic resistance, largely orchestrated by a complex tumor microenvironment (TME) governed by metabolism-immune crosstalk. This review focuses on the spatiotemporal dynamics of the metabolism-immune axis in ovarian cancer progression and resistance, with particular emphasis on how cutting-edge spatial multi-omics technologies reveal previously unrecognized layers of intratumoral heterogeneity and geographic organization that cannot be captured by bulk analyses. Using tools such as MALDI-MSI, GeoMx DSP, and CODEX, these approaches enable high-resolution, spatially resolved mapping of metabolite gradients (e.g., lactate, lipids, kynurenine), immune cell niches, and immunometabolic checkpoints within distinct tumor regions. Such spatial profiling uncovers how metabolic reprogramming-dysregulated glycolysis, lipid metabolism, and glutaminolysis-drives localized immunosuppression and chemoresistance through compartment-specific interactions among tumor cells, cancer-associated fibroblasts (CAFs), adipocytes, and immune populations. These geographically defined insights reshape our understanding of therapeutic failure and highlight precise, location-aware vulnerabilities. Accordingly, we propose spatially informed therapeutic strategies, including regional glycolysis inhibition, glutaminase blockade, lipid pathway interference, and their rational combination with immune checkpoint inhibitors (ICIs), to disrupt pathogenic metabolic-immune circuits and improve immunotherapy outcomes. Looking ahead, advances in vivo spatial imaging, gut microbiota modulation, and AI-powered integrative multi-omics frameworks promise truly personalized treatment of ovarian cancer.