¹H NMR Reveals Predictive Metabolic Signatures of Response to RMP1-14 Treatment in the Plasma of Tumor-Bearing Mice

Understanding and predicting individual responses to immune checkpoint inhibitors (ICIs) are of great importance for advancing precise cancer immunotherapy. This study utilizes ¹H NMR-based plasma phenotyping to reveal the metabolic characteristics and predictive signatures of tumor-bearing mice that are sensitive and nonsensitive to the PD-1 inhibitor RMP1-14. Metabolite set enrichment analysis showed that the high-sensitivity (HS) group may exhibit a more robust immune profile by upregulating sphingolipid metabolism and lactose metabolism before treatment, while the enrichment of branched-chain amino acids and disorder of glucose and lipid metabolism in the low-sensitivity (LS) group may facilitate tumor growth and immune escape. ¹H NMR information on glycoproteins and lipoproteins in predose plasma was significantly correlated with differences in treatment effects. This correlation was validated by the random forest prediction model constructed from the NMR information, which demonstrated a lower out-of-bag error rate. The model further suggested that acetyl-signaling GlycA and GlycB from N-acetyl glycoproteins of predose plasma are predictive signatures for the HS group, whereas lipid-related signals were predictive for the LS group. This study presents an NMR-based framework for investigating and predicting individual variability in responses to ICIs, providing a methodological and data-driven foundation for enhancing precision cancer immunotherapy.