Immune checkpoint inhibitors (ICIs) have improved the metastatic melanoma (MM) treatment. However, a significant proportion of patients show resistance to immunotherapy, and predictive biomarkers for Show more
Immune checkpoint inhibitors (ICIs) have improved the metastatic melanoma (MM) treatment. However, a significant proportion of patients show resistance to immunotherapy, and predictive biomarkers for non-responders or high-risk recurring patients are currently lacking. Recent studies have shown that tumor-related metabolic fingerprints can be useful in predicting prognosis and response to therapy in various cancer types. Our study aimed to identify serum-derived metabolomic signatures that could predict clinical responses in MM patients treated with ICIs. A multivariable model was used to identify distinct prognostic factors for OS. Negative factors included glucose, high-density lipoprotein (HDL) cholesterol, and apolipoprotein B-very low-density lipoprotein (ApoB-VLDL), whereas glutamine and free HDL cholesterol emerged as positive factors. They were then used to construct a risk score model able to stratify patients in prognostic groups. Similarly, a separate predictive risk score model for PFS was developed, focusing solely on glucose and apolipoprotein A1 (ApoA1) HDL. Threefold cross validation resulted in mean concordance indices of 0.72 and 0.74 for PFS and OS, respectively. Importantly, this analysis was replicated in patients who received first-line ICIs. Interestingly, the prognostic score for OS included glutamine, glucose, and LDL (low-density lipoprotein) triglycerides, whereas only glucose negatively influenced PFS. In this subset, the concordance indices increased to 0.81 and 0.9 for PFS and OS, respectively. Our data identified glycolipid signatures as robust predictors of distinct therapeutic outcomes in MM patients treated with ICIs. These results could pave the way for novel therapeutic approaches. Show less