Baseline metabolic signatures predict clinical outcomes in immunotherapy-treated melanoma patients: a pilot study

Simona De Summa¹Giuseppe De Palma¹Veronica Ghini²Benedetta Apollonio¹Ivana De Risi¹Antonio Tufaro¹Sabino Strippoli¹Claudio Luchinat²Leonardo Tenori²Michele Guida^1*

• ¹IRCCS Istituto Tumori "Giovanni Paolo II" Bari, Bari, Italy
• ²University of Florence, Florence, Tuscany, Italy

Background: 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 serumderived metabolomic signatures that could predict clinical responses in MM patients treated with ICIs. Patients and methods: 1 H-NMR (Proton nuclear magnetic resonance) was used to analyze the serum metabolomic profiles from 71 MM patients undergoing anti-PD-1 therapy (43 patients as first-line, 27 as second-line, 1 as third-line). Feature selection was applied to identify key metabolites within these profiles, to develop risk score models predicting overall survival (OS) and progression-free survival (PFS). Results: Multivariable model identified distinct prognostic factors for OS. Negative factors included glucose, high-density lipoprotein (HDL) cholesterol, and apolipoprotein B-very lowdensity lipoprotein (ApoB-VLDL), while 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. 3-fold-cross validation resulted in a mean Concordance Index 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, Concordance Index raised to 0.81 and 0.9 for PFS and OS. Conclusions: Our data identified glyco-lipid signatures as robust predictors of distinct therapeutic outcomes in MM patients treated with ICIs. These results could pave the way for novel therapeutic approaches.