A Transcriptional Signature of Metabolic-Immune Conflict Fails to Provide Independent Prognostic or Predictive Value in Melanoma

BackgroundThe tumor microenvironment is a site of intense metabolic competition between cancer cells and immune cells. In melanoma, tumor-intrinsic glutamine metabolism and the anti-tumor interferon-gamma (IFN{gamma}) immune response are critical opposing factors. We hypothesized that the interplay between these two pathways, termed a metabolic conflict, could be captured by a transcriptional signature to stratify patients and predict clinical outcomes. MethodsWe developed transcriptional signatures for glutamine metabolism and IFN{gamma} response pathways and calculated per-sample enrichment scores using Gene Set Variation Analysis (GSVA). We analyzed a discovery cohort of 469 melanoma patients from The Cancer Genome Atlas (TCGA-SKCM) to assess the signatures prognostic value. We then tested its predictive value for anti-PD-1 therapy in an independent validation cohort of 49 melanoma patients from GEO (GSE91061). ResultsIn the TCGA cohort, the glutamine metabolism and IFN{gamma} response scores were negatively correlated (Pearson r = -0.412), supporting the concept of a metabolic-immune conflict. Stratifying patients into four metabolic-immune groups revealed a trend towards improved overall survival for the Favorable group (Low Glutamine/High IFN{gamma}), but this did not reach statistical significance (log-rank p = 0.081). Cox proportional hazards models confirmed the signature lacked independent prognostic value when adjusted for clinical covariates. In the immunotherapy-treated validation cohort, the signature failed to predict clinical response to anti-PD-1 therapy (Chi-squared p = 0.706). ConclusionA transcriptional signature based on the interplay between glutamine metabolism and IFN{gamma} signaling, while biologically plausible, does not function as an independent prognostic or predictive biomarker in melanoma. Our findings underscore the value of rigorously testing well-founded hypotheses and highlight the complexity of translating metabolic-immune concepts into robust clinical tools.