Metabolic determinants of exceptional response to immune checkpoint inhibition in renal cell carcinoma.

Abstract

571 Background: Immune checkpoint inhibitors (ICI) have led to remarkable, long-lasting responses in a subset of patients with metastatic renal cell carcinoma (RCC). However, the molecular mechanisms driving these exceptional responses (ER) are not fully characterized. We previously observed that a high tertiary lymphoid structure (TLS) signature is associated with ER. Here, we explore metabolic molecular determinants that may influence ER in patients treated with ICI and VEGF inhibitors. Methods: We analyzed genomic and transcriptomic data from 357 treatment-naïve patients with advanced clear cell RCC who were given PD1/L1 and VEGF inhibitors (ICI/VEGFi) in a phase III clinical trial. Patients were categorized into three groups: (a) extreme responders (ER), including those with complete response (CR) and progression-free survival (PFS) of at least 12 months, or partial response (PR) with significant tumor shrinkage and prolonged PFS; (b) intermediate responders (IR), patients with CR or PR not meeting ER criteria; and (c) patients with progressive disease (PD). Whole exome sequencing (WES) was used to analyze somatic mutations, copy number changes, and compute clonal neoantigen load (CNL). Gene set enrichment analysis (GSEA) was performed to explore molecular pathways, and multivariate Cox proportional hazards analysis identified PFS predictors. Results: Metabolic pathway enrichment was notable in ER patients with low TLS signatures, with pathways such as peroxisome function, xenobiotic and bile acid metabolism, adipogenesis, fatty acid metabolism, and oxidative phosphorylation showing upregulation. These findings held after adjusting for tumor purity. Next, we defined a metabolic gene signature based on the enriched metabolic genes in TLS-low ER samples compared to TLS-high ER samples. ER patients with low TLS scores had significantly higher metabolic signature scores than IR (p=0.00062) and PD (p=0.00012) patients. Higher metabolic signature scores correlated with improved PFS (p=0.054) and overall survival (OS) (p=0.0037). Multivariable analysis confirmed that favorable IMDC risk (p<0.001) and a high metabolic signature (p=0.01) were independent predictors of ER. Conclusions: Metabolic pathways are critical drivers of exceptional responses to ICI in RCC, especially in patients with low TLS scores. A high metabolic gene signature may serve as a predictive biomarker for ER, offering insights into new therapeutic strategies targeting metabolic reprogramming in this unique subset of patients.