Renal cell carcinoma (RCC) is a biologically heterogeneous malignancy accounting for 3% of adult cancers globally. Despite advances in immune checkpoint inhibitors (ICIs) and vascular endothelial growth factor (VEGF)-targeted therapies, durable disease control remains elusive for many patients. Increasing evidence implicates the acidic tumour microenvironment (TME) as a critical mediator of RCC progression, immune evasion, and therapeutic resistance. Solid tumours, including RCC, exhibit reversed pH gradients, characterised by acidic extracellular (pH 6.2–6.9) and alkaline intracellular conditions. This dysregulation arises from enhanced glycolysis, hypoxia-driven lactate accumulation, and the overexpression of pH-regulating enzymes such as carbonic anhydrase (CA9). Acidic TMEs impair cytotoxic T-cell and NK-cell activity, promote tumour-associated macrophage (TAM) polarisation towards an immunosuppressive phenotype, and upregulate alternative immune checkpoints. These mechanisms collectively undermine ICI efficacy and contribute to primary and secondary treatment resistance. Proton-sensing G-protein-coupled receptors (GPCRs), notably GPR65, have emerged as pivotal mediators linking extracellular acidosis to immune dysfunction. Preclinical studies demonstrate that GPR65 antagonists restore anti-tumour immune activity by reversing acidosis-driven immunosuppression and enhancing antigen processing. In RCC models, selective GPR65 inhibitors have shown the ability to reduce immunosuppressive cytokine IL-10 production, induce immunoproteasome activation, and synergise with anti-PD-1 therapy. The first-in-class GPR65 inhibitor, PTT-4256, is now under evaluation in the Phase I/II RAISIC-1 trial (NCT06634849) in solid tumours, including RCC. Targeting acid-sensing pathways represents a novel and promising therapeutic strategy in RCC, aiming to remodel the TME and overcome ICI resistance. Integrating GPR65 inhibition with existing immunotherapies may define the next era of RCC management, warranting continued translational and clinical investigation.

5074 Background: Existing clinicopathological tools are unable to accurately identify renal cell carcinoma (RCC) patients who will develop metastases after surgery. As a result, it is unclear how long and how often to follow-up patients post-operatively. Tumor macropathology, as assayed by CT scanning, represents the sum product of tumor biology and microenvironment. We hypothesized that quantitative tumor features extracted from CT scans (termed radiomics) could discriminate between metastatic and non-metastatic RCCs. Methods: This retrospective study incorporated three cohorts of clear-cell RCC patients (n = 279, from TCGA, CPTAC and KiTS19 datasets) treated with nephrectomy. The study cohort was sub-divided into metastatic (n = 54, M1 at diagnosis or recurrence after surgery), high metastatic risk/HMR (n = 85, N1, T3-4, T2G3/4, T1G4) or low metastatic risk/LMR (n = 140, absence of these features) subsets. 3D primary tumor segmentation of arterial contrast CT scans was performed by trained investigators. Features were extracted using pyRadiomics 2.2.0 (n = 839) with gray value and voxel size normalization. For random forest (RF) model training, the cohort was randomly split into training (75%) and validation (25%) sets. Results: Multidimensional clustering of radiomic features by t-SNE analysis showed that metastatic and HMR tumors predominantly cluster together, while LMR tumors cluster separately. Consistent with this, there were no differentially regulated radiomic features (DR-features) between HMR and metastatic tumors. In contrast, we identified 26 DR-features (adjusted p-value < 0.05) between presumed-metastatic (n = 139, HMR and metastatic tumors) and LMR tumors, which were then used as input to a RF binary classifier. In the training set, the trained classifier discriminated between presumed-metastatic and LMR tumors with bootstrapped AUC = 0.81. In the validation set, the classifier discriminated subsets with AUC = 0.80. Conclusions: High-risk and metastatic tumors have similar radiomic properties, suggesting common biology driving metastasis in RCC. We propose a novel radiomic classifier that accurately distinguishes between presumed-metastatic and low-risk tumors. Further work will assess whether this tool can identify patients with micrometastatic disease at diagnosis, who may benefit from adjuvant therapy or closer, long-term surveillance.