Deciphering the mechanistic landscape of immune checkpoint blockade in ccRCC: from molecular drivers to therapeutic responses

Abstract

Immune checkpoint inhibitor (ICI) based combination therapies have revolutionized the management of advanced clear-cell renal cell carcinoma (ccRCC), establishing a new standard of care and significantly improving survival outcomes. However, this success is challenged by substantial heterogeneity in patient response, with primary and acquired resistance remaining major clinical hurdles that limit durable benefit for a substantial proportion of patients. This review synthesizes our current understanding of the multifaceted mechanisms governing these outcomes. We explore the complex interplay between tumor-intrinsic drivers of resistance, such as mutations in key genes like PBRM1, and the profoundly immunosuppressive landscape of the tumor microenvironment (TME), which includes diverse inhibitory cell populations, metabolic reprogramming, and stromal barriers. We then highlight how multi-omics technologies, from single-cell and spatial transcriptomics to proteomics, are decoding the TME’s intricate cellular and spatial architecture to reveal novel biomarkers and therapeutic targets. Crucially, we discuss the pivotal role of artificial intelligence (AI) in translating this high dimensional data into clinically actionable insights. AI-driven models in pathomics and radiomics are creating powerful, non-invasive tools to predict treatment response and prognosis from images, while deep learning algorithms are proving essential for integrating multi-omics data to guide patient selection and accelerate drug discovery. Ultimately, the convergence of these advanced biological insights and computational strategies is paving the way for precision immuno-oncology, with the goal of moving beyond current risk stratification toward truly personalized ICI therapy for patients with ccRCC.