To consolidate knowledge on the mechanisms influencing patient responses to immune checkpoint inhibitors in advanced clear-cell renal cell carcinoma (ccRCC) and their implications for treatment outcomes.
Key Findings:
Significant variability in patient responses to immune checkpoint inhibitors, highlighting the need for personalized approaches.
Primary and acquired resistance are major clinical obstacles that limit the effectiveness of treatments.
Multi-omics approaches are revealing the complex organization of the TME, which is crucial for understanding resistance.
AI is enhancing the prediction of treatment responses and prognoses, potentially leading to better patient management.
Interpretation:
The integration of biological insights and computational methods is essential for advancing precision immuno-oncology and personalizing ICI therapy for ccRCC patients, ultimately improving clinical outcomes.
Limitations:
Variability in patient responses complicates treatment outcomes, necessitating further research.
Resistance mechanisms are not fully understood, which poses challenges for developing effective therapies.
Conclusion:
Advancements in multi-omics and AI are paving the way for personalized immunotherapy in ccRCC, moving beyond traditional risk stratification to enhance patient care and inform future research.
