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Updated: Sep 11, 2025

A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
Published on: April 12, 2017
Decoding Treatment Failures in Metastatic Renal Cell Carcinoma: Predictors Across Immunotherapy and Targeted
Sorin Saftescu1, Vlad-Norin Vornicu1, Dorel-Ionel Popovici1
1Department of Oncology, Faculty of Medicine, Victor Babes University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square 2, 300041 Timisoara, Romania.
Abstract:
Background: Despite recent advances in the management of metastatic renal cell carcinoma (mRCC), real-world outcomes remain heterogeneous, and early treatment failure is common. Predictive biomarkers for time to treatment failure (TTF) outside clinical trials are poorly characterized. Objective: To identify clinical and laboratory predictors associated with early treatment failure in a real-world cohort of mRCC patients treated with immune checkpoint inhibitors (ICIs), tyrosine kinase inhibitors (TKIs), or combination regimens. Methods: We conducted a retrospective, single-center analysis of patients with metastatic non-urothelial RCC treated between 2018 and 2023. Cox proportional hazards regression was used to evaluate the association between baseline biological parameters and TTF for each treatment regimen. Results: Among 137 patients receiving first-line therapy, 50 received Ipilimumab + Nivolumab, 49 Sunitinib, and 17 Avelumab + Axitinib. For Ipilimumab + Nivolumab, elevated AST was significantly associated with shorter TTF. For Avelumab + Axitinib, shorter TTF was associated with lymph node metastases, low lymphocyte count, low creatinine, low BMI, and low hemoglobin. For Cabozantinib in subsequent lines, a higher platelet count, ALT, and presence of liver metastases were associated with shorter TTF. No statistically significant predictors were found for Nivolumab used in the second-line setting. Conclusions: Routine, accessible biomarkers such as AST, hemoglobin, lymphocyte count, and creatinine may serve as predictors of treatment failure in specific therapeutic contexts. These findings support risk-adapted strategies and individualized monitoring in real-world clinical practice, though further validation in larger cohorts is warranted.
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