Neoadjuvant PD-L1 Inhibitor Plus Anlotinib for Kidney Preservation in Complex Renal Cell Carcinoma

Overview

This is a prospective, multicenter, single-arm phase II study evaluating the efficacy and safety of neoadjuvant PD-L1 inhibitor TQB2450 in combination with anlotinib in patients with locally advanced, high-complexity (RENAL score ≥10) clear cell renal cell carcinoma (ccRCC). The primary objective is to determine whether neoadjuvant therapy can increase the rate of successful nephron-sparing surgery. In addition, this study incorporates a pre-specified translational research platform including circulating tumor DNA (ctDNA) methylation-based minimal residual disease (MRD) monitoring, tumor multi-omics profiling, and radiomics analysis. Artificial intelligence-based models will be developed to predict treatment response and surgical conversion, enabling precision neoadjuvant strategies.

Patients with anatomically high-complexity locally advanced ccRCC (RENAL score ≥10) often require radical nephrectomy or technically challenging partial nephrectomy, resulting in increased perioperative risks and long-term renal function impairment. Evidence supporting neoadjuvant strategies in this specific high-complexity population remains limited. This study evaluates a novel neoadjuvant approach combining PD-L1 blockade (TQB2450) with the multi-target tyrosine kinase inhibitor anlotinib. PD-L1 inhibition restores anti-tumor immunity, while antiangiogenic therapy promotes vascular normalization and enhances immune cell infiltration. This dual mechanism may improve tumor shrinkage and facilitate surgical conversion, particularly in central or hilar tumors. Patients will receive 2-4 cycles of neoadjuvant therapy, followed by imaging assessment and multidisciplinary team (MDT) evaluation. Surgery will be performed within a predefined window after treatment completion. A key translational component includes longitudinal ctDNA methylation-based MRD monitoring at four time points (baseline, after 2 cycles, pre-surgery, and post-surgery), combined with tumor tissue RNA expression and DNA methylation profiling, as well as radiomics feature extraction. These data will be integrated using machine learning algorithms to construct predictive models for treatment response and nephron-sparing surgery feasibility.