Fanelli Giuseppe Nicolò, Roviello Giandomenico, Nesi Gabriella
Division of Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa.
First Division of Pathology, Department of Oncology, University Hospital of Pisa, Pisa, Italy.
Curr Opin Urol. 2025 Nov 20. doi: 10.1097/MOU.0000000000001357.
This review outlines a pathology-driven framework that integrates morphology, immunophenotype, and molecular profiling to inform personalized treatment strategies in renal cell carcinoma (RCC), particularly with immunotherapy and tyrosine kinase inhibitors (TKIs).
Systemic therapy for RCC has progressed from cytokine-based regimens to VEGF-targeted TKIs and, more recently, immune checkpoint inhibitors (ICIs), alone or in TKI combinations, resulting in improved survival. Yet, reliable predictive biomarkers remain an unmet need. Programmed death-ligand 1 (PD-L1) expression, while biologically relevant, offers limited clinical utility, as ICI responses occur in both PD-L1-positive and -negative tumors. Tumor microenvironment features (e.g., T-effector and myeloid inflammation signatures) and genomic alterations (e.g., PBRM1, BAP1, SETD2) provide biological and prognostic insights, but have inconsistent predictive value.
Pathology remains essential for accurate histologic classification, grading, and assessment of adverse features such as sarcomatoid changes and necrosis. Molecular profiling is increasingly helpful in non-clear cell RCC, guiding targeted therapies in subtypes such as MET-driven papillary RCC. Emerging tools (liquid biopsy, spatial transcriptomics, and AI-assisted pathology) offer minimally invasive monitoring, refined immune profiling, and multiparametric biomarker integration to advance precision oncology in RCC.
本综述概述了一个病理学驱动的框架，该框架整合了形态学、免疫表型和分子谱分析，以为肾细胞癌（RCC）的个性化治疗策略提供依据，特别是在免疫治疗和酪氨酸激酶抑制剂（TKIs）方面。
RCC的全身治疗已从基于细胞因子的方案发展到VEGF靶向的TKIs，最近又发展到单独或与TKIs联合使用的免疫检查点抑制剂（ICIs），从而提高了生存率。然而，可靠的预测生物标志物仍然是未满足的需求。程序性死亡配体1（PD-L1）表达虽然具有生物学相关性，但其临床应用有限，因为ICI反应在PD-L1阳性和阴性肿瘤中均有发生。肿瘤微环境特征（如T效应细胞和髓系炎症特征）和基因组改变（如PBRM1、BAP1、SETD2）提供了生物学和预后方面的见解，但预测价值不一致。
病理学对于准确的组织学分类、分级以及评估不良特征（如肉瘤样改变和坏死）仍然至关重要。分子谱分析在非透明细胞RCC中越来越有帮助，可指导MET驱动的乳头状RCC等亚型的靶向治疗。新兴工具（液体活检、空间转录组学和人工智能辅助病理学）提供微创监测、精细的免疫谱分析和多参数生物标志物整合，以推动RCC的精准肿瘤学发展。
