泌尿时讯 发表时间:2026/1/14 15:40:05
编者按:从VEGF药物到免疫联合方案及新型HIF-2α抑制剂,肾细胞癌的治疗手段的日益丰富在延长患者生存的同时,也向临床医生提出了更严苛的命题:如何为每一位患者量体裁衣,实现真正的精准医疗?尽管肾癌在很长一段时间内被视为缺乏临床可操作生物标志物的“荒漠”,但随着大规模临床试验与多组学技术的结合,这一困局正迎来转机。肿瘤瞭望-泌尿时讯特别邀请美国范德堡大学医院Brian Rini教授,深度解析肾癌生物标志物研究的进化历程,探讨KIM-1如何助力筛选辅助治疗获益人群,并前瞻性地擘画了未来多维度、动态化精准诊疗的新前景。
01
《肿瘤瞭望-泌尿时讯》:您在肾癌领域已有20多年的丰富经验,能否谈谈在肾细胞癌领域,有哪些相对成熟或有前景的生物标志物,可用于指导预后分组和治疗决策?
Brian Rini 教授:这是一个值得深入探讨的问题。尽管我们投入了20多年的努力,肾癌仍然是实体肿瘤中少数缺乏临床可操作生物标志物的癌种之一。这一现状与乳腺癌、肺癌等肿瘤形成鲜明对比,后者已经建立了较为成熟的生物标志物指导体系。
生物标志物的研究历程的演进可以分为两个阶段:在2005-2015年的VEGF靶向治疗时代,由于临床试验规模有限、生物样本库建设不完善以及分子检测技术的局限性,标志物研究进展缓慢。当时的研究主要聚焦于VEGF通路相关基因,但未能找到具有临床指导意义的标志物。
转机出现在免疫治疗时代。随着CheckMate 025试验证实纳武利尤单抗在晚期肾癌中的生存优势,肾癌治疗正式进入免疫时代。大规模临床试验的开展为生物标志物研究提供了宝贵的样本资源。然而,PD-L1表达在肾癌中的预测价值远不如在肺癌或黑色素瘤中明确。我们的数据显示,PD-L1表达虽然与双免疫治疗方案(伊匹木单抗+纳武利尤单抗)的应答率有一定相关性,但无法有效区分哪些患者更适合双免疫治疗方案还是免疫联合靶向治疗方案。
转录组学技术的突破为肾癌分型带来了新希望。基于IMmotion151研究的分子亚型分类是重要的里程碑。IMmotion151研究通过大规模分析确定了7个分子聚类(Clusters),后续多项研究也逐步验证了肾癌存在血管生成型、免疫型、增殖型等主要分子亚型。尽管分类仍在不断细化,但不同数据集已开始指向一致的生物学内涵。我们团队在OPTIC研究中发现,这种分型能够预测患者对卡博替尼+纳武利尤单抗方案的应答情况。特别值得注意的是,血管生成特征显著的患者对该方案的应答率更高,这为治疗选择提供了分子层面的依据。
我们目前的工作重心,是推动这些发现进入前瞻性临床验证,明确这些分型能否在实际临床中区分不同药物的疗效,特别是在辅助治疗中精准筛选出真正能够获益的人群。
Dr. Brian Rini:This is a question worth exploring in depth. Despite over two decades of effort, kidney cancer remains one of the few solid tumors that lack clinically actionable biomarkers. This stands in stark contrast to breast and lung cancers, which have established relatively mature biomarker-guided systems.
The evolution of biomarker research can be divided into two stages:
The VEGF Era (2005–2015): Progress was slow due to the limited scale of clinical trials, underdeveloped biobanks, and limitations in molecular detection technologies. Research primarily focused on VEGF pathway-related genes but failed to identify markers with clinical utility.
The Immunotherapy Era: The turning point arrived with the CheckMate 025 trial, which confirmed the survival advantage of Nivolumab in advanced RCC. However, the predictive value of PD-L1 expression in kidney cancer is far less clear than in lung cancer or melanoma. Our data indicates that while PD-L1 expression correlates with response rates for dual immunotherapy (Ipilimumab + Nivolumab), it cannot effectively distinguish whether a patient is better suited for dual-IO or IO-TKI combinations.
Breakthroughs in transcriptomic technology have brought new hope for RCC subtyping. The molecular subtyping based on the IMmotion151 study is a major milestone, identifying seven molecular clusters through large-scale analysis. Subsequent studies have validated the existence of major molecular subtypes, such as angiogenic, immune-inflamed, and proliferative. In our OPTIC study, we found these subtypes could predict responses to the Cabozantinib + Nivolumab regimen. Specifically, patients with strong angiogenic signatures showed higher response rates, providing a molecular basis for treatment selection. Our current focus is to drive these findings into prospective clinical validation to determine if these subtypes can differentiate drug efficacy in practice.
02
《肿瘤瞭望-泌尿时讯》:KIM-1是肾细胞癌的新兴生物标志物,ASSURE、IMmotion010等临床试验都使用到了该标志物。您能否分享一些重要的研究进展以及该生物标志物的未来应用潜力?
Brian Rini 教授:KIM-1确实是我们目前看到的最有前景的肾癌生物标志物之一。这个分子最初在急性肾损伤中被发现,因其在肾小管损伤后显著高表达而得名。在肾癌研究中我们发现,KIM-1在肾癌患者血浆中特异性升高,且与疾病进展密切相关。
Dr. Brian Rini:KIM-1 is indeed one of the most promising biomarkers we see today. Originally discovered in acute kidney injury, it is highly expressed following tubular damage. In RCC research, we found KIM-1 is specifically elevated in the plasma of patients and closely correlates with disease progression.
The retrospective analysis of IMmotion 010 is particularly exciting. Although this III trial did not reach its primary endpoint of DFS, it provided a valuable opportunity for biomarker research. Among 2,500 plasma proteins tested, KIM-1 showed the most significant elevation in patients who recurred. Analysis revealed its dual clinical value:Prognostic Value: Patients with high baseline KIM-1 levels (> median) after nephrectomy had a 2.1-fold higher risk of recurrence (HR=2.1, P<0.001).Predictive Value: In patients with high KIM-1 levels, adjuvant Atezolizumab significantly extended disease-free survival (DFS) compared to placebo (HR=0.72), whereas no significant difference was observed in the low-level group (HR=0.98).
This finding is clinically significant because current FDA-approved adjuvant therapies like Pembrolizumab are applied to broad high-risk groups, meaning nearly 40% of patients may face overtreatment. If KIM-1 is prospectively validated, we can more accurately identify those who truly benefit from adjuvant immunotherapy.
03
《肿瘤瞭望-泌尿时讯》:展望未来,您认为肾细胞的分子分型和生物标志物指导的精准治疗,可能会有哪些重要研究方向?
Brian Rini 教授:随着贝组替凡(Belzutifan)等HIF-2α抑制剂的问世,肾癌治疗手段正在向全新的作用机制迈进,这标志着在对抗VHL-HIF通路这一肾癌核心发病机制上取得了直接突破。然而,治疗的进步也带来了“幸福的烦恼”:随着免疫+靶向联合方案成为晚期肾癌的一线标准,以及“免疫+靶向+免疫”或“免疫+靶向+其他机制药物”等三药联合方案的临床研究如火如荼,药物毒性的叠加效应与医疗系统的经济负担已成为我们无法回避的严峻挑战。并非所有患者都需要,或能够耐受如此强度的治疗。因此,未来的核心命题不再是简单地为所有患者寻找“更强”的方案,而是如何为个体患者寻找“最合适”的方案。
在此背景下,肾癌精准医疗的未来发展必须聚焦于以下几个核心方向,实现从“粗放式”用药到“精细化”管理的范式转变:
1. 构建多维度、动态化的生物标志物整合评估体系
单一生物标志物很可能无法捕捉肾癌的全身性及异质性。未来的方向是将循环生物标志物(如血浆中的KIM-1、ctDNA)、组织分子特征(如基于RNA测序的转录组分型、免疫微环境特征)、基因组学信息(如PBRM1、BAP1等基因突变状态)以及影像组学(通过AI分析CT或MRI图像提取的深层特征)进行有机整合。这不仅仅是数据的简单叠加,而是需要通过人工智能和机器学习算法,构建一个综合性的预测模型。这个模型的目标是,在患者诊断之初或治疗决策的关键节点,就能全景式地评估其肿瘤的生物学行为、预后风险以及对不同治疗模式的潜在反应,从而为患者绘制一幅独一无二的“治疗导航图”。
2. 从“选择药物”到“回答关键临床问题”,指导全程化管理
3. 通过严谨的前瞻性临床研究,推动实验室发现向临床工具的转化
我们正处在肾癌生物标志物研究取得突破的黎明前夕,但许多发现仍停留在回顾性分析或假设生成阶段。要将这些充满希望的信号转化为医生手中可信赖的临床工具,前瞻性、干预性的临床验证是唯一的必经之路。这意味着我们需要设计并执行专门的临床试验,例如,将患者随机分配到“基于生物标志物指导的治疗组”和“标准治疗组”,以确凿证据证实这种精准指导能真正改善患者的生存预后。这需要学术界、制药行业和监管机构的通力合作。尽管前路漫长且投入巨大,但这是将精准医疗从理念变为现实的关键一步,它将最终决定我们能否为每一位肾癌患者带来真正意义上的个体化治疗。
Dr. Brian Rini:With the advent of HIF-2α inhibitors like Belzutifan, we are attacking the core VHL-HIF pathogenic mechanism of RCC. However, the rise of IO-TKI doublets and the ongoing research into triplet regimens bring "happy troubles": cumulative drug toxicity and economic burdens. The core mission is no longer just finding a "stronger" regimen for all, but finding the "most suitable" regimen for the individual.
The future must focus on a paradigm shift from "broad-spectrum" to "refined" management.
Integrating Multi-dimensional, Dynamic Assessment Systems: We must integrate circulating biomarkers (KIM-1, ctDNA), tissue features (transcriptomics, immune microenvironment), genomics (PBRM1, BAP1 mutations), and radiomics. By using AI and machine learning, we can build a predictive model to provide a "treatment navigation map" for each patient at the point of diagnosis.
Addressing Key Clinical Questions in Longitudinal Management: Biomarkers should guide treatment de-escalation. Can we use biomarkers like ctDNA clearance to identify patients who can safely stop treatment?. Furthermore, can we identify "functional cure" potential in advanced patients or predict severe immune-related adverse events (irAEs) based on genetic background?.
Driving Translation through Prospective Trials: We are at the dawn of a breakthrough, but many findings remain in the hypothesis-generating stage. Prospective, interventional trials—comparing biomarker-guided groups against standard care—are the only way to turn these signals into trusted clinical tools.
Brian I. Rini 教授
美国范德堡大学医院教授和范德比尔特大学医学中心临床试验主任
他领导着肾癌临床研究工作,并致力于拓展癌症临床研究业务。他曾担任多项III期临床试验的主要研究者,这些试验最终获得了美国食品药品监督管理局(FDA)的批准。他曾在本地、全国乃至国际范围内举办的众多研讨会和特邀讲座上就泌尿生殖系统癌症及其治疗发表演讲。
Brian I. Rini教授是美国临床肿瘤学会(ASCO)、肾癌协会(KCA)和癌症免疫治疗学会(SITC)的成员,并在这些组织中担任领导职务。
曾任肿瘤药物咨询委员会(ODAC)主席。