Huang Xiatong, Qiu Wenjun, Kong Yuyun, Ou Qiyun, Mao Qianqian, Fang Yiran, Fan Zhouyang, Wu Jiani, Lu Xiansheng, Gu Wenchao, Luo Peng, Wang Junfen, Bin Jianping, Liao Yulin, Shi Min, Wu Zuqiang, Sun Huiying, Yu Yunfang, Liao Wangjun, Zeng Dongqiang
Department of Oncology Nanfang Hospital Southern Medical University Guangzhou Guangdong China.
Cancer Center the Sixth Affiliated Hospital School of Medicine South China University of Technology Foshan China.
MedComm (2020). 2025 Aug 25;6(9):e70324. doi: 10.1002/mco2.70324. eCollection 2025 Sep.
While circulating tumor DNA (ctDNA) testing has demonstrated utility in identifying muscle-invasive urothelial carcinoma (MIUC) patients likely to benefit from adjuvant immunotherapy, the prognostic value of transcriptome data from surgical specimens remains underexplored. Using transcriptomic and ctDNA data from the IMvigor010 trial, we developed an artificial intelligence (AI)-driven biomarker to predict immunotherapy response in urothelial carcinoma, termed UAIscore. Patients with high UAIscore had significantly better outcomes in the atezolizumab arm versus the observation arm. Notably, the predictive performance of the UAIscore consistently outperformed that of ctDNA, tTMB, and PD-L1, highlighting its value as an independent biomarker. Moreover, combining ctDNA, tTMB, and PD-L1 with the UAIscore further improved predictive accuracy, underscoring the importance of integrating multi-modality biomarkers. Further analysis of molecular subtypes revealed that the luminal subtype tends to be sensitive to adjuvant immunotherapy, as it may exhibit the highest level of immune infiltration and the lowest degree of hypoxia. Remarkably, we elucidated the role of the NF-κB and TNF-α pathways in mediating immunotherapy resistance within the immune-enriched tumor microenvironment. These findings stratify patients likely to respond to adjuvant immunotherapy, concurrently providing a mechanistic rationale for combination therapies to augment immunotherapy efficacy in urothelial carcinoma.
虽然循环肿瘤DNA（ctDNA）检测已证明在识别可能从辅助免疫治疗中获益的肌层浸润性尿路上皮癌（MIUC）患者方面具有实用性，但手术标本转录组数据的预后价值仍未得到充分探索。利用IMvigor010试验的转录组和ctDNA数据，我们开发了一种人工智能（AI）驱动的生物标志物来预测尿路上皮癌的免疫治疗反应，称为UAIscore。UAIscore高的患者在阿替利珠单抗组的预后明显优于观察组。值得注意的是，UAIscore的预测性能始终优于ctDNA、肿瘤突变负荷（tTMB）和程序性死亡配体1（PD-L1），突出了其作为独立生物标志物的价值。此外，将ctDNA、tTMB和PD-L1与UAIscore相结合进一步提高了预测准确性，强调了整合多模态生物标志物的重要性。分子亚型的进一步分析显示，管腔亚型往往对辅助免疫治疗敏感，因为它可能表现出最高水平的免疫浸润和最低程度的缺氧。值得注意的是，我们阐明了核因子κB（NF-κB）和肿瘤坏死因子-α（TNF-α）通路在免疫丰富的肿瘤微环境中介导免疫治疗耐药性的作用。这些发现对可能对辅助免疫治疗有反应的患者进行了分层，同时为联合治疗增强尿路上皮癌免疫治疗疗效提供了机制依据。
