Abstract：
An important challenge in the real-world management of patients with advanced clear-cell renal cell carcinoma (aRCC) is determining who might benefit from immune checkpoint blockade (ICB). Here we performed a comprehensive multiomics mapping of aRCC in the context of ICB treatment, involving discovery analyses in a real-world data cohort followed by validation in independent cohorts. We cross-connected bulk-tumor transcriptomes across >1,000 patients with validations at single-cell and spatial resolutions, revealing a patient-specific crosstalk between proinflammatory tumor-associated macrophages and (pre-)exhausted CD8+ T cells that was distinguished by a human leukocyte antigen repertoire with higher preference for tumoral neoantigens. A cross-omics machine learning pipeline helped derive a new tumor transcriptomic footprint of neoantigen-favoring human leukocyte antigen alleles. This machine learning signature correlated with positive outcome following ICB treatment in both real-world data and independent clinical cohorts. In experiments using the RENCA-tumor mouse model, CD40 agonism combined with PD1 blockade potentiated both proinflammatory tumor-associated macrophages and CD8+ T cells, thereby achieving maximal antitumor efficacy relative to other tested regimens. Thus, we present a new multiomics and spatial map of the immune-community architecture that drives ICB response in patients with aRCC.
摘要：
在晚期透明细胞肾细胞癌（aRCC）患者的现实管理中，一个重要的挑战是确定谁可能从免疫检查点阻断（ICB）中受益。在这里，我们在ICB治疗的背景下对aRCC进行了全面的多组学映射，涉及真实世界数据队列中的发现分析，然后在独立队列中进行验证。我们交叉连接了超过1,000名患者的大量肿瘤转录组，并在单细胞和空间分辨率下进行验证，揭示了促炎性肿瘤相关巨噬细胞和（前）耗尽的CD8+T细胞之间的患者特异性串扰，其特征在于人类白细胞抗原库对肿瘤新抗原具有更高的偏好。交叉组学机器学习管道有助于获得新抗原有利于人类白细胞抗原等位基因的新肿瘤转录组足迹。在真实世界数据和独立临床队列中,该机器学习特征与ICB治疗后的阳性结果相关。在使用RENCA肿瘤小鼠模型的实验中，CD40激动结合PD1阻断增强了促炎肿瘤相关巨噬细胞和CD8+T细胞，从而实现相对于其他测试方案的最大抗肿瘤功效。因此，我们提出了一种新的免疫群落结构的多组学和空间图谱,该图谱驱动aRCC患者的ICB应答.
