本文由小咖机器人翻译整理

期刊来源：Nat Med

原文链接：https://doi.org/10.1038/s41591-024-02978-9

摘要内容如下：

在晚期透明细胞肾细胞癌（ARCC）患者的实际治疗中，一个重要的挑战是确定谁可能受益于免疫检查点阻断（ICB）。在ICB治疗的背景下，我们对ARCC进行了全面的多组学分析，包括在真实世界数据队列中进行发现分析，然后在独立队列中进行验证。我们交叉连接了超过1，000名患者的大块肿瘤转录组，并在单细胞和空间分辨率上进行了验证，揭示了促炎性肿瘤相关巨噬细胞和（预）耗尽的CD8+T细胞之间的患者特异性串扰，这是通过对肿瘤新抗原具有更高偏好的人类白细胞抗原库来区分的。一条跨组学机器学习管道帮助获得了一种新的肿瘤转录组足迹，即有利于新抗原的人类白细胞抗原等位基因。在真实世界数据和独立临床队列中，这种机器学习特征与ICB治疗后的阳性结果相关。在使用RENCA-Tumor小鼠模型的实验中，CD40激动剂联合PD1阻断增强了促炎性肿瘤相关巨噬细胞和CD8+T细胞，从而相对于其他测试方案实现了最大的抗肿瘤功效。因此，我们提出了一个新的多组学和免疫群落结构的空间图，它驱动ARCC患者的ICB反应。

英文原文如下：

Abstracts

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.

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