人们已经研究了肠道细菌对免疫检查点抑制剂 (ICIs) 反应的影响，但真菌与 ICI 反应之间的关系尚未完全了解。在此，整合了来自 9 个不同队列的 862 个粪便宏基因组，用于鉴定差异丰度的真菌，并随后构建随机森林 (RF) 模型来预测 ICI 反应。真菌标记物表现出优异的性能，平均曲线下面积 (AUC) 为 0.87。它们的性能进一步提高，与细菌标记物结合时，平均 AUC 达到 0.89。正如真菌标记所预测的那样，在应答者中检测到了更高的耗尽 T 细胞富集度。多界网络和功能分析表明，真菌八孢裂殖酵母可能在应答者体内将淀粉发酵成短链脂肪酸。这项研究提供了 ICI 反应的真菌概况，并鉴定了具有良好性能的多界微生物标志物，可以提高 ICI 疗法的整体适用性。版权所有 © 2023 Elsevier Inc. 保留所有权利。

The effect of gut bacteria on the response to immune checkpoint inhibitors (ICIs) has been studied, but the relationship between fungi and ICI responses is not fully understood. Herein, 862 fecal metagenomes from 9 different cohorts were integrated for the identification of differentially abundant fungi and subsequent construction of random forest (RF) models to predict ICI responses. Fungal markers demonstrate excellent performance, with an average area under the curve (AUC) of 0.87. Their performance improves even further, reaching an average AUC of 0.89 when combined with bacterial markers. Higher enrichment of exhausted T cells is detected in responders, as predicted by fungal markers. Multi-kingdom network and functional analysis reveal that the fungus Schizosaccharomyces octosporus may ferment starch into short-chain fatty acids in responders. This study provides a fungal profile of the ICI response and the identification of multi-kingdom microbial markers with good performance that may improve the overall applicability of ICI therapy.Copyright © 2023 Elsevier Inc. All rights reserved.