%0 Journal Article
%A Liu, Chuan
%A Xie, Jindong
%A Lin, Bo
%A Tian, Weihong
%A Wu, Yifan
%A Xin, Shan
%A Hong, Libing
%A Li, Xin
%A Liu, Lulu
%A Jin, Yuzhi
%A Tang, Hailin
%A Deng, Xinpei
%A Zou, Yutian
%A Zheng, Shaoquan
%A Fang, Weijia
%A Cheng, Jinlin
%A Dai, Xiaomeng
%A Bao, Xuanwen
%A Zhao, Peng
%T Pan-Cancer Single-Cell and Spatial-Resolved Profiling Reveals the Immunosuppressive Role of APOE+ Macrophages in Immune Checkpoint Inhibitor Therapy.
%J Advanced science
%V 11
%N 23
%@ 2198-3844
%C Weinheim
%I Wiley-VCH
%M DKFZ-2024-00659
%P e2401061
%D 2024
%Z 2024 Jun;11(23):e2401061
%X The heterogeneity of macrophages influences the response to immune checkpoint inhibitor (ICI) therapy. However, few studies explore the impact of APOE+ macrophages on ICI therapy using single-cell RNA sequencing (scRNA-seq) and machine learning methods. The scRNA-seq and bulk RNA-seq data are Integrated to construct an M.Sig model for predicting ICI response based on the distinct molecular signatures of macrophage and machine learning algorithms. Comprehensive single-cell analysis as well as in vivo and in vitro experiments are applied to explore the potential mechanisms of the APOE+ macrophage in affecting ICI response. The M.Sig model shows clear advantages in predicting the efficacy and prognosis of ICI therapy in pan-cancer patients. The proportion of APOE+ macrophages is higher in ICI non-responders of triple-negative breast cancer compared with responders, and the interaction and longer distance between APOE+ macrophages and CD8+ exhausted T (Tex) cells affecting ICI response is confirmed by multiplex immunohistochemistry. In a 4T1 tumor-bearing mice model, the APOE inhibitor combined with ICI treatment shows the best efficacy. The M.Sig model using real-world immunotherapy data accurately predicts the ICI response of pan-cancer, which may be associated with the interaction between APOE+ macrophages and CD8+ Tex cells.
%K APOE+ macrophages (Other)
%K immune checkpoint inhibitor (Other)
%K machine learning algorithm (Other)
%K pan‐cancer (Other)
%K single‐cell RNA sequencing (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:38569519
%R 10.1002/advs.202401061
%U https://inrepo02.dkfz.de/record/289225