%0 Journal Article
%A Bao, Xuanwen
%A Li, Qiong
%A Chen, Dong
%A Dai, Xiaomeng
%A Liu, Chuan
%A Tian, Weihong
%A Zhang, Hangyu
%A Jin, Yuzhi
%A Wang, Yin
%A Cheng, Jinlin
%A Lai, Chunyu
%A Ye, Chanqi
%A Xin, Shan
%A Li, Xin
%A Su, Ge
%A Ding, Yongfeng
%A Xiong, Yangyang
%A Xie, Jindong
%A Tano, Vincent
%A Wang, Yanfang
%A Fu, Wenguang
%A Deng, Shuiguang
%A Fang, Weijia
%A Sheng, Jianpeng
%A Ruan, Jian
%A Zhao, Peng
%T A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer.
%J Cell reports / Medicine
%V 5
%N 2
%@ 2666-3791
%C Maryland Heights, MO
%I Elsevier
%M DKFZ-2024-00270
%P 101399
%D 2024
%Z 2024 Feb 20;5(2):101399
%X Colorectal cancer (CRC) is a common malignancy involving multiple cellular components. The CRC tumor microenvironment (TME) has been characterized well at single-cell resolution. However, a spatial interaction map of the CRC TME is still elusive. Here, we integrate multiomics analyses and establish a spatial interaction map to improve the prognosis, prediction, and therapeutic development for CRC. We construct a CRC immune module (CCIM) that comprises FOLR2+ macrophages, exhausted CD8+ T cells, tolerant CD8+ T cells, exhausted CD4+ T cells, and regulatory T cells. Multiplex immunohistochemistry is performed to depict the CCIM. Based on this, we utilize advanced deep learning technology to establish a spatial interaction map and predict chemotherapy response. CCIM-Net is constructed, which demonstrates good predictive performance for chemotherapy response in both the training and testing cohorts. Lastly, targeting FOLR2+ macrophage therapeutics is used to disrupt the immunosuppressive CCIM and enhance the chemotherapy response in vivo.
%K FOLR2(+) macrophages (Other)
%K artificial intelligence (Other)
%K colorectal cancer (Other)
%K immuno module (Other)
%K tumor microenvironment (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:38307032
%R 10.1016/j.xcrm.2024.101399
%U https://inrepo02.dkfz.de/record/287616