A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer

作者：Xuanwen Bao, Qiong Li, Dong Chen, Xiaomeng Dai, Chuan Liu, Weihong Tian, Hangyu Zhang, Yuzhi Jin, Yin Wang, Jinlin Cheng, Chun-Yu Lai, Chanqi Ye, Xin Shan, Xin Li, Ge Su, Yongfeng Ding, Yang‐Yang Xiong, Jindong Xie, Vincent Tano, Yanfang Wang, Wenguang Fu, Shuiguang Deng, Weijia Fang, Jianpeng Sheng, Jian Ruan, Peng Zhao · 发表于：Cell Reports Medicine · 年份：2024 · DOI：10.1016/j.xcrm.2024.101399 · 被引用次数：42 · 研究领域：Single-cell and spatial transcriptomics、Immune cells in cancer、Cancer Immunotherapy and Biomarkers

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.