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