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

• Published in: Cell reports. Medicine Vol. 5; no. 2; p. 101399
• Main Authors: Bao, Xuanwen, Li, Qiong, Chen, Dong, Dai, Xiaomeng, Liu, Chuan, Tian, Weihong, Zhang, Hangyu, Jin, Yuzhi, Wang, Yin, Cheng, Jinlin, Lai, Chunyu, Ye, Chanqi, Xin, Shan, Li, Xin, Su, Ge, Ding, Yongfeng, Xiong, Yangyang, Xie, Jindong, Tano, Vincent, Wang, Yanfang, Fu, Wenguang, Deng, Shuiguang, Fang, Weijia, Sheng, Jianpeng, Ruan, Jian, Zhao, Peng
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 20.02.2024; Elsevier
• Subjects: artificial intelligence; CD8-Positive T-Lymphocytes; colorectal cancer; Colorectal Neoplasms - drug therapy; Colorectal Neoplasms - genetics; Deep Learning; Folate Receptor 2; FOLR2+ macrophages; Humans; immuno module; Macrophages; Multiomics; tumor microenvironment; Tumor Microenvironment - genetics; immuno module; colorectal cancer; tumor microenvironment; artificial intelligence; FOLR2+ macrophages; FOLR2(+) macrophages
• ISSN: 2666-3791; 2666-3791
• DOI: 10.1016/j.xcrm.2024.101399

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. [Display omitted] •A FOLR2+ macrophage-oriented cell module is uncovered in colorectal cancer•Deep learning models utilize the macrophage-oriented spatial interaction map•Targeting FOLR2+ resident macrophages enhances the response to chemotherapy Bao et al. utilize a deep learning model supported by multiomics analysis to establish a FOLR2+ macrophage-oriented cell module. This module serves as both a therapeutic target and a prognostic predictor in colorectal cancer. Additionally, the spatial interaction map not only predicts chemotherapy response but also identifies potential therapeutic targets.