A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer
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...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Online Access: | https://hdl.handle.net/10356/178491 |
| _version_ | 1811690174360322048 |
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| author | 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 |
| author2 | Lee Kong Chian School of Medicine (LKCMedicine) |
| author_facet | Lee Kong Chian School of Medicine (LKCMedicine) 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 |
| author_sort | Bao, Xuanwen |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T05:59:48Z |
| format | Journal Article |
| id | ntu-10356/178491 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:59:48Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1784912024-06-30T15:39:21Z A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer 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 Lee Kong Chian School of Medicine (LKCMedicine) Medicine, Health and Life Sciences Artificial intelligence Colorectal cancer 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. Published version This work was supported in part by National Natural Science Foundation of China grants 82101830 (to X.B.), 81874173 (to J.R.), 81472346 (to P.Z.), 82074208 (to P.Z.), and 82102817 (to X.D.) and by Natural Science Foundation of Zhejiang Province grants Y23H160106 (to X.B.), LY20H160033 (to P.Z.), LQ22H160041 (to X.D.), and LY22H160019 (to J.R.). 2024-06-24T05:58:55Z 2024-06-24T05:58:55Z 2024 Journal Article Bao, X., Li, Q., Chen, D., Dai, X., Liu, C., Tian, W., Zhang, H., Jin, Y., Wang, Y., Cheng, J., Lai, C., Ye, C., Xin, S., Li, X., Su, G., Ding, Y., Xiong, Y., Xie, J., Tano, V., ...Zhao, P. (2024). A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer. Cell Reports Medicine, 5(2), 101399-. https://dx.doi.org/10.1016/j.xcrm.2024.101399 2666-3791 https://hdl.handle.net/10356/178491 10.1016/j.xcrm.2024.101399 38307032 2-s2.0-85185311466 2 5 101399 en Cell Reports Medicine © 2024 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). application/pdf |
| spellingShingle | Medicine, Health and Life Sciences Artificial intelligence Colorectal cancer 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 A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer |
| title | A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer |
| title_full | A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer |
| title_fullStr | A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer |
| title_full_unstemmed | A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer |
| title_short | A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer |
| title_sort | multiomics analysis assisted deep learning model identifies a macrophage oriented module as a potential therapeutic target in colorectal cancer |
| topic | Medicine, Health and Life Sciences Artificial intelligence Colorectal cancer |
| url | https://hdl.handle.net/10356/178491 |
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