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@ARTICLE{Bao:287616,
      author       = {X. Bao and Q. Li and D. Chen and X. Dai and C. Liu and W.
                      Tian and H. Zhang and Y. Jin and Y. Wang and J. Cheng and C.
                      Lai and C. Ye and S. Xin and X. Li$^*$ and G. Su and Y. Ding
                      and Y. Xiong and J. Xie and V. Tano and Y. Wang and W. Fu
                      and S. Deng and W. Fang and J. Sheng and J. Ruan and P.
                      Zhao},
      title        = {{A} multiomics analysis-assisted deep learning model
                      identifies a macrophage-oriented module as a potential
                      therapeutic target in colorectal cancer.},
      journal      = {Cell reports / Medicine},
      volume       = {5},
      number       = {2},
      issn         = {2666-3791},
      address      = {Maryland Heights, MO},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2024-00270},
      pages        = {101399},
      year         = {2024},
      note         = {2024 Feb 20;5(2):101399},
      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.},
      keywords     = {FOLR2(+) macrophages (Other) / artificial intelligence
                      (Other) / colorectal cancer (Other) / immuno module (Other)
                      / tumor microenvironment (Other)},
      cin          = {F180 / D440},
      ddc          = {610},
      cid          = {I:(DE-He78)F180-20160331 / I:(DE-He78)D440-20160331},
      pnm          = {314 - Immunologie und Krebs (POF4-314)},
      pid          = {G:(DE-HGF)POF4-314},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38307032},
      doi          = {10.1016/j.xcrm.2024.101399},
      url          = {https://inrepo02.dkfz.de/record/287616},
}