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@ARTICLE{Liu:289225,
      author       = {C. Liu and J. Xie and B. Lin and W. Tian and Y. Wu and S.
                      Xin and L. Hong and X. Li$^*$ and L. Liu and Y. Jin and H.
                      Tang and X. Deng and Y. Zou and S. Zheng and W. Fang and J.
                      Cheng and X. Dai and X. Bao and P. Zhao},
      title        = {{P}an-{C}ancer {S}ingle-{C}ell and {S}patial-{R}esolved
                      {P}rofiling {R}eveals the {I}mmunosuppressive {R}ole of
                      {APOE}+ {M}acrophages in {I}mmune {C}heckpoint {I}nhibitor
                      {T}herapy.},
      journal      = {Advanced science},
      volume       = {11},
      number       = {23},
      issn         = {2198-3844},
      address      = {Weinheim},
      publisher    = {Wiley-VCH},
      reportid     = {DKFZ-2024-00659},
      pages        = {e2401061},
      year         = {2024},
      note         = {2024 Jun;11(23):e2401061},
      abstract     = {The heterogeneity of macrophages influences the response to
                      immune checkpoint inhibitor (ICI) therapy. However, few
                      studies explore the impact of APOE+ macrophages on ICI
                      therapy using single-cell RNA sequencing (scRNA-seq) and
                      machine learning methods. The scRNA-seq and bulk RNA-seq
                      data are Integrated to construct an M.Sig model for
                      predicting ICI response based on the distinct molecular
                      signatures of macrophage and machine learning algorithms.
                      Comprehensive single-cell analysis as well as in vivo and in
                      vitro experiments are applied to explore the potential
                      mechanisms of the APOE+ macrophage in affecting ICI
                      response. The M.Sig model shows clear advantages in
                      predicting the efficacy and prognosis of ICI therapy in
                      pan-cancer patients. The proportion of APOE+ macrophages is
                      higher in ICI non-responders of triple-negative breast
                      cancer compared with responders, and the interaction and
                      longer distance between APOE+ macrophages and CD8+ exhausted
                      T (Tex) cells affecting ICI response is confirmed by
                      multiplex immunohistochemistry. In a 4T1 tumor-bearing mice
                      model, the APOE inhibitor combined with ICI treatment shows
                      the best efficacy. The M.Sig model using real-world
                      immunotherapy data accurately predicts the ICI response of
                      pan-cancer, which may be associated with the interaction
                      between APOE+ macrophages and CD8+ Tex cells.},
      keywords     = {APOE+ macrophages (Other) / immune checkpoint inhibitor
                      (Other) / machine learning algorithm (Other) / pan‐cancer
                      (Other) / single‐cell RNA sequencing (Other)},
      cin          = {F180 / D440},
      ddc          = {624},
      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:38569519},
      doi          = {10.1002/advs.202401061},
      url          = {https://inrepo02.dkfz.de/record/289225},
}