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@ARTICLE{Stachura:301480,
      author       = {P. Stachura$^*$ and Z. Lu$^*$ and R. M. Kronberg and H. C.
                      Xu and W. Liu and J.-W. Tu$^*$ and K. Schaal$^*$ and E.
                      Kameri$^*$ and D. J. Picard$^*$ and S. von Karstedt and U.
                      Fischer$^*$ and S. Bhatia$^*$ and P. A. Lang and A.
                      Borkhardt$^*$ and A. A. Pandyra$^*$},
      title        = {{D}eep transfer learning approach for automated cell death
                      classification reveals novel ferroptosis-inducing agents in
                      subsets of {B}-{ALL}.},
      journal      = {Cell death $\&$ disease},
      volume       = {16},
      number       = {1},
      issn         = {2041-4889},
      address      = {London [u.a.]},
      publisher    = {Nature Publishing Group},
      reportid     = {DKFZ-2025-01022},
      pages        = {396},
      year         = {2025},
      abstract     = {Ferroptosis is a recently described type of regulated
                      necrotic cell death whose induction has anti-cancer
                      therapeutic potential, especially in hematological
                      malignancies. However, efforts to uncover novel
                      ferroptosis-inducing therapeutics have been largely
                      unsuccessful. In the current investigation, we classified
                      brightfield microscopy images of tumor cells undergoing
                      defined modes of cell death using deep transfer learning
                      (DTL). The trained DTL network was subsequently combined
                      with high-throughput pharmacological screening approaches
                      using automated live cell imaging to identify novel
                      ferroptosis-inducing functions of the polo-like kinase
                      inhibitor volasertib. Secondary validation showed that
                      subsets of B-cell acute lymphoblastic leukemia (B-ALL) cell
                      lines, namely 697, NALM6, HAL01, REH and primary patient
                      B-ALL samples were sensitive to ferroptosis induction by
                      volasertib. This was accompanied by an upregulation of
                      ferroptosis-related genes post-volasertib treatment in cell
                      lines and patient samples. Importantly, using several
                      leukemia models, we determined that volasertib delayed tumor
                      growth and induced ferroptosis in vivo. Taken together, we
                      have applied DTL to automated live-cell imaging in
                      pharmacological screening to identify novel
                      ferroptosis-inducing functions of a clinically relevant
                      anti-cancer therapeutic.},
      keywords     = {Ferroptosis: drug effects / Ferroptosis: genetics / Humans
                      / Cell Line, Tumor / Deep Learning / Animals / Mice / Cell
                      Death: drug effects / Antineoplastic Agents: pharmacology /
                      Pteridines / BI 6727 (NLM Chemicals) / Antineoplastic Agents
                      (NLM Chemicals) / Pteridines (NLM Chemicals)},
      cin          = {ED01},
      ddc          = {570},
      cid          = {I:(DE-He78)ED01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40382332},
      pmc          = {pmc:PMC12085637},
      doi          = {10.1038/s41419-025-07704-y},
      url          = {https://inrepo02.dkfz.de/record/301480},
}