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@ARTICLE{Wansch:304584,
      author       = {K. Wansch and U. Pelzer and F. Schneider and F. Dölvers
                      and A. Kühn and M.-P. Dragomir$^*$ and J. Ihlow and G.
                      Hilfenhaus and L. Vecchione and M. Felsenstein and D. Ma and
                      M. Lerchbaumer and C. Jürgensen and M. Bahra and A. E.
                      Granada and G. Duwe and S. Stintzing and U. Keilholz and C.
                      C. M. Neumann},
      title        = {{M}ulti-drug pharmacotyping improves therapy prediction in
                      pancreatic cancer organoids.},
      journal      = {Cancer cell international},
      volume       = {25},
      number       = {1},
      issn         = {1475-2867},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {DKFZ-2025-01907},
      pages        = {321},
      year         = {2025},
      abstract     = {Patient-Derived Organoids (PDOs) represent a promising
                      technology for therapy prediction in pancreatic cancer, with
                      the potential of enhancing treatment outcomes and allowing
                      more effective, personalized treatment choices. However,
                      classification approaches into sensitive and resistant
                      models remain very variable and are based on single-agent
                      testing only, neglecting interactive effects of multi-drug
                      combinations. Here, we established 13 PDOs and performed
                      both single-agent and multi-drug testing. By comparing
                      different clustering approaches of drug-response metrics and
                      establishing a new classification approach based on
                      pharmacokinetic modelling, we were able to evaluate which
                      score best predicts the clinical response of patients. Our
                      newly developed score considered the Area Under The Curve
                      (AUC) of cell viability curves and reached a prediction
                      accuracy of $85\%.$ Our data supports previous findings for
                      PDOs to constitute an effective platform for translational
                      drug testing. Furthermore, our results suggest that the AUC
                      is a more accurate drug-response metric than the half
                      maximal inhibitory concentration (IC50), and that multi-drug
                      testing yields a higher accuracy than single-agent testing.
                      The methodology and outcomes presented in this study are of
                      critical relevance for future PDO-based translational trials
                      as they allow a new physiology-based approach towards
                      multi-drug testing and classification of organoid response,
                      which improves PDO prediction accuracy.},
      keywords     = {Multi-Drug response metrics (Other) / Pancreatic
                      adenocarcinoma (Other) / Patient-derived organoids (Other) /
                      Pharmacokinetic modelling (Other)},
      cin          = {BE01},
      ddc          = {610},
      cid          = {I:(DE-He78)BE01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40946104},
      doi          = {10.1186/s12935-025-03969-7},
      url          = {https://inrepo02.dkfz.de/record/304584},
}