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@ARTICLE{Otto:267558,
      author       = {R. Otto and K. M. Detjen and P. Riemer$^*$ and M. Fattohi
                      and C. Grötzinger and G. Rindi and B. Wiedenmann and C.
                      Sers and U. Leser},
      title        = {{T}ranscriptomic {D}econvolution of {N}euroendocrine
                      {N}eoplasms {P}redicts {C}linically {R}elevant
                      {C}haracteristics.},
      journal      = {Cancers},
      volume       = {15},
      number       = {3},
      issn         = {2072-6694},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {DKFZ-2023-00336},
      pages        = {936},
      year         = {2023},
      abstract     = {Pancreatic neuroendocrine neoplasms (panNENs) are a rare
                      yet diverse type of neoplasia whose precise
                      clinical-pathological classification is frequently
                      challenging. Since incorrect classifications can affect
                      treatment decisions, additional tools which support the
                      diagnosis, such as machine learning (ML) techniques, are
                      critically needed but generally unavailable due to the
                      scarcity of suitable ML training data for rare panNENs.
                      Here, we demonstrate that a multi-step ML framework predicts
                      clinically relevant panNEN characteristics while being
                      exclusively trained on widely available data of a healthy
                      origin. The approach classifies panNENs by deconvolving
                      their transcriptomes into cell type proportions based on
                      shared gene expression profiles with healthy pancreatic cell
                      types. The deconvolution results were found to provide a
                      prognostic value with respect to the prediction of the
                      overall patient survival time, neoplastic grading, and
                      carcinoma versus tumor subclassification. The performance
                      with which a proliferation rate agnostic deconvolution ML
                      model could predict the clinical characteristics was found
                      to be comparable to that of a comparative baseline model
                      trained on the proliferation rate-informed MKI67 levels. The
                      approach is novel in that it complements established
                      proliferation rate-oriented classification schemes whose
                      results can be reproduced and further refined by
                      differentiating between identically graded subgroups. By
                      including non-endocrine cell types, the deconvolution
                      approach furthermore provides an in silico quantification of
                      panNEN dedifferentiation, optimizing it for challenging
                      clinical classification tasks in more aggressive panNEN
                      subtypes.},
      keywords     = {NEN classification (Other) / deconvolution (Other) /
                      machine learning (Other) / neuroendocrine carcinoma (Other)
                      / neuroendocrine neoplasm (Other) / neuroendocrine tumor
                      (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:36765893},
      doi          = {10.3390/cancers15030936},
      url          = {https://inrepo02.dkfz.de/record/267558},
}