% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Jiao:177485,
      author       = {W. Jiao and G. Atwal and P. Polak and R. Karlic and E.
                      Cuppen and PCAWGTumorSubtypesClinicalTranslationWorkingGroup
                      and A. Danyi and J. de Ridder and C. van Herpen and M. P.
                      Lolkema and N. Steeghs and G. Getz and Q. Morris and L. D.
                      Stein and PCAWGConsortium},
      title        = {{A} deep learning system accurately classifies primary and
                      metastatic cancers using passenger mutation patterns.},
      journal      = {Nature Communications},
      volume       = {11},
      number       = {1},
      issn         = {2041-1723},
      address      = {[London]},
      publisher    = {Nature Publishing Group UK},
      reportid     = {DKFZ-2021-02572},
      pages        = {728},
      year         = {2020},
      note         = {siehe Correction: DKFZ Autoren affiliiert im PCAWG
                      Consortium: https://inrepo02.dkfz.de/record/212437 /
                      https://doi.org/10.1038/s41467-022-32329-6},
      abstract     = {In cancer, the primary tumour's organ of origin and
                      histopathology are the strongest determinants of its
                      clinical behaviour, but in $3\%$ of cases a patient presents
                      with a metastatic tumour and no obvious primary. Here, as
                      part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes
                      (PCAWG) Consortium, we train a deep learning classifier to
                      predict cancer type based on patterns of somatic passenger
                      mutations detected in whole genome sequencing (WGS) of 2606
                      tumours representing 24 common cancer types produced by the
                      PCAWG Consortium. Our classifier achieves an accuracy of
                      $91\%$ on held-out tumor samples and $88\%$ and $83\%$
                      respectively on independent primary and metastatic samples,
                      roughly double the accuracy of trained pathologists when
                      presented with a metastatic tumour without knowledge of the
                      primary. Surprisingly, adding information on driver
                      mutations reduced accuracy. Our results have clinical
                      applicability, underscore how patterns of somatic passenger
                      mutations encode the state of the cell of origin, and can
                      inform future strategies to detect the source of circulating
                      tumour DNA.},
      keywords     = {Computational Biology: methods / Deep Learning / Female /
                      Genome, Human / Humans / Male / Mutation / Neoplasm
                      Metastasis / Neoplasms: genetics / Neoplasms: pathology /
                      Reproducibility of Results / Whole Genome Sequencing},
      cin          = {B080 / B240 / B370 / B330 / HD01 / B060 / B360 / BE01 /
                      B062 / B066 / B063 / W190 / B260 / W610 / B087},
      ddc          = {500},
      cid          = {I:(DE-He78)B080-20160331 / I:(DE-He78)B240-20160331 /
                      I:(DE-He78)B370-20160331 / I:(DE-He78)B330-20160331 /
                      I:(DE-He78)HD01-20160331 / I:(DE-He78)B060-20160331 /
                      I:(DE-He78)B360-20160331 / I:(DE-He78)BE01-20160331 /
                      I:(DE-He78)B062-20160331 / I:(DE-He78)B066-20160331 /
                      I:(DE-He78)B063-20160331 / I:(DE-He78)W190-20160331 /
                      I:(DE-He78)B260-20160331 / I:(DE-He78)W610-20160331 /
                      I:(DE-He78)B087-20160331},
      pnm          = {312 - Functional and structural genomics (POF3-312)},
      pid          = {G:(DE-HGF)POF3-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:32024849},
      pmc          = {pmc:PMC7002586},
      doi          = {10.1038/s41467-019-13825-8},
      url          = {https://inrepo02.dkfz.de/record/177485},
}