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@ARTICLE{Barenboim:177380,
      author       = {M. Barenboim and M. Kovac and B. Ameline and D. T. W.
                      Jones$^*$ and O. Witt$^*$ and S. Bielack and S. Burdach$^*$
                      and D. Baumhoer and M. Nathrath},
      title        = {{DNA} methylation-based classifier and gene expression
                      signatures detect {BRCA}ness in osteosarcoma.},
      journal      = {PLoS Computational Biology},
      volume       = {17},
      number       = {11},
      issn         = {1553-7358},
      address      = {San Francisco, Calif.},
      publisher    = {Public Library of Science},
      reportid     = {DKFZ-2021-02472},
      pages        = {e1009562 -},
      year         = {2021},
      abstract     = {Although osteosarcoma (OS) is a rare cancer, it is the most
                      common primary malignant bone tumor in children and
                      adolescents. BRCAness is a phenotypical trait in tumors with
                      a defect in homologous recombination repair, resembling
                      tumors with inactivation of BRCA1/2, rendering these tumors
                      sensitive to poly (ADP)-ribose polymerase inhibitors
                      (PARPi). Recently, OS was shown to exhibit molecular
                      features of BRCAness. Our goal was to develop a method
                      complementing existing genomic methods to aid clinical
                      decision making on administering PARPi in OS patients. OS
                      samples with DNA-methylation data were divided to
                      BRCAness-positive and negative groups based on the degree of
                      their genomic instability (n = 41). Methylation probes were
                      ranked according to decreasing variance difference between
                      two groups. The top 2000 probes were selected for training
                      and cross-validation of the random forest algorithm.
                      Two-thirds of available OS RNA-Seq samples (n = 17) from the
                      top and bottom of the sample list ranked according to genome
                      instability score were subjected to differential expression
                      and, subsequently, to gene set enrichment analysis (GSEA).
                      The combined accuracy of trained random forest was $85\%$
                      and the average area under the ROC curve (AUC) was 0.95.
                      There were 449 upregulated and 1,079 downregulated genes in
                      the BRCAness-positive group (fdr < 0.05). GSEA of
                      upregulated genes detected enrichment of DNA replication and
                      mismatch repair and homologous recombination signatures
                      (FWER < 0.05). Validation of the BRCAness classifier with an
                      independent OS set (n = 20) collected later in the course of
                      study showed AUC of 0.87 with an accuracy of $90\%.$ GSEA
                      signatures computed for this test set were matching the ones
                      observed in the training set enrichment analysis. In
                      conclusion, we developed a new classifier based on
                      DNA-methylation patterns that detects BRCAness in OS samples
                      with high accuracy. GSEA identified genome instability
                      signatures. Machine-learning and gene expression approaches
                      add new epigenomic and transcriptomic aspects to already
                      established genomic methods for evaluation of BRCAness in
                      osteosarcoma and can be extended to cancers characterized by
                      genome instability.},
      cin          = {B360 / B310 / MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)B360-20160331 / I:(DE-He78)B310-20160331 /
                      I:(DE-He78)MU01-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:34762643},
      doi          = {10.1371/journal.pcbi.1009562},
      url          = {https://inrepo02.dkfz.de/record/177380},
}