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@ARTICLE{Zhu:282593,
      author       = {Zhu, Rong and Eason, Katherine and Chin, Suet-Feung and
                      Edwards, Paul A W and Manzano Garcia, Raquel and Moulange,
                      Richard and Pan, Jia Wern and Teo, Soo Hwang and Mukherjee,
                      Sach and Callari, Maurizio and Caldas, Carlos and Sammut,
                      Stephen-John and Rueda, Oscar M},
      title        = {{D}etecting homologous recombination deficiency for breast
                      cancer through integrative analysis of genomic data.},
      journal      = {Molecular oncology},
      volume       = {19},
      number       = {12},
      issn         = {1574-7891},
      address      = {Hoboken, NJ},
      publisher    = {John Wiley $\&$ Sons, Inc.},
      reportid     = {DZNE-2025-01351},
      pages        = {3613 - 3633},
      year         = {2025},
      abstract     = {Homologous recombination deficiency (HRD) leads to genomic
                      instability, and patients with HRD can benefit from
                      HRD-targeting therapies. Previous studies have primarily
                      focused on identifying HRD biomarkers using data from a
                      single technology. Here we integrated features from
                      different genomic data types, including total copy number
                      (CN), allele-specific copy number (ASCN) and single
                      nucleotide variants (SNV). Using a semi-supervised method,
                      we developed HRD classifiers from 1404 breast tumours across
                      two datasets based on their BRCA1/2 status, demonstrating
                      improved HRD identification when aggregating different data
                      types. Notably, HRD-positive tumours in ER-negative disease
                      showed improved survival post-adjuvant chemotherapy, while
                      HRD status strongly correlated with neoadjuvant treatment
                      response. Furthermore, our analysis of cell lines
                      highlighted a sensitivity to PARP inhibitors, particularly
                      rucaparib, among predicted HRD-positive lines. Exploring
                      somatic mutations outside BRCA1/2, we confirmed variants in
                      several genes associated with HRD. Our method for HRD
                      classification can adapt to different data types or
                      resolutions and can be used in various scenarios to help
                      refine patient selection for HRD-targeting therapies that
                      might lead to better clinical outcomes.},
      keywords     = {Humans / Breast Neoplasms: genetics / Breast Neoplasms:
                      drug therapy / Female / Genomics: methods / Homologous
                      Recombination: genetics / DNA Copy Number Variations / Cell
                      Line, Tumor / Poly(ADP-ribose) Polymerase Inhibitors:
                      pharmacology / Polymorphism, Single Nucleotide / Mutation /
                      breast cancer (Other) / cancer genomics (Other) / genomic
                      data integration (Other) / homologous recombination
                      deficiency (Other) / semi‐supervised learning (Other) /
                      tumour biomarkers (Other) / Poly(ADP-ribose) Polymerase
                      Inhibitors (NLM Chemicals)},
      cin          = {AG Mukherjee},
      ddc          = {610},
      cid          = {I:(DE-2719)1013030},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40260608},
      doi          = {10.1002/1878-0261.70041},
      url          = {https://pub.dzne.de/record/282593},
}